From bff01bf718a647d3e7bfc6373a7eb05f6c00bd01 Mon Sep 17 00:00:00 2001
From: Tamar Ervin <tamarervin@gmail.com>
Date: Tue, 26 Oct 2021 16:01:22 -0700
Subject: [PATCH] function documentation

---
 mkdocs/site/404.html                          |  146 +-
 mkdocs/site/about/index.html                  |  148 +-
 mkdocs/site/assets/_mkdocstrings.css          |   16 +
 mkdocs/site/calcs/coords/index.html           |  939 +++++
 mkdocs/site/calcs/mag/index.html              | 1038 +++++
 mkdocs/site/calcs/region/index.html           |  889 +++++
 mkdocs/site/calcs/rv/index.html               |  746 ++++
 mkdocs/site/calcs/vel/index.html              |  949 +++++
 mkdocs/site/{sdo => data}/corr/index.html     |  146 +-
 mkdocs/site/data/data/index.html              |  803 ++++
 .../examples/docs_full_pipeline/index.html    |  170 +-
 mkdocs/site/examples/docs_rv_calcs/index.html | 1785 +++++++++
 .../docs_solar_corrections/index.html         | 3512 +++++++++++++++++
 .../examples/docs_sunpy_example/index.html    | 2475 ++++++++++++
 mkdocs/site/index.html                        |  146 +-
 mkdocs/site/objects.inv                       |  Bin 0 -> 598 bytes
 .../site/package/calculation_funcs/index.html | 3032 ++++++++++++++
 mkdocs/site/package/coord_funcs/index.html    | 1430 +++++++
 mkdocs/site/package/lbc_funcs/index.html      | 1171 ++++++
 .../site/{sdo => package}/pipeline/index.html |  152 +-
 mkdocs/site/package/plotting_funcs/index.html | 2118 ++++++++++
 mkdocs/site/package/rvs/index.html            | 1114 ++++++
 mkdocs/site/package/settings/index.html       | 1127 ++++++
 mkdocs/site/search/search_index.json          |    2 +-
 mkdocs/site/sitemap.xml                       |   58 +-
 mkdocs/site/sitemap.xml.gz                    |  Bin 207 -> 210 bytes
 sdo_hmi_rvs/tools/rvs.py                      |   13 +-
 sdo_hmi_rvs/tools/utilities.py                |   48 +-
 28 files changed, 24044 insertions(+), 129 deletions(-)
 create mode 100644 mkdocs/site/assets/_mkdocstrings.css
 create mode 100644 mkdocs/site/calcs/coords/index.html
 create mode 100644 mkdocs/site/calcs/mag/index.html
 create mode 100644 mkdocs/site/calcs/region/index.html
 create mode 100644 mkdocs/site/calcs/rv/index.html
 create mode 100644 mkdocs/site/calcs/vel/index.html
 rename mkdocs/site/{sdo => data}/corr/index.html (89%)
 create mode 100644 mkdocs/site/data/data/index.html
 rename mkdocs/site/{sdo => }/examples/docs_full_pipeline/index.html (99%)
 create mode 100644 mkdocs/site/examples/docs_rv_calcs/index.html
 create mode 100644 mkdocs/site/examples/docs_solar_corrections/index.html
 create mode 100644 mkdocs/site/examples/docs_sunpy_example/index.html
 create mode 100644 mkdocs/site/objects.inv
 create mode 100644 mkdocs/site/package/calculation_funcs/index.html
 create mode 100644 mkdocs/site/package/coord_funcs/index.html
 create mode 100644 mkdocs/site/package/lbc_funcs/index.html
 rename mkdocs/site/{sdo => package}/pipeline/index.html (89%)
 create mode 100644 mkdocs/site/package/plotting_funcs/index.html
 create mode 100644 mkdocs/site/package/rvs/index.html
 create mode 100644 mkdocs/site/package/settings/index.html

diff --git a/mkdocs/site/404.html b/mkdocs/site/404.html
index 6b920ed..e34e707 100644
--- a/mkdocs/site/404.html
+++ b/mkdocs/site/404.html
@@ -42,6 +42,8 @@
     
     
     
+      <link rel="stylesheet" href="/assets/_mkdocstrings.css">
+    
     
       
 
@@ -261,7 +263,7 @@
   
   
     <li class="md-nav__item">
-      <a href="/sdo/data/" class="md-nav__link">
+      <a href="/data/data/" class="md-nav__link">
         Aquiring Data
       </a>
     </li>
@@ -275,7 +277,7 @@
   
   
     <li class="md-nav__item">
-      <a href="/sdo/corr/" class="md-nav__link">
+      <a href="/data/corr/" class="md-nav__link">
         Data Corrections
       </a>
     </li>
@@ -297,7 +299,7 @@
   
   
     <li class="md-nav__item">
-      <a href="/sdo/coords/" class="md-nav__link">
+      <a href="/calcs/coords/" class="md-nav__link">
         Coordinate Transformations
       </a>
     </li>
@@ -312,7 +314,7 @@
   
   
     <li class="md-nav__item">
-      <a href="/sdo/region/" class="md-nav__link">
+      <a href="/calcs/region/" class="md-nav__link">
         Region Identification
       </a>
     </li>
@@ -327,7 +329,7 @@
   
   
     <li class="md-nav__item">
-      <a href="/sdo/vel/" class="md-nav__link">
+      <a href="/calcs/vel/" class="md-nav__link">
         Velocity Components
       </a>
     </li>
@@ -342,7 +344,7 @@
   
   
     <li class="md-nav__item">
-      <a href="/sdo/rv/" class="md-nav__link">
+      <a href="/calcs/rv/" class="md-nav__link">
         RV Calculation
       </a>
     </li>
@@ -357,7 +359,7 @@
   
   
     <li class="md-nav__item">
-      <a href="/sdo/mag/" class="md-nav__link">
+      <a href="/calcs/mag/" class="md-nav__link">
         Magnetic Observables
       </a>
     </li>
@@ -372,7 +374,7 @@
   
   
     <li class="md-nav__item">
-      <a href="/sdo/pipeline/" class="md-nav__link">
+      <a href="/package/pipeline/" class="md-nav__link">
         Full Pipeline
       </a>
     </li>
@@ -396,12 +398,130 @@
       
       
         <label class="md-nav__link" for="__nav_10">
+          Function Documentation
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Function Documentation" data-md-level="1">
+        <label class="md-nav__title" for="__nav_10">
+          <span class="md-nav__icon md-icon"></span>
+          Function Documentation
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="/package/coord_funcs/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="/package/lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="/package/calculation_funcs/" class="md-nav__link">
+        Calculation Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="/package/rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="/package/plotting_funcs/" class="md-nav__link">
+        Plotting Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="/package/settings/" class="md-nav__link">
+        Settings
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_11" type="checkbox" id="__nav_11" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_11">
           Jupyter Examples
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <nav class="md-nav" aria-label="Jupyter Examples" data-md-level="1">
-        <label class="md-nav__title" for="__nav_10">
+        <label class="md-nav__title" for="__nav_11">
           <span class="md-nav__icon md-icon"></span>
           Jupyter Examples
         </label>
@@ -413,7 +533,7 @@
   
   
     <li class="md-nav__item">
-      <a href="/sdo/examples/docs_sunpy_example/" class="md-nav__link">
+      <a href="/examples/docs_sunpy_example/" class="md-nav__link">
         Sunpy Basics
       </a>
     </li>
@@ -427,7 +547,7 @@
   
   
     <li class="md-nav__item">
-      <a href="/sdo/examples/docs_solar_corrections/" class="md-nav__link">
+      <a href="/examples/docs_solar_corrections/" class="md-nav__link">
         Component Calculations
       </a>
     </li>
@@ -441,7 +561,7 @@
   
   
     <li class="md-nav__item">
-      <a href="/sdo/examples/docs_rv_calcs/" class="md-nav__link">
+      <a href="/examples/docs_rv_calcs/" class="md-nav__link">
         RV Calculation
       </a>
     </li>
@@ -455,7 +575,7 @@
   
   
     <li class="md-nav__item">
-      <a href="/sdo/examples/docs_full_pipeline/" class="md-nav__link">
+      <a href="/examples/docs_full_pipeline/" class="md-nav__link">
         Full Pipeline
       </a>
     </li>
diff --git a/mkdocs/site/about/index.html b/mkdocs/site/about/index.html
index 08c3eef..f774b40 100644
--- a/mkdocs/site/about/index.html
+++ b/mkdocs/site/about/index.html
@@ -42,6 +42,8 @@
     
     
     
+      <link rel="stylesheet" href="../assets/_mkdocstrings.css">
+    
     
       
 
@@ -306,7 +308,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../sdo/data/" class="md-nav__link">
+      <a href="../data/data/" class="md-nav__link">
         Aquiring Data
       </a>
     </li>
@@ -320,7 +322,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../sdo/corr/" class="md-nav__link">
+      <a href="../data/corr/" class="md-nav__link">
         Data Corrections
       </a>
     </li>
@@ -342,7 +344,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../sdo/coords/" class="md-nav__link">
+      <a href="../calcs/coords/" class="md-nav__link">
         Coordinate Transformations
       </a>
     </li>
@@ -357,7 +359,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../sdo/region/" class="md-nav__link">
+      <a href="../calcs/region/" class="md-nav__link">
         Region Identification
       </a>
     </li>
@@ -372,7 +374,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../sdo/vel/" class="md-nav__link">
+      <a href="../calcs/vel/" class="md-nav__link">
         Velocity Components
       </a>
     </li>
@@ -387,7 +389,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../sdo/rv/" class="md-nav__link">
+      <a href="../calcs/rv/" class="md-nav__link">
         RV Calculation
       </a>
     </li>
@@ -402,7 +404,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../sdo/mag/" class="md-nav__link">
+      <a href="../calcs/mag/" class="md-nav__link">
         Magnetic Observables
       </a>
     </li>
@@ -417,7 +419,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../sdo/pipeline/" class="md-nav__link">
+      <a href="../package/pipeline/" class="md-nav__link">
         Full Pipeline
       </a>
     </li>
@@ -441,12 +443,130 @@
       
       
         <label class="md-nav__link" for="__nav_10">
+          Function Documentation
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Function Documentation" data-md-level="1">
+        <label class="md-nav__title" for="__nav_10">
+          <span class="md-nav__icon md-icon"></span>
+          Function Documentation
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../package/coord_funcs/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../package/lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../package/calculation_funcs/" class="md-nav__link">
+        Calculation Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../package/rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../package/plotting_funcs/" class="md-nav__link">
+        Plotting Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../package/settings/" class="md-nav__link">
+        Settings
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_11" type="checkbox" id="__nav_11" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_11">
           Jupyter Examples
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <nav class="md-nav" aria-label="Jupyter Examples" data-md-level="1">
-        <label class="md-nav__title" for="__nav_10">
+        <label class="md-nav__title" for="__nav_11">
           <span class="md-nav__icon md-icon"></span>
           Jupyter Examples
         </label>
@@ -458,7 +578,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../sdo/examples/docs_sunpy_example/" class="md-nav__link">
+      <a href="../examples/docs_sunpy_example/" class="md-nav__link">
         Sunpy Basics
       </a>
     </li>
@@ -472,7 +592,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../sdo/examples/docs_solar_corrections/" class="md-nav__link">
+      <a href="../examples/docs_solar_corrections/" class="md-nav__link">
         Component Calculations
       </a>
     </li>
@@ -486,7 +606,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../sdo/examples/docs_rv_calcs/" class="md-nav__link">
+      <a href="../examples/docs_rv_calcs/" class="md-nav__link">
         RV Calculation
       </a>
     </li>
@@ -500,7 +620,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../sdo/examples/docs_full_pipeline/" class="md-nav__link">
+      <a href="../examples/docs_full_pipeline/" class="md-nav__link">
         Full Pipeline
       </a>
     </li>
@@ -616,7 +736,7 @@ <h2 id="contact">Contact</h2>
       
       
         
-        <a href="../sdo/data/" class="md-footer__link md-footer__link--next" aria-label="Next: Aquiring Data" rel="next">
+        <a href="../data/data/" class="md-footer__link md-footer__link--next" aria-label="Next: Aquiring Data" rel="next">
           <div class="md-footer__title">
             <div class="md-ellipsis">
               <span class="md-footer__direction">
diff --git a/mkdocs/site/assets/_mkdocstrings.css b/mkdocs/site/assets/_mkdocstrings.css
new file mode 100644
index 0000000..b2cceef
--- /dev/null
+++ b/mkdocs/site/assets/_mkdocstrings.css
@@ -0,0 +1,16 @@
+
+/* Don't capitalize names. */
+h5.doc-heading {
+  text-transform: none !important;
+}
+
+/* Avoid breaking parameters name, etc. in table cells. */
+.doc-contents td code {
+  word-break: normal !important;
+}
+
+/* For pieces of Markdown rendered in table cells. */
+.doc-contents td p {
+  margin-top: 0 !important;
+  margin-bottom: 0 !important;
+}
diff --git a/mkdocs/site/calcs/coords/index.html b/mkdocs/site/calcs/coords/index.html
new file mode 100644
index 0000000..e87c552
--- /dev/null
+++ b/mkdocs/site/calcs/coords/index.html
@@ -0,0 +1,939 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="SDO HMI RV Pipeline Documentation">
+      
+      
+      
+      
+      <link rel="icon" href="../../img/sun.png">
+      <meta name="generator" content="mkdocs-1.2.2, mkdocs-material-7.3.1">
+    
+    
+      
+        <title>Coordinate Transformations - SDO HMI RV Pipeline</title>
+      
+    
+    
+      <link rel="stylesheet" href="../../assets/stylesheets/main.1c75ef36.min.css">
+      
+        
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.3f5d1f46.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#4051b5">
+        
+      
+    
+    
+    
+      
+        
+        <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+        <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=:300,400,400i,700%7COswald&display=fallback">
+        <style>:root{--md-text-font-family:"";--md-code-font-family:"Oswald"}</style>
+      
+    
+    
+    
+      <link rel="stylesheet" href="../../assets/_mkdocstrings.css">
+    
+    
+      
+
+
+    
+    
+  </head>
+  
+  
+    
+    
+    
+    
+    
+    <body dir="ltr" data-md-color-scheme="" data-md-color-primary="indigo" data-md-color-accent="light-blue">
+  
+    
+    <script>function __prefix(e){return new URL("../..",location).pathname+"."+e}function __get(e,t=localStorage){return JSON.parse(t.getItem(__prefix(e)))}</script>
+    
+    <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="#coordinate-transformations" 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="SDO HMI RV Pipeline" class="md-header__button md-logo" aria-label="SDO HMI RV Pipeline" data-md-component="logo">
+      
+  <img src="../../img/sun.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">
+            SDO HMI RV Pipeline
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Coordinate Transformations
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+      <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">
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+                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
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+                
+                
+                <h1 id="coordinate-transformations">Coordinate Transformations</h1>
+<p>SDO/HMI Data Products are returned in Helioprojective Cartesian pixel coordinates. In 
+order to calculate the spacecraft and solar rotation velocity corrections, we need to
+convert this to the Heliographic Carrington Coordinate System (sun centered coordinate system). 
+In addition to being sun-centered, Heliographic Carrington coordinates also rotate with
+respect to the Carrington rotation period. Transformations are based off Thompson (2006). </p>
+<p>Coordinate functions are <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/main/sdo_hmi_rvs/tools/coord_funcs.py">here</a>.</p>
+<h2 id="pixel-array-computation">Pixel Array Computation</h2>
+<p>The first step in the coordinate transformation is computation of the pixel-wise
+coordinate arrays with respect to solar center. We do this by taking into account the
+reference pixels from the header file. </p>
+<p>We then calculate the array of <span class="arithmatex"><span class="MathJax_Preview">\mu</span><script type="math/tex">\mu</script></span> and radius values for that image, where <span class="arithmatex"><span class="MathJax_Preview">\mu = cos(\theta)</span><script type="math/tex">\mu = cos(\theta)</script></span>. 
+First, we determine pixel size in physical coordinates (solar radii) and then calculate
+the center-to-limb angles. We get the radius by determining the distance in solar radii 
+to each pixel. </p>
+<pre><code class="language-python">def coordinates(smap):
+    &quot;&quot;&quot;
+    calculate array of mu values and cartesian
+    coordinates for image
+
+    Parameters
+    ----------
+    smap: Sunpy map object
+
+    Returns
+    -------
+    x: array of x coordinates in helioprojective cartesian system
+    y: array of y coordinates in helioprojective cartesian system
+    pd: array of pixel distances
+    r: array of solar radius values
+    d: observer distance from sun in solar radii
+    mu: array of mu values
+
+
+    &quot;&quot;&quot;
+
+    # distance of observer to sun in solar radii
+    d = smap.fits_header['DSUN_OBS'] / smap.fits_header['RSUN_REF']
+
+    # focal length in pixels
+    f = 180. * 3600. / np.pi / smap.fits_header['CDELT1']
+
+    # get cartesian x, y map scales
+    x, y = get_map_scales(smap)
+
+    # distance (in pixels) to pixel
+    pd = np.sqrt(x ** 2 + y ** 2)
+
+    # distance (in solar r) to pixel
+    pr = f * f * pd * pd + pd ** 4 - d * d * pd ** 4 + 0.J
+    r = (d * f * pd - np.sqrt(pr)) / (f * f + pd * pd)
+
+    # separate complex parts
+    r = r.real
+
+    # get mu array
+    pr = 1 - r ** 2 + 0.J
+    cos_alpha = (np.sqrt(pr)).real
+    sin_alpha = r.real
+    cos_theta = ((d - np.sqrt(pr)) / np.sqrt(r ** 2 + (d - np.sqrt(pr)) ** 2)).real
+    sin_theta = (np.sqrt(1 - cos_theta ** 2)).real
+    mu = cos_alpha * cos_theta - sin_alpha * sin_theta
+
+    return x, y, pd, r, d, mu
+</code></pre>
+<h2 id="coordinate-transform">Coordinate Transform</h2>
+<p>We are able to apply the coordinate transformation based on a calculated rotation 
+matrix. We get an array of coordinates in the westward, northward, and radial direction. </p>
+<pre><code class="language-python">def vel_coords(x, y, pd, r, smap):
+    &quot;&quot;&quot;
+    calculate coordinate transformation to heliographic Carrington coordinates
+
+    Parameters
+    ----------
+    x: array of x coordinates in helioprojective cartesian system
+    y: array of y coordinates in helioprojective cartesian system
+    pd: array of pixel distances
+    r: array of solar radius values
+    smap: Sunpy map object
+
+    Returns
+    -------
+    wij: array of pixel coordinates relative to solar center in westward direction
+    nij: array of pixel coordinates relative to solar center in northward direction
+    rij: array of pixel coordinates relative to solar center in radial direction
+
+    &quot;&quot;&quot;
+
+    head = smap.fits_header
+    crota2 = head['CROTA2']  # deg
+
+    # transform each pixel to get into Heliographic CR Coordinates
+    dw = y * np.sin(np.deg2rad(crota2)) + x * np.cos(np.deg2rad(crota2))
+    dn = y * np.cos(np.deg2rad(crota2)) - x * np.sin(np.deg2rad(crota2))
+
+    # get cartesian coordinates for velocity calculations
+    pr = 1 - r ** 2 + 0.J
+    wij = r * dw / pd
+    nij = r * dn / pd
+    rij = (np.sqrt(pr)).real
+
+    return wij, nij, rij
+</code></pre>
+<h2 id="fix-mu-values">Fix Mu Values</h2>
+<p>Lastly, we remove all pixels with mu values less than 0.1 because our limb-brightening model
+does poorly in correcting near the edge of the Solar disk. </p>
+<pre><code class="language-python">def fix_mu(mu, smaps, mu_cutoff=0.3):
+    &quot;&quot;&quot;
+    function to remove pixel values where mu is less than 0.1
+
+    Parameters
+    ----------
+    mu: cosine of the center to limb angle
+    smaps: list of Sunpy map object
+    mu_cutoff: minimum mu cutoff value
+
+    Returns
+    -------
+    mu: corrected cosine of the center to limb angle
+    smap: corrected Sunpy map object
+
+    &quot;&quot;&quot;
+
+    # remove pixel values where mu &lt; mu_cut (0.3)
+    bad_inds = np.where(mu &lt;= mu_cutoff)
+
+    # fix arrays
+    for smap in smaps:
+        smap.data[bad_inds] = 0
+
+    return smaps
+
+</code></pre>
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diff --git a/mkdocs/site/calcs/mag/index.html b/mkdocs/site/calcs/mag/index.html
new file mode 100644
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@@ -0,0 +1,1038 @@
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+      
+      
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+        <label class="md-nav__link" for="__nav_3">
+          HMI Data
+          <span class="md-nav__icon md-icon"></span>
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+      
+      <nav class="md-nav" aria-label="HMI Data" data-md-level="1">
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+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data/data/" class="md-nav__link">
+        Aquiring Data
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data/corr/" class="md-nav__link">
+        Data Corrections
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../coords/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../region/" class="md-nav__link">
+        Region Identification
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../vel/" class="md-nav__link">
+        Velocity Components
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../rv/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
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+  
+  
+    <li class="md-nav__item md-nav__item--active">
+      
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
+      
+      
+        
+      
+      
+        <label class="md-nav__link md-nav__link--active" for="__toc">
+          Magnetic Observables
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <a href="./" class="md-nav__link md-nav__link--active">
+        Magnetic Observables
+      </a>
+      
+        
+<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="#filling-factor" class="md-nav__link">
+    Filling Factor
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#unsigned-magnetic-flux" class="md-nav__link">
+    Unsigned Magnetic Flux
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#area-based-magnetic-observables" class="md-nav__link">
+    Area Based Magnetic Observables
+  </a>
+  
+    <nav class="md-nav" aria-label="Area Based Magnetic Observables">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#area-thresholded-filling-factor" class="md-nav__link">
+    Area Thresholded Filling Factor
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#area-thresholded-unsigned-flux" class="md-nav__link">
+    Area Thresholded Unsigned Flux
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+      
+    </li>
+  
+
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+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/pipeline/" class="md-nav__link">
+        Full Pipeline
+      </a>
+    </li>
+  
+
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+      
+      
+      
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+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
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+          <span class="md-nav__icon md-icon"></span>
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+          
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+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/coord_funcs/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
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+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
+  
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+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/calculation_funcs/" class="md-nav__link">
+        Calculation Functions
+      </a>
+    </li>
+  
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+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/plotting_funcs/" class="md-nav__link">
+        Plotting Functions
+      </a>
+    </li>
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+              
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+      <a href="../../examples/docs_sunpy_example/" class="md-nav__link">
+        Sunpy Basics
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+    </li>
+  
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+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_solar_corrections/" class="md-nav__link">
+        Component Calculations
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+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_rv_calcs/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
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+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_full_pipeline/" class="md-nav__link">
+        Full Pipeline
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+    </li>
+  
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+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#filling-factor" class="md-nav__link">
+    Filling Factor
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#unsigned-magnetic-flux" class="md-nav__link">
+    Unsigned Magnetic Flux
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#area-based-magnetic-observables" class="md-nav__link">
+    Area Based Magnetic Observables
+  </a>
+  
+    <nav class="md-nav" aria-label="Area Based Magnetic Observables">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#area-thresholded-filling-factor" class="md-nav__link">
+    Area Thresholded Filling Factor
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#area-thresholded-unsigned-flux" class="md-nav__link">
+    Area Thresholded Unsigned Flux
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/calcs/mag.md" title="Edit this page" class="md-content__button md-icon">
+                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
+                  </a>
+                
+                
+                <h1 id="calculation-of-solar-magnetic-observables">Calculation of Solar Magnetic Observables</h1>
+<p>Using HMI magnetograms, we are able to calculate magnetic observables that
+both correlate with each other as well as the velocity features. This shows 
+how the magnetic activity is driving the radial velocity variations.  </p>
+<h2 id="filling-factor">Filling Factor</h2>
+<p><strong>Disk averaged filling factors of sunspots and plage which gives
+the percentage of magnetically active pixels.</strong>  </p>
+<p>The filling factor is a calculation of the percentage of active pixels on the solar
+surface at any one time. We calculate three different factors: <span class="arithmatex"><span class="MathJax_Preview">f_{spot}</span><script type="math/tex">f_{spot}</script></span>, <span class="arithmatex"><span class="MathJax_Preview">f_{bright}</span><script type="math/tex">f_{bright}</script></span>,
+and <span class="arithmatex"><span class="MathJax_Preview">f</span><script type="math/tex">f</script></span>. These are the filling factors due to sunspots, faculae/network, and the total
+filling factor which is the sum of the two.  </p>
+<p>We return the filling factor as a percentage:<br />
+<span class="arithmatex"><span class="MathJax_Preview">f = \frac{1}{N_{pix}} \sum_{ij} W_{ij} * 100</span><script type="math/tex">f = \frac{1}{N_{pix}} \sum_{ij} W_{ij} * 100</script></span></p>
+<pre><code class="language-python">def filling_factor(mu, mmap, active, fac_inds, spot_inds, mu_cutoff=0.3):
+    &quot;&quot;&quot;
+    function to calculate filling factor
+
+    Parameters
+    ----------
+    mu: array of mu (cosine theta) values
+    mmap: UNCORRECTED Sunpy map object (Magnetogram)
+    active: weights array where active pixels have weight = 1
+    fac_inds: array of indices where faculae are detected
+    spot_inds: array of indices where sunspots are detected
+    mu_cutoff: minimum mu cutoff value
+
+    Returns
+    -------
+    f_bright: filling factor (%) for bright areas (faculae)
+    f_spot: filling factor (%) for dark areas (sunspots)
+    f_total: filling factor (%) for timestamp
+
+    &quot;&quot;&quot;
+
+    # get good mu values
+    good_mu = np.where(mu &gt; mu_cutoff)
+
+    # get number of pixels
+    npix = len(mmap.data[good_mu])
+
+    # faculae
+    faculae = np.zeros(mmap.data.shape)
+    faculae[fac_inds] = 1.
+    f_bright = np.sum(faculae) / npix * 100
+
+    # sunspots
+    spots = np.zeros(mmap.data.shape)
+    spots[spot_inds] = 1.
+    f_spot = np.sum(spots) / npix * 100
+
+    # get filling factor
+    f_total = np.sum(active) / npix * 100
+
+    return f_bright, f_spot, f_total
+</code></pre>
+<h2 id="unsigned-magnetic-flux">Unsigned Magnetic Flux</h2>
+<p><strong>The disc-averaged, line-of-sight unsigned (unpolarized) magnetic flux of the Sun.</strong></p>
+<p><span class="arithmatex"><span class="MathJax_Preview">|\hat{B_{obs}}| = \frac{\sum_{ij} |B_{obs, ij}| I_{ij}} {\sum_{ij} I_{ij}}</span><script type="math/tex">|\hat{B_{obs}}| = \frac{\sum_{ij} |B_{obs, ij}| I_{ij}} {\sum_{ij} I_{ij}}</script></span></p>
+<pre><code class="language-python">def unsigned_flux(map_mag_obs, imap):
+    &quot;&quot;&quot;
+    calculate unsigned magnetic flux
+
+    Parameters
+    ----------
+    map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram)
+    imap: UNCORRECTED Sunpy map object (Intensitygram)
+
+    Returns
+    -------
+    unsign_flux: unsigned magnetic flux
+
+    &quot;&quot;&quot;
+
+    unsign_flux = np.nansum(np.abs(map_mag_obs.data) * imap.data) / np.nansum(imap.data)
+
+    return np.abs(unsign_flux)
+</code></pre>
+<h2 id="area-based-magnetic-observables">Area Based Magnetic Observables</h2>
+<p>In addition to the unsigned flux and filling factor calculations described above, we calculate
+these quantities with an eye for what types of regions are causing variations in magnetic
+activity. The calculations for area thresholded regions is outlined below.</p>
+<h3 id="area-thresholded-filling-factor">Area Thresholded Filling Factor</h3>
+<pre><code class="language-python">def area_filling_factor(active, area, mu, mmap, fac_inds, athresh=20, mu_cutoff=0.3):
+    &quot;&quot;&quot;
+    calculate filling factor for regions thresholded by area
+    - differentiate between large and small regions
+    - differentiate between plage (large) and network (small) bright regions
+
+    Parameters
+    ----------
+    active: weights array where active pixels have weight = 1
+    area: area of each active region weighted by its intensity
+    mu: array of mu (cosine theta) values
+    mmap: UNCORRECTED Sunpy map object (Magnetogram)
+    fac_inds: array of indices where faculae are detected
+    athresh: area threshold value between large and small regions (in uHem)
+    mu_cutoff: minimum mu cutoff value for usable data
+
+    Returns
+    -------
+    f_small: filling factor (%) for small magnetically active regions
+    f_large: filling factor (%) for large magnetically active regions
+    f_network: filling factor (%) for network (small, bright magnetically active) regions
+    f_plage: filling factor (%) for plage (large, bright magnetically active) regions
+    f_nonconv: filling factor (%) for regions that do not suppress convective blueshift
+
+    &quot;&quot;&quot;
+
+    # get good mu values
+    good_mu = np.where(mu &gt; mu_cutoff)
+
+    # get number of pixels
+    npix = len(mmap.data[good_mu])
+
+    # get quiet pixels
+    quiet = 1 - active
+
+    # get filling factor for 'small' magnetic features
+    small = np.zeros(mmap.data.shape)
+    small_inds = np.logical_and(active &gt; 0.5, area &lt; athresh)
+    small[small_inds] = 1.
+    f_small = np.nansum(small) / npix * 100
+
+    # get filling factor for 'large' magnetic features
+    large = np.zeros(mmap.data.shape)
+    large_inds = np.logical_and(active &gt; 0.5, area &gt; athresh)
+    large[large_inds] = 1.
+    f_large = np.nansum(large) / npix * 100
+
+    # get filling factor for network (small, faculae regions)
+    network = np.zeros(mmap.data.shape)
+    network_inds = np.logical_and(small &gt; 0.5, fac_inds &gt; 0.5)
+    network[network_inds] = 1.
+    f_network = np.nansum(network) / npix * 100
+
+    # get filling factor for plage (large, faculae regions)
+    plage = np.zeros(mmap.data.shape)
+    plage_inds = np.logical_and(large &gt; 0.5, fac_inds &gt; 0.5)
+    plage[plage_inds] = 1.
+    f_plage = np.nansum(plage) / npix * 100
+
+    # get filling factor for small, non-convective regions
+    nonconv = np.zeros(mmap.data.shape)
+    nonconv_inds = np.logical_and(quiet &gt; 0.5, small &gt; 0.5)
+    nonconv[nonconv_inds] = 1.
+    f_nonconv = np.nansum(nonconv) / npix * 100
+
+    return f_small, f_large, f_network, f_plage, f_nonconv
+</code></pre>
+<h3 id="area-thresholded-unsigned-flux">Area Thresholded Unsigned Flux</h3>
+<pre><code class="language-python">def area_unsigned_flux(map_mag_obs, imap, area, active, athresh=20):
+    &quot;&quot;&quot;
+    calculate the magnetic flux for different regions based on area cut
+    and magnetic activitiy
+
+    Parameters
+    ----------
+    map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram)
+    imap: UNCORRECTED Sunpy map object (Intensitygram)
+    area: area of each active region weighted by its intensity
+    active: weights array where active pixels have weight = 1
+
+    Returns
+    -------
+    quiet_flux: magnetic flux of quiet-Sun regions
+    ar_flux: magnetic flux of active regions
+    conv_flux: magnetic flux of regions that suppress convective blueshift
+    pol_flux: magnetic flux of polarized regions
+    pol_conv_flux: magnetic flux of polarized regions that suppress the convective blueshift
+
+    &quot;&quot;&quot;
+
+    # get data arrays
+    i_data = imap.data
+    m_data = map_mag_obs.data
+    mabs_data = np.abs(m_data)
+    quiet = 1 - active
+
+    # get large regions array
+    large = np.zeros(m_data.shape)
+    large_inds = np.logical_and(active &gt; 0.5, area &gt; athresh)
+    large[large_inds] = 1.
+
+    # calculate relevant fluxes
+    quiet_flux = np.nansum(mabs_data * i_data * quiet) / np.nansum(i_data * quiet)
+    ar_flux = np.nansum(mabs_data * i_data * active) / np.nansum(i_data * active)
+    conv_flux = np.nansum(mabs_data * i_data * large) / np.nansum(i_data * large)
+    pol_flux = np.nansum(m_data * i_data) / np.nansum(i_data)
+    pol_conv_flux = np.nansum(m_data * i_data * large) / np.nansum(i_data * large)
+
+    return quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux
+</code></pre>
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+    Intensity Thresholding
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+              
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+                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
+                  </a>
+                
+                
+                <h1 id="identification-of-solar-regions">Identification of Solar Regions</h1>
+<p>In order to calculate the various pertinent velocity components we need
+to differentiate between different types of solar regions. We do two identification
+cuts both based on thresholding values from Yeo et al. 2013.  </p>
+<h2 id="magnetic-thresholding">Magnetic Thresholding</h2>
+<p>The first threshold is to identify active regions. We do this with a simple 
+magnetic threshold value of 24G. The threshold is applied to the unsigned magnetic
+field strength (magnetic field corrected for foreshortening). </p>
+<p>Pixels with magnetic field magnitude above the threshold are marked as active and those below the threshold 
+are quiet Sun. Isolated active pixels are re-identified as quiet Sun based on area thresholding. </p>
+<p>Isolated pixels are denoted as any pixel without four connected neighbors. The connection
+matrix is built using the <code>skimage.regionsprops</code> function.</p>
+<pre><code class="language-python">def mag_thresh(mu, mmap, Br_cutoff=24, mu_cutoff=0.3):
+    &quot;&quot;&quot;
+    function to calculate magnetic threshold and differentiate between magnetically active regions and quiet Sun
+
+    Parameters
+    ----------
+    mu: array of mu (cosine theta) values
+    mmap: corrected (unsigned magnetic field) Sunpy map object (Magnetogram)
+    Br_cutoff: minimum cutoff value (in Gauss) for thresholding active regions
+    mu_cutoff: minimum mu cutoff value for data to ignore
+
+    Returns
+    -------
+    active: weights array where active pixels are 1
+    quiet: weights array where active pixels are 0
+
+    &quot;&quot;&quot;
+
+    # get active region indices
+    active_inds = np.where(np.abs(mmap.data) * mu &gt; Br_cutoff)
+    bad_mu = np.where(mu &lt;= mu_cutoff)
+
+    # make active region array
+    active = np.zeros(mu.shape)
+    active[active_inds] = 1.
+    active[bad_mu] = 0.
+
+    # find isolated pixels
+    # get area
+    y_labeled = label(active, connectivity=2, background=0)
+    y_area = [props.area for props in regionprops(y_labeled)]
+
+    # area constraint
+    good_area = np.where(np.array(y_area) &gt; 5)
+    good_area = good_area[0] + 1
+    active_indices = np.isin(y_labeled, good_area)
+
+    # create weights array
+    active[~active_indices] = 0
+
+    # get quiet indices
+    quiet = 1 - active
+
+    return active, quiet
+</code></pre>
+<h2 id="intensity-thresholding">Intensity Thresholding</h2>
+<p>Once we detect active regions, we want to identify sunspots (dark) and faculae/network regions (bright).
+These regions can be categorized by intensity and therefore an intensity cutoff value
+is calculated relative to the overall mean flattened intensity of the quiet Sun:   </p>
+<p><span class="arithmatex"><span class="MathJax_Preview">I_{quiet} = \frac{\sum_{ij} I_{flat, ij} W_{ij}}{\sum_{ij} W_{ij}}</span><script type="math/tex">I_{quiet} = \frac{\sum_{ij} I_{flat, ij} W_{ij}}{\sum_{ij} W_{ij}}</script></span> where <span class="arithmatex"><span class="MathJax_Preview">W_{ij}</span><script type="math/tex">W_{ij}</script></span> 
+is the quiet Sun weighting array returned from magnetic thresholding.  </p>
+<p>The intensity threshold is then <span class="arithmatex"><span class="MathJax_Preview">I_{thresh} = 0.89I_{quiet}</span><script type="math/tex">I_{thresh} = 0.89I_{quiet}</script></span>.  </p>
+<pre><code class="language-python">def int_thresh(map_int_cor, active, quiet):
+    &quot;&quot;&quot;
+    function to do intensity thresholding and differentiate between faculae (bright) and sunspots (dark)
+
+    Parameters
+    ----------
+    map_int_cor: corrected (limb-darkening) Sunpy map object (Intensitygram)
+    active: weights array where active pixels are 1
+    quiet: weights array where active pixels are 0
+
+    Returns
+    -------
+    fac_inds: array of indices where faculae are detected
+    spot_inds: array of indices where sunspots are detected
+
+    &quot;&quot;&quot;
+    # flattened intensity data
+    Iflat = map_int_cor.data
+
+    # calculate quiet sun intensity
+    int_quiet = np.nansum(Iflat * quiet) / np.nansum(quiet)
+
+    # intensity threshold
+    int_cutoff = 0.89 * int_quiet
+
+    # get faculae
+    fac_inds = np.logical_and((Iflat &gt; int_cutoff), (active &gt; 0.5))
+
+    # get sunspots
+    spot_inds = np.logical_and((Iflat &lt;= int_cutoff), (active &gt; 0.5))
+
+    return fac_inds, spot_inds
+</code></pre>
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+                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
+                  </a>
+                
+                
+                <h1 id="calculation-of-model-rvs">Calculation of Model RVs</h1>
+<p>The model 'sun-as-a-star' RV variation is a linear combination of the two velocity 
+components <span class="arithmatex"><span class="MathJax_Preview">\rm (v_{phot}, v_{conv})</span><script type="math/tex">\rm (v_{phot}, v_{conv})</script></span> along with an offset factor of <span class="arithmatex"><span class="MathJax_Preview">\rm RV_0</span><script type="math/tex">\rm RV_0</script></span>: </p>
+<p><span class="arithmatex"><span class="MathJax_Preview">RV = A \ v_{phot} + B \ v_{conv} + RV_0</span><script type="math/tex">RV = A \ v_{phot} + B \ v_{conv} + RV_0</script></span></p>
+<p>Our ability to take high resolution measurements of the Solar disk is what 
+allows us to decompose the overall RV variation into these two components. </p>
+<p>Because we do not know the relative weights of these two velocity components, we
+rely on regression to determine the best parameters to fit our ground based data.</p>
+<p>We calculate scaling factors A and B, and then offset <span class="arithmatex"><span class="MathJax_Preview">\rm RV_0</span><script type="math/tex">\rm RV_0</script></span> using linear regression. The need for these scaling
+coefficients arises due to the fact that SDO/HMI images the Sun in one wavelength (6173 A)
+while ground based measurements average over thousands of lines. The offset accounts for the zero 
+point of the instrument.  </p>
+<p>An example of the linear regression model used can be found <a href="../examples/docs_rv_calcs/">here</a>.</p>
+                
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+    Photometric Velocity
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+    Convective Velocity
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+                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
+                  </a>
+                
+                
+                <h1 id="calculation-of-rv-components">Calculation of RV Components</h1>
+<p>After the data corrections and identification of solar regions, we are able to calculate
+the individual feature components that make up the SDO derived RV variation. </p>
+<h2 id="photometric-velocity">Photometric Velocity</h2>
+<p><strong>Estimation of rotational Doppler imbalance due to active (faculae/darkspot) regions</strong>  </p>
+<p>Calculation of the photometric velocity due the presence of dark spots/bright faculae causing a Doppler imbalance. Requires
+an active region based magnetic weighting array. A weight of 0 denotes quiet-Sun and
+1 denotes active pixel.</p>
+<p>The equation for this velocity calculation is:
+<span class="arithmatex"><span class="MathJax_Preview">v_{phot} = \frac{\sum_{ij} v_{rot, ij} (I_{ij} - K) W_{ij}}
+ {\sum_{ij} I_{ij}}</span><script type="math/tex">v_{phot} = \frac{\sum_{ij} v_{rot, ij} (I_{ij} - K) W_{ij}}
+ {\sum_{ij} I_{ij}}</script></span>.</p>
+<p>K is a scaling factor based off the limb-brightening correction.
+<span class="arithmatex"><span class="MathJax_Preview">K = \frac{\sum_{ij} I_{ij} L_{ij} W_{ij}}{\sum_{ij} L_{ij}^2 W_{ij}}</span><script type="math/tex">K = \frac{\sum_{ij} I_{ij} L_{ij} W_{ij}}{\sum_{ij} L_{ij}^2 W_{ij}}</script></span></p>
+<p>In this case <span class="arithmatex"><span class="MathJax_Preview">I_{ij}</span><script type="math/tex">I_{ij}</script></span> is the uncorrected (original) intensity data.  </p>
+<p>We are also able to use out intensity weighting to calculate photometric velocities due to 
+just faculae/network or sunspot regions. </p>
+<pre><code class="language-python">def v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds, mu_cutoff=0.3):
+    &quot;&quot;&quot;
+    function to calculate photometric velocity due to rotational Doppler variation
+
+    Parameters
+    ----------
+    quiet: weights array where active pixels have weight = 0
+    active: weights array where active pixels have weight = 1
+    Lij: limb-darkening polynomial function
+    vrot: solar rotational velocity
+    imap: UNCORRECTED Sunpy map object (Intensitygram)
+    mu: array of mu values
+    fac_inds: array of indices where faculae are detected
+    spot_inds: array of indices where sunspots are detected
+    mu_cutoff: minimum mu cutoff value
+
+    Returns
+    -------
+    v_phot: photospheric velocity perturbation (float)
+
+    &quot;&quot;&quot;
+
+    # get good mu values
+    good_mu = np.where(mu &gt; mu_cutoff)
+
+    # calculate K scaling factor
+    K = np.nansum(imap.data * Lij * quiet) / np.sum((Lij[good_mu] ** 2) * quiet[good_mu])
+
+    # calculate photospheric velocity
+    v_phot = np.nansum(np.real(vrot) * (imap.data - K * Lij) * active) / np.nansum(imap.data)
+
+    # faculae driven photospheric velocity
+    vphot_bright = np.nansum(np.real(vrot) * (imap.data - K * Lij) * fac_inds) / np.nansum(imap.data)
+
+    # sunspots driven photospheric velocity
+    vphot_spot = np.nansum(np.real(vrot) * (imap.data - K * Lij) * spot_inds) / np.nansum(imap.data)
+
+    return v_phot, vphot_bright, vphot_spot
+</code></pre>
+<h2 id="convective-velocity">Convective Velocity</h2>
+<p><strong>Estimation due to the suppression of the convective blueshift by magnetically active regions</strong></p>
+<p>Calculation of suppression of convective blueshift due to active regions (mainly faculae).</p>
+<p>The basic premise for calculation is to calculate the disc-averaged velocity of the Sun
+and subtract from that the quiet-Sun velocity.</p>
+<p>We then calculate the convective velocity as what is left:
+<span class="arithmatex"><span class="MathJax_Preview">v_{conv} = v - v_{quiet}</span><script type="math/tex">v_{conv} = v - v_{quiet}</script></span> </p>
+<h3 id="quiet-sun-velocity">Quiet Sun Velocity</h3>
+<p><strong>Estimation of average RV of quiet-Sun due to convective motion</strong>  </p>
+<p>Calculate the velocity due to the convective motion of the quiet-Sun. Requires
+the magnetic weighting array we built in step six, part one. A weight of 0 denotes an
+active pixel, and 1 is quiet-Sun.</p>
+<p>The equation for calculating this quiet-Sun velocity is as such:
+<span class="arithmatex"><span class="MathJax_Preview">v_{quiet} = \frac{\sum_{ij} (v_{ij} - \delta v_{sc, ij} - \delta v_{rot, ij}) I_{ij} W_{ij}}
+ {\sum_{ij} I_{ij} W_{ij}}</span><script type="math/tex">v_{quiet} = \frac{\sum_{ij} (v_{ij} - \delta v_{sc, ij} - \delta v_{rot, ij}) I_{ij} W_{ij}}
+ {\sum_{ij} I_{ij} W_{ij}}</script></span>.</p>
+<p>In this case <span class="arithmatex"><span class="MathJax_Preview">I_{ij}</span><script type="math/tex">I_{ij}</script></span> is the uncorrected (original) intensity data.  </p>
+<pre><code class="language-python">def v_quiet(map_vel_cor, imap, quiet):
+    &quot;&quot;&quot;
+    function to calculate velocity due to convective motion of quiet-Sun
+
+    Parameters
+    ----------
+    map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram)
+    imap: UNCORRECTED Sunpy map object (Intensitygram)
+    quiet: weights array where active pixels have weight = 0
+
+    Returns
+    -------
+    v_quiet: quiet-Sun velocity (float)
+
+    &quot;&quot;&quot;
+
+    v_quiet = np.nansum(map_vel_cor.data * imap.data * quiet) / np.nansum(
+        imap.data * quiet)
+
+    return v_quiet
+</code></pre>
+<h3 id="full-disc-velocity">Full Disc Velocity</h3>
+<p><strong>Total corrected (spacecraft motion, solar rotation) disk-averaged velocity of the Sun</strong>  </p>
+<p>The equation for the disc-averaged velocity is:
+<span class="arithmatex"><span class="MathJax_Preview">v = \frac{\sum_{ij} (v_{ij} - \delta v_{sc, ij} - \delta v_{rot, ij}) I_{ij}}{\sum_{ij} I_{ij}}</span><script type="math/tex">v = \frac{\sum_{ij} (v_{ij} - \delta v_{sc, ij} - \delta v_{rot, ij}) I_{ij}}{\sum_{ij} I_{ij}}</script></span>.  </p>
+<pre><code class="language-python">def v_disc(map_vel_cor, imap):
+    &quot;&quot;&quot;
+    function to calculate disc-averaged velocity of Sun
+
+    Parameters
+    ----------
+    map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram)
+    imap: UNCORRECTED Sunpy map object (Intensitygram)
+
+    Returns
+    -------
+    v_disc: disc averaged velocity of Sun (float)
+
+    &quot;&quot;&quot;
+
+    v_disc = np.nansum(map_vel_cor.data * imap.data) / np.nansum(imap.data)
+
+    return v_disc
+</code></pre>
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+      
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+        <label class="md-nav__link" for="__nav_3">
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+          <span class="md-nav__icon md-icon"></span>
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+      
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+          Aquiring Data
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <a href="./" class="md-nav__link md-nav__link--active">
+        Aquiring Data
+      </a>
+      
+        
+<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="#acquiring-the-data" class="md-nav__link">
+    Acquiring the Data
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+      
+    </li>
+  
+
+            
+          
+            
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+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../corr/" class="md-nav__link">
+        Data Corrections
+      </a>
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+  
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+      </nav>
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+  
+
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+    <li class="md-nav__item">
+      <a href="../../calcs/coords/" class="md-nav__link">
+        Coordinate Transformations
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+    </li>
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+
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+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/region/" class="md-nav__link">
+        Region Identification
+      </a>
+    </li>
+  
+
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+      
+      
+      
+
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+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/vel/" class="md-nav__link">
+        Velocity Components
+      </a>
+    </li>
+  
+
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+      
+      
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+
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+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/rv/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
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+      
+      
+      
+
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+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/mag/" class="md-nav__link">
+        Magnetic Observables
+      </a>
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+  
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+      
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+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/pipeline/" class="md-nav__link">
+        Full Pipeline
+      </a>
+    </li>
+  
+
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+      
+      
+
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+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
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+          Function Documentation
+          <span class="md-nav__icon md-icon"></span>
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+        <label class="md-nav__title" for="__nav_10">
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+          Function Documentation
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+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/coord_funcs/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
+  
+
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+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/calculation_funcs/" class="md-nav__link">
+        Calculation Functions
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+    </li>
+  
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+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/plotting_funcs/" class="md-nav__link">
+        Plotting Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/settings/" class="md-nav__link">
+        Settings
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+    </li>
+  
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+      </nav>
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+  
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+        <label class="md-nav__link" for="__nav_11">
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+          
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+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_sunpy_example/" class="md-nav__link">
+        Sunpy Basics
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_solar_corrections/" class="md-nav__link">
+        Component Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_rv_calcs/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_full_pipeline/" class="md-nav__link">
+        Full Pipeline
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#acquiring-the-data" class="md-nav__link">
+    Acquiring the Data
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/data/data.md" title="Edit this page" class="md-content__button md-icon">
+                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
+                  </a>
+                
+                
+                <h1 id="data-used">Data Used</h1>
+<p>This project requires the use of data from the Helioseismic and Magnetic 
+Imager (HMI) aboard NASA's Solar Dynamics Observatory (SDO). </p>
+<p>HMI has been continuously imaging the Sun since 2012 in the 6173 A line. HMI
+takes images every 45 seconds and provides information regarding the velocity, continuum, and magnetic 
+field of the Solar photosphere. </p>
+<p>Additional information regarding the instrument can be found on the <a href="http://hmi.stanford.edu/">HMI webpage</a>.  </p>
+<p>The three data products used for these calculations are:  </p>
+<ul>
+<li>
+<p><strong>Dopplergrams</strong>: measurements of solar surface velocity  </p>
+</li>
+<li>
+<p><strong>Filtergrams</strong>: continuum intensity measurements of the Solar photosphere  </p>
+</li>
+<li>
+<p><strong>Magnetograms</strong>: measurements of the line-of-sight Solar magnetic field  </p>
+</li>
+</ul>
+<h2 id="acquiring-the-data">Acquiring the Data</h2>
+<p>To get the HMI data products, we utilize Sunpy's querying techniques. 
+This allows us to search by date, physical observable, and instrument.  </p>
+<p>An example of querying Sunpy for data can be found <a href="../examples/docs_sunpy_example/">here</a>.  </p>
+                
+              
+              
+                
+
+
+              
+            </article>
+          </div>
+        </div>
+        
+          <a href="#" class="md-top md-icon" data-md-component="top" data-md-state="hidden">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"/></svg>
+            Back to top
+          </a>
+        
+      </main>
+      
+        
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+  
+    <nav class="md-footer__inner md-grid" aria-label="Footer">
+      
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+        <a href="../../about/" class="md-footer__link md-footer__link--prev" aria-label="Previous: About" rel="prev">
+          <div class="md-footer__button md-icon">
+            <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>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
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+        
+        <a href="../corr/" class="md-footer__link md-footer__link--next" aria-label="Next: Data Corrections" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
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+        </a>
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+    </nav>
+  
+  <div class="md-footer-meta md-typeset">
+    <div class="md-footer-meta__inner md-grid">
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+      <div class="md-dialog__inner md-typeset"></div>
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+    <script id="__config" type="application/json">{"base": "../..", "features": ["navigation.top", "header.autohide"], "translations": {"clipboard.copy": "Copy to clipboard", "clipboard.copied": "Copied to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.placeholder": "Type to start searching", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "search": "../../assets/javascripts/workers/search.e99e714c.min.js", "version": null}</script>
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+</html>
\ No newline at end of file
diff --git a/mkdocs/site/sdo/examples/docs_full_pipeline/index.html b/mkdocs/site/examples/docs_full_pipeline/index.html
similarity index 99%
rename from mkdocs/site/sdo/examples/docs_full_pipeline/index.html
rename to mkdocs/site/examples/docs_full_pipeline/index.html
index cd31f51..3e758b6 100644
--- a/mkdocs/site/sdo/examples/docs_full_pipeline/index.html
+++ b/mkdocs/site/examples/docs_full_pipeline/index.html
@@ -11,7 +11,7 @@
       
       
       
-      <link rel="icon" href="../../../img/sun.png">
+      <link rel="icon" href="../../img/sun.png">
       <meta name="generator" content="mkdocs-1.2.2, mkdocs-material-7.3.1">
     
     
@@ -20,10 +20,10 @@
       
     
     
-      <link rel="stylesheet" href="../../../assets/stylesheets/main.1c75ef36.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.1c75ef36.min.css">
       
         
-        <link rel="stylesheet" href="../../../assets/stylesheets/palette.3f5d1f46.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.3f5d1f46.min.css">
         
           
           
@@ -42,6 +42,8 @@
     
     
     
+      <link rel="stylesheet" href="../../assets/_mkdocstrings.css">
+    
     
       
 
@@ -59,7 +61,7 @@
     <body dir="ltr" data-md-color-scheme="" data-md-color-primary="indigo" data-md-color-accent="light-blue">
   
     
-    <script>function __prefix(e){return new URL("../../..",location).pathname+"."+e}function __get(e,t=localStorage){return JSON.parse(t.getItem(__prefix(e)))}</script>
+    <script>function __prefix(e){return new URL("../..",location).pathname+"."+e}function __get(e,t=localStorage){return JSON.parse(t.getItem(__prefix(e)))}</script>
     
     <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">
@@ -80,9 +82,9 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SDO HMI RV Pipeline" class="md-header__button md-logo" aria-label="SDO HMI RV Pipeline" data-md-component="logo">
+    <a href="../.." title="SDO HMI RV Pipeline" class="md-header__button md-logo" aria-label="SDO HMI RV Pipeline" data-md-component="logo">
       
-  <img src="../../../img/sun.png" alt="logo">
+  <img src="../../img/sun.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -179,9 +181,9 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SDO HMI RV Pipeline" class="md-nav__button md-logo" aria-label="SDO HMI RV Pipeline" data-md-component="logo">
+    <a href="../.." title="SDO HMI RV Pipeline" class="md-nav__button md-logo" aria-label="SDO HMI RV Pipeline" data-md-component="logo">
       
-  <img src="../../../img/sun.png" alt="logo">
+  <img src="../../img/sun.png" alt="logo">
 
     </a>
     SDO HMI RV Pipeline
@@ -210,7 +212,7 @@
   
   
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-      <a href="../../.." class="md-nav__link">
+      <a href="../.." class="md-nav__link">
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@@ -225,7 +227,7 @@
   
   
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-      <a href="../../../about/" class="md-nav__link">
+      <a href="../../about/" class="md-nav__link">
         About
       </a>
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@@ -266,7 +268,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../../data/" class="md-nav__link">
+      <a href="../../data/data/" class="md-nav__link">
         Aquiring Data
       </a>
     </li>
@@ -280,7 +282,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../../corr/" class="md-nav__link">
+      <a href="../../data/corr/" class="md-nav__link">
         Data Corrections
       </a>
     </li>
@@ -302,7 +304,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../../coords/" class="md-nav__link">
+      <a href="../../calcs/coords/" class="md-nav__link">
         Coordinate Transformations
       </a>
     </li>
@@ -317,7 +319,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../../region/" class="md-nav__link">
+      <a href="../../calcs/region/" class="md-nav__link">
         Region Identification
       </a>
     </li>
@@ -332,7 +334,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../../vel/" class="md-nav__link">
+      <a href="../../calcs/vel/" class="md-nav__link">
         Velocity Components
       </a>
     </li>
@@ -347,7 +349,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../../rv/" class="md-nav__link">
+      <a href="../../calcs/rv/" class="md-nav__link">
         RV Calculation
       </a>
     </li>
@@ -362,7 +364,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../../mag/" class="md-nav__link">
+      <a href="../../calcs/mag/" class="md-nav__link">
         Magnetic Observables
       </a>
     </li>
@@ -377,7 +379,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../../pipeline/" class="md-nav__link">
+      <a href="../../package/pipeline/" class="md-nav__link">
         Full Pipeline
       </a>
     </li>
@@ -390,6 +392,124 @@
 
   
   
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_10" type="checkbox" id="__nav_10" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_10">
+          Function Documentation
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Function Documentation" data-md-level="1">
+        <label class="md-nav__title" for="__nav_10">
+          <span class="md-nav__icon md-icon"></span>
+          Function Documentation
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/coord_funcs/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/calculation_funcs/" class="md-nav__link">
+        Calculation Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/plotting_funcs/" class="md-nav__link">
+        Plotting Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/settings/" class="md-nav__link">
+        Settings
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
     
   
   
@@ -397,18 +517,18 @@
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
       
-        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_10" type="checkbox" id="__nav_10" checked>
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_11" type="checkbox" id="__nav_11" checked>
       
       
       
       
-        <label class="md-nav__link" for="__nav_10">
+        <label class="md-nav__link" for="__nav_11">
           Jupyter Examples
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <nav class="md-nav" aria-label="Jupyter Examples" data-md-level="1">
-        <label class="md-nav__title" for="__nav_10">
+        <label class="md-nav__title" for="__nav_11">
           <span class="md-nav__icon md-icon"></span>
           Jupyter Examples
         </label>
@@ -574,7 +694,7 @@
             <article class="md-content__inner md-typeset">
               
                 
-                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/sdo/examples/docs_full_pipeline.ipynb" title="Edit this page" class="md-content__button md-icon">
+                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/examples/docs_full_pipeline.ipynb" title="Edit this page" class="md-content__button md-icon">
                     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
                   </a>
                 
@@ -2036,14 +2156,14 @@ <h2 id="full-rv-calculation">Full RV Calculation<a class="anchor-link" href="#Fu
     <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.top", "header.autohide"], "translations": {"clipboard.copy": "Copy to clipboard", "clipboard.copied": "Copied to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.placeholder": "Type to start searching", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "search": "../../../assets/javascripts/workers/search.e99e714c.min.js", "version": null}</script>
+    <script id="__config" type="application/json">{"base": "../..", "features": ["navigation.top", "header.autohide"], "translations": {"clipboard.copy": "Copy to clipboard", "clipboard.copied": "Copied to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.placeholder": "Type to start searching", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "search": "../../assets/javascripts/workers/search.e99e714c.min.js", "version": null}</script>
     
     
-      <script src="../../../assets/javascripts/bundle.c7d1b464.min.js"></script>
+      <script src="../../assets/javascripts/bundle.c7d1b464.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
-        <script src="../../../javascripts/config.js"></script>
+        <script src="../../javascripts/config.js"></script>
       
         <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
       
diff --git a/mkdocs/site/examples/docs_rv_calcs/index.html b/mkdocs/site/examples/docs_rv_calcs/index.html
new file mode 100644
index 0000000..8166931
--- /dev/null
+++ b/mkdocs/site/examples/docs_rv_calcs/index.html
@@ -0,0 +1,1785 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="SDO HMI RV Pipeline Documentation">
+      
+      
+      
+      
+      <link rel="icon" href="../../img/sun.png">
+      <meta name="generator" content="mkdocs-1.2.2, mkdocs-material-7.3.1">
+    
+    
+      
+        <title>RV Calculation - SDO HMI RV Pipeline</title>
+      
+    
+    
+      <link rel="stylesheet" href="../../assets/stylesheets/main.1c75ef36.min.css">
+      
+        
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.3f5d1f46.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#4051b5">
+        
+      
+    
+    
+    
+      
+        
+        <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+        <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=:300,400,400i,700%7COswald&display=fallback">
+        <style>:root{--md-text-font-family:"";--md-code-font-family:"Oswald"}</style>
+      
+    
+    
+    
+      <link rel="stylesheet" href="../../assets/_mkdocstrings.css">
+    
+    
+      
+
+
+    
+    
+  </head>
+  
+  
+    
+    
+    
+    
+    
+    <body dir="ltr" data-md-color-scheme="" data-md-color-primary="indigo" data-md-color-accent="light-blue">
+  
+    
+    <script>function __prefix(e){return new URL("../..",location).pathname+"."+e}function __get(e,t=localStorage){return JSON.parse(t.getItem(__prefix(e)))}</script>
+    
+    <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="#calculation-of-solar-rv-variations" 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="SDO HMI RV Pipeline" class="md-header__button md-logo" aria-label="SDO HMI RV Pipeline" data-md-component="logo">
+      
+  <img src="../../img/sun.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">
+            SDO HMI RV Pipeline
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              RV Calculation
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+      <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" 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"></ol>
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+    
+    
+      <div class="md-header__source">
+        
+<a href="https://github.com/tamarervin/sdo_hmi_rvs/" 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"><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">
+    GitHub
+  </div>
+</a>
+      </div>
+    
+  </nav>
+  
+</header>
+    
+    <div class="md-container" data-md-component="container">
+      
+      
+        
+          
+        
+      
+      <main class="md-main" data-md-component="main">
+        <div class="md-main__inner md-grid">
+          
+            
+              
+              <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+
+<nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
+  <label class="md-nav__title" for="__drawer">
+    <a href="../.." title="SDO HMI RV Pipeline" class="md-nav__button md-logo" aria-label="SDO HMI RV Pipeline" data-md-component="logo">
+      
+  <img src="../../img/sun.png" alt="logo">
+
+    </a>
+    SDO HMI RV Pipeline
+  </label>
+  
+    <div class="md-nav__source">
+      
+<a href="https://github.com/tamarervin/sdo_hmi_rvs/" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
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+  </div>
+  <div class="md-source__repository">
+    GitHub
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../.." class="md-nav__link">
+        Home
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../about/" class="md-nav__link">
+        About
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          HMI Data
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="HMI Data" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          HMI Data
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data/data/" class="md-nav__link">
+        Aquiring Data
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data/corr/" class="md-nav__link">
+        Data Corrections
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/coords/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/region/" class="md-nav__link">
+        Region Identification
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/vel/" class="md-nav__link">
+        Velocity Components
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/rv/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/mag/" class="md-nav__link">
+        Magnetic Observables
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/pipeline/" class="md-nav__link">
+        Full Pipeline
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_10" type="checkbox" id="__nav_10" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_10">
+          Function Documentation
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Function Documentation" data-md-level="1">
+        <label class="md-nav__title" for="__nav_10">
+          <span class="md-nav__icon md-icon"></span>
+          Function Documentation
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/coord_funcs/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/calculation_funcs/" class="md-nav__link">
+        Calculation Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/plotting_funcs/" class="md-nav__link">
+        Plotting Functions
+      </a>
+    </li>
+  
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+            
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+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/settings/" class="md-nav__link">
+        Settings
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+      
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+        <label class="md-nav__title" for="__nav_11">
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+          Jupyter Examples
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+        <ul class="md-nav__list" data-md-scrollfix>
+          
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+              
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+    <li class="md-nav__item">
+      <a href="../docs_sunpy_example/" class="md-nav__link">
+        Sunpy Basics
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+    </li>
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+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../docs_solar_corrections/" class="md-nav__link">
+        Component Calculations
+      </a>
+    </li>
+  
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+              
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+  
+    
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+    <li class="md-nav__item md-nav__item--active">
+      
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
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+        RV Calculation
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+    </li>
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+          
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+    <li class="md-nav__item">
+      <a href="../docs_full_pipeline/" class="md-nav__link">
+        Full Pipeline
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+</nav>
+                  </div>
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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">
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+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
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+                  </div>
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+            <article class="md-content__inner md-typeset">
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+                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/examples/docs_rv_calcs.ipynb" title="Edit this page" class="md-content__button md-icon">
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+    
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+
+/*# sourceMappingURL=mkdocs-jupyter.css.map*/</style>
+
+    <style type="text/css">
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+The following CSS variables define the main, public API for styling JupyterLab.
+These variables should be used by all plugins wherever possible. In other
+words, plugins should not define custom colors, sizes, etc unless absolutely
+necessary. This enables users to change the visual theme of JupyterLab
+by changing these variables.
+
+Many variables appear in an ordered sequence (0,1,2,3). These sequences
+are designed to work well together, so for example, `--jp-border-color1` should
+be used with `--jp-layout-color1`. The numbers have the following meanings:
+
+* 0: super-primary, reserved for special emphasis
+* 1: primary, most important under normal situations
+* 2: secondary, next most important under normal situations
+* 3: tertiary, next most important under normal situations
+
+Throughout JupyterLab, we are mostly following principles from Google's
+Material Design when selecting colors. We are not, however, following
+all of MD as it is not optimized for dense, information rich UIs.
+*/
+
+:root {
+  /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+  --jp-shadow-base-lightness: 0;
+  --jp-shadow-umbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.2
+  );
+  --jp-shadow-penumbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.14
+  );
+  --jp-shadow-ambient-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.12
+  );
+  --jp-elevation-z0: none;
+  --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+    0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 3px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+    0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 5px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+    0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 10px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+    0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 18px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+    0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+    0px 3px 14px 2px var(--jp-shadow-ambient-color);
+  --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+    0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+    0px 5px 22px 4px var(--jp-shadow-ambient-color);
+  --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+    0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+    0px 6px 30px 5px var(--jp-shadow-ambient-color);
+  --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+    0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+    0px 8px 38px 7px var(--jp-shadow-ambient-color);
+  --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+    0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+    0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+  /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+  --jp-border-width: 1px;
+  --jp-border-color0: var(--md-grey-400);
+  --jp-border-color1: var(--md-grey-400);
+  --jp-border-color2: var(--md-grey-300);
+  --jp-border-color3: var(--md-grey-200);
+  --jp-border-radius: 2px;
+
+  /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-ui-font-scale-factor: 1.2;
+  --jp-ui-font-size0: 0.83333em;
+  --jp-ui-font-size1: 13px; /* Base font size */
+  --jp-ui-font-size2: 1.2em;
+  --jp-ui-font-size3: 1.44em;
+
+  --jp-ui-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica,
+    Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
+
+  /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+  /* Defaults use Material Design specification */
+  --jp-ui-font-color0: rgba(0, 0, 0, 1);
+  --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+  /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+  --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+  --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+  /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-content-line-height: 1.6;
+  --jp-content-font-scale-factor: 1.2;
+  --jp-content-font-size0: 0.83333em;
+  --jp-content-font-size1: 14px; /* Base font size */
+  --jp-content-font-size2: 1.2em;
+  --jp-content-font-size3: 1.44em;
+  --jp-content-font-size4: 1.728em;
+  --jp-content-font-size5: 2.0736em;
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-content-presentation-font-size1: 17px;
+
+  --jp-content-heading-line-height: 1;
+  --jp-content-heading-margin-top: 1.2em;
+  --jp-content-heading-margin-bottom: 0.8em;
+  --jp-content-heading-font-weight: 500;
+
+  /* Defaults use Material Design specification */
+  --jp-content-font-color0: rgba(0, 0, 0, 1);
+  --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+  --jp-content-link-color: var(--md-blue-700);
+
+  --jp-content-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI',
+    Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
+    'Segoe UI Symbol';
+
+  /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+  --jp-code-font-size: 13px;
+  --jp-code-line-height: 1.3077; /* 17px for 13px base */
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+    <!-- End of mathjax configuration --><div class="jupyter-wrapper">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="calculation-of-solar-rv-variations">Calculation of Solar RV Variations<a class="anchor-link" href="#Calculation-of-Solar-RV-Variations">&#182;</a></h1><ol>
+<li><strong>calculate solar velocities:</strong> calculate solar velocities and save to csv</li>
+<li><strong>get component lists:</strong> using pandas to get the lists of each relevant component</li>
+<li><strong>calculate RVs and save to csv:</strong>  calculate RVs using optimized parameters</li>
+</ol>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import numpy as np
+import pandas as pd
+
+from sklearn.linear_model import LinearRegression</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Get ground-based 'sun-as-a-star' RVs.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># get csv with solar RVs</span>
+<span class="n">csv_file</span> <span class="o">=</span> <span class="s1">&#39;file_name&#39;</span>
+
+<span class="c1"># create pandas dataframe</span>
+<span class="n">component_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">csv_file</span><span class="p">)</span>
+
+<span class="c1"># get RV list</span>
+<span class="n">rv_sun</span> <span class="o">=</span> <span class="n">component_df</span><span class="o">.</span><span class="n">rv_sun</span><span class="o">.</span><span class="n">values</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt"># get csv with solar RVs
+csv_file = 'file_name'
+
+# create pandas dataframe
+component_df = pd.read_csv(csv_file)
+
+# get RV list
+rv_sun = component_df.rv_sun.values</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Read in velocities from csv.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># csv file with rv components</span>
+<span class="n">csv_file</span> <span class="o">=</span> <span class="s1">&#39;file_name&#39;</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt"># csv file with rv components
+csv_file = 'file_name'</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Get component lists.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># create pandas dataframe</span>
+<span class="n">component_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">csv_file</span><span class="p">)</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt"># create pandas dataframe
+component_df = pd.read_csv(csv_file)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># get velocities lists</span>
+<span class="n">v_phot</span> <span class="o">=</span> <span class="n">component_df</span><span class="o">.</span><span class="n">v_phot</span><span class="o">.</span><span class="n">values</span>
+<span class="n">v_conv</span> <span class="o">=</span> <span class="n">component_df</span><span class="o">.</span><span class="n">v_conv</span><span class="o">.</span><span class="n">values</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt"># get velocities lists
+v_phot = component_df.v_phot.values
+v_conv = component_df.v_conv.values</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate scaling coefficient values -- using linear regression.</p>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Setup data to plug into linear regression model/</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># components</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rv_sun</span><span class="p">),</span> <span class="mi">2</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="o">=</span> <span class="n">v_phot</span>
+<span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">v_conv</span>
+
+<span class="c1"># data we are fitting to</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">rv_sun</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt"># components
+X = np.zeros(shape=(len(rv_sun), 2))
+X[:, 0] = v_phot
+X[:, 1] = v_conv
+
+# data we are fitting to
+y = rv_sun</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Use sklearn linear regression to get coefficient values.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># apply regression</span>
+<span class="n">reg</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;R^2 for prediction: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)))</span>
+
+<span class="c1"># get scaling factors</span>
+<span class="n">A</span> <span class="o">=</span> <span class="n">reg</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">B</span> <span class="o">=</span> <span class="n">reg</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">RV0</span> <span class="o">=</span> <span class="n">reg</span><span class="o">.</span><span class="n">intercept_</span>
+
+<span class="c1"># print scaling factors</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Scaling Factors:</span><span class="se">\n</span><span class="s2"> A:&quot;</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">B:&quot;</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">RV0:&quot;</span><span class="p">,</span> <span class="n">RV0</span><span class="p">)</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt"># apply regression
+reg = LinearRegression().fit(X, y)
+print('R^2 for prediction: ' + str(reg.score(X, y)))
+
+# get scaling factors
+A = reg.coef_[0]
+B = reg.coef_[1]
+RV0 = reg.intercept_
+
+# print scaling factors
+print("Scaling Factors:\n A:", A, "\nB:", B, "\nRV0:", RV0)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate the Model RV Variation using these coefficients.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">RV</span> <span class="o">=</span> <span class="n">A</span><span class="o">*</span><span class="n">v_phot</span> <span class="o">+</span> <span class="n">B</span><span class="o">*</span><span class="n">v_conv</span> <span class="o">+</span> <span class="n">RV0</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">RV = A*v_phot + B*v_conv + RV0</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Add calculation to csv.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">component_df</span><span class="p">[</span><span class="s2">&quot;rv_model&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">RV</span>
+<span class="n">component_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">csv_file</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt">component_df["rv_model"] = RV
+component_df.to_csv(csv_file, index=False)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
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+        Aquiring Data
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+    <li class="md-nav__item">
+      <a href="../../data/corr/" class="md-nav__link">
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+    <li class="md-nav__item">
+      <a href="../../calcs/coords/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/region/" class="md-nav__link">
+        Region Identification
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/vel/" class="md-nav__link">
+        Velocity Components
+      </a>
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+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/rv/" class="md-nav__link">
+        RV Calculation
+      </a>
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+      
+      
+      
+
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+  
+  
+    <li class="md-nav__item">
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+        Magnetic Observables
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+    <li class="md-nav__item">
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+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/coord_funcs/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
+  
+
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+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/calculation_funcs/" class="md-nav__link">
+        Calculation Functions
+      </a>
+    </li>
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+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../package/plotting_funcs/" class="md-nav__link">
+        Plotting Functions
+      </a>
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+          
+            
+              
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+  
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+    <li class="md-nav__item">
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+        Settings
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+        <label class="md-nav__link" for="__nav_11">
+          Jupyter Examples
+          <span class="md-nav__icon md-icon"></span>
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+      
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+        <label class="md-nav__title" for="__nav_11">
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+          Jupyter Examples
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../docs_sunpy_example/" class="md-nav__link">
+        Sunpy Basics
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+    
+  
+  
+    <li class="md-nav__item md-nav__item--active">
+      
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
+      
+      
+        
+      
+      
+        <label class="md-nav__link md-nav__link--active" for="__toc">
+          Component Calculations
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <a href="./" class="md-nav__link md-nav__link--active">
+        Component Calculations
+      </a>
+      
+        
+<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="#step-one-coordinate-transformation" class="md-nav__link">
+    Step One: Coordinate Transformation
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#step-two-calculate-spacecraft-velocity" class="md-nav__link">
+    Step Two: Calculate Spacecraft Velocity
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#step-three-calculate-velocity-due-to-solar-rotation" class="md-nav__link">
+    Step Three: Calculate Velocity due to Solar Rotation
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#step-four-limb-darkening-correction" class="md-nav__link">
+    Step Four: Limb Darkening Correction
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#step-five-unsigned-magnetic-field-strength" class="md-nav__link">
+    Step Five: Unsigned Magnetic Field Strength
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#step-six-identification-of-solar-regions" class="md-nav__link">
+    Step Six: Identification of Solar Regions
+  </a>
+  
+    <nav class="md-nav" aria-label="Step Six: Identification of Solar Regions">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#part-one-magnetic-thresholding" class="md-nav__link">
+    Part One: Magnetic Thresholding
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#part-two-intensity-thresholding" class="md-nav__link">
+    Part Two: Intensity Thresholding
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+      
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../docs_rv_calcs/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../docs_full_pipeline/" class="md-nav__link">
+        Full Pipeline
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#step-one-coordinate-transformation" class="md-nav__link">
+    Step One: Coordinate Transformation
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#step-two-calculate-spacecraft-velocity" class="md-nav__link">
+    Step Two: Calculate Spacecraft Velocity
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#step-three-calculate-velocity-due-to-solar-rotation" class="md-nav__link">
+    Step Three: Calculate Velocity due to Solar Rotation
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#step-four-limb-darkening-correction" class="md-nav__link">
+    Step Four: Limb Darkening Correction
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#step-five-unsigned-magnetic-field-strength" class="md-nav__link">
+    Step Five: Unsigned Magnetic Field Strength
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#step-six-identification-of-solar-regions" class="md-nav__link">
+    Step Six: Identification of Solar Regions
+  </a>
+  
+    <nav class="md-nav" aria-label="Step Six: Identification of Solar Regions">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#part-one-magnetic-thresholding" class="md-nav__link">
+    Part One: Magnetic Thresholding
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#part-two-intensity-thresholding" class="md-nav__link">
+    Part Two: Intensity Thresholding
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
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+      }
+
+      const node = createNode(text);
+      body.appendChild(node);
+      copyNode(node);
+      body.removeChild(node);
+      return Promise.resolve();
+    }
+
+    function copy(button) {
+      const id = button.getAttribute('for');
+      const text = button.getAttribute('value');
+
+      function trigger() {
+        button.dispatchEvent(new CustomEvent('clipboard-copy', {
+          bubbles: true
+        }));
+      }
+
+      if (text) {
+        copyText(text).then(trigger);
+      } else if (id) {
+        const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
+        const node = root.getElementById(id);
+        if (node) copyTarget(node).then(trigger);
+      }
+    }
+
+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
+      } else {
+        return copyNode(content);
+      }
+    }
+
+    function clicked(event) {
+      const button = event.currentTarget;
+
+      if (button instanceof HTMLElement) {
+        copy(button);
+      }
+    }
+
+    function keydown(event) {
+      if (event.key === ' ' || event.key === 'Enter') {
+        const button = event.currentTarget;
+
+        if (button instanceof HTMLElement) {
+          event.preventDefault();
+          copy(button);
+        }
+      }
+    }
+
+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
+    }
+
+    function blurred(event) {
+      event.currentTarget.removeEventListener('keydown', keydown);
+    }
+
+    class ClipboardCopyElement extends HTMLElement {
+      constructor() {
+        super();
+        this.addEventListener('click', clicked);
+        this.addEventListener('focus', focused);
+        this.addEventListener('blur', blurred);
+      }
+
+      connectedCallback() {
+        if (!this.hasAttribute('tabindex')) {
+          this.setAttribute('tabindex', '0');
+        }
+
+        if (!this.hasAttribute('role')) {
+          this.setAttribute('role', 'button');
+        }
+      }
+
+      get value() {
+        return this.getAttribute('value') || '';
+      }
+
+      set value(text) {
+        this.setAttribute('value', text);
+      }
+
+    }
+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+
+
+
+
+<style type="text/css">
+    
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: 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+
+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*//*!
+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*/}.jupyter-wrapper [data-jp-theme-scrollbars=true]{scrollbar-color:rgb(var(--jp-scrollbar-thumb-color)) var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar{scrollbar-color:rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-corner{background:var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-thumb{background:rgb(var(--jp-scrollbar-thumb-color));border:var(--jp-scrollbar-thumb-margin) solid transparent;background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid 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+
+/*# sourceMappingURL=mkdocs-jupyter.css.map*/</style>
+
+    <style type="text/css">
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+The following CSS variables define the main, public API for styling JupyterLab.
+These variables should be used by all plugins wherever possible. In other
+words, plugins should not define custom colors, sizes, etc unless absolutely
+necessary. This enables users to change the visual theme of JupyterLab
+by changing these variables.
+
+Many variables appear in an ordered sequence (0,1,2,3). These sequences
+are designed to work well together, so for example, `--jp-border-color1` should
+be used with `--jp-layout-color1`. The numbers have the following meanings:
+
+* 0: super-primary, reserved for special emphasis
+* 1: primary, most important under normal situations
+* 2: secondary, next most important under normal situations
+* 3: tertiary, next most important under normal situations
+
+Throughout JupyterLab, we are mostly following principles from Google's
+Material Design when selecting colors. We are not, however, following
+all of MD as it is not optimized for dense, information rich UIs.
+*/
+
+:root {
+  /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+  --jp-shadow-base-lightness: 0;
+  --jp-shadow-umbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.2
+  );
+  --jp-shadow-penumbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.14
+  );
+  --jp-shadow-ambient-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.12
+  );
+  --jp-elevation-z0: none;
+  --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+    0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 3px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+    0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 5px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+    0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 10px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+    0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 18px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+    0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+    0px 3px 14px 2px var(--jp-shadow-ambient-color);
+  --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+    0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+    0px 5px 22px 4px var(--jp-shadow-ambient-color);
+  --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+    0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+    0px 6px 30px 5px var(--jp-shadow-ambient-color);
+  --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+    0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+    0px 8px 38px 7px var(--jp-shadow-ambient-color);
+  --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+    0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+    0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+  /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+  --jp-border-width: 1px;
+  --jp-border-color0: var(--md-grey-400);
+  --jp-border-color1: var(--md-grey-400);
+  --jp-border-color2: var(--md-grey-300);
+  --jp-border-color3: var(--md-grey-200);
+  --jp-border-radius: 2px;
+
+  /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-ui-font-scale-factor: 1.2;
+  --jp-ui-font-size0: 0.83333em;
+  --jp-ui-font-size1: 13px; /* Base font size */
+  --jp-ui-font-size2: 1.2em;
+  --jp-ui-font-size3: 1.44em;
+
+  --jp-ui-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica,
+    Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
+
+  /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+  /* Defaults use Material Design specification */
+  --jp-ui-font-color0: rgba(0, 0, 0, 1);
+  --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+  /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+  --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+  --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+  /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-content-line-height: 1.6;
+  --jp-content-font-scale-factor: 1.2;
+  --jp-content-font-size0: 0.83333em;
+  --jp-content-font-size1: 14px; /* Base font size */
+  --jp-content-font-size2: 1.2em;
+  --jp-content-font-size3: 1.44em;
+  --jp-content-font-size4: 1.728em;
+  --jp-content-font-size5: 2.0736em;
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-content-presentation-font-size1: 17px;
+
+  --jp-content-heading-line-height: 1;
+  --jp-content-heading-margin-top: 1.2em;
+  --jp-content-heading-margin-bottom: 0.8em;
+  --jp-content-heading-font-weight: 500;
+
+  /* Defaults use Material Design specification */
+  --jp-content-font-color0: rgba(0, 0, 0, 1);
+  --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+  --jp-content-link-color: var(--md-blue-700);
+
+  --jp-content-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI',
+    Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
+    'Segoe UI Symbol';
+
+  /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+  --jp-code-font-size: 13px;
+  --jp-code-line-height: 1.3077; /* 17px for 13px base */
+  --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+  --jp-code-font-family-default: Menlo, Consolas, 'DejaVu Sans Mono', monospace;
+  --jp-code-font-family: var(--jp-code-font-family-default);
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-code-presentation-font-size: 16px;
+
+  /* may need to tweak cursor width if you change font size */
+  --jp-code-cursor-width0: 1.4px;
+  --jp-code-cursor-width1: 2px;
+  --jp-code-cursor-width2: 4px;
+
+  /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+  --jp-layout-color0: white;
+  --jp-layout-color1: white;
+  --jp-layout-color2: var(--md-grey-200);
+  --jp-layout-color3: var(--md-grey-400);
+  --jp-layout-color4: var(--md-grey-600);
+
+  /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+  --jp-inverse-layout-color0: #111111;
+  --jp-inverse-layout-color1: var(--md-grey-900);
+  --jp-inverse-layout-color2: var(--md-grey-800);
+  --jp-inverse-layout-color3: var(--md-grey-700);
+  --jp-inverse-layout-color4: var(--md-grey-600);
+
+  /* Brand/accent */
+
+  --jp-brand-color0: var(--md-blue-700);
+  --jp-brand-color1: var(--md-blue-500);
+  --jp-brand-color2: var(--md-blue-300);
+  --jp-brand-color3: var(--md-blue-100);
+  --jp-brand-color4: var(--md-blue-50);
+
+  --jp-accent-color0: var(--md-green-700);
+  --jp-accent-color1: var(--md-green-500);
+  --jp-accent-color2: var(--md-green-300);
+  --jp-accent-color3: var(--md-green-100);
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+
+  --jp-warn-color0: var(--md-orange-700);
+  --jp-warn-color1: var(--md-orange-500);
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+  --jp-error-color2: var(--md-red-300);
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+  --jp-cell-editor-border-color: var(--md-grey-300);
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+  --jp-cell-editor-active-border-color: var(--jp-brand-color1);
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+  --jp-cell-inprompt-font-color: #307fc1;
+  /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+  --jp-cell-outprompt-font-color: #bf5b3d;
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+  /* Notebook specific styles */
+
+  --jp-notebook-padding: 10px;
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+
+  /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+  --jp-notebook-scroll-padding: calc(
+    100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+      var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+  );
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+  /* Rendermime styles */
+
+  --jp-rendermime-error-background: #fdd;
+  --jp-rendermime-table-row-background: var(--md-grey-100);
+  --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+  /* Dialog specific styles */
+
+  --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+  /* Console specific styles */
+
+  --jp-console-padding: 10px;
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+  /* Toolbar specific styles */
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+  --jp-toolbar-border-color: var(--jp-border-color1);
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+  --jp-toolbar-header-margin: 4px 4px 0px 4px;
+  --jp-toolbar-active-background: var(--md-grey-300);
+
+  /* Input field styles */
+
+  --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+  --jp-input-active-background: var(--jp-layout-color1);
+  --jp-input-hover-background: var(--jp-layout-color1);
+  --jp-input-background: var(--md-grey-100);
+  --jp-input-border-color: var(--jp-border-color1);
+  --jp-input-active-border-color: var(--jp-brand-color1);
+  --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+  /* General editor styles */
+
+  --jp-editor-selected-background: #d9d9d9;
+  --jp-editor-selected-focused-background: #d7d4f0;
+  --jp-editor-cursor-color: var(--jp-ui-font-color0);
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+  /* Code mirror specific styles */
+
+  --jp-mirror-editor-keyword-color: #008000;
+  --jp-mirror-editor-atom-color: #88f;
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+  --jp-mirror-editor-variable-color: var(--md-grey-900);
+  --jp-mirror-editor-variable-2-color: #05a;
+  --jp-mirror-editor-variable-3-color: #085;
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+  --jp-mirror-editor-comment-color: #408080;
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+  --jp-mirror-editor-meta-color: #aa22ff;
+  --jp-mirror-editor-qualifier-color: #555;
+  --jp-mirror-editor-builtin-color: #008000;
+  --jp-mirror-editor-bracket-color: #997;
+  --jp-mirror-editor-tag-color: #170;
+  --jp-mirror-editor-attribute-color: #00c;
+  --jp-mirror-editor-header-color: blue;
+  --jp-mirror-editor-quote-color: #090;
+  --jp-mirror-editor-link-color: #00c;
+  --jp-mirror-editor-error-color: #f00;
+  --jp-mirror-editor-hr-color: #999;
+
+  /* Vega extension styles */
+
+  --jp-vega-background: white;
+
+  /* Sidebar-related styles */
+
+  --jp-sidebar-min-width: 250px;
+
+  /* Search-related styles */
+
+  --jp-search-toggle-off-opacity: 0.5;
+  --jp-search-toggle-hover-opacity: 0.8;
+  --jp-search-toggle-on-opacity: 1;
+  --jp-search-selected-match-background-color: rgb(245, 200, 0);
+  --jp-search-selected-match-color: black;
+  --jp-search-unselected-match-background-color: var(
+    --jp-inverse-layout-color0
+  );
+  --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+  /* Icon colors that work well with light or dark backgrounds */
+  --jp-icon-contrast-color0: var(--md-purple-600);
+  --jp-icon-contrast-color1: var(--md-green-600);
+  --jp-icon-contrast-color2: var(--md-pink-600);
+  --jp-icon-contrast-color3: var(--md-blue-600);
+}
+</style>
+
+
+<!-- Load mathjax -->
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+        
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
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+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --><div class="jupyter-wrapper">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="outline-of-corrections-to-sdohmi-images">Outline of corrections to SDO/HMI Images<a class="anchor-link" href="#Outline-of-corrections-to-SDO/HMI-Images">&#182;</a></h1><ol>
+<li><strong>coordinate transformation:</strong> transform from Helioprojective Cartesian coordinates
+to Heliographic Carrington coordinates based on observer</li>
+<li><strong>spacecraft velocity calculation:</strong> correct for the  velocity of spacecraft relative
+to Sun</li>
+<li><strong>solar rotation calculation:</strong> account for velocity due to solar rotation as a function of
+latitude (based on Snodgrass &amp; Ulrich 1990)</li>
+<li><strong>limb darkening correction:</strong> correct for limb darkening in HMI intensity images</li>
+<li><strong>unsigned magnetic field strength:</strong> calculate unsigned magnetic field strength by dividing observed
+field strength by $ \mu = cos(\theta)$</li>
+<li><strong>identify solar regions:</strong> differentiate between quiet Sun, sunspots, faculae/plage regions</li>
+</ol>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">import</span> <span class="nn">astropy.units</span> <span class="k">as</span> <span class="nn">u</span>
+
+<span class="kn">import</span> <span class="nn">sunpy.map</span>
+<span class="kn">from</span> <span class="nn">sunpy.net</span> <span class="kn">import</span> <span class="n">Fido</span>
+<span class="kn">from</span> <span class="nn">sunpy.net</span> <span class="kn">import</span> <span class="n">attrs</span> <span class="k">as</span> <span class="n">a</span>
+<span class="kn">from</span> <span class="nn">sunpy.coordinates</span> <span class="kn">import</span> <span class="n">frames</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
+
+<span class="kn">import</span> <span class="nn">tamar.tools.solar_funcs</span> <span class="k">as</span> <span class="nn">sfuncs</span>
+<span class="kn">import</span> <span class="nn">tamar.tools.lbc_funcs</span> <span class="k">as</span> <span class="nn">lbfuncs</span>
+<span class="kn">import</span> <span class="nn">tamar.tools.coord_funcs</span> <span class="k">as</span> <span class="nn">ctfuncs</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import numpy as np
+
+import astropy.units as u
+
+import sunpy.map
+from sunpy.net import Fido
+from sunpy.net import attrs as a
+from sunpy.coordinates import frames
+
+import matplotlib.pyplot as plt
+
+from scipy import stats
+
+import tamar.tools.solar_funcs as sfuncs
+import tamar.tools.lbc_funcs as lbfuncs
+import tamar.tools.coord_funcs as ctfuncs</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Get HMI data.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># get hmi data products</span>
+<span class="n">cadence</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="mi">24</span><span class="o">*</span><span class="n">u</span><span class="o">.</span><span class="n">hour</span><span class="p">)</span>  <span class="c1"># querying cadence</span>
+<span class="n">start_date</span> <span class="o">=</span> <span class="s1">&#39;2014-01-20T09:43:24.70&#39;</span>  <span class="c1"># start date of query</span>
+<span class="n">end_date</span> <span class="o">=</span> <span class="s1">&#39;2014-01-20T09:44:24.70&#39;</span>
+<span class="n">physobs_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">a</span><span class="o">.</span><span class="n">Physobs</span><span class="o">.</span><span class="n">los_velocity</span><span class="p">,</span> <span class="n">a</span><span class="o">.</span><span class="n">Physobs</span><span class="o">.</span><span class="n">los_magnetic_field</span><span class="p">,</span> <span class="n">a</span><span class="o">.</span><span class="n">Physobs</span><span class="o">.</span><span class="n">intensity</span><span class="p">]</span>
+
+<span class="n">result</span> <span class="o">=</span> <span class="n">Fido</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">Time</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">),</span>
+                     <span class="n">a</span><span class="o">.</span><span class="n">Instrument</span><span class="o">.</span><span class="n">hmi</span><span class="p">,</span> <span class="n">physobs_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">|</span> <span class="n">physobs_list</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">|</span> <span class="n">physobs_list</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">cadence</span><span class="p">)</span>
+
+<span class="c1"># print results</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt"># get hmi data products
+cadence = a.Sample(24*u.hour)  # querying cadence
+start_date = '2014-01-20T09:43:24.70'  # start date of query
+end_date = '2014-01-20T09:44:24.70'
+physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity]
+
+result = Fido.search(a.Time(start_date, end_date),
+                     a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2], cadence)
+
+# print results
+print(result)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Download data files, sort, and build map list.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
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+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># download results</span>
+<span class="n">file_download</span> <span class="o">=</span> <span class="n">Fido</span><span class="o">.</span><span class="n">fetch</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
+<span class="c1"># convert to map sequence</span>
+<span class="n">map_seq</span> <span class="o">=</span> <span class="n">sunpy</span><span class="o">.</span><span class="n">map</span><span class="o">.</span><span class="n">Map</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">file_download</span><span class="p">))</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt"># download results
+file_download = Fido.fetch(result)
+# convert to map sequence
+map_seq = sunpy.map.Map(sorted(file_download))
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Create map lists based on HMI content.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">hmi_vel</span><span class="p">,</span> <span class="n">hmi_mag</span><span class="p">,</span> <span class="n">hmi_int</span> \
+    <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">map_obj</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">map_seq</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;DOPPLERGRAM&#39;</span><span class="p">:</span>
+        <span class="n">hmi_vel</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;MAGNETOGRAM&#39;</span><span class="p">:</span>
+        <span class="n">hmi_mag</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;CONTINUUM INTENSITY&#39;</span><span class="p">:</span>
+        <span class="n">hmi_int</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt">hmi_vel, hmi_mag, hmi_int \
+    = [], [], []
+for i, map_obj in enumerate(map_seq):
+    if map_obj.meta['content'] == 'DOPPLERGRAM':
+        hmi_vel.append(map_obj.rotate(order=3))
+    elif map_obj.meta['content'] == 'MAGNETOGRAM':
+        hmi_mag.append(map_obj.rotate(order=3))
+    elif map_obj.meta['content'] == 'CONTINUUM INTENSITY':
+        hmi_int.append(map_obj.rotate(order=3))</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="step-one-coordinate-transformation">Step One: Coordinate Transformation<a class="anchor-link" href="#Step-One:-Coordinate-Transformation">&#182;</a></h2>
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Apply coordinate transformations for HMI data products where:</p>
+<p><code>x, y, pd, r, d, mu = coord[i][0], coord[i][1], coord[i][2], coord[i][3], coord[i][4], coord[i][5]<code></p>
+<p><code>wij, nij, rij = vel_coords[i][0], vel_coords[i][1], vel_coords[i][2]<code></p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># get cartesian scales from map</span>
+<span class="n">coord</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctfuncs</span><span class="o">.</span><span class="n">coordinates</span><span class="p">(</span><span class="n">hmi_vel</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">))]</span>
+
+<span class="c1"># get mu and radius array</span>
+<span class="n">vel_coords</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctfuncs</span><span class="o">.</span><span class="n">vel_coords</span><span class="p">(</span><span class="n">coord</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">coord</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">coord</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">coord</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">hmi_vel</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt"># get cartesian scales from map
+coord = [ctfuncs.coordinates(hmi_vel[i]) for i in range(0, len(hmi_vel))]
+
+# get mu and radius array
+vel_coords = [ctfuncs.vel_coords(coord[0], coord[1], coord[2], coord[3], hmi_vel[i]) for i in range(0, len(hmi_vel))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Remove bad mu values from each map.</p>
+<p><code>hmi_vel, hmi_mag, imap = maps[i][0], maps[i][1], maps[i][2]<code></p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># fix bad mu values</span>
+<span class="n">maps</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctfuncs</span><span class="o">.</span><span class="n">fix_mu</span><span class="p">(</span><span class="n">coord</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="n">hmi_vel</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>  <span class="n">hmi_mag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>  <span class="n">hmi_int</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">))]</span>
+
+<span class="c1"># return maps to original lists</span>
+<span class="n">hmi_vel</span> <span class="o">=</span> <span class="p">[</span><span class="n">maps</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">))]</span>
+<span class="n">hmi_mag</span> <span class="o">=</span> <span class="p">[</span><span class="n">maps</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">))]</span>
+<span class="n">hmi_int</span> <span class="o">=</span> <span class="p">[</span><span class="n">maps</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt"># fix bad mu values
+maps = [ctfuncs.fix_mu(coord[i][5], [hmi_vel[i],  hmi_mag[i],  hmi_int[i]]) for i in range(0, len(hmi_vel))]
+
+# return maps to original lists
+hmi_vel = [maps[i][0] for i in range(0, len(hmi_vel))]
+hmi_mag = [maps[i][1] for i in range(0, len(hmi_vel))]
+hmi_int = [maps[i][2] for i in range(0, len(hmi_vel))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="step-two-calculate-spacecraft-velocity">Step Two: Calculate Spacecraft Velocity<a class="anchor-link" href="#Step-Two:-Calculate-Spacecraft-Velocity">&#182;</a></h2>
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate relative positions.</p>
+<p><code>deltaw, deltan, deltar, dij = delta[i][0], delta[i][1], delta[i][2], delta[i][3]<code></p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">delta</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">rel_positions</span><span class="p">(</span><span class="n">vel_coords</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">vel_coords</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">vel_coords</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">hmi_vel</span><span class="p">[</span><span class="n">i</span><span class="p">])]</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt">delta = [sfuncs.rel_positions(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], hmi_vel[i])]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate spacecraft velocity.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
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+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">vsc</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">spacecraft_vel</span><span class="p">(</span><span class="n">delta</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">delta</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">delta</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">delta</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">],</span> <span class="n">hmi_vel</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">vsc = [sfuncs.spacecraft_vel(delta[i][0], delta[i][1], delta[i][2], delta[i][3], hmi_vel[i]) for i in range(0, len(hmi_vel))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="step-three-calculate-velocity-due-to-solar-rotation">Step Three: Calculate Velocity due to Solar Rotation<a class="anchor-link" href="#Step-Three:-Calculate-Velocity-due-to-Solar-Rotation">&#182;</a></h2>
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Optimized parameters for calculation of solar rotation.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">a1</span> <span class="o">=</span> <span class="mf">14.713</span>
+<span class="n">a2</span> <span class="o">=</span> <span class="o">-</span><span class="mf">2.396</span>
+<span class="n">a3</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.787</span>
+<span class="n">a_parameters</span> <span class="o">=</span> <span class="p">[</span><span class="n">a1</span><span class="p">,</span> <span class="n">a2</span><span class="p">,</span> <span class="n">a3</span><span class="p">]</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt">a1 = 14.713
+a2 = -2.396
+a3 = -1.787
+a_parameters = [a1, a2, a3]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculation of solar rotation velocity.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-10">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">vrot</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">solar_rot_vel</span><span class="p">(</span><span class="n">vel_coords</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">vel_coords</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">vel_coords</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">delta</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">delta</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">delta</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span>
+                             <span class="n">delta</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">],</span> <span class="n">hmi_vel</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">a_parameters</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-10" class="clipboard-copy-txt">vrot = [sfuncs.solar_rot_vel(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], delta[i][0], delta[i][1], delta[i][2],
+                             delta[i][3], hmi_vel[i], a_parameters) for i in range(0, len(hmi_vel))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate corrected velocity.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-11">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
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+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># subtract vsc and vrot from the Doppler images</span>
+<span class="n">corrected_vel</span> <span class="o">=</span> <span class="p">[</span><span class="n">hmi_vel</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">vsc</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">vrot</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-11" class="clipboard-copy-txt"># subtract vsc and vrot from the Doppler images
+corrected_vel = [hmi_vel[i].data - np.real(vsc[i]) - np.real(vrot[i]) for i in range(0, len(hmi_vel))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Create list of Sunpy map objects with corrected velocity data.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-12">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">map_vel_cor</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">corrected_map</span><span class="p">(</span><span class="n">corrected_vel</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hmi_vel</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">map_type</span><span class="o">=</span><span class="s1">&#39;Corrected-Dopplergram&#39;</span><span class="p">,</span>
+                                    <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-12" class="clipboard-copy-txt">map_vel_cor = [sfuncs.corrected_map(corrected_vel[i], hmi_vel[i], map_type='Corrected-Dopplergram',
+                                    frame=frames.HeliographicCarrington) for i in range(0, len(hmi_vel))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Compare original and corrected velocity maps.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-13">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># plot original map</span>
+<span class="n">hmi_vel</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>
+
+<span class="c1"># plot corrected map</span>
+<span class="n">map_vel_cor</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>
+</pre></div>
+<div id="cell-13" class="clipboard-copy-txt"># plot original map
+hmi_vel[0].peek()
+
+# plot corrected map
+map_vel_cor[0].peek()
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="step-four-limb-darkening-correction">Step Four: Limb Darkening Correction<a class="anchor-link" href="#Step-Four:-Limb-Darkening-Correction">&#182;</a></h2>
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Equation for limb-brightening:
+$ L_{ij} = 1 - u_2 - v_2 + u_2 cos \theta + v_2 cos^2 \theta $</p>
+<p>Coefficient values from Astrophysical Quantities by Allen (1973).</p>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate correction polynomial.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-14">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">Lij</span> <span class="o">=</span> <span class="p">[</span><span class="n">lbfuncs</span><span class="o">.</span><span class="n">limb_polynomial</span><span class="p">(</span><span class="n">hmi_int</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_int</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-14" class="clipboard-copy-txt">Lij = [lbfuncs.limb_polynomial(hmi_int[i]) for i in range(0, len(hmi_int))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate corrected intensity.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-15">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">Iflat</span> <span class="o">=</span> <span class="p">[</span><span class="n">hmi_int</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">/</span><span class="n">Lij</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_int</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-15" class="clipboard-copy-txt">Iflat = [hmi_int[i].data/Lij[i] for i in range(0, len(hmi_int))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Create list of Sunpy map objects with corrected intensity data.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-16">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">map_int_cor</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">corrected_map</span><span class="p">(</span><span class="n">Iflat</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hmi_int</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">map_type</span><span class="o">=</span><span class="s1">&#39;Corrected-Intensitygram&#39;</span><span class="p">,</span> <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_int</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-16" class="clipboard-copy-txt">map_int_cor = [sfuncs.corrected_map(Iflat[i], hmi_int[i], map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Compare original and corrected intensity maps.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-17">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># plot original map</span>
+<span class="n">hmi_int</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>
+
+<span class="c1"># plot corrected map</span>
+<span class="n">map_int_cor</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>
+</pre></div>
+<div id="cell-17" class="clipboard-copy-txt"># plot original map
+hmi_int[0].peek()
+
+# plot corrected map
+map_int_cor[0].peek()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-18">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">int_map</span> <span class="o">=</span> <span class="n">hmi_int</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">cont_map</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;hinodesotintensity&#39;</span><span class="p">)</span>
+<span class="n">int_map</span><span class="o">.</span><span class="n">plot_settings</span><span class="p">[</span><span class="s1">&#39;cmap&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">cont_map</span>
+<span class="n">int_map</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="n">int_map</span> <span class="o">=</span> <span class="n">map_int_cor</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">cont_map</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;hinodesotintensity&#39;</span><span class="p">)</span>
+<span class="n">int_map</span><span class="o">.</span><span class="n">plot_settings</span><span class="p">[</span><span class="s1">&#39;cmap&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">cont_map</span>
+<span class="n">int_map</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-18" class="clipboard-copy-txt">int_map = hmi_int[0]
+cont_map = plt.get_cmap('hinodesotintensity')
+int_map.plot_settings['cmap'] = cont_map
+int_map.plot()
+plt.show()
+
+int_map = map_int_cor[0]
+cont_map = plt.get_cmap('hinodesotintensity')
+int_map.plot_settings['cmap'] = cont_map
+int_map.plot()
+plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="step-five-unsigned-magnetic-field-strength">Step Five: Unsigned Magnetic Field Strength<a class="anchor-link" href="#Step-Five:-Unsigned-Magnetic-Field-Strength">&#182;</a></h2>
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>We calculate radial unsigned magnetic field strength and set pixels with an
+observed magnetic field below the noise value equal to zero.</p>
+<p>The noise level used is from Yeo et al. 2013, and is 8 Gauss.</p>
+<p><code>Bobs, Br = corrected_mag[0], corrected_mag[1]<code></p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-19">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># magnetic noise level</span>
+<span class="n">B_noise</span> <span class="o">=</span> <span class="mi">8</span>
+
+<span class="c1"># calculate unsigned field strength</span>
+<span class="n">corrected_mag</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">mag_field</span><span class="p">(</span><span class="n">coord</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">5</span><span class="p">],</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">B_noise</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-19" class="clipboard-copy-txt"># magnetic noise level
+B_noise = 8
+
+# calculate unsigned field strength
+corrected_mag = [sfuncs.mag_field(coord[i][5], hmi_mag[i], B_noise) for i in range(0, len(hmi_mag))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Create list of Sunpy map objects with corrected observed and radial magnetic data.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-20">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># observed magnetic data</span>
+<span class="n">map_mag_obs</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">corrected_map</span><span class="p">(</span><span class="n">corrected_mag</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">map_type</span><span class="o">=</span><span class="s1">&#39;Corrected-Magnetogram&#39;</span><span class="p">,</span>
+                                    <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">))]</span>
+
+<span class="c1"># radial magnetic data</span>
+<span class="n">map_mag_cor</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">corrected_map</span><span class="p">(</span><span class="n">corrected_mag</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">map_type</span><span class="o">=</span><span class="s1">&#39;Corrected-Magnetogram&#39;</span><span class="p">,</span>
+                                    <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-20" class="clipboard-copy-txt"># observed magnetic data
+map_mag_obs = [sfuncs.corrected_map(corrected_mag[i][0], hmi_mag[i], map_type='Corrected-Magnetogram',
+                                    frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))]
+
+# radial magnetic data
+map_mag_cor = [sfuncs.corrected_map(corrected_mag[i][1], hmi_mag[i], map_type='Corrected-Magnetogram',
+                                    frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Compare original and corrected magnetic maps.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-21">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># plot original map</span>
+<span class="n">hmi_mag</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>
+
+<span class="c1"># plot corrected map</span>
+<span class="n">map_mag_cor</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>
+</pre></div>
+<div id="cell-21" class="clipboard-copy-txt"># plot original map
+hmi_mag[0].peek()
+
+# plot corrected map
+map_mag_cor[0].peek()
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-22">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">mag_map</span> <span class="o">=</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">mag_map</span><span class="o">.</span><span class="n">plot_settings</span><span class="p">[</span><span class="s1">&#39;norm&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="o">-</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">mag_map</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="n">mag_map</span> <span class="o">=</span> <span class="n">map_mag_cor</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">mag_map</span><span class="o">.</span><span class="n">plot_settings</span><span class="p">[</span><span class="s1">&#39;norm&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="o">-</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">mag_map</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-22" class="clipboard-copy-txt">mag_map = hmi_mag[0]
+mag_map.plot_settings['norm'] = plt.Normalize(-100, 100)
+mag_map.plot()
+plt.show()
+
+mag_map = map_mag_cor[0]
+mag_map.plot_settings['norm'] = plt.Normalize(-100, 100)
+mag_map.plot()
+plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="step-six-identification-of-solar-regions">Step Six: Identification of Solar Regions<a class="anchor-link" href="#Step-Six:-Identification-of-Solar-Regions">&#182;</a></h2>
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="part-one-magnetic-thresholding">Part One: Magnetic Thresholding<a class="anchor-link" href="#Part-One:-Magnetic-Thresholding">&#182;</a></h3><p>Differentiate between active Sun and quiet Sun.
+Magnetic thresholding method used from Yeo et al. 2013 where
+$ B_{r, thresh} = 24G / \mu $ .</p>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Magnetic threshold and mu cutoff values.
+Automatically throw everything where $ \mu &lt; 0.1 $ out, for magnetic thresholding we also
+remove $ \mu &lt; 0.3 $ due to correction for radial magnetic field strength.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-23">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># magnetic threshold value (G) from Yeo et. al. 2013</span>
+<span class="n">Br_cutoff</span> <span class="o">=</span> <span class="mi">24</span>
+
+<span class="c1"># mu cutoff value</span>
+<span class="n">mu_cutoff</span> <span class="o">=</span> <span class="mf">0.35</span>
+
+<span class="c1"># area cutoff value (pixels)</span>
+<span class="n">area_cutoff</span> <span class="o">=</span> <span class="mi">45</span>
+</pre></div>
+<div id="cell-23" class="clipboard-copy-txt"># magnetic threshold value (G) from Yeo et. al. 2013
+Br_cutoff = 24
+
+# mu cutoff value
+mu_cutoff = 0.35
+
+# area cutoff value (pixels)
+area_cutoff = 45</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>For weighting, we return an active weights array (active = 1) and quiet weights array (active = 0).</p>
+<p>Get threshold maps where:</p>
+<p><code>active, quiet = mag_thresh[i][0], mag_thresh[i][1]<code></p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-24">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">mag_thresh</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">mag_thresh</span><span class="p">(</span><span class="n">coord</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">5</span><span class="p">],</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">Br_cutoff</span><span class="o">=</span><span class="n">Br_cutoff</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="n">mu_cutoff</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-24" class="clipboard-copy-txt">mag_thresh = [sfuncs.mag_thresh(coord[i][5], hmi_mag[i], Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) for i in range(0, len(hmi_mag))]
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Create list of Sunpy map objects with thresholded magnetic data.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">map_mag_thresh</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">corrected_map</span><span class="p">(</span><span class="n">mag_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">map_type</span><span class="o">=</span><span class="s1">&#39;Magnetic-Threshold&#39;</span><span class="p">,</span> <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-25" class="clipboard-copy-txt">map_mag_thresh = [sfuncs.corrected_map(mag_thresh[i][0], hmi_mag[i], map_type='Magnetic-Threshold', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Compare original and corrected threshold maps.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+            <clipboard-copy for="cell-26">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># plot corrected magnetic map</span>
+<span class="n">map_mag_cor</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>
+
+<span class="c1"># plot corrected magnetic threshold map</span>
+<span class="n">map_mag_thresh</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>
+</pre></div>
+<div id="cell-26" class="clipboard-copy-txt"># plot corrected magnetic map
+map_mag_cor[0].peek()
+
+# plot corrected magnetic threshold map
+map_mag_thresh[0].peek()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="part-two-intensity-thresholding">Part Two: Intensity Thresholding<a class="anchor-link" href="#Part-Two:-Intensity-Thresholding">&#182;</a></h3><p>Differentiate between sunspots (dark) and faculae (bright).
+Using method of Yeo et al. 2013 we calculate the intensity threshold such that
+$ I_{thresh} = 0.89I_{quiet} $ where $ I_{quiet} = \frac{\sum_{ij} I_{flat, ij} W_{ij}}{\sum_{ij} W_{ij}} $
+is the mean pixel flattened intensity over quiet Sun regions.</p>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate intensity threshold value and differentiate between sunspots and faculae where:</p>
+<p><code>fac_inds, spot_inds = int_thresh[0], int_thresh[1]<code></p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-27">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">int_thresh</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">int_thresh</span><span class="p">(</span><span class="n">map_int_cor</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mag_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">mag_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_int</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-27" class="clipboard-copy-txt">int_thresh = [sfuncs.int_thresh(map_int_cor[i], mag_thresh[i][0], mag_thresh[i][1]) for i in range(0, len(hmi_int))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Create list of Sunpy map objects with faculae and sunspot data.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-28">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># create faculae map objects</span>
+<span class="n">fac_map</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">corrected_map</span><span class="p">(</span><span class="n">int_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">map_type</span><span class="o">=</span><span class="s1">&#39;Faculae&#39;</span><span class="p">,</span> <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_int</span><span class="p">))]</span>
+
+<span class="c1"># create sunspot map objects</span>
+<span class="n">spot_map</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">corrected_map</span><span class="p">(</span><span class="n">int_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">map_type</span><span class="o">=</span><span class="s1">&#39;Sunspot&#39;</span><span class="p">,</span> <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_int</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-28" class="clipboard-copy-txt"># create faculae map objects
+fac_map = [sfuncs.corrected_map(int_thresh[i][0], hmi_mag[i], map_type='Faculae', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))]
+
+# create sunspot map objects
+spot_map = [sfuncs.corrected_map(int_thresh[i][1], hmi_mag[i], map_type='Sunspot', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Compare original and corrected threshold maps.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-29">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># plot faculae map</span>
+<span class="n">fac_map</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>
+
+<span class="c1"># plot sunspot map</span>
+<span class="n">spot_map</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>
+</pre></div>
+<div id="cell-29" class="clipboard-copy-txt"># plot faculae map
+fac_map[0].peek()
+
+# plot sunspot map
+spot_map[0].peek()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Create array that combines sunspot (dark: 2) and faculae (bright: 1) regions.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-30">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">thresh_arr</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">thresh_map</span><span class="p">(</span><span class="n">int_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">int_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-30" class="clipboard-copy-txt">thresh_arr = [sfuncs.thresh_map(int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_mag))]
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Create list of Sunpy map objects with threshold data.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-31">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># create threshold map objects</span>
+<span class="n">map_full_thresh</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">corrected_map</span><span class="p">(</span><span class="n">thresh_arr</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">map_type</span><span class="o">=</span><span class="s1">&#39;Threshold&#39;</span><span class="p">,</span> <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_int</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-31" class="clipboard-copy-txt"># create threshold map objects
+map_full_thresh = [sfuncs.corrected_map(thresh_arr[i], hmi_mag[i], map_type='Threshold', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Compare corrected magnetic and threshold maps</p>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>For these threshold maps, we currently only see sunspots detected on the solar
+limb. The solar limb is inherently darker (limb darkening) and since we have yet
+to correct for this it is expected that the overall intensity threshold is lower
+and thus the only values below this threshold are on the limb.</p>
+<p>The Sun is also not currently super magnetically active (solar minima) in the
+time I plotted so this makes sense that there are not many sunspots.</p>
+<p>Could also adjust the $ \mu $ cutoff value to remove these sunspots, but I think limb brightening will fix it.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-32">
+            <div>
+                <span class="notice" hidden>Copied!</span>
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">mag_map</span> <span class="o">=</span> <span class="n">map_mag_cor</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">mag_map</span><span class="o">.</span><span class="n">plot_settings</span><span class="p">[</span><span class="s1">&#39;norm&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="o">-</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">mag_map</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">map_full_thresh</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;bwr&#39;</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Comparison of faculae (blue) and sunspots (red) to magnetic data&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-32" class="clipboard-copy-txt">mag_map = map_mag_cor[0]
+mag_map.plot_settings['norm'] = plt.Normalize(-100, 100)
+mag_map.plot()
+plt.imshow(map_full_thresh[0].data, cmap=plt.get_cmap('bwr'))
+plt.title("Comparison of faculae (blue) and sunspots (red) to magnetic data")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">int_map</span> <span class="o">=</span> <span class="n">map_int_cor</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">cont_map</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;hinodesotintensity&#39;</span><span class="p">)</span>
+<span class="n">int_map</span><span class="o">.</span><span class="n">plot_settings</span><span class="p">[</span><span class="s1">&#39;cmap&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">cont_map</span>
+<span class="n">int_map</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">map_full_thresh</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;bwr&#39;</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Comparison of faculae (blue) and sunspots (red) to intensity data&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-33" class="clipboard-copy-txt">int_map = map_int_cor[0]
+cont_map = plt.get_cmap('hinodesotintensity')
+int_map.plot_settings['cmap'] = cont_map
+int_map.plot()
+plt.imshow(map_full_thresh[0].data, cmap=plt.get_cmap('bwr'))
+plt.title("Comparison of faculae (blue) and sunspots (red) to intensity data")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">int_map</span> <span class="o">=</span> <span class="n">map_int_cor</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">cont_map</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;hinodesotintensity&#39;</span><span class="p">)</span>
+<span class="n">int_map</span><span class="o">.</span><span class="n">plot_settings</span><span class="p">[</span><span class="s1">&#39;cmap&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">cont_map</span>
+<span class="n">int_map</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+
+<span class="n">spots</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">map_full_thresh</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">spots</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;Blues&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Comparison of sunspots to intensity data&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-34" class="clipboard-copy-txt">int_map = map_int_cor[0]
+cont_map = plt.get_cmap('hinodesotintensity')
+int_map.plot_settings['cmap'] = cont_map
+int_map.plot()
+
+spots = np.where(map_full_thresh[0].data == 2, 1, np.nan)
+plt.imshow(spots, cmap='Blues')
+plt.title("Comparison of sunspots to intensity data")
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="outline-of-calculations-of-solar-rvs">Outline of Calculations of Solar RVs<a class="anchor-link" href="#Outline-of-Calculations-of-Solar-RVs">&#182;</a></h1><ol>
+<li><strong>quiet-Sun velocity:</strong> estimation of average RV of quiet-Sun due to convective motion</li>
+<li><strong>photometric velocity:</strong> estimation of rotational Doppler imbalance due to active (faculae/darkspot) regions</li>
+<li><strong>convective blueshift:</strong> convective velocity estimation due to the suppression of the convective blueshift by
+magnetically active regions</li>
+</ol>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="step-one-convective-motion-of-quiet-sun">Step One: Convective Motion of quiet-Sun<a class="anchor-link" href="#Step-One:-Convective-Motion-of-quiet-Sun">&#182;</a></h2><p>Calculate the velocity due to the convective motion of the quiet-Sun. Requires
+the magnetic weighting array we built in step six, part one. A weight of 0 denotes an
+active pixel, and 1 is quiet-Sun.</p>
+<p>The equation for calculating this quiet-Sun velocity is as such:
+$ v_{quiet} = \frac{\sum_{ij} (v_{ij} - \delta v_{sc, ij} - \delta v_{rot, ij}) I_{ij} W_{ij}}
+ {\sum_{ij} I_{ij} W_{ij}} $.</p>
+<p>In this case $ I_{ij} $ is the uncorrected (original) intensity data.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+            <clipboard-copy for="cell-35">
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+                <span class="notice" hidden>Copied!</span>
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">v_quiet</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">v_quiet</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hmi_int</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mag_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-35" class="clipboard-copy-txt">v_quiet = [sfuncs.v_quiet(map_vel_cor[i], hmi_int[i], mag_thresh[i][1]) for i in range(0, len(hmi_mag))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="step-two-photometric-velocity-due-to-rotational-doppler-imbalance">Step Two: Photometric Velocity due to rotational Doppler imbalance<a class="anchor-link" href="#Step-Two:-Photometric-Velocity-due-to-rotational-Doppler-imbalance">&#182;</a></h2><p>Calculation of the photometric velocity due the presence of dark spots/bright faculae causing a Doppler imbalance. Requires
+magnetic weighting array that is flipped from the weighting array previously used. Now, a weight of 0 denotes quiet-Sun and
+1 denotes active pixel.</p>
+<p>The equation for this velocity calculation is:
+$ v_{phot} = \frac{\sum_{ij} (v_{ij} - \delta v_{rot, ij})(I_{ij} - K) W_{ij}}
+ {\sum_{ij} I_{ij}} $.</p>
+<p>K is a scaling factor based off the limb-brightening correction carried out in Step Four of the solar corrections.
+$ K = \frac{\sum_{ij} I_{ij} L_{ij} W_{ij}}{\sum_{ij} L_{ij}^2 W_{ij}} $</p>
+<p>In this case $ I_{ij} $ is the uncorrected (original) intensity data.</p>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate photometric velocity.</p>
+<p><code>v_phot, vphot_bright, vphot_spot = vphot[0], vphot[1], vphot[2]<code></p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+            <clipboard-copy for="cell-36">
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+                <span class="notice" hidden>Copied!</span>
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">v_phot</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">v_phot</span><span class="p">(</span><span class="n">mag_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">mag_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">Lij</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">vrot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hmi_int</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">coord</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">5</span><span class="p">],</span>
+                        <span class="n">int_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">int_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_int</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-36" class="clipboard-copy-txt">v_phot = [sfuncs.v_phot(mag_thresh[i][1], mag_thresh[i][0], Lij[i], vrot[i], hmi_int[i], coord[i][5],
+                        int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_int))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="step-three-convective-blueshift-velocity">Step Three: Convective Blueshift Velocity<a class="anchor-link" href="#Step-Three:-Convective-Blueshift-Velocity">&#182;</a></h2><p>Calculation of suppression of convective blueshift due to active regions (mainly faculae).</p>
+<p>The basic premise for calculation is to calculate the disc-averaged velocity of the Sun
+and subtract from that the quiet-Sun velocity (Step One) and photometric velocity (Step Two) both of which were
+calculated previously.</p>
+<p>Maybe a better way to do this ...</p>
+<p>The equation for the disc-averaged velocity is:
+$ v = \frac{\sum_{ij} (v_{ij} - \delta v_{sc, ij} - \delta v_{rot, ij}) I_{ij}}{\sum_{ij} I_{ij}} $</p>
+<p>We then calculate the convective velocity as what is left:
+$ v_{conv} = v - v_{quiet} $</p>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate disc-averaged velocity of Sun.</p>
+
+</div>
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">v_disc</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">v_disc</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hmi_int</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_int</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-37" class="clipboard-copy-txt">v_disc = [sfuncs.v_disc(map_vel_cor[i], hmi_int[i]) for i in range(0, len(hmi_int))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate convective velocity.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
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+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">v_conv</span> <span class="o">=</span> <span class="p">[</span><span class="n">v_disc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">v_quiet</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-38" class="clipboard-copy-txt">v_conv = [v_disc[i] - v_quiet[i] for i in range(0, len(hmi_mag))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="magnetic-observables">Magnetic Observables<a class="anchor-link" href="#Magnetic-Observables">&#182;</a></h1><p>Calculation of relevant magnetic observables.</p>
+<ol>
+<li><strong>filling factor:</strong> disc averaged filling factors of sunspots and plage which gives
+the percentage of magnetically active pixels</li>
+<li><strong>unsigned magnetic flux:</strong> calculation of disc-averaged, line-of-sight unsigned
+magnetic flux</li>
+</ol>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="filling-factor">Filling Factor<a class="anchor-link" href="#Filling-Factor">&#182;</a></h2><p>Estimate the disc-averaged filling factors of sunspots and plage:
+$ f_{spot, plage} = \frac{1}{N_{pix}} \sum_{ij} W_{ij} $</p>
+<p>In this case, a weight of 1 is an active pixel (active_weights).</p>
+<p><code>f_bright, f_spot, f = filling[0], filling[1], filling[2]<code></p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">filling</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">filling_factor</span><span class="p">(</span><span class="n">coord</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">5</span><span class="p">],</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mag_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">int_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">int_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-39" class="clipboard-copy-txt">filling = [sfuncs.filling_factor(coord[i][5], hmi_mag[i], mag_thresh[i][0], int_thresh[i][0], int_thresh[i][1])
+           for i in range(0, len(hmi_mag))]
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="unsigned-magnetic-flux">Unsigned Magnetic Flux<a class="anchor-link" href="#Unsigned-Magnetic-Flux">&#182;</a></h2><p>Calculate disc-averaged, line-of-sight unsigned (unpolarized) magnetic flux of the Sun.</p>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculation of observed unsigned flux.</p>
+<p>$ |\hat{B_{obs}}| = \frac{\sum_{ij} |B_{obs, ij}| I_{ij}} {\sum_{ij} I_{ij}}$</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">unsigned_obs_flux</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">unsigned_flux</span><span class="p">(</span><span class="n">map_mag_obs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hmi_int</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-40" class="clipboard-copy-txt">unsigned_obs_flux = [sfuncs.unsigned_flux(map_mag_obs[i], hmi_int[i]) for i in range(0, len(hmi_mag))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="area-based-observable-calculations">Area Based Observable Calculations<a class="anchor-link" href="#Area-Based-Observable-Calculations">&#182;</a></h2>
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate intensity weighted area of each active region.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
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+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">pixA_hem</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctfuncs</span><span class="o">.</span><span class="n">pix_area_hem</span><span class="p">(</span><span class="n">vel_coords</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">vel_coords</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">vel_coords</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">hmi_vel</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">))]</span>
+<span class="n">area</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">area_calc</span><span class="p">(</span><span class="n">mag_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">pixA_hem</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-41" class="clipboard-copy-txt">pixA_hem = [ctfuncs.pix_area_hem(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], hmi_vel[i]) for i in range(0, len(hmi_vel))]
+area = [sfuncs.area_calc(mag_thresh[i][0], pixA_hem[i]) for i in range(0, len(hmi_vel))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate the area filling factor.</p>
+<p><code>f_small, f_large, f_network, f_plage, f_nonconv = f_area[0], f_area[1], f_area[2], f_area[3], f_area[4]<code></p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">f_area</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">area_filling_factor</span><span class="p">(</span><span class="n">mag_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">area</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">coord</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">5</span><span class="p">],</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">int_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-42" class="clipboard-copy-txt">f_area = [sfuncs.area_filling_factor(mag_thresh[i][0], area[i], coord[i][5], hmi_mag[i], int_thresh[i][0]) for i in range(0, len(hmi_mag))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Calculate unsigned flux of various polarized and active regions.</p>
+<p><code>quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = flux[0], flux[1], flux[2], flux[3], flux[4], flux[5]<code></p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-43">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">quiet_flux</span><span class="p">,</span> <span class="n">ar_flux</span><span class="p">,</span> <span class="n">conv_flux</span><span class="p">,</span> <span class="n">pol_flux</span><span class="p">,</span> <span class="n">pol_conv_flux</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">area_unsigned_flux</span><span class="p">(</span><span class="n">map_mag_obs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hmi_int</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
+                                <span class="n">area</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">int_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_int</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-43" class="clipboard-copy-txt">quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = [sfuncs.area_unsigned_flux(map_mag_obs[i], hmi_int[i],
+                                area[i], int_thresh[i][0]) for i in range(0, len(hmi_int))]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Get area weighted convective velocities.</p>
+<p><code>vconv_quiet, vconv_large, vconv_small = vconv_area[0], vconv_area[1], vconv_area[2]<code></p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">vconv_area</span> <span class="o">=</span> <span class="p">[</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">area_vconv</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hmi_int</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">int_thresh</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">area</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hmi_int</span><span class="p">))]</span>
+</pre></div>
+<div id="cell-44" class="clipboard-copy-txt">vconv_area = [sfuncs.area_vconv(map_vel_cor[i], hmi_int[i], int_thresh[i][0], area[i]) for i in range(0, len(hmi_int))]
+</div>
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+
+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*//*!
+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+/*# sourceMappingURL=mkdocs-jupyter.css.map*/</style>
+
+    <style type="text/css">
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+The following CSS variables define the main, public API for styling JupyterLab.
+These variables should be used by all plugins wherever possible. In other
+words, plugins should not define custom colors, sizes, etc unless absolutely
+necessary. This enables users to change the visual theme of JupyterLab
+by changing these variables.
+
+Many variables appear in an ordered sequence (0,1,2,3). These sequences
+are designed to work well together, so for example, `--jp-border-color1` should
+be used with `--jp-layout-color1`. The numbers have the following meanings:
+
+* 0: super-primary, reserved for special emphasis
+* 1: primary, most important under normal situations
+* 2: secondary, next most important under normal situations
+* 3: tertiary, next most important under normal situations
+
+Throughout JupyterLab, we are mostly following principles from Google's
+Material Design when selecting colors. We are not, however, following
+all of MD as it is not optimized for dense, information rich UIs.
+*/
+
+:root {
+  /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+  --jp-shadow-base-lightness: 0;
+  --jp-shadow-umbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.2
+  );
+  --jp-shadow-penumbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.14
+  );
+  --jp-shadow-ambient-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.12
+  );
+  --jp-elevation-z0: none;
+  --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+    0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 3px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+    0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 5px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+    0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 10px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+    0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 18px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+    0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+    0px 3px 14px 2px var(--jp-shadow-ambient-color);
+  --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+    0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+    0px 5px 22px 4px var(--jp-shadow-ambient-color);
+  --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+    0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+    0px 6px 30px 5px var(--jp-shadow-ambient-color);
+  --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+    0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+    0px 8px 38px 7px var(--jp-shadow-ambient-color);
+  --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+    0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+    0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+  /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+  --jp-border-width: 1px;
+  --jp-border-color0: var(--md-grey-400);
+  --jp-border-color1: var(--md-grey-400);
+  --jp-border-color2: var(--md-grey-300);
+  --jp-border-color3: var(--md-grey-200);
+  --jp-border-radius: 2px;
+
+  /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-ui-font-scale-factor: 1.2;
+  --jp-ui-font-size0: 0.83333em;
+  --jp-ui-font-size1: 13px; /* Base font size */
+  --jp-ui-font-size2: 1.2em;
+  --jp-ui-font-size3: 1.44em;
+
+  --jp-ui-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica,
+    Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
+
+  /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+  /* Defaults use Material Design specification */
+  --jp-ui-font-color0: rgba(0, 0, 0, 1);
+  --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+  /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+  --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+  --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+  /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-content-line-height: 1.6;
+  --jp-content-font-scale-factor: 1.2;
+  --jp-content-font-size0: 0.83333em;
+  --jp-content-font-size1: 14px; /* Base font size */
+  --jp-content-font-size2: 1.2em;
+  --jp-content-font-size3: 1.44em;
+  --jp-content-font-size4: 1.728em;
+  --jp-content-font-size5: 2.0736em;
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-content-presentation-font-size1: 17px;
+
+  --jp-content-heading-line-height: 1;
+  --jp-content-heading-margin-top: 1.2em;
+  --jp-content-heading-margin-bottom: 0.8em;
+  --jp-content-heading-font-weight: 500;
+
+  /* Defaults use Material Design specification */
+  --jp-content-font-color0: rgba(0, 0, 0, 1);
+  --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+  --jp-content-link-color: var(--md-blue-700);
+
+  --jp-content-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI',
+    Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
+    'Segoe UI Symbol';
+
+  /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+  --jp-code-font-size: 13px;
+  --jp-code-line-height: 1.3077; /* 17px for 13px base */
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+  --jp-layout-color4: var(--md-grey-600);
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+}
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+<!-- Load mathjax -->
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+    <!-- End of mathjax configuration --><div class="jupyter-wrapper">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="examples-using-sunpy-for-querying-and-plotting">Examples using Sunpy for querying and plotting<a class="anchor-link" href="#Examples-using-Sunpy-for-querying-and-plotting">&#182;</a></h1>
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="querying-data">Querying data<a class="anchor-link" href="#Querying-data">&#182;</a></h2>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">matplotlib.animation</span> <span class="k">as</span> <span class="nn">animation</span>
+<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">colors</span>
+
+<span class="kn">import</span> <span class="nn">astropy.units</span> <span class="k">as</span> <span class="nn">u</span>
+
+<span class="kn">import</span> <span class="nn">sunpy.map</span>
+<span class="kn">from</span> <span class="nn">sunpy.net</span> <span class="kn">import</span> <span class="n">Fido</span>
+<span class="kn">from</span> <span class="nn">sunpy.net</span> <span class="kn">import</span> <span class="n">attrs</span> <span class="k">as</span> <span class="n">a</span>
+<span class="kn">import</span> <span class="nn">sunpy.visualization.colormaps</span> <span class="k">as</span> <span class="nn">cm</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import numpy as np
+
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+import matplotlib.colors as colors
+
+import astropy.units as u
+
+import sunpy.map
+from sunpy.net import Fido
+from sunpy.net import attrs as a
+import sunpy.visualization.colormaps as cm</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>To download the required data, we use <code>sunpy.net.Fido</code>, a downloader client,
+to query the Virtual Solar Observatory to acquire AIA and HMI data.</p>
+<p>The following is the data available:</p>
+<p><strong>HMI Bands (A):</strong> 171 (gold), 193 (bronze), 304 (red), 211 (purple), 131 (teal), 335 (blue), 094 (green),
+1600 (yellow/green), 1700 (pink)</p>
+<p><strong>HMI Products:</strong> Magnetogram, Intensitygram, Dopplergram</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
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+                <span class="notice" hidden>Copied!</span>
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+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># data parameters</span>
+<span class="n">cadence</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="mi">24</span><span class="o">*</span><span class="n">u</span><span class="o">.</span><span class="n">hour</span><span class="p">)</span>  <span class="c1"># querying cadence</span>
+<span class="n">start_date</span> <span class="o">=</span> <span class="s1">&#39;2021-1-1T12:00:00&#39;</span>  <span class="c1"># start date of query</span>
+<span class="n">end_date</span> <span class="o">=</span> <span class="s1">&#39;2021-01-5T12:00:00&#39;</span>  <span class="c1"># end date of query</span>
+
+<span class="c1"># AIA specific parameters</span>
+<span class="n">aia_inst</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">Instrument</span><span class="o">.</span><span class="n">aia</span>
+<span class="c1"># list of all available wavelengths for reference</span>
+<span class="n">wavelength_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">a</span><span class="o">.</span><span class="n">Wavelength</span><span class="p">(</span><span class="mi">94</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">angstrom</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">Wavelength</span><span class="p">(</span><span class="mi">131</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">angstrom</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">Wavelength</span><span class="p">(</span><span class="mi">171</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">angstrom</span><span class="p">),</span>
+                   <span class="n">a</span><span class="o">.</span><span class="n">Wavelength</span><span class="p">(</span><span class="mi">193</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">angstrom</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">Wavelength</span><span class="p">(</span><span class="mi">211</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">angstrom</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">Wavelength</span><span class="p">(</span><span class="mi">304</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">angstrom</span><span class="p">),</span>
+                   <span class="n">a</span><span class="o">.</span><span class="n">Wavelength</span><span class="p">(</span><span class="mi">335</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">angstrom</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">Wavelength</span><span class="p">(</span><span class="mi">1600</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">angstrom</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">Wavelength</span><span class="p">(</span><span class="mi">1700</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">angstrom</span><span class="p">)]</span>
+
+<span class="c1"># HMI specific parameters</span>
+<span class="n">hmi_inst</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">Instrument</span><span class="o">.</span><span class="n">hmi</span>
+<span class="c1"># list of physical observables available</span>
+<span class="n">physobs_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">a</span><span class="o">.</span><span class="n">Physobs</span><span class="o">.</span><span class="n">los_velocity</span><span class="p">,</span> <span class="n">a</span><span class="o">.</span><span class="n">Physobs</span><span class="o">.</span><span class="n">los_magnetic_field</span><span class="p">,</span> <span class="n">a</span><span class="o">.</span><span class="n">Physobs</span><span class="o">.</span><span class="n">intensity</span><span class="p">]</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt"># data parameters
+cadence = a.Sample(24*u.hour)  # querying cadence
+start_date = '2021-1-1T12:00:00'  # start date of query
+end_date = '2021-01-5T12:00:00'  # end date of query
+
+# AIA specific parameters
+aia_inst = a.Instrument.aia
+# list of all available wavelengths for reference
+wavelength_list = [a.Wavelength(94 * u.angstrom), a.Wavelength(131 * u.angstrom), a.Wavelength(171 * u.angstrom),
+                   a.Wavelength(193 * u.angstrom), a.Wavelength(211 * u.angstrom), a.Wavelength(304 * u.angstrom),
+                   a.Wavelength(335 * u.angstrom), a.Wavelength(1600 * u.angstrom), a.Wavelength(1700 * u.angstrom)]
+
+# HMI specific parameters
+hmi_inst = a.Instrument.hmi
+# list of physical observables available
+physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity]</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Now we can look at querying for specific information. We can look for results from
+multiple instruments, wavelengths, and physical observables at once.</p>
+<p>It is useful to search for results before download to ensure you don't download excessive
+amounts of data.</p>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Let's say we only want these specific results from AIA. We search for images at a 24 hour cadence between our start
+and end times at specified wavelengths.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">aia_result</span> <span class="o">=</span> <span class="n">Fido</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">Time</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">),</span>
+                     <span class="n">aia_inst</span><span class="p">,</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>  <span class="o">|</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">|</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+                     <span class="o">|</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">cadence</span><span class="p">)</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">aia_result = Fido.search(a.Time(start_date, end_date),
+                     aia_inst, wavelength_list[0]  | wavelength_list[2] | wavelength_list[3]
+                     | wavelength_list[6], cadence)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>We can do the same with HMI data products.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">hmi_result</span> <span class="o">=</span> <span class="n">Fido</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">Time</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">),</span>
+                     <span class="n">a</span><span class="o">.</span><span class="n">Instrument</span><span class="o">.</span><span class="n">hmi</span><span class="p">,</span> <span class="n">physobs_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">|</span> <span class="n">physobs_list</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">|</span> <span class="n">physobs_list</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">cadence</span><span class="p">)</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt">hmi_result = Fido.search(a.Time(start_date, end_date),
+                     a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2], cadence)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>You can also query for both instruments simultaneously.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">Fido</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">Time</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">),</span>
+                     <span class="n">aia_inst</span> <span class="o">|</span> <span class="n">hmi_inst</span><span class="p">,</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">|</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">|</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">|</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+                     <span class="o">|</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">|</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span> <span class="o">|</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span> <span class="o">|</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span> <span class="o">|</span> <span class="n">wavelength_list</span><span class="p">[</span><span class="mi">8</span><span class="p">]</span>
+                     <span class="o">|</span> <span class="n">physobs_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">|</span> <span class="n">physobs_list</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">|</span> <span class="n">physobs_list</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+                     <span class="n">cadence</span><span class="p">)</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">result = Fido.search(a.Time(start_date, end_date),
+                     aia_inst | hmi_inst, wavelength_list[0] | wavelength_list[1] | wavelength_list[2] | wavelength_list[3]
+                     | wavelength_list[4] | wavelength_list[5] | wavelength_list[6] | wavelength_list[7] | wavelength_list[8]
+                     | physobs_list[0] | physobs_list[1] | physobs_list[2],
+                     cadence)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>/Users/tervin/opt/anaconda3/envs/neid_data/lib/python3.7/site-packages/astropy/table/table.py:3197: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future.
+  result = self.as_array() == other
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Next we download the actual data found from our search.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">file_download</span> <span class="o">=</span> <span class="n">Fido</span><span class="o">.</span><span class="n">fetch</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">
+file_download = Fido.fetch(result)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+
+
+
+<div id="7dfee6f4-7dff-42b7-a2ef-b820803d25e8" class="jupyter-widgets jp-OutputArea-output ">
+<script type="text/javascript">
+var element = document.getElementById('7dfee6f4-7dff-42b7-a2ef-b820803d25e8');
+</script>
+<script type="application/vnd.jupyter.widget-view+json">
+{"model_id": "562e80d0aa5b4548a33015eae2ee8169", "version_major": 2, "version_minor": 0}
+</script>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>After downloading our data, we want to sort it by date.
+We then build a map sequence of our data.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">map_seq</span> <span class="o">=</span> <span class="n">sunpy</span><span class="o">.</span><span class="n">map</span><span class="o">.</span><span class="n">Map</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">file_download</span><span class="p">))</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt">map_seq = sunpy.map.Map(sorted(file_download))
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>We then split our results by data product.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[17]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">aia_094</span><span class="p">,</span> <span class="n">aia_131</span><span class="p">,</span> <span class="n">aia_171</span><span class="p">,</span> <span class="n">aia_193</span><span class="p">,</span> <span class="n">aia_211</span><span class="p">,</span> <span class="n">aia_304</span><span class="p">,</span> <span class="n">aia_335</span><span class="p">,</span> <span class="n">aia_1600</span><span class="p">,</span> <span class="n">aia_1700</span><span class="p">,</span> <span class="n">hmi_vel</span><span class="p">,</span> <span class="n">hmi_mag</span><span class="p">,</span> <span class="n">hmi_int</span> \
+    <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
+
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">map_obj</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">map_seq</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">94</span><span class="p">:</span>
+        <span class="n">aia_094</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">131</span><span class="p">:</span>
+        <span class="n">aia_131</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">171</span><span class="p">:</span>
+        <span class="n">aia_171</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">193</span><span class="p">:</span>
+        <span class="n">aia_193</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">211</span><span class="p">:</span>
+        <span class="n">aia_211</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">304</span><span class="p">:</span>
+        <span class="n">aia_304</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">335</span><span class="p">:</span>
+        <span class="n">aia_335</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1600</span><span class="p">:</span>
+        <span class="n">aia_1600</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1700</span><span class="p">:</span>
+        <span class="n">aia_1700</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">6173</span> <span class="ow">and</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;DOPPLERGRAM&#39;</span><span class="p">:</span>
+        <span class="n">hmi_vel</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">6173</span> <span class="ow">and</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;MAGNETOGRAM&#39;</span><span class="p">:</span>
+        <span class="n">hmi_mag</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">6173</span> <span class="ow">and</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;CONTINUUM INTENSITY&#39;</span><span class="p">:</span>
+        <span class="n">hmi_int</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">map_obj</span><span class="p">)</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">aia_094, aia_131, aia_171, aia_193, aia_211, aia_304, aia_335, aia_1600, aia_1700, hmi_vel, hmi_mag, hmi_int \
+    = [], [], [], [], [], [], [], [], [], [], [], []
+
+for i, map_obj in enumerate(map_seq):
+    if map_obj.meta['wavelnth'] == 94:
+        aia_094.append(map_obj)
+    elif map_obj.meta['wavelnth'] == 131:
+        aia_131.append(map_obj)
+    elif map_obj.meta['wavelnth'] == 171:
+        aia_171.append(map_obj)
+    elif map_obj.meta['wavelnth'] == 193:
+        aia_193.append(map_obj)
+    elif map_obj.meta['wavelnth'] == 211:
+        aia_211.append(map_obj)
+    elif map_obj.meta['wavelnth'] == 304:
+        aia_304.append(map_obj)
+    elif map_obj.meta['wavelnth'] == 335:
+        aia_335.append(map_obj)
+    elif map_obj.meta['wavelnth'] == 1600:
+        aia_1600.append(map_obj)
+    elif map_obj.meta['wavelnth'] == 1700:
+        aia_1700.append(map_obj)
+    elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'DOPPLERGRAM':
+        hmi_vel.append(map_obj)
+    elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'MAGNETOGRAM':
+        hmi_mag.append(map_obj)
+    elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'CONTINUUM INTENSITY':
+        hmi_int.append(map_obj)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="plotting-images">Plotting images<a class="anchor-link" href="#Plotting-images">&#182;</a></h2>
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>You can plot individual images using inherent Sunpy features.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[19]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">aia_211</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>  <span class="c1"># this plots the aia image with a colorbar, axes labels, and title</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt">aia_211[0].peek()  # this plots the aia image with a colorbar, axes labels, and title</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img src="data:image/png;base64,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+            <div>
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">aia_211</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>  <span class="c1"># this just plots the data, allows you to customize labels, colormap, and title</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-10" class="clipboard-copy-txt">aia_211[0].plot()  # this just plots the data, allows you to customize labels, colormap, and title
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+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>Plotting HMI images requires us to rotate the image to match the orientation of AIA images.</p>
+<p>The same peek and plot methods work for HMI images.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[21]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-11">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">hmi_vel_rot</span> <span class="o">=</span> <span class="n">hmi_vel</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">hmi_vel_rot</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>
+</pre></div>
+<div id="cell-11" class="clipboard-copy-txt">hmi_vel_rot = hmi_vel[0].rotate(order=3)
+hmi_vel_rot.peek()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
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+
+
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
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>HMI images look nicer when customized a bit.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[22]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-12">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
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+        </div>
+        <div class="CodeMirror cm-s-jupyter">
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># plotting intensity map</span>
+
+<span class="n">int_map</span> <span class="o">=</span> <span class="n">hmi_int</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">int_map</span><span class="o">.</span><span class="n">plot_settings</span><span class="p">[</span><span class="s1">&#39;cmap&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;hinodesotintensity&#39;</span><span class="p">)</span>
+<span class="n">int_map</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-12" class="clipboard-copy-txt"># plotting intensity map
+
+int_map = hmi_int[0].rotate(order=3)
+int_map.plot_settings['cmap'] = plt.get_cmap('hinodesotintensity')
+int_map.plot()
+plt.show()
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
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+
+
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># plotting magnetic map</span>
+
+<span class="n">mag_map</span> <span class="o">=</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">mag_map</span><span class="o">.</span><span class="n">plot_settings</span><span class="p">[</span><span class="s1">&#39;norm&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="o">-</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">mag_map</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-13" class="clipboard-copy-txt"># plotting magnetic map
+
+mag_map = hmi_mag[0].rotate(order=3)
+mag_map.plot_settings['norm'] = plt.Normalize(-100, 100)
+mag_map.plot()
+plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
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+        
+        
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-14">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># plotting doppler map</span>
+
+<span class="n">vel_map</span> <span class="o">=</span> <span class="n">hmi_vel</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">vel_map</span><span class="o">.</span><span class="n">plot_settings</span><span class="p">[</span><span class="s1">&#39;norm&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="o">-</span><span class="mi">5500</span><span class="p">,</span> <span class="mi">5500</span><span class="p">)</span>
+<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">vel_map</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-14" class="clipboard-copy-txt"># plotting doppler map
+
+vel_map = hmi_vel[0].rotate(order=3)
+vel_map.plot_settings['norm'] = plt.Normalize(-5500, 5500)
+fig = plt.figure()
+vel_map.plot()
+plt.show()</div>
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
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>We can now make plots of multiple data types.
+Don't forget to rotate HMI images such that they are oriented the same as AIA.</p>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Images </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">aia_171</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;t_obs&#39;</span><span class="p">])))</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">aia_171</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;telescop&#39;</span><span class="p">]),</span> <span class="nb">str</span><span class="p">(</span><span class="n">aia_171</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">])))</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">aia_211</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;telescop&#39;</span><span class="p">]),</span> <span class="nb">str</span><span class="p">(</span><span class="n">aia_211</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">])))</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;telescop&#39;</span><span class="p">]),</span> <span class="nb">str</span><span class="p">(</span><span class="n">hmi_vel</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">])))</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;telescop&#39;</span><span class="p">]),</span> <span class="nb">str</span><span class="p">(</span><span class="n">hmi_mag</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">])))</span>
+
+<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</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="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span>
+        <span class="n">axs</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
+        <span class="n">axs</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([])</span>
+
+<span class="n">aia_171</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">axes</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">annotate</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">aia_211</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">axes</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">annotate</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># make sure you rotate HMI images</span>
+<span class="n">hmi_vel_rot</span> <span class="o">=</span> <span class="n">hmi_vel</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">hmi_vel_rot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">axes</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">annotate</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">hmi_mag_rot</span> <span class="o">=</span> <span class="n">hmi_mag</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">hmi_mag_rot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">axes</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">annotate</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-15" class="clipboard-copy-txt">fig, axs = plt.subplots(2, 2)
+fig.suptitle("Images %s" % (str(aia_171[0].meta['t_obs'])))
+axs[0, 0].set_title("%s %s" % (str(aia_171[0].meta['telescop']), str(aia_171[0].meta['wavelnth'])))
+axs[0, 1].set_title("%s %s" % (str(aia_211[0].meta['telescop']), str(aia_211[0].meta['wavelnth'])))
+axs[1, 0].set_title("%s %s" % (str(hmi_vel[0].meta['telescop']), str(hmi_vel[0].meta['content'])))
+axs[1, 1].set_title("%s %s" % (str(hmi_mag[0].meta['telescop']), str(hmi_mag[0].meta['content'])))
+
+for j in range(0, 2):
+    for k in range(0, 2):
+        axs[j, k].set_xticks([])
+        axs[j, k].set_yticks([])
+
+aia_171[0].plot(axes=axs[0, 0], annotate=False)
+aia_211[0].plot(axes=axs[0, 1], annotate=False)
+
+# make sure you rotate HMI images
+hmi_vel_rot = hmi_vel[0].rotate(order=3)
+hmi_vel_rot.plot(axes=axs[1, 0], annotate=False)
+hmi_mag_rot = hmi_mag[0].rotate(order=3)
+hmi_mag_rot.plot(axes=axs[1, 1], annotate=False)
+
+plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase]
+WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase]
+WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase]
+WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase]
+WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase]
+WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase]
+WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase]
+WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase]
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
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index f6c9329..b0efd11 100644
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@@ -276,7 +278,7 @@
   
   
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@@ -290,7 +292,7 @@
   
   
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@@ -312,7 +314,7 @@
   
   
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+      <a href="calcs/coords/" class="md-nav__link">
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     </li>
@@ -327,7 +329,7 @@
   
   
     <li class="md-nav__item">
-      <a href="sdo/region/" class="md-nav__link">
+      <a href="calcs/region/" class="md-nav__link">
         Region Identification
       </a>
     </li>
@@ -342,7 +344,7 @@
   
   
     <li class="md-nav__item">
-      <a href="sdo/vel/" class="md-nav__link">
+      <a href="calcs/vel/" class="md-nav__link">
         Velocity Components
       </a>
     </li>
@@ -357,7 +359,7 @@
   
   
     <li class="md-nav__item">
-      <a href="sdo/rv/" class="md-nav__link">
+      <a href="calcs/rv/" class="md-nav__link">
         RV Calculation
       </a>
     </li>
@@ -372,7 +374,7 @@
   
   
     <li class="md-nav__item">
-      <a href="sdo/mag/" class="md-nav__link">
+      <a href="calcs/mag/" class="md-nav__link">
         Magnetic Observables
       </a>
     </li>
@@ -387,7 +389,7 @@
   
   
     <li class="md-nav__item">
-      <a href="sdo/pipeline/" class="md-nav__link">
+      <a href="package/pipeline/" class="md-nav__link">
         Full Pipeline
       </a>
     </li>
@@ -411,12 +413,130 @@
       
       
         <label class="md-nav__link" for="__nav_10">
+          Function Documentation
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Function Documentation" data-md-level="1">
+        <label class="md-nav__title" for="__nav_10">
+          <span class="md-nav__icon md-icon"></span>
+          Function Documentation
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="package/coord_funcs/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="package/lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="package/calculation_funcs/" class="md-nav__link">
+        Calculation Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="package/rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="package/plotting_funcs/" class="md-nav__link">
+        Plotting Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="package/settings/" class="md-nav__link">
+        Settings
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_11" type="checkbox" id="__nav_11" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_11">
           Jupyter Examples
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <nav class="md-nav" aria-label="Jupyter Examples" data-md-level="1">
-        <label class="md-nav__title" for="__nav_10">
+        <label class="md-nav__title" for="__nav_11">
           <span class="md-nav__icon md-icon"></span>
           Jupyter Examples
         </label>
@@ -428,7 +548,7 @@
   
   
     <li class="md-nav__item">
-      <a href="sdo/examples/docs_sunpy_example/" class="md-nav__link">
+      <a href="examples/docs_sunpy_example/" class="md-nav__link">
         Sunpy Basics
       </a>
     </li>
@@ -442,7 +562,7 @@
   
   
     <li class="md-nav__item">
-      <a href="sdo/examples/docs_solar_corrections/" class="md-nav__link">
+      <a href="examples/docs_solar_corrections/" class="md-nav__link">
         Component Calculations
       </a>
     </li>
@@ -456,7 +576,7 @@
   
   
     <li class="md-nav__item">
-      <a href="sdo/examples/docs_rv_calcs/" class="md-nav__link">
+      <a href="examples/docs_rv_calcs/" class="md-nav__link">
         RV Calculation
       </a>
     </li>
@@ -470,7 +590,7 @@
   
   
     <li class="md-nav__item">
-      <a href="sdo/examples/docs_full_pipeline/" class="md-nav__link">
+      <a href="examples/docs_full_pipeline/" class="md-nav__link">
         Full Pipeline
       </a>
     </li>
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diff --git a/mkdocs/site/package/calculation_funcs/index.html b/mkdocs/site/package/calculation_funcs/index.html
new file mode 100644
index 0000000..a938b00
--- /dev/null
+++ b/mkdocs/site/package/calculation_funcs/index.html
@@ -0,0 +1,3032 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="SDO HMI RV Pipeline Documentation">
+      
+      
+      
+      
+      <link rel="icon" href="../../img/sun.png">
+      <meta name="generator" content="mkdocs-1.2.2, mkdocs-material-7.3.1">
+    
+    
+      
+        <title>Calculation Functions - SDO HMI RV Pipeline</title>
+      
+    
+    
+      <link rel="stylesheet" href="../../assets/stylesheets/main.1c75ef36.min.css">
+      
+        
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.3f5d1f46.min.css">
+        
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+          
+          <meta name="theme-color" content="#4051b5">
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+        <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+        <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=:300,400,400i,700%7COswald&display=fallback">
+        <style>:root{--md-text-font-family:"";--md-code-font-family:"Oswald"}</style>
+      
+    
+    
+    
+      <link rel="stylesheet" href="../../assets/_mkdocstrings.css">
+    
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+      
+
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+  </head>
+  
+  
+    
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+    
+    
+    <body dir="ltr" data-md-color-scheme="" data-md-color-primary="indigo" data-md-color-accent="light-blue">
+  
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+    <script>function __prefix(e){return new URL("../..",location).pathname+"."+e}function __get(e,t=localStorage){return JSON.parse(t.getItem(__prefix(e)))}</script>
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+    <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="#calculation-functions-module" 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="SDO HMI RV Pipeline" class="md-header__button md-logo" aria-label="SDO HMI RV Pipeline" data-md-component="logo">
+      
+  <img src="../../img/sun.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">
+            SDO HMI RV Pipeline
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Calculation Functions
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+      <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">
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12z"/></svg>
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+      <nav class="md-search__options" aria-label="Search">
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+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
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+        </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"></ol>
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+    
+    
+      <div class="md-header__source">
+        
+<a href="https://github.com/tamarervin/sdo_hmi_rvs/" 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"><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">
+    GitHub
+  </div>
+</a>
+      </div>
+    
+  </nav>
+  
+</header>
+    
+    <div class="md-container" data-md-component="container">
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+        
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+        
+      
+      <main class="md-main" data-md-component="main">
+        <div class="md-main__inner md-grid">
+          
+            
+              
+              <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
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+<nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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+    <a href="../.." title="SDO HMI RV Pipeline" class="md-nav__button md-logo" aria-label="SDO HMI RV Pipeline" data-md-component="logo">
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+  <img src="../../img/sun.png" alt="logo">
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+  </div>
+  <div class="md-source__repository">
+    GitHub
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../.." class="md-nav__link">
+        Home
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../about/" class="md-nav__link">
+        About
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          HMI Data
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="HMI Data" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          HMI Data
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data/data/" class="md-nav__link">
+        Aquiring Data
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data/corr/" class="md-nav__link">
+        Data Corrections
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/coords/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/region/" class="md-nav__link">
+        Region Identification
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/vel/" class="md-nav__link">
+        Velocity Components
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/rv/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/mag/" class="md-nav__link">
+        Magnetic Observables
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../pipeline/" class="md-nav__link">
+        Full Pipeline
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+    
+  
+  
+    
+    <li class="md-nav__item md-nav__item--active md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_10" type="checkbox" id="__nav_10" checked>
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_10">
+          Function Documentation
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Function Documentation" data-md-level="1">
+        <label class="md-nav__title" for="__nav_10">
+          <span class="md-nav__icon md-icon"></span>
+          Function Documentation
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../coord_funcs/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+    
+  
+  
+    <li class="md-nav__item md-nav__item--active">
+      
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
+      
+      
+        
+      
+      
+        <label class="md-nav__link md-nav__link--active" for="__toc">
+          Calculation Functions
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <a href="./" class="md-nav__link md-nav__link--active">
+        Calculation Functions
+      </a>
+      
+        
+<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="#sdo_hmi_rvs.tools.calculation_funcs" class="md-nav__link">
+    sdo_hmi_rvs.tools.calculation_funcs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_calc" class="md-nav__link">
+    area_calc()
+  </a>
+  
+    <nav class="md-nav" aria-label="area_calc()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_calc--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_calc--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_filling_factor" class="md-nav__link">
+    area_filling_factor()
+  </a>
+  
+    <nav class="md-nav" aria-label="area_filling_factor()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_filling_factor--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_filling_factor--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_unsigned_flux" class="md-nav__link">
+    area_unsigned_flux()
+  </a>
+  
+    <nav class="md-nav" aria-label="area_unsigned_flux()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_unsigned_flux--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_unsigned_flux--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_vconv" class="md-nav__link">
+    area_vconv()
+  </a>
+  
+    <nav class="md-nav" aria-label="area_vconv()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_vconv--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_vconv--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.corrected_map" class="md-nav__link">
+    corrected_map()
+  </a>
+  
+    <nav class="md-nav" aria-label="corrected_map()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.corrected_map--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.corrected_map--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.filling_factor" class="md-nav__link">
+    filling_factor()
+  </a>
+  
+    <nav class="md-nav" aria-label="filling_factor()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.filling_factor--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.filling_factor--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.int_thresh" class="md-nav__link">
+    int_thresh()
+  </a>
+  
+    <nav class="md-nav" aria-label="int_thresh()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.int_thresh--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.int_thresh--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.mag_field" class="md-nav__link">
+    mag_field()
+  </a>
+  
+    <nav class="md-nav" aria-label="mag_field()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.mag_field--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.mag_field--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.mag_thresh" class="md-nav__link">
+    mag_thresh()
+  </a>
+  
+    <nav class="md-nav" aria-label="mag_thresh()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.mag_thresh--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.mag_thresh--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.map_sequence" class="md-nav__link">
+    map_sequence()
+  </a>
+  
+    <nav class="md-nav" aria-label="map_sequence()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.map_sequence--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.map_sequence--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.rel_positions" class="md-nav__link">
+    rel_positions()
+  </a>
+  
+    <nav class="md-nav" aria-label="rel_positions()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.rel_positions--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.rel_positions--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.solar_rot_vel" class="md-nav__link">
+    solar_rot_vel()
+  </a>
+  
+    <nav class="md-nav" aria-label="solar_rot_vel()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.solar_rot_vel--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.solar_rot_vel--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.spacecraft_vel" class="md-nav__link">
+    spacecraft_vel()
+  </a>
+  
+    <nav class="md-nav" aria-label="spacecraft_vel()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.spacecraft_vel--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.spacecraft_vel--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.thresh_map" class="md-nav__link">
+    thresh_map()
+  </a>
+  
+    <nav class="md-nav" aria-label="thresh_map()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.thresh_map--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.thresh_map--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.unsigned_flux" class="md-nav__link">
+    unsigned_flux()
+  </a>
+  
+    <nav class="md-nav" aria-label="unsigned_flux()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.unsigned_flux--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.unsigned_flux--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_disc" class="md-nav__link">
+    v_disc()
+  </a>
+  
+    <nav class="md-nav" aria-label="v_disc()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_disc--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_disc--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_phot" class="md-nav__link">
+    v_phot()
+  </a>
+  
+    <nav class="md-nav" aria-label="v_phot()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_phot--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_phot--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_quiet" class="md-nav__link">
+    v_quiet()
+  </a>
+  
+    <nav class="md-nav" aria-label="v_quiet()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_quiet--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_quiet--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+      
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../plotting_funcs/" class="md-nav__link">
+        Plotting Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../settings/" class="md-nav__link">
+        Settings
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_11" type="checkbox" id="__nav_11" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_11">
+          Jupyter Examples
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Jupyter Examples" data-md-level="1">
+        <label class="md-nav__title" for="__nav_11">
+          <span class="md-nav__icon md-icon"></span>
+          Jupyter Examples
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_sunpy_example/" class="md-nav__link">
+        Sunpy Basics
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_solar_corrections/" class="md-nav__link">
+        Component Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_rv_calcs/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_full_pipeline/" class="md-nav__link">
+        Full Pipeline
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs" class="md-nav__link">
+    sdo_hmi_rvs.tools.calculation_funcs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_calc" class="md-nav__link">
+    area_calc()
+  </a>
+  
+    <nav class="md-nav" aria-label="area_calc()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_calc--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_calc--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_filling_factor" class="md-nav__link">
+    area_filling_factor()
+  </a>
+  
+    <nav class="md-nav" aria-label="area_filling_factor()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_filling_factor--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_filling_factor--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_unsigned_flux" class="md-nav__link">
+    area_unsigned_flux()
+  </a>
+  
+    <nav class="md-nav" aria-label="area_unsigned_flux()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_unsigned_flux--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_unsigned_flux--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_vconv" class="md-nav__link">
+    area_vconv()
+  </a>
+  
+    <nav class="md-nav" aria-label="area_vconv()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_vconv--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.area_vconv--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.corrected_map" class="md-nav__link">
+    corrected_map()
+  </a>
+  
+    <nav class="md-nav" aria-label="corrected_map()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.corrected_map--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.corrected_map--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.filling_factor" class="md-nav__link">
+    filling_factor()
+  </a>
+  
+    <nav class="md-nav" aria-label="filling_factor()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.filling_factor--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.filling_factor--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.int_thresh" class="md-nav__link">
+    int_thresh()
+  </a>
+  
+    <nav class="md-nav" aria-label="int_thresh()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.int_thresh--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.int_thresh--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.mag_field" class="md-nav__link">
+    mag_field()
+  </a>
+  
+    <nav class="md-nav" aria-label="mag_field()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.mag_field--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.mag_field--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.mag_thresh" class="md-nav__link">
+    mag_thresh()
+  </a>
+  
+    <nav class="md-nav" aria-label="mag_thresh()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.mag_thresh--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.mag_thresh--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.map_sequence" class="md-nav__link">
+    map_sequence()
+  </a>
+  
+    <nav class="md-nav" aria-label="map_sequence()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.map_sequence--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.map_sequence--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.rel_positions" class="md-nav__link">
+    rel_positions()
+  </a>
+  
+    <nav class="md-nav" aria-label="rel_positions()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.rel_positions--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.rel_positions--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.solar_rot_vel" class="md-nav__link">
+    solar_rot_vel()
+  </a>
+  
+    <nav class="md-nav" aria-label="solar_rot_vel()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.solar_rot_vel--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.solar_rot_vel--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.spacecraft_vel" class="md-nav__link">
+    spacecraft_vel()
+  </a>
+  
+    <nav class="md-nav" aria-label="spacecraft_vel()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.spacecraft_vel--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.spacecraft_vel--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.thresh_map" class="md-nav__link">
+    thresh_map()
+  </a>
+  
+    <nav class="md-nav" aria-label="thresh_map()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.thresh_map--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.thresh_map--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.unsigned_flux" class="md-nav__link">
+    unsigned_flux()
+  </a>
+  
+    <nav class="md-nav" aria-label="unsigned_flux()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.unsigned_flux--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.unsigned_flux--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_disc" class="md-nav__link">
+    v_disc()
+  </a>
+  
+    <nav class="md-nav" aria-label="v_disc()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_disc--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_disc--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_phot" class="md-nav__link">
+    v_phot()
+  </a>
+  
+    <nav class="md-nav" aria-label="v_phot()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_phot--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_phot--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_quiet" class="md-nav__link">
+    v_quiet()
+  </a>
+  
+    <nav class="md-nav" aria-label="v_quiet()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_quiet--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.calculation_funcs.v_quiet--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/package/calculation_funcs.md" title="Edit this page" class="md-content__button md-icon">
+                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
+                  </a>
+                
+                
+                <h1 id="calculation-functions-module">Calculation Functions Module</h1>
+
+
+  <div class="doc doc-object doc-module">
+
+<a id="sdo_hmi_rvs.tools.calculation_funcs"></a>
+    <div class="doc doc-contents first">
+
+      <p>Tamar Ervin
+Date: June 16, 2021</p>
+<p>functions for calculating Solar velocity corrections
+and components for derivation of SDO/HMI RVs</p>
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.area_calc" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">area_calc</span><span class="p">(</span><span class="n">active</span><span class="p">,</span> <span class="n">pixA_hem</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>calculate area of active pixels for a thresholded map</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.area_calc--parameters">Parameters</h4>
+<p>active: weights array where active pixels have weight = 1
+pixA_hem: pixel areas in uHem</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.area_calc--returns">Returns</h4>
+<p>area: area of each active region weighted by its intensity</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">area_calc</span><span class="p">(</span><span class="n">active</span><span class="p">,</span> <span class="n">pixA_hem</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    calculate area of active pixels for a thresholded map</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    active: weights array where active pixels have weight = 1</span>
+<span class="sd">    pixA_hem: pixel areas in uHem</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    area: area of each active region weighted by its intensity</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># get labeling of image</span>
+    <span class="n">labeled</span> <span class="o">=</span> <span class="n">label</span><span class="p">(</span><span class="n">active</span><span class="p">)</span>
+
+    <span class="c1"># get area of active regions</span>
+    <span class="n">area</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">active</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">props</span> <span class="o">=</span> <span class="n">regionprops</span><span class="p">(</span><span class="n">labeled</span><span class="p">)</span>
+    <span class="n">info</span> <span class="o">=</span> <span class="n">regionprops</span><span class="p">(</span><span class="n">labeled</span><span class="p">,</span> <span class="n">pixA_hem</span><span class="p">)</span>
+
+    <span class="c1"># add area to array</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="nb">len</span><span class="p">(</span><span class="n">info</span><span class="p">)):</span>
+        <span class="n">area</span><span class="p">[</span><span class="n">props</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">coords</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">props</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">coords</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]]</span> <span class="o">=</span> <span class="n">info</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">area</span> <span class="o">*</span> <span class="n">info</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">mean_intensity</span>
+
+    <span class="k">return</span> <span class="n">area</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.area_filling_factor" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">area_filling_factor</span><span class="p">(</span><span class="n">active</span><span class="p">,</span> <span class="n">area</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">fac_inds</span><span class="p">,</span> <span class="n">athresh</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>calculate filling factor for regions thresholded by area
+- differentiate between large and small regions
+- differentiate between plage (large) and network (small) bright regions</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.area_filling_factor--parameters">Parameters</h4>
+<p>active: weights array where active pixels have weight = 1
+area: area of each active region weighted by its intensity
+mu: array of mu (cosine theta) values
+mmap: UNCORRECTED Sunpy map object (Magnetogram)
+fac_inds: array of indices where faculae are detected
+athresh: area threshold value between large and small regions (in uHem)
+mu_cutoff: minimum mu cutoff value for usable data</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.area_filling_factor--returns">Returns</h4>
+<p>f_small: filling factor (%) for small magnetically active regions
+f_large: filling factor (%) for large magnetically active regions
+f_network: filling factor (%) for network (small, bright magnetically active) regions
+f_plage: filling factor (%) for plage (large, bright magnetically active) regions
+f_nonconv: filling factor (%) for regions that do not suppress convective blueshift</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">area_filling_factor</span><span class="p">(</span><span class="n">active</span><span class="p">,</span> <span class="n">area</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">fac_inds</span><span class="p">,</span> <span class="n">athresh</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    calculate filling factor for regions thresholded by area</span>
+<span class="sd">    - differentiate between large and small regions</span>
+<span class="sd">    - differentiate between plage (large) and network (small) bright regions</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    active: weights array where active pixels have weight = 1</span>
+<span class="sd">    area: area of each active region weighted by its intensity</span>
+<span class="sd">    mu: array of mu (cosine theta) values</span>
+<span class="sd">    mmap: UNCORRECTED Sunpy map object (Magnetogram)</span>
+<span class="sd">    fac_inds: array of indices where faculae are detected</span>
+<span class="sd">    athresh: area threshold value between large and small regions (in uHem)</span>
+<span class="sd">    mu_cutoff: minimum mu cutoff value for usable data</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    f_small: filling factor (%) for small magnetically active regions</span>
+<span class="sd">    f_large: filling factor (%) for large magnetically active regions</span>
+<span class="sd">    f_network: filling factor (%) for network (small, bright magnetically active) regions</span>
+<span class="sd">    f_plage: filling factor (%) for plage (large, bright magnetically active) regions</span>
+<span class="sd">    f_nonconv: filling factor (%) for regions that do not suppress convective blueshift</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># get good mu values</span>
+    <span class="n">good_mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mu</span> <span class="o">&gt;</span> <span class="n">mu_cutoff</span><span class="p">)</span>
+
+    <span class="c1"># get number of pixels</span>
+    <span class="n">npix</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">mmap</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">good_mu</span><span class="p">])</span>
+
+    <span class="c1"># get quiet pixels</span>
+    <span class="n">quiet</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">active</span>
+
+    <span class="c1"># get filling factor for &#39;small&#39; magnetic features</span>
+    <span class="n">small</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">mmap</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">small_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">active</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">area</span> <span class="o">&lt;</span> <span class="n">athresh</span><span class="p">)</span>
+    <span class="n">small</span><span class="p">[</span><span class="n">small_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+    <span class="n">f_small</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">small</span><span class="p">)</span> <span class="o">/</span> <span class="n">npix</span> <span class="o">*</span> <span class="mi">100</span>
+
+    <span class="c1"># get filling factor for &#39;large&#39; magnetic features</span>
+    <span class="n">large</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">mmap</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">large_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">active</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">area</span> <span class="o">&gt;</span> <span class="n">athresh</span><span class="p">)</span>
+    <span class="n">large</span><span class="p">[</span><span class="n">large_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+    <span class="n">f_large</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">large</span><span class="p">)</span> <span class="o">/</span> <span class="n">npix</span> <span class="o">*</span> <span class="mi">100</span>
+
+    <span class="c1"># get filling factor for network (small, faculae regions)</span>
+    <span class="n">network</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">mmap</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">network_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">small</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">fac_inds</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">)</span>
+    <span class="n">network</span><span class="p">[</span><span class="n">network_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+    <span class="n">f_network</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">network</span><span class="p">)</span> <span class="o">/</span> <span class="n">npix</span> <span class="o">*</span> <span class="mi">100</span>
+
+    <span class="c1"># get filling factor for plage (large, faculae regions)</span>
+    <span class="n">plage</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">mmap</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">plage_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">large</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">fac_inds</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">)</span>
+    <span class="n">plage</span><span class="p">[</span><span class="n">plage_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+    <span class="n">f_plage</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">plage</span><span class="p">)</span> <span class="o">/</span> <span class="n">npix</span> <span class="o">*</span> <span class="mi">100</span>
+
+    <span class="c1"># get filling factor for small, non-convective regions</span>
+    <span class="n">nonconv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">mmap</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">nonconv_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">quiet</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">small</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">)</span>
+    <span class="n">nonconv</span><span class="p">[</span><span class="n">nonconv_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+    <span class="n">f_nonconv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">nonconv</span><span class="p">)</span> <span class="o">/</span> <span class="n">npix</span> <span class="o">*</span> <span class="mi">100</span>
+
+    <span class="k">return</span> <span class="n">f_small</span><span class="p">,</span> <span class="n">f_large</span><span class="p">,</span> <span class="n">f_network</span><span class="p">,</span> <span class="n">f_plage</span><span class="p">,</span> <span class="n">f_nonconv</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.area_unsigned_flux" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">area_unsigned_flux</span><span class="p">(</span><span class="n">map_mag_obs</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span> <span class="n">area</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">athresh</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>calculate the magnetic flux for different regions based on area cut
+and magnetic activitiy</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.area_unsigned_flux--parameters">Parameters</h4>
+<p>map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram)
+imap: UNCORRECTED Sunpy map object (Intensitygram)
+area: area of each active region weighted by its intensity
+active: weights array where active pixels have weight = 1
+athresh: area threshold value between large and small regions (in uHem)</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.area_unsigned_flux--returns">Returns</h4>
+<p>quiet_flux: magnetic flux of quiet-Sun regions
+ar_flux: magnetic flux of active regions
+conv_flux: magnetic flux of regions that suppress convective blueshift
+pol_flux: magnetic flux of polarized regions
+pol_conv_flux: magnetic flux of polarized regions that suppress the convective blueshift</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">area_unsigned_flux</span><span class="p">(</span><span class="n">map_mag_obs</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span> <span class="n">area</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">athresh</span><span class="o">=</span><span class="mi">20</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    calculate the magnetic flux for different regions based on area cut</span>
+<span class="sd">    and magnetic activitiy</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram)</span>
+<span class="sd">    imap: UNCORRECTED Sunpy map object (Intensitygram)</span>
+<span class="sd">    area: area of each active region weighted by its intensity</span>
+<span class="sd">    active: weights array where active pixels have weight = 1</span>
+<span class="sd">    athresh: area threshold value between large and small regions (in uHem)</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    quiet_flux: magnetic flux of quiet-Sun regions</span>
+<span class="sd">    ar_flux: magnetic flux of active regions</span>
+<span class="sd">    conv_flux: magnetic flux of regions that suppress convective blueshift</span>
+<span class="sd">    pol_flux: magnetic flux of polarized regions</span>
+<span class="sd">    pol_conv_flux: magnetic flux of polarized regions that suppress the convective blueshift</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># get data arrays</span>
+    <span class="n">i_data</span> <span class="o">=</span> <span class="n">imap</span><span class="o">.</span><span class="n">data</span>
+    <span class="n">m_data</span> <span class="o">=</span> <span class="n">map_mag_obs</span><span class="o">.</span><span class="n">data</span>
+    <span class="n">mabs_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">m_data</span><span class="p">)</span>
+    <span class="n">quiet</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">active</span>
+
+    <span class="c1"># get large regions array</span>
+    <span class="n">large</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">m_data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">large_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">active</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">area</span> <span class="o">&gt;</span> <span class="n">athresh</span><span class="p">)</span>
+    <span class="n">large</span><span class="p">[</span><span class="n">large_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+
+    <span class="c1"># calculate relevant fluxes</span>
+    <span class="n">quiet_flux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mabs_data</span> <span class="o">*</span> <span class="n">i_data</span> <span class="o">*</span> <span class="n">quiet</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">i_data</span> <span class="o">*</span> <span class="n">quiet</span><span class="p">)</span>
+    <span class="n">ar_flux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mabs_data</span> <span class="o">*</span> <span class="n">i_data</span> <span class="o">*</span> <span class="n">active</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">i_data</span> <span class="o">*</span> <span class="n">active</span><span class="p">)</span>
+    <span class="n">conv_flux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mabs_data</span> <span class="o">*</span> <span class="n">i_data</span> <span class="o">*</span> <span class="n">large</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">i_data</span> <span class="o">*</span> <span class="n">large</span><span class="p">)</span>
+    <span class="n">pol_flux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">m_data</span> <span class="o">*</span> <span class="n">i_data</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">i_data</span><span class="p">)</span>
+    <span class="n">pol_conv_flux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">m_data</span> <span class="o">*</span> <span class="n">i_data</span> <span class="o">*</span> <span class="n">large</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">i_data</span> <span class="o">*</span> <span class="n">large</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">quiet_flux</span><span class="p">,</span> <span class="n">ar_flux</span><span class="p">,</span> <span class="n">conv_flux</span><span class="p">,</span> <span class="n">pol_flux</span><span class="p">,</span> <span class="n">pol_conv_flux</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.area_vconv" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">area_vconv</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">area</span><span class="p">,</span> <span class="n">athresh</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>calculate convective velocities for different area thresholds</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.area_vconv--parameters">Parameters</h4>
+<p>map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram)
+imap: UNCORRECTED Sunpy map object (Intensitygram)
+active: weights array where active pixels have weight = 1
+area: area of each active region weighted by its intensity
+athresh: area threshold value between large and small regions (in uHem)</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.area_vconv--returns">Returns</h4>
+<p>vconv_quiet: convective velocity due to quiet-Sun regions
+vconv_large: convective velocity due to large active regions
+vconv_small: convective velocity due to small active regions</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">area_vconv</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">area</span><span class="p">,</span> <span class="n">athresh</span><span class="o">=</span><span class="mi">20</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    calculate convective velocities for different area thresholds</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram)</span>
+<span class="sd">    imap: UNCORRECTED Sunpy map object (Intensitygram)</span>
+<span class="sd">    active: weights array where active pixels have weight = 1</span>
+<span class="sd">    area: area of each active region weighted by its intensity</span>
+<span class="sd">    athresh: area threshold value between large and small regions (in uHem)</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    vconv_quiet: convective velocity due to quiet-Sun regions</span>
+<span class="sd">    vconv_large: convective velocity due to large active regions</span>
+<span class="sd">    vconv_small: convective velocity due to small active regions</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># get data arrays</span>
+    <span class="n">v_data</span> <span class="o">=</span> <span class="n">map_vel_cor</span><span class="o">.</span><span class="n">data</span>
+    <span class="n">i_data</span> <span class="o">=</span> <span class="n">imap</span><span class="o">.</span><span class="n">data</span>
+
+    <span class="c1"># get large regions array</span>
+    <span class="n">large</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">v_data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">large_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">active</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">area</span> <span class="o">&gt;</span> <span class="n">athresh</span><span class="p">)</span>
+    <span class="n">large</span><span class="p">[</span><span class="n">large_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+
+    <span class="c1"># get small regions array</span>
+    <span class="n">small</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">v_data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">small_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">active</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">area</span> <span class="o">&lt;</span> <span class="n">athresh</span><span class="p">)</span>
+    <span class="n">small</span><span class="p">[</span><span class="n">small_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+
+    <span class="c1"># label the regions</span>
+    <span class="n">labeled</span> <span class="o">=</span> <span class="n">label</span><span class="p">(</span><span class="n">large</span><span class="p">)</span>
+    <span class="n">v_props</span> <span class="o">=</span> <span class="n">regionprops</span><span class="p">(</span><span class="n">labeled</span><span class="p">,</span> <span class="n">v_data</span><span class="p">)</span>
+    <span class="n">i_props</span> <span class="o">=</span> <span class="n">regionprops</span><span class="p">(</span><span class="n">labeled</span><span class="p">,</span> <span class="n">i_data</span><span class="p">)</span>
+
+    <span class="c1"># labeled for small regions</span>
+    <span class="n">labeled</span> <span class="o">=</span> <span class="n">label</span><span class="p">(</span><span class="n">small</span><span class="p">)</span>
+    <span class="n">v_small</span> <span class="o">=</span> <span class="n">regionprops</span><span class="p">(</span><span class="n">labeled</span><span class="p">,</span> <span class="n">v_data</span><span class="p">)</span>
+    <span class="n">i_small</span> <span class="o">=</span> <span class="n">regionprops</span><span class="p">(</span><span class="n">labeled</span><span class="p">,</span> <span class="n">i_data</span><span class="p">)</span>
+
+    <span class="c1"># get quiet regions array</span>
+    <span class="n">quiet</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">active</span>
+
+    <span class="c1"># array of non-convective regions</span>
+    <span class="n">nonconv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">v_data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">nonconv_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_or</span><span class="p">(</span><span class="n">quiet</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">small</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">)</span>
+    <span class="n">nonconv</span><span class="p">[</span><span class="n">nonconv_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+
+    <span class="c1"># velocities of non convective regions</span>
+    <span class="n">vel_quiet</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">v_data</span> <span class="o">*</span> <span class="n">i_data</span> <span class="o">*</span> <span class="n">quiet</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">i_data</span> <span class="o">*</span> <span class="n">quiet</span><span class="p">)</span>
+    <span class="n">vel_nonconv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">v_data</span> <span class="o">*</span> <span class="n">i_data</span> <span class="o">*</span> <span class="n">nonconv</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">i_data</span> <span class="o">*</span> <span class="n">nonconv</span><span class="p">)</span>
+
+    <span class="c1"># velocities of convective regions</span>
+    <span class="n">vconv_large</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">v_props</span><span class="p">))</span>
+    <span class="n">vconv_small</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">v_props</span><span class="p">))</span>
+    <span class="n">vconv_quiet</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">v_props</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="nb">len</span><span class="p">(</span><span class="n">v_props</span><span class="p">)):</span>
+        <span class="n">vconv_large</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v_props</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">area</span> <span class="o">*</span> <span class="p">(</span><span class="n">v_props</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">mean_intensity</span> <span class="o">-</span> <span class="n">vel_quiet</span><span class="p">)</span> <span class="o">*</span> <span class="n">i_props</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">mean_intensity</span>
+        <span class="n">vconv_small</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v_small</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">area</span> <span class="o">*</span> <span class="p">(</span><span class="n">v_small</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">mean_intensity</span> <span class="o">-</span> <span class="n">vel_quiet</span><span class="p">)</span> <span class="o">*</span> <span class="n">i_small</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">mean_intensity</span>
+        <span class="n">vconv_quiet</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v_props</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">area</span> <span class="o">*</span> <span class="p">(</span><span class="n">v_props</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">mean_intensity</span> <span class="o">-</span> <span class="n">vel_nonconv</span><span class="p">)</span> <span class="o">*</span> <span class="n">i_props</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">mean_intensity</span>
+
+    <span class="c1"># convective velocity of quiet regions</span>
+    <span class="n">vconv_quiet</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">vconv_quiet</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">i_data</span><span class="p">)</span>
+
+    <span class="c1"># convective velocity of large regions</span>
+    <span class="n">vconv_large</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">vconv_large</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">i_data</span><span class="p">)</span>
+
+    <span class="c1"># convective velocity of small regions</span>
+    <span class="n">vconv_small</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">vconv_small</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">i_data</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">vconv_quiet</span><span class="p">,</span> <span class="n">vconv_large</span><span class="p">,</span> <span class="n">vconv_small</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.corrected_map" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">corrected_map</span><span class="p">(</span><span class="n">corrected_data</span><span class="p">,</span> <span class="n">smap</span><span class="p">,</span> <span class="n">map_type</span><span class="p">,</span> <span class="n">frame</span><span class="o">=&lt;</span><span class="k">class</span> <span class="err">&#39;</span><span class="nc">sunpy</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</span><span class="s1">&#39;&gt;)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to make Sunpy map object from corrected data</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.corrected_map--parameters">Parameters</h4>
+<p>corrected_data: corrected velocity data
+smap: original Sunpy map object
+map_type: map type for 'content' section of fits header (string)
+frame: new rotation frame</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.corrected_map--returns">Returns</h4>
+<p>corr_map: Sunpy map object with new frame information and corrected data</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">corrected_map</span><span class="p">(</span><span class="n">corrected_data</span><span class="p">,</span> <span class="n">smap</span><span class="p">,</span> <span class="n">map_type</span><span class="p">,</span> <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to make Sunpy map object from corrected data</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    corrected_data: corrected velocity data</span>
+<span class="sd">    smap: original Sunpy map object</span>
+<span class="sd">    map_type: map type for &#39;content&#39; section of fits header (string)</span>
+<span class="sd">    frame: new rotation frame</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    corr_map: Sunpy map object with new frame information and corrected data</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># build SkyCoord instance in new frame</span>
+    <span class="n">coord</span> <span class="o">=</span> <span class="n">SkyCoord</span><span class="p">(</span><span class="mi">0</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">arcsec</span><span class="p">,</span> <span class="mi">0</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">arcsec</span><span class="p">,</span> <span class="n">obstime</span><span class="o">=</span><span class="n">smap</span><span class="o">.</span><span class="n">date</span><span class="p">,</span> <span class="n">observer</span><span class="o">=</span><span class="n">smap</span><span class="o">.</span><span class="n">observer_coordinate</span><span class="p">,</span>
+                     <span class="n">frame</span><span class="o">=</span><span class="n">frame</span><span class="p">)</span>
+
+    <span class="c1"># create fits header file with data and coordinate system information</span>
+    <span class="n">header</span> <span class="o">=</span> <span class="n">sunpy</span><span class="o">.</span><span class="n">map</span><span class="o">.</span><span class="n">make_fitswcs_header</span><span class="p">(</span><span class="n">corrected_data</span><span class="p">,</span> <span class="n">coord</span><span class="p">)</span>
+
+    <span class="c1"># update fits header with instrument and content information</span>
+    <span class="n">header</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">map_type</span>
+    <span class="n">header</span><span class="p">[</span><span class="s1">&#39;telescop&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">smap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;telescop&#39;</span><span class="p">]</span>
+    <span class="n">header</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">smap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">]</span>
+
+    <span class="c1"># create new Sunpy map instance with corrected data</span>
+    <span class="n">corr_map</span> <span class="o">=</span> <span class="n">sunpy</span><span class="o">.</span><span class="n">map</span><span class="o">.</span><span class="n">Map</span><span class="p">(</span><span class="n">corrected_data</span><span class="p">,</span> <span class="n">header</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">corr_map</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.filling_factor" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">filling_factor</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">fac_inds</span><span class="p">,</span> <span class="n">spot_inds</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to calculate filling factors for faculae, sunspots, and
+total magnetically active regions
+- percentage of magnetically active pixels on the solar surface at any one time</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.filling_factor--parameters">Parameters</h4>
+<p>mu: array of mu (cosine theta) values
+mmap: UNCORRECTED Sunpy map object (Magnetogram)
+active: weights array where active pixels have weight = 1
+fac_inds: array of indices where faculae are detected
+spot_inds: array of indices where sunspots are detected
+mu_cutoff: minimum mu cutoff value</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.filling_factor--returns">Returns</h4>
+<p>f_bright: filling factor (%) for bright areas (faculae)
+f_spot: filling factor (%) for dark areas (sunspots)
+f_total: filling factor (%) for timestamp</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">filling_factor</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">fac_inds</span><span class="p">,</span> <span class="n">spot_inds</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to calculate filling factors for faculae, sunspots, and</span>
+<span class="sd">    total magnetically active regions</span>
+<span class="sd">    - percentage of magnetically active pixels on the solar surface at any one time</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    mu: array of mu (cosine theta) values</span>
+<span class="sd">    mmap: UNCORRECTED Sunpy map object (Magnetogram)</span>
+<span class="sd">    active: weights array where active pixels have weight = 1</span>
+<span class="sd">    fac_inds: array of indices where faculae are detected</span>
+<span class="sd">    spot_inds: array of indices where sunspots are detected</span>
+<span class="sd">    mu_cutoff: minimum mu cutoff value</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    f_bright: filling factor (%) for bright areas (faculae)</span>
+<span class="sd">    f_spot: filling factor (%) for dark areas (sunspots)</span>
+<span class="sd">    f_total: filling factor (%) for timestamp</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># get good mu values</span>
+    <span class="n">good_mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mu</span> <span class="o">&gt;</span> <span class="n">mu_cutoff</span><span class="p">)</span>
+
+    <span class="c1"># get number of pixels</span>
+    <span class="n">npix</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">mmap</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">good_mu</span><span class="p">])</span>
+
+    <span class="c1"># faculae</span>
+    <span class="n">faculae</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">mmap</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">faculae</span><span class="p">[</span><span class="n">fac_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+    <span class="n">f_bright</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">faculae</span><span class="p">)</span> <span class="o">/</span> <span class="n">npix</span> <span class="o">*</span> <span class="mi">100</span>
+
+    <span class="c1"># sunspots</span>
+    <span class="n">spots</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">mmap</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">spots</span><span class="p">[</span><span class="n">spot_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+    <span class="n">f_spot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">spots</span><span class="p">)</span> <span class="o">/</span> <span class="n">npix</span> <span class="o">*</span> <span class="mi">100</span>
+
+    <span class="c1"># get filling factor</span>
+    <span class="n">f_total</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">active</span><span class="p">)</span> <span class="o">/</span> <span class="n">npix</span> <span class="o">*</span> <span class="mi">100</span>
+
+    <span class="k">return</span> <span class="n">f_bright</span><span class="p">,</span> <span class="n">f_spot</span><span class="p">,</span> <span class="n">f_total</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.int_thresh" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">int_thresh</span><span class="p">(</span><span class="n">map_int_cor</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">quiet</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to do intensity thresholding and differentiate between faculae (bright) and sunspots (dark)</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.int_thresh--parameters">Parameters</h4>
+<p>map_int_cor: corrected (limb-darkening) Sunpy map object (Intensitygram)
+active: weights array where active pixels are 1
+quiet: weights array where active pixels are 0</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.int_thresh--returns">Returns</h4>
+<p>fac_inds: array of indices where faculae are detected
+spot_inds: array of indices where sunspots are detected</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">int_thresh</span><span class="p">(</span><span class="n">map_int_cor</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">quiet</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to do intensity thresholding and differentiate between faculae (bright) and sunspots (dark)</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    map_int_cor: corrected (limb-darkening) Sunpy map object (Intensitygram)</span>
+<span class="sd">    active: weights array where active pixels are 1</span>
+<span class="sd">    quiet: weights array where active pixels are 0</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    fac_inds: array of indices where faculae are detected</span>
+<span class="sd">    spot_inds: array of indices where sunspots are detected</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># flattened intensity data</span>
+    <span class="n">Iflat</span> <span class="o">=</span> <span class="n">map_int_cor</span><span class="o">.</span><span class="n">data</span>
+
+    <span class="c1"># calculate quiet sun intensity</span>
+    <span class="n">int_quiet</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">Iflat</span> <span class="o">*</span> <span class="n">quiet</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">quiet</span><span class="p">)</span>
+
+    <span class="c1"># intensity threshold</span>
+    <span class="n">int_cutoff</span> <span class="o">=</span> <span class="mf">0.89</span> <span class="o">*</span> <span class="n">int_quiet</span>
+
+    <span class="c1"># get faculae</span>
+    <span class="n">fac_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">((</span><span class="n">Iflat</span> <span class="o">&gt;</span> <span class="n">int_cutoff</span><span class="p">),</span> <span class="p">(</span><span class="n">active</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">))</span>
+
+    <span class="c1"># get sunspots</span>
+    <span class="n">spot_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">((</span><span class="n">Iflat</span> <span class="o">&lt;=</span> <span class="n">int_cutoff</span><span class="p">),</span> <span class="p">(</span><span class="n">active</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">fac_inds</span><span class="p">,</span> <span class="n">spot_inds</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.mag_field" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">mag_field</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">B_noise</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to correct for unsigned magnetic field strength and magnetic noise</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.mag_field--parameters">Parameters</h4>
+<p>mu: array of mu (cosine theta) values
+mmap: Sunpy map object (Magnetogram)
+B_noise: magnetic noise level in Gauss
+mu_cutoff: minimum mu cutoff value</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.mag_field--returns">Returns</h4>
+<p>Bobs: corrected observed magnetic field strength
+Br: array of corrected unsigned magnetic field strength</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">mag_field</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">B_noise</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to correct for unsigned magnetic field strength and magnetic noise</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    mu: array of mu (cosine theta) values</span>
+<span class="sd">    mmap: Sunpy map object (Magnetogram)</span>
+<span class="sd">    B_noise: magnetic noise level in Gauss</span>
+<span class="sd">    mu_cutoff: minimum mu cutoff value</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    Bobs: corrected observed magnetic field strength</span>
+<span class="sd">    Br: array of corrected unsigned magnetic field strength</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># get valid indices</span>
+    <span class="n">use_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">mu</span> <span class="o">&gt;</span> <span class="n">mu_cutoff</span><span class="p">,</span> <span class="n">mu</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+    <span class="n">mag_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">use_indices</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">mmap</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">B_noise</span><span class="p">)</span>
+
+    <span class="c1"># calculate full magnetic field strength</span>
+    <span class="n">Bobs</span> <span class="o">=</span> <span class="n">mmap</span><span class="o">.</span><span class="n">data</span>
+    <span class="n">Br</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">mmap</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+    <span class="n">Br</span><span class="p">[</span><span class="n">use_indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">Bobs</span><span class="p">[</span><span class="n">use_indices</span><span class="p">]</span> <span class="o">/</span> <span class="n">mu</span><span class="p">[</span><span class="n">use_indices</span><span class="p">]</span>
+    <span class="n">Bobs</span><span class="p">[</span><span class="n">mag_indices</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">Br</span><span class="p">[</span><span class="n">mag_indices</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="k">return</span> <span class="n">Bobs</span><span class="p">,</span> <span class="n">Br</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.mag_thresh" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">mag_thresh</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">Br_cutoff</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to calculate magnetic threshold and differentiate between magnetically active regions and quiet Sun</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.mag_thresh--parameters">Parameters</h4>
+<p>mu: array of mu (cosine theta) values
+mmap: corrected (unsigned magnetic field) Sunpy map object (Magnetogram)
+Br_cutoff: minimum cutoff value (in Gauss) for thresholding active regions
+mu_cutoff: minimum mu cutoff value for data to ignore</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.mag_thresh--returns">Returns</h4>
+<p>active: weights array where active pixels are 1
+quiet: weights array where active pixels are 0</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">mag_thresh</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">Br_cutoff</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to calculate magnetic threshold and differentiate between magnetically active regions and quiet Sun</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    mu: array of mu (cosine theta) values</span>
+<span class="sd">    mmap: corrected (unsigned magnetic field) Sunpy map object (Magnetogram)</span>
+<span class="sd">    Br_cutoff: minimum cutoff value (in Gauss) for thresholding active regions</span>
+<span class="sd">    mu_cutoff: minimum mu cutoff value for data to ignore</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    active: weights array where active pixels are 1</span>
+<span class="sd">    quiet: weights array where active pixels are 0</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># get active region indices</span>
+    <span class="n">active_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">mmap</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="n">mu</span> <span class="o">&gt;</span> <span class="n">Br_cutoff</span><span class="p">)</span>
+    <span class="n">bad_mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mu</span> <span class="o">&lt;=</span> <span class="n">mu_cutoff</span><span class="p">)</span>
+
+    <span class="c1"># make active region array</span>
+    <span class="n">active</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">mu</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">active</span><span class="p">[</span><span class="n">active_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
+    <span class="n">active</span><span class="p">[</span><span class="n">bad_mu</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.</span>
+
+    <span class="c1"># find isolated pixels</span>
+    <span class="c1"># get area</span>
+    <span class="n">y_labeled</span> <span class="o">=</span> <span class="n">label</span><span class="p">(</span><span class="n">active</span><span class="p">,</span> <span class="n">connectivity</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">background</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">y_area</span> <span class="o">=</span> <span class="p">[</span><span class="n">props</span><span class="o">.</span><span class="n">area</span> <span class="k">for</span> <span class="n">props</span> <span class="ow">in</span> <span class="n">regionprops</span><span class="p">(</span><span class="n">y_labeled</span><span class="p">)]</span>
+
+    <span class="c1"># area constraint</span>
+    <span class="n">good_area</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y_area</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">5</span><span class="p">)</span>
+    <span class="n">good_area</span> <span class="o">=</span> <span class="n">good_area</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="n">active_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">y_labeled</span><span class="p">,</span> <span class="n">good_area</span><span class="p">)</span>
+
+    <span class="c1"># create weights array</span>
+    <span class="n">active</span><span class="p">[</span><span class="o">~</span><span class="n">active_indices</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="c1"># get quiet indices</span>
+    <span class="n">quiet</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">active</span>
+
+    <span class="k">return</span> <span class="n">active</span><span class="p">,</span> <span class="n">quiet</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.map_sequence" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">map_sequence</span><span class="p">(</span><span class="n">dates_list</span><span class="p">,</span> <span class="n">time_range</span><span class="o">=</span><span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="mi">6</span><span class="p">),</span> <span class="n">instrument</span><span class="o">=&lt;</span><span class="n">sunpy</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">Instrument</span><span class="p">(</span><span class="n">AIA</span><span class="p">:</span> <span class="n">Atmospheric</span> <span class="n">Imaging</span> <span class="n">Assembly</span><span class="p">)</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x7ff147b83e50</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">wavelength</span><span class="o">=&lt;</span><span class="n">sunpy</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">Wavelength</span><span class="p">(</span><span class="mf">171.0</span><span class="p">,</span> <span class="mf">171.0</span><span class="p">,</span> <span class="s1">&#39;Angstrom&#39;</span><span class="p">)</span><span class="o">&gt;</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to query sunpy for images within certain time range of dates in dates list
+user specified instrument and wavelength, otherwise default values: AIA 171A</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.map_sequence--parameters">Parameters</h4>
+<p>dates_list: list of dates, either datetime or strings
+time_range: plus/minus time range to search for images in comparison to desired timestamp
+instrument: Sunpy instrument of choice (AIA, HMI, LASCO, EIT)
+wavelength: desired wavelength of choice instrument</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.map_sequence--returns">Returns</h4>
+<p>maps: Sunpy map sequence object</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">map_sequence</span><span class="p">(</span><span class="n">dates_list</span><span class="p">,</span> <span class="n">time_range</span><span class="o">=</span><span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="mi">6</span><span class="p">),</span> <span class="n">instrument</span><span class="o">=</span><span class="n">a</span><span class="o">.</span><span class="n">Instrument</span><span class="o">.</span><span class="n">aia</span><span class="p">,</span>
+                 <span class="n">wavelength</span><span class="o">=</span><span class="n">a</span><span class="o">.</span><span class="n">Wavelength</span><span class="p">(</span><span class="mi">171</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">angstrom</span><span class="p">)):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to query sunpy for images within certain time range of dates in dates list</span>
+<span class="sd">    user specified instrument and wavelength, otherwise default values: AIA 171A</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    dates_list: list of dates, either datetime or strings</span>
+<span class="sd">    time_range: plus/minus time range to search for images in comparison to desired timestamp</span>
+<span class="sd">    instrument: Sunpy instrument of choice (AIA, HMI, LASCO, EIT)</span>
+<span class="sd">    wavelength: desired wavelength of choice instrument</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    maps: Sunpy map sequence object</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dates_list</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="n">datetimes</span> <span class="o">=</span> <span class="p">[</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">date</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1">T%H:%M:%S.</span><span class="si">%f</span><span class="s1">&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">date</span> <span class="ow">in</span> <span class="n">dates_list</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">datetimes</span> <span class="o">=</span> <span class="n">dates_list</span>
+
+    <span class="n">downloaded_files</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">ind</span><span class="p">,</span> <span class="n">datetime_object</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">datetimes</span><span class="p">):</span>
+        <span class="c1"># pull image within specified time range</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">Fido</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">Time</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">datetime_object</span> <span class="o">-</span> <span class="n">time_range</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">datetime_object</span> <span class="o">+</span> <span class="n">time_range</span><span class="p">)),</span>
+                             <span class="n">instrument</span><span class="p">,</span> <span class="n">wavelength</span><span class="p">)</span>
+        <span class="c1"># add file to list</span>
+        <span class="n">downloaded_files</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Fido</span><span class="o">.</span><span class="n">fetch</span><span class="p">(</span><span class="n">result</span><span class="p">))</span>
+
+    <span class="c1"># build map sequence from files</span>
+    <span class="n">maps</span> <span class="o">=</span> <span class="n">sunpy</span><span class="o">.</span><span class="n">map</span><span class="o">.</span><span class="n">Map</span><span class="p">(</span><span class="n">downloaded_files</span><span class="p">,</span> <span class="n">sequence</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">maps</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.rel_positions" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">rel_positions</span><span class="p">(</span><span class="n">wij</span><span class="p">,</span> <span class="n">nij</span><span class="p">,</span> <span class="n">rij</span><span class="p">,</span> <span class="n">smap</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to calculate pixel-wise relative positions in new coordinate frame</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.rel_positions--parameters">Parameters</h4>
+<p>wij: array of westward values for image
+nij: array of northward values for image
+rij: array of radius values for image
+smap: Sunpy map object</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.rel_positions--returns">Returns</h4>
+<p>deltaw: relative westward position of pixel
+deltan: relative northward position of pixel
+deltar: relative radial position of pixel
+dij: distance between pixel ij and spacecraft</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">rel_positions</span><span class="p">(</span><span class="n">wij</span><span class="p">,</span> <span class="n">nij</span><span class="p">,</span> <span class="n">rij</span><span class="p">,</span> <span class="n">smap</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to calculate pixel-wise relative positions in new coordinate frame</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    wij: array of westward values for image</span>
+<span class="sd">    nij: array of northward values for image</span>
+<span class="sd">    rij: array of radius values for image</span>
+<span class="sd">    smap: Sunpy map object</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    deltaw: relative westward position of pixel</span>
+<span class="sd">    deltan: relative northward position of pixel</span>
+<span class="sd">    deltar: relative radial position of pixel</span>
+<span class="sd">    dij: distance between pixel ij and spacecraft</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># calculate relative positions of each pixel</span>
+    <span class="n">rsc</span> <span class="o">=</span> <span class="n">smap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;dsun_obs&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">smap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;rsun_ref&#39;</span><span class="p">]</span>
+    <span class="n">deltaw</span> <span class="o">=</span> <span class="n">wij</span>
+    <span class="n">deltan</span> <span class="o">=</span> <span class="n">nij</span>
+    <span class="n">deltar</span> <span class="o">=</span> <span class="n">rij</span> <span class="o">-</span> <span class="n">rsc</span>
+    <span class="n">dij</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">deltaw</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">deltan</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">deltar</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">deltaw</span><span class="p">,</span> <span class="n">deltan</span><span class="p">,</span> <span class="n">deltar</span><span class="p">,</span> <span class="n">dij</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.solar_rot_vel" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">solar_rot_vel</span><span class="p">(</span><span class="n">wij</span><span class="p">,</span> <span class="n">nij</span><span class="p">,</span> <span class="n">rij</span><span class="p">,</span> <span class="n">deltaw</span><span class="p">,</span> <span class="n">deltan</span><span class="p">,</span> <span class="n">deltar</span><span class="p">,</span> <span class="n">dij</span><span class="p">,</span> <span class="n">vmap</span><span class="p">,</span> <span class="n">a_parameters</span><span class="o">=</span><span class="p">[</span><span class="mf">14.713</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.396</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.787</span><span class="p">])</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to calculate pixel-wise velocities due to solar rotation</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.solar_rot_vel--parameters">Parameters</h4>
+<p>wij: array of westward values for image
+nij: array of northward values for image
+rij: array of radius values for image
+deltaw: relative westward position of pixel
+deltan: relative northward position of pixel
+deltar: relative radial position of pixel
+dij: distance between pixel ij and spacecraft
+vmap: Sunpy map object (Dopplergram)
+a_parameters: array of solar differential rotation parameters from Snodgrass &amp; Ulrich (1990).</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.solar_rot_vel--returns">Returns</h4>
+<p>vrot: array of solar rotation velocities</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">solar_rot_vel</span><span class="p">(</span><span class="n">wij</span><span class="p">,</span> <span class="n">nij</span><span class="p">,</span> <span class="n">rij</span><span class="p">,</span> <span class="n">deltaw</span><span class="p">,</span> <span class="n">deltan</span><span class="p">,</span> <span class="n">deltar</span><span class="p">,</span> <span class="n">dij</span><span class="p">,</span> <span class="n">vmap</span><span class="p">,</span> <span class="n">a_parameters</span><span class="o">=</span><span class="p">[</span><span class="mf">14.713</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.396</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.787</span><span class="p">]):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to calculate pixel-wise velocities due to solar rotation</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    wij: array of westward values for image</span>
+<span class="sd">    nij: array of northward values for image</span>
+<span class="sd">    rij: array of radius values for image</span>
+<span class="sd">    deltaw: relative westward position of pixel</span>
+<span class="sd">    deltan: relative northward position of pixel</span>
+<span class="sd">    deltar: relative radial position of pixel</span>
+<span class="sd">    dij: distance between pixel ij and spacecraft</span>
+<span class="sd">    vmap: Sunpy map object (Dopplergram)</span>
+<span class="sd">    a_parameters: array of solar differential rotation parameters from Snodgrass &amp; Ulrich (1990).</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    vrot: array of solar rotation velocities</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># apply to cartesian coordinates</span>
+    <span class="n">x1</span> <span class="o">=</span> <span class="n">wij</span>
+    <span class="n">y1</span> <span class="o">=</span> <span class="n">nij</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crlt_obs&#39;</span><span class="p">]))</span> <span class="o">+</span> <span class="n">rij</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crlt_obs&#39;</span><span class="p">]))</span>
+    <span class="n">z1</span> <span class="o">=</span> <span class="o">-</span> <span class="n">nij</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crlt_obs&#39;</span><span class="p">]))</span> <span class="o">+</span> <span class="n">rij</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crlt_obs&#39;</span><span class="p">]))</span>
+
+    <span class="n">hx</span> <span class="o">=</span> <span class="n">x1</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crln_obs&#39;</span><span class="p">]))</span> <span class="o">+</span> <span class="n">z1</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crln_obs&#39;</span><span class="p">]))</span>
+    <span class="n">hy</span> <span class="o">=</span> <span class="n">y1</span>
+    <span class="n">hz</span> <span class="o">=</span> <span class="o">-</span><span class="n">x1</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crln_obs&#39;</span><span class="p">]))</span> <span class="o">+</span> <span class="n">z1</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crln_obs&#39;</span><span class="p">]))</span>
+
+    <span class="c1"># apply parameters to determine vrot for given image pixel</span>
+    <span class="n">w</span> <span class="o">=</span> <span class="p">(</span><span class="n">a_parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">a_parameters</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">hy</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">a_parameters</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span>
+            <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">hy</span><span class="p">))</span> <span class="o">**</span> <span class="mi">4</span><span class="p">))</span> <span class="o">*</span> <span class="mf">1.</span> <span class="o">/</span> <span class="mf">86400.</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mf">180.</span>
+
+    <span class="c1"># get projection of solar rotation</span>
+    <span class="n">vx_rot</span> <span class="o">=</span> <span class="n">w</span> <span class="o">*</span> <span class="n">hz</span> <span class="o">*</span> <span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;rsun_ref&#39;</span><span class="p">]</span>
+    <span class="n">vy_rot</span> <span class="o">=</span> <span class="mf">0.</span>
+    <span class="n">vz_rot</span> <span class="o">=</span> <span class="o">-</span><span class="n">w</span> <span class="o">*</span> <span class="n">hx</span> <span class="o">*</span> <span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;rsun_ref&#39;</span><span class="p">]</span>
+
+    <span class="n">v1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crln_obs&#39;</span><span class="p">]))</span> <span class="o">*</span> <span class="n">vx_rot</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crln_obs&#39;</span><span class="p">]))</span> <span class="o">*</span> <span class="n">vz_rot</span>
+    <span class="n">v2</span> <span class="o">=</span> <span class="n">vy_rot</span>
+    <span class="n">v3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crln_obs&#39;</span><span class="p">]))</span> <span class="o">*</span> <span class="n">vx_rot</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crln_obs&#39;</span><span class="p">]))</span> <span class="o">*</span> <span class="n">vz_rot</span>
+
+    <span class="c1"># project into correct direction</span>
+    <span class="n">vrotw</span> <span class="o">=</span> <span class="n">v1</span>
+    <span class="n">vrotn</span> <span class="o">=</span> <span class="n">v2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crlt_obs&#39;</span><span class="p">]))</span> <span class="o">-</span> <span class="n">v3</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crlt_obs&#39;</span><span class="p">]))</span>
+    <span class="n">vrotr</span> <span class="o">=</span> <span class="n">v2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crlt_obs&#39;</span><span class="p">]))</span> <span class="o">+</span> <span class="n">v3</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crlt_obs&#39;</span><span class="p">]))</span>
+
+    <span class="c1"># get full rotational velocity</span>
+    <span class="n">vrot</span> <span class="o">=</span> <span class="p">(</span><span class="n">deltaw</span> <span class="o">*</span> <span class="n">vrotw</span> <span class="o">+</span> <span class="n">deltan</span> <span class="o">*</span> <span class="n">vrotn</span> <span class="o">+</span> <span class="n">deltar</span> <span class="o">*</span> <span class="n">vrotr</span><span class="p">)</span> <span class="o">/</span> <span class="n">dij</span>
+
+    <span class="k">return</span> <span class="n">vrot</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.spacecraft_vel" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">spacecraft_vel</span><span class="p">(</span><span class="n">deltaw</span><span class="p">,</span> <span class="n">deltan</span><span class="p">,</span> <span class="n">deltar</span><span class="p">,</span> <span class="n">dij</span><span class="p">,</span> <span class="n">vmap</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to calculate pixel-wise spacecraft velocities for Sunpy map</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.spacecraft_vel--parameters">Parameters</h4>
+<p>deltaw: relative westward position of pixel
+deltan: relative northward position of pixel
+deltar: relative radial position of pixel
+dij: distance between pixel ij and spacecraft
+vmap: Sunpy map object (Dopplergram)</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.spacecraft_vel--returns">Returns</h4>
+<p>vsc: array of spacecraft velocities</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">spacecraft_vel</span><span class="p">(</span><span class="n">deltaw</span><span class="p">,</span> <span class="n">deltan</span><span class="p">,</span> <span class="n">deltar</span><span class="p">,</span> <span class="n">dij</span><span class="p">,</span> <span class="n">vmap</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to calculate pixel-wise spacecraft velocities for Sunpy map</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    deltaw: relative westward position of pixel</span>
+<span class="sd">    deltan: relative northward position of pixel</span>
+<span class="sd">    deltar: relative radial position of pixel</span>
+<span class="sd">    dij: distance between pixel ij and spacecraft</span>
+<span class="sd">    vmap: Sunpy map object (Dopplergram)</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    vsc: array of spacecraft velocities</span>
+
+<span class="sd">   &quot;&quot;&quot;</span>
+
+    <span class="c1"># velocity of spacecraft relative to sun</span>
+    <span class="n">vscw</span> <span class="o">=</span> <span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;obs_vw&#39;</span><span class="p">]</span>
+    <span class="n">vscn</span> <span class="o">=</span> <span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;obs_vn&#39;</span><span class="p">]</span>
+    <span class="n">vscr</span> <span class="o">=</span> <span class="n">vmap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;obs_vr&#39;</span><span class="p">]</span>
+
+    <span class="c1"># pixel-wise magnitude of spacecraft velocity</span>
+    <span class="n">vsc</span> <span class="o">=</span> <span class="o">-</span> <span class="p">(</span><span class="n">deltaw</span> <span class="o">*</span> <span class="n">vscw</span> <span class="o">+</span> <span class="n">deltan</span> <span class="o">*</span> <span class="n">vscn</span> <span class="o">+</span> <span class="n">deltar</span> <span class="o">*</span> <span class="n">vscr</span><span class="p">)</span> <span class="o">/</span> <span class="n">dij</span>
+
+    <span class="k">return</span> <span class="n">vsc</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.thresh_map" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">thresh_map</span><span class="p">(</span><span class="n">fac_inds</span><span class="p">,</span> <span class="n">spot_inds</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function that creates thresholded map of sunspots (1) and faculae (2)</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.thresh_map--parameters">Parameters</h4>
+<p>fac_inds: array of indices where faculae are detected
+spot_inds: array of indices where sunspots are detected</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.thresh_map--returns">Returns</h4>
+<p>thresh_arr: array of values denoting faculae (1) and sunspots (2)</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">thresh_map</span><span class="p">(</span><span class="n">fac_inds</span><span class="p">,</span> <span class="n">spot_inds</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function that creates thresholded map of sunspots (1) and faculae (2)</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    fac_inds: array of indices where faculae are detected</span>
+<span class="sd">    spot_inds: array of indices where sunspots are detected</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    thresh_arr: array of values denoting faculae (1) and sunspots (2)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">thresh_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">fac_inds</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+    <span class="n">thresh_arr</span><span class="p">[</span><span class="n">fac_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">thresh_arr</span><span class="p">[</span><span class="n">spot_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span>
+
+    <span class="k">return</span> <span class="n">thresh_arr</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.unsigned_flux" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">unsigned_flux</span><span class="p">(</span><span class="n">map_mag_obs</span><span class="p">,</span> <span class="n">imap</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>calculate unsigned magnetic flux</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.unsigned_flux--parameters">Parameters</h4>
+<p>map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram)
+imap: UNCORRECTED Sunpy map object (Intensitygram)</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.unsigned_flux--returns">Returns</h4>
+<p>unsign_flux: unsigned magnetic flux</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">unsigned_flux</span><span class="p">(</span><span class="n">map_mag_obs</span><span class="p">,</span> <span class="n">imap</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    calculate unsigned magnetic flux</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram)</span>
+<span class="sd">    imap: UNCORRECTED Sunpy map object (Intensitygram)</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    unsign_flux: unsigned magnetic flux</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># get data arrays</span>
+    <span class="n">i_data</span> <span class="o">=</span> <span class="n">imap</span><span class="o">.</span><span class="n">data</span>
+    <span class="n">m_data</span> <span class="o">=</span> <span class="n">map_mag_obs</span><span class="o">.</span><span class="n">data</span>
+    <span class="n">mabs_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">m_data</span><span class="p">)</span>
+
+    <span class="c1"># unsigned flux</span>
+    <span class="n">unsign_flux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mabs_data</span> <span class="o">*</span> <span class="n">i_data</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">i_data</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">unsign_flux</span><span class="p">)</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.v_disc" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">v_disc</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="p">,</span> <span class="n">imap</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to calculate disc-averaged velocity of Sun</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.v_disc--parameters">Parameters</h4>
+<p>map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram)
+imap: UNCORRECTED Sunpy map object (Intensitygram)</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.v_disc--returns">Returns</h4>
+<p>v_disc: disc averaged velocity of Sun (float)</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">v_disc</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="p">,</span> <span class="n">imap</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to calculate disc-averaged velocity of Sun</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram)</span>
+<span class="sd">    imap: UNCORRECTED Sunpy map object (Intensitygram)</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    v_disc: disc averaged velocity of Sun (float)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">v_disc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="o">.</span><span class="n">data</span> <span class="o">*</span> <span class="n">imap</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">imap</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">v_disc</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.v_phot" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">v_phot</span><span class="p">(</span><span class="n">quiet</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">Lij</span><span class="p">,</span> <span class="n">vrot</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">fac_inds</span><span class="p">,</span> <span class="n">spot_inds</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to calculate photometric velocity due to rotational Doppler variation</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.v_phot--parameters">Parameters</h4>
+<p>quiet: weights array where active pixels have weight = 0
+active: weights array where active pixels have weight = 1
+Lij: limb-darkening polynomial function
+vrot: solar rotational velocity
+imap: UNCORRECTED Sunpy map object (Intensitygram)
+mu: array of mu values
+fac_inds: array of indices where faculae are detected
+spot_inds: array of indices where sunspots are detected
+mu_cutoff: minimum mu cutoff value</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.v_phot--returns">Returns</h4>
+<p>v_phot: photospheric velocity perturbation (float)</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">v_phot</span><span class="p">(</span><span class="n">quiet</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">Lij</span><span class="p">,</span> <span class="n">vrot</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">fac_inds</span><span class="p">,</span> <span class="n">spot_inds</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to calculate photometric velocity due to rotational Doppler variation</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    quiet: weights array where active pixels have weight = 0</span>
+<span class="sd">    active: weights array where active pixels have weight = 1</span>
+<span class="sd">    Lij: limb-darkening polynomial function</span>
+<span class="sd">    vrot: solar rotational velocity</span>
+<span class="sd">    imap: UNCORRECTED Sunpy map object (Intensitygram)</span>
+<span class="sd">    mu: array of mu values</span>
+<span class="sd">    fac_inds: array of indices where faculae are detected</span>
+<span class="sd">    spot_inds: array of indices where sunspots are detected</span>
+<span class="sd">    mu_cutoff: minimum mu cutoff value</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    v_phot: photospheric velocity perturbation (float)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># get good mu values</span>
+    <span class="n">good_mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mu</span> <span class="o">&gt;</span> <span class="n">mu_cutoff</span><span class="p">)</span>
+
+    <span class="c1"># calculate K scaling factor</span>
+    <span class="n">K</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">imap</span><span class="o">.</span><span class="n">data</span> <span class="o">*</span> <span class="n">Lij</span> <span class="o">*</span> <span class="n">quiet</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">Lij</span><span class="p">[</span><span class="n">good_mu</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">quiet</span><span class="p">[</span><span class="n">good_mu</span><span class="p">])</span>
+
+    <span class="c1"># calculate photospheric velocity</span>
+    <span class="n">v_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">vrot</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">imap</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">K</span> <span class="o">*</span> <span class="n">Lij</span><span class="p">)</span> <span class="o">*</span> <span class="n">active</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">imap</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+
+    <span class="c1"># faculae driven photospheric velocity</span>
+    <span class="n">vphot_bright</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">vrot</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">imap</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">K</span> <span class="o">*</span> <span class="n">Lij</span><span class="p">)</span> <span class="o">*</span> <span class="n">fac_inds</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">imap</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+
+    <span class="c1"># sunspots driven photospheric velocity</span>
+    <span class="n">vphot_spot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">vrot</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">imap</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">K</span> <span class="o">*</span> <span class="n">Lij</span><span class="p">)</span> <span class="o">*</span> <span class="n">spot_inds</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">imap</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">v_phot</span><span class="p">,</span> <span class="n">vphot_bright</span><span class="p">,</span> <span class="n">vphot_spot</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.calculation_funcs.v_quiet" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">v_quiet</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span> <span class="n">quiet</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to calculate velocity due to convective motion of quiet-Sun</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.v_quiet--parameters">Parameters</h4>
+<p>map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram)
+imap: UNCORRECTED Sunpy map object (Intensitygram)
+quiet: weights array where active pixels have weight = 0</p>
+<h4 id="sdo_hmi_rvs.tools.calculation_funcs.v_quiet--returns">Returns</h4>
+<p>v_quiet: quiet-Sun velocity (float)</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/calculation_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">v_quiet</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span> <span class="n">quiet</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to calculate velocity due to convective motion of quiet-Sun</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram)</span>
+<span class="sd">    imap: UNCORRECTED Sunpy map object (Intensitygram)</span>
+<span class="sd">    quiet: weights array where active pixels have weight = 0</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    v_quiet: quiet-Sun velocity (float)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">v_quiet</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="o">.</span><span class="n">data</span> <span class="o">*</span> <span class="n">imap</span><span class="o">.</span><span class="n">data</span> <span class="o">*</span> <span class="n">quiet</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span>
+        <span class="n">imap</span><span class="o">.</span><span class="n">data</span> <span class="o">*</span> <span class="n">quiet</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">v_quiet</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+
+
+
+  </div>
+
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+
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diff --git a/mkdocs/site/package/coord_funcs/index.html b/mkdocs/site/package/coord_funcs/index.html
new file mode 100644
index 0000000..52cf4e6
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+          Coordinate Transformations
+          <span class="md-nav__icon md-icon"></span>
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+      
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+        Coordinate Transformations
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+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs" class="md-nav__link">
+    sdo_hmi_rvs.tools.coord_funcs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.coordinates" class="md-nav__link">
+    coordinates()
+  </a>
+  
+    <nav class="md-nav" aria-label="coordinates()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.coordinates--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.coordinates--returns" class="md-nav__link">
+    Returns
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+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.fix_mu" class="md-nav__link">
+    fix_mu()
+  </a>
+  
+    <nav class="md-nav" aria-label="fix_mu()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.fix_mu--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.fix_mu--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.get_map_scales" class="md-nav__link">
+    get_map_scales()
+  </a>
+  
+    <nav class="md-nav" aria-label="get_map_scales()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.get_map_scales--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.get_map_scales--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.pix_area_hem" class="md-nav__link">
+    pix_area_hem()
+  </a>
+  
+    <nav class="md-nav" aria-label="pix_area_hem()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.pix_area_hem--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.pix_area_hem--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.vel_coords" class="md-nav__link">
+    vel_coords()
+  </a>
+  
+    <nav class="md-nav" aria-label="vel_coords()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.vel_coords--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.vel_coords--returns" class="md-nav__link">
+    Returns
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+    <li class="md-nav__item">
+      <a href="../lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
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+          
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+    <li class="md-nav__item">
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+        Calculation Functions
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+        RV Calculations
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+        Plotting Functions
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+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_rv_calcs/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_full_pipeline/" class="md-nav__link">
+        Full Pipeline
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs" class="md-nav__link">
+    sdo_hmi_rvs.tools.coord_funcs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.coordinates" class="md-nav__link">
+    coordinates()
+  </a>
+  
+    <nav class="md-nav" aria-label="coordinates()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.coordinates--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.coordinates--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.fix_mu" class="md-nav__link">
+    fix_mu()
+  </a>
+  
+    <nav class="md-nav" aria-label="fix_mu()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.fix_mu--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.fix_mu--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.get_map_scales" class="md-nav__link">
+    get_map_scales()
+  </a>
+  
+    <nav class="md-nav" aria-label="get_map_scales()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.get_map_scales--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.get_map_scales--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.pix_area_hem" class="md-nav__link">
+    pix_area_hem()
+  </a>
+  
+    <nav class="md-nav" aria-label="pix_area_hem()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.pix_area_hem--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.pix_area_hem--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.vel_coords" class="md-nav__link">
+    vel_coords()
+  </a>
+  
+    <nav class="md-nav" aria-label="vel_coords()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.vel_coords--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.coord_funcs.vel_coords--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/package/coord_funcs.md" title="Edit this page" class="md-content__button md-icon">
+                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
+                  </a>
+                
+                
+                <h1 id="coordinate-transformations-module">Coordinate Transformations Module</h1>
+
+
+  <div class="doc doc-object doc-module">
+
+<a id="sdo_hmi_rvs.tools.coord_funcs"></a>
+    <div class="doc doc-contents first">
+
+      <p>Tamar Ervin
+Date: June 24, 2021</p>
+<p>Coordinate transformation functions to transform from the Helioprojective
+Cartesian to Heliographic Carrington Coordinates.</p>
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.coord_funcs.coordinates" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">coordinates</span><span class="p">(</span><span class="n">smap</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>calculate array of mu values and cartesian
+coordinates for image</p>
+<h4 id="sdo_hmi_rvs.tools.coord_funcs.coordinates--parameters">Parameters</h4>
+<p>smap: Sunpy map object</p>
+<h4 id="sdo_hmi_rvs.tools.coord_funcs.coordinates--returns">Returns</h4>
+<p>x: array of x coordinates in helioprojective cartesian system
+y: array of y coordinates in helioprojective cartesian system
+pd: array of pixel distances
+r: array of solar radius values
+d: observer distance from sun in solar radii
+mu: array of mu values</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/coord_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">coordinates</span><span class="p">(</span><span class="n">smap</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    calculate array of mu values and cartesian</span>
+<span class="sd">    coordinates for image</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    smap: Sunpy map object</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    x: array of x coordinates in helioprojective cartesian system</span>
+<span class="sd">    y: array of y coordinates in helioprojective cartesian system</span>
+<span class="sd">    pd: array of pixel distances</span>
+<span class="sd">    r: array of solar radius values</span>
+<span class="sd">    d: observer distance from sun in solar radii</span>
+<span class="sd">    mu: array of mu values</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># distance of observer to sun in solar radii</span>
+    <span class="n">d</span> <span class="o">=</span> <span class="n">smap</span><span class="o">.</span><span class="n">fits_header</span><span class="p">[</span><span class="s1">&#39;DSUN_OBS&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">smap</span><span class="o">.</span><span class="n">fits_header</span><span class="p">[</span><span class="s1">&#39;RSUN_REF&#39;</span><span class="p">]</span>
+
+    <span class="c1"># focal length in pixels</span>
+    <span class="n">f</span> <span class="o">=</span> <span class="mf">180.</span> <span class="o">*</span> <span class="mf">3600.</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="n">smap</span><span class="o">.</span><span class="n">fits_header</span><span class="p">[</span><span class="s1">&#39;CDELT1&#39;</span><span class="p">]</span>
+
+    <span class="c1"># get cartesian x, y map scales</span>
+    <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">get_map_scales</span><span class="p">(</span><span class="n">smap</span><span class="p">)</span>
+
+    <span class="c1"># distance (in pixels) to pixel</span>
+    <span class="n">pd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">y</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+
+    <span class="c1"># distance (in solar r) to pixel</span>
+    <span class="n">pr</span> <span class="o">=</span> <span class="n">f</span> <span class="o">*</span> <span class="n">f</span> <span class="o">*</span> <span class="n">pd</span> <span class="o">*</span> <span class="n">pd</span> <span class="o">+</span> <span class="n">pd</span> <span class="o">**</span> <span class="mi">4</span> <span class="o">-</span> <span class="n">d</span> <span class="o">*</span> <span class="n">d</span> <span class="o">*</span> <span class="n">pd</span> <span class="o">**</span> <span class="mi">4</span> <span class="o">+</span> <span class="mf">0.</span><span class="n">J</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="p">(</span><span class="n">d</span> <span class="o">*</span> <span class="n">f</span> <span class="o">*</span> <span class="n">pd</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">pr</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span><span class="n">f</span> <span class="o">*</span> <span class="n">f</span> <span class="o">+</span> <span class="n">pd</span> <span class="o">*</span> <span class="n">pd</span><span class="p">)</span>
+
+    <span class="c1"># separate complex parts</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">real</span>
+
+    <span class="c1"># get mu array</span>
+    <span class="n">pr</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">r</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="mf">0.</span><span class="n">J</span>
+    <span class="n">cos_alpha</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">pr</span><span class="p">))</span><span class="o">.</span><span class="n">real</span>
+    <span class="n">sin_alpha</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">real</span>
+    <span class="n">cos_theta</span> <span class="o">=</span> <span class="p">((</span><span class="n">d</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">pr</span><span class="p">))</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">r</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">d</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">pr</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">real</span>
+    <span class="n">sin_theta</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">cos_theta</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">real</span>
+    <span class="n">mu</span> <span class="o">=</span> <span class="n">cos_alpha</span> <span class="o">*</span> <span class="n">cos_theta</span> <span class="o">-</span> <span class="n">sin_alpha</span> <span class="o">*</span> <span class="n">sin_theta</span>
+
+    <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">pd</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">mu</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.coord_funcs.fix_mu" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">fix_mu</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">smaps</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to remove pixel values where mu is less than 0.1</p>
+<h4 id="sdo_hmi_rvs.tools.coord_funcs.fix_mu--parameters">Parameters</h4>
+<p>mu: cosine of the center to limb angle
+smaps: list of Sunpy map object
+mu_cutoff: minimum mu cutoff value</p>
+<h4 id="sdo_hmi_rvs.tools.coord_funcs.fix_mu--returns">Returns</h4>
+<p>mu: corrected cosine of the center to limb angle
+smaps: corrected Sunpy map objects</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/coord_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">fix_mu</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">smaps</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to remove pixel values where mu is less than 0.1</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    mu: cosine of the center to limb angle</span>
+<span class="sd">    smaps: list of Sunpy map object</span>
+<span class="sd">    mu_cutoff: minimum mu cutoff value</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    mu: corrected cosine of the center to limb angle</span>
+<span class="sd">    smaps: corrected Sunpy map objects</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># remove pixel values where mu &lt; mu_cut (0.3)</span>
+    <span class="n">bad_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mu</span> <span class="o">&lt;=</span> <span class="n">mu_cutoff</span><span class="p">)</span>
+
+    <span class="c1"># fix arrays</span>
+    <span class="k">for</span> <span class="n">smap</span> <span class="ow">in</span> <span class="n">smaps</span><span class="p">:</span>
+        <span class="n">smap</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">bad_inds</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="k">return</span> <span class="n">smaps</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.coord_funcs.get_map_scales" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">get_map_scales</span><span class="p">(</span><span class="n">smap</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>compute the solar X and solar Y 2D pixel scale arrays from a sunpy map object
+- If the image has been rotated to solar north up, then the x and y scales will
+  be in the helioprojective cartesian system with units of [Rs].</p>
+<h4 id="sdo_hmi_rvs.tools.coord_funcs.get_map_scales--parameters">Parameters</h4>
+<p>smap: Sunpy map object</p>
+<h4 id="sdo_hmi_rvs.tools.coord_funcs.get_map_scales--returns">Returns</h4>
+<p>x: array of x coordinates in helioprojective cartesian system
+y: array of y coordinates in helioprojective cartesian system</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/coord_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">get_map_scales</span><span class="p">(</span><span class="n">smap</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    compute the solar X and solar Y 2D pixel scale arrays from a sunpy map object</span>
+<span class="sd">    - If the image has been rotated to solar north up, then the x and y scales will</span>
+<span class="sd">      be in the helioprojective cartesian system with units of [Rs].</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    smap: Sunpy map object</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    x: array of x coordinates in helioprojective cartesian system</span>
+<span class="sd">    y: array of y coordinates in helioprojective cartesian system</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">header</span> <span class="o">=</span> <span class="n">smap</span><span class="o">.</span><span class="n">fits_header</span>
+
+    <span class="c1"># pixel array</span>
+    <span class="n">axis1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">header</span><span class="p">[</span><span class="s1">&#39;NAXIS1&#39;</span><span class="p">],</span> <span class="n">header</span><span class="p">[</span><span class="s1">&#39;NAXIS1&#39;</span><span class="p">])</span> <span class="o">-</span> <span class="n">header</span><span class="p">[</span><span class="s1">&#39;CRPIX1&#39;</span><span class="p">]</span>
+    <span class="n">axis2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">header</span><span class="p">[</span><span class="s1">&#39;NAXIS2&#39;</span><span class="p">],</span> <span class="n">header</span><span class="p">[</span><span class="s1">&#39;NAXIS2&#39;</span><span class="p">])</span> <span class="o">-</span> <span class="n">header</span><span class="p">[</span><span class="s1">&#39;CRPIX2&#39;</span><span class="p">]</span>
+
+    <span class="c1"># pixel offset from center of image, x,y</span>
+    <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">axis1</span><span class="p">,</span> <span class="n">axis2</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.coord_funcs.pix_area_hem" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">pix_area_hem</span><span class="p">(</span><span class="n">wij</span><span class="p">,</span> <span class="n">nij</span><span class="p">,</span> <span class="n">rij</span><span class="p">,</span> <span class="n">smap</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>calculate the area of each pixel in uHem for use in area thresholding
+for convective velocity and identification of solar regions</p>
+<h4 id="sdo_hmi_rvs.tools.coord_funcs.pix_area_hem--parameters">Parameters</h4>
+<p>wij: array of pixel coordinates relative to solar center in westward direction
+nij: array of pixel coordinates relative to solar center in northward direction
+rij: array of pixel coordinates relative to solar center in radial direction
+smap</p>
+<h4 id="sdo_hmi_rvs.tools.coord_funcs.pix_area_hem--returns">Returns</h4>
+<p>pixA_hem: pixel areas in uHem</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/coord_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">pix_area_hem</span><span class="p">(</span><span class="n">wij</span><span class="p">,</span> <span class="n">nij</span><span class="p">,</span> <span class="n">rij</span><span class="p">,</span> <span class="n">smap</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    calculate the area of each pixel in uHem for use in area thresholding</span>
+<span class="sd">    for convective velocity and identification of solar regions</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    wij: array of pixel coordinates relative to solar center in westward direction</span>
+<span class="sd">    nij: array of pixel coordinates relative to solar center in northward direction</span>
+<span class="sd">    rij: array of pixel coordinates relative to solar center in radial direction</span>
+<span class="sd">    smap</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    pixA_hem: pixel areas in uHem</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># get x and y arrays</span>
+    <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">get_map_scales</span><span class="p">(</span><span class="n">smap</span><span class="p">)</span>
+
+    <span class="c1"># apply to cartesian coordinates</span>
+    <span class="n">x1</span> <span class="o">=</span> <span class="n">wij</span>
+    <span class="n">y1</span> <span class="o">=</span> <span class="n">nij</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">smap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crlt_obs&#39;</span><span class="p">]))</span> <span class="o">+</span> <span class="n">rij</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">smap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crlt_obs&#39;</span><span class="p">]))</span>
+    <span class="n">z1</span> <span class="o">=</span> <span class="o">-</span> <span class="n">nij</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">smap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crlt_obs&#39;</span><span class="p">]))</span> <span class="o">+</span> <span class="n">rij</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">smap</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;crlt_obs&#39;</span><span class="p">]))</span>
+
+    <span class="c1"># get solar latitude and longitude</span>
+    <span class="n">solar_long</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arctan</span><span class="p">(</span><span class="n">x1</span> <span class="o">/</span> <span class="n">z1</span><span class="p">))</span><span class="o">.</span><span class="n">real</span>
+    <span class="n">solar_long</span> <span class="o">=</span> <span class="o">-</span><span class="nb">abs</span><span class="p">(</span><span class="n">solar_long</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+    <span class="n">y1</span> <span class="o">=</span> <span class="n">y1</span> <span class="o">+</span> <span class="mf">0.</span><span class="n">J</span>
+    <span class="n">solar_lat</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arcsin</span><span class="p">(</span><span class="n">y1</span><span class="p">))</span><span class="o">.</span><span class="n">real</span>
+    <span class="n">d_long</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">solar_long</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">d_lat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">solar_lat</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+    <span class="c1"># interpolate differential longitude and latitude to get same area dimensions as original grid</span>
+    <span class="n">newxx</span> <span class="o">=</span> <span class="n">x</span><span class="p">[:,</span> <span class="mi">0</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="o">/</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">newyx</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:,</span> <span class="mi">0</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="o">/</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">newxy</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:,</span> <span class="p">:]</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">newyy</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:,</span> <span class="p">:]</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="c1"># interpolate latitude array</span>
+    <span class="n">interp</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">interpolate</span><span class="o">.</span><span class="n">interp2d</span><span class="p">(</span><span class="n">newxy</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">newyy</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">d_lat</span><span class="p">)</span>
+    <span class="n">d_lat</span> <span class="o">=</span> <span class="n">interp</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="n">y</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span>
+
+    <span class="c1"># interpolate longitude array</span>
+    <span class="n">interp</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">interpolate</span><span class="o">.</span><span class="n">interp2d</span><span class="p">(</span><span class="n">newxx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">newyx</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">d_long</span><span class="p">)</span>
+    <span class="n">d_long</span> <span class="o">=</span> <span class="n">interp</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="n">y</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span>
+
+    <span class="c1"># calculate co-latitude area in uHem</span>
+    <span class="n">pixA_hem</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">-</span> <span class="n">solar_lat</span><span class="p">)</span> <span class="o">*</span> <span class="nb">abs</span><span class="p">(</span><span class="n">d_long</span><span class="p">)</span> <span class="o">*</span> <span class="nb">abs</span><span class="p">(</span><span class="n">d_lat</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1e6</span>
+
+    <span class="k">return</span> <span class="n">pixA_hem</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.coord_funcs.vel_coords" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">vel_coords</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">pd</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">smap</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>calculate coordinate transformation to heliographic Carrington coordinates</p>
+<h4 id="sdo_hmi_rvs.tools.coord_funcs.vel_coords--parameters">Parameters</h4>
+<p>x: array of x coordinates in helioprojective cartesian system
+y: array of y coordinates in helioprojective cartesian system
+pd: array of pixel distances
+r: array of solar radius values
+smap: Sunpy map object</p>
+<h4 id="sdo_hmi_rvs.tools.coord_funcs.vel_coords--returns">Returns</h4>
+<p>wij: array of pixel coordinates relative to solar center in westward direction
+nij: array of pixel coordinates relative to solar center in northward direction
+rij: array of pixel coordinates relative to solar center in radial direction</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/coord_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">vel_coords</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">pd</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">smap</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    calculate coordinate transformation to heliographic Carrington coordinates</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    x: array of x coordinates in helioprojective cartesian system</span>
+<span class="sd">    y: array of y coordinates in helioprojective cartesian system</span>
+<span class="sd">    pd: array of pixel distances</span>
+<span class="sd">    r: array of solar radius values</span>
+<span class="sd">    smap: Sunpy map object</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    wij: array of pixel coordinates relative to solar center in westward direction</span>
+<span class="sd">    nij: array of pixel coordinates relative to solar center in northward direction</span>
+<span class="sd">    rij: array of pixel coordinates relative to solar center in radial direction</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">head</span> <span class="o">=</span> <span class="n">smap</span><span class="o">.</span><span class="n">fits_header</span>
+    <span class="n">crota2</span> <span class="o">=</span> <span class="n">head</span><span class="p">[</span><span class="s1">&#39;CROTA2&#39;</span><span class="p">]</span>  <span class="c1"># deg</span>
+
+    <span class="c1"># transform each pixel to get into Heliographic CR Coordinates</span>
+    <span class="n">dw</span> <span class="o">=</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">crota2</span><span class="p">))</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">crota2</span><span class="p">))</span>
+    <span class="n">dn</span> <span class="o">=</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">crota2</span><span class="p">))</span> <span class="o">-</span> <span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">crota2</span><span class="p">))</span>
+
+    <span class="c1"># get cartesian coordinates for velocity calculations</span>
+    <span class="n">pr</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">r</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="mf">0.</span><span class="n">J</span>
+    <span class="n">wij</span> <span class="o">=</span> <span class="n">r</span> <span class="o">*</span> <span class="n">dw</span> <span class="o">/</span> <span class="n">pd</span>
+    <span class="n">nij</span> <span class="o">=</span> <span class="n">r</span> <span class="o">*</span> <span class="n">dn</span> <span class="o">/</span> <span class="n">pd</span>
+    <span class="n">rij</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">pr</span><span class="p">))</span><span class="o">.</span><span class="n">real</span>
+
+    <span class="k">return</span> <span class="n">wij</span><span class="p">,</span> <span class="n">nij</span><span class="p">,</span> <span class="n">rij</span>
+</code></pre></div>
+        </details>
+    </div>
+
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+          
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+        Coordinate Transformations
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+  
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+      
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+        
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+      
+        <label class="md-nav__link md-nav__link--active" for="__toc">
+          Limb-Brightening Correction
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <a href="./" class="md-nav__link md-nav__link--active">
+        Limb-Brightening Correction
+      </a>
+      
+        
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
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+      <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="#sdo_hmi_rvs.tools.lbc_funcs" class="md-nav__link">
+    sdo_hmi_rvs.tools.lbc_funcs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.get_u" class="md-nav__link">
+    get_u()
+  </a>
+  
+    <nav class="md-nav" aria-label="get_u()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.get_u--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.get_u--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.get_v" class="md-nav__link">
+    get_v()
+  </a>
+  
+    <nav class="md-nav" aria-label="get_v()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.get_v--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.get_v--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.limb_polynomial" class="md-nav__link">
+    limb_polynomial()
+  </a>
+  
+    <nav class="md-nav" aria-label="limb_polynomial()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.limb_polynomial--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.limb_polynomial--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
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+      
+    </ul>
+  
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+      <a href="../calculation_funcs/" class="md-nav__link">
+        Calculation Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../plotting_funcs/" class="md-nav__link">
+        Plotting Functions
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+        RV Calculation
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+  
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+      Table of contents
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+  <a href="#sdo_hmi_rvs.tools.lbc_funcs" class="md-nav__link">
+    sdo_hmi_rvs.tools.lbc_funcs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.get_u" class="md-nav__link">
+    get_u()
+  </a>
+  
+    <nav class="md-nav" aria-label="get_u()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.get_u--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.get_u--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.get_v" class="md-nav__link">
+    get_v()
+  </a>
+  
+    <nav class="md-nav" aria-label="get_v()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.get_v--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.get_v--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.limb_polynomial" class="md-nav__link">
+    limb_polynomial()
+  </a>
+  
+    <nav class="md-nav" aria-label="limb_polynomial()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.limb_polynomial--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.lbc_funcs.limb_polynomial--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/package/lbc_funcs.md" title="Edit this page" class="md-content__button md-icon">
+                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
+                  </a>
+                
+                
+                <h1 id="limb-brightening-correction-module">Limb-Brightening Correction Module</h1>
+
+
+  <div class="doc doc-object doc-module">
+
+<a id="sdo_hmi_rvs.tools.lbc_funcs"></a>
+    <div class="doc doc-contents first">
+
+      <p>Tamar Ervin
+Date: June 23, 2021</p>
+<p>Limb-darkening correction functions:
+flatten continuum intensity images to correct for limb-darkening
+use fifth order polynomial and constants from Allen 1973</p>
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.lbc_funcs.get_u" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">get_u</span><span class="p">(</span><span class="n">ll</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to get limb darkening coefficient u (Allen 1973) based on wavelength value</p>
+<h4 id="sdo_hmi_rvs.tools.lbc_funcs.get_u--parameters">Parameters</h4>
+<p>ll: wavelength</p>
+<h4 id="sdo_hmi_rvs.tools.lbc_funcs.get_u--returns">Returns</h4>
+<p>vl: coefficient v based on wavelength</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/lbc_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">get_u</span><span class="p">(</span><span class="n">ll</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to get limb darkening coefficient u (Allen 1973) based on wavelength value</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    ll: wavelength</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    vl: coefficient v based on wavelength</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">pll</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">ll</span><span class="p">,</span> <span class="n">ll</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ll</span> <span class="o">**</span> <span class="mi">3</span><span class="p">,</span> <span class="n">ll</span> <span class="o">**</span> <span class="mi">4</span><span class="p">,</span> <span class="n">ll</span> <span class="o">**</span> <span class="mi">5</span><span class="p">])</span>
+    <span class="n">au</span> <span class="o">=</span> <span class="o">-</span><span class="mf">8.9829751</span>
+    <span class="n">bu</span> <span class="o">=</span> <span class="mf">0.0069093916</span>
+    <span class="n">cu</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.8144591e-6</span>
+    <span class="n">du</span> <span class="o">=</span> <span class="mf">2.2540875e-10</span>
+    <span class="n">eu</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.3389747e-14</span>
+    <span class="n">fu</span> <span class="o">=</span> <span class="mf">3.0453572e-19</span>
+    <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">au</span><span class="p">,</span> <span class="n">bu</span><span class="p">,</span> <span class="n">cu</span><span class="p">,</span> <span class="n">du</span><span class="p">,</span> <span class="n">eu</span><span class="p">,</span> <span class="n">fu</span><span class="p">])</span>
+    <span class="n">ul</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">a</span> <span class="o">*</span> <span class="n">pll</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ul</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.lbc_funcs.get_v" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">get_v</span><span class="p">(</span><span class="n">ll</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to get limb darkening coefficient v (Allen 1973) based on wavelength value</p>
+<h4 id="sdo_hmi_rvs.tools.lbc_funcs.get_v--parameters">Parameters</h4>
+<p>ll: wavelength</p>
+<h4 id="sdo_hmi_rvs.tools.lbc_funcs.get_v--returns">Returns</h4>
+<p>vl: coefficient v based on wavelength</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/lbc_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">get_v</span><span class="p">(</span><span class="n">ll</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to get limb darkening coefficient v (Allen 1973) based on wavelength value</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    ll: wavelength</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    vl: coefficient v based on wavelength</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">pll</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">ll</span><span class="p">,</span> <span class="n">ll</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ll</span> <span class="o">**</span> <span class="mi">3</span><span class="p">,</span> <span class="n">ll</span> <span class="o">**</span> <span class="mi">4</span><span class="p">,</span> <span class="n">ll</span> <span class="o">**</span> <span class="mi">5</span><span class="p">])</span>
+    <span class="n">av</span> <span class="o">=</span> <span class="mf">9.2891180</span>
+    <span class="n">bv</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.0062212632</span>
+    <span class="n">cv</span> <span class="o">=</span> <span class="mf">1.5788029e-6</span>
+    <span class="n">dv</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.9359644e-10</span>
+    <span class="n">ev</span> <span class="o">=</span> <span class="mf">1.1444469e-14</span>
+    <span class="n">fv</span> <span class="o">=</span> <span class="o">-</span><span class="mf">2.599494e-19</span>
+    <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">av</span><span class="p">,</span> <span class="n">bv</span><span class="p">,</span> <span class="n">cv</span><span class="p">,</span> <span class="n">dv</span><span class="p">,</span> <span class="n">ev</span><span class="p">,</span> <span class="n">fv</span><span class="p">])</span>
+    <span class="n">vl</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">a</span> <span class="o">*</span> <span class="n">pll</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">vl</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.lbc_funcs.limb_polynomial" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">limb_polynomial</span><span class="p">(</span><span class="n">imap</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to calculate limb darkening correction polynomial based on IDl function:
+https://hesperia.gsfc.nasa.gov/ssw/gen/idl/solar/darklimb_correct.pro</p>
+<h4 id="sdo_hmi_rvs.tools.lbc_funcs.limb_polynomial--parameters">Parameters</h4>
+<p>imap: UNCORRECTED Sunpy map object (Intensitygram)</p>
+<h4 id="sdo_hmi_rvs.tools.lbc_funcs.limb_polynomial--returns">Returns</h4>
+<p>Lij: limb-darkening polynomial array</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/lbc_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">limb_polynomial</span><span class="p">(</span><span class="n">imap</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to calculate limb darkening correction polynomial based on IDl function:</span>
+<span class="sd">    https://hesperia.gsfc.nasa.gov/ssw/gen/idl/solar/darklimb_correct.pro</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    imap: UNCORRECTED Sunpy map object (Intensitygram)</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    Lij: limb-darkening polynomial array</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># get data</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">imap</span><span class="o">.</span><span class="n">data</span>
+
+    <span class="c1"># get header information</span>
+    <span class="n">header</span> <span class="o">=</span> <span class="n">imap</span><span class="o">.</span><span class="n">fits_header</span>
+
+    <span class="c1"># wavelength</span>
+    <span class="n">wavelength</span> <span class="o">=</span> <span class="n">header</span><span class="p">[</span><span class="s1">&#39;WAVELNTH&#39;</span><span class="p">]</span>
+
+    <span class="c1"># coordinates</span>
+    <span class="n">x_center</span> <span class="o">=</span> <span class="n">header</span><span class="p">[</span><span class="s1">&#39;CRPIX1&#39;</span><span class="p">]</span>
+    <span class="n">y_center</span> <span class="o">=</span> <span class="n">header</span><span class="p">[</span><span class="s1">&#39;CRPIX2&#39;</span><span class="p">]</span>
+    <span class="n">radius</span> <span class="o">=</span> <span class="n">header</span><span class="p">[</span><span class="s1">&#39;RSUN_OBS&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">header</span><span class="p">[</span><span class="s1">&#39;CDELT1&#39;</span><span class="p">]</span>
+
+    <span class="c1"># size</span>
+    <span class="n">size</span> <span class="o">=</span> <span class="n">imap</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="c1"># wavelength as float</span>
+    <span class="n">ll</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">*</span> <span class="n">wavelength</span>
+
+    <span class="c1"># convert data to array and set nan values to zero</span>
+    <span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">NaNs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
+    <span class="n">array</span><span class="p">[</span><span class="n">NaNs</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
+
+    <span class="c1"># get correction coefficients based on wavelength</span>
+    <span class="n">ul</span> <span class="o">=</span> <span class="n">get_u</span><span class="p">(</span><span class="n">ll</span><span class="p">)</span>
+    <span class="n">vl</span> <span class="o">=</span> <span class="n">get_v</span><span class="p">(</span><span class="n">ll</span><span class="p">)</span>
+
+    <span class="c1"># make arrays of x and y coordinates</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+
+    <span class="c1"># create grids for x, y arrays</span>
+    <span class="n">x_mat</span><span class="p">,</span> <span class="n">y_mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+    <span class="c1"># make z array such that we get a circle</span>
+    <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">x_mat</span> <span class="o">-</span> <span class="n">x_center</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">y_mat</span> <span class="o">-</span> <span class="n">y_center</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+
+    <span class="c1"># normalize circle to radius of 1</span>
+    <span class="n">grid</span> <span class="o">=</span> <span class="n">z</span> <span class="o">/</span> <span class="n">radius</span>
+    <span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">grid</span> <span class="o">&gt;</span> <span class="mf">1.0</span><span class="p">)</span>
+    <span class="n">grid</span><span class="p">[</span><span class="n">out</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
+
+    <span class="c1"># calculate polynomial</span>
+    <span class="n">Lij</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">ul</span> <span class="o">-</span> <span class="n">vl</span> <span class="o">+</span> <span class="n">ul</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arcsin</span><span class="p">(</span><span class="n">grid</span><span class="p">))</span> <span class="o">+</span> <span class="n">vl</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arcsin</span><span class="p">(</span><span class="n">grid</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span>
+
+    <span class="k">return</span> <span class="n">Lij</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
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+    <script id="__config" type="application/json">{"base": "../..", "features": ["navigation.top", "header.autohide"], "translations": {"clipboard.copy": "Copy to clipboard", "clipboard.copied": "Copied to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.placeholder": "Type to start searching", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "search": "../../assets/javascripts/workers/search.e99e714c.min.js", "version": null}</script>
+    
+    
+      <script src="../../assets/javascripts/bundle.c7d1b464.min.js"></script>
+      
+        <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
+      
+        <script src="../../javascripts/config.js"></script>
+      
+        <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
+      
+        <script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/mkdocs/site/sdo/pipeline/index.html b/mkdocs/site/package/pipeline/index.html
similarity index 89%
rename from mkdocs/site/sdo/pipeline/index.html
rename to mkdocs/site/package/pipeline/index.html
index 88c049c..20b0c2b 100644
--- a/mkdocs/site/sdo/pipeline/index.html
+++ b/mkdocs/site/package/pipeline/index.html
@@ -42,6 +42,8 @@
     
     
     
+      <link rel="stylesheet" href="../../assets/_mkdocstrings.css">
+    
     
       
 
@@ -266,7 +268,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../data/" class="md-nav__link">
+      <a href="../../data/data/" class="md-nav__link">
         Aquiring Data
       </a>
     </li>
@@ -280,7 +282,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../corr/" class="md-nav__link">
+      <a href="../../data/corr/" class="md-nav__link">
         Data Corrections
       </a>
     </li>
@@ -302,7 +304,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../coords/" class="md-nav__link">
+      <a href="../../calcs/coords/" class="md-nav__link">
         Coordinate Transformations
       </a>
     </li>
@@ -317,7 +319,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../region/" class="md-nav__link">
+      <a href="../../calcs/region/" class="md-nav__link">
         Region Identification
       </a>
     </li>
@@ -332,7 +334,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../vel/" class="md-nav__link">
+      <a href="../../calcs/vel/" class="md-nav__link">
         Velocity Components
       </a>
     </li>
@@ -347,7 +349,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../rv/" class="md-nav__link">
+      <a href="../../calcs/rv/" class="md-nav__link">
         RV Calculation
       </a>
     </li>
@@ -362,7 +364,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../mag/" class="md-nav__link">
+      <a href="../../calcs/mag/" class="md-nav__link">
         Magnetic Observables
       </a>
     </li>
@@ -455,12 +457,130 @@
       
       
         <label class="md-nav__link" for="__nav_10">
+          Function Documentation
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Function Documentation" data-md-level="1">
+        <label class="md-nav__title" for="__nav_10">
+          <span class="md-nav__icon md-icon"></span>
+          Function Documentation
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../coord_funcs/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../calculation_funcs/" class="md-nav__link">
+        Calculation Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../plotting_funcs/" class="md-nav__link">
+        Plotting Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../settings/" class="md-nav__link">
+        Settings
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_11" type="checkbox" id="__nav_11" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_11">
           Jupyter Examples
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <nav class="md-nav" aria-label="Jupyter Examples" data-md-level="1">
-        <label class="md-nav__title" for="__nav_10">
+        <label class="md-nav__title" for="__nav_11">
           <span class="md-nav__icon md-icon"></span>
           Jupyter Examples
         </label>
@@ -472,7 +592,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../examples/docs_sunpy_example/" class="md-nav__link">
+      <a href="../../examples/docs_sunpy_example/" class="md-nav__link">
         Sunpy Basics
       </a>
     </li>
@@ -486,7 +606,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../examples/docs_solar_corrections/" class="md-nav__link">
+      <a href="../../examples/docs_solar_corrections/" class="md-nav__link">
         Component Calculations
       </a>
     </li>
@@ -500,7 +620,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../examples/docs_rv_calcs/" class="md-nav__link">
+      <a href="../../examples/docs_rv_calcs/" class="md-nav__link">
         RV Calculation
       </a>
     </li>
@@ -514,7 +634,7 @@
   
   
     <li class="md-nav__item">
-      <a href="../examples/docs_full_pipeline/" class="md-nav__link">
+      <a href="../../examples/docs_full_pipeline/" class="md-nav__link">
         Full Pipeline
       </a>
     </li>
@@ -586,7 +706,7 @@
             <article class="md-content__inner md-typeset">
               
                 
-                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/sdo/pipeline.md" title="Edit this page" class="md-content__button md-icon">
+                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/package/pipeline.md" title="Edit this page" class="md-content__button md-icon">
                     <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
                   </a>
                 
@@ -851,7 +971,7 @@ <h2 id="function">Function</h2>
     <nav class="md-footer__inner md-grid" aria-label="Footer">
       
         
-        <a href="../mag/" class="md-footer__link md-footer__link--prev" aria-label="Previous: Magnetic Observables" rel="prev">
+        <a href="../../calcs/mag/" class="md-footer__link md-footer__link--prev" aria-label="Previous: Magnetic Observables" rel="prev">
           <div class="md-footer__button md-icon">
             <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>
           </div>
@@ -867,13 +987,13 @@ <h2 id="function">Function</h2>
       
       
         
-        <a href="../examples/docs_sunpy_example/" class="md-footer__link md-footer__link--next" aria-label="Next: Sunpy Basics" rel="next">
+        <a href="../coord_funcs/" class="md-footer__link md-footer__link--next" aria-label="Next: Coordinate Transformations" rel="next">
           <div class="md-footer__title">
             <div class="md-ellipsis">
               <span class="md-footer__direction">
                 Next
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-              Sunpy Basics
+              Coordinate Transformations
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           </div>
           <div class="md-footer__button md-icon">
diff --git a/mkdocs/site/package/plotting_funcs/index.html b/mkdocs/site/package/plotting_funcs/index.html
new file mode 100644
index 0000000..c3b0ffb
--- /dev/null
+++ b/mkdocs/site/package/plotting_funcs/index.html
@@ -0,0 +1,2118 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="SDO HMI RV Pipeline Documentation">
+      
+      
+      
+      
+      <link rel="icon" href="../../img/sun.png">
+      <meta name="generator" content="mkdocs-1.2.2, mkdocs-material-7.3.1">
+    
+    
+      
+        <title>Plotting Functions - SDO HMI RV Pipeline</title>
+      
+    
+    
+      <link rel="stylesheet" href="../../assets/stylesheets/main.1c75ef36.min.css">
+      
+        
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.3f5d1f46.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#4051b5">
+        
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+    
+    
+    
+      
+        
+        <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+        <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=:300,400,400i,700%7COswald&display=fallback">
+        <style>:root{--md-text-font-family:"";--md-code-font-family:"Oswald"}</style>
+      
+    
+    
+    
+      <link rel="stylesheet" href="../../assets/_mkdocstrings.css">
+    
+    
+      
+
+
+    
+    
+  </head>
+  
+  
+    
+    
+    
+    
+    
+    <body dir="ltr" data-md-color-scheme="" data-md-color-primary="indigo" data-md-color-accent="light-blue">
+  
+    
+    <script>function __prefix(e){return new URL("../..",location).pathname+"."+e}function __get(e,t=localStorage){return JSON.parse(t.getItem(__prefix(e)))}</script>
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+    <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="#plotting-module" 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="SDO HMI RV Pipeline" class="md-header__button md-logo" aria-label="SDO HMI RV Pipeline" data-md-component="logo">
+      
+  <img src="../../img/sun.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">
+            SDO HMI RV Pipeline
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Plotting Functions
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+      <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" aria-label="Clear" tabindex="-1">
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+        </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"></ol>
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+    
+    
+      <div class="md-header__source">
+        
+<a href="https://github.com/tamarervin/sdo_hmi_rvs/" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><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">
+    GitHub
+  </div>
+</a>
+      </div>
+    
+  </nav>
+  
+</header>
+    
+    <div class="md-container" data-md-component="container">
+      
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+        
+          
+        
+      
+      <main class="md-main" data-md-component="main">
+        <div class="md-main__inner md-grid">
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+              
+              <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
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+<nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
+  <label class="md-nav__title" for="__drawer">
+    <a href="../.." title="SDO HMI RV Pipeline" class="md-nav__button md-logo" aria-label="SDO HMI RV Pipeline" data-md-component="logo">
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+  <img src="../../img/sun.png" alt="logo">
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+  </label>
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+  </div>
+  <div class="md-source__repository">
+    GitHub
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../.." class="md-nav__link">
+        Home
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../about/" class="md-nav__link">
+        About
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          HMI Data
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="HMI Data" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          HMI Data
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data/data/" class="md-nav__link">
+        Aquiring Data
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data/corr/" class="md-nav__link">
+        Data Corrections
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/coords/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/region/" class="md-nav__link">
+        Region Identification
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/vel/" class="md-nav__link">
+        Velocity Components
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/rv/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/mag/" class="md-nav__link">
+        Magnetic Observables
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../pipeline/" class="md-nav__link">
+        Full Pipeline
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+    
+  
+  
+    
+    <li class="md-nav__item md-nav__item--active md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_10" type="checkbox" id="__nav_10" checked>
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_10">
+          Function Documentation
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Function Documentation" data-md-level="1">
+        <label class="md-nav__title" for="__nav_10">
+          <span class="md-nav__icon md-icon"></span>
+          Function Documentation
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../coord_funcs/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../calculation_funcs/" class="md-nav__link">
+        Calculation Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+    
+  
+  
+    <li class="md-nav__item md-nav__item--active">
+      
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
+      
+      
+        
+      
+      
+        <label class="md-nav__link md-nav__link--active" for="__toc">
+          Plotting Functions
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <a href="./" class="md-nav__link md-nav__link--active">
+        Plotting Functions
+      </a>
+      
+        
+<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="#sdo_hmi_rvs.tools.plotting_funcs" class="md-nav__link">
+    sdo_hmi_rvs.tools.plotting_funcs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.animate" class="md-nav__link">
+    animate()
+  </a>
+  
+    <nav class="md-nav" aria-label="animate()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.animate--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.animate--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.correlation_plot" class="md-nav__link">
+    correlation_plot()
+  </a>
+  
+    <nav class="md-nav" aria-label="correlation_plot()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.correlation_plot--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.correlation_plot--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.four_correlation_plots" class="md-nav__link">
+    four_correlation_plots()
+  </a>
+  
+    <nav class="md-nav" aria-label="four_correlation_plots()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.four_correlation_plots--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.four_correlation_plots--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.plot_image" class="md-nav__link">
+    plot_image()
+  </a>
+  
+    <nav class="md-nav" aria-label="plot_image()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.plot_image--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.plot_image--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.plot_timeseries" class="md-nav__link">
+    plot_timeseries()
+  </a>
+  
+    <nav class="md-nav" aria-label="plot_timeseries()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.plot_timeseries--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.plot_timeseries--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.scatter_hist" class="md-nav__link">
+    scatter_hist()
+  </a>
+  
+    <nav class="md-nav" aria-label="scatter_hist()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.scatter_hist--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.scatter_hist--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.scatter_overlaid_timeseries" class="md-nav__link">
+    scatter_overlaid_timeseries()
+  </a>
+  
+    <nav class="md-nav" aria-label="scatter_overlaid_timeseries()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.scatter_overlaid_timeseries--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.scatter_overlaid_timeseries--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.two_correlation_plots" class="md-nav__link">
+    two_correlation_plots()
+  </a>
+  
+    <nav class="md-nav" aria-label="two_correlation_plots()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.two_correlation_plots--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.two_correlation_plots--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.vert_comp_timeseries" class="md-nav__link">
+    vert_comp_timeseries()
+  </a>
+  
+    <nav class="md-nav" aria-label="vert_comp_timeseries()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.vert_comp_timeseries--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.vert_comp_timeseries--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.vert_plot_timeseries" class="md-nav__link">
+    vert_plot_timeseries()
+  </a>
+  
+    <nav class="md-nav" aria-label="vert_plot_timeseries()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.vert_plot_timeseries--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.vert_plot_timeseries--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+      
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../settings/" class="md-nav__link">
+        Settings
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_11" type="checkbox" id="__nav_11" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_11">
+          Jupyter Examples
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Jupyter Examples" data-md-level="1">
+        <label class="md-nav__title" for="__nav_11">
+          <span class="md-nav__icon md-icon"></span>
+          Jupyter Examples
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_sunpy_example/" class="md-nav__link">
+        Sunpy Basics
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_solar_corrections/" class="md-nav__link">
+        Component Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_rv_calcs/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_full_pipeline/" class="md-nav__link">
+        Full Pipeline
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs" class="md-nav__link">
+    sdo_hmi_rvs.tools.plotting_funcs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.animate" class="md-nav__link">
+    animate()
+  </a>
+  
+    <nav class="md-nav" aria-label="animate()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.animate--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.animate--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.correlation_plot" class="md-nav__link">
+    correlation_plot()
+  </a>
+  
+    <nav class="md-nav" aria-label="correlation_plot()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.correlation_plot--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.correlation_plot--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.four_correlation_plots" class="md-nav__link">
+    four_correlation_plots()
+  </a>
+  
+    <nav class="md-nav" aria-label="four_correlation_plots()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.four_correlation_plots--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.four_correlation_plots--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.plot_image" class="md-nav__link">
+    plot_image()
+  </a>
+  
+    <nav class="md-nav" aria-label="plot_image()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.plot_image--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.plot_image--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.plot_timeseries" class="md-nav__link">
+    plot_timeseries()
+  </a>
+  
+    <nav class="md-nav" aria-label="plot_timeseries()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.plot_timeseries--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.plot_timeseries--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.scatter_hist" class="md-nav__link">
+    scatter_hist()
+  </a>
+  
+    <nav class="md-nav" aria-label="scatter_hist()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.scatter_hist--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.scatter_hist--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.scatter_overlaid_timeseries" class="md-nav__link">
+    scatter_overlaid_timeseries()
+  </a>
+  
+    <nav class="md-nav" aria-label="scatter_overlaid_timeseries()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.scatter_overlaid_timeseries--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.scatter_overlaid_timeseries--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.two_correlation_plots" class="md-nav__link">
+    two_correlation_plots()
+  </a>
+  
+    <nav class="md-nav" aria-label="two_correlation_plots()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.two_correlation_plots--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.two_correlation_plots--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.vert_comp_timeseries" class="md-nav__link">
+    vert_comp_timeseries()
+  </a>
+  
+    <nav class="md-nav" aria-label="vert_comp_timeseries()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.vert_comp_timeseries--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.vert_comp_timeseries--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.vert_plot_timeseries" class="md-nav__link">
+    vert_plot_timeseries()
+  </a>
+  
+    <nav class="md-nav" aria-label="vert_plot_timeseries()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.vert_plot_timeseries--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.plotting_funcs.vert_plot_timeseries--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/package/plotting_funcs.md" title="Edit this page" class="md-content__button md-icon">
+                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
+                  </a>
+                
+                
+                <h1 id="plotting-module">Plotting Module</h1>
+
+
+  <div class="doc doc-object doc-module">
+
+<a id="sdo_hmi_rvs.tools.plotting_funcs"></a>
+    <div class="doc doc-contents first">
+
+      <p>Tamar Ervin
+Date: June 23, 2021</p>
+<p>functions for plotting images and various
+solar observable comparison plots</p>
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.plotting_funcs.animate" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">animate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span><span class="p">,</span> <span class="n">aia</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to animate frames to get a movie</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.animate--parameters">Parameters</h4>
+<p>i: index
+fig: figure number
+axs: axes Image object
+aia: image to plot</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.animate--returns">Returns</h4>
+<p>plot_obj: array of plot objects for FuncAnimation function</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/plotting_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">animate</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span><span class="p">,</span> <span class="n">aia</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to animate frames to get a movie</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    i: index</span>
+<span class="sd">    fig: figure number</span>
+<span class="sd">    axs: axes Image object</span>
+<span class="sd">    aia: image to plot</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    plot_obj: array of plot objects for FuncAnimation function</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;AIA </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">aia</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;t_obs&#39;</span><span class="p">])))</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;AIA </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">aia</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">])))</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;AIA </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">aia</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">])))</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;AIA </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">aia</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">])))</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;AIA </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">aia</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;wavelnth&#39;</span><span class="p">])))</span>
+    <span class="n">plot_obj0</span> <span class="o">=</span> <span class="n">aia</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">axes</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+    <span class="c1"># plot_obj0.set_data(aia[0][i].data)</span>
+    <span class="n">plot_obj1</span> <span class="o">=</span> <span class="n">aia</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">axes</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="c1"># plot_obj1.set_data(aia[1][i].data)</span>
+    <span class="n">plot_obj2</span> <span class="o">=</span> <span class="n">aia</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">axes</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+    <span class="c1"># plot_obj2.set_data(aia[2][i].data)</span>
+    <span class="n">plot_obj3</span> <span class="o">=</span> <span class="n">aia</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">axes</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="c1"># plot_obj3.set_data(aia[3][i].data)</span>
+    <span class="k">return</span> <span class="n">plot_obj0</span><span class="p">,</span> <span class="n">plot_obj1</span><span class="p">,</span> <span class="n">plot_obj2</span><span class="p">,</span> <span class="n">plot_obj3</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.plotting_funcs.correlation_plot" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">correlation_plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to create correlation plot and calculate correlation coefficient</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.correlation_plot--parameters">Parameters</h4>
+<p>x: x data to plot
+y: y data to plot
+title: title of plot
+xlabel: label for x axes
+ylabel: label for y axes
+save_fig: path to save file, None if not saving</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.correlation_plot--returns">Returns</h4>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/plotting_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">correlation_plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to create correlation plot and calculate correlation coefficient</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    x: x data to plot</span>
+<span class="sd">    y: y data to plot</span>
+<span class="sd">    title: title of plot</span>
+<span class="sd">    xlabel: label for x axes</span>
+<span class="sd">    ylabel: label for y axes</span>
+<span class="sd">    save_fig: path to save file, None if not saving</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># plot style</span>
+    <span class="n">plot_style</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">PlotDir</span><span class="o">.</span><span class="n">MPL</span><span class="p">,</span> <span class="s1">&#39;mystyle.mplstyle&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="n">plot_style</span><span class="p">)</span>
+
+    <span class="c1"># calculate correlation coefficient</span>
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">()</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mf">0.85</span><span class="p">)</span>
+
+    <span class="n">correlation</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">spearmanr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+    <span class="c1"># label correlation coefficients</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">correlation</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span>
+            <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">)</span>
+
+    <span class="c1"># plot data</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;deepskyblue&#39;</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+    <span class="c1"># title and axes</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">)</span>
+
+    <span class="c1"># save if needed</span>
+    <span class="k">if</span> <span class="n">save_fig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_fig</span><span class="p">)</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.plotting_funcs.four_correlation_plots" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">four_correlation_plots</span><span class="p">(</span><span class="n">x_list</span><span class="p">,</span> <span class="n">y_list</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">xlabel_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ylabel_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function for multiple correlation plots</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.four_correlation_plots--parameters">Parameters</h4>
+<p>x_list: list of x data to plot
+y_list: list of y data to plot
+title: title of plot
+xlabel_list: list of labels for x axes
+ylabel_list: list of labels for y axes
+save_fig: path to save file, None if not saving</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.four_correlation_plots--returns">Returns</h4>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/plotting_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">four_correlation_plots</span><span class="p">(</span><span class="n">x_list</span><span class="p">,</span> <span class="n">y_list</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">xlabel_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ylabel_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function for multiple correlation plots</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    x_list: list of x data to plot</span>
+<span class="sd">    y_list: list of y data to plot</span>
+<span class="sd">    title: title of plot</span>
+<span class="sd">    xlabel_list: list of labels for x axes</span>
+<span class="sd">    ylabel_list: list of labels for y axes</span>
+<span class="sd">    save_fig: path to save file, None if not saving</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># plot style</span>
+    <span class="n">plot_style</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">PlotDir</span><span class="o">.</span><span class="n">MPL</span><span class="p">,</span> <span class="s1">&#39;mystyle.mplstyle&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="n">plot_style</span><span class="p">)</span>
+
+    <span class="c1"># calculate correlation coefficient</span>
+    <span class="n">corr_one</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">spearmanr</span><span class="p">(</span><span class="n">x_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y_list</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">corr_two</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">spearmanr</span><span class="p">(</span><span class="n">x_list</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">y_list</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">corr_three</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">spearmanr</span><span class="p">(</span><span class="n">x_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y_list</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">corr_four</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">spearmanr</span><span class="p">(</span><span class="n">x_list</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">y_list</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="c1"># setup the plot</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="s1">&#39;row&#39;</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="s1">&#39;col&#39;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;hspace&#39;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span> <span class="s1">&#39;wspace&#39;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">})</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">ylabel</span><span class="o">=</span><span class="n">ylabel_list</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="c1"># axs[0, 1].set(xlabel=xlabel_list[1])</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="n">xlabel_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ylabel</span><span class="o">=</span><span class="n">ylabel_list</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="n">xlabel_list</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="c1"># plot data</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;violet&#39;</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x_list</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">y_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;violet&#39;</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y_list</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;violet&#39;</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x_list</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">y_list</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;violet&#39;</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+    <span class="c1"># label correlation coefficients</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">corr_one</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span>
+                <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">corr_two</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span>
+                <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">corr_three</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span>
+                <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">corr_four</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span>
+                <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">)</span>
+
+    <span class="c1"># remove tick marks</span>
+    <span class="c1"># for ax in axs:</span>
+    <span class="c1">#     ax.tick_params(labelbottom=True)</span>
+
+    <span class="c1"># save if needed</span>
+    <span class="k">if</span> <span class="n">save_fig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_fig</span><span class="p">)</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.plotting_funcs.plot_image" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">plot_image</span><span class="p">(</span><span class="n">los_image</span><span class="p">,</span> <span class="n">nfig</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to plot AIA los disk images</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.plot_image--parameters">Parameters</h4>
+<p>los_image: 2D line of sight image to plot
+nfig: figure number
+cmap: colormap to use
+title: figure title</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.plot_image--returns">Returns</h4>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/plotting_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">plot_image</span><span class="p">(</span><span class="n">los_image</span><span class="p">,</span> <span class="n">nfig</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to plot AIA los disk images</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    los_image: 2D line of sight image to plot</span>
+<span class="sd">    nfig: figure number</span>
+<span class="sd">    cmap: colormap to use</span>
+<span class="sd">    title: figure title</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">plot_arr</span> <span class="o">=</span> <span class="n">los_image</span><span class="o">.</span><span class="n">data</span>
+
+    <span class="n">plot_arr</span><span class="p">[</span><span class="n">plot_arr</span> <span class="o">&lt;</span> <span class="mf">.001</span><span class="p">]</span> <span class="o">=</span> <span class="mf">.001</span>
+
+    <span class="n">norm_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mf">1.01</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">plot_arr</span><span class="p">))</span>
+    <span class="n">norm</span> <span class="o">=</span> <span class="n">colors</span><span class="o">.</span><span class="n">LogNorm</span><span class="p">(</span><span class="n">vmin</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">norm_max</span><span class="p">)</span>
+
+    <span class="c1"># plot the initial image</span>
+    <span class="k">if</span> <span class="n">nfig</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">cur_figs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_fignums</span><span class="p">()</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">nfig</span><span class="p">:</span>
+            <span class="n">nfig</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">nfig</span> <span class="o">=</span> <span class="n">cur_figs</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">nfig</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">plot_arr</span><span class="p">,</span> <span class="n">extent</span><span class="o">=</span><span class="p">[</span><span class="n">los_image</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">los_image</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">los_image</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">los_image</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">max</span><span class="p">()],</span>
+               <span class="n">origin</span><span class="o">=</span><span class="s2">&quot;lower&quot;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="s2">&quot;equal&quot;</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Latitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Carrinton Longitude&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="kc">None</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.plotting_funcs.plot_timeseries" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">plot_timeseries</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>simple mpl function to plot relevant observables</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.plot_timeseries--parameters">Parameters</h4>
+<p>x: x data to plot
+y: y data to plot
+xlabel: label for x axes
+ylabel: label for y axes
+title: title of plot
+save_fig: path to save file, None if not saving</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.plot_timeseries--returns">Returns</h4>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/plotting_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">plot_timeseries</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    simple mpl function to plot relevant observables</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    x: x data to plot</span>
+<span class="sd">    y: y data to plot</span>
+<span class="sd">    xlabel: label for x axes</span>
+<span class="sd">    ylabel: label for y axes</span>
+<span class="sd">    title: title of plot</span>
+<span class="sd">    save_fig: path to save file, None if not saving</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># plot style</span>
+    <span class="n">plot_style</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">PlotDir</span><span class="o">.</span><span class="n">MPL</span><span class="p">,</span> <span class="s1">&#39;timeseries.mplstyle&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="n">plot_style</span><span class="p">)</span>
+
+    <span class="c1"># plot data</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;cornflowerblue&#39;</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">)</span>
+
+    <span class="c1"># save if needed</span>
+    <span class="k">if</span> <span class="n">save_fig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_fig</span><span class="p">)</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.plotting_funcs.scatter_hist" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">scatter_hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">bins</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to create a scatter plot with associated histograms</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.scatter_hist--parameters">Parameters</h4>
+<p>x: 2D array of x values
+y: 2D array of y values
+bins: number of bins (integer)</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.scatter_hist--returns">Returns</h4>
+<p>scatter_hist: scatter plot with corresponding histograms</p>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/plotting_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">scatter_hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">bins</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to create a scatter plot with associated histograms</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    x: 2D array of x values</span>
+<span class="sd">    y: 2D array of y values</span>
+<span class="sd">    bins: number of bins (integer)</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    scatter_hist: scatter plot with corresponding histograms</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># make sure bins value is an integer</span>
+    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">bins</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="n">bins</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">bins</span><span class="p">)</span>
+
+    <span class="c1"># definitions for the axes</span>
+    <span class="n">left</span><span class="p">,</span> <span class="n">width</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.65</span>
+    <span class="n">bottom</span><span class="p">,</span> <span class="n">height</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.65</span>
+    <span class="n">spacing</span> <span class="o">=</span> <span class="mf">0.005</span>
+
+    <span class="n">rect_scatter</span> <span class="o">=</span> <span class="p">[</span><span class="n">left</span><span class="p">,</span> <span class="n">bottom</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">]</span>
+    <span class="n">rect_histx</span> <span class="o">=</span> <span class="p">[</span><span class="n">left</span><span class="p">,</span> <span class="n">bottom</span> <span class="o">+</span> <span class="n">height</span> <span class="o">+</span> <span class="n">spacing</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]</span>
+    <span class="n">rect_histy</span> <span class="o">=</span> <span class="p">[</span><span class="n">left</span> <span class="o">+</span> <span class="n">width</span> <span class="o">+</span> <span class="n">spacing</span><span class="p">,</span> <span class="n">bottom</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">height</span><span class="p">]</span>
+
+    <span class="c1"># setup the plot</span>
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">(</span><span class="n">rect_scatter</span><span class="p">)</span>
+    <span class="n">ax_histx</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">(</span><span class="n">rect_histx</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+    <span class="n">ax_histy</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">(</span><span class="n">rect_histy</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+
+    <span class="c1"># no labels</span>
+    <span class="n">ax_histx</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="n">labelbottom</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">ax_histy</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s2">&quot;y&quot;</span><span class="p">,</span> <span class="n">labelleft</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="c1"># scatter plot</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+
+    <span class="c1"># plot histograms</span>
+    <span class="n">ax_histx</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">)</span>
+    <span class="n">ax_histy</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">)</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.plotting_funcs.scatter_overlaid_timeseries" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">scatter_overlaid_timeseries</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_list</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel_list</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to overlay scatter plots</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.scatter_overlaid_timeseries--parameters">Parameters</h4>
+<p>x: x data to plot
+y_list: list of y data to plot
+title: title of plot
+xlabel: label for x axes
+ylabel_list: list of labels for y axes
+save_fig: path to save file, None if not saving</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.scatter_overlaid_timeseries--returns">Returns</h4>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/plotting_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">scatter_overlaid_timeseries</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_list</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel_list</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to overlay scatter plots</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    x: x data to plot</span>
+<span class="sd">    y_list: list of y data to plot</span>
+<span class="sd">    title: title of plot</span>
+<span class="sd">    xlabel: label for x axes</span>
+<span class="sd">    ylabel_list: list of labels for y axes</span>
+<span class="sd">    save_fig: path to save file, None if not saving</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># plot style</span>
+    <span class="n">plot_style</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">PlotDir</span><span class="o">.</span><span class="n">MPL</span><span class="p">,</span> <span class="s1">&#39;mystyle.mplstyle&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="n">plot_style</span><span class="p">)</span>
+
+    <span class="c1"># plot data</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+
+    <span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ylabel_list</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;forestgreen&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">ylabel_list</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+    <span class="n">ax2</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
+
+    <span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ylabel_list</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">ax2</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_list</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;darkviolet&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">ylabel_list</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+    <span class="c1"># save if needed</span>
+    <span class="k">if</span> <span class="n">save_fig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_fig</span><span class="p">)</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.plotting_funcs.two_correlation_plots" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">two_correlation_plots</span><span class="p">(</span><span class="n">x_list</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">xlabel_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function for multiple correlation plots</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.two_correlation_plots--parameters">Parameters</h4>
+<p>x_list: list of x data to plot
+y: y data to plot
+title: title of plot
+xlabel_list: list of labels for x axes
+ylabel: label for y axes
+save_fig: path to save file, None if not saving</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.two_correlation_plots--returns">Returns</h4>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/plotting_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">two_correlation_plots</span><span class="p">(</span><span class="n">x_list</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">xlabel_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function for multiple correlation plots</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    x_list: list of x data to plot</span>
+<span class="sd">    y: y data to plot</span>
+<span class="sd">    title: title of plot</span>
+<span class="sd">    xlabel_list: list of labels for x axes</span>
+<span class="sd">    ylabel: label for y axes</span>
+<span class="sd">    save_fig: path to save file, None if not saving</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># plot style</span>
+    <span class="n">plot_style</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">PlotDir</span><span class="o">.</span><span class="n">MPL</span><span class="p">,</span> <span class="s1">&#39;mystyle.mplstyle&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="n">plot_style</span><span class="p">)</span>
+
+    <span class="c1"># calculate correlation coefficient</span>
+    <span class="n">corr_one</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">spearmanr</span><span class="p">(</span><span class="n">x_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y</span><span class="p">)</span>
+    <span class="n">corr_two</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">spearmanr</span><span class="p">(</span><span class="n">x_list</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">y</span><span class="p">)</span>
+
+    <span class="c1"># setup the plot</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;wspace&#39;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">})</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="n">xlabel_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ylabel</span><span class="o">=</span><span class="n">ylabel</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="n">xlabel_list</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="c1"># plot data</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;violet&#39;</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x_list</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;violet&#39;</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+    <span class="c1"># label correlation coefficients</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">corr_one</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span>
+                <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">corr_two</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span>
+                <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">)</span>
+
+    <span class="c1"># remove tick marks</span>
+    <span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axs</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">labelbottom</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="c1"># save if needed</span>
+    <span class="k">if</span> <span class="n">save_fig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_fig</span><span class="p">)</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.plotting_funcs.vert_comp_timeseries" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">vert_comp_timeseries</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_list</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ylabel_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to plot stacked timeseries for comparison</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.vert_comp_timeseries--parameters">Parameters</h4>
+<p>x: x data to plot
+y_list: list of y data to plot
+title: title of plot
+xlabel: label for x axes
+ylabel_list: list of labels for y axes
+save_fig: path to save file, None if not saving</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.vert_comp_timeseries--returns">Returns</h4>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/plotting_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">vert_comp_timeseries</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_list</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ylabel_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to plot stacked timeseries for comparison</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    x: x data to plot</span>
+<span class="sd">    y_list: list of y data to plot</span>
+<span class="sd">    title: title of plot</span>
+<span class="sd">    xlabel: label for x axes</span>
+<span class="sd">    ylabel_list: list of labels for y axes</span>
+<span class="sd">    save_fig: path to save file, None if not saving</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># plot style</span>
+    <span class="n">plot_style</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">PlotDir</span><span class="o">.</span><span class="n">MPL</span><span class="p">,</span> <span class="s1">&#39;timeseries.mplstyle&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;mathtext.fontset&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;custom&#39;</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;mathtext.cal&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;Helvetica&#39;</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="n">plot_style</span><span class="p">)</span>
+
+    <span class="c1"># set up figure</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_list</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">y_list</span><span class="p">)],</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;hspace&#39;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">})</span>
+    <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+
+    <span class="c1"># set up axes labels</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">axs</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">axs</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="n">ylabel_list</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">ylabel</span><span class="o">=</span><span class="n">ylabel_list</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+    <span class="c1"># plot data</span>
+    <span class="n">color_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;lightcoral&#39;</span><span class="p">,</span> <span class="s1">&#39;lightblue&#39;</span><span class="p">,</span> <span class="s1">&#39;plum&#39;</span><span class="p">,</span> <span class="s1">&#39;pink&#39;</span><span class="p">,</span> <span class="s1">&#39;navajowhite&#39;</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">axs</span><span class="p">)):</span>
+        <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_list</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">color_list</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+        <span class="c1"># axs[i].locator_params(axis=&quot;y&quot;, nbins=5)</span>
+    <span class="c1"># axs[1].scatter(x, y_list[1], color=&#39;lightblue&#39;, edgecolors=&#39;k&#39;, linewidths=1.0)</span>
+
+    <span class="c1"># set axes tick marks</span>
+    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">labelbottom</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="c1"># align y axis labels</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">align_ylabels</span><span class="p">(</span><span class="n">axs</span><span class="p">)</span>
+
+    <span class="c1"># save if needed</span>
+    <span class="k">if</span> <span class="n">save_fig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_fig</span><span class="p">)</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.plotting_funcs.vert_plot_timeseries" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">vert_plot_timeseries</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_list</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ylabel_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to plot stacked timeseries for comparison</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.vert_plot_timeseries--parameters">Parameters</h4>
+<p>x: x data to plot
+y_list: list of y data to plot
+title: title of plot
+xlabel: label for x axes
+ylabel_list: list of labels for y axes
+save_fig: path to save file, None if not saving</p>
+<h4 id="sdo_hmi_rvs.tools.plotting_funcs.vert_plot_timeseries--returns">Returns</h4>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/plotting_funcs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">vert_plot_timeseries</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_list</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ylabel_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_fig</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to plot stacked timeseries for comparison</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    x: x data to plot</span>
+<span class="sd">    y_list: list of y data to plot</span>
+<span class="sd">    title: title of plot</span>
+<span class="sd">    xlabel: label for x axes</span>
+<span class="sd">    ylabel_list: list of labels for y axes</span>
+<span class="sd">    save_fig: path to save file, None if not saving</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># plot style</span>
+    <span class="n">plot_style</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">PlotDir</span><span class="o">.</span><span class="n">MPL</span><span class="p">,</span> <span class="s1">&#39;timeseries.mplstyle&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;mathtext.fontset&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;custom&#39;</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;mathtext.cal&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;Helvetica Neue LT Pro&#39;</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="n">plot_style</span><span class="p">)</span>
+
+    <span class="c1"># set up figure</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_list</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_list</span><span class="p">)],</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;hspace&#39;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">})</span>
+    <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+
+    <span class="c1"># set up axes labels</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">axs</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">axs</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="n">ylabel_list</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">ylabel</span><span class="o">=</span><span class="n">ylabel_list</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+    <span class="c1"># plot data</span>
+    <span class="n">color_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;lightcoral&#39;</span><span class="p">,</span> <span class="s1">&#39;lightblue&#39;</span><span class="p">,</span> <span class="s1">&#39;plum&#39;</span><span class="p">,</span> <span class="s1">&#39;pink&#39;</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">axs</span><span class="p">)):</span>
+        <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_list</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">color_list</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.2</span><span class="p">)</span>
+    <span class="c1"># axs[1].plot(x, y_list[1], color=&#39;steelblue&#39;, linewidth=0.8)</span>
+
+    <span class="c1"># set axes tick marks</span>
+    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">labelbottom</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="c1"># align y axis labels</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">align_ylabels</span><span class="p">(</span><span class="n">axs</span><span class="p">)</span>
+
+    <span class="c1"># save if needed</span>
+    <span class="k">if</span> <span class="n">save_fig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_fig</span><span class="p">)</span>
+</code></pre></div>
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+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../calculation_funcs/" class="md-nav__link">
+        Calculation Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+    
+  
+  
+    <li class="md-nav__item md-nav__item--active">
+      
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
+      
+      
+        
+      
+      
+        <label class="md-nav__link md-nav__link--active" for="__toc">
+          RV Calculations
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <a href="./" class="md-nav__link md-nav__link--active">
+        RV Calculations
+      </a>
+      
+        
+<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="#sdo_hmi_rvs.tools.rvs" class="md-nav__link">
+    sdo_hmi_rvs.tools.rvs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.rvs.rvs" class="md-nav__link">
+    rvs()
+  </a>
+  
+    <nav class="md-nav" aria-label="rvs()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.rvs.rvs--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.rvs.rvs--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+      
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../plotting_funcs/" class="md-nav__link">
+        Plotting Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../settings/" class="md-nav__link">
+        Settings
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_11" type="checkbox" id="__nav_11" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_11">
+          Jupyter Examples
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Jupyter Examples" data-md-level="1">
+        <label class="md-nav__title" for="__nav_11">
+          <span class="md-nav__icon md-icon"></span>
+          Jupyter Examples
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_sunpy_example/" class="md-nav__link">
+        Sunpy Basics
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_solar_corrections/" class="md-nav__link">
+        Component Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_rv_calcs/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_full_pipeline/" class="md-nav__link">
+        Full Pipeline
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </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="#sdo_hmi_rvs.tools.rvs" class="md-nav__link">
+    sdo_hmi_rvs.tools.rvs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.rvs.rvs" class="md-nav__link">
+    rvs()
+  </a>
+  
+    <nav class="md-nav" aria-label="rvs()">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.rvs.rvs--parameters" class="md-nav__link">
+    Parameters
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.rvs.rvs--returns" class="md-nav__link">
+    Returns
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/package/rvs.md" title="Edit this page" class="md-content__button md-icon">
+                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
+                  </a>
+                
+                
+                <h1 id="rv-calculation-module">RV Calculation Module</h1>
+
+
+  <div class="doc doc-object doc-module">
+
+<a id="sdo_hmi_rvs.tools.rvs"></a>
+    <div class="doc doc-contents first">
+
+      <p>Tamar Ervin
+Date: October 7, 2021</p>
+<p>pipeline function to calculate 'sun-as-a-star' RVs</p>
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+  <div class="doc doc-object doc-function">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.rvs.rvs" class="doc doc-heading">
+<code class="highlight language-python"><span class="n">rvs</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">cadence</span><span class="p">,</span> <span class="n">csv_name</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>function to calculate rv components using pipeline functions</p>
+<h4 id="sdo_hmi_rvs.tools.rvs.rvs--parameters">Parameters</h4>
+<p>start_date: start date of RV calculations
+end_date: end date of RV calculations
+cadence: how often to calculate RV components
+csv_name: name of file to store calculations in</p>
+<h4 id="sdo_hmi_rvs.tools.rvs.rvs--returns">Returns</h4>
+
+        <details class="quote">
+          <summary>Source code in <code>sdo_hmi_rvs/tools/rvs.py</code></summary>
+          <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">rvs</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">cadence</span><span class="p">,</span> <span class="n">csv_name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    function to calculate rv components using pipeline functions</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    start_date: start date of RV calculations</span>
+<span class="sd">    end_date: end date of RV calculations</span>
+<span class="sd">    cadence: how often to calculate RV components</span>
+<span class="sd">    csv_name: name of file to store calculations in</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># create file names</span>
+    <span class="k">if</span> <span class="n">csv_name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">csv_name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="p">)</span>
+    <span class="n">csv_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">CsvDir</span><span class="o">.</span><span class="n">CALC</span><span class="p">,</span> <span class="n">csv_name</span> <span class="o">+</span> <span class="s1">&#39;.csv&#39;</span><span class="p">)</span>
+    <span class="n">bad_dates_csv</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">CsvDir</span><span class="o">.</span><span class="n">CALC</span><span class="p">,</span> <span class="n">csv_name</span> <span class="o">+</span> <span class="s1">&#39;_bad_dates.csv&#39;</span><span class="p">)</span>
+
+    <span class="c1"># print out csv title</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Beginning calculation of values for csv file: &quot;</span> <span class="o">+</span> <span class="n">csv_name</span><span class="p">)</span>
+
+    <span class="c1"># List of header strings</span>
+    <span class="n">row_contents</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;date_obs&#39;</span><span class="p">,</span> <span class="s1">&#39;date_jd&#39;</span><span class="p">,</span> <span class="s1">&#39;v_quiet&#39;</span><span class="p">,</span> <span class="s1">&#39;v_disc&#39;</span><span class="p">,</span> <span class="s1">&#39;v_phot&#39;</span><span class="p">,</span> <span class="s1">&#39;v_conv&#39;</span><span class="p">,</span> <span class="s1">&#39;f_bright&#39;</span><span class="p">,</span> <span class="s1">&#39;f_spot&#39;</span><span class="p">,</span> <span class="s1">&#39;f&#39;</span><span class="p">,</span> <span class="s1">&#39;Bobs&#39;</span><span class="p">,</span>
+                    <span class="s1">&#39;vphot_bright&#39;</span><span class="p">,</span> <span class="s1">&#39;vphot_spot&#39;</span><span class="p">,</span> <span class="s1">&#39;f_small&#39;</span><span class="p">,</span> <span class="s1">&#39;f_large&#39;</span><span class="p">,</span> <span class="s1">&#39;f_network&#39;</span><span class="p">,</span> <span class="s1">&#39;f_plage&#39;</span><span class="p">,</span> <span class="s1">&#39;f_nonconv&#39;</span><span class="p">,</span>
+                    <span class="s1">&#39;quiet_flux&#39;</span><span class="p">,</span> <span class="s1">&#39;ar_flux&#39;</span><span class="p">,</span> <span class="s1">&#39;conv_flux&#39;</span><span class="p">,</span> <span class="s1">&#39;pol_flux&#39;</span><span class="p">,</span> <span class="s1">&#39;pol_conv_flux&#39;</span><span class="p">,</span> <span class="s1">&#39;vconv_quiet&#39;</span><span class="p">,</span> <span class="s1">&#39;vconv_large&#39;</span><span class="p">,</span>
+                    <span class="s1">&#39;vconv_small&#39;</span><span class="p">]</span>
+
+    <span class="c1"># Append a list as new line to an old csv file</span>
+    <span class="n">utils</span><span class="o">.</span><span class="n">append_list_as_row</span><span class="p">(</span><span class="n">csv_name</span><span class="p">,</span> <span class="n">row_contents</span><span class="p">)</span>
+
+    <span class="c1"># get hmi data products</span>
+    <span class="n">time_range</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="mi">22</span><span class="p">)</span>
+    <span class="n">physobs_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">a</span><span class="o">.</span><span class="n">Physobs</span><span class="o">.</span><span class="n">los_velocity</span><span class="p">,</span> <span class="n">a</span><span class="o">.</span><span class="n">Physobs</span><span class="o">.</span><span class="n">los_magnetic_field</span><span class="p">,</span> <span class="n">a</span><span class="o">.</span><span class="n">Physobs</span><span class="o">.</span><span class="n">intensity</span><span class="p">]</span>
+
+    <span class="c1"># get dates list</span>
+    <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">end_date</span> <span class="o">-</span> <span class="n">start_date</span><span class="p">)</span><span class="o">.</span><span class="n">seconds</span> <span class="o">+</span> <span class="p">(</span><span class="n">end_date</span> <span class="o">-</span> <span class="n">start_date</span><span class="p">)</span><span class="o">.</span><span class="n">days</span> <span class="o">*</span> <span class="mi">24</span> <span class="o">*</span> <span class="mi">3600</span>
+    <span class="n">dates_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">start_date</span> <span class="o">+</span> <span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="n">cadence</span> <span class="o">*</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="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">xy</span> <span class="o">/</span> <span class="n">cadence</span><span class="p">))]</span>
+
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">date</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dates_list</span><span class="p">):</span>
+        <span class="c1"># convert the date to a string -- required for use in csv file</span>
+        <span class="n">date_str</span><span class="p">,</span> <span class="n">date_obj</span><span class="p">,</span> <span class="n">date_jd</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">get_dates</span><span class="p">(</span><span class="n">date</span><span class="p">)</span>
+
+        <span class="c1"># pull image within specified time range</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">Fido</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">Time</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">date_obj</span> <span class="o">-</span> <span class="n">time_range</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">date_obj</span> <span class="o">+</span> <span class="n">time_range</span><span class="p">)),</span>
+                             <span class="n">a</span><span class="o">.</span><span class="n">Instrument</span><span class="o">.</span><span class="n">hmi</span><span class="p">,</span> <span class="n">physobs_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">|</span> <span class="n">physobs_list</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">|</span> <span class="n">physobs_list</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+
+        <span class="c1"># add file to list</span>
+        <span class="n">file_download</span> <span class="o">=</span> <span class="n">Fido</span><span class="o">.</span><span class="n">fetch</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
+
+        <span class="c1"># remove unusable file types</span>
+        <span class="n">good_files</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">file_download</span><span class="p">:</span>
+            <span class="n">name</span><span class="p">,</span> <span class="n">extension</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">extension</span> <span class="o">==</span> <span class="s1">&#39;.fits&#39;</span><span class="p">:</span>
+                <span class="n">good_files</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">good_files</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="c1"># append these values to the csv file</span>
+            <span class="n">save_vals</span> <span class="o">=</span> <span class="p">[</span><span class="n">date_str</span><span class="p">,</span> <span class="s1">&#39;not three good files&#39;</span><span class="p">]</span>
+            <span class="n">utils</span><span class="o">.</span><span class="n">append_list_as_row</span><span class="p">(</span><span class="n">bad_dates_csv</span><span class="p">,</span> <span class="n">save_vals</span><span class="p">)</span>
+            <span class="c1"># print that the files are missing</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Not three good files: &#39;</span> <span class="o">+</span> <span class="n">date_str</span> <span class="o">+</span> <span class="s1">&#39; index: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+
+            <span class="k">pass</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># convert to map sequence</span>
+            <span class="n">map_seq</span> <span class="o">=</span> <span class="n">sunpy</span><span class="o">.</span><span class="n">map</span><span class="o">.</span><span class="n">Map</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">good_files</span><span class="p">))</span>
+
+            <span class="c1"># check for missing data types</span>
+            <span class="n">missing_map</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="c1"># split into data types</span>
+            <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">map_obj</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">map_seq</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;DOPPLERGRAM&#39;</span><span class="p">:</span>
+                    <span class="n">vmap</span> <span class="o">=</span> <span class="n">map_obj</span>
+                <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;MAGNETOGRAM&#39;</span><span class="p">:</span>
+                    <span class="n">mmap</span> <span class="o">=</span> <span class="n">map_obj</span>
+                <span class="k">elif</span> <span class="n">map_obj</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;CONTINUUM INTENSITY&#39;</span><span class="p">:</span>
+                    <span class="n">imap</span> <span class="o">=</span> <span class="n">map_obj</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">missing_map</span> <span class="o">=</span> <span class="kc">True</span>
+
+            <span class="k">if</span> <span class="n">missing_map</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Missing a data product for &quot;</span> <span class="o">+</span> <span class="n">date_str</span><span class="p">)</span>
+
+                <span class="c1"># add the data</span>
+                <span class="c1"># append these values to the csv file</span>
+                <span class="n">save_vals</span> <span class="o">=</span> <span class="p">[</span><span class="n">date_str</span><span class="p">,</span> <span class="s1">&#39;missing data product&#39;</span><span class="p">]</span>
+                <span class="n">utils</span><span class="o">.</span><span class="n">append_list_as_row</span><span class="p">(</span><span class="n">bad_dates_csv</span><span class="p">,</span> <span class="n">save_vals</span><span class="p">)</span>
+                <span class="k">pass</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1"># coordinate transformation for maps</span>
+                <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">pd</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">mu</span> <span class="o">=</span> <span class="n">ctfuncs</span><span class="o">.</span><span class="n">coordinates</span><span class="p">(</span><span class="n">vmap</span><span class="p">)</span>
+                <span class="n">wij</span><span class="p">,</span> <span class="n">nij</span><span class="p">,</span> <span class="n">rij</span> <span class="o">=</span> <span class="n">ctfuncs</span><span class="o">.</span><span class="n">vel_coords</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">pd</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">vmap</span><span class="p">)</span>
+
+                <span class="c1"># remove bad mu values</span>
+                <span class="n">vmap</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">imap</span> <span class="o">=</span> <span class="n">ctfuncs</span><span class="o">.</span><span class="n">fix_mu</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="p">[</span><span class="n">vmap</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">imap</span><span class="p">])</span>
+
+                <span class="c1"># calculate relative positions</span>
+                <span class="n">deltaw</span><span class="p">,</span> <span class="n">deltan</span><span class="p">,</span> <span class="n">deltar</span><span class="p">,</span> <span class="n">dij</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">rel_positions</span><span class="p">(</span><span class="n">wij</span><span class="p">,</span> <span class="n">nij</span><span class="p">,</span> <span class="n">rij</span><span class="p">,</span> <span class="n">vmap</span><span class="p">)</span>
+
+                <span class="c1"># calculate spacecraft velocity</span>
+                <span class="n">vsc</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">spacecraft_vel</span><span class="p">(</span><span class="n">deltaw</span><span class="p">,</span> <span class="n">deltan</span><span class="p">,</span> <span class="n">deltar</span><span class="p">,</span> <span class="n">dij</span><span class="p">,</span> <span class="n">vmap</span><span class="p">)</span>
+
+                <span class="c1"># optimized solar rotation parameters</span>
+                <span class="n">a1</span> <span class="o">=</span> <span class="mf">14.713</span>
+                <span class="n">a2</span> <span class="o">=</span> <span class="o">-</span><span class="mf">2.396</span>
+                <span class="n">a3</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.787</span>
+                <span class="n">a_parameters</span> <span class="o">=</span> <span class="p">[</span><span class="n">a1</span><span class="p">,</span> <span class="n">a2</span><span class="p">,</span> <span class="n">a3</span><span class="p">]</span>
+
+                <span class="c1"># calculation of solar rotation velocity</span>
+                <span class="n">vrot</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">solar_rot_vel</span><span class="p">(</span><span class="n">wij</span><span class="p">,</span> <span class="n">nij</span><span class="p">,</span> <span class="n">rij</span><span class="p">,</span> <span class="n">deltaw</span><span class="p">,</span> <span class="n">deltan</span><span class="p">,</span> <span class="n">deltar</span><span class="p">,</span> <span class="n">dij</span><span class="p">,</span> <span class="n">vmap</span><span class="p">,</span> <span class="n">a_parameters</span><span class="p">)</span>
+
+                <span class="c1"># calculate corrected velocity</span>
+                <span class="n">corrected_vel</span> <span class="o">=</span> <span class="n">vmap</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">vsc</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">vrot</span><span class="p">)</span>
+
+                <span class="c1"># corrected velocity maps</span>
+                <span class="n">map_vel_cor</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">corrected_map</span><span class="p">(</span><span class="n">corrected_vel</span><span class="p">,</span> <span class="n">vmap</span><span class="p">,</span> <span class="n">map_type</span><span class="o">=</span><span class="s1">&#39;Corrected-Dopplergram&#39;</span><span class="p">,</span>
+                                            <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</span><span class="p">)</span>
+
+                <span class="c1"># limb brightening</span>
+                <span class="n">Lij</span> <span class="o">=</span> <span class="n">lbfuncs</span><span class="o">.</span><span class="n">limb_polynomial</span><span class="p">(</span><span class="n">imap</span><span class="p">)</span>
+
+                <span class="c1"># calculate corrected data</span>
+                <span class="n">Iflat</span> <span class="o">=</span> <span class="n">imap</span><span class="o">.</span><span class="n">data</span> <span class="o">/</span> <span class="n">Lij</span>
+
+                <span class="c1"># corrected intensity maps</span>
+                <span class="n">map_int_cor</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">corrected_map</span><span class="p">(</span><span class="n">Iflat</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span> <span class="n">map_type</span><span class="o">=</span><span class="s1">&#39;Corrected-Intensitygram&#39;</span><span class="p">,</span>
+                                            <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</span><span class="p">)</span>
+
+                <span class="c1"># magnetic noise level</span>
+                <span class="n">B_noise</span> <span class="o">=</span> <span class="mi">8</span>
+
+                <span class="c1"># calculate unsigned field strength</span>
+                <span class="n">Bobs</span><span class="p">,</span> <span class="n">Br</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">mag_field</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">B_noise</span><span class="p">)</span>
+
+                <span class="c1"># corrected observed magnetic data map</span>
+                <span class="n">map_mag_obs</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">corrected_map</span><span class="p">(</span><span class="n">Bobs</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">map_type</span><span class="o">=</span><span class="s1">&#39;Corrected-Magnetogram&#39;</span><span class="p">,</span>
+                                            <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</span><span class="p">)</span>
+
+
+                <span class="c1">### calculate magnetic threshold</span>
+                <span class="c1"># magnetic threshold value (G) from Yeo et. al. 2013</span>
+                <span class="n">Br_cutoff</span> <span class="o">=</span> <span class="mi">24</span>
+                <span class="c1"># mu cutoff value</span>
+                <span class="n">mu_cutoff</span> <span class="o">=</span> <span class="mf">0.3</span>
+
+                <span class="c1"># calculate magnetic threshold</span>
+                <span class="n">active</span><span class="p">,</span> <span class="n">quiet</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">mag_thresh</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">Br_cutoff</span><span class="o">=</span><span class="n">Br_cutoff</span><span class="p">,</span> <span class="n">mu_cutoff</span><span class="o">=</span><span class="n">mu_cutoff</span><span class="p">)</span>
+
+                <span class="c1"># calculate intensity threshold</span>
+                <span class="n">fac_inds</span><span class="p">,</span> <span class="n">spot_inds</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">int_thresh</span><span class="p">(</span><span class="n">map_int_cor</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">quiet</span><span class="p">)</span>
+
+                <span class="c1"># create threshold array</span>
+                <span class="n">thresh_arr</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">thresh_map</span><span class="p">(</span><span class="n">fac_inds</span><span class="p">,</span> <span class="n">spot_inds</span><span class="p">)</span>
+
+                <span class="c1"># full threshold maps</span>
+                <span class="n">map_full_thresh</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">corrected_map</span><span class="p">(</span><span class="n">thresh_arr</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">map_type</span><span class="o">=</span><span class="s1">&#39;Threshold&#39;</span><span class="p">,</span>
+                                                <span class="n">frame</span><span class="o">=</span><span class="n">frames</span><span class="o">.</span><span class="n">HeliographicCarrington</span><span class="p">)</span>
+
+                <span class="c1">### velocity contribution due to convective motion of quiet-Sun</span>
+                <span class="n">v_quiet</span> <span class="o">=</span> <span class="n">v_quiet</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span> <span class="n">quiet</span><span class="p">)</span>
+
+                <span class="c1">### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots)</span>
+                <span class="c1"># calculate photospheric velocity</span>
+                <span class="n">v_phot</span><span class="p">,</span> <span class="n">vphot_bright</span><span class="p">,</span> <span class="n">vphot_spot</span> <span class="o">=</span> <span class="n">v_phot</span><span class="p">(</span><span class="n">quiet</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">Lij</span><span class="p">,</span> <span class="n">vrot</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">fac_inds</span><span class="p">,</span>
+                                                          <span class="n">spot_inds</span><span class="p">)</span>
+
+                <span class="c1">### velocity contribution due to suppression of convective blueshift by active regions</span>
+                <span class="c1"># calculate disc-averaged velocity</span>
+                <span class="n">v_disc</span> <span class="o">=</span> <span class="n">v_disc</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="p">,</span> <span class="n">imap</span><span class="p">)</span>
+
+                <span class="c1"># calculate convective velocity</span>
+                <span class="n">v_conv</span> <span class="o">=</span> <span class="n">v_disc</span> <span class="o">-</span> <span class="n">v_quiet</span>
+
+                <span class="c1">### filling factor</span>
+                <span class="c1"># calculate filling factor</span>
+                <span class="n">f_bright</span><span class="p">,</span> <span class="n">f_spot</span><span class="p">,</span> <span class="n">f</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">filling_factor</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">fac_inds</span><span class="p">,</span> <span class="n">spot_inds</span><span class="p">)</span>
+
+                <span class="c1">### unsigned magnetic flux</span>
+                <span class="c1"># unsigned observed flux</span>
+                <span class="n">unsigned_obs_flux</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">unsigned_flux</span><span class="p">(</span><span class="n">map_mag_obs</span><span class="p">,</span> <span class="n">imap</span><span class="p">)</span>
+
+                <span class="c1">### calculate the area filling factor</span>
+                <span class="n">pixA_hem</span> <span class="o">=</span> <span class="n">ctfuncs</span><span class="o">.</span><span class="n">pix_area_hem</span><span class="p">(</span><span class="n">wij</span><span class="p">,</span> <span class="n">nij</span><span class="p">,</span> <span class="n">rij</span><span class="p">,</span> <span class="n">vmap</span><span class="p">)</span>
+                <span class="n">area</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">area_calc</span><span class="p">(</span><span class="n">active</span><span class="p">,</span> <span class="n">pixA_hem</span><span class="p">)</span>
+                <span class="n">f_small</span><span class="p">,</span> <span class="n">f_large</span><span class="p">,</span> <span class="n">f_network</span><span class="p">,</span> <span class="n">f_plage</span><span class="p">,</span> <span class="n">f_nonconv</span> <span class="o">=</span><span class="n">sfuncs</span><span class="o">.</span><span class="n">area_filling_factor</span><span class="p">(</span><span class="n">active</span><span class="p">,</span> <span class="n">area</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">mmap</span><span class="p">,</span>
+                                                                                      <span class="n">fac_inds</span><span class="p">)</span>
+
+                <span class="c1">### get the unsigned flux</span>
+                <span class="n">quiet_flux</span><span class="p">,</span> <span class="n">ar_flux</span><span class="p">,</span> <span class="n">conv_flux</span><span class="p">,</span> <span class="n">pol_flux</span><span class="p">,</span> <span class="n">pol_conv_flux</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">area_unsigned_flux</span><span class="p">(</span><span class="n">map_mag_obs</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span>
+                                                                                             <span class="n">area</span><span class="p">,</span>
+                                                                                             <span class="n">active</span><span class="p">)</span>
+
+                <span class="c1">### get area weighted convective velocities</span>
+                <span class="n">vconv_quiet</span><span class="p">,</span> <span class="n">vconv_large</span><span class="p">,</span> <span class="n">vconv_small</span> <span class="o">=</span> <span class="n">sfuncs</span><span class="o">.</span><span class="n">area_vconv</span><span class="p">(</span><span class="n">map_vel_cor</span><span class="p">,</span> <span class="n">imap</span><span class="p">,</span> <span class="n">active</span><span class="p">,</span> <span class="n">area</span><span class="p">)</span>
+
+                <span class="c1"># make array of what we want to save</span>
+                <span class="n">save_vals</span> <span class="o">=</span> <span class="p">[</span><span class="n">v_quiet</span><span class="p">,</span> <span class="n">v_disc</span><span class="p">,</span> <span class="n">v_phot</span><span class="p">,</span> <span class="n">v_conv</span><span class="p">,</span> <span class="n">f_bright</span><span class="p">,</span> <span class="n">f_spot</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">unsigned_obs_flux</span><span class="p">,</span> <span class="n">vphot_bright</span><span class="p">,</span>
+                             <span class="n">vphot_spot</span><span class="p">,</span> <span class="n">f_small</span><span class="p">,</span> <span class="n">f_large</span><span class="p">,</span> <span class="n">f_network</span><span class="p">,</span> <span class="n">f_plage</span><span class="p">,</span> <span class="n">f_nonconv</span><span class="p">,</span> <span class="n">quiet_flux</span><span class="p">,</span> <span class="n">ar_flux</span><span class="p">,</span>
+                             <span class="n">conv_flux</span><span class="p">,</span> <span class="n">pol_flux</span><span class="p">,</span> <span class="n">pol_conv_flux</span><span class="p">,</span> <span class="n">vconv_quiet</span><span class="p">,</span> <span class="n">vconv_large</span><span class="p">,</span> <span class="n">vconv_small</span><span class="p">]</span>
+
+                <span class="c1"># round stuff</span>
+                <span class="n">rounded</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">save_vals</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+                <span class="n">round_vals</span> <span class="o">=</span> <span class="p">[</span><span class="n">date_str</span><span class="p">,</span> <span class="n">date_jd</span><span class="p">]</span>
+                <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">rounded</span><span class="p">:</span>
+                    <span class="n">round_vals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+
+                <span class="c1"># append these values to the csv file</span>
+                <span class="n">utils</span><span class="o">.</span><span class="n">append_list_as_row</span><span class="p">(</span><span class="n">csv_name</span><span class="p">,</span> <span class="n">round_vals</span><span class="p">)</span>
+
+                <span class="c1"># print that the date is completed</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Calculations and save to file complete for &#39;</span> <span class="o">+</span> <span class="n">date_str</span> <span class="o">+</span> <span class="s1">&#39; index: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Calculation complete for dates:&#39;</span><span class="p">,</span> <span class="n">start_date</span><span class="p">,</span> <span class="s1">&#39;to&#39;</span><span class="p">,</span> <span class="n">end_date</span><span class="p">)</span>
+</code></pre></div>
+        </details>
+    </div>
+
+  </div>
+
+
+
+
+
+
+  </div>
+
+    </div>
+
+  </div>
+                
+              
+              
+                
+
+
+              
+            </article>
+          </div>
+        </div>
+        
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+      
+      
+        <label class="md-nav__link" for="__nav_3">
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+          <span class="md-nav__icon md-icon"></span>
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+          
+            
+              
+  
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+  
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+        Aquiring Data
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+    </li>
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+          
+            
+              
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+  
+  
+    <li class="md-nav__item">
+      <a href="../../data/corr/" class="md-nav__link">
+        Data Corrections
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+    </li>
+  
+
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+          
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+      </nav>
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+  
+
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+      
+      
+      
+
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+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/coords/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/region/" class="md-nav__link">
+        Region Identification
+      </a>
+    </li>
+  
+
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+      
+      
+      
+
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+  
+  
+    <li class="md-nav__item">
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+        Velocity Components
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+      
+      
+      
+
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+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/rv/" class="md-nav__link">
+        RV Calculation
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+      
+      
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+  
+  
+    <li class="md-nav__item">
+      <a href="../../calcs/mag/" class="md-nav__link">
+        Magnetic Observables
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+  
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+    <li class="md-nav__item">
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+        Full Pipeline
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+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../coord_funcs/" class="md-nav__link">
+        Coordinate Transformations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../lbc_funcs/" class="md-nav__link">
+        Limb-Brightening Correction
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../calculation_funcs/" class="md-nav__link">
+        Calculation Functions
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../rvs/" class="md-nav__link">
+        RV Calculations
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../plotting_funcs/" class="md-nav__link">
+        Plotting Functions
+      </a>
+    </li>
+  
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+            
+          
+            
+              
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+  
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+  
+    <li class="md-nav__item md-nav__item--active">
+      
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+        Settings
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+        
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+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings" class="md-nav__link">
+    sdo_hmi_rvs.tools.settings
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings.BaseDir" class="md-nav__link">
+    BaseDir
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings.CsvDir" class="md-nav__link">
+    CsvDir
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings.HARPSN" class="md-nav__link">
+    HARPSN
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings.ImgDir" class="md-nav__link">
+    ImgDir
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings.NEID" class="md-nav__link">
+    NEID
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings.Scaling" class="md-nav__link">
+    Scaling
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+        Sunpy Basics
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+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_rv_calcs/" class="md-nav__link">
+        RV Calculation
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../examples/docs_full_pipeline/" class="md-nav__link">
+        Full Pipeline
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings" class="md-nav__link">
+    sdo_hmi_rvs.tools.settings
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings.BaseDir" class="md-nav__link">
+    BaseDir
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings.CsvDir" class="md-nav__link">
+    CsvDir
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings.HARPSN" class="md-nav__link">
+    HARPSN
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings.ImgDir" class="md-nav__link">
+    ImgDir
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings.NEID" class="md-nav__link">
+    NEID
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sdo_hmi_rvs.tools.settings.Scaling" class="md-nav__link">
+    Scaling
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+                  <a href="https://github.com/tamarervin/sdo_hmi_rvs/blob/master/mkdocs/docs/package/settings.md" title="Edit this page" class="md-content__button md-icon">
+                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
+                  </a>
+                
+                
+                <h1 id="settings-module">Settings Module</h1>
+
+
+  <div class="doc doc-object doc-module">
+
+<a id="sdo_hmi_rvs.tools.settings"></a>
+    <div class="doc doc-contents first">
+
+      <p>Tamar Ervin</p>
+<p>Date: June 14, 2021</p>
+<p>Settings file for paths
+Update to work with your system.</p>
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+  <div class="doc doc-object doc-class">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.settings.BaseDir" class="doc doc-heading">
+        <code>
+BaseDir        </code>
+
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>Base directory. Update this and follow same file structure and all will be well :)</p>
+
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+
+
+
+
+  </div>
+
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-class">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.settings.CsvDir" class="doc doc-heading">
+        <code>
+CsvDir        </code>
+
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>CSV directories</p>
+
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+
+
+
+
+
+  </div>
+
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-class">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.settings.HARPSN" class="doc doc-heading">
+        <code>
+HARPSN            (<a title="sdo_hmi_rvs.tools.settings.Scaling" href="#sdo_hmi_rvs.tools.settings.Scaling">Scaling</a>)
+        </code>
+
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>Class that holds HARPS-N scaling coefficients for RV calculations.</p>
+
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  </div>
+
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-class">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.settings.ImgDir" class="doc doc-heading">
+        <code>
+ImgDir        </code>
+
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>Image directories</p>
+
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+
+
+
+
+  </div>
+
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-class">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.settings.NEID" class="doc doc-heading">
+        <code>
+NEID            (<a title="sdo_hmi_rvs.tools.settings.Scaling" href="#sdo_hmi_rvs.tools.settings.Scaling">Scaling</a>)
+        </code>
+
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>Class that holds NEID scaling coefficients for RV calculations.</p>
+
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  </div>
+
+    </div>
+
+  </div>
+
+
+
+  <div class="doc doc-object doc-class">
+
+
+
+<h2 id="sdo_hmi_rvs.tools.settings.Scaling" class="doc doc-heading">
+        <code>
+Scaling        </code>
+
+
+
+</h2>
+
+    <div class="doc doc-contents ">
+
+      <p>Class that holds scaling coefficients for RV calculations.</p>
+
+
+
+    </div>
+
+  </div>
+
+
+
+
+
+
+
+  </div>
+
+    </div>
+
+  </div>
+                
+              
+              
+                
+
+
+              
+            </article>
+          </div>
+        </div>
+        
+          <a href="#" class="md-top md-icon" data-md-component="top" data-md-state="hidden">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"/></svg>
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diff --git a/mkdocs/site/search/search_index.json b/mkdocs/site/search/search_index.json
index d321c35..9746bfb 100644
--- a/mkdocs/site/search/search_index.json
+++ b/mkdocs/site/search/search_index.json
@@ -1 +1 @@
-{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"SDO/HMI Derived RVs Welcome to the documentation site for the SDO/HMI Derived RVs package. Additional information can be found by navigating through the links on the left. For the source code check out our GitHub !","title":"Home"},{"location":"#sdohmi-derived-rvs","text":"Welcome to the documentation site for the SDO/HMI Derived RVs package. Additional information can be found by navigating through the links on the left. For the source code check out our GitHub !","title":"SDO/HMI Derived RVs"},{"location":"about/","text":"About the Project This package provides the necessary querying, analysis, and calculation methods to independently derive 'sun-as-a-star' RV variations using SDO/HMI data for any time span since SDO has begun observing. Scaling factors are provided in order to calculate RVs comparable to magnitudes measured by ground-based spectrographs (HARPS-N and NEID). In addition, there are routines to calculate magnetic observables to compare with RV variations and determine what is driving Solar activity. A huge thank you to Raphaelle Haywood for her guidance and support on this project and for her papers that outlined the methodology of calculation. Contact Tamar Ervin : tamar.ervin@nasa.jpl.gov Sam Halverson : samuel.halverson@jpl.nasa.gov","title":"About"},{"location":"about/#about-the-project","text":"This package provides the necessary querying, analysis, and calculation methods to independently derive 'sun-as-a-star' RV variations using SDO/HMI data for any time span since SDO has begun observing. Scaling factors are provided in order to calculate RVs comparable to magnitudes measured by ground-based spectrographs (HARPS-N and NEID). In addition, there are routines to calculate magnetic observables to compare with RV variations and determine what is driving Solar activity. A huge thank you to Raphaelle Haywood for her guidance and support on this project and for her papers that outlined the methodology of calculation.","title":"About the Project"},{"location":"about/#contact","text":"Tamar Ervin : tamar.ervin@nasa.jpl.gov Sam Halverson : samuel.halverson@jpl.nasa.gov","title":"Contact"},{"location":"sdo/coords/","text":"Coordinate Transformations SDO/HMI Data Products are returned in Helioprojective Cartesian pixel coordinates. In order to calculate the spacecraft and solar rotation velocity corrections, we need to convert this to the Heliographic Carrington Coordinate System (sun centered coordinate system). In addition to being sun-centered, Heliographic Carrington coordinates also rotate with respect to the Carrington rotation period. Transformations are based off Thompson (2006). Coordinate functions are here . Pixel Array Computation The first step in the coordinate transformation is computation of the pixel-wise coordinate arrays with respect to solar center. We do this by taking into account the reference pixels from the header file. We then calculate the array of \\mu \\mu and radius values for that image, where \\mu = cos(\\theta) \\mu = cos(\\theta) . First, we determine pixel size in physical coordinates (solar radii) and then calculate the center-to-limb angles. We get the radius by determining the distance in solar radii to each pixel. def coordinates(smap): \"\"\" calculate array of mu values and cartesian coordinates for image Parameters ---------- smap: Sunpy map object Returns ------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values d: observer distance from sun in solar radii mu: array of mu values \"\"\" # distance of observer to sun in solar radii d = smap.fits_header['DSUN_OBS'] / smap.fits_header['RSUN_REF'] # focal length in pixels f = 180. * 3600. / np.pi / smap.fits_header['CDELT1'] # get cartesian x, y map scales x, y = get_map_scales(smap) # distance (in pixels) to pixel pd = np.sqrt(x ** 2 + y ** 2) # distance (in solar r) to pixel pr = f * f * pd * pd + pd ** 4 - d * d * pd ** 4 + 0.J r = (d * f * pd - np.sqrt(pr)) / (f * f + pd * pd) # separate complex parts r = r.real # get mu array pr = 1 - r ** 2 + 0.J cos_alpha = (np.sqrt(pr)).real sin_alpha = r.real cos_theta = ((d - np.sqrt(pr)) / np.sqrt(r ** 2 + (d - np.sqrt(pr)) ** 2)).real sin_theta = (np.sqrt(1 - cos_theta ** 2)).real mu = cos_alpha * cos_theta - sin_alpha * sin_theta return x, y, pd, r, d, mu Coordinate Transform We are able to apply the coordinate transformation based on a calculated rotation matrix. We get an array of coordinates in the westward, northward, and radial direction. def vel_coords(x, y, pd, r, smap): \"\"\" calculate coordinate transformation to heliographic Carrington coordinates Parameters ---------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values smap: Sunpy map object Returns ------- wij: array of pixel coordinates relative to solar center in westward direction nij: array of pixel coordinates relative to solar center in northward direction rij: array of pixel coordinates relative to solar center in radial direction \"\"\" head = smap.fits_header crota2 = head['CROTA2'] # deg # transform each pixel to get into Heliographic CR Coordinates dw = y * np.sin(np.deg2rad(crota2)) + x * np.cos(np.deg2rad(crota2)) dn = y * np.cos(np.deg2rad(crota2)) - x * np.sin(np.deg2rad(crota2)) # get cartesian coordinates for velocity calculations pr = 1 - r ** 2 + 0.J wij = r * dw / pd nij = r * dn / pd rij = (np.sqrt(pr)).real return wij, nij, rij Fix Mu Values Lastly, we remove all pixels with mu values less than 0.1 because our limb-brightening model does poorly in correcting near the edge of the Solar disk. def fix_mu(mu, smaps, mu_cutoff=0.3): \"\"\" function to remove pixel values where mu is less than 0.1 Parameters ---------- mu: cosine of the center to limb angle smaps: list of Sunpy map object mu_cutoff: minimum mu cutoff value Returns ------- mu: corrected cosine of the center to limb angle smap: corrected Sunpy map object \"\"\" # remove pixel values where mu < mu_cut (0.3) bad_inds = np.where(mu <= mu_cutoff) # fix arrays for smap in smaps: smap.data[bad_inds] = 0 return smaps","title":"Coordinate Transformations"},{"location":"sdo/coords/#coordinate-transformations","text":"SDO/HMI Data Products are returned in Helioprojective Cartesian pixel coordinates. In order to calculate the spacecraft and solar rotation velocity corrections, we need to convert this to the Heliographic Carrington Coordinate System (sun centered coordinate system). In addition to being sun-centered, Heliographic Carrington coordinates also rotate with respect to the Carrington rotation period. Transformations are based off Thompson (2006). Coordinate functions are here .","title":"Coordinate Transformations"},{"location":"sdo/coords/#pixel-array-computation","text":"The first step in the coordinate transformation is computation of the pixel-wise coordinate arrays with respect to solar center. We do this by taking into account the reference pixels from the header file. We then calculate the array of \\mu \\mu and radius values for that image, where \\mu = cos(\\theta) \\mu = cos(\\theta) . First, we determine pixel size in physical coordinates (solar radii) and then calculate the center-to-limb angles. We get the radius by determining the distance in solar radii to each pixel. def coordinates(smap): \"\"\" calculate array of mu values and cartesian coordinates for image Parameters ---------- smap: Sunpy map object Returns ------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values d: observer distance from sun in solar radii mu: array of mu values \"\"\" # distance of observer to sun in solar radii d = smap.fits_header['DSUN_OBS'] / smap.fits_header['RSUN_REF'] # focal length in pixels f = 180. * 3600. / np.pi / smap.fits_header['CDELT1'] # get cartesian x, y map scales x, y = get_map_scales(smap) # distance (in pixels) to pixel pd = np.sqrt(x ** 2 + y ** 2) # distance (in solar r) to pixel pr = f * f * pd * pd + pd ** 4 - d * d * pd ** 4 + 0.J r = (d * f * pd - np.sqrt(pr)) / (f * f + pd * pd) # separate complex parts r = r.real # get mu array pr = 1 - r ** 2 + 0.J cos_alpha = (np.sqrt(pr)).real sin_alpha = r.real cos_theta = ((d - np.sqrt(pr)) / np.sqrt(r ** 2 + (d - np.sqrt(pr)) ** 2)).real sin_theta = (np.sqrt(1 - cos_theta ** 2)).real mu = cos_alpha * cos_theta - sin_alpha * sin_theta return x, y, pd, r, d, mu","title":"Pixel Array Computation"},{"location":"sdo/coords/#coordinate-transform","text":"We are able to apply the coordinate transformation based on a calculated rotation matrix. We get an array of coordinates in the westward, northward, and radial direction. def vel_coords(x, y, pd, r, smap): \"\"\" calculate coordinate transformation to heliographic Carrington coordinates Parameters ---------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values smap: Sunpy map object Returns ------- wij: array of pixel coordinates relative to solar center in westward direction nij: array of pixel coordinates relative to solar center in northward direction rij: array of pixel coordinates relative to solar center in radial direction \"\"\" head = smap.fits_header crota2 = head['CROTA2'] # deg # transform each pixel to get into Heliographic CR Coordinates dw = y * np.sin(np.deg2rad(crota2)) + x * np.cos(np.deg2rad(crota2)) dn = y * np.cos(np.deg2rad(crota2)) - x * np.sin(np.deg2rad(crota2)) # get cartesian coordinates for velocity calculations pr = 1 - r ** 2 + 0.J wij = r * dw / pd nij = r * dn / pd rij = (np.sqrt(pr)).real return wij, nij, rij","title":"Coordinate Transform"},{"location":"sdo/coords/#fix-mu-values","text":"Lastly, we remove all pixels with mu values less than 0.1 because our limb-brightening model does poorly in correcting near the edge of the Solar disk. def fix_mu(mu, smaps, mu_cutoff=0.3): \"\"\" function to remove pixel values where mu is less than 0.1 Parameters ---------- mu: cosine of the center to limb angle smaps: list of Sunpy map object mu_cutoff: minimum mu cutoff value Returns ------- mu: corrected cosine of the center to limb angle smap: corrected Sunpy map object \"\"\" # remove pixel values where mu < mu_cut (0.3) bad_inds = np.where(mu <= mu_cutoff) # fix arrays for smap in smaps: smap.data[bad_inds] = 0 return smaps","title":"Fix Mu Values"},{"location":"sdo/corr/","text":"Data Corrections For the calculation of independent RV's, we used the three main HMI data products: Dopplergrams, Intensitygrams, Magnetograms. These data products must be corrected for inherent issues such as relative velocityn between the spacecraft and Sun, limb-darkening, and foreshortening. Velocity Corrections Dopplergrams are maps of solar surface velocity that must be corrected for the relative spacecraft velocity, and the velocity due to solar rotation. Spacecraft Velocity Correction: v_{sc, ij} = - \\frac{\\delta w_{ij} v_{sc, w_{ij}} + \\delta n_{ij} v_{sc, n{ij}} + \\delta r_{ij} v_{sc, r_{ij}}}{d_{ij}} v_{sc, ij} = - \\frac{\\delta w_{ij} v_{sc, w_{ij}} + \\delta n_{ij} v_{sc, n{ij}} + \\delta r_{ij} v_{sc, r_{ij}}}{d_{ij}} where d_{ij} = \\sqrt{\\delta w_{ij} ^ 2 + \\delta n_{ij} ^ 2 + \\delta r_{ij} ^ 2 } d_{ij} = \\sqrt{\\delta w_{ij} ^ 2 + \\delta n_{ij} ^ 2 + \\delta r_{ij} ^ 2 } . def spacecraft_vel(deltaw, deltan, deltar, dij, vmap): \"\"\" function to calculate pixel-wise spacecraft velocities for Sunpy map Parameters ---------- deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) Returns ------- vsc: array of spacecraft velocities \"\"\" # velocity of spacecraft relative to sun vscw = vmap.meta['obs_vw'] vscn = vmap.meta['obs_vn'] vscr = vmap.meta['obs_vr'] # pixel-wise magnitude of spacecraft velocity vsc = - (deltaw * vscw + deltan * vscn + deltar * vscr) / dij return vsc Solar Rotational Velocity: We calculate the solar differential rotation profile based on Snodgrass & Ulrich (1990). \\omega(\\theta) = \\alpha_1 + \\alpha_2 sin^2{\\theta} + \\alpha_3 sin^4{\\theta} \\omega(\\theta) = \\alpha_1 + \\alpha_2 sin^2{\\theta} + \\alpha_3 sin^4{\\theta} \\theta \\theta is latitude and the \\alpha \\alpha values are 14.713, -2.396, -1.787 14.713, -2.396, -1.787 respectively. def solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters=[14.713, -2.396, -1.787]): \"\"\" function to calculate pixel-wise velocities due to solar rotation Parameters ---------- wij: array of westward values for image nij: array of northward values for image rij: array of radius values for image deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) a_parameters: array of solar differential rotation parameters from Snodgrass & Ulrich (1990). Returns ------- vrot: array of solar rotation velocities \"\"\" # apply to cartesian coordinates x1 = wij y1 = nij * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) + rij * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) z1 = - nij * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) + rij * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) hx = x1 * np.cos(np.deg2rad(vmap.meta['crln_obs'])) + z1 * np.sin(np.deg2rad(vmap.meta['crln_obs'])) hy = y1 hz = -x1 * np.sin(np.deg2rad(vmap.meta['crln_obs'])) + z1 * np.cos(np.deg2rad(vmap.meta['crln_obs'])) # apply parameters to determine vrot for given image pixel w = (a_parameters[0] + a_parameters[1] * ((np.sin(hy)) ** 2) + a_parameters[2] * ( (np.sin(hy)) ** 4)) * 1. / 86400. * np.pi / 180. # get projection of solar rotation vx_rot = w * hz * vmap.meta['rsun_ref'] vy_rot = 0. vz_rot = -w * hx * vmap.meta['rsun_ref'] v1 = np.cos(np.deg2rad(vmap.meta['crln_obs'])) * vx_rot - np.sin(np.deg2rad(vmap.meta['crln_obs'])) * vz_rot v2 = vy_rot v3 = np.sin(np.deg2rad(vmap.meta['crln_obs'])) * vx_rot + np.cos(np.deg2rad(vmap.meta['crln_obs'])) * vz_rot # project into correct direction vrotw = v1 vrotn = v2 * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) - v3 * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) vrotr = v2 * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) + v3 * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) # get full rotational velocity vrot = (deltaw * vrotw + deltan * vrotn + deltar * vrotr) / dij return vrot Intensity Corrections Intensitygrams are continuum filtergrams that need to be corrected for limb-darkening. We account for limb-darkening and flatten the continuum using the limb-darkening polynomial calculated in Allen's Astrophysical Quantities (1993). L_{ij} = 1 - u_2 - v_2 + u_2 cos \\theta + v_2 cos^2 \\theta L_{ij} = 1 - u_2 - v_2 + u_2 cos \\theta + v_2 cos^2 \\theta The functions for limb-darkening corrections can be found here . Magnetic Field Corrections Magnetograms (line-of-sight longitudinal magnetic field maps) are used to determine magnetic observables that show strong correlation with RV variation and velocity components. Due to foreshortening, the observed magnetic field (magnetogram data) is less than the true radial field by a factor of \\mu = cos(\\theta) \\mu = cos(\\theta) . We correct for this as seen here: B_{r, ij} = B{obs, ij} / \\mu_{ij} B_{r, ij} = B{obs, ij} / \\mu_{ij} . Additionally, we set all magnetic pixels with magnitude lower than the noise level (8G) to 0.","title":"Data Corrections"},{"location":"sdo/corr/#data-corrections","text":"For the calculation of independent RV's, we used the three main HMI data products: Dopplergrams, Intensitygrams, Magnetograms. These data products must be corrected for inherent issues such as relative velocityn between the spacecraft and Sun, limb-darkening, and foreshortening.","title":"Data Corrections"},{"location":"sdo/corr/#velocity-corrections","text":"Dopplergrams are maps of solar surface velocity that must be corrected for the relative spacecraft velocity, and the velocity due to solar rotation. Spacecraft Velocity Correction: v_{sc, ij} = - \\frac{\\delta w_{ij} v_{sc, w_{ij}} + \\delta n_{ij} v_{sc, n{ij}} + \\delta r_{ij} v_{sc, r_{ij}}}{d_{ij}} v_{sc, ij} = - \\frac{\\delta w_{ij} v_{sc, w_{ij}} + \\delta n_{ij} v_{sc, n{ij}} + \\delta r_{ij} v_{sc, r_{ij}}}{d_{ij}} where d_{ij} = \\sqrt{\\delta w_{ij} ^ 2 + \\delta n_{ij} ^ 2 + \\delta r_{ij} ^ 2 } d_{ij} = \\sqrt{\\delta w_{ij} ^ 2 + \\delta n_{ij} ^ 2 + \\delta r_{ij} ^ 2 } . def spacecraft_vel(deltaw, deltan, deltar, dij, vmap): \"\"\" function to calculate pixel-wise spacecraft velocities for Sunpy map Parameters ---------- deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) Returns ------- vsc: array of spacecraft velocities \"\"\" # velocity of spacecraft relative to sun vscw = vmap.meta['obs_vw'] vscn = vmap.meta['obs_vn'] vscr = vmap.meta['obs_vr'] # pixel-wise magnitude of spacecraft velocity vsc = - (deltaw * vscw + deltan * vscn + deltar * vscr) / dij return vsc Solar Rotational Velocity: We calculate the solar differential rotation profile based on Snodgrass & Ulrich (1990). \\omega(\\theta) = \\alpha_1 + \\alpha_2 sin^2{\\theta} + \\alpha_3 sin^4{\\theta} \\omega(\\theta) = \\alpha_1 + \\alpha_2 sin^2{\\theta} + \\alpha_3 sin^4{\\theta} \\theta \\theta is latitude and the \\alpha \\alpha values are 14.713, -2.396, -1.787 14.713, -2.396, -1.787 respectively. def solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters=[14.713, -2.396, -1.787]): \"\"\" function to calculate pixel-wise velocities due to solar rotation Parameters ---------- wij: array of westward values for image nij: array of northward values for image rij: array of radius values for image deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) a_parameters: array of solar differential rotation parameters from Snodgrass & Ulrich (1990). Returns ------- vrot: array of solar rotation velocities \"\"\" # apply to cartesian coordinates x1 = wij y1 = nij * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) + rij * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) z1 = - nij * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) + rij * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) hx = x1 * np.cos(np.deg2rad(vmap.meta['crln_obs'])) + z1 * np.sin(np.deg2rad(vmap.meta['crln_obs'])) hy = y1 hz = -x1 * np.sin(np.deg2rad(vmap.meta['crln_obs'])) + z1 * np.cos(np.deg2rad(vmap.meta['crln_obs'])) # apply parameters to determine vrot for given image pixel w = (a_parameters[0] + a_parameters[1] * ((np.sin(hy)) ** 2) + a_parameters[2] * ( (np.sin(hy)) ** 4)) * 1. / 86400. * np.pi / 180. # get projection of solar rotation vx_rot = w * hz * vmap.meta['rsun_ref'] vy_rot = 0. vz_rot = -w * hx * vmap.meta['rsun_ref'] v1 = np.cos(np.deg2rad(vmap.meta['crln_obs'])) * vx_rot - np.sin(np.deg2rad(vmap.meta['crln_obs'])) * vz_rot v2 = vy_rot v3 = np.sin(np.deg2rad(vmap.meta['crln_obs'])) * vx_rot + np.cos(np.deg2rad(vmap.meta['crln_obs'])) * vz_rot # project into correct direction vrotw = v1 vrotn = v2 * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) - v3 * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) vrotr = v2 * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) + v3 * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) # get full rotational velocity vrot = (deltaw * vrotw + deltan * vrotn + deltar * vrotr) / dij return vrot","title":"Velocity Corrections"},{"location":"sdo/corr/#intensity-corrections","text":"Intensitygrams are continuum filtergrams that need to be corrected for limb-darkening. We account for limb-darkening and flatten the continuum using the limb-darkening polynomial calculated in Allen's Astrophysical Quantities (1993). L_{ij} = 1 - u_2 - v_2 + u_2 cos \\theta + v_2 cos^2 \\theta L_{ij} = 1 - u_2 - v_2 + u_2 cos \\theta + v_2 cos^2 \\theta The functions for limb-darkening corrections can be found here .","title":"Intensity Corrections"},{"location":"sdo/corr/#magnetic-field-corrections","text":"Magnetograms (line-of-sight longitudinal magnetic field maps) are used to determine magnetic observables that show strong correlation with RV variation and velocity components. Due to foreshortening, the observed magnetic field (magnetogram data) is less than the true radial field by a factor of \\mu = cos(\\theta) \\mu = cos(\\theta) . We correct for this as seen here: B_{r, ij} = B{obs, ij} / \\mu_{ij} B_{r, ij} = B{obs, ij} / \\mu_{ij} . Additionally, we set all magnetic pixels with magnitude lower than the noise level (8G) to 0.","title":"Magnetic Field Corrections"},{"location":"sdo/data/","text":"Data Used This project requires the use of data from the Helioseismic and Magnetic Imager (HMI) aboard NASA's Solar Dynamics Observatory (SDO). HMI has been continuously imaging the Sun since 2012 in the 6173 A line. HMI takes images every 45 seconds and provides information regarding the velocity, continuum, and magnetic field of the Solar photosphere. Additional information regarding the instrument can be found on the HMI webpage . The three data products used for these calculations are: Dopplergrams : measurements of solar surface velocity Filtergrams : continuum intensity measurements of the Solar photosphere Magnetograms : measurements of the line-of-sight Solar magnetic field Acquiring the Data To get the HMI data products, we utilize Sunpy's querying techniques. This allows us to search by date, physical observable, and instrument. An example of querying Sunpy for data can be found here .","title":"Aquiring Data"},{"location":"sdo/data/#data-used","text":"This project requires the use of data from the Helioseismic and Magnetic Imager (HMI) aboard NASA's Solar Dynamics Observatory (SDO). HMI has been continuously imaging the Sun since 2012 in the 6173 A line. HMI takes images every 45 seconds and provides information regarding the velocity, continuum, and magnetic field of the Solar photosphere. Additional information regarding the instrument can be found on the HMI webpage . The three data products used for these calculations are: Dopplergrams : measurements of solar surface velocity Filtergrams : continuum intensity measurements of the Solar photosphere Magnetograms : measurements of the line-of-sight Solar magnetic field","title":"Data Used"},{"location":"sdo/data/#acquiring-the-data","text":"To get the HMI data products, we utilize Sunpy's querying techniques. This allows us to search by date, physical observable, and instrument. An example of querying Sunpy for data can be found here .","title":"Acquiring the Data"},{"location":"sdo/mag/","text":"Calculation of Solar Magnetic Observables Using HMI magnetograms, we are able to calculate magnetic observables that both correlate with each other as well as the velocity features. This shows how the magnetic activity is driving the radial velocity variations. Filling Factor Disk averaged filling factors of sunspots and plage which gives the percentage of magnetically active pixels. The filling factor is a calculation of the percentage of active pixels on the solar surface at any one time. We calculate three different factors: f_{spot} f_{spot} , f_{bright} f_{bright} , and f f . These are the filling factors due to sunspots, faculae/network, and the total filling factor which is the sum of the two. We return the filling factor as a percentage: f = \\frac{1}{N_{pix}} \\sum_{ij} W_{ij} * 100 f = \\frac{1}{N_{pix}} \\sum_{ij} W_{ij} * 100 def filling_factor(mu, mmap, active, fac_inds, spot_inds, mu_cutoff=0.3): \"\"\" function to calculate filling factor Parameters ---------- mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) active: weights array where active pixels have weight = 1 fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns ------- f_bright: filling factor (%) for bright areas (faculae) f_spot: filling factor (%) for dark areas (sunspots) f_total: filling factor (%) for timestamp \"\"\" # get good mu values good_mu = np.where(mu > mu_cutoff) # get number of pixels npix = len(mmap.data[good_mu]) # faculae faculae = np.zeros(mmap.data.shape) faculae[fac_inds] = 1. f_bright = np.sum(faculae) / npix * 100 # sunspots spots = np.zeros(mmap.data.shape) spots[spot_inds] = 1. f_spot = np.sum(spots) / npix * 100 # get filling factor f_total = np.sum(active) / npix * 100 return f_bright, f_spot, f_total Unsigned Magnetic Flux The disc-averaged, line-of-sight unsigned (unpolarized) magnetic flux of the Sun. |\\hat{B_{obs}}| = \\frac{\\sum_{ij} |B_{obs, ij}| I_{ij}} {\\sum_{ij} I_{ij}} |\\hat{B_{obs}}| = \\frac{\\sum_{ij} |B_{obs, ij}| I_{ij}} {\\sum_{ij} I_{ij}} def unsigned_flux(map_mag_obs, imap): \"\"\" calculate unsigned magnetic flux Parameters ---------- map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- unsign_flux: unsigned magnetic flux \"\"\" unsign_flux = np.nansum(np.abs(map_mag_obs.data) * imap.data) / np.nansum(imap.data) return np.abs(unsign_flux) Area Based Magnetic Observables In addition to the unsigned flux and filling factor calculations described above, we calculate these quantities with an eye for what types of regions are causing variations in magnetic activity. The calculations for area thresholded regions is outlined below. Area Thresholded Filling Factor def area_filling_factor(active, area, mu, mmap, fac_inds, athresh=20, mu_cutoff=0.3): \"\"\" calculate filling factor for regions thresholded by area - differentiate between large and small regions - differentiate between plage (large) and network (small) bright regions Parameters ---------- active: weights array where active pixels have weight = 1 area: area of each active region weighted by its intensity mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) fac_inds: array of indices where faculae are detected athresh: area threshold value between large and small regions (in uHem) mu_cutoff: minimum mu cutoff value for usable data Returns ------- f_small: filling factor (%) for small magnetically active regions f_large: filling factor (%) for large magnetically active regions f_network: filling factor (%) for network (small, bright magnetically active) regions f_plage: filling factor (%) for plage (large, bright magnetically active) regions f_nonconv: filling factor (%) for regions that do not suppress convective blueshift \"\"\" # get good mu values good_mu = np.where(mu > mu_cutoff) # get number of pixels npix = len(mmap.data[good_mu]) # get quiet pixels quiet = 1 - active # get filling factor for 'small' magnetic features small = np.zeros(mmap.data.shape) small_inds = np.logical_and(active > 0.5, area < athresh) small[small_inds] = 1. f_small = np.nansum(small) / npix * 100 # get filling factor for 'large' magnetic features large = np.zeros(mmap.data.shape) large_inds = np.logical_and(active > 0.5, area > athresh) large[large_inds] = 1. f_large = np.nansum(large) / npix * 100 # get filling factor for network (small, faculae regions) network = np.zeros(mmap.data.shape) network_inds = np.logical_and(small > 0.5, fac_inds > 0.5) network[network_inds] = 1. f_network = np.nansum(network) / npix * 100 # get filling factor for plage (large, faculae regions) plage = np.zeros(mmap.data.shape) plage_inds = np.logical_and(large > 0.5, fac_inds > 0.5) plage[plage_inds] = 1. f_plage = np.nansum(plage) / npix * 100 # get filling factor for small, non-convective regions nonconv = np.zeros(mmap.data.shape) nonconv_inds = np.logical_and(quiet > 0.5, small > 0.5) nonconv[nonconv_inds] = 1. f_nonconv = np.nansum(nonconv) / npix * 100 return f_small, f_large, f_network, f_plage, f_nonconv Area Thresholded Unsigned Flux def area_unsigned_flux(map_mag_obs, imap, area, active, athresh=20): \"\"\" calculate the magnetic flux for different regions based on area cut and magnetic activitiy Parameters ---------- map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) area: area of each active region weighted by its intensity active: weights array where active pixels have weight = 1 Returns ------- quiet_flux: magnetic flux of quiet-Sun regions ar_flux: magnetic flux of active regions conv_flux: magnetic flux of regions that suppress convective blueshift pol_flux: magnetic flux of polarized regions pol_conv_flux: magnetic flux of polarized regions that suppress the convective blueshift \"\"\" # get data arrays i_data = imap.data m_data = map_mag_obs.data mabs_data = np.abs(m_data) quiet = 1 - active # get large regions array large = np.zeros(m_data.shape) large_inds = np.logical_and(active > 0.5, area > athresh) large[large_inds] = 1. # calculate relevant fluxes quiet_flux = np.nansum(mabs_data * i_data * quiet) / np.nansum(i_data * quiet) ar_flux = np.nansum(mabs_data * i_data * active) / np.nansum(i_data * active) conv_flux = np.nansum(mabs_data * i_data * large) / np.nansum(i_data * large) pol_flux = np.nansum(m_data * i_data) / np.nansum(i_data) pol_conv_flux = np.nansum(m_data * i_data * large) / np.nansum(i_data * large) return quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux","title":"Magnetic Observables"},{"location":"sdo/mag/#calculation-of-solar-magnetic-observables","text":"Using HMI magnetograms, we are able to calculate magnetic observables that both correlate with each other as well as the velocity features. This shows how the magnetic activity is driving the radial velocity variations.","title":"Calculation of Solar Magnetic Observables"},{"location":"sdo/mag/#filling-factor","text":"Disk averaged filling factors of sunspots and plage which gives the percentage of magnetically active pixels. The filling factor is a calculation of the percentage of active pixels on the solar surface at any one time. We calculate three different factors: f_{spot} f_{spot} , f_{bright} f_{bright} , and f f . These are the filling factors due to sunspots, faculae/network, and the total filling factor which is the sum of the two. We return the filling factor as a percentage: f = \\frac{1}{N_{pix}} \\sum_{ij} W_{ij} * 100 f = \\frac{1}{N_{pix}} \\sum_{ij} W_{ij} * 100 def filling_factor(mu, mmap, active, fac_inds, spot_inds, mu_cutoff=0.3): \"\"\" function to calculate filling factor Parameters ---------- mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) active: weights array where active pixels have weight = 1 fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns ------- f_bright: filling factor (%) for bright areas (faculae) f_spot: filling factor (%) for dark areas (sunspots) f_total: filling factor (%) for timestamp \"\"\" # get good mu values good_mu = np.where(mu > mu_cutoff) # get number of pixels npix = len(mmap.data[good_mu]) # faculae faculae = np.zeros(mmap.data.shape) faculae[fac_inds] = 1. f_bright = np.sum(faculae) / npix * 100 # sunspots spots = np.zeros(mmap.data.shape) spots[spot_inds] = 1. f_spot = np.sum(spots) / npix * 100 # get filling factor f_total = np.sum(active) / npix * 100 return f_bright, f_spot, f_total","title":"Filling Factor"},{"location":"sdo/mag/#unsigned-magnetic-flux","text":"The disc-averaged, line-of-sight unsigned (unpolarized) magnetic flux of the Sun. |\\hat{B_{obs}}| = \\frac{\\sum_{ij} |B_{obs, ij}| I_{ij}} {\\sum_{ij} I_{ij}} |\\hat{B_{obs}}| = \\frac{\\sum_{ij} |B_{obs, ij}| I_{ij}} {\\sum_{ij} I_{ij}} def unsigned_flux(map_mag_obs, imap): \"\"\" calculate unsigned magnetic flux Parameters ---------- map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- unsign_flux: unsigned magnetic flux \"\"\" unsign_flux = np.nansum(np.abs(map_mag_obs.data) * imap.data) / np.nansum(imap.data) return np.abs(unsign_flux)","title":"Unsigned Magnetic Flux"},{"location":"sdo/mag/#area-based-magnetic-observables","text":"In addition to the unsigned flux and filling factor calculations described above, we calculate these quantities with an eye for what types of regions are causing variations in magnetic activity. The calculations for area thresholded regions is outlined below.","title":"Area Based Magnetic Observables"},{"location":"sdo/mag/#area-thresholded-filling-factor","text":"def area_filling_factor(active, area, mu, mmap, fac_inds, athresh=20, mu_cutoff=0.3): \"\"\" calculate filling factor for regions thresholded by area - differentiate between large and small regions - differentiate between plage (large) and network (small) bright regions Parameters ---------- active: weights array where active pixels have weight = 1 area: area of each active region weighted by its intensity mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) fac_inds: array of indices where faculae are detected athresh: area threshold value between large and small regions (in uHem) mu_cutoff: minimum mu cutoff value for usable data Returns ------- f_small: filling factor (%) for small magnetically active regions f_large: filling factor (%) for large magnetically active regions f_network: filling factor (%) for network (small, bright magnetically active) regions f_plage: filling factor (%) for plage (large, bright magnetically active) regions f_nonconv: filling factor (%) for regions that do not suppress convective blueshift \"\"\" # get good mu values good_mu = np.where(mu > mu_cutoff) # get number of pixels npix = len(mmap.data[good_mu]) # get quiet pixels quiet = 1 - active # get filling factor for 'small' magnetic features small = np.zeros(mmap.data.shape) small_inds = np.logical_and(active > 0.5, area < athresh) small[small_inds] = 1. f_small = np.nansum(small) / npix * 100 # get filling factor for 'large' magnetic features large = np.zeros(mmap.data.shape) large_inds = np.logical_and(active > 0.5, area > athresh) large[large_inds] = 1. f_large = np.nansum(large) / npix * 100 # get filling factor for network (small, faculae regions) network = np.zeros(mmap.data.shape) network_inds = np.logical_and(small > 0.5, fac_inds > 0.5) network[network_inds] = 1. f_network = np.nansum(network) / npix * 100 # get filling factor for plage (large, faculae regions) plage = np.zeros(mmap.data.shape) plage_inds = np.logical_and(large > 0.5, fac_inds > 0.5) plage[plage_inds] = 1. f_plage = np.nansum(plage) / npix * 100 # get filling factor for small, non-convective regions nonconv = np.zeros(mmap.data.shape) nonconv_inds = np.logical_and(quiet > 0.5, small > 0.5) nonconv[nonconv_inds] = 1. f_nonconv = np.nansum(nonconv) / npix * 100 return f_small, f_large, f_network, f_plage, f_nonconv","title":"Area Thresholded Filling Factor"},{"location":"sdo/mag/#area-thresholded-unsigned-flux","text":"def area_unsigned_flux(map_mag_obs, imap, area, active, athresh=20): \"\"\" calculate the magnetic flux for different regions based on area cut and magnetic activitiy Parameters ---------- map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) area: area of each active region weighted by its intensity active: weights array where active pixels have weight = 1 Returns ------- quiet_flux: magnetic flux of quiet-Sun regions ar_flux: magnetic flux of active regions conv_flux: magnetic flux of regions that suppress convective blueshift pol_flux: magnetic flux of polarized regions pol_conv_flux: magnetic flux of polarized regions that suppress the convective blueshift \"\"\" # get data arrays i_data = imap.data m_data = map_mag_obs.data mabs_data = np.abs(m_data) quiet = 1 - active # get large regions array large = np.zeros(m_data.shape) large_inds = np.logical_and(active > 0.5, area > athresh) large[large_inds] = 1. # calculate relevant fluxes quiet_flux = np.nansum(mabs_data * i_data * quiet) / np.nansum(i_data * quiet) ar_flux = np.nansum(mabs_data * i_data * active) / np.nansum(i_data * active) conv_flux = np.nansum(mabs_data * i_data * large) / np.nansum(i_data * large) pol_flux = np.nansum(m_data * i_data) / np.nansum(i_data) pol_conv_flux = np.nansum(m_data * i_data * large) / np.nansum(i_data * large) return quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux","title":"Area Thresholded Unsigned Flux"},{"location":"sdo/pipeline/","text":"Full Pipeline Inputs start_date: start date of RV calculations end_date: end date of RV calculations cadence: how often you want to calculate [seconds] csv_name: file name to save calculations Example usage start_date = datetime.datetime(2021, 8, 26, 8, 00, 0) end_date = datetime.datetime(2021, 9, 3, 0, 00, 0) cadence = 24*3600 csv_name = 'csv_name' component_calc(start_date, end_date, cadence, csv_name) Function def component_calc(start_date, end_date, cadence, csv_name=None): \"\"\" function to calculate rv components using pipeline functions Parameters ---------- start_date: start date of RV calculations end_date: end date of RV calculations cadence: how often to calculate RV components csv_name: name of file to store calculations in Returns ------- \"\"\" # create file names if csv_name is None: csv_name = str(start_date) csv_name = os.path.join(CsvDir.CALC, csv_name + '.csv') bad_dates_csv = os.path.join(CsvDir.CALC, csv_name + '_bad_dates.csv') # print out csv title print(\"Beginning calculation of values for csv file: \" + csv_name) # List of header strings row_contents = ['date_obs', 'date_jd', 'v_quiet', 'v_disc', 'v_phot', 'v_conv', 'f_bright', 'f_spot', 'f', 'Bobs', 'vphot_bright', 'vphot_spot', 'f_small', 'f_large', 'f_network', 'f_plage', 'f_nonconv', 'quiet_flux', 'ar_flux', 'conv_flux', 'pol_flux', 'pol_conv_flux', 'vconv_quiet', 'vconv_large', 'vconv_small'] # Append a list as new line to an old csv file utils.append_list_as_row(csv_name, row_contents) # get hmi data products time_range = datetime.timedelta(seconds=22) physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] # get dates list xy = (end_date - start_date).seconds + (end_date - start_date).days * 24 * 3600 dates_list = [start_date + datetime.timedelta(seconds=cadence * x) for x in range(0, int(xy / cadence))] for i, date in enumerate(dates_list): # convert the date to a string -- required for use in csv file date_str, date_obj, date_jd = utils.get_dates(date) # pull image within specified time range result = Fido.search(a.Time(str(date_obj - time_range), str(date_obj + time_range)), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2]) # add file to list file_download = Fido.fetch(result) # remove unusable file types good_files = [] for file in file_download: name, extension = os.path.splitext(file) if extension == '.fits': good_files.append(file) if len(good_files) != 3: # append these values to the csv file save_vals = [date_str, 'not three good files'] utils.append_list_as_row(bad_dates_csv, save_vals) # print that the files are missing print('\\nNot three good files: ' + date_str + ' index: ' + str(i)) pass else: # convert to map sequence map_seq = sunpy.map.Map(sorted(good_files)) # check for missing data types missing_map = False # split into data types for j, map_obj in enumerate(map_seq): if map_obj.meta['content'] == 'DOPPLERGRAM': vmap = map_obj elif map_obj.meta['content'] == 'MAGNETOGRAM': mmap = map_obj elif map_obj.meta['content'] == 'CONTINUUM INTENSITY': imap = map_obj else: missing_map = True if missing_map: print(\"Missing a data product for \" + date_str) # add the data # append these values to the csv file save_vals = [date_str, 'missing data product'] utils.append_list_as_row(bad_dates_csv, save_vals) pass else: # coordinate transformation for maps x, y, pd, r, d, mu = ctfuncs.coordinates(vmap) wij, nij, rij = ctfuncs.vel_coords(x, y, pd, r, vmap) # remove bad mu values vmap, mmap, imap = ctfuncs.fix_mu(mu, [vmap, mmap, imap]) # calculate relative positions deltaw, deltan, deltar, dij = sfuncs.rel_positions(wij, nij, rij, vmap) # calculate spacecraft velocity vsc = sfuncs.spacecraft_vel(deltaw, deltan, deltar, dij, vmap) # optimized solar rotation parameters a1 = 14.713 a2 = -2.396 a3 = -1.787 a_parameters = [a1, a2, a3] # calculation of solar rotation velocity vrot = sfuncs.solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters) # calculate corrected velocity corrected_vel = vmap.data - np.real(vsc) - np.real(vrot) # corrected velocity maps map_vel_cor = sfuncs.corrected_map(corrected_vel, vmap, map_type='Corrected-Dopplergram', frame=frames.HeliographicCarrington) # limb brightening Lij = lbfuncs.limb_polynomial(imap) # calculate corrected data Iflat = imap.data / Lij # corrected intensity maps map_int_cor = sfuncs.corrected_map(Iflat, imap, map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs, Br = sfuncs.mag_field(mu, mmap, B_noise) # corrected observed magnetic data map map_mag_obs = sfuncs.corrected_map(Bobs, mmap, map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active, quiet = sfuncs.mag_thresh(mu, mmap, Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) # calculate intensity threshold fac_inds, spot_inds = sfuncs.int_thresh(map_int_cor, active, quiet) # create threshold array thresh_arr = sfuncs.thresh_map(fac_inds, spot_inds) # full threshold maps map_full_thresh = sfuncs.corrected_map(thresh_arr, mmap, map_type='Threshold', frame=frames.HeliographicCarrington) ### velocity contribution due to convective motion of quiet-Sun v_quiet = v_quiet(map_vel_cor, imap, quiet) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot, vphot_bright, vphot_spot = v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = v_disc(map_vel_cor, imap) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright, f_spot, f = sfuncs.filling_factor(mu, mmap, active, fac_inds, spot_inds) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs.unsigned_flux(map_mag_obs, imap) ### calculate the area filling factor pixA_hem = ctfuncs.pix_area_hem(wij, nij, rij, vmap) area = sfuncs.area_calc(active, pixA_hem) f_small, f_large, f_network, f_plage, f_nonconv =sfuncs.area_filling_factor(active, area, mu, mmap, fac_inds) ### get the unsigned flux quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = sfuncs.area_unsigned_flux(map_mag_obs, imap, area, active) ### get area weighted convective velocities vconv_quiet, vconv_large, vconv_small = sfuncs.area_vconv(map_vel_cor, imap, active, area) # make array of what we want to save save_vals = [v_quiet, v_disc, v_phot, v_conv, f_bright, f_spot, f, unsigned_obs_flux, vphot_bright, vphot_spot, f_small, f_large, f_network, f_plage, f_nonconv, quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux, vconv_quiet, vconv_large, vconv_small] # round stuff rounded = np.around(save_vals, 3) round_vals = [date_str, date_jd] for val in rounded: round_vals.append(val) # append these values to the csv file utils.append_list_as_row(csv_name, round_vals) # print that the date is completed print('\\nCalculations and save to file complete for ' + date_str + ' index: ' + str(i)) print('Calculation complete for dates:', start_date, 'to', end_date)","title":"Full Pipeline"},{"location":"sdo/pipeline/#full-pipeline","text":"","title":"Full Pipeline"},{"location":"sdo/pipeline/#inputs","text":"start_date: start date of RV calculations end_date: end date of RV calculations cadence: how often you want to calculate [seconds] csv_name: file name to save calculations","title":"Inputs"},{"location":"sdo/pipeline/#example-usage","text":"start_date = datetime.datetime(2021, 8, 26, 8, 00, 0) end_date = datetime.datetime(2021, 9, 3, 0, 00, 0) cadence = 24*3600 csv_name = 'csv_name' component_calc(start_date, end_date, cadence, csv_name)","title":"Example usage"},{"location":"sdo/pipeline/#function","text":"def component_calc(start_date, end_date, cadence, csv_name=None): \"\"\" function to calculate rv components using pipeline functions Parameters ---------- start_date: start date of RV calculations end_date: end date of RV calculations cadence: how often to calculate RV components csv_name: name of file to store calculations in Returns ------- \"\"\" # create file names if csv_name is None: csv_name = str(start_date) csv_name = os.path.join(CsvDir.CALC, csv_name + '.csv') bad_dates_csv = os.path.join(CsvDir.CALC, csv_name + '_bad_dates.csv') # print out csv title print(\"Beginning calculation of values for csv file: \" + csv_name) # List of header strings row_contents = ['date_obs', 'date_jd', 'v_quiet', 'v_disc', 'v_phot', 'v_conv', 'f_bright', 'f_spot', 'f', 'Bobs', 'vphot_bright', 'vphot_spot', 'f_small', 'f_large', 'f_network', 'f_plage', 'f_nonconv', 'quiet_flux', 'ar_flux', 'conv_flux', 'pol_flux', 'pol_conv_flux', 'vconv_quiet', 'vconv_large', 'vconv_small'] # Append a list as new line to an old csv file utils.append_list_as_row(csv_name, row_contents) # get hmi data products time_range = datetime.timedelta(seconds=22) physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] # get dates list xy = (end_date - start_date).seconds + (end_date - start_date).days * 24 * 3600 dates_list = [start_date + datetime.timedelta(seconds=cadence * x) for x in range(0, int(xy / cadence))] for i, date in enumerate(dates_list): # convert the date to a string -- required for use in csv file date_str, date_obj, date_jd = utils.get_dates(date) # pull image within specified time range result = Fido.search(a.Time(str(date_obj - time_range), str(date_obj + time_range)), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2]) # add file to list file_download = Fido.fetch(result) # remove unusable file types good_files = [] for file in file_download: name, extension = os.path.splitext(file) if extension == '.fits': good_files.append(file) if len(good_files) != 3: # append these values to the csv file save_vals = [date_str, 'not three good files'] utils.append_list_as_row(bad_dates_csv, save_vals) # print that the files are missing print('\\nNot three good files: ' + date_str + ' index: ' + str(i)) pass else: # convert to map sequence map_seq = sunpy.map.Map(sorted(good_files)) # check for missing data types missing_map = False # split into data types for j, map_obj in enumerate(map_seq): if map_obj.meta['content'] == 'DOPPLERGRAM': vmap = map_obj elif map_obj.meta['content'] == 'MAGNETOGRAM': mmap = map_obj elif map_obj.meta['content'] == 'CONTINUUM INTENSITY': imap = map_obj else: missing_map = True if missing_map: print(\"Missing a data product for \" + date_str) # add the data # append these values to the csv file save_vals = [date_str, 'missing data product'] utils.append_list_as_row(bad_dates_csv, save_vals) pass else: # coordinate transformation for maps x, y, pd, r, d, mu = ctfuncs.coordinates(vmap) wij, nij, rij = ctfuncs.vel_coords(x, y, pd, r, vmap) # remove bad mu values vmap, mmap, imap = ctfuncs.fix_mu(mu, [vmap, mmap, imap]) # calculate relative positions deltaw, deltan, deltar, dij = sfuncs.rel_positions(wij, nij, rij, vmap) # calculate spacecraft velocity vsc = sfuncs.spacecraft_vel(deltaw, deltan, deltar, dij, vmap) # optimized solar rotation parameters a1 = 14.713 a2 = -2.396 a3 = -1.787 a_parameters = [a1, a2, a3] # calculation of solar rotation velocity vrot = sfuncs.solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters) # calculate corrected velocity corrected_vel = vmap.data - np.real(vsc) - np.real(vrot) # corrected velocity maps map_vel_cor = sfuncs.corrected_map(corrected_vel, vmap, map_type='Corrected-Dopplergram', frame=frames.HeliographicCarrington) # limb brightening Lij = lbfuncs.limb_polynomial(imap) # calculate corrected data Iflat = imap.data / Lij # corrected intensity maps map_int_cor = sfuncs.corrected_map(Iflat, imap, map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs, Br = sfuncs.mag_field(mu, mmap, B_noise) # corrected observed magnetic data map map_mag_obs = sfuncs.corrected_map(Bobs, mmap, map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active, quiet = sfuncs.mag_thresh(mu, mmap, Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) # calculate intensity threshold fac_inds, spot_inds = sfuncs.int_thresh(map_int_cor, active, quiet) # create threshold array thresh_arr = sfuncs.thresh_map(fac_inds, spot_inds) # full threshold maps map_full_thresh = sfuncs.corrected_map(thresh_arr, mmap, map_type='Threshold', frame=frames.HeliographicCarrington) ### velocity contribution due to convective motion of quiet-Sun v_quiet = v_quiet(map_vel_cor, imap, quiet) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot, vphot_bright, vphot_spot = v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = v_disc(map_vel_cor, imap) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright, f_spot, f = sfuncs.filling_factor(mu, mmap, active, fac_inds, spot_inds) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs.unsigned_flux(map_mag_obs, imap) ### calculate the area filling factor pixA_hem = ctfuncs.pix_area_hem(wij, nij, rij, vmap) area = sfuncs.area_calc(active, pixA_hem) f_small, f_large, f_network, f_plage, f_nonconv =sfuncs.area_filling_factor(active, area, mu, mmap, fac_inds) ### get the unsigned flux quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = sfuncs.area_unsigned_flux(map_mag_obs, imap, area, active) ### get area weighted convective velocities vconv_quiet, vconv_large, vconv_small = sfuncs.area_vconv(map_vel_cor, imap, active, area) # make array of what we want to save save_vals = [v_quiet, v_disc, v_phot, v_conv, f_bright, f_spot, f, unsigned_obs_flux, vphot_bright, vphot_spot, f_small, f_large, f_network, f_plage, f_nonconv, quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux, vconv_quiet, vconv_large, vconv_small] # round stuff rounded = np.around(save_vals, 3) round_vals = [date_str, date_jd] for val in rounded: round_vals.append(val) # append these values to the csv file utils.append_list_as_row(csv_name, round_vals) # print that the date is completed print('\\nCalculations and save to file complete for ' + date_str + ' index: ' + str(i)) print('Calculation complete for dates:', start_date, 'to', end_date)","title":"Function"},{"location":"sdo/region/","text":"Identification of Solar Regions In order to calculate the various pertinent velocity components we need to differentiate between different types of solar regions. We do two identification cuts both based on thresholding values from Yeo et al. 2013. Magnetic Thresholding The first threshold is to identify active regions. We do this with a simple magnetic threshold value of 24G. The threshold is applied to the unsigned magnetic field strength (magnetic field corrected for foreshortening). Pixels with magnetic field magnitude above the threshold are marked as active and those below the threshold are quiet Sun. Isolated active pixels are re-identified as quiet Sun based on area thresholding. Isolated pixels are denoted as any pixel without four connected neighbors. The connection matrix is built using the skimage.regionsprops function. def mag_thresh(mu, mmap, Br_cutoff=24, mu_cutoff=0.3): \"\"\" function to calculate magnetic threshold and differentiate between magnetically active regions and quiet Sun Parameters ---------- mu: array of mu (cosine theta) values mmap: corrected (unsigned magnetic field) Sunpy map object (Magnetogram) Br_cutoff: minimum cutoff value (in Gauss) for thresholding active regions mu_cutoff: minimum mu cutoff value for data to ignore Returns ------- active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 \"\"\" # get active region indices active_inds = np.where(np.abs(mmap.data) * mu > Br_cutoff) bad_mu = np.where(mu <= mu_cutoff) # make active region array active = np.zeros(mu.shape) active[active_inds] = 1. active[bad_mu] = 0. # find isolated pixels # get area y_labeled = label(active, connectivity=2, background=0) y_area = [props.area for props in regionprops(y_labeled)] # area constraint good_area = np.where(np.array(y_area) > 5) good_area = good_area[0] + 1 active_indices = np.isin(y_labeled, good_area) # create weights array active[~active_indices] = 0 # get quiet indices quiet = 1 - active return active, quiet Intensity Thresholding Once we detect active regions, we want to identify sunspots (dark) and faculae/network regions (bright). These regions can be categorized by intensity and therefore an intensity cutoff value is calculated relative to the overall mean flattened intensity of the quiet Sun: I_{quiet} = \\frac{\\sum_{ij} I_{flat, ij} W_{ij}}{\\sum_{ij} W_{ij}} I_{quiet} = \\frac{\\sum_{ij} I_{flat, ij} W_{ij}}{\\sum_{ij} W_{ij}} where W_{ij} W_{ij} is the quiet Sun weighting array returned from magnetic thresholding. The intensity threshold is then I_{thresh} = 0.89I_{quiet} I_{thresh} = 0.89I_{quiet} . def int_thresh(map_int_cor, active, quiet): \"\"\" function to do intensity thresholding and differentiate between faculae (bright) and sunspots (dark) Parameters ---------- map_int_cor: corrected (limb-darkening) Sunpy map object (Intensitygram) active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 Returns ------- fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected \"\"\" # flattened intensity data Iflat = map_int_cor.data # calculate quiet sun intensity int_quiet = np.nansum(Iflat * quiet) / np.nansum(quiet) # intensity threshold int_cutoff = 0.89 * int_quiet # get faculae fac_inds = np.logical_and((Iflat > int_cutoff), (active > 0.5)) # get sunspots spot_inds = np.logical_and((Iflat <= int_cutoff), (active > 0.5)) return fac_inds, spot_inds","title":"Region Identification"},{"location":"sdo/region/#identification-of-solar-regions","text":"In order to calculate the various pertinent velocity components we need to differentiate between different types of solar regions. We do two identification cuts both based on thresholding values from Yeo et al. 2013.","title":"Identification of Solar Regions"},{"location":"sdo/region/#magnetic-thresholding","text":"The first threshold is to identify active regions. We do this with a simple magnetic threshold value of 24G. The threshold is applied to the unsigned magnetic field strength (magnetic field corrected for foreshortening). Pixels with magnetic field magnitude above the threshold are marked as active and those below the threshold are quiet Sun. Isolated active pixels are re-identified as quiet Sun based on area thresholding. Isolated pixels are denoted as any pixel without four connected neighbors. The connection matrix is built using the skimage.regionsprops function. def mag_thresh(mu, mmap, Br_cutoff=24, mu_cutoff=0.3): \"\"\" function to calculate magnetic threshold and differentiate between magnetically active regions and quiet Sun Parameters ---------- mu: array of mu (cosine theta) values mmap: corrected (unsigned magnetic field) Sunpy map object (Magnetogram) Br_cutoff: minimum cutoff value (in Gauss) for thresholding active regions mu_cutoff: minimum mu cutoff value for data to ignore Returns ------- active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 \"\"\" # get active region indices active_inds = np.where(np.abs(mmap.data) * mu > Br_cutoff) bad_mu = np.where(mu <= mu_cutoff) # make active region array active = np.zeros(mu.shape) active[active_inds] = 1. active[bad_mu] = 0. # find isolated pixels # get area y_labeled = label(active, connectivity=2, background=0) y_area = [props.area for props in regionprops(y_labeled)] # area constraint good_area = np.where(np.array(y_area) > 5) good_area = good_area[0] + 1 active_indices = np.isin(y_labeled, good_area) # create weights array active[~active_indices] = 0 # get quiet indices quiet = 1 - active return active, quiet","title":"Magnetic Thresholding"},{"location":"sdo/region/#intensity-thresholding","text":"Once we detect active regions, we want to identify sunspots (dark) and faculae/network regions (bright). These regions can be categorized by intensity and therefore an intensity cutoff value is calculated relative to the overall mean flattened intensity of the quiet Sun: I_{quiet} = \\frac{\\sum_{ij} I_{flat, ij} W_{ij}}{\\sum_{ij} W_{ij}} I_{quiet} = \\frac{\\sum_{ij} I_{flat, ij} W_{ij}}{\\sum_{ij} W_{ij}} where W_{ij} W_{ij} is the quiet Sun weighting array returned from magnetic thresholding. The intensity threshold is then I_{thresh} = 0.89I_{quiet} I_{thresh} = 0.89I_{quiet} . def int_thresh(map_int_cor, active, quiet): \"\"\" function to do intensity thresholding and differentiate between faculae (bright) and sunspots (dark) Parameters ---------- map_int_cor: corrected (limb-darkening) Sunpy map object (Intensitygram) active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 Returns ------- fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected \"\"\" # flattened intensity data Iflat = map_int_cor.data # calculate quiet sun intensity int_quiet = np.nansum(Iflat * quiet) / np.nansum(quiet) # intensity threshold int_cutoff = 0.89 * int_quiet # get faculae fac_inds = np.logical_and((Iflat > int_cutoff), (active > 0.5)) # get sunspots spot_inds = np.logical_and((Iflat <= int_cutoff), (active > 0.5)) return fac_inds, spot_inds","title":"Intensity Thresholding"},{"location":"sdo/rv/","text":"Calculation of Model RVs The model 'sun-as-a-star' RV variation is a linear combination of the two velocity components \\rm (v_{phot}, v_{conv}) \\rm (v_{phot}, v_{conv}) along with an offset factor of \\rm RV_0 \\rm RV_0 : RV = A \\ v_{phot} + B \\ v_{conv} + RV_0 RV = A \\ v_{phot} + B \\ v_{conv} + RV_0 Our ability to take high resolution measurements of the Solar disk is what allows us to decompose the overall RV variation into these two components. Because we do not know the relative weights of these two velocity components, we rely on regression to determine the best parameters to fit our ground based data. We calculate scaling factors A and B, and then offset \\rm RV_0 \\rm RV_0 using linear regression. The need for these scaling coefficients arises due to the fact that SDO/HMI images the Sun in one wavelength (6173 A) while ground based measurements average over thousands of lines. The offset accounts for the zero point of the instrument. An example of the linear regression model used can be found here .","title":"RV Calculation"},{"location":"sdo/rv/#calculation-of-model-rvs","text":"The model 'sun-as-a-star' RV variation is a linear combination of the two velocity components \\rm (v_{phot}, v_{conv}) \\rm (v_{phot}, v_{conv}) along with an offset factor of \\rm RV_0 \\rm RV_0 : RV = A \\ v_{phot} + B \\ v_{conv} + RV_0 RV = A \\ v_{phot} + B \\ v_{conv} + RV_0 Our ability to take high resolution measurements of the Solar disk is what allows us to decompose the overall RV variation into these two components. Because we do not know the relative weights of these two velocity components, we rely on regression to determine the best parameters to fit our ground based data. We calculate scaling factors A and B, and then offset \\rm RV_0 \\rm RV_0 using linear regression. The need for these scaling coefficients arises due to the fact that SDO/HMI images the Sun in one wavelength (6173 A) while ground based measurements average over thousands of lines. The offset accounts for the zero point of the instrument. An example of the linear regression model used can be found here .","title":"Calculation of Model RVs"},{"location":"sdo/vel/","text":"Calculation of RV Components After the data corrections and identification of solar regions, we are able to calculate the individual feature components that make up the SDO derived RV variation. Photometric Velocity Estimation of rotational Doppler imbalance due to active (faculae/darkspot) regions Calculation of the photometric velocity due the presence of dark spots/bright faculae causing a Doppler imbalance. Requires an active region based magnetic weighting array. A weight of 0 denotes quiet-Sun and 1 denotes active pixel. The equation for this velocity calculation is: v_{phot} = \\frac{\\sum_{ij} v_{rot, ij} (I_{ij} - K) W_{ij}} {\\sum_{ij} I_{ij}} v_{phot} = \\frac{\\sum_{ij} v_{rot, ij} (I_{ij} - K) W_{ij}} {\\sum_{ij} I_{ij}} . K is a scaling factor based off the limb-brightening correction. K = \\frac{\\sum_{ij} I_{ij} L_{ij} W_{ij}}{\\sum_{ij} L_{ij}^2 W_{ij}} K = \\frac{\\sum_{ij} I_{ij} L_{ij} W_{ij}}{\\sum_{ij} L_{ij}^2 W_{ij}} In this case I_{ij} I_{ij} is the uncorrected (original) intensity data. We are also able to use out intensity weighting to calculate photometric velocities due to just faculae/network or sunspot regions. def v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds, mu_cutoff=0.3): \"\"\" function to calculate photometric velocity due to rotational Doppler variation Parameters ---------- quiet: weights array where active pixels have weight = 0 active: weights array where active pixels have weight = 1 Lij: limb-darkening polynomial function vrot: solar rotational velocity imap: UNCORRECTED Sunpy map object (Intensitygram) mu: array of mu values fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns ------- v_phot: photospheric velocity perturbation (float) \"\"\" # get good mu values good_mu = np.where(mu > mu_cutoff) # calculate K scaling factor K = np.nansum(imap.data * Lij * quiet) / np.sum((Lij[good_mu] ** 2) * quiet[good_mu]) # calculate photospheric velocity v_phot = np.nansum(np.real(vrot) * (imap.data - K * Lij) * active) / np.nansum(imap.data) # faculae driven photospheric velocity vphot_bright = np.nansum(np.real(vrot) * (imap.data - K * Lij) * fac_inds) / np.nansum(imap.data) # sunspots driven photospheric velocity vphot_spot = np.nansum(np.real(vrot) * (imap.data - K * Lij) * spot_inds) / np.nansum(imap.data) return v_phot, vphot_bright, vphot_spot Convective Velocity Estimation due to the suppression of the convective blueshift by magnetically active regions Calculation of suppression of convective blueshift due to active regions (mainly faculae). The basic premise for calculation is to calculate the disc-averaged velocity of the Sun and subtract from that the quiet-Sun velocity. We then calculate the convective velocity as what is left: v_{conv} = v - v_{quiet} v_{conv} = v - v_{quiet} Quiet Sun Velocity Estimation of average RV of quiet-Sun due to convective motion Calculate the velocity due to the convective motion of the quiet-Sun. Requires the magnetic weighting array we built in step six, part one. A weight of 0 denotes an active pixel, and 1 is quiet-Sun. The equation for calculating this quiet-Sun velocity is as such: v_{quiet} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij} W_{ij}} {\\sum_{ij} I_{ij} W_{ij}} v_{quiet} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij} W_{ij}} {\\sum_{ij} I_{ij} W_{ij}} . In this case I_{ij} I_{ij} is the uncorrected (original) intensity data. def v_quiet(map_vel_cor, imap, quiet): \"\"\" function to calculate velocity due to convective motion of quiet-Sun Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) quiet: weights array where active pixels have weight = 0 Returns ------- v_quiet: quiet-Sun velocity (float) \"\"\" v_quiet = np.nansum(map_vel_cor.data * imap.data * quiet) / np.nansum( imap.data * quiet) return v_quiet Full Disc Velocity Total corrected (spacecraft motion, solar rotation) disk-averaged velocity of the Sun The equation for the disc-averaged velocity is: v = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij}}{\\sum_{ij} I_{ij}} v = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij}}{\\sum_{ij} I_{ij}} . def v_disc(map_vel_cor, imap): \"\"\" function to calculate disc-averaged velocity of Sun Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- v_disc: disc averaged velocity of Sun (float) \"\"\" v_disc = np.nansum(map_vel_cor.data * imap.data) / np.nansum(imap.data) return v_disc","title":"Velocity Components"},{"location":"sdo/vel/#calculation-of-rv-components","text":"After the data corrections and identification of solar regions, we are able to calculate the individual feature components that make up the SDO derived RV variation.","title":"Calculation of RV Components"},{"location":"sdo/vel/#photometric-velocity","text":"Estimation of rotational Doppler imbalance due to active (faculae/darkspot) regions Calculation of the photometric velocity due the presence of dark spots/bright faculae causing a Doppler imbalance. Requires an active region based magnetic weighting array. A weight of 0 denotes quiet-Sun and 1 denotes active pixel. The equation for this velocity calculation is: v_{phot} = \\frac{\\sum_{ij} v_{rot, ij} (I_{ij} - K) W_{ij}} {\\sum_{ij} I_{ij}} v_{phot} = \\frac{\\sum_{ij} v_{rot, ij} (I_{ij} - K) W_{ij}} {\\sum_{ij} I_{ij}} . K is a scaling factor based off the limb-brightening correction. K = \\frac{\\sum_{ij} I_{ij} L_{ij} W_{ij}}{\\sum_{ij} L_{ij}^2 W_{ij}} K = \\frac{\\sum_{ij} I_{ij} L_{ij} W_{ij}}{\\sum_{ij} L_{ij}^2 W_{ij}} In this case I_{ij} I_{ij} is the uncorrected (original) intensity data. We are also able to use out intensity weighting to calculate photometric velocities due to just faculae/network or sunspot regions. def v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds, mu_cutoff=0.3): \"\"\" function to calculate photometric velocity due to rotational Doppler variation Parameters ---------- quiet: weights array where active pixels have weight = 0 active: weights array where active pixels have weight = 1 Lij: limb-darkening polynomial function vrot: solar rotational velocity imap: UNCORRECTED Sunpy map object (Intensitygram) mu: array of mu values fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns ------- v_phot: photospheric velocity perturbation (float) \"\"\" # get good mu values good_mu = np.where(mu > mu_cutoff) # calculate K scaling factor K = np.nansum(imap.data * Lij * quiet) / np.sum((Lij[good_mu] ** 2) * quiet[good_mu]) # calculate photospheric velocity v_phot = np.nansum(np.real(vrot) * (imap.data - K * Lij) * active) / np.nansum(imap.data) # faculae driven photospheric velocity vphot_bright = np.nansum(np.real(vrot) * (imap.data - K * Lij) * fac_inds) / np.nansum(imap.data) # sunspots driven photospheric velocity vphot_spot = np.nansum(np.real(vrot) * (imap.data - K * Lij) * spot_inds) / np.nansum(imap.data) return v_phot, vphot_bright, vphot_spot","title":"Photometric Velocity"},{"location":"sdo/vel/#convective-velocity","text":"Estimation due to the suppression of the convective blueshift by magnetically active regions Calculation of suppression of convective blueshift due to active regions (mainly faculae). The basic premise for calculation is to calculate the disc-averaged velocity of the Sun and subtract from that the quiet-Sun velocity. We then calculate the convective velocity as what is left: v_{conv} = v - v_{quiet} v_{conv} = v - v_{quiet}","title":"Convective Velocity"},{"location":"sdo/vel/#quiet-sun-velocity","text":"Estimation of average RV of quiet-Sun due to convective motion Calculate the velocity due to the convective motion of the quiet-Sun. Requires the magnetic weighting array we built in step six, part one. A weight of 0 denotes an active pixel, and 1 is quiet-Sun. The equation for calculating this quiet-Sun velocity is as such: v_{quiet} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij} W_{ij}} {\\sum_{ij} I_{ij} W_{ij}} v_{quiet} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij} W_{ij}} {\\sum_{ij} I_{ij} W_{ij}} . In this case I_{ij} I_{ij} is the uncorrected (original) intensity data. def v_quiet(map_vel_cor, imap, quiet): \"\"\" function to calculate velocity due to convective motion of quiet-Sun Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) quiet: weights array where active pixels have weight = 0 Returns ------- v_quiet: quiet-Sun velocity (float) \"\"\" v_quiet = np.nansum(map_vel_cor.data * imap.data * quiet) / np.nansum( imap.data * quiet) return v_quiet","title":"Quiet Sun Velocity"},{"location":"sdo/vel/#full-disc-velocity","text":"Total corrected (spacecraft motion, solar rotation) disk-averaged velocity of the Sun The equation for the disc-averaged velocity is: v = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij}}{\\sum_{ij} I_{ij}} v = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij}}{\\sum_{ij} I_{ij}} . def v_disc(map_vel_cor, imap): \"\"\" function to calculate disc-averaged velocity of Sun Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- v_disc: disc averaged velocity of Sun (float) \"\"\" v_disc = np.nansum(map_vel_cor.data * imap.data) / np.nansum(imap.data) return v_disc","title":"Full Disc Velocity"},{"location":"sdo/examples/docs_full_pipeline/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. 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In [ ]: Copied! # name of csv file to store calculations csv_name = 'csv_name' # date range start_date = datetime . datetime ( 2021 , 8 , 26 , 8 , 00 , 0 ) end_date = datetime . datetime ( 2021 , 9 , 3 , 0 , 00 , 0 ) # query cadence in seconds cadence = 24 * 3600 # name of csv file to store calculations csv_name = 'csv_name' # date range start_date = datetime.datetime(2021, 8, 26, 8, 00, 0) end_date = datetime.datetime(2021, 9, 3, 0, 00, 0) # query cadence in seconds cadence = 24*3600 Setup CSV file and SDO/HMI querying parameters. In [ ]: Copied! # create file names csv_name = os . path . join ( CsvDir . CALC , csv_name + '.csv' ) bad_dates_csv = os . path . join ( CsvDir . CALC , csv_name + '_bad_dates.csv' ) # print out csv title print ( \"Beginning calculation of values for csv file: \" + csv_name ) # List of header strings row_contents = [ 'date_obs' , 'date_jd' , 'v_quiet' , 'v_disc' , 'v_phot' , 'v_conv' , 'f_bright' , 'f_spot' , 'f' , 'Bobs' , 'vphot_bright' , 'vphot_spot' , 'f_small' , 'f_large' , 'f_network' , 'f_plage' , 'f_nonconv' , 'quiet_flux' , 'ar_flux' , 'conv_flux' , 'pol_flux' , 'pol_conv_flux' , 'vconv_quiet' , 'vconv_large' , 'vconv_small' ] # Append a list as new line to an old csv file utils . append_list_as_row ( csv_name , row_contents ) # get hmi data products time_range = datetime . timedelta ( seconds = 22 ) physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] # get dates list xy = ( end_date - start_date ) . seconds + ( end_date - start_date ) . days * 24 * 3600 dates_list = [ start_date + datetime . timedelta ( seconds = cadence * x ) for x in range ( 0 , int ( xy / cadence ))] # create file names csv_name = os.path.join(CsvDir.CALC, csv_name+'.csv') bad_dates_csv = os.path.join(CsvDir.CALC, csv_name+'_bad_dates.csv') # print out csv title print(\"Beginning calculation of values for csv file: \" + csv_name) # List of header strings row_contents = ['date_obs', 'date_jd', 'v_quiet', 'v_disc', 'v_phot', 'v_conv', 'f_bright', 'f_spot', 'f', 'Bobs', 'vphot_bright', 'vphot_spot', 'f_small', 'f_large', 'f_network', 'f_plage', 'f_nonconv', 'quiet_flux', 'ar_flux', 'conv_flux', 'pol_flux', 'pol_conv_flux', 'vconv_quiet', 'vconv_large', 'vconv_small'] # Append a list as new line to an old csv file utils.append_list_as_row(csv_name, row_contents) # get hmi data products time_range = datetime.timedelta(seconds=22) physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] # get dates list xy = (end_date - start_date).seconds + (end_date - start_date).days * 24 * 3600 dates_list = [start_date + datetime.timedelta(seconds=cadence*x) for x in range(0, int(xy/cadence))] Component calculations \u00b6 Run through list to calculate and save values. In [ ]: Copied! for i , date in enumerate ( dates_list ): # convert the date to a string -- required for use in csv file date_str , date_obj , date_jd = utils . get_dates ( date ) # pull image within specified time range result = Fido . search ( a . Time ( str ( date_obj - time_range ), str ( date_obj + time_range )), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ]) # add file to list file_download = Fido . fetch ( result ) # remove unusable file types good_files = [] for file in file_download : name , extension = os . path . splitext ( file ) if extension == '.fits' : good_files . append ( file ) if len ( good_files ) != 3 : # add the data # append these values to the csv file save_vals = [ date_str , 'not three good files' ] utils . append_list_as_row ( bad_dates_csv , save_vals ) # print that the files are missing print ( ' \\n Not three good files: ' + date_str + ' index: ' + str ( i )) pass else : # convert to map sequence map_seq = sunpy . map . Map ( sorted ( good_files )) # check for missing data types missing_map = False # split into data types for j , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : vmap = map_obj elif map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : mmap = map_obj elif map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : imap = map_obj else : missing_map = True if missing_map : print ( \"Missing a data product for \" + date_str ) # add the data # append these values to the csv file save_vals = [ date_str , 'missing data product' ] utils . append_list_as_row ( bad_dates_csv , save_vals ) pass else : # coordinate transformation for maps x , y , pd , r , d , mu = ctfuncs . coordinates ( vmap ) wij , nij , rij = ctfuncs . vel_coords ( x , y , pd , r , vmap ) # remove bad mu values vmap , mmap , imap = ctfuncs . fix_mu ( mu , [ vmap , mmap , imap ]) # calculate relative positions deltaw , deltan , deltar , dij = sfuncs . rel_positions ( wij , nij , rij , vmap ) # calculate spacecraft velocity vsc = sfuncs . spacecraft_vel ( deltaw , deltan , deltar , dij , vmap ) # optimized solar rotation parameters a1 = 14.713 a2 = - 2.396 a3 = - 1.787 a_parameters = [ a1 , a2 , a3 ] # calculation of solar rotation velocity vrot = sfuncs . solar_rot_vel ( wij , nij , rij , deltaw , deltan , deltar , dij , vmap , a_parameters ) # calculate corrected velocity corrected_vel = vmap . data - np . real ( vsc ) - np . real ( vrot ) # corrected velocity maps map_vel_cor = sfuncs . corrected_map ( corrected_vel , vmap , map_type = 'Corrected-Dopplergram' , frame = frames . HeliographicCarrington ) # limb brightening Lij = lbfuncs . limb_polynomial ( imap ) # calculate corrected data Iflat = imap . data / Lij # corrected intensity maps map_int_cor = sfuncs . corrected_map ( Iflat , imap , map_type = 'Corrected-Intensitygram' , frame = frames . HeliographicCarrington ) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs , Br = sfuncs . mag_field ( mu , mmap , B_noise ) # corrected observed magnetic data map map_mag_obs = sfuncs . corrected_map ( Bobs , mmap , map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) # radial magnetic data map map_mag_cor = sfuncs . corrected_map ( Br , mmap , map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active , quiet = sfuncs . mag_thresh ( mu , mmap , Br_cutoff = Br_cutoff , mu_cutoff = mu_cutoff ) # calculate intensity threshold fac_inds , spot_inds = sfuncs . int_thresh ( map_int_cor , active , quiet ) # create threshold array thresh_arr = sfuncs . thresh_map ( fac_inds , spot_inds ) # full threshold maps map_full_thresh = sfuncs . corrected_map ( thresh_arr , mmap , map_type = 'Threshold' , frame = frames . HeliographicCarrington ) ### velocity contribution due to convective motion of quiet-Sun v_quiet = sfuncs . v_quiet ( map_vel_cor , imap , quiet ) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot , vphot_bright , vphot_spot = sfuncs . v_phot ( quiet , active , Lij , vrot , imap , mu , fac_inds , spot_inds ) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = sfuncs . v_disc ( map_vel_cor , imap ) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright , f_spot , f = sfuncs . filling_factor ( mu , mmap , active , fac_inds , spot_inds ) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs . unsigned_flux ( map_mag_obs , imap ) ### calculate the area filling factor pixA_hem = ctfuncs . pix_area_hem ( wij , nij , rij , vmap ) area = sfuncs . area_calc ( active , pixA_hem ) f_small , f_large , f_network , f_plage , f_nonconv = sfuncs . area_filling_factor ( active , area , mu , mmap , fac_inds ) ### get the unsigned flux quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux = sfuncs . area_unsigned_flux ( map_mag_obs , imap , area , active ) ### get area weighted convective velocities vconv_quiet , vconv_large , vconv_small = sfuncs . area_vconv ( map_vel_cor , imap , active , area ) # make array of what we want to save save_vals = [ v_quiet , v_disc , v_phot , v_conv , f_bright , f_spot , f , unsigned_obs_flux , vphot_bright , vphot_spot , f_small , f_large , f_network , f_plage , f_nonconv , quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux , vconv_quiet , vconv_large , vconv_small ] # round stuff rounded = np . around ( save_vals , 3 ) round_vals = [ date_str , date_jd ] for val in rounded : round_vals . append ( val ) # append these values to the csv file utils . append_list_as_row ( csv_name , round_vals ) # print that the date is completed print ( ' \\n Calculations and save to file complete for ' + date_str + ' index: ' + str ( i )) print ( 'Calculation complete for dates:' , start_date , 'to' , end_date ) for i, date in enumerate(dates_list): # convert the date to a string -- required for use in csv file date_str, date_obj, date_jd = utils.get_dates(date) # pull image within specified time range result = Fido.search(a.Time(str(date_obj - time_range), str(date_obj + time_range)), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2]) # add file to list file_download = Fido.fetch(result) # remove unusable file types good_files = [] for file in file_download: name, extension = os.path.splitext(file) if extension == '.fits': good_files.append(file) if len(good_files) != 3: # add the data # append these values to the csv file save_vals = [date_str, 'not three good files'] utils.append_list_as_row(bad_dates_csv, save_vals) # print that the files are missing print('\\nNot three good files: ' + date_str + ' index: ' + str(i)) pass else: # convert to map sequence map_seq = sunpy.map.Map(sorted(good_files)) # check for missing data types missing_map = False # split into data types for j, map_obj in enumerate(map_seq): if map_obj.meta['content'] == 'DOPPLERGRAM': vmap = map_obj elif map_obj.meta['content'] == 'MAGNETOGRAM': mmap = map_obj elif map_obj.meta['content'] == 'CONTINUUM INTENSITY': imap = map_obj else: missing_map = True if missing_map: print(\"Missing a data product for \" + date_str) # add the data # append these values to the csv file save_vals = [date_str, 'missing data product'] utils.append_list_as_row(bad_dates_csv, save_vals) pass else: # coordinate transformation for maps x, y, pd, r, d, mu = ctfuncs.coordinates(vmap) wij, nij, rij = ctfuncs.vel_coords(x, y, pd, r, vmap) # remove bad mu values vmap, mmap, imap = ctfuncs.fix_mu(mu, [vmap, mmap, imap]) # calculate relative positions deltaw, deltan, deltar, dij = sfuncs.rel_positions(wij, nij, rij, vmap) # calculate spacecraft velocity vsc = sfuncs.spacecraft_vel(deltaw, deltan, deltar, dij, vmap) # optimized solar rotation parameters a1 = 14.713 a2 = -2.396 a3 = -1.787 a_parameters = [a1, a2, a3] # calculation of solar rotation velocity vrot = sfuncs.solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters) # calculate corrected velocity corrected_vel = vmap.data - np.real(vsc) - np.real(vrot) # corrected velocity maps map_vel_cor = sfuncs.corrected_map(corrected_vel, vmap, map_type='Corrected-Dopplergram', frame=frames.HeliographicCarrington) # limb brightening Lij = lbfuncs.limb_polynomial(imap) # calculate corrected data Iflat = imap.data / Lij # corrected intensity maps map_int_cor = sfuncs.corrected_map(Iflat, imap, map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs, Br = sfuncs.mag_field(mu, mmap, B_noise) # corrected observed magnetic data map map_mag_obs = sfuncs.corrected_map(Bobs, mmap, map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) # radial magnetic data map map_mag_cor = sfuncs.corrected_map(Br, mmap, map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active, quiet = sfuncs.mag_thresh(mu, mmap, Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) # calculate intensity threshold fac_inds, spot_inds = sfuncs.int_thresh(map_int_cor, active, quiet) # create threshold array thresh_arr = sfuncs.thresh_map(fac_inds, spot_inds) # full threshold maps map_full_thresh = sfuncs.corrected_map(thresh_arr, mmap, map_type='Threshold', frame=frames.HeliographicCarrington) ### velocity contribution due to convective motion of quiet-Sun v_quiet = sfuncs.v_quiet(map_vel_cor, imap, quiet) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot, vphot_bright, vphot_spot = sfuncs.v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = sfuncs.v_disc(map_vel_cor, imap) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright, f_spot, f = sfuncs.filling_factor(mu, mmap, active, fac_inds, spot_inds) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs.unsigned_flux(map_mag_obs, imap) ### calculate the area filling factor pixA_hem = ctfuncs.pix_area_hem(wij, nij, rij, vmap) area = sfuncs.area_calc(active, pixA_hem) f_small, f_large, f_network, f_plage, f_nonconv = sfuncs.area_filling_factor(active, area, mu, mmap, fac_inds) ### get the unsigned flux quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = sfuncs.area_unsigned_flux(map_mag_obs, imap, area, active) ### get area weighted convective velocities vconv_quiet, vconv_large, vconv_small = sfuncs.area_vconv(map_vel_cor, imap, active, area) # make array of what we want to save save_vals = [v_quiet, v_disc, v_phot, v_conv, f_bright, f_spot, f, unsigned_obs_flux, vphot_bright, vphot_spot, f_small, f_large, f_network, f_plage, f_nonconv, quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux, vconv_quiet, vconv_large, vconv_small] # round stuff rounded = np.around(save_vals, 3) round_vals = [date_str, date_jd] for val in rounded: round_vals.append(val) # append these values to the csv file utils.append_list_as_row(csv_name, round_vals) # print that the date is completed print('\\nCalculations and save to file complete for ' + date_str + ' index: ' + str(i)) print('Calculation complete for dates:', start_date, 'to', end_date) Full RV Calculation \u00b6 Use the photometric and convective velocity components to calculate the full RV. Read in CSV with data. In [ ]: Copied! # csv file with rv components csv_file = os . path . join ( CsvDir . CALC , 'csv_name.csv' ) # create pandas dataframe component_df = pd . read_csv ( csv_file ) # csv file with rv components csv_file = os.path.join(CsvDir.CALC, 'csv_name.csv') # create pandas dataframe component_df = pd.read_csv(csv_file) In [ ]: Copied! # get velocities lists v_phot = component_df . v_phot . values v_conv = component_df . v_conv . values # get scaling factors A = Scaling . HARPSN . A B = Scaling . HARPSN . B RV0 = Scaling . HARPSN . RV0 # get velocities lists v_phot = component_df.v_phot.values v_conv = component_df.v_conv.values # get scaling factors A = Scaling.HARPSN.A B = Scaling.HARPSN.B RV0 = Scaling.HARPSN.RV0 In [ ]: Copied! RV = A * v_phot + B * v_conv + RV0 component_df [ \"rv_model\" ] = RV component_df . to_csv ( csv_file , index = False ) RV = A*v_phot + B*v_conv + RV0 component_df[\"rv_model\"] = RV component_df.to_csv(csv_file, index=False)","title":"Full Pipeline"},{"location":"sdo/examples/docs_full_pipeline/#outline-of-correction-and-velocity-calculations-from-sdohmi-images","text":"In [ ]: Copied! import os import time import datetime import pandas as pd import numpy as np import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a from sunpy.coordinates import frames import sdo_hmi_rvs.tools.calculation_funcs as sfuncs import sdo_hmi_rvs.tools.lbc_funcs as lbfuncs import sdo_hmi_rvs.tools.coord_funcs as ctfuncs import sdo_hmi_rvs.tools.utilities as utils from sdo_hmi_rvs.tools.settings import CsvDir from sdo_hmi_rvs.tools.settings import Scaling from sklearn.linear_model import LinearRegression import os import time import datetime import pandas as pd import numpy as np import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a from sunpy.coordinates import frames import sdo_hmi_rvs.tools.calculation_funcs as sfuncs import sdo_hmi_rvs.tools.lbc_funcs as lbfuncs import sdo_hmi_rvs.tools.coord_funcs as ctfuncs import sdo_hmi_rvs.tools.utilities as utils from sdo_hmi_rvs.tools.settings import CsvDir from sdo_hmi_rvs.tools.settings import Scaling from sklearn.linear_model import LinearRegression User inputted parameters. In [ ]: Copied! # name of csv file to store calculations csv_name = 'csv_name' # date range start_date = datetime . datetime ( 2021 , 8 , 26 , 8 , 00 , 0 ) end_date = datetime . datetime ( 2021 , 9 , 3 , 0 , 00 , 0 ) # query cadence in seconds cadence = 24 * 3600 # name of csv file to store calculations csv_name = 'csv_name' # date range start_date = datetime.datetime(2021, 8, 26, 8, 00, 0) end_date = datetime.datetime(2021, 9, 3, 0, 00, 0) # query cadence in seconds cadence = 24*3600 Setup CSV file and SDO/HMI querying parameters. In [ ]: Copied! # create file names csv_name = os . path . join ( CsvDir . CALC , csv_name + '.csv' ) bad_dates_csv = os . path . join ( CsvDir . CALC , csv_name + '_bad_dates.csv' ) # print out csv title print ( \"Beginning calculation of values for csv file: \" + csv_name ) # List of header strings row_contents = [ 'date_obs' , 'date_jd' , 'v_quiet' , 'v_disc' , 'v_phot' , 'v_conv' , 'f_bright' , 'f_spot' , 'f' , 'Bobs' , 'vphot_bright' , 'vphot_spot' , 'f_small' , 'f_large' , 'f_network' , 'f_plage' , 'f_nonconv' , 'quiet_flux' , 'ar_flux' , 'conv_flux' , 'pol_flux' , 'pol_conv_flux' , 'vconv_quiet' , 'vconv_large' , 'vconv_small' ] # Append a list as new line to an old csv file utils . append_list_as_row ( csv_name , row_contents ) # get hmi data products time_range = datetime . timedelta ( seconds = 22 ) physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] # get dates list xy = ( end_date - start_date ) . seconds + ( end_date - start_date ) . days * 24 * 3600 dates_list = [ start_date + datetime . timedelta ( seconds = cadence * x ) for x in range ( 0 , int ( xy / cadence ))] # create file names csv_name = os.path.join(CsvDir.CALC, csv_name+'.csv') bad_dates_csv = os.path.join(CsvDir.CALC, csv_name+'_bad_dates.csv') # print out csv title print(\"Beginning calculation of values for csv file: \" + csv_name) # List of header strings row_contents = ['date_obs', 'date_jd', 'v_quiet', 'v_disc', 'v_phot', 'v_conv', 'f_bright', 'f_spot', 'f', 'Bobs', 'vphot_bright', 'vphot_spot', 'f_small', 'f_large', 'f_network', 'f_plage', 'f_nonconv', 'quiet_flux', 'ar_flux', 'conv_flux', 'pol_flux', 'pol_conv_flux', 'vconv_quiet', 'vconv_large', 'vconv_small'] # Append a list as new line to an old csv file utils.append_list_as_row(csv_name, row_contents) # get hmi data products time_range = datetime.timedelta(seconds=22) physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] # get dates list xy = (end_date - start_date).seconds + (end_date - start_date).days * 24 * 3600 dates_list = [start_date + datetime.timedelta(seconds=cadence*x) for x in range(0, int(xy/cadence))]","title":"Outline of correction and velocity calculations from SDO/HMI Images"},{"location":"sdo/examples/docs_full_pipeline/#component-calculations","text":"Run through list to calculate and save values. In [ ]: Copied! for i , date in enumerate ( dates_list ): # convert the date to a string -- required for use in csv file date_str , date_obj , date_jd = utils . get_dates ( date ) # pull image within specified time range result = Fido . search ( a . Time ( str ( date_obj - time_range ), str ( date_obj + time_range )), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ]) # add file to list file_download = Fido . fetch ( result ) # remove unusable file types good_files = [] for file in file_download : name , extension = os . path . splitext ( file ) if extension == '.fits' : good_files . append ( file ) if len ( good_files ) != 3 : # add the data # append these values to the csv file save_vals = [ date_str , 'not three good files' ] utils . append_list_as_row ( bad_dates_csv , save_vals ) # print that the files are missing print ( ' \\n Not three good files: ' + date_str + ' index: ' + str ( i )) pass else : # convert to map sequence map_seq = sunpy . map . Map ( sorted ( good_files )) # check for missing data types missing_map = False # split into data types for j , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : vmap = map_obj elif map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : mmap = map_obj elif map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : imap = map_obj else : missing_map = True if missing_map : print ( \"Missing a data product for \" + date_str ) # add the data # append these values to the csv file save_vals = [ date_str , 'missing data product' ] utils . append_list_as_row ( bad_dates_csv , save_vals ) pass else : # coordinate transformation for maps x , y , pd , r , d , mu = ctfuncs . coordinates ( vmap ) wij , nij , rij = ctfuncs . vel_coords ( x , y , pd , r , vmap ) # remove bad mu values vmap , mmap , imap = ctfuncs . fix_mu ( mu , [ vmap , mmap , imap ]) # calculate relative positions deltaw , deltan , deltar , dij = sfuncs . rel_positions ( wij , nij , rij , vmap ) # calculate spacecraft velocity vsc = sfuncs . spacecraft_vel ( deltaw , deltan , deltar , dij , vmap ) # optimized solar rotation parameters a1 = 14.713 a2 = - 2.396 a3 = - 1.787 a_parameters = [ a1 , a2 , a3 ] # calculation of solar rotation velocity vrot = sfuncs . solar_rot_vel ( wij , nij , rij , deltaw , deltan , deltar , dij , vmap , a_parameters ) # calculate corrected velocity corrected_vel = vmap . data - np . real ( vsc ) - np . real ( vrot ) # corrected velocity maps map_vel_cor = sfuncs . corrected_map ( corrected_vel , vmap , map_type = 'Corrected-Dopplergram' , frame = frames . HeliographicCarrington ) # limb brightening Lij = lbfuncs . limb_polynomial ( imap ) # calculate corrected data Iflat = imap . data / Lij # corrected intensity maps map_int_cor = sfuncs . corrected_map ( Iflat , imap , map_type = 'Corrected-Intensitygram' , frame = frames . HeliographicCarrington ) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs , Br = sfuncs . mag_field ( mu , mmap , B_noise ) # corrected observed magnetic data map map_mag_obs = sfuncs . corrected_map ( Bobs , mmap , map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) # radial magnetic data map map_mag_cor = sfuncs . corrected_map ( Br , mmap , map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active , quiet = sfuncs . mag_thresh ( mu , mmap , Br_cutoff = Br_cutoff , mu_cutoff = mu_cutoff ) # calculate intensity threshold fac_inds , spot_inds = sfuncs . int_thresh ( map_int_cor , active , quiet ) # create threshold array thresh_arr = sfuncs . thresh_map ( fac_inds , spot_inds ) # full threshold maps map_full_thresh = sfuncs . corrected_map ( thresh_arr , mmap , map_type = 'Threshold' , frame = frames . HeliographicCarrington ) ### velocity contribution due to convective motion of quiet-Sun v_quiet = sfuncs . v_quiet ( map_vel_cor , imap , quiet ) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot , vphot_bright , vphot_spot = sfuncs . v_phot ( quiet , active , Lij , vrot , imap , mu , fac_inds , spot_inds ) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = sfuncs . v_disc ( map_vel_cor , imap ) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright , f_spot , f = sfuncs . filling_factor ( mu , mmap , active , fac_inds , spot_inds ) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs . unsigned_flux ( map_mag_obs , imap ) ### calculate the area filling factor pixA_hem = ctfuncs . pix_area_hem ( wij , nij , rij , vmap ) area = sfuncs . area_calc ( active , pixA_hem ) f_small , f_large , f_network , f_plage , f_nonconv = sfuncs . area_filling_factor ( active , area , mu , mmap , fac_inds ) ### get the unsigned flux quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux = sfuncs . area_unsigned_flux ( map_mag_obs , imap , area , active ) ### get area weighted convective velocities vconv_quiet , vconv_large , vconv_small = sfuncs . area_vconv ( map_vel_cor , imap , active , area ) # make array of what we want to save save_vals = [ v_quiet , v_disc , v_phot , v_conv , f_bright , f_spot , f , unsigned_obs_flux , vphot_bright , vphot_spot , f_small , f_large , f_network , f_plage , f_nonconv , quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux , vconv_quiet , vconv_large , vconv_small ] # round stuff rounded = np . around ( save_vals , 3 ) round_vals = [ date_str , date_jd ] for val in rounded : round_vals . append ( val ) # append these values to the csv file utils . append_list_as_row ( csv_name , round_vals ) # print that the date is completed print ( ' \\n Calculations and save to file complete for ' + date_str + ' index: ' + str ( i )) print ( 'Calculation complete for dates:' , start_date , 'to' , end_date ) for i, date in enumerate(dates_list): # convert the date to a string -- required for use in csv file date_str, date_obj, date_jd = utils.get_dates(date) # pull image within specified time range result = Fido.search(a.Time(str(date_obj - time_range), str(date_obj + time_range)), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2]) # add file to list file_download = Fido.fetch(result) # remove unusable file types good_files = [] for file in file_download: name, extension = os.path.splitext(file) if extension == '.fits': good_files.append(file) if len(good_files) != 3: # add the data # append these values to the csv file save_vals = [date_str, 'not three good files'] utils.append_list_as_row(bad_dates_csv, save_vals) # print that the files are missing print('\\nNot three good files: ' + date_str + ' index: ' + str(i)) pass else: # convert to map sequence map_seq = sunpy.map.Map(sorted(good_files)) # check for missing data types missing_map = False # split into data types for j, map_obj in enumerate(map_seq): if map_obj.meta['content'] == 'DOPPLERGRAM': vmap = map_obj elif map_obj.meta['content'] == 'MAGNETOGRAM': mmap = map_obj elif map_obj.meta['content'] == 'CONTINUUM INTENSITY': imap = map_obj else: missing_map = True if missing_map: print(\"Missing a data product for \" + date_str) # add the data # append these values to the csv file save_vals = [date_str, 'missing data product'] utils.append_list_as_row(bad_dates_csv, save_vals) pass else: # coordinate transformation for maps x, y, pd, r, d, mu = ctfuncs.coordinates(vmap) wij, nij, rij = ctfuncs.vel_coords(x, y, pd, r, vmap) # remove bad mu values vmap, mmap, imap = ctfuncs.fix_mu(mu, [vmap, mmap, imap]) # calculate relative positions deltaw, deltan, deltar, dij = sfuncs.rel_positions(wij, nij, rij, vmap) # calculate spacecraft velocity vsc = sfuncs.spacecraft_vel(deltaw, deltan, deltar, dij, vmap) # optimized solar rotation parameters a1 = 14.713 a2 = -2.396 a3 = -1.787 a_parameters = [a1, a2, a3] # calculation of solar rotation velocity vrot = sfuncs.solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters) # calculate corrected velocity corrected_vel = vmap.data - np.real(vsc) - np.real(vrot) # corrected velocity maps map_vel_cor = sfuncs.corrected_map(corrected_vel, vmap, map_type='Corrected-Dopplergram', frame=frames.HeliographicCarrington) # limb brightening Lij = lbfuncs.limb_polynomial(imap) # calculate corrected data Iflat = imap.data / Lij # corrected intensity maps map_int_cor = sfuncs.corrected_map(Iflat, imap, map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs, Br = sfuncs.mag_field(mu, mmap, B_noise) # corrected observed magnetic data map map_mag_obs = sfuncs.corrected_map(Bobs, mmap, map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) # radial magnetic data map map_mag_cor = sfuncs.corrected_map(Br, mmap, map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active, quiet = sfuncs.mag_thresh(mu, mmap, Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) # calculate intensity threshold fac_inds, spot_inds = sfuncs.int_thresh(map_int_cor, active, quiet) # create threshold array thresh_arr = sfuncs.thresh_map(fac_inds, spot_inds) # full threshold maps map_full_thresh = sfuncs.corrected_map(thresh_arr, mmap, map_type='Threshold', frame=frames.HeliographicCarrington) ### velocity contribution due to convective motion of quiet-Sun v_quiet = sfuncs.v_quiet(map_vel_cor, imap, quiet) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot, vphot_bright, vphot_spot = sfuncs.v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = sfuncs.v_disc(map_vel_cor, imap) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright, f_spot, f = sfuncs.filling_factor(mu, mmap, active, fac_inds, spot_inds) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs.unsigned_flux(map_mag_obs, imap) ### calculate the area filling factor pixA_hem = ctfuncs.pix_area_hem(wij, nij, rij, vmap) area = sfuncs.area_calc(active, pixA_hem) f_small, f_large, f_network, f_plage, f_nonconv = sfuncs.area_filling_factor(active, area, mu, mmap, fac_inds) ### get the unsigned flux quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = sfuncs.area_unsigned_flux(map_mag_obs, imap, area, active) ### get area weighted convective velocities vconv_quiet, vconv_large, vconv_small = sfuncs.area_vconv(map_vel_cor, imap, active, area) # make array of what we want to save save_vals = [v_quiet, v_disc, v_phot, v_conv, f_bright, f_spot, f, unsigned_obs_flux, vphot_bright, vphot_spot, f_small, f_large, f_network, f_plage, f_nonconv, quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux, vconv_quiet, vconv_large, vconv_small] # round stuff rounded = np.around(save_vals, 3) round_vals = [date_str, date_jd] for val in rounded: round_vals.append(val) # append these values to the csv file utils.append_list_as_row(csv_name, round_vals) # print that the date is completed print('\\nCalculations and save to file complete for ' + date_str + ' index: ' + str(i)) print('Calculation complete for dates:', start_date, 'to', end_date)","title":"Component calculations"},{"location":"sdo/examples/docs_full_pipeline/#full-rv-calculation","text":"Use the photometric and convective velocity components to calculate the full RV. Read in CSV with data. In [ ]: Copied! # csv file with rv components csv_file = os . path . join ( CsvDir . CALC , 'csv_name.csv' ) # create pandas dataframe component_df = pd . read_csv ( csv_file ) # csv file with rv components csv_file = os.path.join(CsvDir.CALC, 'csv_name.csv') # create pandas dataframe component_df = pd.read_csv(csv_file) In [ ]: Copied! # get velocities lists v_phot = component_df . v_phot . values v_conv = component_df . v_conv . values # get scaling factors A = Scaling . HARPSN . A B = Scaling . HARPSN . B RV0 = Scaling . HARPSN . RV0 # get velocities lists v_phot = component_df.v_phot.values v_conv = component_df.v_conv.values # get scaling factors A = Scaling.HARPSN.A B = Scaling.HARPSN.B RV0 = Scaling.HARPSN.RV0 In [ ]: Copied! RV = A * v_phot + B * v_conv + RV0 component_df [ \"rv_model\" ] = RV component_df . to_csv ( csv_file , index = False ) RV = A*v_phot + B*v_conv + RV0 component_df[\"rv_model\"] = RV component_df.to_csv(csv_file, index=False)","title":"Full RV Calculation"},{"location":"sdo/examples/docs_rv_calcs/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. 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In [ ]: Copied! # get csv with solar RVs csv_file = 'file_name' # create pandas dataframe component_df = pd . read_csv ( csv_file ) # get RV list rv_sun = component_df . rv_sun . values # get csv with solar RVs csv_file = 'file_name' # create pandas dataframe component_df = pd.read_csv(csv_file) # get RV list rv_sun = component_df.rv_sun.values Read in velocities from csv. In [2]: Copied! # csv file with rv components csv_file = 'file_name' # csv file with rv components csv_file = 'file_name' Get component lists. In [ ]: Copied! # create pandas dataframe component_df = pd . read_csv ( csv_file ) # create pandas dataframe component_df = pd.read_csv(csv_file) In [4]: Copied! # get velocities lists v_phot = component_df . v_phot . values v_conv = component_df . v_conv . values # get velocities lists v_phot = component_df.v_phot.values v_conv = component_df.v_conv.values Calculate scaling coefficient values -- using linear regression. Setup data to plug into linear regression model/ In [ ]: Copied! # components X = np . zeros ( shape = ( len ( rv_sun ), 2 )) X [:, 0 ] = v_phot X [:, 1 ] = v_conv # data we are fitting to y = rv_sun # components X = np.zeros(shape=(len(rv_sun), 2)) X[:, 0] = v_phot X[:, 1] = v_conv # data we are fitting to y = rv_sun Use sklearn linear regression to get coefficient values. In [ ]: Copied! # apply regression reg = LinearRegression () . fit ( X , y ) print ( 'R^2 for prediction: ' + str ( reg . score ( X , y ))) # get scaling factors A = reg . coef_ [ 0 ] B = reg . coef_ [ 1 ] RV0 = reg . intercept_ # print scaling factors print ( \"Scaling Factors: \\n A:\" , A , \" \\n B:\" , B , \" \\n RV0:\" , RV0 ) # apply regression reg = LinearRegression().fit(X, y) print('R^2 for prediction: ' + str(reg.score(X, y))) # get scaling factors A = reg.coef_[0] B = reg.coef_[1] RV0 = reg.intercept_ # print scaling factors print(\"Scaling Factors:\\n A:\", A, \"\\nB:\", B, \"\\nRV0:\", RV0) Calculate the Model RV Variation using these coefficients. In [ ]: Copied! RV = A * v_phot + B * v_conv + RV0 RV = A*v_phot + B*v_conv + RV0 Add calculation to csv. In [ ]: Copied! component_df [ \"rv_model\" ] = RV component_df . to_csv ( csv_file , index = False ) component_df[\"rv_model\"] = RV component_df.to_csv(csv_file, index=False)","title":"RV Calculation"},{"location":"sdo/examples/docs_rv_calcs/#calculation-of-solar-rv-variations","text":"calculate solar velocities: calculate solar velocities and save to csv get component lists: using pandas to get the lists of each relevant component calculate RVs and save to csv: calculate RVs using optimized parameters In [1]: Copied! import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression Get ground-based 'sun-as-a-star' RVs. In [ ]: Copied! # get csv with solar RVs csv_file = 'file_name' # create pandas dataframe component_df = pd . read_csv ( csv_file ) # get RV list rv_sun = component_df . rv_sun . values # get csv with solar RVs csv_file = 'file_name' # create pandas dataframe component_df = pd.read_csv(csv_file) # get RV list rv_sun = component_df.rv_sun.values Read in velocities from csv. In [2]: Copied! # csv file with rv components csv_file = 'file_name' # csv file with rv components csv_file = 'file_name' Get component lists. In [ ]: Copied! # create pandas dataframe component_df = pd . read_csv ( csv_file ) # create pandas dataframe component_df = pd.read_csv(csv_file) In [4]: Copied! # get velocities lists v_phot = component_df . v_phot . values v_conv = component_df . v_conv . values # get velocities lists v_phot = component_df.v_phot.values v_conv = component_df.v_conv.values Calculate scaling coefficient values -- using linear regression. Setup data to plug into linear regression model/ In [ ]: Copied! # components X = np . zeros ( shape = ( len ( rv_sun ), 2 )) X [:, 0 ] = v_phot X [:, 1 ] = v_conv # data we are fitting to y = rv_sun # components X = np.zeros(shape=(len(rv_sun), 2)) X[:, 0] = v_phot X[:, 1] = v_conv # data we are fitting to y = rv_sun Use sklearn linear regression to get coefficient values. In [ ]: Copied! # apply regression reg = LinearRegression () . fit ( X , y ) print ( 'R^2 for prediction: ' + str ( reg . score ( X , y ))) # get scaling factors A = reg . coef_ [ 0 ] B = reg . coef_ [ 1 ] RV0 = reg . intercept_ # print scaling factors print ( \"Scaling Factors: \\n A:\" , A , \" \\n B:\" , B , \" \\n RV0:\" , RV0 ) # apply regression reg = LinearRegression().fit(X, y) print('R^2 for prediction: ' + str(reg.score(X, y))) # get scaling factors A = reg.coef_[0] B = reg.coef_[1] RV0 = reg.intercept_ # print scaling factors print(\"Scaling Factors:\\n A:\", A, \"\\nB:\", B, \"\\nRV0:\", RV0) Calculate the Model RV Variation using these coefficients. In [ ]: Copied! RV = A * v_phot + B * v_conv + RV0 RV = A*v_phot + B*v_conv + RV0 Add calculation to csv. In [ ]: Copied! component_df [ \"rv_model\" ] = RV component_df . to_csv ( csv_file , index = False ) component_df[\"rv_model\"] = RV component_df.to_csv(csv_file, index=False)","title":"Calculation of Solar RV Variations"},{"location":"sdo/examples/docs_solar_corrections/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. 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import numpy as np import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a from sunpy.coordinates import frames import matplotlib.pyplot as plt from scipy import stats import tamar.tools.solar_funcs as sfuncs import tamar.tools.lbc_funcs as lbfuncs import tamar.tools.coord_funcs as ctfuncs import numpy as np import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a from sunpy.coordinates import frames import matplotlib.pyplot as plt from scipy import stats import tamar.tools.solar_funcs as sfuncs import tamar.tools.lbc_funcs as lbfuncs import tamar.tools.coord_funcs as ctfuncs Get HMI data. In [ ]: Copied! # get hmi data products cadence = a . Sample ( 24 * u . hour ) # querying cadence start_date = '2014-01-20T09:43:24.70' # start date of query end_date = '2014-01-20T09:44:24.70' physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] result = Fido . search ( a . Time ( start_date , end_date ), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ], cadence ) # print results print ( result ) # get hmi data products cadence = a.Sample(24*u.hour) # querying cadence start_date = '2014-01-20T09:43:24.70' # start date of query end_date = '2014-01-20T09:44:24.70' physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] result = Fido.search(a.Time(start_date, end_date), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2], cadence) # print results print(result) Download data files, sort, and build map list. In [ ]: Copied! # download results file_download = Fido . fetch ( result ) # convert to map sequence map_seq = sunpy . map . Map ( sorted ( file_download )) # download results file_download = Fido.fetch(result) # convert to map sequence map_seq = sunpy.map.Map(sorted(file_download)) Create map lists based on HMI content. In [ ]: Copied! hmi_vel , hmi_mag , hmi_int \\ = [], [], [] for i , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : hmi_vel . append ( map_obj . rotate ( order = 3 )) elif map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : hmi_mag . append ( map_obj . rotate ( order = 3 )) elif map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : hmi_int . append ( map_obj . rotate ( order = 3 )) hmi_vel, hmi_mag, hmi_int \\ = [], [], [] for i, map_obj in enumerate(map_seq): if map_obj.meta['content'] == 'DOPPLERGRAM': hmi_vel.append(map_obj.rotate(order=3)) elif map_obj.meta['content'] == 'MAGNETOGRAM': hmi_mag.append(map_obj.rotate(order=3)) elif map_obj.meta['content'] == 'CONTINUUM INTENSITY': hmi_int.append(map_obj.rotate(order=3)) Step One: Coordinate Transformation \u00b6 Apply coordinate transformations for HMI data products where: x, y, pd, r, d, mu = coord[i][0], coord[i][1], coord[i][2], coord[i][3], coord[i][4], coord[i][5] wij, nij, rij = vel_coords[i][0], vel_coords[i][1], vel_coords[i][2] In [ ]: Copied! # get cartesian scales from map coord = [ ctfuncs . coordinates ( hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] # get mu and radius array vel_coords = [ ctfuncs . vel_coords ( coord [ 0 ], coord [ 1 ], coord [ 2 ], coord [ 3 ], hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] # get cartesian scales from map coord = [ctfuncs.coordinates(hmi_vel[i]) for i in range(0, len(hmi_vel))] # get mu and radius array vel_coords = [ctfuncs.vel_coords(coord[0], coord[1], coord[2], coord[3], hmi_vel[i]) for i in range(0, len(hmi_vel))] Remove bad mu values from each map. hmi_vel, hmi_mag, imap = maps[i][0], maps[i][1], maps[i][2] In [ ]: Copied! # fix bad mu values maps = [ ctfuncs . fix_mu ( coord [ i ][ 5 ], [ hmi_vel [ i ], hmi_mag [ i ], hmi_int [ i ]]) for i in range ( 0 , len ( hmi_vel ))] # return maps to original lists hmi_vel = [ maps [ i ][ 0 ] for i in range ( 0 , len ( hmi_vel ))] hmi_mag = [ maps [ i ][ 1 ] for i in range ( 0 , len ( hmi_vel ))] hmi_int = [ maps [ i ][ 2 ] for i in range ( 0 , len ( hmi_vel ))] # fix bad mu values maps = [ctfuncs.fix_mu(coord[i][5], [hmi_vel[i], hmi_mag[i], hmi_int[i]]) for i in range(0, len(hmi_vel))] # return maps to original lists hmi_vel = [maps[i][0] for i in range(0, len(hmi_vel))] hmi_mag = [maps[i][1] for i in range(0, len(hmi_vel))] hmi_int = [maps[i][2] for i in range(0, len(hmi_vel))] Step Two: Calculate Spacecraft Velocity \u00b6 Calculate relative positions. deltaw, deltan, deltar, dij = delta[i][0], delta[i][1], delta[i][2], delta[i][3] In [ ]: Copied! delta = [ sfuncs . rel_positions ( vel_coords [ i ][ 0 ], vel_coords [ i ][ 1 ], vel_coords [ i ][ 2 ], hmi_vel [ i ])] delta = [sfuncs.rel_positions(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], hmi_vel[i])] Calculate spacecraft velocity. In [ ]: Copied! vsc = [ sfuncs . spacecraft_vel ( delta [ i ][ 0 ], delta [ i ][ 1 ], delta [ i ][ 2 ], delta [ i ][ 3 ], hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] vsc = [sfuncs.spacecraft_vel(delta[i][0], delta[i][1], delta[i][2], delta[i][3], hmi_vel[i]) for i in range(0, len(hmi_vel))] Step Three: Calculate Velocity due to Solar Rotation \u00b6 Optimized parameters for calculation of solar rotation. In [ ]: Copied! a1 = 14.713 a2 = - 2.396 a3 = - 1.787 a_parameters = [ a1 , a2 , a3 ] a1 = 14.713 a2 = -2.396 a3 = -1.787 a_parameters = [a1, a2, a3] Calculation of solar rotation velocity. In [ ]: Copied! vrot = [ sfuncs . solar_rot_vel ( vel_coords [ i ][ 0 ], vel_coords [ i ][ 1 ], vel_coords [ i ][ 2 ], delta [ i ][ 0 ], delta [ i ][ 1 ], delta [ i ][ 2 ], delta [ i ][ 3 ], hmi_vel [ i ], a_parameters ) for i in range ( 0 , len ( hmi_vel ))] vrot = [sfuncs.solar_rot_vel(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], delta[i][0], delta[i][1], delta[i][2], delta[i][3], hmi_vel[i], a_parameters) for i in range(0, len(hmi_vel))] Calculate corrected velocity. In [ ]: Copied! # subtract vsc and vrot from the Doppler images corrected_vel = [ hmi_vel [ i ] . data - np . real ( vsc [ i ]) - np . real ( vrot [ i ]) for i in range ( 0 , len ( hmi_vel ))] # subtract vsc and vrot from the Doppler images corrected_vel = [hmi_vel[i].data - np.real(vsc[i]) - np.real(vrot[i]) for i in range(0, len(hmi_vel))] Create list of Sunpy map objects with corrected velocity data. In [ ]: Copied! map_vel_cor = [ sfuncs . corrected_map ( corrected_vel [ i ], hmi_vel [ i ], map_type = 'Corrected-Dopplergram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_vel ))] map_vel_cor = [sfuncs.corrected_map(corrected_vel[i], hmi_vel[i], map_type='Corrected-Dopplergram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_vel))] Compare original and corrected velocity maps. In [ ]: Copied! # plot original map hmi_vel [ 0 ] . peek () # plot corrected map map_vel_cor [ 0 ] . peek () # plot original map hmi_vel[0].peek() # plot corrected map map_vel_cor[0].peek() Step Four: Limb Darkening Correction \u00b6 Equation for limb-brightening: $ L_{ij} = 1 - u_2 - v_2 + u_2 cos \\theta + v_2 cos^2 \\theta $ Coefficient values from Astrophysical Quantities by Allen (1973). Calculate correction polynomial. In [ ]: Copied! Lij = [ lbfuncs . limb_polynomial ( hmi_int [ i ]) for i in range ( 0 , len ( hmi_int ))] Lij = [lbfuncs.limb_polynomial(hmi_int[i]) for i in range(0, len(hmi_int))] Calculate corrected intensity. In [ ]: Copied! Iflat = [ hmi_int [ i ] . data / Lij [ i ] for i in range ( 0 , len ( hmi_int ))] Iflat = [hmi_int[i].data/Lij[i] for i in range(0, len(hmi_int))] Create list of Sunpy map objects with corrected intensity data. In [ ]: Copied! map_int_cor = [ sfuncs . corrected_map ( Iflat [ i ], hmi_int [ i ], map_type = 'Corrected-Intensitygram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] map_int_cor = [sfuncs.corrected_map(Iflat[i], hmi_int[i], map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] Compare original and corrected intensity maps. In [ ]: Copied! # plot original map hmi_int [ 0 ] . peek () # plot corrected map map_int_cor [ 0 ] . peek () # plot original map hmi_int[0].peek() # plot corrected map map_int_cor[0].peek() In [ ]: Copied! int_map = hmi_int [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () plt . show () int_map = map_int_cor [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () plt . show () int_map = hmi_int[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() plt.show() int_map = map_int_cor[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() plt.show() Step Five: Unsigned Magnetic Field Strength \u00b6 We calculate radial unsigned magnetic field strength and set pixels with an observed magnetic field below the noise value equal to zero. The noise level used is from Yeo et al. 2013, and is 8 Gauss. Bobs, Br = corrected_mag[0], corrected_mag[1] In [ ]: Copied! # magnetic noise level B_noise = 8 # calculate unsigned field strength corrected_mag = [ sfuncs . mag_field ( coord [ i ][ 5 ], hmi_mag [ i ], B_noise ) for i in range ( 0 , len ( hmi_mag ))] # magnetic noise level B_noise = 8 # calculate unsigned field strength corrected_mag = [sfuncs.mag_field(coord[i][5], hmi_mag[i], B_noise) for i in range(0, len(hmi_mag))] Create list of Sunpy map objects with corrected observed and radial magnetic data. In [ ]: Copied! # observed magnetic data map_mag_obs = [ sfuncs . corrected_map ( corrected_mag [ i ][ 0 ], hmi_mag [ i ], map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_mag ))] # radial magnetic data map_mag_cor = [ sfuncs . corrected_map ( corrected_mag [ i ][ 1 ], hmi_mag [ i ], map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_mag ))] # observed magnetic data map_mag_obs = [sfuncs.corrected_map(corrected_mag[i][0], hmi_mag[i], map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))] # radial magnetic data map_mag_cor = [sfuncs.corrected_map(corrected_mag[i][1], hmi_mag[i], map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))] Compare original and corrected magnetic maps. In [ ]: Copied! # plot original map hmi_mag [ 0 ] . peek () # plot corrected map map_mag_cor [ 0 ] . peek () # plot original map hmi_mag[0].peek() # plot corrected map map_mag_cor[0].peek() In [ ]: Copied! mag_map = hmi_mag [ 0 ] mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . show () mag_map = map_mag_cor [ 0 ] mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . show () mag_map = hmi_mag[0] mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.show() mag_map = map_mag_cor[0] mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.show() Step Six: Identification of Solar Regions \u00b6 Part One: Magnetic Thresholding \u00b6 Differentiate between active Sun and quiet Sun. Magnetic thresholding method used from Yeo et al. 2013 where $ B_{r, thresh} = 24G / \\mu $ . Magnetic threshold and mu cutoff values. Automatically throw everything where $ \\mu < 0.1 $ out, for magnetic thresholding we also remove $ \\mu < 0.3 $ due to correction for radial magnetic field strength. In [ ]: Copied! # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.35 # area cutoff value (pixels) area_cutoff = 45 # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.35 # area cutoff value (pixels) area_cutoff = 45 For weighting, we return an active weights array (active = 1) and quiet weights array (active = 0). Get threshold maps where: active, quiet = mag_thresh[i][0], mag_thresh[i][1] In [ ]: Copied! mag_thresh = [ sfuncs . mag_thresh ( coord [ i ][ 5 ], hmi_mag [ i ], Br_cutoff = Br_cutoff , mu_cutoff = mu_cutoff ) for i in range ( 0 , len ( hmi_mag ))] mag_thresh = [sfuncs.mag_thresh(coord[i][5], hmi_mag[i], Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) for i in range(0, len(hmi_mag))] Create list of Sunpy map objects with thresholded magnetic data. In [ ]: Copied! map_mag_thresh = [ sfuncs . corrected_map ( mag_thresh [ i ][ 0 ], hmi_mag [ i ], map_type = 'Magnetic-Threshold' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_mag ))] map_mag_thresh = [sfuncs.corrected_map(mag_thresh[i][0], hmi_mag[i], map_type='Magnetic-Threshold', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))] Compare original and corrected threshold maps. In [ ]: Copied! # plot corrected magnetic map map_mag_cor [ 0 ] . peek () # plot corrected magnetic threshold map map_mag_thresh [ 0 ] . peek () # plot corrected magnetic map map_mag_cor[0].peek() # plot corrected magnetic threshold map map_mag_thresh[0].peek() Part Two: Intensity Thresholding \u00b6 Differentiate between sunspots (dark) and faculae (bright). Using method of Yeo et al. 2013 we calculate the intensity threshold such that $ I_{thresh} = 0.89I_{quiet} $ where $ I_{quiet} = \\frac{\\sum_{ij} I_{flat, ij} W_{ij}}{\\sum_{ij} W_{ij}} $ is the mean pixel flattened intensity over quiet Sun regions. Calculate intensity threshold value and differentiate between sunspots and faculae where: fac_inds, spot_inds = int_thresh[0], int_thresh[1] In [ ]: Copied! int_thresh = [ sfuncs . int_thresh ( map_int_cor [ i ], mag_thresh [ i ][ 0 ], mag_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_int ))] int_thresh = [sfuncs.int_thresh(map_int_cor[i], mag_thresh[i][0], mag_thresh[i][1]) for i in range(0, len(hmi_int))] Create list of Sunpy map objects with faculae and sunspot data. In [ ]: Copied! # create faculae map objects fac_map = [ sfuncs . corrected_map ( int_thresh [ i ][ 0 ], hmi_mag [ i ], map_type = 'Faculae' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] # create sunspot map objects spot_map = [ sfuncs . corrected_map ( int_thresh [ i ][ 1 ], hmi_mag [ i ], map_type = 'Sunspot' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] # create faculae map objects fac_map = [sfuncs.corrected_map(int_thresh[i][0], hmi_mag[i], map_type='Faculae', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] # create sunspot map objects spot_map = [sfuncs.corrected_map(int_thresh[i][1], hmi_mag[i], map_type='Sunspot', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] Compare original and corrected threshold maps. In [ ]: Copied! # plot faculae map fac_map [ 0 ] . peek () # plot sunspot map spot_map [ 0 ] . peek () # plot faculae map fac_map[0].peek() # plot sunspot map spot_map[0].peek() Create array that combines sunspot (dark: 2) and faculae (bright: 1) regions. In [ ]: Copied! thresh_arr = [ sfuncs . thresh_map ( int_thresh [ i ][ 0 ], int_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_mag ))] thresh_arr = [sfuncs.thresh_map(int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_mag))] Create list of Sunpy map objects with threshold data. In [ ]: Copied! # create threshold map objects map_full_thresh = [ sfuncs . corrected_map ( thresh_arr [ i ], hmi_mag [ i ], map_type = 'Threshold' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] # create threshold map objects map_full_thresh = [sfuncs.corrected_map(thresh_arr[i], hmi_mag[i], map_type='Threshold', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] Compare corrected magnetic and threshold maps For these threshold maps, we currently only see sunspots detected on the solar limb. The solar limb is inherently darker (limb darkening) and since we have yet to correct for this it is expected that the overall intensity threshold is lower and thus the only values below this threshold are on the limb. The Sun is also not currently super magnetically active (solar minima) in the time I plotted so this makes sense that there are not many sunspots. Could also adjust the $ \\mu $ cutoff value to remove these sunspots, but I think limb brightening will fix it. In [ ]: Copied! mag_map = map_mag_cor [ 0 ] mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . imshow ( map_full_thresh [ 0 ] . data , cmap = plt . get_cmap ( 'bwr' )) plt . title ( \"Comparison of faculae (blue) and sunspots (red) to magnetic data\" ) mag_map = map_mag_cor[0] mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.imshow(map_full_thresh[0].data, cmap=plt.get_cmap('bwr')) plt.title(\"Comparison of faculae (blue) and sunspots (red) to magnetic data\") In [ ]: Copied! int_map = map_int_cor [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () plt . imshow ( map_full_thresh [ 0 ] . data , cmap = plt . get_cmap ( 'bwr' )) plt . title ( \"Comparison of faculae (blue) and sunspots (red) to intensity data\" ) int_map = map_int_cor[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() plt.imshow(map_full_thresh[0].data, cmap=plt.get_cmap('bwr')) plt.title(\"Comparison of faculae (blue) and sunspots (red) to intensity data\") In [ ]: Copied! int_map = map_int_cor [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () spots = np . where ( map_full_thresh [ 0 ] . data == 2 , 1 , np . nan ) plt . imshow ( spots , cmap = 'Blues' ) plt . title ( \"Comparison of sunspots to intensity data\" ) int_map = map_int_cor[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() spots = np.where(map_full_thresh[0].data == 2, 1, np.nan) plt.imshow(spots, cmap='Blues') plt.title(\"Comparison of sunspots to intensity data\") Outline of Calculations of Solar RVs \u00b6 quiet-Sun velocity: estimation of average RV of quiet-Sun due to convective motion photometric velocity: estimation of rotational Doppler imbalance due to active (faculae/darkspot) regions convective blueshift: convective velocity estimation due to the suppression of the convective blueshift by magnetically active regions Step One: Convective Motion of quiet-Sun \u00b6 Calculate the velocity due to the convective motion of the quiet-Sun. Requires the magnetic weighting array we built in step six, part one. A weight of 0 denotes an active pixel, and 1 is quiet-Sun. The equation for calculating this quiet-Sun velocity is as such: $ v_{quiet} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij} W_{ij}} {\\sum_{ij} I_{ij} W_{ij}} $. In this case $ I_{ij} $ is the uncorrected (original) intensity data. In [ ]: Copied! v_quiet = [ sfuncs . v_quiet ( map_vel_cor [ i ], hmi_int [ i ], mag_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_mag ))] v_quiet = [sfuncs.v_quiet(map_vel_cor[i], hmi_int[i], mag_thresh[i][1]) for i in range(0, len(hmi_mag))] Step Two: Photometric Velocity due to rotational Doppler imbalance \u00b6 Calculation of the photometric velocity due the presence of dark spots/bright faculae causing a Doppler imbalance. Requires magnetic weighting array that is flipped from the weighting array previously used. Now, a weight of 0 denotes quiet-Sun and 1 denotes active pixel. The equation for this velocity calculation is: $ v_{phot} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{rot, ij})(I_{ij} - K) W_{ij}} {\\sum_{ij} I_{ij}} $. K is a scaling factor based off the limb-brightening correction carried out in Step Four of the solar corrections. $ K = \\frac{\\sum_{ij} I_{ij} L_{ij} W_{ij}}{\\sum_{ij} L_{ij}^2 W_{ij}} $ In this case $ I_{ij} $ is the uncorrected (original) intensity data. Calculate photometric velocity. v_phot, vphot_bright, vphot_spot = vphot[0], vphot[1], vphot[2] In [ ]: Copied! v_phot = [ sfuncs . v_phot ( mag_thresh [ i ][ 1 ], mag_thresh [ i ][ 0 ], Lij [ i ], vrot [ i ], hmi_int [ i ], coord [ i ][ 5 ], int_thresh [ i ][ 0 ], int_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_int ))] v_phot = [sfuncs.v_phot(mag_thresh[i][1], mag_thresh[i][0], Lij[i], vrot[i], hmi_int[i], coord[i][5], int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_int))] Step Three: Convective Blueshift Velocity \u00b6 Calculation of suppression of convective blueshift due to active regions (mainly faculae). The basic premise for calculation is to calculate the disc-averaged velocity of the Sun and subtract from that the quiet-Sun velocity (Step One) and photometric velocity (Step Two) both of which were calculated previously. Maybe a better way to do this ... The equation for the disc-averaged velocity is: $ v = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij}}{\\sum_{ij} I_{ij}} $ We then calculate the convective velocity as what is left: $ v_{conv} = v - v_{quiet} $ Calculate disc-averaged velocity of Sun. In [ ]: Copied! v_disc = [ sfuncs . v_disc ( map_vel_cor [ i ], hmi_int [ i ]) for i in range ( 0 , len ( hmi_int ))] v_disc = [sfuncs.v_disc(map_vel_cor[i], hmi_int[i]) for i in range(0, len(hmi_int))] Calculate convective velocity. In [ ]: Copied! v_conv = [ v_disc [ i ] - v_quiet [ i ] for i in range ( 0 , len ( hmi_mag ))] v_conv = [v_disc[i] - v_quiet[i] for i in range(0, len(hmi_mag))] Magnetic Observables \u00b6 Calculation of relevant magnetic observables. filling factor: disc averaged filling factors of sunspots and plage which gives the percentage of magnetically active pixels unsigned magnetic flux: calculation of disc-averaged, line-of-sight unsigned magnetic flux Filling Factor \u00b6 Estimate the disc-averaged filling factors of sunspots and plage: $ f_{spot, plage} = \\frac{1}{N_{pix}} \\sum_{ij} W_{ij} $ In this case, a weight of 1 is an active pixel (active_weights). f_bright, f_spot, f = filling[0], filling[1], filling[2] In [ ]: Copied! filling = [ sfuncs . filling_factor ( coord [ i ][ 5 ], hmi_mag [ i ], mag_thresh [ i ][ 0 ], int_thresh [ i ][ 0 ], int_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_mag ))] filling = [sfuncs.filling_factor(coord[i][5], hmi_mag[i], mag_thresh[i][0], int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_mag))] Unsigned Magnetic Flux \u00b6 Calculate disc-averaged, line-of-sight unsigned (unpolarized) magnetic flux of the Sun. Calculation of observed unsigned flux. $ |\\hat{B_{obs}}| = \\frac{\\sum_{ij} |B_{obs, ij}| I_{ij}} {\\sum_{ij} I_{ij}}$ In [ ]: Copied! unsigned_obs_flux = [ sfuncs . unsigned_flux ( map_mag_obs [ i ], hmi_int [ i ]) for i in range ( 0 , len ( hmi_mag ))] unsigned_obs_flux = [sfuncs.unsigned_flux(map_mag_obs[i], hmi_int[i]) for i in range(0, len(hmi_mag))] Area Based Observable Calculations \u00b6 Calculate intensity weighted area of each active region. In [ ]: Copied! pixA_hem = [ ctfuncs . pix_area_hem ( vel_coords [ i ][ 0 ], vel_coords [ i ][ 1 ], vel_coords [ i ][ 2 ], hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] area = [ sfuncs . area_calc ( mag_thresh [ i ][ 0 ], pixA_hem [ i ]) for i in range ( 0 , len ( hmi_vel ))] pixA_hem = [ctfuncs.pix_area_hem(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], hmi_vel[i]) for i in range(0, len(hmi_vel))] area = [sfuncs.area_calc(mag_thresh[i][0], pixA_hem[i]) for i in range(0, len(hmi_vel))] Calculate the area filling factor. f_small, f_large, f_network, f_plage, f_nonconv = f_area[0], f_area[1], f_area[2], f_area[3], f_area[4] In [ ]: Copied! f_area = [ sfuncs . area_filling_factor ( mag_thresh [ i ][ 0 ], area [ i ], coord [ i ][ 5 ], hmi_mag [ i ], int_thresh [ i ][ 0 ]) for i in range ( 0 , len ( hmi_mag ))] f_area = [sfuncs.area_filling_factor(mag_thresh[i][0], area[i], coord[i][5], hmi_mag[i], int_thresh[i][0]) for i in range(0, len(hmi_mag))] Calculate unsigned flux of various polarized and active regions. quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = flux[0], flux[1], flux[2], flux[3], flux[4], flux[5] In [ ]: Copied! quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux = [ sfuncs . area_unsigned_flux ( map_mag_obs [ i ], hmi_int [ i ], area [ i ], int_thresh [ i ][ 0 ]) for i in range ( 0 , len ( hmi_int ))] quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = [sfuncs.area_unsigned_flux(map_mag_obs[i], hmi_int[i], area[i], int_thresh[i][0]) for i in range(0, len(hmi_int))] Get area weighted convective velocities. vconv_quiet, vconv_large, vconv_small = vconv_area[0], vconv_area[1], vconv_area[2] In [ ]: Copied! vconv_area = [ sfuncs . area_vconv ( map_vel_cor [ i ], hmi_int [ i ], int_thresh [ i ][ 0 ], area [ i ]) for i in range ( 0 , len ( hmi_int ))] vconv_area = [sfuncs.area_vconv(map_vel_cor[i], hmi_int[i], int_thresh[i][0], area[i]) for i in range(0, len(hmi_int))]","title":"Component Calculations"},{"location":"sdo/examples/docs_solar_corrections/#outline-of-corrections-to-sdohmi-images","text":"coordinate transformation: transform from Helioprojective Cartesian coordinates to Heliographic Carrington coordinates based on observer spacecraft velocity calculation: correct for the velocity of spacecraft relative to Sun solar rotation calculation: account for velocity due to solar rotation as a function of latitude (based on Snodgrass & Ulrich 1990) limb darkening correction: correct for limb darkening in HMI intensity images unsigned magnetic field strength: calculate unsigned magnetic field strength by dividing observed field strength by $ \\mu = cos(\\theta)$ identify solar regions: differentiate between quiet Sun, sunspots, faculae/plage regions In [ ]: Copied! import numpy as np import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a from sunpy.coordinates import frames import matplotlib.pyplot as plt from scipy import stats import tamar.tools.solar_funcs as sfuncs import tamar.tools.lbc_funcs as lbfuncs import tamar.tools.coord_funcs as ctfuncs import numpy as np import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a from sunpy.coordinates import frames import matplotlib.pyplot as plt from scipy import stats import tamar.tools.solar_funcs as sfuncs import tamar.tools.lbc_funcs as lbfuncs import tamar.tools.coord_funcs as ctfuncs Get HMI data. In [ ]: Copied! # get hmi data products cadence = a . Sample ( 24 * u . hour ) # querying cadence start_date = '2014-01-20T09:43:24.70' # start date of query end_date = '2014-01-20T09:44:24.70' physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] result = Fido . search ( a . Time ( start_date , end_date ), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ], cadence ) # print results print ( result ) # get hmi data products cadence = a.Sample(24*u.hour) # querying cadence start_date = '2014-01-20T09:43:24.70' # start date of query end_date = '2014-01-20T09:44:24.70' physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] result = Fido.search(a.Time(start_date, end_date), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2], cadence) # print results print(result) Download data files, sort, and build map list. In [ ]: Copied! # download results file_download = Fido . fetch ( result ) # convert to map sequence map_seq = sunpy . map . Map ( sorted ( file_download )) # download results file_download = Fido.fetch(result) # convert to map sequence map_seq = sunpy.map.Map(sorted(file_download)) Create map lists based on HMI content. In [ ]: Copied! hmi_vel , hmi_mag , hmi_int \\ = [], [], [] for i , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : hmi_vel . append ( map_obj . rotate ( order = 3 )) elif map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : hmi_mag . append ( map_obj . rotate ( order = 3 )) elif map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : hmi_int . append ( map_obj . rotate ( order = 3 )) hmi_vel, hmi_mag, hmi_int \\ = [], [], [] for i, map_obj in enumerate(map_seq): if map_obj.meta['content'] == 'DOPPLERGRAM': hmi_vel.append(map_obj.rotate(order=3)) elif map_obj.meta['content'] == 'MAGNETOGRAM': hmi_mag.append(map_obj.rotate(order=3)) elif map_obj.meta['content'] == 'CONTINUUM INTENSITY': hmi_int.append(map_obj.rotate(order=3))","title":"Outline of corrections to SDO/HMI Images"},{"location":"sdo/examples/docs_solar_corrections/#step-one-coordinate-transformation","text":"Apply coordinate transformations for HMI data products where: x, y, pd, r, d, mu = coord[i][0], coord[i][1], coord[i][2], coord[i][3], coord[i][4], coord[i][5] wij, nij, rij = vel_coords[i][0], vel_coords[i][1], vel_coords[i][2] In [ ]: Copied! # get cartesian scales from map coord = [ ctfuncs . coordinates ( hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] # get mu and radius array vel_coords = [ ctfuncs . vel_coords ( coord [ 0 ], coord [ 1 ], coord [ 2 ], coord [ 3 ], hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] # get cartesian scales from map coord = [ctfuncs.coordinates(hmi_vel[i]) for i in range(0, len(hmi_vel))] # get mu and radius array vel_coords = [ctfuncs.vel_coords(coord[0], coord[1], coord[2], coord[3], hmi_vel[i]) for i in range(0, len(hmi_vel))] Remove bad mu values from each map. hmi_vel, hmi_mag, imap = maps[i][0], maps[i][1], maps[i][2] In [ ]: Copied! # fix bad mu values maps = [ ctfuncs . fix_mu ( coord [ i ][ 5 ], [ hmi_vel [ i ], hmi_mag [ i ], hmi_int [ i ]]) for i in range ( 0 , len ( hmi_vel ))] # return maps to original lists hmi_vel = [ maps [ i ][ 0 ] for i in range ( 0 , len ( hmi_vel ))] hmi_mag = [ maps [ i ][ 1 ] for i in range ( 0 , len ( hmi_vel ))] hmi_int = [ maps [ i ][ 2 ] for i in range ( 0 , len ( hmi_vel ))] # fix bad mu values maps = [ctfuncs.fix_mu(coord[i][5], [hmi_vel[i], hmi_mag[i], hmi_int[i]]) for i in range(0, len(hmi_vel))] # return maps to original lists hmi_vel = [maps[i][0] for i in range(0, len(hmi_vel))] hmi_mag = [maps[i][1] for i in range(0, len(hmi_vel))] hmi_int = [maps[i][2] for i in range(0, len(hmi_vel))]","title":"Step One: Coordinate Transformation"},{"location":"sdo/examples/docs_solar_corrections/#step-two-calculate-spacecraft-velocity","text":"Calculate relative positions. deltaw, deltan, deltar, dij = delta[i][0], delta[i][1], delta[i][2], delta[i][3] In [ ]: Copied! delta = [ sfuncs . rel_positions ( vel_coords [ i ][ 0 ], vel_coords [ i ][ 1 ], vel_coords [ i ][ 2 ], hmi_vel [ i ])] delta = [sfuncs.rel_positions(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], hmi_vel[i])] Calculate spacecraft velocity. In [ ]: Copied! vsc = [ sfuncs . spacecraft_vel ( delta [ i ][ 0 ], delta [ i ][ 1 ], delta [ i ][ 2 ], delta [ i ][ 3 ], hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] vsc = [sfuncs.spacecraft_vel(delta[i][0], delta[i][1], delta[i][2], delta[i][3], hmi_vel[i]) for i in range(0, len(hmi_vel))]","title":"Step Two: Calculate Spacecraft Velocity"},{"location":"sdo/examples/docs_solar_corrections/#step-three-calculate-velocity-due-to-solar-rotation","text":"Optimized parameters for calculation of solar rotation. In [ ]: Copied! a1 = 14.713 a2 = - 2.396 a3 = - 1.787 a_parameters = [ a1 , a2 , a3 ] a1 = 14.713 a2 = -2.396 a3 = -1.787 a_parameters = [a1, a2, a3] Calculation of solar rotation velocity. In [ ]: Copied! vrot = [ sfuncs . solar_rot_vel ( vel_coords [ i ][ 0 ], vel_coords [ i ][ 1 ], vel_coords [ i ][ 2 ], delta [ i ][ 0 ], delta [ i ][ 1 ], delta [ i ][ 2 ], delta [ i ][ 3 ], hmi_vel [ i ], a_parameters ) for i in range ( 0 , len ( hmi_vel ))] vrot = [sfuncs.solar_rot_vel(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], delta[i][0], delta[i][1], delta[i][2], delta[i][3], hmi_vel[i], a_parameters) for i in range(0, len(hmi_vel))] Calculate corrected velocity. In [ ]: Copied! # subtract vsc and vrot from the Doppler images corrected_vel = [ hmi_vel [ i ] . data - np . real ( vsc [ i ]) - np . real ( vrot [ i ]) for i in range ( 0 , len ( hmi_vel ))] # subtract vsc and vrot from the Doppler images corrected_vel = [hmi_vel[i].data - np.real(vsc[i]) - np.real(vrot[i]) for i in range(0, len(hmi_vel))] Create list of Sunpy map objects with corrected velocity data. In [ ]: Copied! map_vel_cor = [ sfuncs . corrected_map ( corrected_vel [ i ], hmi_vel [ i ], map_type = 'Corrected-Dopplergram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_vel ))] map_vel_cor = [sfuncs.corrected_map(corrected_vel[i], hmi_vel[i], map_type='Corrected-Dopplergram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_vel))] Compare original and corrected velocity maps. In [ ]: Copied! # plot original map hmi_vel [ 0 ] . peek () # plot corrected map map_vel_cor [ 0 ] . peek () # plot original map hmi_vel[0].peek() # plot corrected map map_vel_cor[0].peek()","title":"Step Three: Calculate Velocity due to Solar Rotation"},{"location":"sdo/examples/docs_solar_corrections/#step-four-limb-darkening-correction","text":"Equation for limb-brightening: $ L_{ij} = 1 - u_2 - v_2 + u_2 cos \\theta + v_2 cos^2 \\theta $ Coefficient values from Astrophysical Quantities by Allen (1973). Calculate correction polynomial. In [ ]: Copied! Lij = [ lbfuncs . limb_polynomial ( hmi_int [ i ]) for i in range ( 0 , len ( hmi_int ))] Lij = [lbfuncs.limb_polynomial(hmi_int[i]) for i in range(0, len(hmi_int))] Calculate corrected intensity. In [ ]: Copied! Iflat = [ hmi_int [ i ] . data / Lij [ i ] for i in range ( 0 , len ( hmi_int ))] Iflat = [hmi_int[i].data/Lij[i] for i in range(0, len(hmi_int))] Create list of Sunpy map objects with corrected intensity data. In [ ]: Copied! map_int_cor = [ sfuncs . corrected_map ( Iflat [ i ], hmi_int [ i ], map_type = 'Corrected-Intensitygram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] map_int_cor = [sfuncs.corrected_map(Iflat[i], hmi_int[i], map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] Compare original and corrected intensity maps. In [ ]: Copied! # plot original map hmi_int [ 0 ] . peek () # plot corrected map map_int_cor [ 0 ] . peek () # plot original map hmi_int[0].peek() # plot corrected map map_int_cor[0].peek() In [ ]: Copied! int_map = hmi_int [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () plt . show () int_map = map_int_cor [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () plt . show () int_map = hmi_int[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() plt.show() int_map = map_int_cor[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() plt.show()","title":"Step Four: Limb Darkening Correction"},{"location":"sdo/examples/docs_solar_corrections/#step-five-unsigned-magnetic-field-strength","text":"We calculate radial unsigned magnetic field strength and set pixels with an observed magnetic field below the noise value equal to zero. The noise level used is from Yeo et al. 2013, and is 8 Gauss. Bobs, Br = corrected_mag[0], corrected_mag[1] In [ ]: Copied! # magnetic noise level B_noise = 8 # calculate unsigned field strength corrected_mag = [ sfuncs . mag_field ( coord [ i ][ 5 ], hmi_mag [ i ], B_noise ) for i in range ( 0 , len ( hmi_mag ))] # magnetic noise level B_noise = 8 # calculate unsigned field strength corrected_mag = [sfuncs.mag_field(coord[i][5], hmi_mag[i], B_noise) for i in range(0, len(hmi_mag))] Create list of Sunpy map objects with corrected observed and radial magnetic data. In [ ]: Copied! # observed magnetic data map_mag_obs = [ sfuncs . corrected_map ( corrected_mag [ i ][ 0 ], hmi_mag [ i ], map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_mag ))] # radial magnetic data map_mag_cor = [ sfuncs . corrected_map ( corrected_mag [ i ][ 1 ], hmi_mag [ i ], map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_mag ))] # observed magnetic data map_mag_obs = [sfuncs.corrected_map(corrected_mag[i][0], hmi_mag[i], map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))] # radial magnetic data map_mag_cor = [sfuncs.corrected_map(corrected_mag[i][1], hmi_mag[i], map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))] Compare original and corrected magnetic maps. In [ ]: Copied! # plot original map hmi_mag [ 0 ] . peek () # plot corrected map map_mag_cor [ 0 ] . peek () # plot original map hmi_mag[0].peek() # plot corrected map map_mag_cor[0].peek() In [ ]: Copied! mag_map = hmi_mag [ 0 ] mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . show () mag_map = map_mag_cor [ 0 ] mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . show () mag_map = hmi_mag[0] mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.show() mag_map = map_mag_cor[0] mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.show()","title":"Step Five: Unsigned Magnetic Field Strength"},{"location":"sdo/examples/docs_solar_corrections/#step-six-identification-of-solar-regions","text":"","title":"Step Six: Identification of Solar Regions"},{"location":"sdo/examples/docs_solar_corrections/#part-one-magnetic-thresholding","text":"Differentiate between active Sun and quiet Sun. Magnetic thresholding method used from Yeo et al. 2013 where $ B_{r, thresh} = 24G / \\mu $ . Magnetic threshold and mu cutoff values. Automatically throw everything where $ \\mu < 0.1 $ out, for magnetic thresholding we also remove $ \\mu < 0.3 $ due to correction for radial magnetic field strength. In [ ]: Copied! # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.35 # area cutoff value (pixels) area_cutoff = 45 # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.35 # area cutoff value (pixels) area_cutoff = 45 For weighting, we return an active weights array (active = 1) and quiet weights array (active = 0). Get threshold maps where: active, quiet = mag_thresh[i][0], mag_thresh[i][1] In [ ]: Copied! mag_thresh = [ sfuncs . mag_thresh ( coord [ i ][ 5 ], hmi_mag [ i ], Br_cutoff = Br_cutoff , mu_cutoff = mu_cutoff ) for i in range ( 0 , len ( hmi_mag ))] mag_thresh = [sfuncs.mag_thresh(coord[i][5], hmi_mag[i], Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) for i in range(0, len(hmi_mag))] Create list of Sunpy map objects with thresholded magnetic data. In [ ]: Copied! map_mag_thresh = [ sfuncs . corrected_map ( mag_thresh [ i ][ 0 ], hmi_mag [ i ], map_type = 'Magnetic-Threshold' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_mag ))] map_mag_thresh = [sfuncs.corrected_map(mag_thresh[i][0], hmi_mag[i], map_type='Magnetic-Threshold', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))] Compare original and corrected threshold maps. In [ ]: Copied! # plot corrected magnetic map map_mag_cor [ 0 ] . peek () # plot corrected magnetic threshold map map_mag_thresh [ 0 ] . peek () # plot corrected magnetic map map_mag_cor[0].peek() # plot corrected magnetic threshold map map_mag_thresh[0].peek()","title":"Part One: Magnetic Thresholding"},{"location":"sdo/examples/docs_solar_corrections/#part-two-intensity-thresholding","text":"Differentiate between sunspots (dark) and faculae (bright). Using method of Yeo et al. 2013 we calculate the intensity threshold such that $ I_{thresh} = 0.89I_{quiet} $ where $ I_{quiet} = \\frac{\\sum_{ij} I_{flat, ij} W_{ij}}{\\sum_{ij} W_{ij}} $ is the mean pixel flattened intensity over quiet Sun regions. Calculate intensity threshold value and differentiate between sunspots and faculae where: fac_inds, spot_inds = int_thresh[0], int_thresh[1] In [ ]: Copied! int_thresh = [ sfuncs . int_thresh ( map_int_cor [ i ], mag_thresh [ i ][ 0 ], mag_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_int ))] int_thresh = [sfuncs.int_thresh(map_int_cor[i], mag_thresh[i][0], mag_thresh[i][1]) for i in range(0, len(hmi_int))] Create list of Sunpy map objects with faculae and sunspot data. In [ ]: Copied! # create faculae map objects fac_map = [ sfuncs . corrected_map ( int_thresh [ i ][ 0 ], hmi_mag [ i ], map_type = 'Faculae' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] # create sunspot map objects spot_map = [ sfuncs . corrected_map ( int_thresh [ i ][ 1 ], hmi_mag [ i ], map_type = 'Sunspot' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] # create faculae map objects fac_map = [sfuncs.corrected_map(int_thresh[i][0], hmi_mag[i], map_type='Faculae', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] # create sunspot map objects spot_map = [sfuncs.corrected_map(int_thresh[i][1], hmi_mag[i], map_type='Sunspot', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] Compare original and corrected threshold maps. In [ ]: Copied! # plot faculae map fac_map [ 0 ] . peek () # plot sunspot map spot_map [ 0 ] . peek () # plot faculae map fac_map[0].peek() # plot sunspot map spot_map[0].peek() Create array that combines sunspot (dark: 2) and faculae (bright: 1) regions. In [ ]: Copied! thresh_arr = [ sfuncs . thresh_map ( int_thresh [ i ][ 0 ], int_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_mag ))] thresh_arr = [sfuncs.thresh_map(int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_mag))] Create list of Sunpy map objects with threshold data. In [ ]: Copied! # create threshold map objects map_full_thresh = [ sfuncs . corrected_map ( thresh_arr [ i ], hmi_mag [ i ], map_type = 'Threshold' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] # create threshold map objects map_full_thresh = [sfuncs.corrected_map(thresh_arr[i], hmi_mag[i], map_type='Threshold', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] Compare corrected magnetic and threshold maps For these threshold maps, we currently only see sunspots detected on the solar limb. The solar limb is inherently darker (limb darkening) and since we have yet to correct for this it is expected that the overall intensity threshold is lower and thus the only values below this threshold are on the limb. The Sun is also not currently super magnetically active (solar minima) in the time I plotted so this makes sense that there are not many sunspots. Could also adjust the $ \\mu $ cutoff value to remove these sunspots, but I think limb brightening will fix it. In [ ]: Copied! mag_map = map_mag_cor [ 0 ] mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . imshow ( map_full_thresh [ 0 ] . data , cmap = plt . get_cmap ( 'bwr' )) plt . title ( \"Comparison of faculae (blue) and sunspots (red) to magnetic data\" ) mag_map = map_mag_cor[0] mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.imshow(map_full_thresh[0].data, cmap=plt.get_cmap('bwr')) plt.title(\"Comparison of faculae (blue) and sunspots (red) to magnetic data\") In [ ]: Copied! int_map = map_int_cor [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () plt . imshow ( map_full_thresh [ 0 ] . data , cmap = plt . get_cmap ( 'bwr' )) plt . title ( \"Comparison of faculae (blue) and sunspots (red) to intensity data\" ) int_map = map_int_cor[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() plt.imshow(map_full_thresh[0].data, cmap=plt.get_cmap('bwr')) plt.title(\"Comparison of faculae (blue) and sunspots (red) to intensity data\") In [ ]: Copied! int_map = map_int_cor [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () spots = np . where ( map_full_thresh [ 0 ] . data == 2 , 1 , np . nan ) plt . imshow ( spots , cmap = 'Blues' ) plt . title ( \"Comparison of sunspots to intensity data\" ) int_map = map_int_cor[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() spots = np.where(map_full_thresh[0].data == 2, 1, np.nan) plt.imshow(spots, cmap='Blues') plt.title(\"Comparison of sunspots to intensity data\")","title":"Part Two: Intensity Thresholding"},{"location":"sdo/examples/docs_solar_corrections/#outline-of-calculations-of-solar-rvs","text":"quiet-Sun velocity: estimation of average RV of quiet-Sun due to convective motion photometric velocity: estimation of rotational Doppler imbalance due to active (faculae/darkspot) regions convective blueshift: convective velocity estimation due to the suppression of the convective blueshift by magnetically active regions","title":"Outline of Calculations of Solar RVs"},{"location":"sdo/examples/docs_solar_corrections/#step-one-convective-motion-of-quiet-sun","text":"Calculate the velocity due to the convective motion of the quiet-Sun. Requires the magnetic weighting array we built in step six, part one. A weight of 0 denotes an active pixel, and 1 is quiet-Sun. The equation for calculating this quiet-Sun velocity is as such: $ v_{quiet} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij} W_{ij}} {\\sum_{ij} I_{ij} W_{ij}} $. In this case $ I_{ij} $ is the uncorrected (original) intensity data. In [ ]: Copied! v_quiet = [ sfuncs . v_quiet ( map_vel_cor [ i ], hmi_int [ i ], mag_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_mag ))] v_quiet = [sfuncs.v_quiet(map_vel_cor[i], hmi_int[i], mag_thresh[i][1]) for i in range(0, len(hmi_mag))]","title":"Step One: Convective Motion of quiet-Sun"},{"location":"sdo/examples/docs_solar_corrections/#step-two-photometric-velocity-due-to-rotational-doppler-imbalance","text":"Calculation of the photometric velocity due the presence of dark spots/bright faculae causing a Doppler imbalance. Requires magnetic weighting array that is flipped from the weighting array previously used. Now, a weight of 0 denotes quiet-Sun and 1 denotes active pixel. The equation for this velocity calculation is: $ v_{phot} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{rot, ij})(I_{ij} - K) W_{ij}} {\\sum_{ij} I_{ij}} $. K is a scaling factor based off the limb-brightening correction carried out in Step Four of the solar corrections. $ K = \\frac{\\sum_{ij} I_{ij} L_{ij} W_{ij}}{\\sum_{ij} L_{ij}^2 W_{ij}} $ In this case $ I_{ij} $ is the uncorrected (original) intensity data. Calculate photometric velocity. v_phot, vphot_bright, vphot_spot = vphot[0], vphot[1], vphot[2] In [ ]: Copied! v_phot = [ sfuncs . v_phot ( mag_thresh [ i ][ 1 ], mag_thresh [ i ][ 0 ], Lij [ i ], vrot [ i ], hmi_int [ i ], coord [ i ][ 5 ], int_thresh [ i ][ 0 ], int_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_int ))] v_phot = [sfuncs.v_phot(mag_thresh[i][1], mag_thresh[i][0], Lij[i], vrot[i], hmi_int[i], coord[i][5], int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_int))]","title":"Step Two: Photometric Velocity due to rotational Doppler imbalance"},{"location":"sdo/examples/docs_solar_corrections/#step-three-convective-blueshift-velocity","text":"Calculation of suppression of convective blueshift due to active regions (mainly faculae). The basic premise for calculation is to calculate the disc-averaged velocity of the Sun and subtract from that the quiet-Sun velocity (Step One) and photometric velocity (Step Two) both of which were calculated previously. Maybe a better way to do this ... The equation for the disc-averaged velocity is: $ v = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij}}{\\sum_{ij} I_{ij}} $ We then calculate the convective velocity as what is left: $ v_{conv} = v - v_{quiet} $ Calculate disc-averaged velocity of Sun. In [ ]: Copied! v_disc = [ sfuncs . v_disc ( map_vel_cor [ i ], hmi_int [ i ]) for i in range ( 0 , len ( hmi_int ))] v_disc = [sfuncs.v_disc(map_vel_cor[i], hmi_int[i]) for i in range(0, len(hmi_int))] Calculate convective velocity. In [ ]: Copied! v_conv = [ v_disc [ i ] - v_quiet [ i ] for i in range ( 0 , len ( hmi_mag ))] v_conv = [v_disc[i] - v_quiet[i] for i in range(0, len(hmi_mag))]","title":"Step Three: Convective Blueshift Velocity"},{"location":"sdo/examples/docs_solar_corrections/#magnetic-observables","text":"Calculation of relevant magnetic observables. filling factor: disc averaged filling factors of sunspots and plage which gives the percentage of magnetically active pixels unsigned magnetic flux: calculation of disc-averaged, line-of-sight unsigned magnetic flux","title":"Magnetic Observables"},{"location":"sdo/examples/docs_solar_corrections/#filling-factor","text":"Estimate the disc-averaged filling factors of sunspots and plage: $ f_{spot, plage} = \\frac{1}{N_{pix}} \\sum_{ij} W_{ij} $ In this case, a weight of 1 is an active pixel (active_weights). f_bright, f_spot, f = filling[0], filling[1], filling[2] In [ ]: Copied! filling = [ sfuncs . filling_factor ( coord [ i ][ 5 ], hmi_mag [ i ], mag_thresh [ i ][ 0 ], int_thresh [ i ][ 0 ], int_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_mag ))] filling = [sfuncs.filling_factor(coord[i][5], hmi_mag[i], mag_thresh[i][0], int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_mag))]","title":"Filling Factor"},{"location":"sdo/examples/docs_solar_corrections/#unsigned-magnetic-flux","text":"Calculate disc-averaged, line-of-sight unsigned (unpolarized) magnetic flux of the Sun. Calculation of observed unsigned flux. $ |\\hat{B_{obs}}| = \\frac{\\sum_{ij} |B_{obs, ij}| I_{ij}} {\\sum_{ij} I_{ij}}$ In [ ]: Copied! unsigned_obs_flux = [ sfuncs . unsigned_flux ( map_mag_obs [ i ], hmi_int [ i ]) for i in range ( 0 , len ( hmi_mag ))] unsigned_obs_flux = [sfuncs.unsigned_flux(map_mag_obs[i], hmi_int[i]) for i in range(0, len(hmi_mag))]","title":"Unsigned Magnetic Flux"},{"location":"sdo/examples/docs_solar_corrections/#area-based-observable-calculations","text":"Calculate intensity weighted area of each active region. In [ ]: Copied! pixA_hem = [ ctfuncs . pix_area_hem ( vel_coords [ i ][ 0 ], vel_coords [ i ][ 1 ], vel_coords [ i ][ 2 ], hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] area = [ sfuncs . area_calc ( mag_thresh [ i ][ 0 ], pixA_hem [ i ]) for i in range ( 0 , len ( hmi_vel ))] pixA_hem = [ctfuncs.pix_area_hem(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], hmi_vel[i]) for i in range(0, len(hmi_vel))] area = [sfuncs.area_calc(mag_thresh[i][0], pixA_hem[i]) for i in range(0, len(hmi_vel))] Calculate the area filling factor. f_small, f_large, f_network, f_plage, f_nonconv = f_area[0], f_area[1], f_area[2], f_area[3], f_area[4] In [ ]: Copied! f_area = [ sfuncs . area_filling_factor ( mag_thresh [ i ][ 0 ], area [ i ], coord [ i ][ 5 ], hmi_mag [ i ], int_thresh [ i ][ 0 ]) for i in range ( 0 , len ( hmi_mag ))] f_area = [sfuncs.area_filling_factor(mag_thresh[i][0], area[i], coord[i][5], hmi_mag[i], int_thresh[i][0]) for i in range(0, len(hmi_mag))] Calculate unsigned flux of various polarized and active regions. quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = flux[0], flux[1], flux[2], flux[3], flux[4], flux[5] In [ ]: Copied! quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux = [ sfuncs . area_unsigned_flux ( map_mag_obs [ i ], hmi_int [ i ], area [ i ], int_thresh [ i ][ 0 ]) for i in range ( 0 , len ( hmi_int ))] quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = [sfuncs.area_unsigned_flux(map_mag_obs[i], hmi_int[i], area[i], int_thresh[i][0]) for i in range(0, len(hmi_int))] Get area weighted convective velocities. vconv_quiet, vconv_large, vconv_small = vconv_area[0], vconv_area[1], vconv_area[2] In [ ]: Copied! vconv_area = [ sfuncs . area_vconv ( map_vel_cor [ i ], hmi_int [ i ], int_thresh [ i ][ 0 ], area [ i ]) for i in range ( 0 , len ( hmi_int ))] vconv_area = [sfuncs.area_vconv(map_vel_cor[i], hmi_int[i], int_thresh[i][0], area[i]) for i in range(0, len(hmi_int))]","title":"Area Based Observable Calculations"},{"location":"sdo/examples/docs_sunpy_example/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, 'DejaVu Sans Mono', monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } }, \"HTML-CSS\": { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Examples using Sunpy for querying and plotting \u00b6 Querying data \u00b6 In [10]: Copied! import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation import matplotlib.colors as colors import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a import sunpy.visualization.colormaps as cm import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation import matplotlib.colors as colors import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a import sunpy.visualization.colormaps as cm To download the required data, we use sunpy.net.Fido , a downloader client, to query the Virtual Solar Observatory to acquire AIA and HMI data. The following is the data available: HMI Bands (A): 171 (gold), 193 (bronze), 304 (red), 211 (purple), 131 (teal), 335 (blue), 094 (green), 1600 (yellow/green), 1700 (pink) HMI Products: Magnetogram, Intensitygram, Dopplergram In [11]: Copied! # data parameters cadence = a . Sample ( 24 * u . hour ) # querying cadence start_date = '2021-1-1T12:00:00' # start date of query end_date = '2021-01-5T12:00:00' # end date of query # AIA specific parameters aia_inst = a . Instrument . aia # list of all available wavelengths for reference wavelength_list = [ a . Wavelength ( 94 * u . angstrom ), a . Wavelength ( 131 * u . angstrom ), a . Wavelength ( 171 * u . angstrom ), a . Wavelength ( 193 * u . angstrom ), a . Wavelength ( 211 * u . angstrom ), a . Wavelength ( 304 * u . angstrom ), a . Wavelength ( 335 * u . angstrom ), a . Wavelength ( 1600 * u . angstrom ), a . Wavelength ( 1700 * u . angstrom )] # HMI specific parameters hmi_inst = a . Instrument . hmi # list of physical observables available physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] # data parameters cadence = a.Sample(24*u.hour) # querying cadence start_date = '2021-1-1T12:00:00' # start date of query end_date = '2021-01-5T12:00:00' # end date of query # AIA specific parameters aia_inst = a.Instrument.aia # list of all available wavelengths for reference wavelength_list = [a.Wavelength(94 * u.angstrom), a.Wavelength(131 * u.angstrom), a.Wavelength(171 * u.angstrom), a.Wavelength(193 * u.angstrom), a.Wavelength(211 * u.angstrom), a.Wavelength(304 * u.angstrom), a.Wavelength(335 * u.angstrom), a.Wavelength(1600 * u.angstrom), a.Wavelength(1700 * u.angstrom)] # HMI specific parameters hmi_inst = a.Instrument.hmi # list of physical observables available physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] Now we can look at querying for specific information. We can look for results from multiple instruments, wavelengths, and physical observables at once. It is useful to search for results before download to ensure you don't download excessive amounts of data. Let's say we only want these specific results from AIA. We search for images at a 24 hour cadence between our start and end times at specified wavelengths. In [12]: Copied! aia_result = Fido . search ( a . Time ( start_date , end_date ), aia_inst , wavelength_list [ 0 ] | wavelength_list [ 2 ] | wavelength_list [ 3 ] | wavelength_list [ 6 ], cadence ) aia_result = Fido.search(a.Time(start_date, end_date), aia_inst, wavelength_list[0] | wavelength_list[2] | wavelength_list[3] | wavelength_list[6], cadence) We can do the same with HMI data products. In [13]: Copied! hmi_result = Fido . search ( a . Time ( start_date , end_date ), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ], cadence ) hmi_result = Fido.search(a.Time(start_date, end_date), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2], cadence) You can also query for both instruments simultaneously. In [14]: Copied! result = Fido . search ( a . Time ( start_date , end_date ), aia_inst | hmi_inst , wavelength_list [ 0 ] | wavelength_list [ 1 ] | wavelength_list [ 2 ] | wavelength_list [ 3 ] | wavelength_list [ 4 ] | wavelength_list [ 5 ] | wavelength_list [ 6 ] | wavelength_list [ 7 ] | wavelength_list [ 8 ] | physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ], cadence ) result = Fido.search(a.Time(start_date, end_date), aia_inst | hmi_inst, wavelength_list[0] | wavelength_list[1] | wavelength_list[2] | wavelength_list[3] | wavelength_list[4] | wavelength_list[5] | wavelength_list[6] | wavelength_list[7] | wavelength_list[8] | physobs_list[0] | physobs_list[1] | physobs_list[2], cadence) /Users/tervin/opt/anaconda3/envs/neid_data/lib/python3.7/site-packages/astropy/table/table.py:3197: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future. result = self.as_array() == other Next we download the actual data found from our search. In [15]: Copied! file_download = Fido . fetch ( result ) file_download = Fido.fetch(result) var element = document.getElementById('68bff78d-ed7f-4d68-a27f-649b6eaa0ce5'); {\"model_id\": \"562e80d0aa5b4548a33015eae2ee8169\", \"version_major\": 2, \"version_minor\": 0} After downloading our data, we want to sort it by date. We then build a map sequence of our data. In [16]: Copied! map_seq = sunpy . map . Map ( sorted ( file_download )) map_seq = sunpy.map.Map(sorted(file_download)) We then split our results by data product. In [17]: Copied! aia_094 , aia_131 , aia_171 , aia_193 , aia_211 , aia_304 , aia_335 , aia_1600 , aia_1700 , hmi_vel , hmi_mag , hmi_int \\ = [], [], [], [], [], [], [], [], [], [], [], [] for i , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'wavelnth' ] == 94 : aia_094 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 131 : aia_131 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 171 : aia_171 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 193 : aia_193 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 211 : aia_211 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 304 : aia_304 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 335 : aia_335 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 1600 : aia_1600 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 1700 : aia_1700 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 6173 and map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : hmi_vel . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 6173 and map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : hmi_mag . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 6173 and map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : hmi_int . append ( map_obj ) aia_094, aia_131, aia_171, aia_193, aia_211, aia_304, aia_335, aia_1600, aia_1700, hmi_vel, hmi_mag, hmi_int \\ = [], [], [], [], [], [], [], [], [], [], [], [] for i, map_obj in enumerate(map_seq): if map_obj.meta['wavelnth'] == 94: aia_094.append(map_obj) elif map_obj.meta['wavelnth'] == 131: aia_131.append(map_obj) elif map_obj.meta['wavelnth'] == 171: aia_171.append(map_obj) elif map_obj.meta['wavelnth'] == 193: aia_193.append(map_obj) elif map_obj.meta['wavelnth'] == 211: aia_211.append(map_obj) elif map_obj.meta['wavelnth'] == 304: aia_304.append(map_obj) elif map_obj.meta['wavelnth'] == 335: aia_335.append(map_obj) elif map_obj.meta['wavelnth'] == 1600: aia_1600.append(map_obj) elif map_obj.meta['wavelnth'] == 1700: aia_1700.append(map_obj) elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'DOPPLERGRAM': hmi_vel.append(map_obj) elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'MAGNETOGRAM': hmi_mag.append(map_obj) elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'CONTINUUM INTENSITY': hmi_int.append(map_obj) Plotting images \u00b6 You can plot individual images using inherent Sunpy features. In [19]: Copied! aia_211 [ 0 ] . peek () # this plots the aia image with a colorbar, axes labels, and title aia_211[0].peek() # this plots the aia image with a colorbar, axes labels, and title In [20]: Copied! aia_211 [ 0 ] . plot () # this just plots the data, allows you to customize labels, colormap, and title plt . show () aia_211[0].plot() # this just plots the data, allows you to customize labels, colormap, and title plt.show() Plotting HMI images requires us to rotate the image to match the orientation of AIA images. The same peek and plot methods work for HMI images. In [21]: Copied! hmi_vel_rot = hmi_vel [ 0 ] . rotate ( order = 3 ) hmi_vel_rot . peek () hmi_vel_rot = hmi_vel[0].rotate(order=3) hmi_vel_rot.peek() HMI images look nicer when customized a bit. In [22]: Copied! # plotting intensity map int_map = hmi_int [ 0 ] . rotate ( order = 3 ) int_map . plot_settings [ 'cmap' ] = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot () plt . show () # plotting intensity map int_map = hmi_int[0].rotate(order=3) int_map.plot_settings['cmap'] = plt.get_cmap('hinodesotintensity') int_map.plot() plt.show() In [23]: Copied! # plotting magnetic map mag_map = hmi_mag [ 0 ] . rotate ( order = 3 ) mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . show () # plotting magnetic map mag_map = hmi_mag[0].rotate(order=3) mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.show() In [24]: Copied! # plotting doppler map vel_map = hmi_vel [ 0 ] . rotate ( order = 3 ) vel_map . plot_settings [ 'norm' ] = plt . Normalize ( - 5500 , 5500 ) fig = plt . figure () vel_map . plot () plt . show () # plotting doppler map vel_map = hmi_vel[0].rotate(order=3) vel_map.plot_settings['norm'] = plt.Normalize(-5500, 5500) fig = plt.figure() vel_map.plot() plt.show() We can now make plots of multiple data types. Don't forget to rotate HMI images such that they are oriented the same as AIA. In [25]: Copied! fig , axs = plt . subplots ( 2 , 2 ) fig . suptitle ( \"Images %s \" % ( str ( aia_171 [ 0 ] . meta [ 't_obs' ]))) axs [ 0 , 0 ] . set_title ( \" %s %s \" % ( str ( aia_171 [ 0 ] . meta [ 'telescop' ]), str ( aia_171 [ 0 ] . meta [ 'wavelnth' ]))) axs [ 0 , 1 ] . set_title ( \" %s %s \" % ( str ( aia_211 [ 0 ] . meta [ 'telescop' ]), str ( aia_211 [ 0 ] . meta [ 'wavelnth' ]))) axs [ 1 , 0 ] . set_title ( \" %s %s \" % ( str ( hmi_vel [ 0 ] . meta [ 'telescop' ]), str ( hmi_vel [ 0 ] . meta [ 'content' ]))) axs [ 1 , 1 ] . set_title ( \" %s %s \" % ( str ( hmi_mag [ 0 ] . meta [ 'telescop' ]), str ( hmi_mag [ 0 ] . meta [ 'content' ]))) for j in range ( 0 , 2 ): for k in range ( 0 , 2 ): axs [ j , k ] . set_xticks ([]) axs [ j , k ] . set_yticks ([]) aia_171 [ 0 ] . plot ( axes = axs [ 0 , 0 ], annotate = False ) aia_211 [ 0 ] . plot ( axes = axs [ 0 , 1 ], annotate = False ) # make sure you rotate HMI images hmi_vel_rot = hmi_vel [ 0 ] . rotate ( order = 3 ) hmi_vel_rot . plot ( axes = axs [ 1 , 0 ], annotate = False ) hmi_mag_rot = hmi_mag [ 0 ] . rotate ( order = 3 ) hmi_mag_rot . plot ( axes = axs [ 1 , 1 ], annotate = False ) plt . show () fig, axs = plt.subplots(2, 2) fig.suptitle(\"Images %s\" % (str(aia_171[0].meta['t_obs']))) axs[0, 0].set_title(\"%s %s\" % (str(aia_171[0].meta['telescop']), str(aia_171[0].meta['wavelnth']))) axs[0, 1].set_title(\"%s %s\" % (str(aia_211[0].meta['telescop']), str(aia_211[0].meta['wavelnth']))) axs[1, 0].set_title(\"%s %s\" % (str(hmi_vel[0].meta['telescop']), str(hmi_vel[0].meta['content']))) axs[1, 1].set_title(\"%s %s\" % (str(hmi_mag[0].meta['telescop']), str(hmi_mag[0].meta['content']))) for j in range(0, 2): for k in range(0, 2): axs[j, k].set_xticks([]) axs[j, k].set_yticks([]) aia_171[0].plot(axes=axs[0, 0], annotate=False) aia_211[0].plot(axes=axs[0, 1], annotate=False) # make sure you rotate HMI images hmi_vel_rot = hmi_vel[0].rotate(order=3) hmi_vel_rot.plot(axes=axs[1, 0], annotate=False) hmi_mag_rot = hmi_mag[0].rotate(order=3) hmi_mag_rot.plot(axes=axs[1, 1], annotate=False) plt.show() WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] In [ ]: Copied!","title":"Sunpy Basics"},{"location":"sdo/examples/docs_sunpy_example/#examples-using-sunpy-for-querying-and-plotting","text":"","title":"Examples using Sunpy for querying and plotting"},{"location":"sdo/examples/docs_sunpy_example/#querying-data","text":"In [10]: Copied! import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation import matplotlib.colors as colors import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a import sunpy.visualization.colormaps as cm import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation import matplotlib.colors as colors import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a import sunpy.visualization.colormaps as cm To download the required data, we use sunpy.net.Fido , a downloader client, to query the Virtual Solar Observatory to acquire AIA and HMI data. The following is the data available: HMI Bands (A): 171 (gold), 193 (bronze), 304 (red), 211 (purple), 131 (teal), 335 (blue), 094 (green), 1600 (yellow/green), 1700 (pink) HMI Products: Magnetogram, Intensitygram, Dopplergram In [11]: Copied! # data parameters cadence = a . Sample ( 24 * u . hour ) # querying cadence start_date = '2021-1-1T12:00:00' # start date of query end_date = '2021-01-5T12:00:00' # end date of query # AIA specific parameters aia_inst = a . Instrument . aia # list of all available wavelengths for reference wavelength_list = [ a . Wavelength ( 94 * u . angstrom ), a . Wavelength ( 131 * u . angstrom ), a . Wavelength ( 171 * u . angstrom ), a . Wavelength ( 193 * u . angstrom ), a . Wavelength ( 211 * u . angstrom ), a . Wavelength ( 304 * u . angstrom ), a . Wavelength ( 335 * u . angstrom ), a . Wavelength ( 1600 * u . angstrom ), a . Wavelength ( 1700 * u . angstrom )] # HMI specific parameters hmi_inst = a . Instrument . hmi # list of physical observables available physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] # data parameters cadence = a.Sample(24*u.hour) # querying cadence start_date = '2021-1-1T12:00:00' # start date of query end_date = '2021-01-5T12:00:00' # end date of query # AIA specific parameters aia_inst = a.Instrument.aia # list of all available wavelengths for reference wavelength_list = [a.Wavelength(94 * u.angstrom), a.Wavelength(131 * u.angstrom), a.Wavelength(171 * u.angstrom), a.Wavelength(193 * u.angstrom), a.Wavelength(211 * u.angstrom), a.Wavelength(304 * u.angstrom), a.Wavelength(335 * u.angstrom), a.Wavelength(1600 * u.angstrom), a.Wavelength(1700 * u.angstrom)] # HMI specific parameters hmi_inst = a.Instrument.hmi # list of physical observables available physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] Now we can look at querying for specific information. We can look for results from multiple instruments, wavelengths, and physical observables at once. It is useful to search for results before download to ensure you don't download excessive amounts of data. Let's say we only want these specific results from AIA. We search for images at a 24 hour cadence between our start and end times at specified wavelengths. In [12]: Copied! aia_result = Fido . search ( a . Time ( start_date , end_date ), aia_inst , wavelength_list [ 0 ] | wavelength_list [ 2 ] | wavelength_list [ 3 ] | wavelength_list [ 6 ], cadence ) aia_result = Fido.search(a.Time(start_date, end_date), aia_inst, wavelength_list[0] | wavelength_list[2] | wavelength_list[3] | wavelength_list[6], cadence) We can do the same with HMI data products. In [13]: Copied! hmi_result = Fido . search ( a . Time ( start_date , end_date ), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ], cadence ) hmi_result = Fido.search(a.Time(start_date, end_date), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2], cadence) You can also query for both instruments simultaneously. In [14]: Copied! result = Fido . search ( a . Time ( start_date , end_date ), aia_inst | hmi_inst , wavelength_list [ 0 ] | wavelength_list [ 1 ] | wavelength_list [ 2 ] | wavelength_list [ 3 ] | wavelength_list [ 4 ] | wavelength_list [ 5 ] | wavelength_list [ 6 ] | wavelength_list [ 7 ] | wavelength_list [ 8 ] | physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ], cadence ) result = Fido.search(a.Time(start_date, end_date), aia_inst | hmi_inst, wavelength_list[0] | wavelength_list[1] | wavelength_list[2] | wavelength_list[3] | wavelength_list[4] | wavelength_list[5] | wavelength_list[6] | wavelength_list[7] | wavelength_list[8] | physobs_list[0] | physobs_list[1] | physobs_list[2], cadence) /Users/tervin/opt/anaconda3/envs/neid_data/lib/python3.7/site-packages/astropy/table/table.py:3197: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future. result = self.as_array() == other Next we download the actual data found from our search. In [15]: Copied! file_download = Fido . fetch ( result ) file_download = Fido.fetch(result) var element = document.getElementById('68bff78d-ed7f-4d68-a27f-649b6eaa0ce5'); {\"model_id\": \"562e80d0aa5b4548a33015eae2ee8169\", \"version_major\": 2, \"version_minor\": 0} After downloading our data, we want to sort it by date. We then build a map sequence of our data. In [16]: Copied! map_seq = sunpy . map . Map ( sorted ( file_download )) map_seq = sunpy.map.Map(sorted(file_download)) We then split our results by data product. In [17]: Copied! aia_094 , aia_131 , aia_171 , aia_193 , aia_211 , aia_304 , aia_335 , aia_1600 , aia_1700 , hmi_vel , hmi_mag , hmi_int \\ = [], [], [], [], [], [], [], [], [], [], [], [] for i , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'wavelnth' ] == 94 : aia_094 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 131 : aia_131 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 171 : aia_171 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 193 : aia_193 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 211 : aia_211 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 304 : aia_304 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 335 : aia_335 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 1600 : aia_1600 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 1700 : aia_1700 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 6173 and map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : hmi_vel . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 6173 and map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : hmi_mag . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 6173 and map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : hmi_int . append ( map_obj ) aia_094, aia_131, aia_171, aia_193, aia_211, aia_304, aia_335, aia_1600, aia_1700, hmi_vel, hmi_mag, hmi_int \\ = [], [], [], [], [], [], [], [], [], [], [], [] for i, map_obj in enumerate(map_seq): if map_obj.meta['wavelnth'] == 94: aia_094.append(map_obj) elif map_obj.meta['wavelnth'] == 131: aia_131.append(map_obj) elif map_obj.meta['wavelnth'] == 171: aia_171.append(map_obj) elif map_obj.meta['wavelnth'] == 193: aia_193.append(map_obj) elif map_obj.meta['wavelnth'] == 211: aia_211.append(map_obj) elif map_obj.meta['wavelnth'] == 304: aia_304.append(map_obj) elif map_obj.meta['wavelnth'] == 335: aia_335.append(map_obj) elif map_obj.meta['wavelnth'] == 1600: aia_1600.append(map_obj) elif map_obj.meta['wavelnth'] == 1700: aia_1700.append(map_obj) elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'DOPPLERGRAM': hmi_vel.append(map_obj) elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'MAGNETOGRAM': hmi_mag.append(map_obj) elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'CONTINUUM INTENSITY': hmi_int.append(map_obj)","title":"Querying data"},{"location":"sdo/examples/docs_sunpy_example/#plotting-images","text":"You can plot individual images using inherent Sunpy features. In [19]: Copied! aia_211 [ 0 ] . peek () # this plots the aia image with a colorbar, axes labels, and title aia_211[0].peek() # this plots the aia image with a colorbar, axes labels, and title In [20]: Copied! aia_211 [ 0 ] . plot () # this just plots the data, allows you to customize labels, colormap, and title plt . show () aia_211[0].plot() # this just plots the data, allows you to customize labels, colormap, and title plt.show() Plotting HMI images requires us to rotate the image to match the orientation of AIA images. The same peek and plot methods work for HMI images. In [21]: Copied! hmi_vel_rot = hmi_vel [ 0 ] . rotate ( order = 3 ) hmi_vel_rot . peek () hmi_vel_rot = hmi_vel[0].rotate(order=3) hmi_vel_rot.peek() HMI images look nicer when customized a bit. In [22]: Copied! # plotting intensity map int_map = hmi_int [ 0 ] . rotate ( order = 3 ) int_map . plot_settings [ 'cmap' ] = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot () plt . show () # plotting intensity map int_map = hmi_int[0].rotate(order=3) int_map.plot_settings['cmap'] = plt.get_cmap('hinodesotintensity') int_map.plot() plt.show() In [23]: Copied! # plotting magnetic map mag_map = hmi_mag [ 0 ] . rotate ( order = 3 ) mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . show () # plotting magnetic map mag_map = hmi_mag[0].rotate(order=3) mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.show() In [24]: Copied! # plotting doppler map vel_map = hmi_vel [ 0 ] . rotate ( order = 3 ) vel_map . plot_settings [ 'norm' ] = plt . Normalize ( - 5500 , 5500 ) fig = plt . figure () vel_map . plot () plt . show () # plotting doppler map vel_map = hmi_vel[0].rotate(order=3) vel_map.plot_settings['norm'] = plt.Normalize(-5500, 5500) fig = plt.figure() vel_map.plot() plt.show() We can now make plots of multiple data types. Don't forget to rotate HMI images such that they are oriented the same as AIA. In [25]: Copied! fig , axs = plt . subplots ( 2 , 2 ) fig . suptitle ( \"Images %s \" % ( str ( aia_171 [ 0 ] . meta [ 't_obs' ]))) axs [ 0 , 0 ] . set_title ( \" %s %s \" % ( str ( aia_171 [ 0 ] . meta [ 'telescop' ]), str ( aia_171 [ 0 ] . meta [ 'wavelnth' ]))) axs [ 0 , 1 ] . set_title ( \" %s %s \" % ( str ( aia_211 [ 0 ] . meta [ 'telescop' ]), str ( aia_211 [ 0 ] . meta [ 'wavelnth' ]))) axs [ 1 , 0 ] . set_title ( \" %s %s \" % ( str ( hmi_vel [ 0 ] . meta [ 'telescop' ]), str ( hmi_vel [ 0 ] . meta [ 'content' ]))) axs [ 1 , 1 ] . set_title ( \" %s %s \" % ( str ( hmi_mag [ 0 ] . meta [ 'telescop' ]), str ( hmi_mag [ 0 ] . meta [ 'content' ]))) for j in range ( 0 , 2 ): for k in range ( 0 , 2 ): axs [ j , k ] . set_xticks ([]) axs [ j , k ] . set_yticks ([]) aia_171 [ 0 ] . plot ( axes = axs [ 0 , 0 ], annotate = False ) aia_211 [ 0 ] . plot ( axes = axs [ 0 , 1 ], annotate = False ) # make sure you rotate HMI images hmi_vel_rot = hmi_vel [ 0 ] . rotate ( order = 3 ) hmi_vel_rot . plot ( axes = axs [ 1 , 0 ], annotate = False ) hmi_mag_rot = hmi_mag [ 0 ] . rotate ( order = 3 ) hmi_mag_rot . plot ( axes = axs [ 1 , 1 ], annotate = False ) plt . show () fig, axs = plt.subplots(2, 2) fig.suptitle(\"Images %s\" % (str(aia_171[0].meta['t_obs']))) axs[0, 0].set_title(\"%s %s\" % (str(aia_171[0].meta['telescop']), str(aia_171[0].meta['wavelnth']))) axs[0, 1].set_title(\"%s %s\" % (str(aia_211[0].meta['telescop']), str(aia_211[0].meta['wavelnth']))) axs[1, 0].set_title(\"%s %s\" % (str(hmi_vel[0].meta['telescop']), str(hmi_vel[0].meta['content']))) axs[1, 1].set_title(\"%s %s\" % (str(hmi_mag[0].meta['telescop']), str(hmi_mag[0].meta['content']))) for j in range(0, 2): for k in range(0, 2): axs[j, k].set_xticks([]) axs[j, k].set_yticks([]) aia_171[0].plot(axes=axs[0, 0], annotate=False) aia_211[0].plot(axes=axs[0, 1], annotate=False) # make sure you rotate HMI images hmi_vel_rot = hmi_vel[0].rotate(order=3) hmi_vel_rot.plot(axes=axs[1, 0], annotate=False) hmi_mag_rot = hmi_mag[0].rotate(order=3) hmi_mag_rot.plot(axes=axs[1, 1], annotate=False) plt.show() WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] In [ ]: Copied!","title":"Plotting images"}]}
\ No newline at end of file
+{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"SDO/HMI Derived RVs Welcome to the documentation site for the SDO/HMI Derived RVs package. Additional information can be found by navigating through the links on the left. For the source code check out our GitHub !","title":"Home"},{"location":"#sdohmi-derived-rvs","text":"Welcome to the documentation site for the SDO/HMI Derived RVs package. Additional information can be found by navigating through the links on the left. For the source code check out our GitHub !","title":"SDO/HMI Derived RVs"},{"location":"about/","text":"About the Project This package provides the necessary querying, analysis, and calculation methods to independently derive 'sun-as-a-star' RV variations using SDO/HMI data for any time span since SDO has begun observing. Scaling factors are provided in order to calculate RVs comparable to magnitudes measured by ground-based spectrographs (HARPS-N and NEID). In addition, there are routines to calculate magnetic observables to compare with RV variations and determine what is driving Solar activity. A huge thank you to Raphaelle Haywood for her guidance and support on this project and for her papers that outlined the methodology of calculation. Contact Tamar Ervin : tamar.ervin@nasa.jpl.gov Sam Halverson : samuel.halverson@jpl.nasa.gov","title":"About"},{"location":"about/#about-the-project","text":"This package provides the necessary querying, analysis, and calculation methods to independently derive 'sun-as-a-star' RV variations using SDO/HMI data for any time span since SDO has begun observing. Scaling factors are provided in order to calculate RVs comparable to magnitudes measured by ground-based spectrographs (HARPS-N and NEID). In addition, there are routines to calculate magnetic observables to compare with RV variations and determine what is driving Solar activity. A huge thank you to Raphaelle Haywood for her guidance and support on this project and for her papers that outlined the methodology of calculation.","title":"About the Project"},{"location":"about/#contact","text":"Tamar Ervin : tamar.ervin@nasa.jpl.gov Sam Halverson : samuel.halverson@jpl.nasa.gov","title":"Contact"},{"location":"calcs/coords/","text":"Coordinate Transformations SDO/HMI Data Products are returned in Helioprojective Cartesian pixel coordinates. In order to calculate the spacecraft and solar rotation velocity corrections, we need to convert this to the Heliographic Carrington Coordinate System (sun centered coordinate system). In addition to being sun-centered, Heliographic Carrington coordinates also rotate with respect to the Carrington rotation period. Transformations are based off Thompson (2006). Coordinate functions are here . Pixel Array Computation The first step in the coordinate transformation is computation of the pixel-wise coordinate arrays with respect to solar center. We do this by taking into account the reference pixels from the header file. We then calculate the array of \\mu \\mu and radius values for that image, where \\mu = cos(\\theta) \\mu = cos(\\theta) . First, we determine pixel size in physical coordinates (solar radii) and then calculate the center-to-limb angles. We get the radius by determining the distance in solar radii to each pixel. def coordinates(smap): \"\"\" calculate array of mu values and cartesian coordinates for image Parameters ---------- smap: Sunpy map object Returns ------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values d: observer distance from sun in solar radii mu: array of mu values \"\"\" # distance of observer to sun in solar radii d = smap.fits_header['DSUN_OBS'] / smap.fits_header['RSUN_REF'] # focal length in pixels f = 180. * 3600. / np.pi / smap.fits_header['CDELT1'] # get cartesian x, y map scales x, y = get_map_scales(smap) # distance (in pixels) to pixel pd = np.sqrt(x ** 2 + y ** 2) # distance (in solar r) to pixel pr = f * f * pd * pd + pd ** 4 - d * d * pd ** 4 + 0.J r = (d * f * pd - np.sqrt(pr)) / (f * f + pd * pd) # separate complex parts r = r.real # get mu array pr = 1 - r ** 2 + 0.J cos_alpha = (np.sqrt(pr)).real sin_alpha = r.real cos_theta = ((d - np.sqrt(pr)) / np.sqrt(r ** 2 + (d - np.sqrt(pr)) ** 2)).real sin_theta = (np.sqrt(1 - cos_theta ** 2)).real mu = cos_alpha * cos_theta - sin_alpha * sin_theta return x, y, pd, r, d, mu Coordinate Transform We are able to apply the coordinate transformation based on a calculated rotation matrix. We get an array of coordinates in the westward, northward, and radial direction. def vel_coords(x, y, pd, r, smap): \"\"\" calculate coordinate transformation to heliographic Carrington coordinates Parameters ---------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values smap: Sunpy map object Returns ------- wij: array of pixel coordinates relative to solar center in westward direction nij: array of pixel coordinates relative to solar center in northward direction rij: array of pixel coordinates relative to solar center in radial direction \"\"\" head = smap.fits_header crota2 = head['CROTA2'] # deg # transform each pixel to get into Heliographic CR Coordinates dw = y * np.sin(np.deg2rad(crota2)) + x * np.cos(np.deg2rad(crota2)) dn = y * np.cos(np.deg2rad(crota2)) - x * np.sin(np.deg2rad(crota2)) # get cartesian coordinates for velocity calculations pr = 1 - r ** 2 + 0.J wij = r * dw / pd nij = r * dn / pd rij = (np.sqrt(pr)).real return wij, nij, rij Fix Mu Values Lastly, we remove all pixels with mu values less than 0.1 because our limb-brightening model does poorly in correcting near the edge of the Solar disk. def fix_mu(mu, smaps, mu_cutoff=0.3): \"\"\" function to remove pixel values where mu is less than 0.1 Parameters ---------- mu: cosine of the center to limb angle smaps: list of Sunpy map object mu_cutoff: minimum mu cutoff value Returns ------- mu: corrected cosine of the center to limb angle smap: corrected Sunpy map object \"\"\" # remove pixel values where mu < mu_cut (0.3) bad_inds = np.where(mu <= mu_cutoff) # fix arrays for smap in smaps: smap.data[bad_inds] = 0 return smaps","title":"Coordinate Transformations"},{"location":"calcs/coords/#coordinate-transformations","text":"SDO/HMI Data Products are returned in Helioprojective Cartesian pixel coordinates. In order to calculate the spacecraft and solar rotation velocity corrections, we need to convert this to the Heliographic Carrington Coordinate System (sun centered coordinate system). In addition to being sun-centered, Heliographic Carrington coordinates also rotate with respect to the Carrington rotation period. Transformations are based off Thompson (2006). Coordinate functions are here .","title":"Coordinate Transformations"},{"location":"calcs/coords/#pixel-array-computation","text":"The first step in the coordinate transformation is computation of the pixel-wise coordinate arrays with respect to solar center. We do this by taking into account the reference pixels from the header file. We then calculate the array of \\mu \\mu and radius values for that image, where \\mu = cos(\\theta) \\mu = cos(\\theta) . First, we determine pixel size in physical coordinates (solar radii) and then calculate the center-to-limb angles. We get the radius by determining the distance in solar radii to each pixel. def coordinates(smap): \"\"\" calculate array of mu values and cartesian coordinates for image Parameters ---------- smap: Sunpy map object Returns ------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values d: observer distance from sun in solar radii mu: array of mu values \"\"\" # distance of observer to sun in solar radii d = smap.fits_header['DSUN_OBS'] / smap.fits_header['RSUN_REF'] # focal length in pixels f = 180. * 3600. / np.pi / smap.fits_header['CDELT1'] # get cartesian x, y map scales x, y = get_map_scales(smap) # distance (in pixels) to pixel pd = np.sqrt(x ** 2 + y ** 2) # distance (in solar r) to pixel pr = f * f * pd * pd + pd ** 4 - d * d * pd ** 4 + 0.J r = (d * f * pd - np.sqrt(pr)) / (f * f + pd * pd) # separate complex parts r = r.real # get mu array pr = 1 - r ** 2 + 0.J cos_alpha = (np.sqrt(pr)).real sin_alpha = r.real cos_theta = ((d - np.sqrt(pr)) / np.sqrt(r ** 2 + (d - np.sqrt(pr)) ** 2)).real sin_theta = (np.sqrt(1 - cos_theta ** 2)).real mu = cos_alpha * cos_theta - sin_alpha * sin_theta return x, y, pd, r, d, mu","title":"Pixel Array Computation"},{"location":"calcs/coords/#coordinate-transform","text":"We are able to apply the coordinate transformation based on a calculated rotation matrix. We get an array of coordinates in the westward, northward, and radial direction. def vel_coords(x, y, pd, r, smap): \"\"\" calculate coordinate transformation to heliographic Carrington coordinates Parameters ---------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values smap: Sunpy map object Returns ------- wij: array of pixel coordinates relative to solar center in westward direction nij: array of pixel coordinates relative to solar center in northward direction rij: array of pixel coordinates relative to solar center in radial direction \"\"\" head = smap.fits_header crota2 = head['CROTA2'] # deg # transform each pixel to get into Heliographic CR Coordinates dw = y * np.sin(np.deg2rad(crota2)) + x * np.cos(np.deg2rad(crota2)) dn = y * np.cos(np.deg2rad(crota2)) - x * np.sin(np.deg2rad(crota2)) # get cartesian coordinates for velocity calculations pr = 1 - r ** 2 + 0.J wij = r * dw / pd nij = r * dn / pd rij = (np.sqrt(pr)).real return wij, nij, rij","title":"Coordinate Transform"},{"location":"calcs/coords/#fix-mu-values","text":"Lastly, we remove all pixels with mu values less than 0.1 because our limb-brightening model does poorly in correcting near the edge of the Solar disk. def fix_mu(mu, smaps, mu_cutoff=0.3): \"\"\" function to remove pixel values where mu is less than 0.1 Parameters ---------- mu: cosine of the center to limb angle smaps: list of Sunpy map object mu_cutoff: minimum mu cutoff value Returns ------- mu: corrected cosine of the center to limb angle smap: corrected Sunpy map object \"\"\" # remove pixel values where mu < mu_cut (0.3) bad_inds = np.where(mu <= mu_cutoff) # fix arrays for smap in smaps: smap.data[bad_inds] = 0 return smaps","title":"Fix Mu Values"},{"location":"calcs/mag/","text":"Calculation of Solar Magnetic Observables Using HMI magnetograms, we are able to calculate magnetic observables that both correlate with each other as well as the velocity features. This shows how the magnetic activity is driving the radial velocity variations. Filling Factor Disk averaged filling factors of sunspots and plage which gives the percentage of magnetically active pixels. The filling factor is a calculation of the percentage of active pixels on the solar surface at any one time. We calculate three different factors: f_{spot} f_{spot} , f_{bright} f_{bright} , and f f . These are the filling factors due to sunspots, faculae/network, and the total filling factor which is the sum of the two. We return the filling factor as a percentage: f = \\frac{1}{N_{pix}} \\sum_{ij} W_{ij} * 100 f = \\frac{1}{N_{pix}} \\sum_{ij} W_{ij} * 100 def filling_factor(mu, mmap, active, fac_inds, spot_inds, mu_cutoff=0.3): \"\"\" function to calculate filling factor Parameters ---------- mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) active: weights array where active pixels have weight = 1 fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns ------- f_bright: filling factor (%) for bright areas (faculae) f_spot: filling factor (%) for dark areas (sunspots) f_total: filling factor (%) for timestamp \"\"\" # get good mu values good_mu = np.where(mu > mu_cutoff) # get number of pixels npix = len(mmap.data[good_mu]) # faculae faculae = np.zeros(mmap.data.shape) faculae[fac_inds] = 1. f_bright = np.sum(faculae) / npix * 100 # sunspots spots = np.zeros(mmap.data.shape) spots[spot_inds] = 1. f_spot = np.sum(spots) / npix * 100 # get filling factor f_total = np.sum(active) / npix * 100 return f_bright, f_spot, f_total Unsigned Magnetic Flux The disc-averaged, line-of-sight unsigned (unpolarized) magnetic flux of the Sun. |\\hat{B_{obs}}| = \\frac{\\sum_{ij} |B_{obs, ij}| I_{ij}} {\\sum_{ij} I_{ij}} |\\hat{B_{obs}}| = \\frac{\\sum_{ij} |B_{obs, ij}| I_{ij}} {\\sum_{ij} I_{ij}} def unsigned_flux(map_mag_obs, imap): \"\"\" calculate unsigned magnetic flux Parameters ---------- map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- unsign_flux: unsigned magnetic flux \"\"\" unsign_flux = np.nansum(np.abs(map_mag_obs.data) * imap.data) / np.nansum(imap.data) return np.abs(unsign_flux) Area Based Magnetic Observables In addition to the unsigned flux and filling factor calculations described above, we calculate these quantities with an eye for what types of regions are causing variations in magnetic activity. The calculations for area thresholded regions is outlined below. Area Thresholded Filling Factor def area_filling_factor(active, area, mu, mmap, fac_inds, athresh=20, mu_cutoff=0.3): \"\"\" calculate filling factor for regions thresholded by area - differentiate between large and small regions - differentiate between plage (large) and network (small) bright regions Parameters ---------- active: weights array where active pixels have weight = 1 area: area of each active region weighted by its intensity mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) fac_inds: array of indices where faculae are detected athresh: area threshold value between large and small regions (in uHem) mu_cutoff: minimum mu cutoff value for usable data Returns ------- f_small: filling factor (%) for small magnetically active regions f_large: filling factor (%) for large magnetically active regions f_network: filling factor (%) for network (small, bright magnetically active) regions f_plage: filling factor (%) for plage (large, bright magnetically active) regions f_nonconv: filling factor (%) for regions that do not suppress convective blueshift \"\"\" # get good mu values good_mu = np.where(mu > mu_cutoff) # get number of pixels npix = len(mmap.data[good_mu]) # get quiet pixels quiet = 1 - active # get filling factor for 'small' magnetic features small = np.zeros(mmap.data.shape) small_inds = np.logical_and(active > 0.5, area < athresh) small[small_inds] = 1. f_small = np.nansum(small) / npix * 100 # get filling factor for 'large' magnetic features large = np.zeros(mmap.data.shape) large_inds = np.logical_and(active > 0.5, area > athresh) large[large_inds] = 1. f_large = np.nansum(large) / npix * 100 # get filling factor for network (small, faculae regions) network = np.zeros(mmap.data.shape) network_inds = np.logical_and(small > 0.5, fac_inds > 0.5) network[network_inds] = 1. f_network = np.nansum(network) / npix * 100 # get filling factor for plage (large, faculae regions) plage = np.zeros(mmap.data.shape) plage_inds = np.logical_and(large > 0.5, fac_inds > 0.5) plage[plage_inds] = 1. f_plage = np.nansum(plage) / npix * 100 # get filling factor for small, non-convective regions nonconv = np.zeros(mmap.data.shape) nonconv_inds = np.logical_and(quiet > 0.5, small > 0.5) nonconv[nonconv_inds] = 1. f_nonconv = np.nansum(nonconv) / npix * 100 return f_small, f_large, f_network, f_plage, f_nonconv Area Thresholded Unsigned Flux def area_unsigned_flux(map_mag_obs, imap, area, active, athresh=20): \"\"\" calculate the magnetic flux for different regions based on area cut and magnetic activitiy Parameters ---------- map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) area: area of each active region weighted by its intensity active: weights array where active pixels have weight = 1 Returns ------- quiet_flux: magnetic flux of quiet-Sun regions ar_flux: magnetic flux of active regions conv_flux: magnetic flux of regions that suppress convective blueshift pol_flux: magnetic flux of polarized regions pol_conv_flux: magnetic flux of polarized regions that suppress the convective blueshift \"\"\" # get data arrays i_data = imap.data m_data = map_mag_obs.data mabs_data = np.abs(m_data) quiet = 1 - active # get large regions array large = np.zeros(m_data.shape) large_inds = np.logical_and(active > 0.5, area > athresh) large[large_inds] = 1. # calculate relevant fluxes quiet_flux = np.nansum(mabs_data * i_data * quiet) / np.nansum(i_data * quiet) ar_flux = np.nansum(mabs_data * i_data * active) / np.nansum(i_data * active) conv_flux = np.nansum(mabs_data * i_data * large) / np.nansum(i_data * large) pol_flux = np.nansum(m_data * i_data) / np.nansum(i_data) pol_conv_flux = np.nansum(m_data * i_data * large) / np.nansum(i_data * large) return quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux","title":"Magnetic Observables"},{"location":"calcs/mag/#calculation-of-solar-magnetic-observables","text":"Using HMI magnetograms, we are able to calculate magnetic observables that both correlate with each other as well as the velocity features. This shows how the magnetic activity is driving the radial velocity variations.","title":"Calculation of Solar Magnetic Observables"},{"location":"calcs/mag/#filling-factor","text":"Disk averaged filling factors of sunspots and plage which gives the percentage of magnetically active pixels. The filling factor is a calculation of the percentage of active pixels on the solar surface at any one time. We calculate three different factors: f_{spot} f_{spot} , f_{bright} f_{bright} , and f f . These are the filling factors due to sunspots, faculae/network, and the total filling factor which is the sum of the two. We return the filling factor as a percentage: f = \\frac{1}{N_{pix}} \\sum_{ij} W_{ij} * 100 f = \\frac{1}{N_{pix}} \\sum_{ij} W_{ij} * 100 def filling_factor(mu, mmap, active, fac_inds, spot_inds, mu_cutoff=0.3): \"\"\" function to calculate filling factor Parameters ---------- mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) active: weights array where active pixels have weight = 1 fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns ------- f_bright: filling factor (%) for bright areas (faculae) f_spot: filling factor (%) for dark areas (sunspots) f_total: filling factor (%) for timestamp \"\"\" # get good mu values good_mu = np.where(mu > mu_cutoff) # get number of pixels npix = len(mmap.data[good_mu]) # faculae faculae = np.zeros(mmap.data.shape) faculae[fac_inds] = 1. f_bright = np.sum(faculae) / npix * 100 # sunspots spots = np.zeros(mmap.data.shape) spots[spot_inds] = 1. f_spot = np.sum(spots) / npix * 100 # get filling factor f_total = np.sum(active) / npix * 100 return f_bright, f_spot, f_total","title":"Filling Factor"},{"location":"calcs/mag/#unsigned-magnetic-flux","text":"The disc-averaged, line-of-sight unsigned (unpolarized) magnetic flux of the Sun. |\\hat{B_{obs}}| = \\frac{\\sum_{ij} |B_{obs, ij}| I_{ij}} {\\sum_{ij} I_{ij}} |\\hat{B_{obs}}| = \\frac{\\sum_{ij} |B_{obs, ij}| I_{ij}} {\\sum_{ij} I_{ij}} def unsigned_flux(map_mag_obs, imap): \"\"\" calculate unsigned magnetic flux Parameters ---------- map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- unsign_flux: unsigned magnetic flux \"\"\" unsign_flux = np.nansum(np.abs(map_mag_obs.data) * imap.data) / np.nansum(imap.data) return np.abs(unsign_flux)","title":"Unsigned Magnetic Flux"},{"location":"calcs/mag/#area-based-magnetic-observables","text":"In addition to the unsigned flux and filling factor calculations described above, we calculate these quantities with an eye for what types of regions are causing variations in magnetic activity. The calculations for area thresholded regions is outlined below.","title":"Area Based Magnetic Observables"},{"location":"calcs/mag/#area-thresholded-filling-factor","text":"def area_filling_factor(active, area, mu, mmap, fac_inds, athresh=20, mu_cutoff=0.3): \"\"\" calculate filling factor for regions thresholded by area - differentiate between large and small regions - differentiate between plage (large) and network (small) bright regions Parameters ---------- active: weights array where active pixels have weight = 1 area: area of each active region weighted by its intensity mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) fac_inds: array of indices where faculae are detected athresh: area threshold value between large and small regions (in uHem) mu_cutoff: minimum mu cutoff value for usable data Returns ------- f_small: filling factor (%) for small magnetically active regions f_large: filling factor (%) for large magnetically active regions f_network: filling factor (%) for network (small, bright magnetically active) regions f_plage: filling factor (%) for plage (large, bright magnetically active) regions f_nonconv: filling factor (%) for regions that do not suppress convective blueshift \"\"\" # get good mu values good_mu = np.where(mu > mu_cutoff) # get number of pixels npix = len(mmap.data[good_mu]) # get quiet pixels quiet = 1 - active # get filling factor for 'small' magnetic features small = np.zeros(mmap.data.shape) small_inds = np.logical_and(active > 0.5, area < athresh) small[small_inds] = 1. f_small = np.nansum(small) / npix * 100 # get filling factor for 'large' magnetic features large = np.zeros(mmap.data.shape) large_inds = np.logical_and(active > 0.5, area > athresh) large[large_inds] = 1. f_large = np.nansum(large) / npix * 100 # get filling factor for network (small, faculae regions) network = np.zeros(mmap.data.shape) network_inds = np.logical_and(small > 0.5, fac_inds > 0.5) network[network_inds] = 1. f_network = np.nansum(network) / npix * 100 # get filling factor for plage (large, faculae regions) plage = np.zeros(mmap.data.shape) plage_inds = np.logical_and(large > 0.5, fac_inds > 0.5) plage[plage_inds] = 1. f_plage = np.nansum(plage) / npix * 100 # get filling factor for small, non-convective regions nonconv = np.zeros(mmap.data.shape) nonconv_inds = np.logical_and(quiet > 0.5, small > 0.5) nonconv[nonconv_inds] = 1. f_nonconv = np.nansum(nonconv) / npix * 100 return f_small, f_large, f_network, f_plage, f_nonconv","title":"Area Thresholded Filling Factor"},{"location":"calcs/mag/#area-thresholded-unsigned-flux","text":"def area_unsigned_flux(map_mag_obs, imap, area, active, athresh=20): \"\"\" calculate the magnetic flux for different regions based on area cut and magnetic activitiy Parameters ---------- map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) area: area of each active region weighted by its intensity active: weights array where active pixels have weight = 1 Returns ------- quiet_flux: magnetic flux of quiet-Sun regions ar_flux: magnetic flux of active regions conv_flux: magnetic flux of regions that suppress convective blueshift pol_flux: magnetic flux of polarized regions pol_conv_flux: magnetic flux of polarized regions that suppress the convective blueshift \"\"\" # get data arrays i_data = imap.data m_data = map_mag_obs.data mabs_data = np.abs(m_data) quiet = 1 - active # get large regions array large = np.zeros(m_data.shape) large_inds = np.logical_and(active > 0.5, area > athresh) large[large_inds] = 1. # calculate relevant fluxes quiet_flux = np.nansum(mabs_data * i_data * quiet) / np.nansum(i_data * quiet) ar_flux = np.nansum(mabs_data * i_data * active) / np.nansum(i_data * active) conv_flux = np.nansum(mabs_data * i_data * large) / np.nansum(i_data * large) pol_flux = np.nansum(m_data * i_data) / np.nansum(i_data) pol_conv_flux = np.nansum(m_data * i_data * large) / np.nansum(i_data * large) return quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux","title":"Area Thresholded Unsigned Flux"},{"location":"calcs/region/","text":"Identification of Solar Regions In order to calculate the various pertinent velocity components we need to differentiate between different types of solar regions. We do two identification cuts both based on thresholding values from Yeo et al. 2013. Magnetic Thresholding The first threshold is to identify active regions. We do this with a simple magnetic threshold value of 24G. The threshold is applied to the unsigned magnetic field strength (magnetic field corrected for foreshortening). Pixels with magnetic field magnitude above the threshold are marked as active and those below the threshold are quiet Sun. Isolated active pixels are re-identified as quiet Sun based on area thresholding. Isolated pixels are denoted as any pixel without four connected neighbors. The connection matrix is built using the skimage.regionsprops function. def mag_thresh(mu, mmap, Br_cutoff=24, mu_cutoff=0.3): \"\"\" function to calculate magnetic threshold and differentiate between magnetically active regions and quiet Sun Parameters ---------- mu: array of mu (cosine theta) values mmap: corrected (unsigned magnetic field) Sunpy map object (Magnetogram) Br_cutoff: minimum cutoff value (in Gauss) for thresholding active regions mu_cutoff: minimum mu cutoff value for data to ignore Returns ------- active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 \"\"\" # get active region indices active_inds = np.where(np.abs(mmap.data) * mu > Br_cutoff) bad_mu = np.where(mu <= mu_cutoff) # make active region array active = np.zeros(mu.shape) active[active_inds] = 1. active[bad_mu] = 0. # find isolated pixels # get area y_labeled = label(active, connectivity=2, background=0) y_area = [props.area for props in regionprops(y_labeled)] # area constraint good_area = np.where(np.array(y_area) > 5) good_area = good_area[0] + 1 active_indices = np.isin(y_labeled, good_area) # create weights array active[~active_indices] = 0 # get quiet indices quiet = 1 - active return active, quiet Intensity Thresholding Once we detect active regions, we want to identify sunspots (dark) and faculae/network regions (bright). These regions can be categorized by intensity and therefore an intensity cutoff value is calculated relative to the overall mean flattened intensity of the quiet Sun: I_{quiet} = \\frac{\\sum_{ij} I_{flat, ij} W_{ij}}{\\sum_{ij} W_{ij}} I_{quiet} = \\frac{\\sum_{ij} I_{flat, ij} W_{ij}}{\\sum_{ij} W_{ij}} where W_{ij} W_{ij} is the quiet Sun weighting array returned from magnetic thresholding. The intensity threshold is then I_{thresh} = 0.89I_{quiet} I_{thresh} = 0.89I_{quiet} . def int_thresh(map_int_cor, active, quiet): \"\"\" function to do intensity thresholding and differentiate between faculae (bright) and sunspots (dark) Parameters ---------- map_int_cor: corrected (limb-darkening) Sunpy map object (Intensitygram) active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 Returns ------- fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected \"\"\" # flattened intensity data Iflat = map_int_cor.data # calculate quiet sun intensity int_quiet = np.nansum(Iflat * quiet) / np.nansum(quiet) # intensity threshold int_cutoff = 0.89 * int_quiet # get faculae fac_inds = np.logical_and((Iflat > int_cutoff), (active > 0.5)) # get sunspots spot_inds = np.logical_and((Iflat <= int_cutoff), (active > 0.5)) return fac_inds, spot_inds","title":"Region Identification"},{"location":"calcs/region/#identification-of-solar-regions","text":"In order to calculate the various pertinent velocity components we need to differentiate between different types of solar regions. We do two identification cuts both based on thresholding values from Yeo et al. 2013.","title":"Identification of Solar Regions"},{"location":"calcs/region/#magnetic-thresholding","text":"The first threshold is to identify active regions. We do this with a simple magnetic threshold value of 24G. The threshold is applied to the unsigned magnetic field strength (magnetic field corrected for foreshortening). Pixels with magnetic field magnitude above the threshold are marked as active and those below the threshold are quiet Sun. Isolated active pixels are re-identified as quiet Sun based on area thresholding. Isolated pixels are denoted as any pixel without four connected neighbors. The connection matrix is built using the skimage.regionsprops function. def mag_thresh(mu, mmap, Br_cutoff=24, mu_cutoff=0.3): \"\"\" function to calculate magnetic threshold and differentiate between magnetically active regions and quiet Sun Parameters ---------- mu: array of mu (cosine theta) values mmap: corrected (unsigned magnetic field) Sunpy map object (Magnetogram) Br_cutoff: minimum cutoff value (in Gauss) for thresholding active regions mu_cutoff: minimum mu cutoff value for data to ignore Returns ------- active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 \"\"\" # get active region indices active_inds = np.where(np.abs(mmap.data) * mu > Br_cutoff) bad_mu = np.where(mu <= mu_cutoff) # make active region array active = np.zeros(mu.shape) active[active_inds] = 1. active[bad_mu] = 0. # find isolated pixels # get area y_labeled = label(active, connectivity=2, background=0) y_area = [props.area for props in regionprops(y_labeled)] # area constraint good_area = np.where(np.array(y_area) > 5) good_area = good_area[0] + 1 active_indices = np.isin(y_labeled, good_area) # create weights array active[~active_indices] = 0 # get quiet indices quiet = 1 - active return active, quiet","title":"Magnetic Thresholding"},{"location":"calcs/region/#intensity-thresholding","text":"Once we detect active regions, we want to identify sunspots (dark) and faculae/network regions (bright). These regions can be categorized by intensity and therefore an intensity cutoff value is calculated relative to the overall mean flattened intensity of the quiet Sun: I_{quiet} = \\frac{\\sum_{ij} I_{flat, ij} W_{ij}}{\\sum_{ij} W_{ij}} I_{quiet} = \\frac{\\sum_{ij} I_{flat, ij} W_{ij}}{\\sum_{ij} W_{ij}} where W_{ij} W_{ij} is the quiet Sun weighting array returned from magnetic thresholding. The intensity threshold is then I_{thresh} = 0.89I_{quiet} I_{thresh} = 0.89I_{quiet} . def int_thresh(map_int_cor, active, quiet): \"\"\" function to do intensity thresholding and differentiate between faculae (bright) and sunspots (dark) Parameters ---------- map_int_cor: corrected (limb-darkening) Sunpy map object (Intensitygram) active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 Returns ------- fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected \"\"\" # flattened intensity data Iflat = map_int_cor.data # calculate quiet sun intensity int_quiet = np.nansum(Iflat * quiet) / np.nansum(quiet) # intensity threshold int_cutoff = 0.89 * int_quiet # get faculae fac_inds = np.logical_and((Iflat > int_cutoff), (active > 0.5)) # get sunspots spot_inds = np.logical_and((Iflat <= int_cutoff), (active > 0.5)) return fac_inds, spot_inds","title":"Intensity Thresholding"},{"location":"calcs/rv/","text":"Calculation of Model RVs The model 'sun-as-a-star' RV variation is a linear combination of the two velocity components \\rm (v_{phot}, v_{conv}) \\rm (v_{phot}, v_{conv}) along with an offset factor of \\rm RV_0 \\rm RV_0 : RV = A \\ v_{phot} + B \\ v_{conv} + RV_0 RV = A \\ v_{phot} + B \\ v_{conv} + RV_0 Our ability to take high resolution measurements of the Solar disk is what allows us to decompose the overall RV variation into these two components. Because we do not know the relative weights of these two velocity components, we rely on regression to determine the best parameters to fit our ground based data. We calculate scaling factors A and B, and then offset \\rm RV_0 \\rm RV_0 using linear regression. The need for these scaling coefficients arises due to the fact that SDO/HMI images the Sun in one wavelength (6173 A) while ground based measurements average over thousands of lines. The offset accounts for the zero point of the instrument. An example of the linear regression model used can be found here .","title":"RV Calculation"},{"location":"calcs/rv/#calculation-of-model-rvs","text":"The model 'sun-as-a-star' RV variation is a linear combination of the two velocity components \\rm (v_{phot}, v_{conv}) \\rm (v_{phot}, v_{conv}) along with an offset factor of \\rm RV_0 \\rm RV_0 : RV = A \\ v_{phot} + B \\ v_{conv} + RV_0 RV = A \\ v_{phot} + B \\ v_{conv} + RV_0 Our ability to take high resolution measurements of the Solar disk is what allows us to decompose the overall RV variation into these two components. Because we do not know the relative weights of these two velocity components, we rely on regression to determine the best parameters to fit our ground based data. We calculate scaling factors A and B, and then offset \\rm RV_0 \\rm RV_0 using linear regression. The need for these scaling coefficients arises due to the fact that SDO/HMI images the Sun in one wavelength (6173 A) while ground based measurements average over thousands of lines. The offset accounts for the zero point of the instrument. An example of the linear regression model used can be found here .","title":"Calculation of Model RVs"},{"location":"calcs/vel/","text":"Calculation of RV Components After the data corrections and identification of solar regions, we are able to calculate the individual feature components that make up the SDO derived RV variation. Photometric Velocity Estimation of rotational Doppler imbalance due to active (faculae/darkspot) regions Calculation of the photometric velocity due the presence of dark spots/bright faculae causing a Doppler imbalance. Requires an active region based magnetic weighting array. A weight of 0 denotes quiet-Sun and 1 denotes active pixel. The equation for this velocity calculation is: v_{phot} = \\frac{\\sum_{ij} v_{rot, ij} (I_{ij} - K) W_{ij}} {\\sum_{ij} I_{ij}} v_{phot} = \\frac{\\sum_{ij} v_{rot, ij} (I_{ij} - K) W_{ij}} {\\sum_{ij} I_{ij}} . K is a scaling factor based off the limb-brightening correction. K = \\frac{\\sum_{ij} I_{ij} L_{ij} W_{ij}}{\\sum_{ij} L_{ij}^2 W_{ij}} K = \\frac{\\sum_{ij} I_{ij} L_{ij} W_{ij}}{\\sum_{ij} L_{ij}^2 W_{ij}} In this case I_{ij} I_{ij} is the uncorrected (original) intensity data. We are also able to use out intensity weighting to calculate photometric velocities due to just faculae/network or sunspot regions. def v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds, mu_cutoff=0.3): \"\"\" function to calculate photometric velocity due to rotational Doppler variation Parameters ---------- quiet: weights array where active pixels have weight = 0 active: weights array where active pixels have weight = 1 Lij: limb-darkening polynomial function vrot: solar rotational velocity imap: UNCORRECTED Sunpy map object (Intensitygram) mu: array of mu values fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns ------- v_phot: photospheric velocity perturbation (float) \"\"\" # get good mu values good_mu = np.where(mu > mu_cutoff) # calculate K scaling factor K = np.nansum(imap.data * Lij * quiet) / np.sum((Lij[good_mu] ** 2) * quiet[good_mu]) # calculate photospheric velocity v_phot = np.nansum(np.real(vrot) * (imap.data - K * Lij) * active) / np.nansum(imap.data) # faculae driven photospheric velocity vphot_bright = np.nansum(np.real(vrot) * (imap.data - K * Lij) * fac_inds) / np.nansum(imap.data) # sunspots driven photospheric velocity vphot_spot = np.nansum(np.real(vrot) * (imap.data - K * Lij) * spot_inds) / np.nansum(imap.data) return v_phot, vphot_bright, vphot_spot Convective Velocity Estimation due to the suppression of the convective blueshift by magnetically active regions Calculation of suppression of convective blueshift due to active regions (mainly faculae). The basic premise for calculation is to calculate the disc-averaged velocity of the Sun and subtract from that the quiet-Sun velocity. We then calculate the convective velocity as what is left: v_{conv} = v - v_{quiet} v_{conv} = v - v_{quiet} Quiet Sun Velocity Estimation of average RV of quiet-Sun due to convective motion Calculate the velocity due to the convective motion of the quiet-Sun. Requires the magnetic weighting array we built in step six, part one. A weight of 0 denotes an active pixel, and 1 is quiet-Sun. The equation for calculating this quiet-Sun velocity is as such: v_{quiet} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij} W_{ij}} {\\sum_{ij} I_{ij} W_{ij}} v_{quiet} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij} W_{ij}} {\\sum_{ij} I_{ij} W_{ij}} . In this case I_{ij} I_{ij} is the uncorrected (original) intensity data. def v_quiet(map_vel_cor, imap, quiet): \"\"\" function to calculate velocity due to convective motion of quiet-Sun Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) quiet: weights array where active pixels have weight = 0 Returns ------- v_quiet: quiet-Sun velocity (float) \"\"\" v_quiet = np.nansum(map_vel_cor.data * imap.data * quiet) / np.nansum( imap.data * quiet) return v_quiet Full Disc Velocity Total corrected (spacecraft motion, solar rotation) disk-averaged velocity of the Sun The equation for the disc-averaged velocity is: v = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij}}{\\sum_{ij} I_{ij}} v = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij}}{\\sum_{ij} I_{ij}} . def v_disc(map_vel_cor, imap): \"\"\" function to calculate disc-averaged velocity of Sun Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- v_disc: disc averaged velocity of Sun (float) \"\"\" v_disc = np.nansum(map_vel_cor.data * imap.data) / np.nansum(imap.data) return v_disc","title":"Velocity Components"},{"location":"calcs/vel/#calculation-of-rv-components","text":"After the data corrections and identification of solar regions, we are able to calculate the individual feature components that make up the SDO derived RV variation.","title":"Calculation of RV Components"},{"location":"calcs/vel/#photometric-velocity","text":"Estimation of rotational Doppler imbalance due to active (faculae/darkspot) regions Calculation of the photometric velocity due the presence of dark spots/bright faculae causing a Doppler imbalance. Requires an active region based magnetic weighting array. A weight of 0 denotes quiet-Sun and 1 denotes active pixel. The equation for this velocity calculation is: v_{phot} = \\frac{\\sum_{ij} v_{rot, ij} (I_{ij} - K) W_{ij}} {\\sum_{ij} I_{ij}} v_{phot} = \\frac{\\sum_{ij} v_{rot, ij} (I_{ij} - K) W_{ij}} {\\sum_{ij} I_{ij}} . K is a scaling factor based off the limb-brightening correction. K = \\frac{\\sum_{ij} I_{ij} L_{ij} W_{ij}}{\\sum_{ij} L_{ij}^2 W_{ij}} K = \\frac{\\sum_{ij} I_{ij} L_{ij} W_{ij}}{\\sum_{ij} L_{ij}^2 W_{ij}} In this case I_{ij} I_{ij} is the uncorrected (original) intensity data. We are also able to use out intensity weighting to calculate photometric velocities due to just faculae/network or sunspot regions. def v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds, mu_cutoff=0.3): \"\"\" function to calculate photometric velocity due to rotational Doppler variation Parameters ---------- quiet: weights array where active pixels have weight = 0 active: weights array where active pixels have weight = 1 Lij: limb-darkening polynomial function vrot: solar rotational velocity imap: UNCORRECTED Sunpy map object (Intensitygram) mu: array of mu values fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns ------- v_phot: photospheric velocity perturbation (float) \"\"\" # get good mu values good_mu = np.where(mu > mu_cutoff) # calculate K scaling factor K = np.nansum(imap.data * Lij * quiet) / np.sum((Lij[good_mu] ** 2) * quiet[good_mu]) # calculate photospheric velocity v_phot = np.nansum(np.real(vrot) * (imap.data - K * Lij) * active) / np.nansum(imap.data) # faculae driven photospheric velocity vphot_bright = np.nansum(np.real(vrot) * (imap.data - K * Lij) * fac_inds) / np.nansum(imap.data) # sunspots driven photospheric velocity vphot_spot = np.nansum(np.real(vrot) * (imap.data - K * Lij) * spot_inds) / np.nansum(imap.data) return v_phot, vphot_bright, vphot_spot","title":"Photometric Velocity"},{"location":"calcs/vel/#convective-velocity","text":"Estimation due to the suppression of the convective blueshift by magnetically active regions Calculation of suppression of convective blueshift due to active regions (mainly faculae). The basic premise for calculation is to calculate the disc-averaged velocity of the Sun and subtract from that the quiet-Sun velocity. We then calculate the convective velocity as what is left: v_{conv} = v - v_{quiet} v_{conv} = v - v_{quiet}","title":"Convective Velocity"},{"location":"calcs/vel/#quiet-sun-velocity","text":"Estimation of average RV of quiet-Sun due to convective motion Calculate the velocity due to the convective motion of the quiet-Sun. Requires the magnetic weighting array we built in step six, part one. A weight of 0 denotes an active pixel, and 1 is quiet-Sun. The equation for calculating this quiet-Sun velocity is as such: v_{quiet} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij} W_{ij}} {\\sum_{ij} I_{ij} W_{ij}} v_{quiet} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij} W_{ij}} {\\sum_{ij} I_{ij} W_{ij}} . In this case I_{ij} I_{ij} is the uncorrected (original) intensity data. def v_quiet(map_vel_cor, imap, quiet): \"\"\" function to calculate velocity due to convective motion of quiet-Sun Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) quiet: weights array where active pixels have weight = 0 Returns ------- v_quiet: quiet-Sun velocity (float) \"\"\" v_quiet = np.nansum(map_vel_cor.data * imap.data * quiet) / np.nansum( imap.data * quiet) return v_quiet","title":"Quiet Sun Velocity"},{"location":"calcs/vel/#full-disc-velocity","text":"Total corrected (spacecraft motion, solar rotation) disk-averaged velocity of the Sun The equation for the disc-averaged velocity is: v = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij}}{\\sum_{ij} I_{ij}} v = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij}}{\\sum_{ij} I_{ij}} . def v_disc(map_vel_cor, imap): \"\"\" function to calculate disc-averaged velocity of Sun Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- v_disc: disc averaged velocity of Sun (float) \"\"\" v_disc = np.nansum(map_vel_cor.data * imap.data) / np.nansum(imap.data) return v_disc","title":"Full Disc Velocity"},{"location":"data/corr/","text":"Data Corrections For the calculation of independent RV's, we used the three main HMI data products: Dopplergrams, Intensitygrams, Magnetograms. These data products must be corrected for inherent issues such as relative velocityn between the spacecraft and Sun, limb-darkening, and foreshortening. Velocity Corrections Dopplergrams are maps of solar surface velocity that must be corrected for the relative spacecraft velocity, and the velocity due to solar rotation. Spacecraft Velocity Correction: v_{sc, ij} = - \\frac{\\delta w_{ij} v_{sc, w_{ij}} + \\delta n_{ij} v_{sc, n{ij}} + \\delta r_{ij} v_{sc, r_{ij}}}{d_{ij}} v_{sc, ij} = - \\frac{\\delta w_{ij} v_{sc, w_{ij}} + \\delta n_{ij} v_{sc, n{ij}} + \\delta r_{ij} v_{sc, r_{ij}}}{d_{ij}} where d_{ij} = \\sqrt{\\delta w_{ij} ^ 2 + \\delta n_{ij} ^ 2 + \\delta r_{ij} ^ 2 } d_{ij} = \\sqrt{\\delta w_{ij} ^ 2 + \\delta n_{ij} ^ 2 + \\delta r_{ij} ^ 2 } . def spacecraft_vel(deltaw, deltan, deltar, dij, vmap): \"\"\" function to calculate pixel-wise spacecraft velocities for Sunpy map Parameters ---------- deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) Returns ------- vsc: array of spacecraft velocities \"\"\" # velocity of spacecraft relative to sun vscw = vmap.meta['obs_vw'] vscn = vmap.meta['obs_vn'] vscr = vmap.meta['obs_vr'] # pixel-wise magnitude of spacecraft velocity vsc = - (deltaw * vscw + deltan * vscn + deltar * vscr) / dij return vsc Solar Rotational Velocity: We calculate the solar differential rotation profile based on Snodgrass & Ulrich (1990). \\omega(\\theta) = \\alpha_1 + \\alpha_2 sin^2{\\theta} + \\alpha_3 sin^4{\\theta} \\omega(\\theta) = \\alpha_1 + \\alpha_2 sin^2{\\theta} + \\alpha_3 sin^4{\\theta} \\theta \\theta is latitude and the \\alpha \\alpha values are 14.713, -2.396, -1.787 14.713, -2.396, -1.787 respectively. def solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters=[14.713, -2.396, -1.787]): \"\"\" function to calculate pixel-wise velocities due to solar rotation Parameters ---------- wij: array of westward values for image nij: array of northward values for image rij: array of radius values for image deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) a_parameters: array of solar differential rotation parameters from Snodgrass & Ulrich (1990). Returns ------- vrot: array of solar rotation velocities \"\"\" # apply to cartesian coordinates x1 = wij y1 = nij * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) + rij * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) z1 = - nij * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) + rij * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) hx = x1 * np.cos(np.deg2rad(vmap.meta['crln_obs'])) + z1 * np.sin(np.deg2rad(vmap.meta['crln_obs'])) hy = y1 hz = -x1 * np.sin(np.deg2rad(vmap.meta['crln_obs'])) + z1 * np.cos(np.deg2rad(vmap.meta['crln_obs'])) # apply parameters to determine vrot for given image pixel w = (a_parameters[0] + a_parameters[1] * ((np.sin(hy)) ** 2) + a_parameters[2] * ( (np.sin(hy)) ** 4)) * 1. / 86400. * np.pi / 180. # get projection of solar rotation vx_rot = w * hz * vmap.meta['rsun_ref'] vy_rot = 0. vz_rot = -w * hx * vmap.meta['rsun_ref'] v1 = np.cos(np.deg2rad(vmap.meta['crln_obs'])) * vx_rot - np.sin(np.deg2rad(vmap.meta['crln_obs'])) * vz_rot v2 = vy_rot v3 = np.sin(np.deg2rad(vmap.meta['crln_obs'])) * vx_rot + np.cos(np.deg2rad(vmap.meta['crln_obs'])) * vz_rot # project into correct direction vrotw = v1 vrotn = v2 * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) - v3 * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) vrotr = v2 * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) + v3 * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) # get full rotational velocity vrot = (deltaw * vrotw + deltan * vrotn + deltar * vrotr) / dij return vrot Intensity Corrections Intensitygrams are continuum filtergrams that need to be corrected for limb-darkening. We account for limb-darkening and flatten the continuum using the limb-darkening polynomial calculated in Allen's Astrophysical Quantities (1993). L_{ij} = 1 - u_2 - v_2 + u_2 cos \\theta + v_2 cos^2 \\theta L_{ij} = 1 - u_2 - v_2 + u_2 cos \\theta + v_2 cos^2 \\theta The functions for limb-darkening corrections can be found here . Magnetic Field Corrections Magnetograms (line-of-sight longitudinal magnetic field maps) are used to determine magnetic observables that show strong correlation with RV variation and velocity components. Due to foreshortening, the observed magnetic field (magnetogram data) is less than the true radial field by a factor of \\mu = cos(\\theta) \\mu = cos(\\theta) . We correct for this as seen here: B_{r, ij} = B{obs, ij} / \\mu_{ij} B_{r, ij} = B{obs, ij} / \\mu_{ij} . Additionally, we set all magnetic pixels with magnitude lower than the noise level (8G) to 0.","title":"Data Corrections"},{"location":"data/corr/#data-corrections","text":"For the calculation of independent RV's, we used the three main HMI data products: Dopplergrams, Intensitygrams, Magnetograms. These data products must be corrected for inherent issues such as relative velocityn between the spacecraft and Sun, limb-darkening, and foreshortening.","title":"Data Corrections"},{"location":"data/corr/#velocity-corrections","text":"Dopplergrams are maps of solar surface velocity that must be corrected for the relative spacecraft velocity, and the velocity due to solar rotation. Spacecraft Velocity Correction: v_{sc, ij} = - \\frac{\\delta w_{ij} v_{sc, w_{ij}} + \\delta n_{ij} v_{sc, n{ij}} + \\delta r_{ij} v_{sc, r_{ij}}}{d_{ij}} v_{sc, ij} = - \\frac{\\delta w_{ij} v_{sc, w_{ij}} + \\delta n_{ij} v_{sc, n{ij}} + \\delta r_{ij} v_{sc, r_{ij}}}{d_{ij}} where d_{ij} = \\sqrt{\\delta w_{ij} ^ 2 + \\delta n_{ij} ^ 2 + \\delta r_{ij} ^ 2 } d_{ij} = \\sqrt{\\delta w_{ij} ^ 2 + \\delta n_{ij} ^ 2 + \\delta r_{ij} ^ 2 } . def spacecraft_vel(deltaw, deltan, deltar, dij, vmap): \"\"\" function to calculate pixel-wise spacecraft velocities for Sunpy map Parameters ---------- deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) Returns ------- vsc: array of spacecraft velocities \"\"\" # velocity of spacecraft relative to sun vscw = vmap.meta['obs_vw'] vscn = vmap.meta['obs_vn'] vscr = vmap.meta['obs_vr'] # pixel-wise magnitude of spacecraft velocity vsc = - (deltaw * vscw + deltan * vscn + deltar * vscr) / dij return vsc Solar Rotational Velocity: We calculate the solar differential rotation profile based on Snodgrass & Ulrich (1990). \\omega(\\theta) = \\alpha_1 + \\alpha_2 sin^2{\\theta} + \\alpha_3 sin^4{\\theta} \\omega(\\theta) = \\alpha_1 + \\alpha_2 sin^2{\\theta} + \\alpha_3 sin^4{\\theta} \\theta \\theta is latitude and the \\alpha \\alpha values are 14.713, -2.396, -1.787 14.713, -2.396, -1.787 respectively. def solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters=[14.713, -2.396, -1.787]): \"\"\" function to calculate pixel-wise velocities due to solar rotation Parameters ---------- wij: array of westward values for image nij: array of northward values for image rij: array of radius values for image deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) a_parameters: array of solar differential rotation parameters from Snodgrass & Ulrich (1990). Returns ------- vrot: array of solar rotation velocities \"\"\" # apply to cartesian coordinates x1 = wij y1 = nij * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) + rij * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) z1 = - nij * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) + rij * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) hx = x1 * np.cos(np.deg2rad(vmap.meta['crln_obs'])) + z1 * np.sin(np.deg2rad(vmap.meta['crln_obs'])) hy = y1 hz = -x1 * np.sin(np.deg2rad(vmap.meta['crln_obs'])) + z1 * np.cos(np.deg2rad(vmap.meta['crln_obs'])) # apply parameters to determine vrot for given image pixel w = (a_parameters[0] + a_parameters[1] * ((np.sin(hy)) ** 2) + a_parameters[2] * ( (np.sin(hy)) ** 4)) * 1. / 86400. * np.pi / 180. # get projection of solar rotation vx_rot = w * hz * vmap.meta['rsun_ref'] vy_rot = 0. vz_rot = -w * hx * vmap.meta['rsun_ref'] v1 = np.cos(np.deg2rad(vmap.meta['crln_obs'])) * vx_rot - np.sin(np.deg2rad(vmap.meta['crln_obs'])) * vz_rot v2 = vy_rot v3 = np.sin(np.deg2rad(vmap.meta['crln_obs'])) * vx_rot + np.cos(np.deg2rad(vmap.meta['crln_obs'])) * vz_rot # project into correct direction vrotw = v1 vrotn = v2 * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) - v3 * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) vrotr = v2 * np.sin(np.deg2rad(vmap.meta['crlt_obs'])) + v3 * np.cos(np.deg2rad(vmap.meta['crlt_obs'])) # get full rotational velocity vrot = (deltaw * vrotw + deltan * vrotn + deltar * vrotr) / dij return vrot","title":"Velocity Corrections"},{"location":"data/corr/#intensity-corrections","text":"Intensitygrams are continuum filtergrams that need to be corrected for limb-darkening. We account for limb-darkening and flatten the continuum using the limb-darkening polynomial calculated in Allen's Astrophysical Quantities (1993). L_{ij} = 1 - u_2 - v_2 + u_2 cos \\theta + v_2 cos^2 \\theta L_{ij} = 1 - u_2 - v_2 + u_2 cos \\theta + v_2 cos^2 \\theta The functions for limb-darkening corrections can be found here .","title":"Intensity Corrections"},{"location":"data/corr/#magnetic-field-corrections","text":"Magnetograms (line-of-sight longitudinal magnetic field maps) are used to determine magnetic observables that show strong correlation with RV variation and velocity components. Due to foreshortening, the observed magnetic field (magnetogram data) is less than the true radial field by a factor of \\mu = cos(\\theta) \\mu = cos(\\theta) . We correct for this as seen here: B_{r, ij} = B{obs, ij} / \\mu_{ij} B_{r, ij} = B{obs, ij} / \\mu_{ij} . Additionally, we set all magnetic pixels with magnitude lower than the noise level (8G) to 0.","title":"Magnetic Field Corrections"},{"location":"data/data/","text":"Data Used This project requires the use of data from the Helioseismic and Magnetic Imager (HMI) aboard NASA's Solar Dynamics Observatory (SDO). HMI has been continuously imaging the Sun since 2012 in the 6173 A line. HMI takes images every 45 seconds and provides information regarding the velocity, continuum, and magnetic field of the Solar photosphere. Additional information regarding the instrument can be found on the HMI webpage . The three data products used for these calculations are: Dopplergrams : measurements of solar surface velocity Filtergrams : continuum intensity measurements of the Solar photosphere Magnetograms : measurements of the line-of-sight Solar magnetic field Acquiring the Data To get the HMI data products, we utilize Sunpy's querying techniques. This allows us to search by date, physical observable, and instrument. An example of querying Sunpy for data can be found here .","title":"Aquiring Data"},{"location":"data/data/#data-used","text":"This project requires the use of data from the Helioseismic and Magnetic Imager (HMI) aboard NASA's Solar Dynamics Observatory (SDO). HMI has been continuously imaging the Sun since 2012 in the 6173 A line. HMI takes images every 45 seconds and provides information regarding the velocity, continuum, and magnetic field of the Solar photosphere. Additional information regarding the instrument can be found on the HMI webpage . The three data products used for these calculations are: Dopplergrams : measurements of solar surface velocity Filtergrams : continuum intensity measurements of the Solar photosphere Magnetograms : measurements of the line-of-sight Solar magnetic field","title":"Data Used"},{"location":"data/data/#acquiring-the-data","text":"To get the HMI data products, we utilize Sunpy's querying techniques. This allows us to search by date, physical observable, and instrument. An example of querying Sunpy for data can be found here .","title":"Acquiring the Data"},{"location":"examples/docs_full_pipeline/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. 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In [ ]: Copied! # name of csv file to store calculations csv_name = 'csv_name' # date range start_date = datetime . datetime ( 2021 , 8 , 26 , 8 , 00 , 0 ) end_date = datetime . datetime ( 2021 , 9 , 3 , 0 , 00 , 0 ) # query cadence in seconds cadence = 24 * 3600 # name of csv file to store calculations csv_name = 'csv_name' # date range start_date = datetime.datetime(2021, 8, 26, 8, 00, 0) end_date = datetime.datetime(2021, 9, 3, 0, 00, 0) # query cadence in seconds cadence = 24*3600 Setup CSV file and SDO/HMI querying parameters. In [ ]: Copied! # create file names csv_name = os . path . join ( CsvDir . CALC , csv_name + '.csv' ) bad_dates_csv = os . path . join ( CsvDir . CALC , csv_name + '_bad_dates.csv' ) # print out csv title print ( \"Beginning calculation of values for csv file: \" + csv_name ) # List of header strings row_contents = [ 'date_obs' , 'date_jd' , 'v_quiet' , 'v_disc' , 'v_phot' , 'v_conv' , 'f_bright' , 'f_spot' , 'f' , 'Bobs' , 'vphot_bright' , 'vphot_spot' , 'f_small' , 'f_large' , 'f_network' , 'f_plage' , 'f_nonconv' , 'quiet_flux' , 'ar_flux' , 'conv_flux' , 'pol_flux' , 'pol_conv_flux' , 'vconv_quiet' , 'vconv_large' , 'vconv_small' ] # Append a list as new line to an old csv file utils . append_list_as_row ( csv_name , row_contents ) # get hmi data products time_range = datetime . timedelta ( seconds = 22 ) physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] # get dates list xy = ( end_date - start_date ) . seconds + ( end_date - start_date ) . days * 24 * 3600 dates_list = [ start_date + datetime . timedelta ( seconds = cadence * x ) for x in range ( 0 , int ( xy / cadence ))] # create file names csv_name = os.path.join(CsvDir.CALC, csv_name+'.csv') bad_dates_csv = os.path.join(CsvDir.CALC, csv_name+'_bad_dates.csv') # print out csv title print(\"Beginning calculation of values for csv file: \" + csv_name) # List of header strings row_contents = ['date_obs', 'date_jd', 'v_quiet', 'v_disc', 'v_phot', 'v_conv', 'f_bright', 'f_spot', 'f', 'Bobs', 'vphot_bright', 'vphot_spot', 'f_small', 'f_large', 'f_network', 'f_plage', 'f_nonconv', 'quiet_flux', 'ar_flux', 'conv_flux', 'pol_flux', 'pol_conv_flux', 'vconv_quiet', 'vconv_large', 'vconv_small'] # Append a list as new line to an old csv file utils.append_list_as_row(csv_name, row_contents) # get hmi data products time_range = datetime.timedelta(seconds=22) physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] # get dates list xy = (end_date - start_date).seconds + (end_date - start_date).days * 24 * 3600 dates_list = [start_date + datetime.timedelta(seconds=cadence*x) for x in range(0, int(xy/cadence))] Component calculations \u00b6 Run through list to calculate and save values. In [ ]: Copied! for i , date in enumerate ( dates_list ): # convert the date to a string -- required for use in csv file date_str , date_obj , date_jd = utils . get_dates ( date ) # pull image within specified time range result = Fido . search ( a . Time ( str ( date_obj - time_range ), str ( date_obj + time_range )), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ]) # add file to list file_download = Fido . fetch ( result ) # remove unusable file types good_files = [] for file in file_download : name , extension = os . path . splitext ( file ) if extension == '.fits' : good_files . append ( file ) if len ( good_files ) != 3 : # add the data # append these values to the csv file save_vals = [ date_str , 'not three good files' ] utils . append_list_as_row ( bad_dates_csv , save_vals ) # print that the files are missing print ( ' \\n Not three good files: ' + date_str + ' index: ' + str ( i )) pass else : # convert to map sequence map_seq = sunpy . map . Map ( sorted ( good_files )) # check for missing data types missing_map = False # split into data types for j , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : vmap = map_obj elif map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : mmap = map_obj elif map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : imap = map_obj else : missing_map = True if missing_map : print ( \"Missing a data product for \" + date_str ) # add the data # append these values to the csv file save_vals = [ date_str , 'missing data product' ] utils . append_list_as_row ( bad_dates_csv , save_vals ) pass else : # coordinate transformation for maps x , y , pd , r , d , mu = ctfuncs . coordinates ( vmap ) wij , nij , rij = ctfuncs . vel_coords ( x , y , pd , r , vmap ) # remove bad mu values vmap , mmap , imap = ctfuncs . fix_mu ( mu , [ vmap , mmap , imap ]) # calculate relative positions deltaw , deltan , deltar , dij = sfuncs . rel_positions ( wij , nij , rij , vmap ) # calculate spacecraft velocity vsc = sfuncs . spacecraft_vel ( deltaw , deltan , deltar , dij , vmap ) # optimized solar rotation parameters a1 = 14.713 a2 = - 2.396 a3 = - 1.787 a_parameters = [ a1 , a2 , a3 ] # calculation of solar rotation velocity vrot = sfuncs . solar_rot_vel ( wij , nij , rij , deltaw , deltan , deltar , dij , vmap , a_parameters ) # calculate corrected velocity corrected_vel = vmap . data - np . real ( vsc ) - np . real ( vrot ) # corrected velocity maps map_vel_cor = sfuncs . corrected_map ( corrected_vel , vmap , map_type = 'Corrected-Dopplergram' , frame = frames . HeliographicCarrington ) # limb brightening Lij = lbfuncs . limb_polynomial ( imap ) # calculate corrected data Iflat = imap . data / Lij # corrected intensity maps map_int_cor = sfuncs . corrected_map ( Iflat , imap , map_type = 'Corrected-Intensitygram' , frame = frames . HeliographicCarrington ) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs , Br = sfuncs . mag_field ( mu , mmap , B_noise ) # corrected observed magnetic data map map_mag_obs = sfuncs . corrected_map ( Bobs , mmap , map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) # radial magnetic data map map_mag_cor = sfuncs . corrected_map ( Br , mmap , map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active , quiet = sfuncs . mag_thresh ( mu , mmap , Br_cutoff = Br_cutoff , mu_cutoff = mu_cutoff ) # calculate intensity threshold fac_inds , spot_inds = sfuncs . int_thresh ( map_int_cor , active , quiet ) # create threshold array thresh_arr = sfuncs . thresh_map ( fac_inds , spot_inds ) # full threshold maps map_full_thresh = sfuncs . corrected_map ( thresh_arr , mmap , map_type = 'Threshold' , frame = frames . HeliographicCarrington ) ### velocity contribution due to convective motion of quiet-Sun v_quiet = sfuncs . v_quiet ( map_vel_cor , imap , quiet ) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot , vphot_bright , vphot_spot = sfuncs . v_phot ( quiet , active , Lij , vrot , imap , mu , fac_inds , spot_inds ) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = sfuncs . v_disc ( map_vel_cor , imap ) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright , f_spot , f = sfuncs . filling_factor ( mu , mmap , active , fac_inds , spot_inds ) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs . unsigned_flux ( map_mag_obs , imap ) ### calculate the area filling factor pixA_hem = ctfuncs . pix_area_hem ( wij , nij , rij , vmap ) area = sfuncs . area_calc ( active , pixA_hem ) f_small , f_large , f_network , f_plage , f_nonconv = sfuncs . area_filling_factor ( active , area , mu , mmap , fac_inds ) ### get the unsigned flux quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux = sfuncs . area_unsigned_flux ( map_mag_obs , imap , area , active ) ### get area weighted convective velocities vconv_quiet , vconv_large , vconv_small = sfuncs . area_vconv ( map_vel_cor , imap , active , area ) # make array of what we want to save save_vals = [ v_quiet , v_disc , v_phot , v_conv , f_bright , f_spot , f , unsigned_obs_flux , vphot_bright , vphot_spot , f_small , f_large , f_network , f_plage , f_nonconv , quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux , vconv_quiet , vconv_large , vconv_small ] # round stuff rounded = np . around ( save_vals , 3 ) round_vals = [ date_str , date_jd ] for val in rounded : round_vals . append ( val ) # append these values to the csv file utils . append_list_as_row ( csv_name , round_vals ) # print that the date is completed print ( ' \\n Calculations and save to file complete for ' + date_str + ' index: ' + str ( i )) print ( 'Calculation complete for dates:' , start_date , 'to' , end_date ) for i, date in enumerate(dates_list): # convert the date to a string -- required for use in csv file date_str, date_obj, date_jd = utils.get_dates(date) # pull image within specified time range result = Fido.search(a.Time(str(date_obj - time_range), str(date_obj + time_range)), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2]) # add file to list file_download = Fido.fetch(result) # remove unusable file types good_files = [] for file in file_download: name, extension = os.path.splitext(file) if extension == '.fits': good_files.append(file) if len(good_files) != 3: # add the data # append these values to the csv file save_vals = [date_str, 'not three good files'] utils.append_list_as_row(bad_dates_csv, save_vals) # print that the files are missing print('\\nNot three good files: ' + date_str + ' index: ' + str(i)) pass else: # convert to map sequence map_seq = sunpy.map.Map(sorted(good_files)) # check for missing data types missing_map = False # split into data types for j, map_obj in enumerate(map_seq): if map_obj.meta['content'] == 'DOPPLERGRAM': vmap = map_obj elif map_obj.meta['content'] == 'MAGNETOGRAM': mmap = map_obj elif map_obj.meta['content'] == 'CONTINUUM INTENSITY': imap = map_obj else: missing_map = True if missing_map: print(\"Missing a data product for \" + date_str) # add the data # append these values to the csv file save_vals = [date_str, 'missing data product'] utils.append_list_as_row(bad_dates_csv, save_vals) pass else: # coordinate transformation for maps x, y, pd, r, d, mu = ctfuncs.coordinates(vmap) wij, nij, rij = ctfuncs.vel_coords(x, y, pd, r, vmap) # remove bad mu values vmap, mmap, imap = ctfuncs.fix_mu(mu, [vmap, mmap, imap]) # calculate relative positions deltaw, deltan, deltar, dij = sfuncs.rel_positions(wij, nij, rij, vmap) # calculate spacecraft velocity vsc = sfuncs.spacecraft_vel(deltaw, deltan, deltar, dij, vmap) # optimized solar rotation parameters a1 = 14.713 a2 = -2.396 a3 = -1.787 a_parameters = [a1, a2, a3] # calculation of solar rotation velocity vrot = sfuncs.solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters) # calculate corrected velocity corrected_vel = vmap.data - np.real(vsc) - np.real(vrot) # corrected velocity maps map_vel_cor = sfuncs.corrected_map(corrected_vel, vmap, map_type='Corrected-Dopplergram', frame=frames.HeliographicCarrington) # limb brightening Lij = lbfuncs.limb_polynomial(imap) # calculate corrected data Iflat = imap.data / Lij # corrected intensity maps map_int_cor = sfuncs.corrected_map(Iflat, imap, map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs, Br = sfuncs.mag_field(mu, mmap, B_noise) # corrected observed magnetic data map map_mag_obs = sfuncs.corrected_map(Bobs, mmap, map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) # radial magnetic data map map_mag_cor = sfuncs.corrected_map(Br, mmap, map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active, quiet = sfuncs.mag_thresh(mu, mmap, Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) # calculate intensity threshold fac_inds, spot_inds = sfuncs.int_thresh(map_int_cor, active, quiet) # create threshold array thresh_arr = sfuncs.thresh_map(fac_inds, spot_inds) # full threshold maps map_full_thresh = sfuncs.corrected_map(thresh_arr, mmap, map_type='Threshold', frame=frames.HeliographicCarrington) ### velocity contribution due to convective motion of quiet-Sun v_quiet = sfuncs.v_quiet(map_vel_cor, imap, quiet) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot, vphot_bright, vphot_spot = sfuncs.v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = sfuncs.v_disc(map_vel_cor, imap) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright, f_spot, f = sfuncs.filling_factor(mu, mmap, active, fac_inds, spot_inds) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs.unsigned_flux(map_mag_obs, imap) ### calculate the area filling factor pixA_hem = ctfuncs.pix_area_hem(wij, nij, rij, vmap) area = sfuncs.area_calc(active, pixA_hem) f_small, f_large, f_network, f_plage, f_nonconv = sfuncs.area_filling_factor(active, area, mu, mmap, fac_inds) ### get the unsigned flux quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = sfuncs.area_unsigned_flux(map_mag_obs, imap, area, active) ### get area weighted convective velocities vconv_quiet, vconv_large, vconv_small = sfuncs.area_vconv(map_vel_cor, imap, active, area) # make array of what we want to save save_vals = [v_quiet, v_disc, v_phot, v_conv, f_bright, f_spot, f, unsigned_obs_flux, vphot_bright, vphot_spot, f_small, f_large, f_network, f_plage, f_nonconv, quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux, vconv_quiet, vconv_large, vconv_small] # round stuff rounded = np.around(save_vals, 3) round_vals = [date_str, date_jd] for val in rounded: round_vals.append(val) # append these values to the csv file utils.append_list_as_row(csv_name, round_vals) # print that the date is completed print('\\nCalculations and save to file complete for ' + date_str + ' index: ' + str(i)) print('Calculation complete for dates:', start_date, 'to', end_date) Full RV Calculation \u00b6 Use the photometric and convective velocity components to calculate the full RV. Read in CSV with data. In [ ]: Copied! # csv file with rv components csv_file = os . path . join ( CsvDir . CALC , 'csv_name.csv' ) # create pandas dataframe component_df = pd . read_csv ( csv_file ) # csv file with rv components csv_file = os.path.join(CsvDir.CALC, 'csv_name.csv') # create pandas dataframe component_df = pd.read_csv(csv_file) In [ ]: Copied! # get velocities lists v_phot = component_df . v_phot . values v_conv = component_df . v_conv . values # get scaling factors A = Scaling . HARPSN . A B = Scaling . HARPSN . B RV0 = Scaling . HARPSN . RV0 # get velocities lists v_phot = component_df.v_phot.values v_conv = component_df.v_conv.values # get scaling factors A = Scaling.HARPSN.A B = Scaling.HARPSN.B RV0 = Scaling.HARPSN.RV0 In [ ]: Copied! RV = A * v_phot + B * v_conv + RV0 component_df [ \"rv_model\" ] = RV component_df . to_csv ( csv_file , index = False ) RV = A*v_phot + B*v_conv + RV0 component_df[\"rv_model\"] = RV component_df.to_csv(csv_file, index=False)","title":"Full Pipeline"},{"location":"examples/docs_full_pipeline/#outline-of-correction-and-velocity-calculations-from-sdohmi-images","text":"In [ ]: Copied! import os import time import datetime import pandas as pd import numpy as np import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a from sunpy.coordinates import frames import sdo_hmi_rvs.tools.calculation_funcs as sfuncs import sdo_hmi_rvs.tools.lbc_funcs as lbfuncs import sdo_hmi_rvs.tools.coord_funcs as ctfuncs import sdo_hmi_rvs.tools.utilities as utils from sdo_hmi_rvs.tools.settings import CsvDir from sdo_hmi_rvs.tools.settings import Scaling from sklearn.linear_model import LinearRegression import os import time import datetime import pandas as pd import numpy as np import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a from sunpy.coordinates import frames import sdo_hmi_rvs.tools.calculation_funcs as sfuncs import sdo_hmi_rvs.tools.lbc_funcs as lbfuncs import sdo_hmi_rvs.tools.coord_funcs as ctfuncs import sdo_hmi_rvs.tools.utilities as utils from sdo_hmi_rvs.tools.settings import CsvDir from sdo_hmi_rvs.tools.settings import Scaling from sklearn.linear_model import LinearRegression User inputted parameters. In [ ]: Copied! # name of csv file to store calculations csv_name = 'csv_name' # date range start_date = datetime . datetime ( 2021 , 8 , 26 , 8 , 00 , 0 ) end_date = datetime . datetime ( 2021 , 9 , 3 , 0 , 00 , 0 ) # query cadence in seconds cadence = 24 * 3600 # name of csv file to store calculations csv_name = 'csv_name' # date range start_date = datetime.datetime(2021, 8, 26, 8, 00, 0) end_date = datetime.datetime(2021, 9, 3, 0, 00, 0) # query cadence in seconds cadence = 24*3600 Setup CSV file and SDO/HMI querying parameters. In [ ]: Copied! # create file names csv_name = os . path . join ( CsvDir . CALC , csv_name + '.csv' ) bad_dates_csv = os . path . join ( CsvDir . CALC , csv_name + '_bad_dates.csv' ) # print out csv title print ( \"Beginning calculation of values for csv file: \" + csv_name ) # List of header strings row_contents = [ 'date_obs' , 'date_jd' , 'v_quiet' , 'v_disc' , 'v_phot' , 'v_conv' , 'f_bright' , 'f_spot' , 'f' , 'Bobs' , 'vphot_bright' , 'vphot_spot' , 'f_small' , 'f_large' , 'f_network' , 'f_plage' , 'f_nonconv' , 'quiet_flux' , 'ar_flux' , 'conv_flux' , 'pol_flux' , 'pol_conv_flux' , 'vconv_quiet' , 'vconv_large' , 'vconv_small' ] # Append a list as new line to an old csv file utils . append_list_as_row ( csv_name , row_contents ) # get hmi data products time_range = datetime . timedelta ( seconds = 22 ) physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] # get dates list xy = ( end_date - start_date ) . seconds + ( end_date - start_date ) . days * 24 * 3600 dates_list = [ start_date + datetime . timedelta ( seconds = cadence * x ) for x in range ( 0 , int ( xy / cadence ))] # create file names csv_name = os.path.join(CsvDir.CALC, csv_name+'.csv') bad_dates_csv = os.path.join(CsvDir.CALC, csv_name+'_bad_dates.csv') # print out csv title print(\"Beginning calculation of values for csv file: \" + csv_name) # List of header strings row_contents = ['date_obs', 'date_jd', 'v_quiet', 'v_disc', 'v_phot', 'v_conv', 'f_bright', 'f_spot', 'f', 'Bobs', 'vphot_bright', 'vphot_spot', 'f_small', 'f_large', 'f_network', 'f_plage', 'f_nonconv', 'quiet_flux', 'ar_flux', 'conv_flux', 'pol_flux', 'pol_conv_flux', 'vconv_quiet', 'vconv_large', 'vconv_small'] # Append a list as new line to an old csv file utils.append_list_as_row(csv_name, row_contents) # get hmi data products time_range = datetime.timedelta(seconds=22) physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] # get dates list xy = (end_date - start_date).seconds + (end_date - start_date).days * 24 * 3600 dates_list = [start_date + datetime.timedelta(seconds=cadence*x) for x in range(0, int(xy/cadence))]","title":"Outline of correction and velocity calculations from SDO/HMI Images"},{"location":"examples/docs_full_pipeline/#component-calculations","text":"Run through list to calculate and save values. In [ ]: Copied! for i , date in enumerate ( dates_list ): # convert the date to a string -- required for use in csv file date_str , date_obj , date_jd = utils . get_dates ( date ) # pull image within specified time range result = Fido . search ( a . Time ( str ( date_obj - time_range ), str ( date_obj + time_range )), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ]) # add file to list file_download = Fido . fetch ( result ) # remove unusable file types good_files = [] for file in file_download : name , extension = os . path . splitext ( file ) if extension == '.fits' : good_files . append ( file ) if len ( good_files ) != 3 : # add the data # append these values to the csv file save_vals = [ date_str , 'not three good files' ] utils . append_list_as_row ( bad_dates_csv , save_vals ) # print that the files are missing print ( ' \\n Not three good files: ' + date_str + ' index: ' + str ( i )) pass else : # convert to map sequence map_seq = sunpy . map . Map ( sorted ( good_files )) # check for missing data types missing_map = False # split into data types for j , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : vmap = map_obj elif map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : mmap = map_obj elif map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : imap = map_obj else : missing_map = True if missing_map : print ( \"Missing a data product for \" + date_str ) # add the data # append these values to the csv file save_vals = [ date_str , 'missing data product' ] utils . append_list_as_row ( bad_dates_csv , save_vals ) pass else : # coordinate transformation for maps x , y , pd , r , d , mu = ctfuncs . coordinates ( vmap ) wij , nij , rij = ctfuncs . vel_coords ( x , y , pd , r , vmap ) # remove bad mu values vmap , mmap , imap = ctfuncs . fix_mu ( mu , [ vmap , mmap , imap ]) # calculate relative positions deltaw , deltan , deltar , dij = sfuncs . rel_positions ( wij , nij , rij , vmap ) # calculate spacecraft velocity vsc = sfuncs . spacecraft_vel ( deltaw , deltan , deltar , dij , vmap ) # optimized solar rotation parameters a1 = 14.713 a2 = - 2.396 a3 = - 1.787 a_parameters = [ a1 , a2 , a3 ] # calculation of solar rotation velocity vrot = sfuncs . solar_rot_vel ( wij , nij , rij , deltaw , deltan , deltar , dij , vmap , a_parameters ) # calculate corrected velocity corrected_vel = vmap . data - np . real ( vsc ) - np . real ( vrot ) # corrected velocity maps map_vel_cor = sfuncs . corrected_map ( corrected_vel , vmap , map_type = 'Corrected-Dopplergram' , frame = frames . HeliographicCarrington ) # limb brightening Lij = lbfuncs . limb_polynomial ( imap ) # calculate corrected data Iflat = imap . data / Lij # corrected intensity maps map_int_cor = sfuncs . corrected_map ( Iflat , imap , map_type = 'Corrected-Intensitygram' , frame = frames . HeliographicCarrington ) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs , Br = sfuncs . mag_field ( mu , mmap , B_noise ) # corrected observed magnetic data map map_mag_obs = sfuncs . corrected_map ( Bobs , mmap , map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) # radial magnetic data map map_mag_cor = sfuncs . corrected_map ( Br , mmap , map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active , quiet = sfuncs . mag_thresh ( mu , mmap , Br_cutoff = Br_cutoff , mu_cutoff = mu_cutoff ) # calculate intensity threshold fac_inds , spot_inds = sfuncs . int_thresh ( map_int_cor , active , quiet ) # create threshold array thresh_arr = sfuncs . thresh_map ( fac_inds , spot_inds ) # full threshold maps map_full_thresh = sfuncs . corrected_map ( thresh_arr , mmap , map_type = 'Threshold' , frame = frames . HeliographicCarrington ) ### velocity contribution due to convective motion of quiet-Sun v_quiet = sfuncs . v_quiet ( map_vel_cor , imap , quiet ) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot , vphot_bright , vphot_spot = sfuncs . v_phot ( quiet , active , Lij , vrot , imap , mu , fac_inds , spot_inds ) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = sfuncs . v_disc ( map_vel_cor , imap ) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright , f_spot , f = sfuncs . filling_factor ( mu , mmap , active , fac_inds , spot_inds ) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs . unsigned_flux ( map_mag_obs , imap ) ### calculate the area filling factor pixA_hem = ctfuncs . pix_area_hem ( wij , nij , rij , vmap ) area = sfuncs . area_calc ( active , pixA_hem ) f_small , f_large , f_network , f_plage , f_nonconv = sfuncs . area_filling_factor ( active , area , mu , mmap , fac_inds ) ### get the unsigned flux quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux = sfuncs . area_unsigned_flux ( map_mag_obs , imap , area , active ) ### get area weighted convective velocities vconv_quiet , vconv_large , vconv_small = sfuncs . area_vconv ( map_vel_cor , imap , active , area ) # make array of what we want to save save_vals = [ v_quiet , v_disc , v_phot , v_conv , f_bright , f_spot , f , unsigned_obs_flux , vphot_bright , vphot_spot , f_small , f_large , f_network , f_plage , f_nonconv , quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux , vconv_quiet , vconv_large , vconv_small ] # round stuff rounded = np . around ( save_vals , 3 ) round_vals = [ date_str , date_jd ] for val in rounded : round_vals . append ( val ) # append these values to the csv file utils . append_list_as_row ( csv_name , round_vals ) # print that the date is completed print ( ' \\n Calculations and save to file complete for ' + date_str + ' index: ' + str ( i )) print ( 'Calculation complete for dates:' , start_date , 'to' , end_date ) for i, date in enumerate(dates_list): # convert the date to a string -- required for use in csv file date_str, date_obj, date_jd = utils.get_dates(date) # pull image within specified time range result = Fido.search(a.Time(str(date_obj - time_range), str(date_obj + time_range)), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2]) # add file to list file_download = Fido.fetch(result) # remove unusable file types good_files = [] for file in file_download: name, extension = os.path.splitext(file) if extension == '.fits': good_files.append(file) if len(good_files) != 3: # add the data # append these values to the csv file save_vals = [date_str, 'not three good files'] utils.append_list_as_row(bad_dates_csv, save_vals) # print that the files are missing print('\\nNot three good files: ' + date_str + ' index: ' + str(i)) pass else: # convert to map sequence map_seq = sunpy.map.Map(sorted(good_files)) # check for missing data types missing_map = False # split into data types for j, map_obj in enumerate(map_seq): if map_obj.meta['content'] == 'DOPPLERGRAM': vmap = map_obj elif map_obj.meta['content'] == 'MAGNETOGRAM': mmap = map_obj elif map_obj.meta['content'] == 'CONTINUUM INTENSITY': imap = map_obj else: missing_map = True if missing_map: print(\"Missing a data product for \" + date_str) # add the data # append these values to the csv file save_vals = [date_str, 'missing data product'] utils.append_list_as_row(bad_dates_csv, save_vals) pass else: # coordinate transformation for maps x, y, pd, r, d, mu = ctfuncs.coordinates(vmap) wij, nij, rij = ctfuncs.vel_coords(x, y, pd, r, vmap) # remove bad mu values vmap, mmap, imap = ctfuncs.fix_mu(mu, [vmap, mmap, imap]) # calculate relative positions deltaw, deltan, deltar, dij = sfuncs.rel_positions(wij, nij, rij, vmap) # calculate spacecraft velocity vsc = sfuncs.spacecraft_vel(deltaw, deltan, deltar, dij, vmap) # optimized solar rotation parameters a1 = 14.713 a2 = -2.396 a3 = -1.787 a_parameters = [a1, a2, a3] # calculation of solar rotation velocity vrot = sfuncs.solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters) # calculate corrected velocity corrected_vel = vmap.data - np.real(vsc) - np.real(vrot) # corrected velocity maps map_vel_cor = sfuncs.corrected_map(corrected_vel, vmap, map_type='Corrected-Dopplergram', frame=frames.HeliographicCarrington) # limb brightening Lij = lbfuncs.limb_polynomial(imap) # calculate corrected data Iflat = imap.data / Lij # corrected intensity maps map_int_cor = sfuncs.corrected_map(Iflat, imap, map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs, Br = sfuncs.mag_field(mu, mmap, B_noise) # corrected observed magnetic data map map_mag_obs = sfuncs.corrected_map(Bobs, mmap, map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) # radial magnetic data map map_mag_cor = sfuncs.corrected_map(Br, mmap, map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active, quiet = sfuncs.mag_thresh(mu, mmap, Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) # calculate intensity threshold fac_inds, spot_inds = sfuncs.int_thresh(map_int_cor, active, quiet) # create threshold array thresh_arr = sfuncs.thresh_map(fac_inds, spot_inds) # full threshold maps map_full_thresh = sfuncs.corrected_map(thresh_arr, mmap, map_type='Threshold', frame=frames.HeliographicCarrington) ### velocity contribution due to convective motion of quiet-Sun v_quiet = sfuncs.v_quiet(map_vel_cor, imap, quiet) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot, vphot_bright, vphot_spot = sfuncs.v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = sfuncs.v_disc(map_vel_cor, imap) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright, f_spot, f = sfuncs.filling_factor(mu, mmap, active, fac_inds, spot_inds) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs.unsigned_flux(map_mag_obs, imap) ### calculate the area filling factor pixA_hem = ctfuncs.pix_area_hem(wij, nij, rij, vmap) area = sfuncs.area_calc(active, pixA_hem) f_small, f_large, f_network, f_plage, f_nonconv = sfuncs.area_filling_factor(active, area, mu, mmap, fac_inds) ### get the unsigned flux quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = sfuncs.area_unsigned_flux(map_mag_obs, imap, area, active) ### get area weighted convective velocities vconv_quiet, vconv_large, vconv_small = sfuncs.area_vconv(map_vel_cor, imap, active, area) # make array of what we want to save save_vals = [v_quiet, v_disc, v_phot, v_conv, f_bright, f_spot, f, unsigned_obs_flux, vphot_bright, vphot_spot, f_small, f_large, f_network, f_plage, f_nonconv, quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux, vconv_quiet, vconv_large, vconv_small] # round stuff rounded = np.around(save_vals, 3) round_vals = [date_str, date_jd] for val in rounded: round_vals.append(val) # append these values to the csv file utils.append_list_as_row(csv_name, round_vals) # print that the date is completed print('\\nCalculations and save to file complete for ' + date_str + ' index: ' + str(i)) print('Calculation complete for dates:', start_date, 'to', end_date)","title":"Component calculations"},{"location":"examples/docs_full_pipeline/#full-rv-calculation","text":"Use the photometric and convective velocity components to calculate the full RV. Read in CSV with data. In [ ]: Copied! # csv file with rv components csv_file = os . path . join ( CsvDir . CALC , 'csv_name.csv' ) # create pandas dataframe component_df = pd . read_csv ( csv_file ) # csv file with rv components csv_file = os.path.join(CsvDir.CALC, 'csv_name.csv') # create pandas dataframe component_df = pd.read_csv(csv_file) In [ ]: Copied! # get velocities lists v_phot = component_df . v_phot . values v_conv = component_df . v_conv . values # get scaling factors A = Scaling . HARPSN . A B = Scaling . HARPSN . B RV0 = Scaling . HARPSN . RV0 # get velocities lists v_phot = component_df.v_phot.values v_conv = component_df.v_conv.values # get scaling factors A = Scaling.HARPSN.A B = Scaling.HARPSN.B RV0 = Scaling.HARPSN.RV0 In [ ]: Copied! RV = A * v_phot + B * v_conv + RV0 component_df [ \"rv_model\" ] = RV component_df . to_csv ( csv_file , index = False ) RV = A*v_phot + B*v_conv + RV0 component_df[\"rv_model\"] = RV component_df.to_csv(csv_file, index=False)","title":"Full RV Calculation"},{"location":"examples/docs_rv_calcs/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. 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In [ ]: Copied! # get csv with solar RVs csv_file = 'file_name' # create pandas dataframe component_df = pd . read_csv ( csv_file ) # get RV list rv_sun = component_df . rv_sun . values # get csv with solar RVs csv_file = 'file_name' # create pandas dataframe component_df = pd.read_csv(csv_file) # get RV list rv_sun = component_df.rv_sun.values Read in velocities from csv. In [2]: Copied! # csv file with rv components csv_file = 'file_name' # csv file with rv components csv_file = 'file_name' Get component lists. In [ ]: Copied! # create pandas dataframe component_df = pd . read_csv ( csv_file ) # create pandas dataframe component_df = pd.read_csv(csv_file) In [4]: Copied! # get velocities lists v_phot = component_df . v_phot . values v_conv = component_df . v_conv . values # get velocities lists v_phot = component_df.v_phot.values v_conv = component_df.v_conv.values Calculate scaling coefficient values -- using linear regression. Setup data to plug into linear regression model/ In [ ]: Copied! # components X = np . zeros ( shape = ( len ( rv_sun ), 2 )) X [:, 0 ] = v_phot X [:, 1 ] = v_conv # data we are fitting to y = rv_sun # components X = np.zeros(shape=(len(rv_sun), 2)) X[:, 0] = v_phot X[:, 1] = v_conv # data we are fitting to y = rv_sun Use sklearn linear regression to get coefficient values. In [ ]: Copied! # apply regression reg = LinearRegression () . fit ( X , y ) print ( 'R^2 for prediction: ' + str ( reg . score ( X , y ))) # get scaling factors A = reg . coef_ [ 0 ] B = reg . coef_ [ 1 ] RV0 = reg . intercept_ # print scaling factors print ( \"Scaling Factors: \\n A:\" , A , \" \\n B:\" , B , \" \\n RV0:\" , RV0 ) # apply regression reg = LinearRegression().fit(X, y) print('R^2 for prediction: ' + str(reg.score(X, y))) # get scaling factors A = reg.coef_[0] B = reg.coef_[1] RV0 = reg.intercept_ # print scaling factors print(\"Scaling Factors:\\n A:\", A, \"\\nB:\", B, \"\\nRV0:\", RV0) Calculate the Model RV Variation using these coefficients. In [ ]: Copied! RV = A * v_phot + B * v_conv + RV0 RV = A*v_phot + B*v_conv + RV0 Add calculation to csv. In [ ]: Copied! component_df [ \"rv_model\" ] = RV component_df . to_csv ( csv_file , index = False ) component_df[\"rv_model\"] = RV component_df.to_csv(csv_file, index=False)","title":"RV Calculation"},{"location":"examples/docs_rv_calcs/#calculation-of-solar-rv-variations","text":"calculate solar velocities: calculate solar velocities and save to csv get component lists: using pandas to get the lists of each relevant component calculate RVs and save to csv: calculate RVs using optimized parameters In [1]: Copied! import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression Get ground-based 'sun-as-a-star' RVs. In [ ]: Copied! # get csv with solar RVs csv_file = 'file_name' # create pandas dataframe component_df = pd . read_csv ( csv_file ) # get RV list rv_sun = component_df . rv_sun . values # get csv with solar RVs csv_file = 'file_name' # create pandas dataframe component_df = pd.read_csv(csv_file) # get RV list rv_sun = component_df.rv_sun.values Read in velocities from csv. In [2]: Copied! # csv file with rv components csv_file = 'file_name' # csv file with rv components csv_file = 'file_name' Get component lists. In [ ]: Copied! # create pandas dataframe component_df = pd . read_csv ( csv_file ) # create pandas dataframe component_df = pd.read_csv(csv_file) In [4]: Copied! # get velocities lists v_phot = component_df . v_phot . values v_conv = component_df . v_conv . values # get velocities lists v_phot = component_df.v_phot.values v_conv = component_df.v_conv.values Calculate scaling coefficient values -- using linear regression. Setup data to plug into linear regression model/ In [ ]: Copied! # components X = np . zeros ( shape = ( len ( rv_sun ), 2 )) X [:, 0 ] = v_phot X [:, 1 ] = v_conv # data we are fitting to y = rv_sun # components X = np.zeros(shape=(len(rv_sun), 2)) X[:, 0] = v_phot X[:, 1] = v_conv # data we are fitting to y = rv_sun Use sklearn linear regression to get coefficient values. In [ ]: Copied! # apply regression reg = LinearRegression () . fit ( X , y ) print ( 'R^2 for prediction: ' + str ( reg . score ( X , y ))) # get scaling factors A = reg . coef_ [ 0 ] B = reg . coef_ [ 1 ] RV0 = reg . intercept_ # print scaling factors print ( \"Scaling Factors: \\n A:\" , A , \" \\n B:\" , B , \" \\n RV0:\" , RV0 ) # apply regression reg = LinearRegression().fit(X, y) print('R^2 for prediction: ' + str(reg.score(X, y))) # get scaling factors A = reg.coef_[0] B = reg.coef_[1] RV0 = reg.intercept_ # print scaling factors print(\"Scaling Factors:\\n A:\", A, \"\\nB:\", B, \"\\nRV0:\", RV0) Calculate the Model RV Variation using these coefficients. In [ ]: Copied! RV = A * v_phot + B * v_conv + RV0 RV = A*v_phot + B*v_conv + RV0 Add calculation to csv. In [ ]: Copied! component_df [ \"rv_model\" ] = RV component_df . to_csv ( csv_file , index = False ) component_df[\"rv_model\"] = RV component_df.to_csv(csv_file, index=False)","title":"Calculation of Solar RV Variations"},{"location":"examples/docs_solar_corrections/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. 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import numpy as np import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a from sunpy.coordinates import frames import matplotlib.pyplot as plt from scipy import stats import tamar.tools.solar_funcs as sfuncs import tamar.tools.lbc_funcs as lbfuncs import tamar.tools.coord_funcs as ctfuncs import numpy as np import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a from sunpy.coordinates import frames import matplotlib.pyplot as plt from scipy import stats import tamar.tools.solar_funcs as sfuncs import tamar.tools.lbc_funcs as lbfuncs import tamar.tools.coord_funcs as ctfuncs Get HMI data. In [ ]: Copied! # get hmi data products cadence = a . Sample ( 24 * u . hour ) # querying cadence start_date = '2014-01-20T09:43:24.70' # start date of query end_date = '2014-01-20T09:44:24.70' physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] result = Fido . search ( a . Time ( start_date , end_date ), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ], cadence ) # print results print ( result ) # get hmi data products cadence = a.Sample(24*u.hour) # querying cadence start_date = '2014-01-20T09:43:24.70' # start date of query end_date = '2014-01-20T09:44:24.70' physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] result = Fido.search(a.Time(start_date, end_date), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2], cadence) # print results print(result) Download data files, sort, and build map list. In [ ]: Copied! # download results file_download = Fido . fetch ( result ) # convert to map sequence map_seq = sunpy . map . Map ( sorted ( file_download )) # download results file_download = Fido.fetch(result) # convert to map sequence map_seq = sunpy.map.Map(sorted(file_download)) Create map lists based on HMI content. In [ ]: Copied! hmi_vel , hmi_mag , hmi_int \\ = [], [], [] for i , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : hmi_vel . append ( map_obj . rotate ( order = 3 )) elif map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : hmi_mag . append ( map_obj . rotate ( order = 3 )) elif map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : hmi_int . append ( map_obj . rotate ( order = 3 )) hmi_vel, hmi_mag, hmi_int \\ = [], [], [] for i, map_obj in enumerate(map_seq): if map_obj.meta['content'] == 'DOPPLERGRAM': hmi_vel.append(map_obj.rotate(order=3)) elif map_obj.meta['content'] == 'MAGNETOGRAM': hmi_mag.append(map_obj.rotate(order=3)) elif map_obj.meta['content'] == 'CONTINUUM INTENSITY': hmi_int.append(map_obj.rotate(order=3)) Step One: Coordinate Transformation \u00b6 Apply coordinate transformations for HMI data products where: x, y, pd, r, d, mu = coord[i][0], coord[i][1], coord[i][2], coord[i][3], coord[i][4], coord[i][5] wij, nij, rij = vel_coords[i][0], vel_coords[i][1], vel_coords[i][2] In [ ]: Copied! # get cartesian scales from map coord = [ ctfuncs . coordinates ( hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] # get mu and radius array vel_coords = [ ctfuncs . vel_coords ( coord [ 0 ], coord [ 1 ], coord [ 2 ], coord [ 3 ], hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] # get cartesian scales from map coord = [ctfuncs.coordinates(hmi_vel[i]) for i in range(0, len(hmi_vel))] # get mu and radius array vel_coords = [ctfuncs.vel_coords(coord[0], coord[1], coord[2], coord[3], hmi_vel[i]) for i in range(0, len(hmi_vel))] Remove bad mu values from each map. hmi_vel, hmi_mag, imap = maps[i][0], maps[i][1], maps[i][2] In [ ]: Copied! # fix bad mu values maps = [ ctfuncs . fix_mu ( coord [ i ][ 5 ], [ hmi_vel [ i ], hmi_mag [ i ], hmi_int [ i ]]) for i in range ( 0 , len ( hmi_vel ))] # return maps to original lists hmi_vel = [ maps [ i ][ 0 ] for i in range ( 0 , len ( hmi_vel ))] hmi_mag = [ maps [ i ][ 1 ] for i in range ( 0 , len ( hmi_vel ))] hmi_int = [ maps [ i ][ 2 ] for i in range ( 0 , len ( hmi_vel ))] # fix bad mu values maps = [ctfuncs.fix_mu(coord[i][5], [hmi_vel[i], hmi_mag[i], hmi_int[i]]) for i in range(0, len(hmi_vel))] # return maps to original lists hmi_vel = [maps[i][0] for i in range(0, len(hmi_vel))] hmi_mag = [maps[i][1] for i in range(0, len(hmi_vel))] hmi_int = [maps[i][2] for i in range(0, len(hmi_vel))] Step Two: Calculate Spacecraft Velocity \u00b6 Calculate relative positions. deltaw, deltan, deltar, dij = delta[i][0], delta[i][1], delta[i][2], delta[i][3] In [ ]: Copied! delta = [ sfuncs . rel_positions ( vel_coords [ i ][ 0 ], vel_coords [ i ][ 1 ], vel_coords [ i ][ 2 ], hmi_vel [ i ])] delta = [sfuncs.rel_positions(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], hmi_vel[i])] Calculate spacecraft velocity. In [ ]: Copied! vsc = [ sfuncs . spacecraft_vel ( delta [ i ][ 0 ], delta [ i ][ 1 ], delta [ i ][ 2 ], delta [ i ][ 3 ], hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] vsc = [sfuncs.spacecraft_vel(delta[i][0], delta[i][1], delta[i][2], delta[i][3], hmi_vel[i]) for i in range(0, len(hmi_vel))] Step Three: Calculate Velocity due to Solar Rotation \u00b6 Optimized parameters for calculation of solar rotation. In [ ]: Copied! a1 = 14.713 a2 = - 2.396 a3 = - 1.787 a_parameters = [ a1 , a2 , a3 ] a1 = 14.713 a2 = -2.396 a3 = -1.787 a_parameters = [a1, a2, a3] Calculation of solar rotation velocity. In [ ]: Copied! vrot = [ sfuncs . solar_rot_vel ( vel_coords [ i ][ 0 ], vel_coords [ i ][ 1 ], vel_coords [ i ][ 2 ], delta [ i ][ 0 ], delta [ i ][ 1 ], delta [ i ][ 2 ], delta [ i ][ 3 ], hmi_vel [ i ], a_parameters ) for i in range ( 0 , len ( hmi_vel ))] vrot = [sfuncs.solar_rot_vel(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], delta[i][0], delta[i][1], delta[i][2], delta[i][3], hmi_vel[i], a_parameters) for i in range(0, len(hmi_vel))] Calculate corrected velocity. In [ ]: Copied! # subtract vsc and vrot from the Doppler images corrected_vel = [ hmi_vel [ i ] . data - np . real ( vsc [ i ]) - np . real ( vrot [ i ]) for i in range ( 0 , len ( hmi_vel ))] # subtract vsc and vrot from the Doppler images corrected_vel = [hmi_vel[i].data - np.real(vsc[i]) - np.real(vrot[i]) for i in range(0, len(hmi_vel))] Create list of Sunpy map objects with corrected velocity data. In [ ]: Copied! map_vel_cor = [ sfuncs . corrected_map ( corrected_vel [ i ], hmi_vel [ i ], map_type = 'Corrected-Dopplergram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_vel ))] map_vel_cor = [sfuncs.corrected_map(corrected_vel[i], hmi_vel[i], map_type='Corrected-Dopplergram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_vel))] Compare original and corrected velocity maps. In [ ]: Copied! # plot original map hmi_vel [ 0 ] . peek () # plot corrected map map_vel_cor [ 0 ] . peek () # plot original map hmi_vel[0].peek() # plot corrected map map_vel_cor[0].peek() Step Four: Limb Darkening Correction \u00b6 Equation for limb-brightening: $ L_{ij} = 1 - u_2 - v_2 + u_2 cos \\theta + v_2 cos^2 \\theta $ Coefficient values from Astrophysical Quantities by Allen (1973). Calculate correction polynomial. In [ ]: Copied! Lij = [ lbfuncs . limb_polynomial ( hmi_int [ i ]) for i in range ( 0 , len ( hmi_int ))] Lij = [lbfuncs.limb_polynomial(hmi_int[i]) for i in range(0, len(hmi_int))] Calculate corrected intensity. In [ ]: Copied! Iflat = [ hmi_int [ i ] . data / Lij [ i ] for i in range ( 0 , len ( hmi_int ))] Iflat = [hmi_int[i].data/Lij[i] for i in range(0, len(hmi_int))] Create list of Sunpy map objects with corrected intensity data. In [ ]: Copied! map_int_cor = [ sfuncs . corrected_map ( Iflat [ i ], hmi_int [ i ], map_type = 'Corrected-Intensitygram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] map_int_cor = [sfuncs.corrected_map(Iflat[i], hmi_int[i], map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] Compare original and corrected intensity maps. In [ ]: Copied! # plot original map hmi_int [ 0 ] . peek () # plot corrected map map_int_cor [ 0 ] . peek () # plot original map hmi_int[0].peek() # plot corrected map map_int_cor[0].peek() In [ ]: Copied! int_map = hmi_int [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () plt . show () int_map = map_int_cor [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () plt . show () int_map = hmi_int[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() plt.show() int_map = map_int_cor[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() plt.show() Step Five: Unsigned Magnetic Field Strength \u00b6 We calculate radial unsigned magnetic field strength and set pixels with an observed magnetic field below the noise value equal to zero. The noise level used is from Yeo et al. 2013, and is 8 Gauss. Bobs, Br = corrected_mag[0], corrected_mag[1] In [ ]: Copied! # magnetic noise level B_noise = 8 # calculate unsigned field strength corrected_mag = [ sfuncs . mag_field ( coord [ i ][ 5 ], hmi_mag [ i ], B_noise ) for i in range ( 0 , len ( hmi_mag ))] # magnetic noise level B_noise = 8 # calculate unsigned field strength corrected_mag = [sfuncs.mag_field(coord[i][5], hmi_mag[i], B_noise) for i in range(0, len(hmi_mag))] Create list of Sunpy map objects with corrected observed and radial magnetic data. In [ ]: Copied! # observed magnetic data map_mag_obs = [ sfuncs . corrected_map ( corrected_mag [ i ][ 0 ], hmi_mag [ i ], map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_mag ))] # radial magnetic data map_mag_cor = [ sfuncs . corrected_map ( corrected_mag [ i ][ 1 ], hmi_mag [ i ], map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_mag ))] # observed magnetic data map_mag_obs = [sfuncs.corrected_map(corrected_mag[i][0], hmi_mag[i], map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))] # radial magnetic data map_mag_cor = [sfuncs.corrected_map(corrected_mag[i][1], hmi_mag[i], map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))] Compare original and corrected magnetic maps. In [ ]: Copied! # plot original map hmi_mag [ 0 ] . peek () # plot corrected map map_mag_cor [ 0 ] . peek () # plot original map hmi_mag[0].peek() # plot corrected map map_mag_cor[0].peek() In [ ]: Copied! mag_map = hmi_mag [ 0 ] mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . show () mag_map = map_mag_cor [ 0 ] mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . show () mag_map = hmi_mag[0] mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.show() mag_map = map_mag_cor[0] mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.show() Step Six: Identification of Solar Regions \u00b6 Part One: Magnetic Thresholding \u00b6 Differentiate between active Sun and quiet Sun. Magnetic thresholding method used from Yeo et al. 2013 where $ B_{r, thresh} = 24G / \\mu $ . Magnetic threshold and mu cutoff values. Automatically throw everything where $ \\mu < 0.1 $ out, for magnetic thresholding we also remove $ \\mu < 0.3 $ due to correction for radial magnetic field strength. In [ ]: Copied! # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.35 # area cutoff value (pixels) area_cutoff = 45 # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.35 # area cutoff value (pixels) area_cutoff = 45 For weighting, we return an active weights array (active = 1) and quiet weights array (active = 0). Get threshold maps where: active, quiet = mag_thresh[i][0], mag_thresh[i][1] In [ ]: Copied! mag_thresh = [ sfuncs . mag_thresh ( coord [ i ][ 5 ], hmi_mag [ i ], Br_cutoff = Br_cutoff , mu_cutoff = mu_cutoff ) for i in range ( 0 , len ( hmi_mag ))] mag_thresh = [sfuncs.mag_thresh(coord[i][5], hmi_mag[i], Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) for i in range(0, len(hmi_mag))] Create list of Sunpy map objects with thresholded magnetic data. In [ ]: Copied! map_mag_thresh = [ sfuncs . corrected_map ( mag_thresh [ i ][ 0 ], hmi_mag [ i ], map_type = 'Magnetic-Threshold' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_mag ))] map_mag_thresh = [sfuncs.corrected_map(mag_thresh[i][0], hmi_mag[i], map_type='Magnetic-Threshold', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))] Compare original and corrected threshold maps. In [ ]: Copied! # plot corrected magnetic map map_mag_cor [ 0 ] . peek () # plot corrected magnetic threshold map map_mag_thresh [ 0 ] . peek () # plot corrected magnetic map map_mag_cor[0].peek() # plot corrected magnetic threshold map map_mag_thresh[0].peek() Part Two: Intensity Thresholding \u00b6 Differentiate between sunspots (dark) and faculae (bright). Using method of Yeo et al. 2013 we calculate the intensity threshold such that $ I_{thresh} = 0.89I_{quiet} $ where $ I_{quiet} = \\frac{\\sum_{ij} I_{flat, ij} W_{ij}}{\\sum_{ij} W_{ij}} $ is the mean pixel flattened intensity over quiet Sun regions. Calculate intensity threshold value and differentiate between sunspots and faculae where: fac_inds, spot_inds = int_thresh[0], int_thresh[1] In [ ]: Copied! int_thresh = [ sfuncs . int_thresh ( map_int_cor [ i ], mag_thresh [ i ][ 0 ], mag_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_int ))] int_thresh = [sfuncs.int_thresh(map_int_cor[i], mag_thresh[i][0], mag_thresh[i][1]) for i in range(0, len(hmi_int))] Create list of Sunpy map objects with faculae and sunspot data. In [ ]: Copied! # create faculae map objects fac_map = [ sfuncs . corrected_map ( int_thresh [ i ][ 0 ], hmi_mag [ i ], map_type = 'Faculae' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] # create sunspot map objects spot_map = [ sfuncs . corrected_map ( int_thresh [ i ][ 1 ], hmi_mag [ i ], map_type = 'Sunspot' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] # create faculae map objects fac_map = [sfuncs.corrected_map(int_thresh[i][0], hmi_mag[i], map_type='Faculae', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] # create sunspot map objects spot_map = [sfuncs.corrected_map(int_thresh[i][1], hmi_mag[i], map_type='Sunspot', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] Compare original and corrected threshold maps. In [ ]: Copied! # plot faculae map fac_map [ 0 ] . peek () # plot sunspot map spot_map [ 0 ] . peek () # plot faculae map fac_map[0].peek() # plot sunspot map spot_map[0].peek() Create array that combines sunspot (dark: 2) and faculae (bright: 1) regions. In [ ]: Copied! thresh_arr = [ sfuncs . thresh_map ( int_thresh [ i ][ 0 ], int_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_mag ))] thresh_arr = [sfuncs.thresh_map(int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_mag))] Create list of Sunpy map objects with threshold data. In [ ]: Copied! # create threshold map objects map_full_thresh = [ sfuncs . corrected_map ( thresh_arr [ i ], hmi_mag [ i ], map_type = 'Threshold' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] # create threshold map objects map_full_thresh = [sfuncs.corrected_map(thresh_arr[i], hmi_mag[i], map_type='Threshold', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] Compare corrected magnetic and threshold maps For these threshold maps, we currently only see sunspots detected on the solar limb. The solar limb is inherently darker (limb darkening) and since we have yet to correct for this it is expected that the overall intensity threshold is lower and thus the only values below this threshold are on the limb. The Sun is also not currently super magnetically active (solar minima) in the time I plotted so this makes sense that there are not many sunspots. Could also adjust the $ \\mu $ cutoff value to remove these sunspots, but I think limb brightening will fix it. In [ ]: Copied! mag_map = map_mag_cor [ 0 ] mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . imshow ( map_full_thresh [ 0 ] . data , cmap = plt . get_cmap ( 'bwr' )) plt . title ( \"Comparison of faculae (blue) and sunspots (red) to magnetic data\" ) mag_map = map_mag_cor[0] mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.imshow(map_full_thresh[0].data, cmap=plt.get_cmap('bwr')) plt.title(\"Comparison of faculae (blue) and sunspots (red) to magnetic data\") In [ ]: Copied! int_map = map_int_cor [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () plt . imshow ( map_full_thresh [ 0 ] . data , cmap = plt . get_cmap ( 'bwr' )) plt . title ( \"Comparison of faculae (blue) and sunspots (red) to intensity data\" ) int_map = map_int_cor[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() plt.imshow(map_full_thresh[0].data, cmap=plt.get_cmap('bwr')) plt.title(\"Comparison of faculae (blue) and sunspots (red) to intensity data\") In [ ]: Copied! int_map = map_int_cor [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () spots = np . where ( map_full_thresh [ 0 ] . data == 2 , 1 , np . nan ) plt . imshow ( spots , cmap = 'Blues' ) plt . title ( \"Comparison of sunspots to intensity data\" ) int_map = map_int_cor[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() spots = np.where(map_full_thresh[0].data == 2, 1, np.nan) plt.imshow(spots, cmap='Blues') plt.title(\"Comparison of sunspots to intensity data\") Outline of Calculations of Solar RVs \u00b6 quiet-Sun velocity: estimation of average RV of quiet-Sun due to convective motion photometric velocity: estimation of rotational Doppler imbalance due to active (faculae/darkspot) regions convective blueshift: convective velocity estimation due to the suppression of the convective blueshift by magnetically active regions Step One: Convective Motion of quiet-Sun \u00b6 Calculate the velocity due to the convective motion of the quiet-Sun. Requires the magnetic weighting array we built in step six, part one. A weight of 0 denotes an active pixel, and 1 is quiet-Sun. The equation for calculating this quiet-Sun velocity is as such: $ v_{quiet} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij} W_{ij}} {\\sum_{ij} I_{ij} W_{ij}} $. In this case $ I_{ij} $ is the uncorrected (original) intensity data. In [ ]: Copied! v_quiet = [ sfuncs . v_quiet ( map_vel_cor [ i ], hmi_int [ i ], mag_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_mag ))] v_quiet = [sfuncs.v_quiet(map_vel_cor[i], hmi_int[i], mag_thresh[i][1]) for i in range(0, len(hmi_mag))] Step Two: Photometric Velocity due to rotational Doppler imbalance \u00b6 Calculation of the photometric velocity due the presence of dark spots/bright faculae causing a Doppler imbalance. Requires magnetic weighting array that is flipped from the weighting array previously used. Now, a weight of 0 denotes quiet-Sun and 1 denotes active pixel. The equation for this velocity calculation is: $ v_{phot} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{rot, ij})(I_{ij} - K) W_{ij}} {\\sum_{ij} I_{ij}} $. K is a scaling factor based off the limb-brightening correction carried out in Step Four of the solar corrections. $ K = \\frac{\\sum_{ij} I_{ij} L_{ij} W_{ij}}{\\sum_{ij} L_{ij}^2 W_{ij}} $ In this case $ I_{ij} $ is the uncorrected (original) intensity data. Calculate photometric velocity. v_phot, vphot_bright, vphot_spot = vphot[0], vphot[1], vphot[2] In [ ]: Copied! v_phot = [ sfuncs . v_phot ( mag_thresh [ i ][ 1 ], mag_thresh [ i ][ 0 ], Lij [ i ], vrot [ i ], hmi_int [ i ], coord [ i ][ 5 ], int_thresh [ i ][ 0 ], int_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_int ))] v_phot = [sfuncs.v_phot(mag_thresh[i][1], mag_thresh[i][0], Lij[i], vrot[i], hmi_int[i], coord[i][5], int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_int))] Step Three: Convective Blueshift Velocity \u00b6 Calculation of suppression of convective blueshift due to active regions (mainly faculae). The basic premise for calculation is to calculate the disc-averaged velocity of the Sun and subtract from that the quiet-Sun velocity (Step One) and photometric velocity (Step Two) both of which were calculated previously. Maybe a better way to do this ... The equation for the disc-averaged velocity is: $ v = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij}}{\\sum_{ij} I_{ij}} $ We then calculate the convective velocity as what is left: $ v_{conv} = v - v_{quiet} $ Calculate disc-averaged velocity of Sun. In [ ]: Copied! v_disc = [ sfuncs . v_disc ( map_vel_cor [ i ], hmi_int [ i ]) for i in range ( 0 , len ( hmi_int ))] v_disc = [sfuncs.v_disc(map_vel_cor[i], hmi_int[i]) for i in range(0, len(hmi_int))] Calculate convective velocity. In [ ]: Copied! v_conv = [ v_disc [ i ] - v_quiet [ i ] for i in range ( 0 , len ( hmi_mag ))] v_conv = [v_disc[i] - v_quiet[i] for i in range(0, len(hmi_mag))] Magnetic Observables \u00b6 Calculation of relevant magnetic observables. filling factor: disc averaged filling factors of sunspots and plage which gives the percentage of magnetically active pixels unsigned magnetic flux: calculation of disc-averaged, line-of-sight unsigned magnetic flux Filling Factor \u00b6 Estimate the disc-averaged filling factors of sunspots and plage: $ f_{spot, plage} = \\frac{1}{N_{pix}} \\sum_{ij} W_{ij} $ In this case, a weight of 1 is an active pixel (active_weights). f_bright, f_spot, f = filling[0], filling[1], filling[2] In [ ]: Copied! filling = [ sfuncs . filling_factor ( coord [ i ][ 5 ], hmi_mag [ i ], mag_thresh [ i ][ 0 ], int_thresh [ i ][ 0 ], int_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_mag ))] filling = [sfuncs.filling_factor(coord[i][5], hmi_mag[i], mag_thresh[i][0], int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_mag))] Unsigned Magnetic Flux \u00b6 Calculate disc-averaged, line-of-sight unsigned (unpolarized) magnetic flux of the Sun. Calculation of observed unsigned flux. $ |\\hat{B_{obs}}| = \\frac{\\sum_{ij} |B_{obs, ij}| I_{ij}} {\\sum_{ij} I_{ij}}$ In [ ]: Copied! unsigned_obs_flux = [ sfuncs . unsigned_flux ( map_mag_obs [ i ], hmi_int [ i ]) for i in range ( 0 , len ( hmi_mag ))] unsigned_obs_flux = [sfuncs.unsigned_flux(map_mag_obs[i], hmi_int[i]) for i in range(0, len(hmi_mag))] Area Based Observable Calculations \u00b6 Calculate intensity weighted area of each active region. In [ ]: Copied! pixA_hem = [ ctfuncs . pix_area_hem ( vel_coords [ i ][ 0 ], vel_coords [ i ][ 1 ], vel_coords [ i ][ 2 ], hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] area = [ sfuncs . area_calc ( mag_thresh [ i ][ 0 ], pixA_hem [ i ]) for i in range ( 0 , len ( hmi_vel ))] pixA_hem = [ctfuncs.pix_area_hem(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], hmi_vel[i]) for i in range(0, len(hmi_vel))] area = [sfuncs.area_calc(mag_thresh[i][0], pixA_hem[i]) for i in range(0, len(hmi_vel))] Calculate the area filling factor. f_small, f_large, f_network, f_plage, f_nonconv = f_area[0], f_area[1], f_area[2], f_area[3], f_area[4] In [ ]: Copied! f_area = [ sfuncs . area_filling_factor ( mag_thresh [ i ][ 0 ], area [ i ], coord [ i ][ 5 ], hmi_mag [ i ], int_thresh [ i ][ 0 ]) for i in range ( 0 , len ( hmi_mag ))] f_area = [sfuncs.area_filling_factor(mag_thresh[i][0], area[i], coord[i][5], hmi_mag[i], int_thresh[i][0]) for i in range(0, len(hmi_mag))] Calculate unsigned flux of various polarized and active regions. quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = flux[0], flux[1], flux[2], flux[3], flux[4], flux[5] In [ ]: Copied! quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux = [ sfuncs . area_unsigned_flux ( map_mag_obs [ i ], hmi_int [ i ], area [ i ], int_thresh [ i ][ 0 ]) for i in range ( 0 , len ( hmi_int ))] quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = [sfuncs.area_unsigned_flux(map_mag_obs[i], hmi_int[i], area[i], int_thresh[i][0]) for i in range(0, len(hmi_int))] Get area weighted convective velocities. vconv_quiet, vconv_large, vconv_small = vconv_area[0], vconv_area[1], vconv_area[2] In [ ]: Copied! vconv_area = [ sfuncs . area_vconv ( map_vel_cor [ i ], hmi_int [ i ], int_thresh [ i ][ 0 ], area [ i ]) for i in range ( 0 , len ( hmi_int ))] vconv_area = [sfuncs.area_vconv(map_vel_cor[i], hmi_int[i], int_thresh[i][0], area[i]) for i in range(0, len(hmi_int))]","title":"Component Calculations"},{"location":"examples/docs_solar_corrections/#outline-of-corrections-to-sdohmi-images","text":"coordinate transformation: transform from Helioprojective Cartesian coordinates to Heliographic Carrington coordinates based on observer spacecraft velocity calculation: correct for the velocity of spacecraft relative to Sun solar rotation calculation: account for velocity due to solar rotation as a function of latitude (based on Snodgrass & Ulrich 1990) limb darkening correction: correct for limb darkening in HMI intensity images unsigned magnetic field strength: calculate unsigned magnetic field strength by dividing observed field strength by $ \\mu = cos(\\theta)$ identify solar regions: differentiate between quiet Sun, sunspots, faculae/plage regions In [ ]: Copied! import numpy as np import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a from sunpy.coordinates import frames import matplotlib.pyplot as plt from scipy import stats import tamar.tools.solar_funcs as sfuncs import tamar.tools.lbc_funcs as lbfuncs import tamar.tools.coord_funcs as ctfuncs import numpy as np import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a from sunpy.coordinates import frames import matplotlib.pyplot as plt from scipy import stats import tamar.tools.solar_funcs as sfuncs import tamar.tools.lbc_funcs as lbfuncs import tamar.tools.coord_funcs as ctfuncs Get HMI data. In [ ]: Copied! # get hmi data products cadence = a . Sample ( 24 * u . hour ) # querying cadence start_date = '2014-01-20T09:43:24.70' # start date of query end_date = '2014-01-20T09:44:24.70' physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] result = Fido . search ( a . Time ( start_date , end_date ), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ], cadence ) # print results print ( result ) # get hmi data products cadence = a.Sample(24*u.hour) # querying cadence start_date = '2014-01-20T09:43:24.70' # start date of query end_date = '2014-01-20T09:44:24.70' physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] result = Fido.search(a.Time(start_date, end_date), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2], cadence) # print results print(result) Download data files, sort, and build map list. In [ ]: Copied! # download results file_download = Fido . fetch ( result ) # convert to map sequence map_seq = sunpy . map . Map ( sorted ( file_download )) # download results file_download = Fido.fetch(result) # convert to map sequence map_seq = sunpy.map.Map(sorted(file_download)) Create map lists based on HMI content. In [ ]: Copied! hmi_vel , hmi_mag , hmi_int \\ = [], [], [] for i , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : hmi_vel . append ( map_obj . rotate ( order = 3 )) elif map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : hmi_mag . append ( map_obj . rotate ( order = 3 )) elif map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : hmi_int . append ( map_obj . rotate ( order = 3 )) hmi_vel, hmi_mag, hmi_int \\ = [], [], [] for i, map_obj in enumerate(map_seq): if map_obj.meta['content'] == 'DOPPLERGRAM': hmi_vel.append(map_obj.rotate(order=3)) elif map_obj.meta['content'] == 'MAGNETOGRAM': hmi_mag.append(map_obj.rotate(order=3)) elif map_obj.meta['content'] == 'CONTINUUM INTENSITY': hmi_int.append(map_obj.rotate(order=3))","title":"Outline of corrections to SDO/HMI Images"},{"location":"examples/docs_solar_corrections/#step-one-coordinate-transformation","text":"Apply coordinate transformations for HMI data products where: x, y, pd, r, d, mu = coord[i][0], coord[i][1], coord[i][2], coord[i][3], coord[i][4], coord[i][5] wij, nij, rij = vel_coords[i][0], vel_coords[i][1], vel_coords[i][2] In [ ]: Copied! # get cartesian scales from map coord = [ ctfuncs . coordinates ( hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] # get mu and radius array vel_coords = [ ctfuncs . vel_coords ( coord [ 0 ], coord [ 1 ], coord [ 2 ], coord [ 3 ], hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] # get cartesian scales from map coord = [ctfuncs.coordinates(hmi_vel[i]) for i in range(0, len(hmi_vel))] # get mu and radius array vel_coords = [ctfuncs.vel_coords(coord[0], coord[1], coord[2], coord[3], hmi_vel[i]) for i in range(0, len(hmi_vel))] Remove bad mu values from each map. hmi_vel, hmi_mag, imap = maps[i][0], maps[i][1], maps[i][2] In [ ]: Copied! # fix bad mu values maps = [ ctfuncs . fix_mu ( coord [ i ][ 5 ], [ hmi_vel [ i ], hmi_mag [ i ], hmi_int [ i ]]) for i in range ( 0 , len ( hmi_vel ))] # return maps to original lists hmi_vel = [ maps [ i ][ 0 ] for i in range ( 0 , len ( hmi_vel ))] hmi_mag = [ maps [ i ][ 1 ] for i in range ( 0 , len ( hmi_vel ))] hmi_int = [ maps [ i ][ 2 ] for i in range ( 0 , len ( hmi_vel ))] # fix bad mu values maps = [ctfuncs.fix_mu(coord[i][5], [hmi_vel[i], hmi_mag[i], hmi_int[i]]) for i in range(0, len(hmi_vel))] # return maps to original lists hmi_vel = [maps[i][0] for i in range(0, len(hmi_vel))] hmi_mag = [maps[i][1] for i in range(0, len(hmi_vel))] hmi_int = [maps[i][2] for i in range(0, len(hmi_vel))]","title":"Step One: Coordinate Transformation"},{"location":"examples/docs_solar_corrections/#step-two-calculate-spacecraft-velocity","text":"Calculate relative positions. deltaw, deltan, deltar, dij = delta[i][0], delta[i][1], delta[i][2], delta[i][3] In [ ]: Copied! delta = [ sfuncs . rel_positions ( vel_coords [ i ][ 0 ], vel_coords [ i ][ 1 ], vel_coords [ i ][ 2 ], hmi_vel [ i ])] delta = [sfuncs.rel_positions(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], hmi_vel[i])] Calculate spacecraft velocity. In [ ]: Copied! vsc = [ sfuncs . spacecraft_vel ( delta [ i ][ 0 ], delta [ i ][ 1 ], delta [ i ][ 2 ], delta [ i ][ 3 ], hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] vsc = [sfuncs.spacecraft_vel(delta[i][0], delta[i][1], delta[i][2], delta[i][3], hmi_vel[i]) for i in range(0, len(hmi_vel))]","title":"Step Two: Calculate Spacecraft Velocity"},{"location":"examples/docs_solar_corrections/#step-three-calculate-velocity-due-to-solar-rotation","text":"Optimized parameters for calculation of solar rotation. In [ ]: Copied! a1 = 14.713 a2 = - 2.396 a3 = - 1.787 a_parameters = [ a1 , a2 , a3 ] a1 = 14.713 a2 = -2.396 a3 = -1.787 a_parameters = [a1, a2, a3] Calculation of solar rotation velocity. In [ ]: Copied! vrot = [ sfuncs . solar_rot_vel ( vel_coords [ i ][ 0 ], vel_coords [ i ][ 1 ], vel_coords [ i ][ 2 ], delta [ i ][ 0 ], delta [ i ][ 1 ], delta [ i ][ 2 ], delta [ i ][ 3 ], hmi_vel [ i ], a_parameters ) for i in range ( 0 , len ( hmi_vel ))] vrot = [sfuncs.solar_rot_vel(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], delta[i][0], delta[i][1], delta[i][2], delta[i][3], hmi_vel[i], a_parameters) for i in range(0, len(hmi_vel))] Calculate corrected velocity. In [ ]: Copied! # subtract vsc and vrot from the Doppler images corrected_vel = [ hmi_vel [ i ] . data - np . real ( vsc [ i ]) - np . real ( vrot [ i ]) for i in range ( 0 , len ( hmi_vel ))] # subtract vsc and vrot from the Doppler images corrected_vel = [hmi_vel[i].data - np.real(vsc[i]) - np.real(vrot[i]) for i in range(0, len(hmi_vel))] Create list of Sunpy map objects with corrected velocity data. In [ ]: Copied! map_vel_cor = [ sfuncs . corrected_map ( corrected_vel [ i ], hmi_vel [ i ], map_type = 'Corrected-Dopplergram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_vel ))] map_vel_cor = [sfuncs.corrected_map(corrected_vel[i], hmi_vel[i], map_type='Corrected-Dopplergram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_vel))] Compare original and corrected velocity maps. In [ ]: Copied! # plot original map hmi_vel [ 0 ] . peek () # plot corrected map map_vel_cor [ 0 ] . peek () # plot original map hmi_vel[0].peek() # plot corrected map map_vel_cor[0].peek()","title":"Step Three: Calculate Velocity due to Solar Rotation"},{"location":"examples/docs_solar_corrections/#step-four-limb-darkening-correction","text":"Equation for limb-brightening: $ L_{ij} = 1 - u_2 - v_2 + u_2 cos \\theta + v_2 cos^2 \\theta $ Coefficient values from Astrophysical Quantities by Allen (1973). Calculate correction polynomial. In [ ]: Copied! Lij = [ lbfuncs . limb_polynomial ( hmi_int [ i ]) for i in range ( 0 , len ( hmi_int ))] Lij = [lbfuncs.limb_polynomial(hmi_int[i]) for i in range(0, len(hmi_int))] Calculate corrected intensity. In [ ]: Copied! Iflat = [ hmi_int [ i ] . data / Lij [ i ] for i in range ( 0 , len ( hmi_int ))] Iflat = [hmi_int[i].data/Lij[i] for i in range(0, len(hmi_int))] Create list of Sunpy map objects with corrected intensity data. In [ ]: Copied! map_int_cor = [ sfuncs . corrected_map ( Iflat [ i ], hmi_int [ i ], map_type = 'Corrected-Intensitygram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] map_int_cor = [sfuncs.corrected_map(Iflat[i], hmi_int[i], map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] Compare original and corrected intensity maps. In [ ]: Copied! # plot original map hmi_int [ 0 ] . peek () # plot corrected map map_int_cor [ 0 ] . peek () # plot original map hmi_int[0].peek() # plot corrected map map_int_cor[0].peek() In [ ]: Copied! int_map = hmi_int [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () plt . show () int_map = map_int_cor [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () plt . show () int_map = hmi_int[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() plt.show() int_map = map_int_cor[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() plt.show()","title":"Step Four: Limb Darkening Correction"},{"location":"examples/docs_solar_corrections/#step-five-unsigned-magnetic-field-strength","text":"We calculate radial unsigned magnetic field strength and set pixels with an observed magnetic field below the noise value equal to zero. The noise level used is from Yeo et al. 2013, and is 8 Gauss. Bobs, Br = corrected_mag[0], corrected_mag[1] In [ ]: Copied! # magnetic noise level B_noise = 8 # calculate unsigned field strength corrected_mag = [ sfuncs . mag_field ( coord [ i ][ 5 ], hmi_mag [ i ], B_noise ) for i in range ( 0 , len ( hmi_mag ))] # magnetic noise level B_noise = 8 # calculate unsigned field strength corrected_mag = [sfuncs.mag_field(coord[i][5], hmi_mag[i], B_noise) for i in range(0, len(hmi_mag))] Create list of Sunpy map objects with corrected observed and radial magnetic data. In [ ]: Copied! # observed magnetic data map_mag_obs = [ sfuncs . corrected_map ( corrected_mag [ i ][ 0 ], hmi_mag [ i ], map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_mag ))] # radial magnetic data map_mag_cor = [ sfuncs . corrected_map ( corrected_mag [ i ][ 1 ], hmi_mag [ i ], map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_mag ))] # observed magnetic data map_mag_obs = [sfuncs.corrected_map(corrected_mag[i][0], hmi_mag[i], map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))] # radial magnetic data map_mag_cor = [sfuncs.corrected_map(corrected_mag[i][1], hmi_mag[i], map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))] Compare original and corrected magnetic maps. In [ ]: Copied! # plot original map hmi_mag [ 0 ] . peek () # plot corrected map map_mag_cor [ 0 ] . peek () # plot original map hmi_mag[0].peek() # plot corrected map map_mag_cor[0].peek() In [ ]: Copied! mag_map = hmi_mag [ 0 ] mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . show () mag_map = map_mag_cor [ 0 ] mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . show () mag_map = hmi_mag[0] mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.show() mag_map = map_mag_cor[0] mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.show()","title":"Step Five: Unsigned Magnetic Field Strength"},{"location":"examples/docs_solar_corrections/#step-six-identification-of-solar-regions","text":"","title":"Step Six: Identification of Solar Regions"},{"location":"examples/docs_solar_corrections/#part-one-magnetic-thresholding","text":"Differentiate between active Sun and quiet Sun. Magnetic thresholding method used from Yeo et al. 2013 where $ B_{r, thresh} = 24G / \\mu $ . Magnetic threshold and mu cutoff values. Automatically throw everything where $ \\mu < 0.1 $ out, for magnetic thresholding we also remove $ \\mu < 0.3 $ due to correction for radial magnetic field strength. In [ ]: Copied! # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.35 # area cutoff value (pixels) area_cutoff = 45 # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.35 # area cutoff value (pixels) area_cutoff = 45 For weighting, we return an active weights array (active = 1) and quiet weights array (active = 0). Get threshold maps where: active, quiet = mag_thresh[i][0], mag_thresh[i][1] In [ ]: Copied! mag_thresh = [ sfuncs . mag_thresh ( coord [ i ][ 5 ], hmi_mag [ i ], Br_cutoff = Br_cutoff , mu_cutoff = mu_cutoff ) for i in range ( 0 , len ( hmi_mag ))] mag_thresh = [sfuncs.mag_thresh(coord[i][5], hmi_mag[i], Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) for i in range(0, len(hmi_mag))] Create list of Sunpy map objects with thresholded magnetic data. In [ ]: Copied! map_mag_thresh = [ sfuncs . corrected_map ( mag_thresh [ i ][ 0 ], hmi_mag [ i ], map_type = 'Magnetic-Threshold' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_mag ))] map_mag_thresh = [sfuncs.corrected_map(mag_thresh[i][0], hmi_mag[i], map_type='Magnetic-Threshold', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_mag))] Compare original and corrected threshold maps. In [ ]: Copied! # plot corrected magnetic map map_mag_cor [ 0 ] . peek () # plot corrected magnetic threshold map map_mag_thresh [ 0 ] . peek () # plot corrected magnetic map map_mag_cor[0].peek() # plot corrected magnetic threshold map map_mag_thresh[0].peek()","title":"Part One: Magnetic Thresholding"},{"location":"examples/docs_solar_corrections/#part-two-intensity-thresholding","text":"Differentiate between sunspots (dark) and faculae (bright). Using method of Yeo et al. 2013 we calculate the intensity threshold such that $ I_{thresh} = 0.89I_{quiet} $ where $ I_{quiet} = \\frac{\\sum_{ij} I_{flat, ij} W_{ij}}{\\sum_{ij} W_{ij}} $ is the mean pixel flattened intensity over quiet Sun regions. Calculate intensity threshold value and differentiate between sunspots and faculae where: fac_inds, spot_inds = int_thresh[0], int_thresh[1] In [ ]: Copied! int_thresh = [ sfuncs . int_thresh ( map_int_cor [ i ], mag_thresh [ i ][ 0 ], mag_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_int ))] int_thresh = [sfuncs.int_thresh(map_int_cor[i], mag_thresh[i][0], mag_thresh[i][1]) for i in range(0, len(hmi_int))] Create list of Sunpy map objects with faculae and sunspot data. In [ ]: Copied! # create faculae map objects fac_map = [ sfuncs . corrected_map ( int_thresh [ i ][ 0 ], hmi_mag [ i ], map_type = 'Faculae' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] # create sunspot map objects spot_map = [ sfuncs . corrected_map ( int_thresh [ i ][ 1 ], hmi_mag [ i ], map_type = 'Sunspot' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] # create faculae map objects fac_map = [sfuncs.corrected_map(int_thresh[i][0], hmi_mag[i], map_type='Faculae', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] # create sunspot map objects spot_map = [sfuncs.corrected_map(int_thresh[i][1], hmi_mag[i], map_type='Sunspot', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] Compare original and corrected threshold maps. In [ ]: Copied! # plot faculae map fac_map [ 0 ] . peek () # plot sunspot map spot_map [ 0 ] . peek () # plot faculae map fac_map[0].peek() # plot sunspot map spot_map[0].peek() Create array that combines sunspot (dark: 2) and faculae (bright: 1) regions. In [ ]: Copied! thresh_arr = [ sfuncs . thresh_map ( int_thresh [ i ][ 0 ], int_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_mag ))] thresh_arr = [sfuncs.thresh_map(int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_mag))] Create list of Sunpy map objects with threshold data. In [ ]: Copied! # create threshold map objects map_full_thresh = [ sfuncs . corrected_map ( thresh_arr [ i ], hmi_mag [ i ], map_type = 'Threshold' , frame = frames . HeliographicCarrington ) for i in range ( 0 , len ( hmi_int ))] # create threshold map objects map_full_thresh = [sfuncs.corrected_map(thresh_arr[i], hmi_mag[i], map_type='Threshold', frame=frames.HeliographicCarrington) for i in range(0, len(hmi_int))] Compare corrected magnetic and threshold maps For these threshold maps, we currently only see sunspots detected on the solar limb. The solar limb is inherently darker (limb darkening) and since we have yet to correct for this it is expected that the overall intensity threshold is lower and thus the only values below this threshold are on the limb. The Sun is also not currently super magnetically active (solar minima) in the time I plotted so this makes sense that there are not many sunspots. Could also adjust the $ \\mu $ cutoff value to remove these sunspots, but I think limb brightening will fix it. In [ ]: Copied! mag_map = map_mag_cor [ 0 ] mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . imshow ( map_full_thresh [ 0 ] . data , cmap = plt . get_cmap ( 'bwr' )) plt . title ( \"Comparison of faculae (blue) and sunspots (red) to magnetic data\" ) mag_map = map_mag_cor[0] mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.imshow(map_full_thresh[0].data, cmap=plt.get_cmap('bwr')) plt.title(\"Comparison of faculae (blue) and sunspots (red) to magnetic data\") In [ ]: Copied! int_map = map_int_cor [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () plt . imshow ( map_full_thresh [ 0 ] . data , cmap = plt . get_cmap ( 'bwr' )) plt . title ( \"Comparison of faculae (blue) and sunspots (red) to intensity data\" ) int_map = map_int_cor[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() plt.imshow(map_full_thresh[0].data, cmap=plt.get_cmap('bwr')) plt.title(\"Comparison of faculae (blue) and sunspots (red) to intensity data\") In [ ]: Copied! int_map = map_int_cor [ 0 ] cont_map = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot_settings [ 'cmap' ] = cont_map int_map . plot () spots = np . where ( map_full_thresh [ 0 ] . data == 2 , 1 , np . nan ) plt . imshow ( spots , cmap = 'Blues' ) plt . title ( \"Comparison of sunspots to intensity data\" ) int_map = map_int_cor[0] cont_map = plt.get_cmap('hinodesotintensity') int_map.plot_settings['cmap'] = cont_map int_map.plot() spots = np.where(map_full_thresh[0].data == 2, 1, np.nan) plt.imshow(spots, cmap='Blues') plt.title(\"Comparison of sunspots to intensity data\")","title":"Part Two: Intensity Thresholding"},{"location":"examples/docs_solar_corrections/#outline-of-calculations-of-solar-rvs","text":"quiet-Sun velocity: estimation of average RV of quiet-Sun due to convective motion photometric velocity: estimation of rotational Doppler imbalance due to active (faculae/darkspot) regions convective blueshift: convective velocity estimation due to the suppression of the convective blueshift by magnetically active regions","title":"Outline of Calculations of Solar RVs"},{"location":"examples/docs_solar_corrections/#step-one-convective-motion-of-quiet-sun","text":"Calculate the velocity due to the convective motion of the quiet-Sun. Requires the magnetic weighting array we built in step six, part one. A weight of 0 denotes an active pixel, and 1 is quiet-Sun. The equation for calculating this quiet-Sun velocity is as such: $ v_{quiet} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij} W_{ij}} {\\sum_{ij} I_{ij} W_{ij}} $. In this case $ I_{ij} $ is the uncorrected (original) intensity data. In [ ]: Copied! v_quiet = [ sfuncs . v_quiet ( map_vel_cor [ i ], hmi_int [ i ], mag_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_mag ))] v_quiet = [sfuncs.v_quiet(map_vel_cor[i], hmi_int[i], mag_thresh[i][1]) for i in range(0, len(hmi_mag))]","title":"Step One: Convective Motion of quiet-Sun"},{"location":"examples/docs_solar_corrections/#step-two-photometric-velocity-due-to-rotational-doppler-imbalance","text":"Calculation of the photometric velocity due the presence of dark spots/bright faculae causing a Doppler imbalance. Requires magnetic weighting array that is flipped from the weighting array previously used. Now, a weight of 0 denotes quiet-Sun and 1 denotes active pixel. The equation for this velocity calculation is: $ v_{phot} = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{rot, ij})(I_{ij} - K) W_{ij}} {\\sum_{ij} I_{ij}} $. K is a scaling factor based off the limb-brightening correction carried out in Step Four of the solar corrections. $ K = \\frac{\\sum_{ij} I_{ij} L_{ij} W_{ij}}{\\sum_{ij} L_{ij}^2 W_{ij}} $ In this case $ I_{ij} $ is the uncorrected (original) intensity data. Calculate photometric velocity. v_phot, vphot_bright, vphot_spot = vphot[0], vphot[1], vphot[2] In [ ]: Copied! v_phot = [ sfuncs . v_phot ( mag_thresh [ i ][ 1 ], mag_thresh [ i ][ 0 ], Lij [ i ], vrot [ i ], hmi_int [ i ], coord [ i ][ 5 ], int_thresh [ i ][ 0 ], int_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_int ))] v_phot = [sfuncs.v_phot(mag_thresh[i][1], mag_thresh[i][0], Lij[i], vrot[i], hmi_int[i], coord[i][5], int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_int))]","title":"Step Two: Photometric Velocity due to rotational Doppler imbalance"},{"location":"examples/docs_solar_corrections/#step-three-convective-blueshift-velocity","text":"Calculation of suppression of convective blueshift due to active regions (mainly faculae). The basic premise for calculation is to calculate the disc-averaged velocity of the Sun and subtract from that the quiet-Sun velocity (Step One) and photometric velocity (Step Two) both of which were calculated previously. Maybe a better way to do this ... The equation for the disc-averaged velocity is: $ v = \\frac{\\sum_{ij} (v_{ij} - \\delta v_{sc, ij} - \\delta v_{rot, ij}) I_{ij}}{\\sum_{ij} I_{ij}} $ We then calculate the convective velocity as what is left: $ v_{conv} = v - v_{quiet} $ Calculate disc-averaged velocity of Sun. In [ ]: Copied! v_disc = [ sfuncs . v_disc ( map_vel_cor [ i ], hmi_int [ i ]) for i in range ( 0 , len ( hmi_int ))] v_disc = [sfuncs.v_disc(map_vel_cor[i], hmi_int[i]) for i in range(0, len(hmi_int))] Calculate convective velocity. In [ ]: Copied! v_conv = [ v_disc [ i ] - v_quiet [ i ] for i in range ( 0 , len ( hmi_mag ))] v_conv = [v_disc[i] - v_quiet[i] for i in range(0, len(hmi_mag))]","title":"Step Three: Convective Blueshift Velocity"},{"location":"examples/docs_solar_corrections/#magnetic-observables","text":"Calculation of relevant magnetic observables. filling factor: disc averaged filling factors of sunspots and plage which gives the percentage of magnetically active pixels unsigned magnetic flux: calculation of disc-averaged, line-of-sight unsigned magnetic flux","title":"Magnetic Observables"},{"location":"examples/docs_solar_corrections/#filling-factor","text":"Estimate the disc-averaged filling factors of sunspots and plage: $ f_{spot, plage} = \\frac{1}{N_{pix}} \\sum_{ij} W_{ij} $ In this case, a weight of 1 is an active pixel (active_weights). f_bright, f_spot, f = filling[0], filling[1], filling[2] In [ ]: Copied! filling = [ sfuncs . filling_factor ( coord [ i ][ 5 ], hmi_mag [ i ], mag_thresh [ i ][ 0 ], int_thresh [ i ][ 0 ], int_thresh [ i ][ 1 ]) for i in range ( 0 , len ( hmi_mag ))] filling = [sfuncs.filling_factor(coord[i][5], hmi_mag[i], mag_thresh[i][0], int_thresh[i][0], int_thresh[i][1]) for i in range(0, len(hmi_mag))]","title":"Filling Factor"},{"location":"examples/docs_solar_corrections/#unsigned-magnetic-flux","text":"Calculate disc-averaged, line-of-sight unsigned (unpolarized) magnetic flux of the Sun. Calculation of observed unsigned flux. $ |\\hat{B_{obs}}| = \\frac{\\sum_{ij} |B_{obs, ij}| I_{ij}} {\\sum_{ij} I_{ij}}$ In [ ]: Copied! unsigned_obs_flux = [ sfuncs . unsigned_flux ( map_mag_obs [ i ], hmi_int [ i ]) for i in range ( 0 , len ( hmi_mag ))] unsigned_obs_flux = [sfuncs.unsigned_flux(map_mag_obs[i], hmi_int[i]) for i in range(0, len(hmi_mag))]","title":"Unsigned Magnetic Flux"},{"location":"examples/docs_solar_corrections/#area-based-observable-calculations","text":"Calculate intensity weighted area of each active region. In [ ]: Copied! pixA_hem = [ ctfuncs . pix_area_hem ( vel_coords [ i ][ 0 ], vel_coords [ i ][ 1 ], vel_coords [ i ][ 2 ], hmi_vel [ i ]) for i in range ( 0 , len ( hmi_vel ))] area = [ sfuncs . area_calc ( mag_thresh [ i ][ 0 ], pixA_hem [ i ]) for i in range ( 0 , len ( hmi_vel ))] pixA_hem = [ctfuncs.pix_area_hem(vel_coords[i][0], vel_coords[i][1], vel_coords[i][2], hmi_vel[i]) for i in range(0, len(hmi_vel))] area = [sfuncs.area_calc(mag_thresh[i][0], pixA_hem[i]) for i in range(0, len(hmi_vel))] Calculate the area filling factor. f_small, f_large, f_network, f_plage, f_nonconv = f_area[0], f_area[1], f_area[2], f_area[3], f_area[4] In [ ]: Copied! f_area = [ sfuncs . area_filling_factor ( mag_thresh [ i ][ 0 ], area [ i ], coord [ i ][ 5 ], hmi_mag [ i ], int_thresh [ i ][ 0 ]) for i in range ( 0 , len ( hmi_mag ))] f_area = [sfuncs.area_filling_factor(mag_thresh[i][0], area[i], coord[i][5], hmi_mag[i], int_thresh[i][0]) for i in range(0, len(hmi_mag))] Calculate unsigned flux of various polarized and active regions. quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = flux[0], flux[1], flux[2], flux[3], flux[4], flux[5] In [ ]: Copied! quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux = [ sfuncs . area_unsigned_flux ( map_mag_obs [ i ], hmi_int [ i ], area [ i ], int_thresh [ i ][ 0 ]) for i in range ( 0 , len ( hmi_int ))] quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = [sfuncs.area_unsigned_flux(map_mag_obs[i], hmi_int[i], area[i], int_thresh[i][0]) for i in range(0, len(hmi_int))] Get area weighted convective velocities. vconv_quiet, vconv_large, vconv_small = vconv_area[0], vconv_area[1], vconv_area[2] In [ ]: Copied! vconv_area = [ sfuncs . area_vconv ( map_vel_cor [ i ], hmi_int [ i ], int_thresh [ i ][ 0 ], area [ i ]) for i in range ( 0 , len ( hmi_int ))] vconv_area = [sfuncs.area_vconv(map_vel_cor[i], hmi_int[i], int_thresh[i][0], area[i]) for i in range(0, len(hmi_int))]","title":"Area Based Observable Calculations"},{"location":"examples/docs_sunpy_example/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, 'DejaVu Sans Mono', monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } }, \"HTML-CSS\": { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Examples using Sunpy for querying and plotting \u00b6 Querying data \u00b6 In [10]: Copied! import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation import matplotlib.colors as colors import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a import sunpy.visualization.colormaps as cm import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation import matplotlib.colors as colors import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a import sunpy.visualization.colormaps as cm To download the required data, we use sunpy.net.Fido , a downloader client, to query the Virtual Solar Observatory to acquire AIA and HMI data. The following is the data available: HMI Bands (A): 171 (gold), 193 (bronze), 304 (red), 211 (purple), 131 (teal), 335 (blue), 094 (green), 1600 (yellow/green), 1700 (pink) HMI Products: Magnetogram, Intensitygram, Dopplergram In [11]: Copied! # data parameters cadence = a . Sample ( 24 * u . hour ) # querying cadence start_date = '2021-1-1T12:00:00' # start date of query end_date = '2021-01-5T12:00:00' # end date of query # AIA specific parameters aia_inst = a . Instrument . aia # list of all available wavelengths for reference wavelength_list = [ a . Wavelength ( 94 * u . angstrom ), a . Wavelength ( 131 * u . angstrom ), a . Wavelength ( 171 * u . angstrom ), a . Wavelength ( 193 * u . angstrom ), a . Wavelength ( 211 * u . angstrom ), a . Wavelength ( 304 * u . angstrom ), a . Wavelength ( 335 * u . angstrom ), a . Wavelength ( 1600 * u . angstrom ), a . Wavelength ( 1700 * u . angstrom )] # HMI specific parameters hmi_inst = a . Instrument . hmi # list of physical observables available physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] # data parameters cadence = a.Sample(24*u.hour) # querying cadence start_date = '2021-1-1T12:00:00' # start date of query end_date = '2021-01-5T12:00:00' # end date of query # AIA specific parameters aia_inst = a.Instrument.aia # list of all available wavelengths for reference wavelength_list = [a.Wavelength(94 * u.angstrom), a.Wavelength(131 * u.angstrom), a.Wavelength(171 * u.angstrom), a.Wavelength(193 * u.angstrom), a.Wavelength(211 * u.angstrom), a.Wavelength(304 * u.angstrom), a.Wavelength(335 * u.angstrom), a.Wavelength(1600 * u.angstrom), a.Wavelength(1700 * u.angstrom)] # HMI specific parameters hmi_inst = a.Instrument.hmi # list of physical observables available physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] Now we can look at querying for specific information. We can look for results from multiple instruments, wavelengths, and physical observables at once. It is useful to search for results before download to ensure you don't download excessive amounts of data. Let's say we only want these specific results from AIA. We search for images at a 24 hour cadence between our start and end times at specified wavelengths. In [12]: Copied! aia_result = Fido . search ( a . Time ( start_date , end_date ), aia_inst , wavelength_list [ 0 ] | wavelength_list [ 2 ] | wavelength_list [ 3 ] | wavelength_list [ 6 ], cadence ) aia_result = Fido.search(a.Time(start_date, end_date), aia_inst, wavelength_list[0] | wavelength_list[2] | wavelength_list[3] | wavelength_list[6], cadence) We can do the same with HMI data products. In [13]: Copied! hmi_result = Fido . search ( a . Time ( start_date , end_date ), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ], cadence ) hmi_result = Fido.search(a.Time(start_date, end_date), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2], cadence) You can also query for both instruments simultaneously. In [14]: Copied! result = Fido . search ( a . Time ( start_date , end_date ), aia_inst | hmi_inst , wavelength_list [ 0 ] | wavelength_list [ 1 ] | wavelength_list [ 2 ] | wavelength_list [ 3 ] | wavelength_list [ 4 ] | wavelength_list [ 5 ] | wavelength_list [ 6 ] | wavelength_list [ 7 ] | wavelength_list [ 8 ] | physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ], cadence ) result = Fido.search(a.Time(start_date, end_date), aia_inst | hmi_inst, wavelength_list[0] | wavelength_list[1] | wavelength_list[2] | wavelength_list[3] | wavelength_list[4] | wavelength_list[5] | wavelength_list[6] | wavelength_list[7] | wavelength_list[8] | physobs_list[0] | physobs_list[1] | physobs_list[2], cadence) /Users/tervin/opt/anaconda3/envs/neid_data/lib/python3.7/site-packages/astropy/table/table.py:3197: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future. result = self.as_array() == other Next we download the actual data found from our search. In [15]: Copied! file_download = Fido . fetch ( result ) file_download = Fido.fetch(result) var element = document.getElementById('7dfee6f4-7dff-42b7-a2ef-b820803d25e8'); {\"model_id\": \"562e80d0aa5b4548a33015eae2ee8169\", \"version_major\": 2, \"version_minor\": 0} After downloading our data, we want to sort it by date. We then build a map sequence of our data. In [16]: Copied! map_seq = sunpy . map . Map ( sorted ( file_download )) map_seq = sunpy.map.Map(sorted(file_download)) We then split our results by data product. In [17]: Copied! aia_094 , aia_131 , aia_171 , aia_193 , aia_211 , aia_304 , aia_335 , aia_1600 , aia_1700 , hmi_vel , hmi_mag , hmi_int \\ = [], [], [], [], [], [], [], [], [], [], [], [] for i , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'wavelnth' ] == 94 : aia_094 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 131 : aia_131 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 171 : aia_171 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 193 : aia_193 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 211 : aia_211 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 304 : aia_304 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 335 : aia_335 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 1600 : aia_1600 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 1700 : aia_1700 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 6173 and map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : hmi_vel . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 6173 and map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : hmi_mag . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 6173 and map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : hmi_int . append ( map_obj ) aia_094, aia_131, aia_171, aia_193, aia_211, aia_304, aia_335, aia_1600, aia_1700, hmi_vel, hmi_mag, hmi_int \\ = [], [], [], [], [], [], [], [], [], [], [], [] for i, map_obj in enumerate(map_seq): if map_obj.meta['wavelnth'] == 94: aia_094.append(map_obj) elif map_obj.meta['wavelnth'] == 131: aia_131.append(map_obj) elif map_obj.meta['wavelnth'] == 171: aia_171.append(map_obj) elif map_obj.meta['wavelnth'] == 193: aia_193.append(map_obj) elif map_obj.meta['wavelnth'] == 211: aia_211.append(map_obj) elif map_obj.meta['wavelnth'] == 304: aia_304.append(map_obj) elif map_obj.meta['wavelnth'] == 335: aia_335.append(map_obj) elif map_obj.meta['wavelnth'] == 1600: aia_1600.append(map_obj) elif map_obj.meta['wavelnth'] == 1700: aia_1700.append(map_obj) elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'DOPPLERGRAM': hmi_vel.append(map_obj) elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'MAGNETOGRAM': hmi_mag.append(map_obj) elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'CONTINUUM INTENSITY': hmi_int.append(map_obj) Plotting images \u00b6 You can plot individual images using inherent Sunpy features. In [19]: Copied! aia_211 [ 0 ] . peek () # this plots the aia image with a colorbar, axes labels, and title aia_211[0].peek() # this plots the aia image with a colorbar, axes labels, and title In [20]: Copied! aia_211 [ 0 ] . plot () # this just plots the data, allows you to customize labels, colormap, and title plt . show () aia_211[0].plot() # this just plots the data, allows you to customize labels, colormap, and title plt.show() Plotting HMI images requires us to rotate the image to match the orientation of AIA images. The same peek and plot methods work for HMI images. In [21]: Copied! hmi_vel_rot = hmi_vel [ 0 ] . rotate ( order = 3 ) hmi_vel_rot . peek () hmi_vel_rot = hmi_vel[0].rotate(order=3) hmi_vel_rot.peek() HMI images look nicer when customized a bit. In [22]: Copied! # plotting intensity map int_map = hmi_int [ 0 ] . rotate ( order = 3 ) int_map . plot_settings [ 'cmap' ] = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot () plt . show () # plotting intensity map int_map = hmi_int[0].rotate(order=3) int_map.plot_settings['cmap'] = plt.get_cmap('hinodesotintensity') int_map.plot() plt.show() In [23]: Copied! # plotting magnetic map mag_map = hmi_mag [ 0 ] . rotate ( order = 3 ) mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . show () # plotting magnetic map mag_map = hmi_mag[0].rotate(order=3) mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.show() In [24]: Copied! # plotting doppler map vel_map = hmi_vel [ 0 ] . rotate ( order = 3 ) vel_map . plot_settings [ 'norm' ] = plt . Normalize ( - 5500 , 5500 ) fig = plt . figure () vel_map . plot () plt . show () # plotting doppler map vel_map = hmi_vel[0].rotate(order=3) vel_map.plot_settings['norm'] = plt.Normalize(-5500, 5500) fig = plt.figure() vel_map.plot() plt.show() We can now make plots of multiple data types. Don't forget to rotate HMI images such that they are oriented the same as AIA. In [25]: Copied! fig , axs = plt . subplots ( 2 , 2 ) fig . suptitle ( \"Images %s \" % ( str ( aia_171 [ 0 ] . meta [ 't_obs' ]))) axs [ 0 , 0 ] . set_title ( \" %s %s \" % ( str ( aia_171 [ 0 ] . meta [ 'telescop' ]), str ( aia_171 [ 0 ] . meta [ 'wavelnth' ]))) axs [ 0 , 1 ] . set_title ( \" %s %s \" % ( str ( aia_211 [ 0 ] . meta [ 'telescop' ]), str ( aia_211 [ 0 ] . meta [ 'wavelnth' ]))) axs [ 1 , 0 ] . set_title ( \" %s %s \" % ( str ( hmi_vel [ 0 ] . meta [ 'telescop' ]), str ( hmi_vel [ 0 ] . meta [ 'content' ]))) axs [ 1 , 1 ] . set_title ( \" %s %s \" % ( str ( hmi_mag [ 0 ] . meta [ 'telescop' ]), str ( hmi_mag [ 0 ] . meta [ 'content' ]))) for j in range ( 0 , 2 ): for k in range ( 0 , 2 ): axs [ j , k ] . set_xticks ([]) axs [ j , k ] . set_yticks ([]) aia_171 [ 0 ] . plot ( axes = axs [ 0 , 0 ], annotate = False ) aia_211 [ 0 ] . plot ( axes = axs [ 0 , 1 ], annotate = False ) # make sure you rotate HMI images hmi_vel_rot = hmi_vel [ 0 ] . rotate ( order = 3 ) hmi_vel_rot . plot ( axes = axs [ 1 , 0 ], annotate = False ) hmi_mag_rot = hmi_mag [ 0 ] . rotate ( order = 3 ) hmi_mag_rot . plot ( axes = axs [ 1 , 1 ], annotate = False ) plt . show () fig, axs = plt.subplots(2, 2) fig.suptitle(\"Images %s\" % (str(aia_171[0].meta['t_obs']))) axs[0, 0].set_title(\"%s %s\" % (str(aia_171[0].meta['telescop']), str(aia_171[0].meta['wavelnth']))) axs[0, 1].set_title(\"%s %s\" % (str(aia_211[0].meta['telescop']), str(aia_211[0].meta['wavelnth']))) axs[1, 0].set_title(\"%s %s\" % (str(hmi_vel[0].meta['telescop']), str(hmi_vel[0].meta['content']))) axs[1, 1].set_title(\"%s %s\" % (str(hmi_mag[0].meta['telescop']), str(hmi_mag[0].meta['content']))) for j in range(0, 2): for k in range(0, 2): axs[j, k].set_xticks([]) axs[j, k].set_yticks([]) aia_171[0].plot(axes=axs[0, 0], annotate=False) aia_211[0].plot(axes=axs[0, 1], annotate=False) # make sure you rotate HMI images hmi_vel_rot = hmi_vel[0].rotate(order=3) hmi_vel_rot.plot(axes=axs[1, 0], annotate=False) hmi_mag_rot = hmi_mag[0].rotate(order=3) hmi_mag_rot.plot(axes=axs[1, 1], annotate=False) plt.show() WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] In [ ]: Copied!","title":"Sunpy Basics"},{"location":"examples/docs_sunpy_example/#examples-using-sunpy-for-querying-and-plotting","text":"","title":"Examples using Sunpy for querying and plotting"},{"location":"examples/docs_sunpy_example/#querying-data","text":"In [10]: Copied! import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation import matplotlib.colors as colors import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a import sunpy.visualization.colormaps as cm import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation import matplotlib.colors as colors import astropy.units as u import sunpy.map from sunpy.net import Fido from sunpy.net import attrs as a import sunpy.visualization.colormaps as cm To download the required data, we use sunpy.net.Fido , a downloader client, to query the Virtual Solar Observatory to acquire AIA and HMI data. The following is the data available: HMI Bands (A): 171 (gold), 193 (bronze), 304 (red), 211 (purple), 131 (teal), 335 (blue), 094 (green), 1600 (yellow/green), 1700 (pink) HMI Products: Magnetogram, Intensitygram, Dopplergram In [11]: Copied! # data parameters cadence = a . Sample ( 24 * u . hour ) # querying cadence start_date = '2021-1-1T12:00:00' # start date of query end_date = '2021-01-5T12:00:00' # end date of query # AIA specific parameters aia_inst = a . Instrument . aia # list of all available wavelengths for reference wavelength_list = [ a . Wavelength ( 94 * u . angstrom ), a . Wavelength ( 131 * u . angstrom ), a . Wavelength ( 171 * u . angstrom ), a . Wavelength ( 193 * u . angstrom ), a . Wavelength ( 211 * u . angstrom ), a . Wavelength ( 304 * u . angstrom ), a . Wavelength ( 335 * u . angstrom ), a . Wavelength ( 1600 * u . angstrom ), a . Wavelength ( 1700 * u . angstrom )] # HMI specific parameters hmi_inst = a . Instrument . hmi # list of physical observables available physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] # data parameters cadence = a.Sample(24*u.hour) # querying cadence start_date = '2021-1-1T12:00:00' # start date of query end_date = '2021-01-5T12:00:00' # end date of query # AIA specific parameters aia_inst = a.Instrument.aia # list of all available wavelengths for reference wavelength_list = [a.Wavelength(94 * u.angstrom), a.Wavelength(131 * u.angstrom), a.Wavelength(171 * u.angstrom), a.Wavelength(193 * u.angstrom), a.Wavelength(211 * u.angstrom), a.Wavelength(304 * u.angstrom), a.Wavelength(335 * u.angstrom), a.Wavelength(1600 * u.angstrom), a.Wavelength(1700 * u.angstrom)] # HMI specific parameters hmi_inst = a.Instrument.hmi # list of physical observables available physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] Now we can look at querying for specific information. We can look for results from multiple instruments, wavelengths, and physical observables at once. It is useful to search for results before download to ensure you don't download excessive amounts of data. Let's say we only want these specific results from AIA. We search for images at a 24 hour cadence between our start and end times at specified wavelengths. In [12]: Copied! aia_result = Fido . search ( a . Time ( start_date , end_date ), aia_inst , wavelength_list [ 0 ] | wavelength_list [ 2 ] | wavelength_list [ 3 ] | wavelength_list [ 6 ], cadence ) aia_result = Fido.search(a.Time(start_date, end_date), aia_inst, wavelength_list[0] | wavelength_list[2] | wavelength_list[3] | wavelength_list[6], cadence) We can do the same with HMI data products. In [13]: Copied! hmi_result = Fido . search ( a . Time ( start_date , end_date ), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ], cadence ) hmi_result = Fido.search(a.Time(start_date, end_date), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2], cadence) You can also query for both instruments simultaneously. In [14]: Copied! result = Fido . search ( a . Time ( start_date , end_date ), aia_inst | hmi_inst , wavelength_list [ 0 ] | wavelength_list [ 1 ] | wavelength_list [ 2 ] | wavelength_list [ 3 ] | wavelength_list [ 4 ] | wavelength_list [ 5 ] | wavelength_list [ 6 ] | wavelength_list [ 7 ] | wavelength_list [ 8 ] | physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ], cadence ) result = Fido.search(a.Time(start_date, end_date), aia_inst | hmi_inst, wavelength_list[0] | wavelength_list[1] | wavelength_list[2] | wavelength_list[3] | wavelength_list[4] | wavelength_list[5] | wavelength_list[6] | wavelength_list[7] | wavelength_list[8] | physobs_list[0] | physobs_list[1] | physobs_list[2], cadence) /Users/tervin/opt/anaconda3/envs/neid_data/lib/python3.7/site-packages/astropy/table/table.py:3197: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future. result = self.as_array() == other Next we download the actual data found from our search. In [15]: Copied! file_download = Fido . fetch ( result ) file_download = Fido.fetch(result) var element = document.getElementById('7dfee6f4-7dff-42b7-a2ef-b820803d25e8'); {\"model_id\": \"562e80d0aa5b4548a33015eae2ee8169\", \"version_major\": 2, \"version_minor\": 0} After downloading our data, we want to sort it by date. We then build a map sequence of our data. In [16]: Copied! map_seq = sunpy . map . Map ( sorted ( file_download )) map_seq = sunpy.map.Map(sorted(file_download)) We then split our results by data product. In [17]: Copied! aia_094 , aia_131 , aia_171 , aia_193 , aia_211 , aia_304 , aia_335 , aia_1600 , aia_1700 , hmi_vel , hmi_mag , hmi_int \\ = [], [], [], [], [], [], [], [], [], [], [], [] for i , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'wavelnth' ] == 94 : aia_094 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 131 : aia_131 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 171 : aia_171 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 193 : aia_193 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 211 : aia_211 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 304 : aia_304 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 335 : aia_335 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 1600 : aia_1600 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 1700 : aia_1700 . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 6173 and map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : hmi_vel . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 6173 and map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : hmi_mag . append ( map_obj ) elif map_obj . meta [ 'wavelnth' ] == 6173 and map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : hmi_int . append ( map_obj ) aia_094, aia_131, aia_171, aia_193, aia_211, aia_304, aia_335, aia_1600, aia_1700, hmi_vel, hmi_mag, hmi_int \\ = [], [], [], [], [], [], [], [], [], [], [], [] for i, map_obj in enumerate(map_seq): if map_obj.meta['wavelnth'] == 94: aia_094.append(map_obj) elif map_obj.meta['wavelnth'] == 131: aia_131.append(map_obj) elif map_obj.meta['wavelnth'] == 171: aia_171.append(map_obj) elif map_obj.meta['wavelnth'] == 193: aia_193.append(map_obj) elif map_obj.meta['wavelnth'] == 211: aia_211.append(map_obj) elif map_obj.meta['wavelnth'] == 304: aia_304.append(map_obj) elif map_obj.meta['wavelnth'] == 335: aia_335.append(map_obj) elif map_obj.meta['wavelnth'] == 1600: aia_1600.append(map_obj) elif map_obj.meta['wavelnth'] == 1700: aia_1700.append(map_obj) elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'DOPPLERGRAM': hmi_vel.append(map_obj) elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'MAGNETOGRAM': hmi_mag.append(map_obj) elif map_obj.meta['wavelnth'] == 6173 and map_obj.meta['content'] == 'CONTINUUM INTENSITY': hmi_int.append(map_obj)","title":"Querying data"},{"location":"examples/docs_sunpy_example/#plotting-images","text":"You can plot individual images using inherent Sunpy features. In [19]: Copied! aia_211 [ 0 ] . peek () # this plots the aia image with a colorbar, axes labels, and title aia_211[0].peek() # this plots the aia image with a colorbar, axes labels, and title In [20]: Copied! aia_211 [ 0 ] . plot () # this just plots the data, allows you to customize labels, colormap, and title plt . show () aia_211[0].plot() # this just plots the data, allows you to customize labels, colormap, and title plt.show() Plotting HMI images requires us to rotate the image to match the orientation of AIA images. The same peek and plot methods work for HMI images. In [21]: Copied! hmi_vel_rot = hmi_vel [ 0 ] . rotate ( order = 3 ) hmi_vel_rot . peek () hmi_vel_rot = hmi_vel[0].rotate(order=3) hmi_vel_rot.peek() HMI images look nicer when customized a bit. In [22]: Copied! # plotting intensity map int_map = hmi_int [ 0 ] . rotate ( order = 3 ) int_map . plot_settings [ 'cmap' ] = plt . get_cmap ( 'hinodesotintensity' ) int_map . plot () plt . show () # plotting intensity map int_map = hmi_int[0].rotate(order=3) int_map.plot_settings['cmap'] = plt.get_cmap('hinodesotintensity') int_map.plot() plt.show() In [23]: Copied! # plotting magnetic map mag_map = hmi_mag [ 0 ] . rotate ( order = 3 ) mag_map . plot_settings [ 'norm' ] = plt . Normalize ( - 100 , 100 ) mag_map . plot () plt . show () # plotting magnetic map mag_map = hmi_mag[0].rotate(order=3) mag_map.plot_settings['norm'] = plt.Normalize(-100, 100) mag_map.plot() plt.show() In [24]: Copied! # plotting doppler map vel_map = hmi_vel [ 0 ] . rotate ( order = 3 ) vel_map . plot_settings [ 'norm' ] = plt . Normalize ( - 5500 , 5500 ) fig = plt . figure () vel_map . plot () plt . show () # plotting doppler map vel_map = hmi_vel[0].rotate(order=3) vel_map.plot_settings['norm'] = plt.Normalize(-5500, 5500) fig = plt.figure() vel_map.plot() plt.show() We can now make plots of multiple data types. Don't forget to rotate HMI images such that they are oriented the same as AIA. In [25]: Copied! fig , axs = plt . subplots ( 2 , 2 ) fig . suptitle ( \"Images %s \" % ( str ( aia_171 [ 0 ] . meta [ 't_obs' ]))) axs [ 0 , 0 ] . set_title ( \" %s %s \" % ( str ( aia_171 [ 0 ] . meta [ 'telescop' ]), str ( aia_171 [ 0 ] . meta [ 'wavelnth' ]))) axs [ 0 , 1 ] . set_title ( \" %s %s \" % ( str ( aia_211 [ 0 ] . meta [ 'telescop' ]), str ( aia_211 [ 0 ] . meta [ 'wavelnth' ]))) axs [ 1 , 0 ] . set_title ( \" %s %s \" % ( str ( hmi_vel [ 0 ] . meta [ 'telescop' ]), str ( hmi_vel [ 0 ] . meta [ 'content' ]))) axs [ 1 , 1 ] . set_title ( \" %s %s \" % ( str ( hmi_mag [ 0 ] . meta [ 'telescop' ]), str ( hmi_mag [ 0 ] . meta [ 'content' ]))) for j in range ( 0 , 2 ): for k in range ( 0 , 2 ): axs [ j , k ] . set_xticks ([]) axs [ j , k ] . set_yticks ([]) aia_171 [ 0 ] . plot ( axes = axs [ 0 , 0 ], annotate = False ) aia_211 [ 0 ] . plot ( axes = axs [ 0 , 1 ], annotate = False ) # make sure you rotate HMI images hmi_vel_rot = hmi_vel [ 0 ] . rotate ( order = 3 ) hmi_vel_rot . plot ( axes = axs [ 1 , 0 ], annotate = False ) hmi_mag_rot = hmi_mag [ 0 ] . rotate ( order = 3 ) hmi_mag_rot . plot ( axes = axs [ 1 , 1 ], annotate = False ) plt . show () fig, axs = plt.subplots(2, 2) fig.suptitle(\"Images %s\" % (str(aia_171[0].meta['t_obs']))) axs[0, 0].set_title(\"%s %s\" % (str(aia_171[0].meta['telescop']), str(aia_171[0].meta['wavelnth']))) axs[0, 1].set_title(\"%s %s\" % (str(aia_211[0].meta['telescop']), str(aia_211[0].meta['wavelnth']))) axs[1, 0].set_title(\"%s %s\" % (str(hmi_vel[0].meta['telescop']), str(hmi_vel[0].meta['content']))) axs[1, 1].set_title(\"%s %s\" % (str(hmi_mag[0].meta['telescop']), str(hmi_mag[0].meta['content']))) for j in range(0, 2): for k in range(0, 2): axs[j, k].set_xticks([]) axs[j, k].set_yticks([]) aia_171[0].plot(axes=axs[0, 0], annotate=False) aia_211[0].plot(axes=axs[0, 1], annotate=False) # make sure you rotate HMI images hmi_vel_rot = hmi_vel[0].rotate(order=3) hmi_vel_rot.plot(axes=axs[1, 0], annotate=False) hmi_mag_rot = hmi_mag[0].rotate(order=3) hmi_mag_rot.plot(axes=axs[1, 1], annotate=False) plt.show() WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] WARNING: SunpyUserWarning: The axes of this map are not aligned to the pixel grid. Plot axes may be incorrect. [sunpy.map.mapbase] WARNING: SunpyDeprecationWarning: WCSAxes not being used as the axes object for this plot. Support for this is deprecated, and will be removed in sunpy 3.1. To fix this pass set the `projection` keyword to this map when creating the axes. [sunpy.map.mapbase] In [ ]: Copied!","title":"Plotting images"},{"location":"package/calculation_funcs/","text":"Calculation Functions Module Tamar Ervin Date: June 16, 2021 functions for calculating Solar velocity corrections and components for derivation of SDO/HMI RVs area_calc ( active , pixA_hem ) calculate area of active pixels for a thresholded map Parameters active: weights array where active pixels have weight = 1 pixA_hem: pixel areas in uHem Returns area: area of each active region weighted by its intensity Source code in sdo_hmi_rvs/tools/calculation_funcs.py def area_calc ( active , pixA_hem ): \"\"\" calculate area of active pixels for a thresholded map Parameters ---------- active: weights array where active pixels have weight = 1 pixA_hem: pixel areas in uHem Returns ------- area: area of each active region weighted by its intensity \"\"\" # get labeling of image labeled = label ( active ) # get area of active regions area = np . zeros ( active . shape ) props = regionprops ( labeled ) info = regionprops ( labeled , pixA_hem ) # add area to array for k in range ( 1 , len ( info )): area [ props [ k ] . coords [:, 0 ], props [ k ] . coords [:, 1 ]] = info [ k ] . area * info [ k ] . mean_intensity return area area_filling_factor ( active , area , mu , mmap , fac_inds , athresh = 20 , mu_cutoff = 0.3 ) calculate filling factor for regions thresholded by area - differentiate between large and small regions - differentiate between plage (large) and network (small) bright regions Parameters active: weights array where active pixels have weight = 1 area: area of each active region weighted by its intensity mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) fac_inds: array of indices where faculae are detected athresh: area threshold value between large and small regions (in uHem) mu_cutoff: minimum mu cutoff value for usable data Returns f_small: filling factor (%) for small magnetically active regions f_large: filling factor (%) for large magnetically active regions f_network: filling factor (%) for network (small, bright magnetically active) regions f_plage: filling factor (%) for plage (large, bright magnetically active) regions f_nonconv: filling factor (%) for regions that do not suppress convective blueshift Source code in sdo_hmi_rvs/tools/calculation_funcs.py def area_filling_factor ( active , area , mu , mmap , fac_inds , athresh = 20 , mu_cutoff = 0.3 ): \"\"\" calculate filling factor for regions thresholded by area - differentiate between large and small regions - differentiate between plage (large) and network (small) bright regions Parameters ---------- active: weights array where active pixels have weight = 1 area: area of each active region weighted by its intensity mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) fac_inds: array of indices where faculae are detected athresh: area threshold value between large and small regions (in uHem) mu_cutoff: minimum mu cutoff value for usable data Returns ------- f_small: filling factor (%) for small magnetically active regions f_large: filling factor (%) for large magnetically active regions f_network: filling factor (%) for network (small, bright magnetically active) regions f_plage: filling factor (%) for plage (large, bright magnetically active) regions f_nonconv: filling factor (%) for regions that do not suppress convective blueshift \"\"\" # get good mu values good_mu = np . where ( mu > mu_cutoff ) # get number of pixels npix = len ( mmap . data [ good_mu ]) # get quiet pixels quiet = 1 - active # get filling factor for 'small' magnetic features small = np . zeros ( mmap . data . shape ) small_inds = np . logical_and ( active > 0.5 , area < athresh ) small [ small_inds ] = 1. f_small = np . nansum ( small ) / npix * 100 # get filling factor for 'large' magnetic features large = np . zeros ( mmap . data . shape ) large_inds = np . logical_and ( active > 0.5 , area > athresh ) large [ large_inds ] = 1. f_large = np . nansum ( large ) / npix * 100 # get filling factor for network (small, faculae regions) network = np . zeros ( mmap . data . shape ) network_inds = np . logical_and ( small > 0.5 , fac_inds > 0.5 ) network [ network_inds ] = 1. f_network = np . nansum ( network ) / npix * 100 # get filling factor for plage (large, faculae regions) plage = np . zeros ( mmap . data . shape ) plage_inds = np . logical_and ( large > 0.5 , fac_inds > 0.5 ) plage [ plage_inds ] = 1. f_plage = np . nansum ( plage ) / npix * 100 # get filling factor for small, non-convective regions nonconv = np . zeros ( mmap . data . shape ) nonconv_inds = np . logical_and ( quiet > 0.5 , small > 0.5 ) nonconv [ nonconv_inds ] = 1. f_nonconv = np . nansum ( nonconv ) / npix * 100 return f_small , f_large , f_network , f_plage , f_nonconv area_unsigned_flux ( map_mag_obs , imap , area , active , athresh = 20 ) calculate the magnetic flux for different regions based on area cut and magnetic activitiy Parameters map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) area: area of each active region weighted by its intensity active: weights array where active pixels have weight = 1 athresh: area threshold value between large and small regions (in uHem) Returns quiet_flux: magnetic flux of quiet-Sun regions ar_flux: magnetic flux of active regions conv_flux: magnetic flux of regions that suppress convective blueshift pol_flux: magnetic flux of polarized regions pol_conv_flux: magnetic flux of polarized regions that suppress the convective blueshift Source code in sdo_hmi_rvs/tools/calculation_funcs.py def area_unsigned_flux ( map_mag_obs , imap , area , active , athresh = 20 ): \"\"\" calculate the magnetic flux for different regions based on area cut and magnetic activitiy Parameters ---------- map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) area: area of each active region weighted by its intensity active: weights array where active pixels have weight = 1 athresh: area threshold value between large and small regions (in uHem) Returns ------- quiet_flux: magnetic flux of quiet-Sun regions ar_flux: magnetic flux of active regions conv_flux: magnetic flux of regions that suppress convective blueshift pol_flux: magnetic flux of polarized regions pol_conv_flux: magnetic flux of polarized regions that suppress the convective blueshift \"\"\" # get data arrays i_data = imap . data m_data = map_mag_obs . data mabs_data = np . abs ( m_data ) quiet = 1 - active # get large regions array large = np . zeros ( m_data . shape ) large_inds = np . logical_and ( active > 0.5 , area > athresh ) large [ large_inds ] = 1. # calculate relevant fluxes quiet_flux = np . nansum ( mabs_data * i_data * quiet ) / np . nansum ( i_data * quiet ) ar_flux = np . nansum ( mabs_data * i_data * active ) / np . nansum ( i_data * active ) conv_flux = np . nansum ( mabs_data * i_data * large ) / np . nansum ( i_data * large ) pol_flux = np . nansum ( m_data * i_data ) / np . nansum ( i_data ) pol_conv_flux = np . nansum ( m_data * i_data * large ) / np . nansum ( i_data * large ) return quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux area_vconv ( map_vel_cor , imap , active , area , athresh = 20 ) calculate convective velocities for different area thresholds Parameters map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) active: weights array where active pixels have weight = 1 area: area of each active region weighted by its intensity athresh: area threshold value between large and small regions (in uHem) Returns vconv_quiet: convective velocity due to quiet-Sun regions vconv_large: convective velocity due to large active regions vconv_small: convective velocity due to small active regions Source code in sdo_hmi_rvs/tools/calculation_funcs.py def area_vconv ( map_vel_cor , imap , active , area , athresh = 20 ): \"\"\" calculate convective velocities for different area thresholds Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) active: weights array where active pixels have weight = 1 area: area of each active region weighted by its intensity athresh: area threshold value between large and small regions (in uHem) Returns ------- vconv_quiet: convective velocity due to quiet-Sun regions vconv_large: convective velocity due to large active regions vconv_small: convective velocity due to small active regions \"\"\" # get data arrays v_data = map_vel_cor . data i_data = imap . data # get large regions array large = np . zeros ( v_data . shape ) large_inds = np . logical_and ( active > 0.5 , area > athresh ) large [ large_inds ] = 1. # get small regions array small = np . zeros ( v_data . shape ) small_inds = np . logical_and ( active > 0.5 , area < athresh ) small [ small_inds ] = 1. # label the regions labeled = label ( large ) v_props = regionprops ( labeled , v_data ) i_props = regionprops ( labeled , i_data ) # labeled for small regions labeled = label ( small ) v_small = regionprops ( labeled , v_data ) i_small = regionprops ( labeled , i_data ) # get quiet regions array quiet = 1 - active # array of non-convective regions nonconv = np . zeros ( v_data . shape ) nonconv_inds = np . logical_or ( quiet > 0.5 , small > 0.5 ) nonconv [ nonconv_inds ] = 1. # velocities of non convective regions vel_quiet = np . nansum ( v_data * i_data * quiet ) / np . sum ( i_data * quiet ) vel_nonconv = np . nansum ( v_data * i_data * nonconv ) / np . sum ( i_data * nonconv ) # velocities of convective regions vconv_large = np . zeros ( len ( v_props )) vconv_small = np . zeros ( len ( v_props )) vconv_quiet = np . zeros ( len ( v_props )) for k in range ( len ( v_props )): vconv_large [ k ] = v_props [ k ] . area * ( v_props [ k ] . mean_intensity - vel_quiet ) * i_props [ k ] . mean_intensity vconv_small [ k ] = v_small [ k ] . area * ( v_small [ k ] . mean_intensity - vel_quiet ) * i_small [ k ] . mean_intensity vconv_quiet [ k ] = v_props [ k ] . area * ( v_props [ k ] . mean_intensity - vel_nonconv ) * i_props [ k ] . mean_intensity # convective velocity of quiet regions vconv_quiet = np . nansum ( vconv_quiet ) / np . sum ( i_data ) # convective velocity of large regions vconv_large = np . nansum ( vconv_large ) / np . sum ( i_data ) # convective velocity of small regions vconv_small = np . nansum ( vconv_small ) / np . sum ( i_data ) return vconv_quiet , vconv_large , vconv_small corrected_map ( corrected_data , smap , map_type , frame =< class ' sunpy . coordinates . frames . HeliographicCarrington '>) function to make Sunpy map object from corrected data Parameters corrected_data: corrected velocity data smap: original Sunpy map object map_type: map type for 'content' section of fits header (string) frame: new rotation frame Returns corr_map: Sunpy map object with new frame information and corrected data Source code in sdo_hmi_rvs/tools/calculation_funcs.py def corrected_map ( corrected_data , smap , map_type , frame = frames . HeliographicCarrington ): \"\"\" function to make Sunpy map object from corrected data Parameters ---------- corrected_data: corrected velocity data smap: original Sunpy map object map_type: map type for 'content' section of fits header (string) frame: new rotation frame Returns ------- corr_map: Sunpy map object with new frame information and corrected data \"\"\" # build SkyCoord instance in new frame coord = SkyCoord ( 0 * u . arcsec , 0 * u . arcsec , obstime = smap . date , observer = smap . observer_coordinate , frame = frame ) # create fits header file with data and coordinate system information header = sunpy . map . make_fitswcs_header ( corrected_data , coord ) # update fits header with instrument and content information header [ 'content' ] = map_type header [ 'telescop' ] = smap . meta [ 'telescop' ] header [ 'wavelnth' ] = smap . meta [ 'wavelnth' ] # create new Sunpy map instance with corrected data corr_map = sunpy . map . Map ( corrected_data , header ) return corr_map filling_factor ( mu , mmap , active , fac_inds , spot_inds , mu_cutoff = 0.3 ) function to calculate filling factors for faculae, sunspots, and total magnetically active regions - percentage of magnetically active pixels on the solar surface at any one time Parameters mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) active: weights array where active pixels have weight = 1 fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns f_bright: filling factor (%) for bright areas (faculae) f_spot: filling factor (%) for dark areas (sunspots) f_total: filling factor (%) for timestamp Source code in sdo_hmi_rvs/tools/calculation_funcs.py def filling_factor ( mu , mmap , active , fac_inds , spot_inds , mu_cutoff = 0.3 ): \"\"\" function to calculate filling factors for faculae, sunspots, and total magnetically active regions - percentage of magnetically active pixels on the solar surface at any one time Parameters ---------- mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) active: weights array where active pixels have weight = 1 fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns ------- f_bright: filling factor (%) for bright areas (faculae) f_spot: filling factor (%) for dark areas (sunspots) f_total: filling factor (%) for timestamp \"\"\" # get good mu values good_mu = np . where ( mu > mu_cutoff ) # get number of pixels npix = len ( mmap . data [ good_mu ]) # faculae faculae = np . zeros ( mmap . data . shape ) faculae [ fac_inds ] = 1. f_bright = np . sum ( faculae ) / npix * 100 # sunspots spots = np . zeros ( mmap . data . shape ) spots [ spot_inds ] = 1. f_spot = np . sum ( spots ) / npix * 100 # get filling factor f_total = np . sum ( active ) / npix * 100 return f_bright , f_spot , f_total int_thresh ( map_int_cor , active , quiet ) function to do intensity thresholding and differentiate between faculae (bright) and sunspots (dark) Parameters map_int_cor: corrected (limb-darkening) Sunpy map object (Intensitygram) active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 Returns fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected Source code in sdo_hmi_rvs/tools/calculation_funcs.py def int_thresh ( map_int_cor , active , quiet ): \"\"\" function to do intensity thresholding and differentiate between faculae (bright) and sunspots (dark) Parameters ---------- map_int_cor: corrected (limb-darkening) Sunpy map object (Intensitygram) active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 Returns ------- fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected \"\"\" # flattened intensity data Iflat = map_int_cor . data # calculate quiet sun intensity int_quiet = np . nansum ( Iflat * quiet ) / np . nansum ( quiet ) # intensity threshold int_cutoff = 0.89 * int_quiet # get faculae fac_inds = np . logical_and (( Iflat > int_cutoff ), ( active > 0.5 )) # get sunspots spot_inds = np . logical_and (( Iflat <= int_cutoff ), ( active > 0.5 )) return fac_inds , spot_inds mag_field ( mu , mmap , B_noise = 8 , mu_cutoff = 0.3 ) function to correct for unsigned magnetic field strength and magnetic noise Parameters mu: array of mu (cosine theta) values mmap: Sunpy map object (Magnetogram) B_noise: magnetic noise level in Gauss mu_cutoff: minimum mu cutoff value Returns Bobs: corrected observed magnetic field strength Br: array of corrected unsigned magnetic field strength Source code in sdo_hmi_rvs/tools/calculation_funcs.py def mag_field ( mu , mmap , B_noise = 8 , mu_cutoff = 0.3 ): \"\"\" function to correct for unsigned magnetic field strength and magnetic noise Parameters ---------- mu: array of mu (cosine theta) values mmap: Sunpy map object (Magnetogram) B_noise: magnetic noise level in Gauss mu_cutoff: minimum mu cutoff value Returns ------- Bobs: corrected observed magnetic field strength Br: array of corrected unsigned magnetic field strength \"\"\" # get valid indices use_indices = np . logical_and ( mu > mu_cutoff , mu != np . nan ) mag_indices = np . logical_and ( use_indices , np . abs ( mmap . data ) < B_noise ) # calculate full magnetic field strength Bobs = mmap . data Br = np . full ( shape = mmap . data . shape , fill_value = np . nan ) Br [ use_indices ] = Bobs [ use_indices ] / mu [ use_indices ] Bobs [ mag_indices ] = 0 Br [ mag_indices ] = 0 return Bobs , Br mag_thresh ( mu , mmap , Br_cutoff = 24 , mu_cutoff = 0.3 ) function to calculate magnetic threshold and differentiate between magnetically active regions and quiet Sun Parameters mu: array of mu (cosine theta) values mmap: corrected (unsigned magnetic field) Sunpy map object (Magnetogram) Br_cutoff: minimum cutoff value (in Gauss) for thresholding active regions mu_cutoff: minimum mu cutoff value for data to ignore Returns active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 Source code in sdo_hmi_rvs/tools/calculation_funcs.py def mag_thresh ( mu , mmap , Br_cutoff = 24 , mu_cutoff = 0.3 ): \"\"\" function to calculate magnetic threshold and differentiate between magnetically active regions and quiet Sun Parameters ---------- mu: array of mu (cosine theta) values mmap: corrected (unsigned magnetic field) Sunpy map object (Magnetogram) Br_cutoff: minimum cutoff value (in Gauss) for thresholding active regions mu_cutoff: minimum mu cutoff value for data to ignore Returns ------- active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 \"\"\" # get active region indices active_inds = np . where ( np . abs ( mmap . data ) * mu > Br_cutoff ) bad_mu = np . where ( mu <= mu_cutoff ) # make active region array active = np . zeros ( mu . shape ) active [ active_inds ] = 1. active [ bad_mu ] = 0. # find isolated pixels # get area y_labeled = label ( active , connectivity = 2 , background = 0 ) y_area = [ props . area for props in regionprops ( y_labeled )] # area constraint good_area = np . where ( np . array ( y_area ) > 5 ) good_area = good_area [ 0 ] + 1 active_indices = np . isin ( y_labeled , good_area ) # create weights array active [ ~ active_indices ] = 0 # get quiet indices quiet = 1 - active return active , quiet map_sequence ( dates_list , time_range = datetime . timedelta ( seconds = 6 ), instrument =< sunpy . net . attrs . Instrument ( AIA : Atmospheric Imaging Assembly ) object at 0x7ff147b83e50 > , wavelength =< sunpy . net . attrs . Wavelength ( 171.0 , 171.0 , 'Angstrom' ) > ) function to query sunpy for images within certain time range of dates in dates list user specified instrument and wavelength, otherwise default values: AIA 171A Parameters dates_list: list of dates, either datetime or strings time_range: plus/minus time range to search for images in comparison to desired timestamp instrument: Sunpy instrument of choice (AIA, HMI, LASCO, EIT) wavelength: desired wavelength of choice instrument Returns maps: Sunpy map sequence object Source code in sdo_hmi_rvs/tools/calculation_funcs.py def map_sequence ( dates_list , time_range = datetime . timedelta ( seconds = 6 ), instrument = a . Instrument . aia , wavelength = a . Wavelength ( 171 * u . angstrom )): \"\"\" function to query sunpy for images within certain time range of dates in dates list user specified instrument and wavelength, otherwise default values: AIA 171A Parameters ---------- dates_list: list of dates, either datetime or strings time_range: plus/minus time range to search for images in comparison to desired timestamp instrument: Sunpy instrument of choice (AIA, HMI, LASCO, EIT) wavelength: desired wavelength of choice instrument Returns ------- maps: Sunpy map sequence object \"\"\" if isinstance ( dates_list [ 0 ][ 0 ], str ): datetimes = [ datetime . datetime . strptime ( date [ 0 ], '%Y-%m- %d T%H:%M:%S. %f ' ) for date in dates_list ] else : datetimes = dates_list downloaded_files = [] for ind , datetime_object in enumerate ( datetimes ): # pull image within specified time range result = Fido . search ( a . Time ( str ( datetime_object - time_range ), str ( datetime_object + time_range )), instrument , wavelength ) # add file to list downloaded_files . append ( Fido . fetch ( result )) # build map sequence from files maps = sunpy . map . Map ( downloaded_files , sequence = True ) return maps rel_positions ( wij , nij , rij , smap ) function to calculate pixel-wise relative positions in new coordinate frame Parameters wij: array of westward values for image nij: array of northward values for image rij: array of radius values for image smap: Sunpy map object Returns deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft Source code in sdo_hmi_rvs/tools/calculation_funcs.py def rel_positions ( wij , nij , rij , smap ): \"\"\" function to calculate pixel-wise relative positions in new coordinate frame Parameters ---------- wij: array of westward values for image nij: array of northward values for image rij: array of radius values for image smap: Sunpy map object Returns ------- deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft \"\"\" # calculate relative positions of each pixel rsc = smap . meta [ 'dsun_obs' ] / smap . meta [ 'rsun_ref' ] deltaw = wij deltan = nij deltar = rij - rsc dij = np . sqrt ( deltaw ** 2 + deltan ** 2 + deltar ** 2 ) return deltaw , deltan , deltar , dij solar_rot_vel ( wij , nij , rij , deltaw , deltan , deltar , dij , vmap , a_parameters = [ 14.713 , - 2.396 , - 1.787 ]) function to calculate pixel-wise velocities due to solar rotation Parameters wij: array of westward values for image nij: array of northward values for image rij: array of radius values for image deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) a_parameters: array of solar differential rotation parameters from Snodgrass & Ulrich (1990). Returns vrot: array of solar rotation velocities Source code in sdo_hmi_rvs/tools/calculation_funcs.py def solar_rot_vel ( wij , nij , rij , deltaw , deltan , deltar , dij , vmap , a_parameters = [ 14.713 , - 2.396 , - 1.787 ]): \"\"\" function to calculate pixel-wise velocities due to solar rotation Parameters ---------- wij: array of westward values for image nij: array of northward values for image rij: array of radius values for image deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) a_parameters: array of solar differential rotation parameters from Snodgrass & Ulrich (1990). Returns ------- vrot: array of solar rotation velocities \"\"\" # apply to cartesian coordinates x1 = wij y1 = nij * np . cos ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) + rij * np . sin ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) z1 = - nij * np . sin ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) + rij * np . cos ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) hx = x1 * np . cos ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) + z1 * np . sin ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) hy = y1 hz = - x1 * np . sin ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) + z1 * np . cos ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) # apply parameters to determine vrot for given image pixel w = ( a_parameters [ 0 ] + a_parameters [ 1 ] * (( np . sin ( hy )) ** 2 ) + a_parameters [ 2 ] * ( ( np . sin ( hy )) ** 4 )) * 1. / 86400. * np . pi / 180. # get projection of solar rotation vx_rot = w * hz * vmap . meta [ 'rsun_ref' ] vy_rot = 0. vz_rot = - w * hx * vmap . meta [ 'rsun_ref' ] v1 = np . cos ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) * vx_rot - np . sin ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) * vz_rot v2 = vy_rot v3 = np . sin ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) * vx_rot + np . cos ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) * vz_rot # project into correct direction vrotw = v1 vrotn = v2 * np . cos ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) - v3 * np . sin ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) vrotr = v2 * np . sin ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) + v3 * np . cos ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) # get full rotational velocity vrot = ( deltaw * vrotw + deltan * vrotn + deltar * vrotr ) / dij return vrot spacecraft_vel ( deltaw , deltan , deltar , dij , vmap ) function to calculate pixel-wise spacecraft velocities for Sunpy map Parameters deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) Returns vsc: array of spacecraft velocities Source code in sdo_hmi_rvs/tools/calculation_funcs.py def spacecraft_vel ( deltaw , deltan , deltar , dij , vmap ): \"\"\" function to calculate pixel-wise spacecraft velocities for Sunpy map Parameters ---------- deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) Returns ------- vsc: array of spacecraft velocities \"\"\" # velocity of spacecraft relative to sun vscw = vmap . meta [ 'obs_vw' ] vscn = vmap . meta [ 'obs_vn' ] vscr = vmap . meta [ 'obs_vr' ] # pixel-wise magnitude of spacecraft velocity vsc = - ( deltaw * vscw + deltan * vscn + deltar * vscr ) / dij return vsc thresh_map ( fac_inds , spot_inds ) function that creates thresholded map of sunspots (1) and faculae (2) Parameters fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected Returns thresh_arr: array of values denoting faculae (1) and sunspots (2) Source code in sdo_hmi_rvs/tools/calculation_funcs.py def thresh_map ( fac_inds , spot_inds ): \"\"\" function that creates thresholded map of sunspots (1) and faculae (2) Parameters ---------- fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected Returns ------- thresh_arr: array of values denoting faculae (1) and sunspots (2) \"\"\" thresh_arr = np . full ( shape = fac_inds . shape , fill_value = np . nan ) thresh_arr [ fac_inds ] = 1 thresh_arr [ spot_inds ] = 2 return thresh_arr unsigned_flux ( map_mag_obs , imap ) calculate unsigned magnetic flux Parameters map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns unsign_flux: unsigned magnetic flux Source code in sdo_hmi_rvs/tools/calculation_funcs.py def unsigned_flux ( map_mag_obs , imap ): \"\"\" calculate unsigned magnetic flux Parameters ---------- map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- unsign_flux: unsigned magnetic flux \"\"\" # get data arrays i_data = imap . data m_data = map_mag_obs . data mabs_data = np . abs ( m_data ) # unsigned flux unsign_flux = np . nansum ( mabs_data * i_data ) / np . nansum ( i_data ) return np . abs ( unsign_flux ) v_disc ( map_vel_cor , imap ) function to calculate disc-averaged velocity of Sun Parameters map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns v_disc: disc averaged velocity of Sun (float) Source code in sdo_hmi_rvs/tools/calculation_funcs.py def v_disc ( map_vel_cor , imap ): \"\"\" function to calculate disc-averaged velocity of Sun Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- v_disc: disc averaged velocity of Sun (float) \"\"\" v_disc = np . nansum ( map_vel_cor . data * imap . data ) / np . nansum ( imap . data ) return v_disc v_phot ( quiet , active , Lij , vrot , imap , mu , fac_inds , spot_inds , mu_cutoff = 0.3 ) function to calculate photometric velocity due to rotational Doppler variation Parameters quiet: weights array where active pixels have weight = 0 active: weights array where active pixels have weight = 1 Lij: limb-darkening polynomial function vrot: solar rotational velocity imap: UNCORRECTED Sunpy map object (Intensitygram) mu: array of mu values fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns v_phot: photospheric velocity perturbation (float) Source code in sdo_hmi_rvs/tools/calculation_funcs.py def v_phot ( quiet , active , Lij , vrot , imap , mu , fac_inds , spot_inds , mu_cutoff = 0.3 ): \"\"\" function to calculate photometric velocity due to rotational Doppler variation Parameters ---------- quiet: weights array where active pixels have weight = 0 active: weights array where active pixels have weight = 1 Lij: limb-darkening polynomial function vrot: solar rotational velocity imap: UNCORRECTED Sunpy map object (Intensitygram) mu: array of mu values fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns ------- v_phot: photospheric velocity perturbation (float) \"\"\" # get good mu values good_mu = np . where ( mu > mu_cutoff ) # calculate K scaling factor K = np . nansum ( imap . data * Lij * quiet ) / np . sum (( Lij [ good_mu ] ** 2 ) * quiet [ good_mu ]) # calculate photospheric velocity v_phot = np . nansum ( np . real ( vrot ) * ( imap . data - K * Lij ) * active ) / np . nansum ( imap . data ) # faculae driven photospheric velocity vphot_bright = np . nansum ( np . real ( vrot ) * ( imap . data - K * Lij ) * fac_inds ) / np . nansum ( imap . data ) # sunspots driven photospheric velocity vphot_spot = np . nansum ( np . real ( vrot ) * ( imap . data - K * Lij ) * spot_inds ) / np . nansum ( imap . data ) return v_phot , vphot_bright , vphot_spot v_quiet ( map_vel_cor , imap , quiet ) function to calculate velocity due to convective motion of quiet-Sun Parameters map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) quiet: weights array where active pixels have weight = 0 Returns v_quiet: quiet-Sun velocity (float) Source code in sdo_hmi_rvs/tools/calculation_funcs.py def v_quiet ( map_vel_cor , imap , quiet ): \"\"\" function to calculate velocity due to convective motion of quiet-Sun Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) quiet: weights array where active pixels have weight = 0 Returns ------- v_quiet: quiet-Sun velocity (float) \"\"\" v_quiet = np . nansum ( map_vel_cor . data * imap . data * quiet ) / np . nansum ( imap . data * quiet ) return v_quiet","title":"Calculation Functions"},{"location":"package/calculation_funcs/#calculation-functions-module","text":"Tamar Ervin Date: June 16, 2021 functions for calculating Solar velocity corrections and components for derivation of SDO/HMI RVs","title":"Calculation Functions Module"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.area_calc","text":"calculate area of active pixels for a thresholded map","title":"area_calc()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.area_calc--parameters","text":"active: weights array where active pixels have weight = 1 pixA_hem: pixel areas in uHem","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.area_calc--returns","text":"area: area of each active region weighted by its intensity Source code in sdo_hmi_rvs/tools/calculation_funcs.py def area_calc ( active , pixA_hem ): \"\"\" calculate area of active pixels for a thresholded map Parameters ---------- active: weights array where active pixels have weight = 1 pixA_hem: pixel areas in uHem Returns ------- area: area of each active region weighted by its intensity \"\"\" # get labeling of image labeled = label ( active ) # get area of active regions area = np . zeros ( active . shape ) props = regionprops ( labeled ) info = regionprops ( labeled , pixA_hem ) # add area to array for k in range ( 1 , len ( info )): area [ props [ k ] . coords [:, 0 ], props [ k ] . coords [:, 1 ]] = info [ k ] . area * info [ k ] . mean_intensity return area","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.area_filling_factor","text":"calculate filling factor for regions thresholded by area - differentiate between large and small regions - differentiate between plage (large) and network (small) bright regions","title":"area_filling_factor()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.area_filling_factor--parameters","text":"active: weights array where active pixels have weight = 1 area: area of each active region weighted by its intensity mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) fac_inds: array of indices where faculae are detected athresh: area threshold value between large and small regions (in uHem) mu_cutoff: minimum mu cutoff value for usable data","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.area_filling_factor--returns","text":"f_small: filling factor (%) for small magnetically active regions f_large: filling factor (%) for large magnetically active regions f_network: filling factor (%) for network (small, bright magnetically active) regions f_plage: filling factor (%) for plage (large, bright magnetically active) regions f_nonconv: filling factor (%) for regions that do not suppress convective blueshift Source code in sdo_hmi_rvs/tools/calculation_funcs.py def area_filling_factor ( active , area , mu , mmap , fac_inds , athresh = 20 , mu_cutoff = 0.3 ): \"\"\" calculate filling factor for regions thresholded by area - differentiate between large and small regions - differentiate between plage (large) and network (small) bright regions Parameters ---------- active: weights array where active pixels have weight = 1 area: area of each active region weighted by its intensity mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) fac_inds: array of indices where faculae are detected athresh: area threshold value between large and small regions (in uHem) mu_cutoff: minimum mu cutoff value for usable data Returns ------- f_small: filling factor (%) for small magnetically active regions f_large: filling factor (%) for large magnetically active regions f_network: filling factor (%) for network (small, bright magnetically active) regions f_plage: filling factor (%) for plage (large, bright magnetically active) regions f_nonconv: filling factor (%) for regions that do not suppress convective blueshift \"\"\" # get good mu values good_mu = np . where ( mu > mu_cutoff ) # get number of pixels npix = len ( mmap . data [ good_mu ]) # get quiet pixels quiet = 1 - active # get filling factor for 'small' magnetic features small = np . zeros ( mmap . data . shape ) small_inds = np . logical_and ( active > 0.5 , area < athresh ) small [ small_inds ] = 1. f_small = np . nansum ( small ) / npix * 100 # get filling factor for 'large' magnetic features large = np . zeros ( mmap . data . shape ) large_inds = np . logical_and ( active > 0.5 , area > athresh ) large [ large_inds ] = 1. f_large = np . nansum ( large ) / npix * 100 # get filling factor for network (small, faculae regions) network = np . zeros ( mmap . data . shape ) network_inds = np . logical_and ( small > 0.5 , fac_inds > 0.5 ) network [ network_inds ] = 1. f_network = np . nansum ( network ) / npix * 100 # get filling factor for plage (large, faculae regions) plage = np . zeros ( mmap . data . shape ) plage_inds = np . logical_and ( large > 0.5 , fac_inds > 0.5 ) plage [ plage_inds ] = 1. f_plage = np . nansum ( plage ) / npix * 100 # get filling factor for small, non-convective regions nonconv = np . zeros ( mmap . data . shape ) nonconv_inds = np . logical_and ( quiet > 0.5 , small > 0.5 ) nonconv [ nonconv_inds ] = 1. f_nonconv = np . nansum ( nonconv ) / npix * 100 return f_small , f_large , f_network , f_plage , f_nonconv","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.area_unsigned_flux","text":"calculate the magnetic flux for different regions based on area cut and magnetic activitiy","title":"area_unsigned_flux()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.area_unsigned_flux--parameters","text":"map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) area: area of each active region weighted by its intensity active: weights array where active pixels have weight = 1 athresh: area threshold value between large and small regions (in uHem)","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.area_unsigned_flux--returns","text":"quiet_flux: magnetic flux of quiet-Sun regions ar_flux: magnetic flux of active regions conv_flux: magnetic flux of regions that suppress convective blueshift pol_flux: magnetic flux of polarized regions pol_conv_flux: magnetic flux of polarized regions that suppress the convective blueshift Source code in sdo_hmi_rvs/tools/calculation_funcs.py def area_unsigned_flux ( map_mag_obs , imap , area , active , athresh = 20 ): \"\"\" calculate the magnetic flux for different regions based on area cut and magnetic activitiy Parameters ---------- map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) area: area of each active region weighted by its intensity active: weights array where active pixels have weight = 1 athresh: area threshold value between large and small regions (in uHem) Returns ------- quiet_flux: magnetic flux of quiet-Sun regions ar_flux: magnetic flux of active regions conv_flux: magnetic flux of regions that suppress convective blueshift pol_flux: magnetic flux of polarized regions pol_conv_flux: magnetic flux of polarized regions that suppress the convective blueshift \"\"\" # get data arrays i_data = imap . data m_data = map_mag_obs . data mabs_data = np . abs ( m_data ) quiet = 1 - active # get large regions array large = np . zeros ( m_data . shape ) large_inds = np . logical_and ( active > 0.5 , area > athresh ) large [ large_inds ] = 1. # calculate relevant fluxes quiet_flux = np . nansum ( mabs_data * i_data * quiet ) / np . nansum ( i_data * quiet ) ar_flux = np . nansum ( mabs_data * i_data * active ) / np . nansum ( i_data * active ) conv_flux = np . nansum ( mabs_data * i_data * large ) / np . nansum ( i_data * large ) pol_flux = np . nansum ( m_data * i_data ) / np . nansum ( i_data ) pol_conv_flux = np . nansum ( m_data * i_data * large ) / np . nansum ( i_data * large ) return quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.area_vconv","text":"calculate convective velocities for different area thresholds","title":"area_vconv()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.area_vconv--parameters","text":"map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) active: weights array where active pixels have weight = 1 area: area of each active region weighted by its intensity athresh: area threshold value between large and small regions (in uHem)","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.area_vconv--returns","text":"vconv_quiet: convective velocity due to quiet-Sun regions vconv_large: convective velocity due to large active regions vconv_small: convective velocity due to small active regions Source code in sdo_hmi_rvs/tools/calculation_funcs.py def area_vconv ( map_vel_cor , imap , active , area , athresh = 20 ): \"\"\" calculate convective velocities for different area thresholds Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) active: weights array where active pixels have weight = 1 area: area of each active region weighted by its intensity athresh: area threshold value between large and small regions (in uHem) Returns ------- vconv_quiet: convective velocity due to quiet-Sun regions vconv_large: convective velocity due to large active regions vconv_small: convective velocity due to small active regions \"\"\" # get data arrays v_data = map_vel_cor . data i_data = imap . data # get large regions array large = np . zeros ( v_data . shape ) large_inds = np . logical_and ( active > 0.5 , area > athresh ) large [ large_inds ] = 1. # get small regions array small = np . zeros ( v_data . shape ) small_inds = np . logical_and ( active > 0.5 , area < athresh ) small [ small_inds ] = 1. # label the regions labeled = label ( large ) v_props = regionprops ( labeled , v_data ) i_props = regionprops ( labeled , i_data ) # labeled for small regions labeled = label ( small ) v_small = regionprops ( labeled , v_data ) i_small = regionprops ( labeled , i_data ) # get quiet regions array quiet = 1 - active # array of non-convective regions nonconv = np . zeros ( v_data . shape ) nonconv_inds = np . logical_or ( quiet > 0.5 , small > 0.5 ) nonconv [ nonconv_inds ] = 1. # velocities of non convective regions vel_quiet = np . nansum ( v_data * i_data * quiet ) / np . sum ( i_data * quiet ) vel_nonconv = np . nansum ( v_data * i_data * nonconv ) / np . sum ( i_data * nonconv ) # velocities of convective regions vconv_large = np . zeros ( len ( v_props )) vconv_small = np . zeros ( len ( v_props )) vconv_quiet = np . zeros ( len ( v_props )) for k in range ( len ( v_props )): vconv_large [ k ] = v_props [ k ] . area * ( v_props [ k ] . mean_intensity - vel_quiet ) * i_props [ k ] . mean_intensity vconv_small [ k ] = v_small [ k ] . area * ( v_small [ k ] . mean_intensity - vel_quiet ) * i_small [ k ] . mean_intensity vconv_quiet [ k ] = v_props [ k ] . area * ( v_props [ k ] . mean_intensity - vel_nonconv ) * i_props [ k ] . mean_intensity # convective velocity of quiet regions vconv_quiet = np . nansum ( vconv_quiet ) / np . sum ( i_data ) # convective velocity of large regions vconv_large = np . nansum ( vconv_large ) / np . sum ( i_data ) # convective velocity of small regions vconv_small = np . nansum ( vconv_small ) / np . sum ( i_data ) return vconv_quiet , vconv_large , vconv_small","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.corrected_map","text":"function to make Sunpy map object from corrected data","title":"corrected_map()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.corrected_map--parameters","text":"corrected_data: corrected velocity data smap: original Sunpy map object map_type: map type for 'content' section of fits header (string) frame: new rotation frame","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.corrected_map--returns","text":"corr_map: Sunpy map object with new frame information and corrected data Source code in sdo_hmi_rvs/tools/calculation_funcs.py def corrected_map ( corrected_data , smap , map_type , frame = frames . HeliographicCarrington ): \"\"\" function to make Sunpy map object from corrected data Parameters ---------- corrected_data: corrected velocity data smap: original Sunpy map object map_type: map type for 'content' section of fits header (string) frame: new rotation frame Returns ------- corr_map: Sunpy map object with new frame information and corrected data \"\"\" # build SkyCoord instance in new frame coord = SkyCoord ( 0 * u . arcsec , 0 * u . arcsec , obstime = smap . date , observer = smap . observer_coordinate , frame = frame ) # create fits header file with data and coordinate system information header = sunpy . map . make_fitswcs_header ( corrected_data , coord ) # update fits header with instrument and content information header [ 'content' ] = map_type header [ 'telescop' ] = smap . meta [ 'telescop' ] header [ 'wavelnth' ] = smap . meta [ 'wavelnth' ] # create new Sunpy map instance with corrected data corr_map = sunpy . map . Map ( corrected_data , header ) return corr_map","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.filling_factor","text":"function to calculate filling factors for faculae, sunspots, and total magnetically active regions - percentage of magnetically active pixels on the solar surface at any one time","title":"filling_factor()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.filling_factor--parameters","text":"mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) active: weights array where active pixels have weight = 1 fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.filling_factor--returns","text":"f_bright: filling factor (%) for bright areas (faculae) f_spot: filling factor (%) for dark areas (sunspots) f_total: filling factor (%) for timestamp Source code in sdo_hmi_rvs/tools/calculation_funcs.py def filling_factor ( mu , mmap , active , fac_inds , spot_inds , mu_cutoff = 0.3 ): \"\"\" function to calculate filling factors for faculae, sunspots, and total magnetically active regions - percentage of magnetically active pixels on the solar surface at any one time Parameters ---------- mu: array of mu (cosine theta) values mmap: UNCORRECTED Sunpy map object (Magnetogram) active: weights array where active pixels have weight = 1 fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns ------- f_bright: filling factor (%) for bright areas (faculae) f_spot: filling factor (%) for dark areas (sunspots) f_total: filling factor (%) for timestamp \"\"\" # get good mu values good_mu = np . where ( mu > mu_cutoff ) # get number of pixels npix = len ( mmap . data [ good_mu ]) # faculae faculae = np . zeros ( mmap . data . shape ) faculae [ fac_inds ] = 1. f_bright = np . sum ( faculae ) / npix * 100 # sunspots spots = np . zeros ( mmap . data . shape ) spots [ spot_inds ] = 1. f_spot = np . sum ( spots ) / npix * 100 # get filling factor f_total = np . sum ( active ) / npix * 100 return f_bright , f_spot , f_total","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.int_thresh","text":"function to do intensity thresholding and differentiate between faculae (bright) and sunspots (dark)","title":"int_thresh()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.int_thresh--parameters","text":"map_int_cor: corrected (limb-darkening) Sunpy map object (Intensitygram) active: weights array where active pixels are 1 quiet: weights array where active pixels are 0","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.int_thresh--returns","text":"fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected Source code in sdo_hmi_rvs/tools/calculation_funcs.py def int_thresh ( map_int_cor , active , quiet ): \"\"\" function to do intensity thresholding and differentiate between faculae (bright) and sunspots (dark) Parameters ---------- map_int_cor: corrected (limb-darkening) Sunpy map object (Intensitygram) active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 Returns ------- fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected \"\"\" # flattened intensity data Iflat = map_int_cor . data # calculate quiet sun intensity int_quiet = np . nansum ( Iflat * quiet ) / np . nansum ( quiet ) # intensity threshold int_cutoff = 0.89 * int_quiet # get faculae fac_inds = np . logical_and (( Iflat > int_cutoff ), ( active > 0.5 )) # get sunspots spot_inds = np . logical_and (( Iflat <= int_cutoff ), ( active > 0.5 )) return fac_inds , spot_inds","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.mag_field","text":"function to correct for unsigned magnetic field strength and magnetic noise","title":"mag_field()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.mag_field--parameters","text":"mu: array of mu (cosine theta) values mmap: Sunpy map object (Magnetogram) B_noise: magnetic noise level in Gauss mu_cutoff: minimum mu cutoff value","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.mag_field--returns","text":"Bobs: corrected observed magnetic field strength Br: array of corrected unsigned magnetic field strength Source code in sdo_hmi_rvs/tools/calculation_funcs.py def mag_field ( mu , mmap , B_noise = 8 , mu_cutoff = 0.3 ): \"\"\" function to correct for unsigned magnetic field strength and magnetic noise Parameters ---------- mu: array of mu (cosine theta) values mmap: Sunpy map object (Magnetogram) B_noise: magnetic noise level in Gauss mu_cutoff: minimum mu cutoff value Returns ------- Bobs: corrected observed magnetic field strength Br: array of corrected unsigned magnetic field strength \"\"\" # get valid indices use_indices = np . logical_and ( mu > mu_cutoff , mu != np . nan ) mag_indices = np . logical_and ( use_indices , np . abs ( mmap . data ) < B_noise ) # calculate full magnetic field strength Bobs = mmap . data Br = np . full ( shape = mmap . data . shape , fill_value = np . nan ) Br [ use_indices ] = Bobs [ use_indices ] / mu [ use_indices ] Bobs [ mag_indices ] = 0 Br [ mag_indices ] = 0 return Bobs , Br","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.mag_thresh","text":"function to calculate magnetic threshold and differentiate between magnetically active regions and quiet Sun","title":"mag_thresh()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.mag_thresh--parameters","text":"mu: array of mu (cosine theta) values mmap: corrected (unsigned magnetic field) Sunpy map object (Magnetogram) Br_cutoff: minimum cutoff value (in Gauss) for thresholding active regions mu_cutoff: minimum mu cutoff value for data to ignore","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.mag_thresh--returns","text":"active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 Source code in sdo_hmi_rvs/tools/calculation_funcs.py def mag_thresh ( mu , mmap , Br_cutoff = 24 , mu_cutoff = 0.3 ): \"\"\" function to calculate magnetic threshold and differentiate between magnetically active regions and quiet Sun Parameters ---------- mu: array of mu (cosine theta) values mmap: corrected (unsigned magnetic field) Sunpy map object (Magnetogram) Br_cutoff: minimum cutoff value (in Gauss) for thresholding active regions mu_cutoff: minimum mu cutoff value for data to ignore Returns ------- active: weights array where active pixels are 1 quiet: weights array where active pixels are 0 \"\"\" # get active region indices active_inds = np . where ( np . abs ( mmap . data ) * mu > Br_cutoff ) bad_mu = np . where ( mu <= mu_cutoff ) # make active region array active = np . zeros ( mu . shape ) active [ active_inds ] = 1. active [ bad_mu ] = 0. # find isolated pixels # get area y_labeled = label ( active , connectivity = 2 , background = 0 ) y_area = [ props . area for props in regionprops ( y_labeled )] # area constraint good_area = np . where ( np . array ( y_area ) > 5 ) good_area = good_area [ 0 ] + 1 active_indices = np . isin ( y_labeled , good_area ) # create weights array active [ ~ active_indices ] = 0 # get quiet indices quiet = 1 - active return active , quiet","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.map_sequence","text":"function to query sunpy for images within certain time range of dates in dates list user specified instrument and wavelength, otherwise default values: AIA 171A","title":"map_sequence()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.map_sequence--parameters","text":"dates_list: list of dates, either datetime or strings time_range: plus/minus time range to search for images in comparison to desired timestamp instrument: Sunpy instrument of choice (AIA, HMI, LASCO, EIT) wavelength: desired wavelength of choice instrument","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.map_sequence--returns","text":"maps: Sunpy map sequence object Source code in sdo_hmi_rvs/tools/calculation_funcs.py def map_sequence ( dates_list , time_range = datetime . timedelta ( seconds = 6 ), instrument = a . Instrument . aia , wavelength = a . Wavelength ( 171 * u . angstrom )): \"\"\" function to query sunpy for images within certain time range of dates in dates list user specified instrument and wavelength, otherwise default values: AIA 171A Parameters ---------- dates_list: list of dates, either datetime or strings time_range: plus/minus time range to search for images in comparison to desired timestamp instrument: Sunpy instrument of choice (AIA, HMI, LASCO, EIT) wavelength: desired wavelength of choice instrument Returns ------- maps: Sunpy map sequence object \"\"\" if isinstance ( dates_list [ 0 ][ 0 ], str ): datetimes = [ datetime . datetime . strptime ( date [ 0 ], '%Y-%m- %d T%H:%M:%S. %f ' ) for date in dates_list ] else : datetimes = dates_list downloaded_files = [] for ind , datetime_object in enumerate ( datetimes ): # pull image within specified time range result = Fido . search ( a . Time ( str ( datetime_object - time_range ), str ( datetime_object + time_range )), instrument , wavelength ) # add file to list downloaded_files . append ( Fido . fetch ( result )) # build map sequence from files maps = sunpy . map . Map ( downloaded_files , sequence = True ) return maps","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.rel_positions","text":"function to calculate pixel-wise relative positions in new coordinate frame","title":"rel_positions()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.rel_positions--parameters","text":"wij: array of westward values for image nij: array of northward values for image rij: array of radius values for image smap: Sunpy map object","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.rel_positions--returns","text":"deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft Source code in sdo_hmi_rvs/tools/calculation_funcs.py def rel_positions ( wij , nij , rij , smap ): \"\"\" function to calculate pixel-wise relative positions in new coordinate frame Parameters ---------- wij: array of westward values for image nij: array of northward values for image rij: array of radius values for image smap: Sunpy map object Returns ------- deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft \"\"\" # calculate relative positions of each pixel rsc = smap . meta [ 'dsun_obs' ] / smap . meta [ 'rsun_ref' ] deltaw = wij deltan = nij deltar = rij - rsc dij = np . sqrt ( deltaw ** 2 + deltan ** 2 + deltar ** 2 ) return deltaw , deltan , deltar , dij","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.solar_rot_vel","text":"function to calculate pixel-wise velocities due to solar rotation","title":"solar_rot_vel()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.solar_rot_vel--parameters","text":"wij: array of westward values for image nij: array of northward values for image rij: array of radius values for image deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) a_parameters: array of solar differential rotation parameters from Snodgrass & Ulrich (1990).","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.solar_rot_vel--returns","text":"vrot: array of solar rotation velocities Source code in sdo_hmi_rvs/tools/calculation_funcs.py def solar_rot_vel ( wij , nij , rij , deltaw , deltan , deltar , dij , vmap , a_parameters = [ 14.713 , - 2.396 , - 1.787 ]): \"\"\" function to calculate pixel-wise velocities due to solar rotation Parameters ---------- wij: array of westward values for image nij: array of northward values for image rij: array of radius values for image deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) a_parameters: array of solar differential rotation parameters from Snodgrass & Ulrich (1990). Returns ------- vrot: array of solar rotation velocities \"\"\" # apply to cartesian coordinates x1 = wij y1 = nij * np . cos ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) + rij * np . sin ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) z1 = - nij * np . sin ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) + rij * np . cos ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) hx = x1 * np . cos ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) + z1 * np . sin ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) hy = y1 hz = - x1 * np . sin ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) + z1 * np . cos ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) # apply parameters to determine vrot for given image pixel w = ( a_parameters [ 0 ] + a_parameters [ 1 ] * (( np . sin ( hy )) ** 2 ) + a_parameters [ 2 ] * ( ( np . sin ( hy )) ** 4 )) * 1. / 86400. * np . pi / 180. # get projection of solar rotation vx_rot = w * hz * vmap . meta [ 'rsun_ref' ] vy_rot = 0. vz_rot = - w * hx * vmap . meta [ 'rsun_ref' ] v1 = np . cos ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) * vx_rot - np . sin ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) * vz_rot v2 = vy_rot v3 = np . sin ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) * vx_rot + np . cos ( np . deg2rad ( vmap . meta [ 'crln_obs' ])) * vz_rot # project into correct direction vrotw = v1 vrotn = v2 * np . cos ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) - v3 * np . sin ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) vrotr = v2 * np . sin ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) + v3 * np . cos ( np . deg2rad ( vmap . meta [ 'crlt_obs' ])) # get full rotational velocity vrot = ( deltaw * vrotw + deltan * vrotn + deltar * vrotr ) / dij return vrot","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.spacecraft_vel","text":"function to calculate pixel-wise spacecraft velocities for Sunpy map","title":"spacecraft_vel()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.spacecraft_vel--parameters","text":"deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram)","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.spacecraft_vel--returns","text":"vsc: array of spacecraft velocities Source code in sdo_hmi_rvs/tools/calculation_funcs.py def spacecraft_vel ( deltaw , deltan , deltar , dij , vmap ): \"\"\" function to calculate pixel-wise spacecraft velocities for Sunpy map Parameters ---------- deltaw: relative westward position of pixel deltan: relative northward position of pixel deltar: relative radial position of pixel dij: distance between pixel ij and spacecraft vmap: Sunpy map object (Dopplergram) Returns ------- vsc: array of spacecraft velocities \"\"\" # velocity of spacecraft relative to sun vscw = vmap . meta [ 'obs_vw' ] vscn = vmap . meta [ 'obs_vn' ] vscr = vmap . meta [ 'obs_vr' ] # pixel-wise magnitude of spacecraft velocity vsc = - ( deltaw * vscw + deltan * vscn + deltar * vscr ) / dij return vsc","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.thresh_map","text":"function that creates thresholded map of sunspots (1) and faculae (2)","title":"thresh_map()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.thresh_map--parameters","text":"fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.thresh_map--returns","text":"thresh_arr: array of values denoting faculae (1) and sunspots (2) Source code in sdo_hmi_rvs/tools/calculation_funcs.py def thresh_map ( fac_inds , spot_inds ): \"\"\" function that creates thresholded map of sunspots (1) and faculae (2) Parameters ---------- fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected Returns ------- thresh_arr: array of values denoting faculae (1) and sunspots (2) \"\"\" thresh_arr = np . full ( shape = fac_inds . shape , fill_value = np . nan ) thresh_arr [ fac_inds ] = 1 thresh_arr [ spot_inds ] = 2 return thresh_arr","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.unsigned_flux","text":"calculate unsigned magnetic flux","title":"unsigned_flux()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.unsigned_flux--parameters","text":"map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram)","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.unsigned_flux--returns","text":"unsign_flux: unsigned magnetic flux Source code in sdo_hmi_rvs/tools/calculation_funcs.py def unsigned_flux ( map_mag_obs , imap ): \"\"\" calculate unsigned magnetic flux Parameters ---------- map_mag_obs: corrected observed magnetic field strength Sunpy map object (Magnetogram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- unsign_flux: unsigned magnetic flux \"\"\" # get data arrays i_data = imap . data m_data = map_mag_obs . data mabs_data = np . abs ( m_data ) # unsigned flux unsign_flux = np . nansum ( mabs_data * i_data ) / np . nansum ( i_data ) return np . abs ( unsign_flux )","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.v_disc","text":"function to calculate disc-averaged velocity of Sun","title":"v_disc()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.v_disc--parameters","text":"map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram)","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.v_disc--returns","text":"v_disc: disc averaged velocity of Sun (float) Source code in sdo_hmi_rvs/tools/calculation_funcs.py def v_disc ( map_vel_cor , imap ): \"\"\" function to calculate disc-averaged velocity of Sun Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- v_disc: disc averaged velocity of Sun (float) \"\"\" v_disc = np . nansum ( map_vel_cor . data * imap . data ) / np . nansum ( imap . data ) return v_disc","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.v_phot","text":"function to calculate photometric velocity due to rotational Doppler variation","title":"v_phot()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.v_phot--parameters","text":"quiet: weights array where active pixels have weight = 0 active: weights array where active pixels have weight = 1 Lij: limb-darkening polynomial function vrot: solar rotational velocity imap: UNCORRECTED Sunpy map object (Intensitygram) mu: array of mu values fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.v_phot--returns","text":"v_phot: photospheric velocity perturbation (float) Source code in sdo_hmi_rvs/tools/calculation_funcs.py def v_phot ( quiet , active , Lij , vrot , imap , mu , fac_inds , spot_inds , mu_cutoff = 0.3 ): \"\"\" function to calculate photometric velocity due to rotational Doppler variation Parameters ---------- quiet: weights array where active pixels have weight = 0 active: weights array where active pixels have weight = 1 Lij: limb-darkening polynomial function vrot: solar rotational velocity imap: UNCORRECTED Sunpy map object (Intensitygram) mu: array of mu values fac_inds: array of indices where faculae are detected spot_inds: array of indices where sunspots are detected mu_cutoff: minimum mu cutoff value Returns ------- v_phot: photospheric velocity perturbation (float) \"\"\" # get good mu values good_mu = np . where ( mu > mu_cutoff ) # calculate K scaling factor K = np . nansum ( imap . data * Lij * quiet ) / np . sum (( Lij [ good_mu ] ** 2 ) * quiet [ good_mu ]) # calculate photospheric velocity v_phot = np . nansum ( np . real ( vrot ) * ( imap . data - K * Lij ) * active ) / np . nansum ( imap . data ) # faculae driven photospheric velocity vphot_bright = np . nansum ( np . real ( vrot ) * ( imap . data - K * Lij ) * fac_inds ) / np . nansum ( imap . data ) # sunspots driven photospheric velocity vphot_spot = np . nansum ( np . real ( vrot ) * ( imap . data - K * Lij ) * spot_inds ) / np . nansum ( imap . data ) return v_phot , vphot_bright , vphot_spot","title":"Returns"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.v_quiet","text":"function to calculate velocity due to convective motion of quiet-Sun","title":"v_quiet()"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.v_quiet--parameters","text":"map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) quiet: weights array where active pixels have weight = 0","title":"Parameters"},{"location":"package/calculation_funcs/#sdo_hmi_rvs.tools.calculation_funcs.v_quiet--returns","text":"v_quiet: quiet-Sun velocity (float) Source code in sdo_hmi_rvs/tools/calculation_funcs.py def v_quiet ( map_vel_cor , imap , quiet ): \"\"\" function to calculate velocity due to convective motion of quiet-Sun Parameters ---------- map_vel_cor: corrected (velocities) Sunpy map object (Dopplergram) imap: UNCORRECTED Sunpy map object (Intensitygram) quiet: weights array where active pixels have weight = 0 Returns ------- v_quiet: quiet-Sun velocity (float) \"\"\" v_quiet = np . nansum ( map_vel_cor . data * imap . data * quiet ) / np . nansum ( imap . data * quiet ) return v_quiet","title":"Returns"},{"location":"package/coord_funcs/","text":"Coordinate Transformations Module Tamar Ervin Date: June 24, 2021 Coordinate transformation functions to transform from the Helioprojective Cartesian to Heliographic Carrington Coordinates. coordinates ( smap ) calculate array of mu values and cartesian coordinates for image Parameters smap: Sunpy map object Returns x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values d: observer distance from sun in solar radii mu: array of mu values Source code in sdo_hmi_rvs/tools/coord_funcs.py def coordinates ( smap ): \"\"\" calculate array of mu values and cartesian coordinates for image Parameters ---------- smap: Sunpy map object Returns ------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values d: observer distance from sun in solar radii mu: array of mu values \"\"\" # distance of observer to sun in solar radii d = smap . fits_header [ 'DSUN_OBS' ] / smap . fits_header [ 'RSUN_REF' ] # focal length in pixels f = 180. * 3600. / np . pi / smap . fits_header [ 'CDELT1' ] # get cartesian x, y map scales x , y = get_map_scales ( smap ) # distance (in pixels) to pixel pd = np . sqrt ( x ** 2 + y ** 2 ) # distance (in solar r) to pixel pr = f * f * pd * pd + pd ** 4 - d * d * pd ** 4 + 0. J r = ( d * f * pd - np . sqrt ( pr )) / ( f * f + pd * pd ) # separate complex parts r = r . real # get mu array pr = 1 - r ** 2 + 0. J cos_alpha = ( np . sqrt ( pr )) . real sin_alpha = r . real cos_theta = (( d - np . sqrt ( pr )) / np . sqrt ( r ** 2 + ( d - np . sqrt ( pr )) ** 2 )) . real sin_theta = ( np . sqrt ( 1 - cos_theta ** 2 )) . real mu = cos_alpha * cos_theta - sin_alpha * sin_theta return x , y , pd , r , d , mu fix_mu ( mu , smaps , mu_cutoff = 0.3 ) function to remove pixel values where mu is less than 0.1 Parameters mu: cosine of the center to limb angle smaps: list of Sunpy map object mu_cutoff: minimum mu cutoff value Returns mu: corrected cosine of the center to limb angle smaps: corrected Sunpy map objects Source code in sdo_hmi_rvs/tools/coord_funcs.py def fix_mu ( mu , smaps , mu_cutoff = 0.3 ): \"\"\" function to remove pixel values where mu is less than 0.1 Parameters ---------- mu: cosine of the center to limb angle smaps: list of Sunpy map object mu_cutoff: minimum mu cutoff value Returns ------- mu: corrected cosine of the center to limb angle smaps: corrected Sunpy map objects \"\"\" # remove pixel values where mu < mu_cut (0.3) bad_inds = np . where ( mu <= mu_cutoff ) # fix arrays for smap in smaps : smap . data [ bad_inds ] = 0 return smaps get_map_scales ( smap ) compute the solar X and solar Y 2D pixel scale arrays from a sunpy map object - If the image has been rotated to solar north up, then the x and y scales will be in the helioprojective cartesian system with units of [Rs]. Parameters smap: Sunpy map object Returns x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system Source code in sdo_hmi_rvs/tools/coord_funcs.py def get_map_scales ( smap ): \"\"\" compute the solar X and solar Y 2D pixel scale arrays from a sunpy map object - If the image has been rotated to solar north up, then the x and y scales will be in the helioprojective cartesian system with units of [Rs]. Parameters ---------- smap: Sunpy map object Returns ------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system \"\"\" header = smap . fits_header # pixel array axis1 = np . linspace ( 1 , header [ 'NAXIS1' ], header [ 'NAXIS1' ]) - header [ 'CRPIX1' ] axis2 = np . linspace ( 1 , header [ 'NAXIS2' ], header [ 'NAXIS2' ]) - header [ 'CRPIX2' ] # pixel offset from center of image, x,y x , y = np . meshgrid ( axis1 , axis2 ) return x , y pix_area_hem ( wij , nij , rij , smap ) calculate the area of each pixel in uHem for use in area thresholding for convective velocity and identification of solar regions Parameters wij: array of pixel coordinates relative to solar center in westward direction nij: array of pixel coordinates relative to solar center in northward direction rij: array of pixel coordinates relative to solar center in radial direction smap Returns pixA_hem: pixel areas in uHem Source code in sdo_hmi_rvs/tools/coord_funcs.py def pix_area_hem ( wij , nij , rij , smap ): \"\"\" calculate the area of each pixel in uHem for use in area thresholding for convective velocity and identification of solar regions Parameters ---------- wij: array of pixel coordinates relative to solar center in westward direction nij: array of pixel coordinates relative to solar center in northward direction rij: array of pixel coordinates relative to solar center in radial direction smap Returns ------- pixA_hem: pixel areas in uHem \"\"\" # get x and y arrays x , y = get_map_scales ( smap ) # apply to cartesian coordinates x1 = wij y1 = nij * np . cos ( np . deg2rad ( smap . meta [ 'crlt_obs' ])) + rij * np . sin ( np . deg2rad ( smap . meta [ 'crlt_obs' ])) z1 = - nij * np . sin ( np . deg2rad ( smap . meta [ 'crlt_obs' ])) + rij * np . cos ( np . deg2rad ( smap . meta [ 'crlt_obs' ])) # get solar latitude and longitude solar_long = ( np . arctan ( x1 / z1 )) . real solar_long = - abs ( solar_long ) * np . sign ( x ) y1 = y1 + 0. J solar_lat = ( np . arcsin ( y1 )) . real d_long = np . diff ( solar_long , 1 , 1 ) d_lat = np . diff ( solar_lat , 1 , 0 ) # interpolate differential longitude and latitude to get same area dimensions as original grid newxx = x [:, 0 : - 1 :] + 1 / 2 * np . diff ( x , axis = 1 ) newyx = y [:, 0 : - 1 :] + 1 / 2 * np . diff ( y , axis = 1 ) newxy = x [ 0 : - 1 :, :] + 1 / 2 * np . diff ( x , axis = 0 ) newyy = y [ 0 : - 1 :, :] + 1 / 2 * np . diff ( y , axis = 0 ) # interpolate latitude array interp = scipy . interpolate . interp2d ( newxy [ 0 ], newyy [:, 0 ], d_lat ) d_lat = interp ( x [ 0 ], y [:, 0 ]) # interpolate longitude array interp = scipy . interpolate . interp2d ( newxx [ 0 ], newyx [:, 0 ], d_long ) d_long = interp ( x [ 0 ], y [:, 0 ]) # calculate co-latitude area in uHem pixA_hem = np . sin ( np . pi / 2 - solar_lat ) * abs ( d_long ) * abs ( d_lat ) / ( 2 * np . pi ) * 1e6 return pixA_hem vel_coords ( x , y , pd , r , smap ) calculate coordinate transformation to heliographic Carrington coordinates Parameters x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values smap: Sunpy map object Returns wij: array of pixel coordinates relative to solar center in westward direction nij: array of pixel coordinates relative to solar center in northward direction rij: array of pixel coordinates relative to solar center in radial direction Source code in sdo_hmi_rvs/tools/coord_funcs.py def vel_coords ( x , y , pd , r , smap ): \"\"\" calculate coordinate transformation to heliographic Carrington coordinates Parameters ---------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values smap: Sunpy map object Returns ------- wij: array of pixel coordinates relative to solar center in westward direction nij: array of pixel coordinates relative to solar center in northward direction rij: array of pixel coordinates relative to solar center in radial direction \"\"\" head = smap . fits_header crota2 = head [ 'CROTA2' ] # deg # transform each pixel to get into Heliographic CR Coordinates dw = y * np . sin ( np . deg2rad ( crota2 )) + x * np . cos ( np . deg2rad ( crota2 )) dn = y * np . cos ( np . deg2rad ( crota2 )) - x * np . sin ( np . deg2rad ( crota2 )) # get cartesian coordinates for velocity calculations pr = 1 - r ** 2 + 0. J wij = r * dw / pd nij = r * dn / pd rij = ( np . sqrt ( pr )) . real return wij , nij , rij","title":"Coordinate Transformations"},{"location":"package/coord_funcs/#coordinate-transformations-module","text":"Tamar Ervin Date: June 24, 2021 Coordinate transformation functions to transform from the Helioprojective Cartesian to Heliographic Carrington Coordinates.","title":"Coordinate Transformations Module"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.coordinates","text":"calculate array of mu values and cartesian coordinates for image","title":"coordinates()"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.coordinates--parameters","text":"smap: Sunpy map object","title":"Parameters"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.coordinates--returns","text":"x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values d: observer distance from sun in solar radii mu: array of mu values Source code in sdo_hmi_rvs/tools/coord_funcs.py def coordinates ( smap ): \"\"\" calculate array of mu values and cartesian coordinates for image Parameters ---------- smap: Sunpy map object Returns ------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values d: observer distance from sun in solar radii mu: array of mu values \"\"\" # distance of observer to sun in solar radii d = smap . fits_header [ 'DSUN_OBS' ] / smap . fits_header [ 'RSUN_REF' ] # focal length in pixels f = 180. * 3600. / np . pi / smap . fits_header [ 'CDELT1' ] # get cartesian x, y map scales x , y = get_map_scales ( smap ) # distance (in pixels) to pixel pd = np . sqrt ( x ** 2 + y ** 2 ) # distance (in solar r) to pixel pr = f * f * pd * pd + pd ** 4 - d * d * pd ** 4 + 0. J r = ( d * f * pd - np . sqrt ( pr )) / ( f * f + pd * pd ) # separate complex parts r = r . real # get mu array pr = 1 - r ** 2 + 0. J cos_alpha = ( np . sqrt ( pr )) . real sin_alpha = r . real cos_theta = (( d - np . sqrt ( pr )) / np . sqrt ( r ** 2 + ( d - np . sqrt ( pr )) ** 2 )) . real sin_theta = ( np . sqrt ( 1 - cos_theta ** 2 )) . real mu = cos_alpha * cos_theta - sin_alpha * sin_theta return x , y , pd , r , d , mu","title":"Returns"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.fix_mu","text":"function to remove pixel values where mu is less than 0.1","title":"fix_mu()"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.fix_mu--parameters","text":"mu: cosine of the center to limb angle smaps: list of Sunpy map object mu_cutoff: minimum mu cutoff value","title":"Parameters"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.fix_mu--returns","text":"mu: corrected cosine of the center to limb angle smaps: corrected Sunpy map objects Source code in sdo_hmi_rvs/tools/coord_funcs.py def fix_mu ( mu , smaps , mu_cutoff = 0.3 ): \"\"\" function to remove pixel values where mu is less than 0.1 Parameters ---------- mu: cosine of the center to limb angle smaps: list of Sunpy map object mu_cutoff: minimum mu cutoff value Returns ------- mu: corrected cosine of the center to limb angle smaps: corrected Sunpy map objects \"\"\" # remove pixel values where mu < mu_cut (0.3) bad_inds = np . where ( mu <= mu_cutoff ) # fix arrays for smap in smaps : smap . data [ bad_inds ] = 0 return smaps","title":"Returns"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.get_map_scales","text":"compute the solar X and solar Y 2D pixel scale arrays from a sunpy map object - If the image has been rotated to solar north up, then the x and y scales will be in the helioprojective cartesian system with units of [Rs].","title":"get_map_scales()"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.get_map_scales--parameters","text":"smap: Sunpy map object","title":"Parameters"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.get_map_scales--returns","text":"x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system Source code in sdo_hmi_rvs/tools/coord_funcs.py def get_map_scales ( smap ): \"\"\" compute the solar X and solar Y 2D pixel scale arrays from a sunpy map object - If the image has been rotated to solar north up, then the x and y scales will be in the helioprojective cartesian system with units of [Rs]. Parameters ---------- smap: Sunpy map object Returns ------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system \"\"\" header = smap . fits_header # pixel array axis1 = np . linspace ( 1 , header [ 'NAXIS1' ], header [ 'NAXIS1' ]) - header [ 'CRPIX1' ] axis2 = np . linspace ( 1 , header [ 'NAXIS2' ], header [ 'NAXIS2' ]) - header [ 'CRPIX2' ] # pixel offset from center of image, x,y x , y = np . meshgrid ( axis1 , axis2 ) return x , y","title":"Returns"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.pix_area_hem","text":"calculate the area of each pixel in uHem for use in area thresholding for convective velocity and identification of solar regions","title":"pix_area_hem()"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.pix_area_hem--parameters","text":"wij: array of pixel coordinates relative to solar center in westward direction nij: array of pixel coordinates relative to solar center in northward direction rij: array of pixel coordinates relative to solar center in radial direction smap","title":"Parameters"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.pix_area_hem--returns","text":"pixA_hem: pixel areas in uHem Source code in sdo_hmi_rvs/tools/coord_funcs.py def pix_area_hem ( wij , nij , rij , smap ): \"\"\" calculate the area of each pixel in uHem for use in area thresholding for convective velocity and identification of solar regions Parameters ---------- wij: array of pixel coordinates relative to solar center in westward direction nij: array of pixel coordinates relative to solar center in northward direction rij: array of pixel coordinates relative to solar center in radial direction smap Returns ------- pixA_hem: pixel areas in uHem \"\"\" # get x and y arrays x , y = get_map_scales ( smap ) # apply to cartesian coordinates x1 = wij y1 = nij * np . cos ( np . deg2rad ( smap . meta [ 'crlt_obs' ])) + rij * np . sin ( np . deg2rad ( smap . meta [ 'crlt_obs' ])) z1 = - nij * np . sin ( np . deg2rad ( smap . meta [ 'crlt_obs' ])) + rij * np . cos ( np . deg2rad ( smap . meta [ 'crlt_obs' ])) # get solar latitude and longitude solar_long = ( np . arctan ( x1 / z1 )) . real solar_long = - abs ( solar_long ) * np . sign ( x ) y1 = y1 + 0. J solar_lat = ( np . arcsin ( y1 )) . real d_long = np . diff ( solar_long , 1 , 1 ) d_lat = np . diff ( solar_lat , 1 , 0 ) # interpolate differential longitude and latitude to get same area dimensions as original grid newxx = x [:, 0 : - 1 :] + 1 / 2 * np . diff ( x , axis = 1 ) newyx = y [:, 0 : - 1 :] + 1 / 2 * np . diff ( y , axis = 1 ) newxy = x [ 0 : - 1 :, :] + 1 / 2 * np . diff ( x , axis = 0 ) newyy = y [ 0 : - 1 :, :] + 1 / 2 * np . diff ( y , axis = 0 ) # interpolate latitude array interp = scipy . interpolate . interp2d ( newxy [ 0 ], newyy [:, 0 ], d_lat ) d_lat = interp ( x [ 0 ], y [:, 0 ]) # interpolate longitude array interp = scipy . interpolate . interp2d ( newxx [ 0 ], newyx [:, 0 ], d_long ) d_long = interp ( x [ 0 ], y [:, 0 ]) # calculate co-latitude area in uHem pixA_hem = np . sin ( np . pi / 2 - solar_lat ) * abs ( d_long ) * abs ( d_lat ) / ( 2 * np . pi ) * 1e6 return pixA_hem","title":"Returns"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.vel_coords","text":"calculate coordinate transformation to heliographic Carrington coordinates","title":"vel_coords()"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.vel_coords--parameters","text":"x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values smap: Sunpy map object","title":"Parameters"},{"location":"package/coord_funcs/#sdo_hmi_rvs.tools.coord_funcs.vel_coords--returns","text":"wij: array of pixel coordinates relative to solar center in westward direction nij: array of pixel coordinates relative to solar center in northward direction rij: array of pixel coordinates relative to solar center in radial direction Source code in sdo_hmi_rvs/tools/coord_funcs.py def vel_coords ( x , y , pd , r , smap ): \"\"\" calculate coordinate transformation to heliographic Carrington coordinates Parameters ---------- x: array of x coordinates in helioprojective cartesian system y: array of y coordinates in helioprojective cartesian system pd: array of pixel distances r: array of solar radius values smap: Sunpy map object Returns ------- wij: array of pixel coordinates relative to solar center in westward direction nij: array of pixel coordinates relative to solar center in northward direction rij: array of pixel coordinates relative to solar center in radial direction \"\"\" head = smap . fits_header crota2 = head [ 'CROTA2' ] # deg # transform each pixel to get into Heliographic CR Coordinates dw = y * np . sin ( np . deg2rad ( crota2 )) + x * np . cos ( np . deg2rad ( crota2 )) dn = y * np . cos ( np . deg2rad ( crota2 )) - x * np . sin ( np . deg2rad ( crota2 )) # get cartesian coordinates for velocity calculations pr = 1 - r ** 2 + 0. J wij = r * dw / pd nij = r * dn / pd rij = ( np . sqrt ( pr )) . real return wij , nij , rij","title":"Returns"},{"location":"package/lbc_funcs/","text":"Limb-Brightening Correction Module Tamar Ervin Date: June 23, 2021 Limb-darkening correction functions: flatten continuum intensity images to correct for limb-darkening use fifth order polynomial and constants from Allen 1973 get_u ( ll ) function to get limb darkening coefficient u (Allen 1973) based on wavelength value Parameters ll: wavelength Returns vl: coefficient v based on wavelength Source code in sdo_hmi_rvs/tools/lbc_funcs.py def get_u ( ll ): \"\"\" function to get limb darkening coefficient u (Allen 1973) based on wavelength value Parameters ---------- ll: wavelength Returns ------- vl: coefficient v based on wavelength \"\"\" pll = np . array ([ 1.0 , ll , ll ** 2 , ll ** 3 , ll ** 4 , ll ** 5 ]) au = - 8.9829751 bu = 0.0069093916 cu = - 1.8144591e-6 du = 2.2540875e-10 eu = - 1.3389747e-14 fu = 3.0453572e-19 a = np . array ([ au , bu , cu , du , eu , fu ]) ul = sum ( a * pll ) return ul get_v ( ll ) function to get limb darkening coefficient v (Allen 1973) based on wavelength value Parameters ll: wavelength Returns vl: coefficient v based on wavelength Source code in sdo_hmi_rvs/tools/lbc_funcs.py def get_v ( ll ): \"\"\" function to get limb darkening coefficient v (Allen 1973) based on wavelength value Parameters ---------- ll: wavelength Returns ------- vl: coefficient v based on wavelength \"\"\" pll = np . array ([ 1.0 , ll , ll ** 2 , ll ** 3 , ll ** 4 , ll ** 5 ]) av = 9.2891180 bv = - 0.0062212632 cv = 1.5788029e-6 dv = - 1.9359644e-10 ev = 1.1444469e-14 fv = - 2.599494e-19 a = np . array ([ av , bv , cv , dv , ev , fv ]) vl = sum ( a * pll ) return vl limb_polynomial ( imap ) function to calculate limb darkening correction polynomial based on IDl function: https://hesperia.gsfc.nasa.gov/ssw/gen/idl/solar/darklimb_correct.pro Parameters imap: UNCORRECTED Sunpy map object (Intensitygram) Returns Lij: limb-darkening polynomial array Source code in sdo_hmi_rvs/tools/lbc_funcs.py def limb_polynomial ( imap ): \"\"\" function to calculate limb darkening correction polynomial based on IDl function: https://hesperia.gsfc.nasa.gov/ssw/gen/idl/solar/darklimb_correct.pro Parameters ---------- imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- Lij: limb-darkening polynomial array \"\"\" # get data data = imap . data # get header information header = imap . fits_header # wavelength wavelength = header [ 'WAVELNTH' ] # coordinates x_center = header [ 'CRPIX1' ] y_center = header [ 'CRPIX2' ] radius = header [ 'RSUN_OBS' ] / header [ 'CDELT1' ] # size size = imap . data . shape [ 0 ] # wavelength as float ll = 1.0 * wavelength # convert data to array and set nan values to zero array = np . array ( data ) NaNs = np . isnan ( array ) array [ NaNs ] = 0.0 # get correction coefficients based on wavelength ul = get_u ( ll ) vl = get_v ( ll ) # make arrays of x and y coordinates x = np . arange ( 0 , size , 1.0 ) y = np . arange ( 0 , size , 1.0 ) # create grids for x, y arrays x_mat , y_mat = np . meshgrid ( x , y ) # make z array such that we get a circle z = np . sqrt (( x_mat - x_center ) ** 2 + ( y_mat - y_center ) ** 2 ) # normalize circle to radius of 1 grid = z / radius out = np . where ( grid > 1.0 ) grid [ out ] = 0.0 # calculate polynomial Lij = 1.0 - ul - vl + ul * np . cos ( np . arcsin ( grid )) + vl * np . cos ( np . arcsin ( grid )) ** 2 return Lij","title":"Limb-Brightening Correction"},{"location":"package/lbc_funcs/#limb-brightening-correction-module","text":"Tamar Ervin Date: June 23, 2021 Limb-darkening correction functions: flatten continuum intensity images to correct for limb-darkening use fifth order polynomial and constants from Allen 1973","title":"Limb-Brightening Correction Module"},{"location":"package/lbc_funcs/#sdo_hmi_rvs.tools.lbc_funcs.get_u","text":"function to get limb darkening coefficient u (Allen 1973) based on wavelength value","title":"get_u()"},{"location":"package/lbc_funcs/#sdo_hmi_rvs.tools.lbc_funcs.get_u--parameters","text":"ll: wavelength","title":"Parameters"},{"location":"package/lbc_funcs/#sdo_hmi_rvs.tools.lbc_funcs.get_u--returns","text":"vl: coefficient v based on wavelength Source code in sdo_hmi_rvs/tools/lbc_funcs.py def get_u ( ll ): \"\"\" function to get limb darkening coefficient u (Allen 1973) based on wavelength value Parameters ---------- ll: wavelength Returns ------- vl: coefficient v based on wavelength \"\"\" pll = np . array ([ 1.0 , ll , ll ** 2 , ll ** 3 , ll ** 4 , ll ** 5 ]) au = - 8.9829751 bu = 0.0069093916 cu = - 1.8144591e-6 du = 2.2540875e-10 eu = - 1.3389747e-14 fu = 3.0453572e-19 a = np . array ([ au , bu , cu , du , eu , fu ]) ul = sum ( a * pll ) return ul","title":"Returns"},{"location":"package/lbc_funcs/#sdo_hmi_rvs.tools.lbc_funcs.get_v","text":"function to get limb darkening coefficient v (Allen 1973) based on wavelength value","title":"get_v()"},{"location":"package/lbc_funcs/#sdo_hmi_rvs.tools.lbc_funcs.get_v--parameters","text":"ll: wavelength","title":"Parameters"},{"location":"package/lbc_funcs/#sdo_hmi_rvs.tools.lbc_funcs.get_v--returns","text":"vl: coefficient v based on wavelength Source code in sdo_hmi_rvs/tools/lbc_funcs.py def get_v ( ll ): \"\"\" function to get limb darkening coefficient v (Allen 1973) based on wavelength value Parameters ---------- ll: wavelength Returns ------- vl: coefficient v based on wavelength \"\"\" pll = np . array ([ 1.0 , ll , ll ** 2 , ll ** 3 , ll ** 4 , ll ** 5 ]) av = 9.2891180 bv = - 0.0062212632 cv = 1.5788029e-6 dv = - 1.9359644e-10 ev = 1.1444469e-14 fv = - 2.599494e-19 a = np . array ([ av , bv , cv , dv , ev , fv ]) vl = sum ( a * pll ) return vl","title":"Returns"},{"location":"package/lbc_funcs/#sdo_hmi_rvs.tools.lbc_funcs.limb_polynomial","text":"function to calculate limb darkening correction polynomial based on IDl function: https://hesperia.gsfc.nasa.gov/ssw/gen/idl/solar/darklimb_correct.pro","title":"limb_polynomial()"},{"location":"package/lbc_funcs/#sdo_hmi_rvs.tools.lbc_funcs.limb_polynomial--parameters","text":"imap: UNCORRECTED Sunpy map object (Intensitygram)","title":"Parameters"},{"location":"package/lbc_funcs/#sdo_hmi_rvs.tools.lbc_funcs.limb_polynomial--returns","text":"Lij: limb-darkening polynomial array Source code in sdo_hmi_rvs/tools/lbc_funcs.py def limb_polynomial ( imap ): \"\"\" function to calculate limb darkening correction polynomial based on IDl function: https://hesperia.gsfc.nasa.gov/ssw/gen/idl/solar/darklimb_correct.pro Parameters ---------- imap: UNCORRECTED Sunpy map object (Intensitygram) Returns ------- Lij: limb-darkening polynomial array \"\"\" # get data data = imap . data # get header information header = imap . fits_header # wavelength wavelength = header [ 'WAVELNTH' ] # coordinates x_center = header [ 'CRPIX1' ] y_center = header [ 'CRPIX2' ] radius = header [ 'RSUN_OBS' ] / header [ 'CDELT1' ] # size size = imap . data . shape [ 0 ] # wavelength as float ll = 1.0 * wavelength # convert data to array and set nan values to zero array = np . array ( data ) NaNs = np . isnan ( array ) array [ NaNs ] = 0.0 # get correction coefficients based on wavelength ul = get_u ( ll ) vl = get_v ( ll ) # make arrays of x and y coordinates x = np . arange ( 0 , size , 1.0 ) y = np . arange ( 0 , size , 1.0 ) # create grids for x, y arrays x_mat , y_mat = np . meshgrid ( x , y ) # make z array such that we get a circle z = np . sqrt (( x_mat - x_center ) ** 2 + ( y_mat - y_center ) ** 2 ) # normalize circle to radius of 1 grid = z / radius out = np . where ( grid > 1.0 ) grid [ out ] = 0.0 # calculate polynomial Lij = 1.0 - ul - vl + ul * np . cos ( np . arcsin ( grid )) + vl * np . cos ( np . arcsin ( grid )) ** 2 return Lij","title":"Returns"},{"location":"package/pipeline/","text":"Full Pipeline Inputs start_date: start date of RV calculations end_date: end date of RV calculations cadence: how often you want to calculate [seconds] csv_name: file name to save calculations Example usage start_date = datetime.datetime(2021, 8, 26, 8, 00, 0) end_date = datetime.datetime(2021, 9, 3, 0, 00, 0) cadence = 24*3600 csv_name = 'csv_name' component_calc(start_date, end_date, cadence, csv_name) Function def component_calc(start_date, end_date, cadence, csv_name=None): \"\"\" function to calculate rv components using pipeline functions Parameters ---------- start_date: start date of RV calculations end_date: end date of RV calculations cadence: how often to calculate RV components csv_name: name of file to store calculations in Returns ------- \"\"\" # create file names if csv_name is None: csv_name = str(start_date) csv_name = os.path.join(CsvDir.CALC, csv_name + '.csv') bad_dates_csv = os.path.join(CsvDir.CALC, csv_name + '_bad_dates.csv') # print out csv title print(\"Beginning calculation of values for csv file: \" + csv_name) # List of header strings row_contents = ['date_obs', 'date_jd', 'v_quiet', 'v_disc', 'v_phot', 'v_conv', 'f_bright', 'f_spot', 'f', 'Bobs', 'vphot_bright', 'vphot_spot', 'f_small', 'f_large', 'f_network', 'f_plage', 'f_nonconv', 'quiet_flux', 'ar_flux', 'conv_flux', 'pol_flux', 'pol_conv_flux', 'vconv_quiet', 'vconv_large', 'vconv_small'] # Append a list as new line to an old csv file utils.append_list_as_row(csv_name, row_contents) # get hmi data products time_range = datetime.timedelta(seconds=22) physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] # get dates list xy = (end_date - start_date).seconds + (end_date - start_date).days * 24 * 3600 dates_list = [start_date + datetime.timedelta(seconds=cadence * x) for x in range(0, int(xy / cadence))] for i, date in enumerate(dates_list): # convert the date to a string -- required for use in csv file date_str, date_obj, date_jd = utils.get_dates(date) # pull image within specified time range result = Fido.search(a.Time(str(date_obj - time_range), str(date_obj + time_range)), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2]) # add file to list file_download = Fido.fetch(result) # remove unusable file types good_files = [] for file in file_download: name, extension = os.path.splitext(file) if extension == '.fits': good_files.append(file) if len(good_files) != 3: # append these values to the csv file save_vals = [date_str, 'not three good files'] utils.append_list_as_row(bad_dates_csv, save_vals) # print that the files are missing print('\\nNot three good files: ' + date_str + ' index: ' + str(i)) pass else: # convert to map sequence map_seq = sunpy.map.Map(sorted(good_files)) # check for missing data types missing_map = False # split into data types for j, map_obj in enumerate(map_seq): if map_obj.meta['content'] == 'DOPPLERGRAM': vmap = map_obj elif map_obj.meta['content'] == 'MAGNETOGRAM': mmap = map_obj elif map_obj.meta['content'] == 'CONTINUUM INTENSITY': imap = map_obj else: missing_map = True if missing_map: print(\"Missing a data product for \" + date_str) # add the data # append these values to the csv file save_vals = [date_str, 'missing data product'] utils.append_list_as_row(bad_dates_csv, save_vals) pass else: # coordinate transformation for maps x, y, pd, r, d, mu = ctfuncs.coordinates(vmap) wij, nij, rij = ctfuncs.vel_coords(x, y, pd, r, vmap) # remove bad mu values vmap, mmap, imap = ctfuncs.fix_mu(mu, [vmap, mmap, imap]) # calculate relative positions deltaw, deltan, deltar, dij = sfuncs.rel_positions(wij, nij, rij, vmap) # calculate spacecraft velocity vsc = sfuncs.spacecraft_vel(deltaw, deltan, deltar, dij, vmap) # optimized solar rotation parameters a1 = 14.713 a2 = -2.396 a3 = -1.787 a_parameters = [a1, a2, a3] # calculation of solar rotation velocity vrot = sfuncs.solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters) # calculate corrected velocity corrected_vel = vmap.data - np.real(vsc) - np.real(vrot) # corrected velocity maps map_vel_cor = sfuncs.corrected_map(corrected_vel, vmap, map_type='Corrected-Dopplergram', frame=frames.HeliographicCarrington) # limb brightening Lij = lbfuncs.limb_polynomial(imap) # calculate corrected data Iflat = imap.data / Lij # corrected intensity maps map_int_cor = sfuncs.corrected_map(Iflat, imap, map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs, Br = sfuncs.mag_field(mu, mmap, B_noise) # corrected observed magnetic data map map_mag_obs = sfuncs.corrected_map(Bobs, mmap, map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active, quiet = sfuncs.mag_thresh(mu, mmap, Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) # calculate intensity threshold fac_inds, spot_inds = sfuncs.int_thresh(map_int_cor, active, quiet) # create threshold array thresh_arr = sfuncs.thresh_map(fac_inds, spot_inds) # full threshold maps map_full_thresh = sfuncs.corrected_map(thresh_arr, mmap, map_type='Threshold', frame=frames.HeliographicCarrington) ### velocity contribution due to convective motion of quiet-Sun v_quiet = v_quiet(map_vel_cor, imap, quiet) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot, vphot_bright, vphot_spot = v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = v_disc(map_vel_cor, imap) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright, f_spot, f = sfuncs.filling_factor(mu, mmap, active, fac_inds, spot_inds) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs.unsigned_flux(map_mag_obs, imap) ### calculate the area filling factor pixA_hem = ctfuncs.pix_area_hem(wij, nij, rij, vmap) area = sfuncs.area_calc(active, pixA_hem) f_small, f_large, f_network, f_plage, f_nonconv =sfuncs.area_filling_factor(active, area, mu, mmap, fac_inds) ### get the unsigned flux quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = sfuncs.area_unsigned_flux(map_mag_obs, imap, area, active) ### get area weighted convective velocities vconv_quiet, vconv_large, vconv_small = sfuncs.area_vconv(map_vel_cor, imap, active, area) # make array of what we want to save save_vals = [v_quiet, v_disc, v_phot, v_conv, f_bright, f_spot, f, unsigned_obs_flux, vphot_bright, vphot_spot, f_small, f_large, f_network, f_plage, f_nonconv, quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux, vconv_quiet, vconv_large, vconv_small] # round stuff rounded = np.around(save_vals, 3) round_vals = [date_str, date_jd] for val in rounded: round_vals.append(val) # append these values to the csv file utils.append_list_as_row(csv_name, round_vals) # print that the date is completed print('\\nCalculations and save to file complete for ' + date_str + ' index: ' + str(i)) print('Calculation complete for dates:', start_date, 'to', end_date)","title":"Full Pipeline"},{"location":"package/pipeline/#full-pipeline","text":"","title":"Full Pipeline"},{"location":"package/pipeline/#inputs","text":"start_date: start date of RV calculations end_date: end date of RV calculations cadence: how often you want to calculate [seconds] csv_name: file name to save calculations","title":"Inputs"},{"location":"package/pipeline/#example-usage","text":"start_date = datetime.datetime(2021, 8, 26, 8, 00, 0) end_date = datetime.datetime(2021, 9, 3, 0, 00, 0) cadence = 24*3600 csv_name = 'csv_name' component_calc(start_date, end_date, cadence, csv_name)","title":"Example usage"},{"location":"package/pipeline/#function","text":"def component_calc(start_date, end_date, cadence, csv_name=None): \"\"\" function to calculate rv components using pipeline functions Parameters ---------- start_date: start date of RV calculations end_date: end date of RV calculations cadence: how often to calculate RV components csv_name: name of file to store calculations in Returns ------- \"\"\" # create file names if csv_name is None: csv_name = str(start_date) csv_name = os.path.join(CsvDir.CALC, csv_name + '.csv') bad_dates_csv = os.path.join(CsvDir.CALC, csv_name + '_bad_dates.csv') # print out csv title print(\"Beginning calculation of values for csv file: \" + csv_name) # List of header strings row_contents = ['date_obs', 'date_jd', 'v_quiet', 'v_disc', 'v_phot', 'v_conv', 'f_bright', 'f_spot', 'f', 'Bobs', 'vphot_bright', 'vphot_spot', 'f_small', 'f_large', 'f_network', 'f_plage', 'f_nonconv', 'quiet_flux', 'ar_flux', 'conv_flux', 'pol_flux', 'pol_conv_flux', 'vconv_quiet', 'vconv_large', 'vconv_small'] # Append a list as new line to an old csv file utils.append_list_as_row(csv_name, row_contents) # get hmi data products time_range = datetime.timedelta(seconds=22) physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity] # get dates list xy = (end_date - start_date).seconds + (end_date - start_date).days * 24 * 3600 dates_list = [start_date + datetime.timedelta(seconds=cadence * x) for x in range(0, int(xy / cadence))] for i, date in enumerate(dates_list): # convert the date to a string -- required for use in csv file date_str, date_obj, date_jd = utils.get_dates(date) # pull image within specified time range result = Fido.search(a.Time(str(date_obj - time_range), str(date_obj + time_range)), a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2]) # add file to list file_download = Fido.fetch(result) # remove unusable file types good_files = [] for file in file_download: name, extension = os.path.splitext(file) if extension == '.fits': good_files.append(file) if len(good_files) != 3: # append these values to the csv file save_vals = [date_str, 'not three good files'] utils.append_list_as_row(bad_dates_csv, save_vals) # print that the files are missing print('\\nNot three good files: ' + date_str + ' index: ' + str(i)) pass else: # convert to map sequence map_seq = sunpy.map.Map(sorted(good_files)) # check for missing data types missing_map = False # split into data types for j, map_obj in enumerate(map_seq): if map_obj.meta['content'] == 'DOPPLERGRAM': vmap = map_obj elif map_obj.meta['content'] == 'MAGNETOGRAM': mmap = map_obj elif map_obj.meta['content'] == 'CONTINUUM INTENSITY': imap = map_obj else: missing_map = True if missing_map: print(\"Missing a data product for \" + date_str) # add the data # append these values to the csv file save_vals = [date_str, 'missing data product'] utils.append_list_as_row(bad_dates_csv, save_vals) pass else: # coordinate transformation for maps x, y, pd, r, d, mu = ctfuncs.coordinates(vmap) wij, nij, rij = ctfuncs.vel_coords(x, y, pd, r, vmap) # remove bad mu values vmap, mmap, imap = ctfuncs.fix_mu(mu, [vmap, mmap, imap]) # calculate relative positions deltaw, deltan, deltar, dij = sfuncs.rel_positions(wij, nij, rij, vmap) # calculate spacecraft velocity vsc = sfuncs.spacecraft_vel(deltaw, deltan, deltar, dij, vmap) # optimized solar rotation parameters a1 = 14.713 a2 = -2.396 a3 = -1.787 a_parameters = [a1, a2, a3] # calculation of solar rotation velocity vrot = sfuncs.solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters) # calculate corrected velocity corrected_vel = vmap.data - np.real(vsc) - np.real(vrot) # corrected velocity maps map_vel_cor = sfuncs.corrected_map(corrected_vel, vmap, map_type='Corrected-Dopplergram', frame=frames.HeliographicCarrington) # limb brightening Lij = lbfuncs.limb_polynomial(imap) # calculate corrected data Iflat = imap.data / Lij # corrected intensity maps map_int_cor = sfuncs.corrected_map(Iflat, imap, map_type='Corrected-Intensitygram', frame=frames.HeliographicCarrington) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs, Br = sfuncs.mag_field(mu, mmap, B_noise) # corrected observed magnetic data map map_mag_obs = sfuncs.corrected_map(Bobs, mmap, map_type='Corrected-Magnetogram', frame=frames.HeliographicCarrington) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active, quiet = sfuncs.mag_thresh(mu, mmap, Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff) # calculate intensity threshold fac_inds, spot_inds = sfuncs.int_thresh(map_int_cor, active, quiet) # create threshold array thresh_arr = sfuncs.thresh_map(fac_inds, spot_inds) # full threshold maps map_full_thresh = sfuncs.corrected_map(thresh_arr, mmap, map_type='Threshold', frame=frames.HeliographicCarrington) ### velocity contribution due to convective motion of quiet-Sun v_quiet = v_quiet(map_vel_cor, imap, quiet) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot, vphot_bright, vphot_spot = v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds, spot_inds) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = v_disc(map_vel_cor, imap) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright, f_spot, f = sfuncs.filling_factor(mu, mmap, active, fac_inds, spot_inds) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs.unsigned_flux(map_mag_obs, imap) ### calculate the area filling factor pixA_hem = ctfuncs.pix_area_hem(wij, nij, rij, vmap) area = sfuncs.area_calc(active, pixA_hem) f_small, f_large, f_network, f_plage, f_nonconv =sfuncs.area_filling_factor(active, area, mu, mmap, fac_inds) ### get the unsigned flux quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = sfuncs.area_unsigned_flux(map_mag_obs, imap, area, active) ### get area weighted convective velocities vconv_quiet, vconv_large, vconv_small = sfuncs.area_vconv(map_vel_cor, imap, active, area) # make array of what we want to save save_vals = [v_quiet, v_disc, v_phot, v_conv, f_bright, f_spot, f, unsigned_obs_flux, vphot_bright, vphot_spot, f_small, f_large, f_network, f_plage, f_nonconv, quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux, vconv_quiet, vconv_large, vconv_small] # round stuff rounded = np.around(save_vals, 3) round_vals = [date_str, date_jd] for val in rounded: round_vals.append(val) # append these values to the csv file utils.append_list_as_row(csv_name, round_vals) # print that the date is completed print('\\nCalculations and save to file complete for ' + date_str + ' index: ' + str(i)) print('Calculation complete for dates:', start_date, 'to', end_date)","title":"Function"},{"location":"package/plotting_funcs/","text":"Plotting Module Tamar Ervin Date: June 23, 2021 functions for plotting images and various solar observable comparison plots animate ( i , fig , axs , aia ) function to animate frames to get a movie Parameters i: index fig: figure number axs: axes Image object aia: image to plot Returns plot_obj: array of plot objects for FuncAnimation function Source code in sdo_hmi_rvs/tools/plotting_funcs.py def animate ( i , fig , axs , aia ): \"\"\" function to animate frames to get a movie Parameters ---------- i: index fig: figure number axs: axes Image object aia: image to plot Returns ------- plot_obj: array of plot objects for FuncAnimation function \"\"\" fig . suptitle ( \"AIA %s \" % ( str ( aia [ 0 ][ i ] . meta [ 't_obs' ]))) axs [ 0 , 0 ] . set_title ( \"AIA %s \" % ( str ( aia [ 0 ][ i ] . meta [ 'wavelnth' ]))) axs [ 0 , 1 ] . set_title ( \"AIA %s \" % ( str ( aia [ 1 ][ i ] . meta [ 'wavelnth' ]))) axs [ 1 , 0 ] . set_title ( \"AIA %s \" % ( str ( aia [ 2 ][ i ] . meta [ 'wavelnth' ]))) axs [ 1 , 1 ] . set_title ( \"AIA %s \" % ( str ( aia [ 3 ][ i ] . meta [ 'wavelnth' ]))) plot_obj0 = aia [ 0 ][ i ] . plot ( axes = axs [ 0 , 0 ]) # plot_obj0.set_data(aia[0][i].data) plot_obj1 = aia [ 1 ][ i ] . plot ( axes = axs [ 0 , 1 ]) # plot_obj1.set_data(aia[1][i].data) plot_obj2 = aia [ 2 ][ i ] . plot ( axes = axs [ 1 , 0 ]) # plot_obj2.set_data(aia[2][i].data) plot_obj3 = aia [ 3 ][ i ] . plot ( axes = axs [ 1 , 1 ]) # plot_obj3.set_data(aia[3][i].data) return plot_obj0 , plot_obj1 , plot_obj2 , plot_obj3 correlation_plot ( x , y , title , xlabel , ylabel , save_fig = None ) function to create correlation plot and calculate correlation coefficient Parameters x: x data to plot y: y data to plot title: title of plot xlabel: label for x axes ylabel: label for y axes save_fig: path to save file, None if not saving Returns Source code in sdo_hmi_rvs/tools/plotting_funcs.py def correlation_plot ( x , y , title , xlabel , ylabel , save_fig = None ): \"\"\" function to create correlation plot and calculate correlation coefficient Parameters ---------- x: x data to plot y: y data to plot title: title of plot xlabel: label for x axes ylabel: label for y axes save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'mystyle.mplstyle' ) plt . style . use ( plot_style ) # calculate correlation coefficient fig = plt . figure () plt . rcParams [ 'figure.figsize' ] = [ 12 , 6 ] ax = fig . add_subplot () fig . subplots_adjust ( top = 0.85 ) correlation = stats . spearmanr ( x , y ) # label correlation coefficients ax . text ( 0.1 , 0.95 , str ( np . around ( correlation [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = ax . transAxes , weight = 'normal' ) # plot data plt . scatter ( x , y , color = 'deepskyblue' , edgecolors = 'k' , linewidths = 1.0 ) # title and axes fig . suptitle ( title ) ax . set_xlabel ( xlabel ) ax . set_ylabel ( ylabel ) # save if needed if save_fig is not None : plt . savefig ( save_fig ) four_correlation_plots ( x_list , y_list , title , xlabel_list = None , ylabel_list = None , save_fig = None ) function for multiple correlation plots Parameters x_list: list of x data to plot y_list: list of y data to plot title: title of plot xlabel_list: list of labels for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving Returns Source code in sdo_hmi_rvs/tools/plotting_funcs.py def four_correlation_plots ( x_list , y_list , title , xlabel_list = None , ylabel_list = None , save_fig = None ): \"\"\" function for multiple correlation plots Parameters ---------- x_list: list of x data to plot y_list: list of y data to plot title: title of plot xlabel_list: list of labels for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'mystyle.mplstyle' ) plt . style . use ( plot_style ) # calculate correlation coefficient corr_one = stats . spearmanr ( x_list [ 0 ], y_list [ 0 ]) corr_two = stats . spearmanr ( x_list [ 1 ], y_list [ 0 ]) corr_three = stats . spearmanr ( x_list [ 0 ], y_list [ 1 ]) corr_four = stats . spearmanr ( x_list [ 1 ], y_list [ 1 ]) # setup the plot fig , axs = plt . subplots ( 2 , 2 , sharey = 'row' , sharex = 'col' , figsize = [ 12 , 6 ], gridspec_kw = { 'hspace' : 0.1 , 'wspace' : 0.1 }) fig . suptitle ( title ) axs [ 0 , 0 ] . set ( ylabel = ylabel_list [ 0 ]) # axs[0, 1].set(xlabel=xlabel_list[1]) axs [ 1 , 0 ] . set ( xlabel = xlabel_list [ 0 ], ylabel = ylabel_list [ 1 ]) axs [ 1 , 1 ] . set ( xlabel = xlabel_list [ 1 ]) # plot data axs [ 0 , 0 ] . scatter ( x_list [ 0 ], y_list [ 0 ], color = 'violet' , edgecolors = 'k' , linewidths = 1.0 ) axs [ 0 , 1 ] . scatter ( x_list [ 1 ], y_list [ 0 ], color = 'violet' , edgecolors = 'k' , linewidths = 1.0 ) axs [ 1 , 0 ] . scatter ( x_list [ 0 ], y_list [ 1 ], color = 'violet' , edgecolors = 'k' , linewidths = 1.0 ) axs [ 1 , 1 ] . scatter ( x_list [ 1 ], y_list [ 1 ], color = 'violet' , edgecolors = 'k' , linewidths = 1.0 ) # label correlation coefficients axs [ 0 , 0 ] . text ( 0.1 , 0.95 , str ( np . around ( corr_one [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = axs [ 0 , 0 ] . transAxes , weight = 'normal' ) axs [ 0 , 1 ] . text ( 0.1 , 0.95 , str ( np . around ( corr_two [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = axs [ 0 , 1 ] . transAxes , weight = 'normal' ) axs [ 1 , 0 ] . text ( 0.1 , 0.95 , str ( np . around ( corr_three [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = axs [ 1 , 0 ] . transAxes , weight = 'normal' ) axs [ 1 , 1 ] . text ( 0.1 , 0.95 , str ( np . around ( corr_four [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = axs [ 1 , 1 ] . transAxes , weight = 'normal' ) # remove tick marks # for ax in axs: # ax.tick_params(labelbottom=True) # save if needed if save_fig is not None : plt . savefig ( save_fig ) plot_image ( los_image , nfig = None , cmap = 'gray' , title = None ) function to plot AIA los disk images Parameters los_image: 2D line of sight image to plot nfig: figure number cmap: colormap to use title: figure title Returns Source code in sdo_hmi_rvs/tools/plotting_funcs.py def plot_image ( los_image , nfig = None , cmap = 'gray' , title = None ): \"\"\" function to plot AIA los disk images Parameters ---------- los_image: 2D line of sight image to plot nfig: figure number cmap: colormap to use title: figure title Returns ------- \"\"\" plot_arr = los_image . data plot_arr [ plot_arr < .001 ] = .001 norm_max = max ( 1.01 , np . nanmax ( plot_arr )) norm = colors . LogNorm ( vmin = 1.0 , vmax = norm_max ) # plot the initial image if nfig is None : cur_figs = plt . get_fignums () if not nfig : nfig = 0 else : nfig = cur_figs . max () + 1 plt . figure ( nfig ) plt . imshow ( plot_arr , extent = [ los_image . x . min (), los_image . x . max (), los_image . y . min (), los_image . y . max ()], origin = \"lower\" , cmap = cmap , aspect = \"equal\" , norm = norm ) plt . xlabel ( \"Latitude\" ) plt . ylabel ( \"Carrinton Longitude\" ) if title is not None : plt . title ( title ) return None plot_timeseries ( x , y , title = None , xlabel = None , ylabel = None , save_fig = None ) simple mpl function to plot relevant observables Parameters x: x data to plot y: y data to plot xlabel: label for x axes ylabel: label for y axes title: title of plot save_fig: path to save file, None if not saving Returns Source code in sdo_hmi_rvs/tools/plotting_funcs.py def plot_timeseries ( x , y , title = None , xlabel = None , ylabel = None , save_fig = None ): \"\"\" simple mpl function to plot relevant observables Parameters ---------- x: x data to plot y: y data to plot xlabel: label for x axes ylabel: label for y axes title: title of plot save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'timeseries.mplstyle' ) plt . style . use ( plot_style ) # plot data plt . rcParams [ 'figure.figsize' ] = [ 12 , 4 ] plt . scatter ( x , y , color = 'cornflowerblue' , edgecolors = 'k' , linewidths = 1.0 ) plt . title ( title ) plt . xlabel ( xlabel ) plt . ylabel ( ylabel ) # save if needed if save_fig is not None : plt . savefig ( save_fig ) scatter_hist ( x , y , bins ) function to create a scatter plot with associated histograms Parameters x: 2D array of x values y: 2D array of y values bins: number of bins (integer) Returns scatter_hist: scatter plot with corresponding histograms Source code in sdo_hmi_rvs/tools/plotting_funcs.py def scatter_hist ( x , y , bins ): \"\"\" function to create a scatter plot with associated histograms Parameters ---------- x: 2D array of x values y: 2D array of y values bins: number of bins (integer) Returns ------- scatter_hist: scatter plot with corresponding histograms \"\"\" # make sure bins value is an integer if type ( bins ) != int : bins = int ( bins ) # definitions for the axes left , width = 0.1 , 0.65 bottom , height = 0.1 , 0.65 spacing = 0.005 rect_scatter = [ left , bottom , width , height ] rect_histx = [ left , bottom + height + spacing , width , 0.2 ] rect_histy = [ left + width + spacing , bottom , 0.2 , height ] # setup the plot fig = plt . figure ( figsize = ( 8 , 8 )) ax = fig . add_axes ( rect_scatter ) ax_histx = fig . add_axes ( rect_histx , sharex = ax ) ax_histy = fig . add_axes ( rect_histy , sharey = ax ) # no labels ax_histx . tick_params ( axis = \"x\" , labelbottom = False ) ax_histy . tick_params ( axis = \"y\" , labelleft = False ) # scatter plot ax . scatter ( x , y , edgecolors = 'k' ) # plot histograms ax_histx . hist ( x , bins = bins ) ax_histy . hist ( y , bins = bins , orientation = 'horizontal' ) scatter_overlaid_timeseries ( x , y_list , title , xlabel , ylabel_list , save_fig = None ) function to overlay scatter plots Parameters x: x data to plot y_list: list of y data to plot title: title of plot xlabel: label for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving Returns Source code in sdo_hmi_rvs/tools/plotting_funcs.py def scatter_overlaid_timeseries ( x , y_list , title , xlabel , ylabel_list , save_fig = None ): \"\"\" function to overlay scatter plots Parameters ---------- x: x data to plot y_list: list of y data to plot title: title of plot xlabel: label for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'mystyle.mplstyle' ) plt . style . use ( plot_style ) # plot data plt . rcParams [ 'figure.figsize' ] = [ 12 , 6 ] fig , ax1 = plt . subplots () ax1 . set_xlabel ( xlabel ) ax1 . set_ylabel ( ylabel_list [ 0 ]) ax1 . set_title ( title ) ax1 . scatter ( x , y_list [ 0 ], color = 'forestgreen' , label = ylabel_list [ 0 ]) ax1 . tick_params ( axis = 'y' ) plt . legend () ax2 = ax1 . twinx () ax2 . set_ylabel ( ylabel_list [ 1 ]) ax2 . scatter ( x , y_list [ 1 ], color = 'darkviolet' , label = ylabel_list [ 1 ]) ax2 . tick_params ( axis = 'y' ) plt . title ( title ) plt . legend () # save if needed if save_fig is not None : plt . savefig ( save_fig ) two_correlation_plots ( x_list , y , title , xlabel_list = None , ylabel = None , save_fig = None ) function for multiple correlation plots Parameters x_list: list of x data to plot y: y data to plot title: title of plot xlabel_list: list of labels for x axes ylabel: label for y axes save_fig: path to save file, None if not saving Returns Source code in sdo_hmi_rvs/tools/plotting_funcs.py def two_correlation_plots ( x_list , y , title , xlabel_list = None , ylabel = None , save_fig = None ): \"\"\" function for multiple correlation plots Parameters ---------- x_list: list of x data to plot y: y data to plot title: title of plot xlabel_list: list of labels for x axes ylabel: label for y axes save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'mystyle.mplstyle' ) plt . style . use ( plot_style ) # calculate correlation coefficient corr_one = stats . spearmanr ( x_list [ 0 ], y ) corr_two = stats . spearmanr ( x_list [ 1 ], y ) # setup the plot fig , axs = plt . subplots ( 1 , 2 , sharey = 'all' , figsize = [ 12 , 6 ], gridspec_kw = { 'wspace' : 0.1 }) fig . suptitle ( title ) axs [ 0 ] . set ( xlabel = xlabel_list [ 0 ], ylabel = ylabel ) axs [ 1 ] . set ( xlabel = xlabel_list [ 1 ]) # plot data axs [ 0 ] . scatter ( x_list [ 0 ], y , color = 'violet' , edgecolors = 'k' , linewidths = 1.0 ) axs [ 1 ] . scatter ( x_list [ 1 ], y , color = 'violet' , edgecolors = 'k' , linewidths = 1.0 ) # label correlation coefficients axs [ 0 ] . text ( 0.1 , 0.95 , str ( np . around ( corr_one [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = axs [ 0 ] . transAxes , weight = 'normal' ) axs [ 1 ] . text ( 0.1 , 0.95 , str ( np . around ( corr_two [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = axs [ 1 ] . transAxes , weight = 'normal' ) # remove tick marks for ax in axs : ax . tick_params ( labelbottom = True ) # save if needed if save_fig is not None : plt . savefig ( save_fig ) vert_comp_timeseries ( x , y_list , title , xlabel = None , ylabel_list = None , save_fig = None ) function to plot stacked timeseries for comparison Parameters x: x data to plot y_list: list of y data to plot title: title of plot xlabel: label for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving Returns Source code in sdo_hmi_rvs/tools/plotting_funcs.py def vert_comp_timeseries ( x , y_list , title , xlabel = None , ylabel_list = None , save_fig = None ): \"\"\" function to plot stacked timeseries for comparison Parameters ---------- x: x data to plot y_list: list of y data to plot title: title of plot xlabel: label for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'timeseries.mplstyle' ) plt . rcParams [ 'mathtext.fontset' ] = 'custom' plt . rcParams [ 'mathtext.cal' ] = 'Helvetica' plt . style . use ( plot_style ) # set up figure fig , axs = plt . subplots ( len ( y_list ), 1 , sharex = 'all' , figsize = [ 12 , 3 * len ( y_list )], gridspec_kw = { 'hspace' : 0.0 }) if title is not None : fig . suptitle ( title ) # set up axes labels for i in range ( 0 , len ( axs )): if i == len ( axs ) - 1 : axs [ i ] . set ( xlabel = xlabel , ylabel = ylabel_list [ i ]) else : axs [ i ] . set ( ylabel = ylabel_list [ i ]) # plot data color_list = [ 'lightcoral' , 'lightblue' , 'plum' , 'pink' , 'navajowhite' ] for i in range ( 0 , len ( axs )): axs [ i ] . scatter ( x , y_list [ i ], color = color_list [ i ], edgecolors = 'k' , linewidths = 1.0 ) # axs[i].locator_params(axis=\"y\", nbins=5) # axs[1].scatter(x, y_list[1], color='lightblue', edgecolors='k', linewidths=1.0) # set axes tick marks axs [ i ] . tick_params ( labelbottom = True ) # align y axis labels fig . align_ylabels ( axs ) # save if needed if save_fig is not None : plt . savefig ( save_fig ) vert_plot_timeseries ( x , y_list , title , xlabel = None , ylabel_list = None , save_fig = None ) function to plot stacked timeseries for comparison Parameters x: x data to plot y_list: list of y data to plot title: title of plot xlabel: label for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving Returns Source code in sdo_hmi_rvs/tools/plotting_funcs.py def vert_plot_timeseries ( x , y_list , title , xlabel = None , ylabel_list = None , save_fig = None ): \"\"\" function to plot stacked timeseries for comparison Parameters ---------- x: x data to plot y_list: list of y data to plot title: title of plot xlabel: label for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'timeseries.mplstyle' ) plt . rcParams [ 'mathtext.fontset' ] = 'custom' plt . rcParams [ 'mathtext.cal' ] = 'Helvetica Neue LT Pro' plt . style . use ( plot_style ) # set up figure fig , axs = plt . subplots ( len ( y_list ), 1 , sharex = 'all' , figsize = [ 12 , 2 * len ( y_list )], gridspec_kw = { 'hspace' : 0.0 }) if title is not None : fig . suptitle ( title ) # set up axes labels for i in range ( 0 , len ( axs )): if i == len ( axs ) - 1 : axs [ i ] . set ( xlabel = xlabel , ylabel = ylabel_list [ i ]) else : axs [ i ] . set ( ylabel = ylabel_list [ i ]) # plot data color_list = [ 'lightcoral' , 'lightblue' , 'plum' , 'pink' ] for i in range ( 0 , len ( axs )): axs [ i ] . plot ( x , y_list [ i ], color = color_list [ i ], linewidth = 1.2 ) # axs[1].plot(x, y_list[1], color='steelblue', linewidth=0.8) # set axes tick marks axs [ i ] . tick_params ( labelbottom = True ) # align y axis labels fig . align_ylabels ( axs ) # save if needed if save_fig is not None : plt . savefig ( save_fig )","title":"Plotting Functions"},{"location":"package/plotting_funcs/#plotting-module","text":"Tamar Ervin Date: June 23, 2021 functions for plotting images and various solar observable comparison plots","title":"Plotting Module"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.animate","text":"function to animate frames to get a movie","title":"animate()"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.animate--parameters","text":"i: index fig: figure number axs: axes Image object aia: image to plot","title":"Parameters"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.animate--returns","text":"plot_obj: array of plot objects for FuncAnimation function Source code in sdo_hmi_rvs/tools/plotting_funcs.py def animate ( i , fig , axs , aia ): \"\"\" function to animate frames to get a movie Parameters ---------- i: index fig: figure number axs: axes Image object aia: image to plot Returns ------- plot_obj: array of plot objects for FuncAnimation function \"\"\" fig . suptitle ( \"AIA %s \" % ( str ( aia [ 0 ][ i ] . meta [ 't_obs' ]))) axs [ 0 , 0 ] . set_title ( \"AIA %s \" % ( str ( aia [ 0 ][ i ] . meta [ 'wavelnth' ]))) axs [ 0 , 1 ] . set_title ( \"AIA %s \" % ( str ( aia [ 1 ][ i ] . meta [ 'wavelnth' ]))) axs [ 1 , 0 ] . set_title ( \"AIA %s \" % ( str ( aia [ 2 ][ i ] . meta [ 'wavelnth' ]))) axs [ 1 , 1 ] . set_title ( \"AIA %s \" % ( str ( aia [ 3 ][ i ] . meta [ 'wavelnth' ]))) plot_obj0 = aia [ 0 ][ i ] . plot ( axes = axs [ 0 , 0 ]) # plot_obj0.set_data(aia[0][i].data) plot_obj1 = aia [ 1 ][ i ] . plot ( axes = axs [ 0 , 1 ]) # plot_obj1.set_data(aia[1][i].data) plot_obj2 = aia [ 2 ][ i ] . plot ( axes = axs [ 1 , 0 ]) # plot_obj2.set_data(aia[2][i].data) plot_obj3 = aia [ 3 ][ i ] . plot ( axes = axs [ 1 , 1 ]) # plot_obj3.set_data(aia[3][i].data) return plot_obj0 , plot_obj1 , plot_obj2 , plot_obj3","title":"Returns"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.correlation_plot","text":"function to create correlation plot and calculate correlation coefficient","title":"correlation_plot()"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.correlation_plot--parameters","text":"x: x data to plot y: y data to plot title: title of plot xlabel: label for x axes ylabel: label for y axes save_fig: path to save file, None if not saving","title":"Parameters"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.correlation_plot--returns","text":"Source code in sdo_hmi_rvs/tools/plotting_funcs.py def correlation_plot ( x , y , title , xlabel , ylabel , save_fig = None ): \"\"\" function to create correlation plot and calculate correlation coefficient Parameters ---------- x: x data to plot y: y data to plot title: title of plot xlabel: label for x axes ylabel: label for y axes save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'mystyle.mplstyle' ) plt . style . use ( plot_style ) # calculate correlation coefficient fig = plt . figure () plt . rcParams [ 'figure.figsize' ] = [ 12 , 6 ] ax = fig . add_subplot () fig . subplots_adjust ( top = 0.85 ) correlation = stats . spearmanr ( x , y ) # label correlation coefficients ax . text ( 0.1 , 0.95 , str ( np . around ( correlation [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = ax . transAxes , weight = 'normal' ) # plot data plt . scatter ( x , y , color = 'deepskyblue' , edgecolors = 'k' , linewidths = 1.0 ) # title and axes fig . suptitle ( title ) ax . set_xlabel ( xlabel ) ax . set_ylabel ( ylabel ) # save if needed if save_fig is not None : plt . savefig ( save_fig )","title":"Returns"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.four_correlation_plots","text":"function for multiple correlation plots","title":"four_correlation_plots()"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.four_correlation_plots--parameters","text":"x_list: list of x data to plot y_list: list of y data to plot title: title of plot xlabel_list: list of labels for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving","title":"Parameters"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.four_correlation_plots--returns","text":"Source code in sdo_hmi_rvs/tools/plotting_funcs.py def four_correlation_plots ( x_list , y_list , title , xlabel_list = None , ylabel_list = None , save_fig = None ): \"\"\" function for multiple correlation plots Parameters ---------- x_list: list of x data to plot y_list: list of y data to plot title: title of plot xlabel_list: list of labels for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'mystyle.mplstyle' ) plt . style . use ( plot_style ) # calculate correlation coefficient corr_one = stats . spearmanr ( x_list [ 0 ], y_list [ 0 ]) corr_two = stats . spearmanr ( x_list [ 1 ], y_list [ 0 ]) corr_three = stats . spearmanr ( x_list [ 0 ], y_list [ 1 ]) corr_four = stats . spearmanr ( x_list [ 1 ], y_list [ 1 ]) # setup the plot fig , axs = plt . subplots ( 2 , 2 , sharey = 'row' , sharex = 'col' , figsize = [ 12 , 6 ], gridspec_kw = { 'hspace' : 0.1 , 'wspace' : 0.1 }) fig . suptitle ( title ) axs [ 0 , 0 ] . set ( ylabel = ylabel_list [ 0 ]) # axs[0, 1].set(xlabel=xlabel_list[1]) axs [ 1 , 0 ] . set ( xlabel = xlabel_list [ 0 ], ylabel = ylabel_list [ 1 ]) axs [ 1 , 1 ] . set ( xlabel = xlabel_list [ 1 ]) # plot data axs [ 0 , 0 ] . scatter ( x_list [ 0 ], y_list [ 0 ], color = 'violet' , edgecolors = 'k' , linewidths = 1.0 ) axs [ 0 , 1 ] . scatter ( x_list [ 1 ], y_list [ 0 ], color = 'violet' , edgecolors = 'k' , linewidths = 1.0 ) axs [ 1 , 0 ] . scatter ( x_list [ 0 ], y_list [ 1 ], color = 'violet' , edgecolors = 'k' , linewidths = 1.0 ) axs [ 1 , 1 ] . scatter ( x_list [ 1 ], y_list [ 1 ], color = 'violet' , edgecolors = 'k' , linewidths = 1.0 ) # label correlation coefficients axs [ 0 , 0 ] . text ( 0.1 , 0.95 , str ( np . around ( corr_one [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = axs [ 0 , 0 ] . transAxes , weight = 'normal' ) axs [ 0 , 1 ] . text ( 0.1 , 0.95 , str ( np . around ( corr_two [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = axs [ 0 , 1 ] . transAxes , weight = 'normal' ) axs [ 1 , 0 ] . text ( 0.1 , 0.95 , str ( np . around ( corr_three [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = axs [ 1 , 0 ] . transAxes , weight = 'normal' ) axs [ 1 , 1 ] . text ( 0.1 , 0.95 , str ( np . around ( corr_four [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = axs [ 1 , 1 ] . transAxes , weight = 'normal' ) # remove tick marks # for ax in axs: # ax.tick_params(labelbottom=True) # save if needed if save_fig is not None : plt . savefig ( save_fig )","title":"Returns"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.plot_image","text":"function to plot AIA los disk images","title":"plot_image()"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.plot_image--parameters","text":"los_image: 2D line of sight image to plot nfig: figure number cmap: colormap to use title: figure title","title":"Parameters"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.plot_image--returns","text":"Source code in sdo_hmi_rvs/tools/plotting_funcs.py def plot_image ( los_image , nfig = None , cmap = 'gray' , title = None ): \"\"\" function to plot AIA los disk images Parameters ---------- los_image: 2D line of sight image to plot nfig: figure number cmap: colormap to use title: figure title Returns ------- \"\"\" plot_arr = los_image . data plot_arr [ plot_arr < .001 ] = .001 norm_max = max ( 1.01 , np . nanmax ( plot_arr )) norm = colors . LogNorm ( vmin = 1.0 , vmax = norm_max ) # plot the initial image if nfig is None : cur_figs = plt . get_fignums () if not nfig : nfig = 0 else : nfig = cur_figs . max () + 1 plt . figure ( nfig ) plt . imshow ( plot_arr , extent = [ los_image . x . min (), los_image . x . max (), los_image . y . min (), los_image . y . max ()], origin = \"lower\" , cmap = cmap , aspect = \"equal\" , norm = norm ) plt . xlabel ( \"Latitude\" ) plt . ylabel ( \"Carrinton Longitude\" ) if title is not None : plt . title ( title ) return None","title":"Returns"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.plot_timeseries","text":"simple mpl function to plot relevant observables","title":"plot_timeseries()"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.plot_timeseries--parameters","text":"x: x data to plot y: y data to plot xlabel: label for x axes ylabel: label for y axes title: title of plot save_fig: path to save file, None if not saving","title":"Parameters"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.plot_timeseries--returns","text":"Source code in sdo_hmi_rvs/tools/plotting_funcs.py def plot_timeseries ( x , y , title = None , xlabel = None , ylabel = None , save_fig = None ): \"\"\" simple mpl function to plot relevant observables Parameters ---------- x: x data to plot y: y data to plot xlabel: label for x axes ylabel: label for y axes title: title of plot save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'timeseries.mplstyle' ) plt . style . use ( plot_style ) # plot data plt . rcParams [ 'figure.figsize' ] = [ 12 , 4 ] plt . scatter ( x , y , color = 'cornflowerblue' , edgecolors = 'k' , linewidths = 1.0 ) plt . title ( title ) plt . xlabel ( xlabel ) plt . ylabel ( ylabel ) # save if needed if save_fig is not None : plt . savefig ( save_fig )","title":"Returns"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.scatter_hist","text":"function to create a scatter plot with associated histograms","title":"scatter_hist()"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.scatter_hist--parameters","text":"x: 2D array of x values y: 2D array of y values bins: number of bins (integer)","title":"Parameters"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.scatter_hist--returns","text":"scatter_hist: scatter plot with corresponding histograms Source code in sdo_hmi_rvs/tools/plotting_funcs.py def scatter_hist ( x , y , bins ): \"\"\" function to create a scatter plot with associated histograms Parameters ---------- x: 2D array of x values y: 2D array of y values bins: number of bins (integer) Returns ------- scatter_hist: scatter plot with corresponding histograms \"\"\" # make sure bins value is an integer if type ( bins ) != int : bins = int ( bins ) # definitions for the axes left , width = 0.1 , 0.65 bottom , height = 0.1 , 0.65 spacing = 0.005 rect_scatter = [ left , bottom , width , height ] rect_histx = [ left , bottom + height + spacing , width , 0.2 ] rect_histy = [ left + width + spacing , bottom , 0.2 , height ] # setup the plot fig = plt . figure ( figsize = ( 8 , 8 )) ax = fig . add_axes ( rect_scatter ) ax_histx = fig . add_axes ( rect_histx , sharex = ax ) ax_histy = fig . add_axes ( rect_histy , sharey = ax ) # no labels ax_histx . tick_params ( axis = \"x\" , labelbottom = False ) ax_histy . tick_params ( axis = \"y\" , labelleft = False ) # scatter plot ax . scatter ( x , y , edgecolors = 'k' ) # plot histograms ax_histx . hist ( x , bins = bins ) ax_histy . hist ( y , bins = bins , orientation = 'horizontal' )","title":"Returns"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.scatter_overlaid_timeseries","text":"function to overlay scatter plots","title":"scatter_overlaid_timeseries()"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.scatter_overlaid_timeseries--parameters","text":"x: x data to plot y_list: list of y data to plot title: title of plot xlabel: label for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving","title":"Parameters"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.scatter_overlaid_timeseries--returns","text":"Source code in sdo_hmi_rvs/tools/plotting_funcs.py def scatter_overlaid_timeseries ( x , y_list , title , xlabel , ylabel_list , save_fig = None ): \"\"\" function to overlay scatter plots Parameters ---------- x: x data to plot y_list: list of y data to plot title: title of plot xlabel: label for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'mystyle.mplstyle' ) plt . style . use ( plot_style ) # plot data plt . rcParams [ 'figure.figsize' ] = [ 12 , 6 ] fig , ax1 = plt . subplots () ax1 . set_xlabel ( xlabel ) ax1 . set_ylabel ( ylabel_list [ 0 ]) ax1 . set_title ( title ) ax1 . scatter ( x , y_list [ 0 ], color = 'forestgreen' , label = ylabel_list [ 0 ]) ax1 . tick_params ( axis = 'y' ) plt . legend () ax2 = ax1 . twinx () ax2 . set_ylabel ( ylabel_list [ 1 ]) ax2 . scatter ( x , y_list [ 1 ], color = 'darkviolet' , label = ylabel_list [ 1 ]) ax2 . tick_params ( axis = 'y' ) plt . title ( title ) plt . legend () # save if needed if save_fig is not None : plt . savefig ( save_fig )","title":"Returns"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.two_correlation_plots","text":"function for multiple correlation plots","title":"two_correlation_plots()"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.two_correlation_plots--parameters","text":"x_list: list of x data to plot y: y data to plot title: title of plot xlabel_list: list of labels for x axes ylabel: label for y axes save_fig: path to save file, None if not saving","title":"Parameters"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.two_correlation_plots--returns","text":"Source code in sdo_hmi_rvs/tools/plotting_funcs.py def two_correlation_plots ( x_list , y , title , xlabel_list = None , ylabel = None , save_fig = None ): \"\"\" function for multiple correlation plots Parameters ---------- x_list: list of x data to plot y: y data to plot title: title of plot xlabel_list: list of labels for x axes ylabel: label for y axes save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'mystyle.mplstyle' ) plt . style . use ( plot_style ) # calculate correlation coefficient corr_one = stats . spearmanr ( x_list [ 0 ], y ) corr_two = stats . spearmanr ( x_list [ 1 ], y ) # setup the plot fig , axs = plt . subplots ( 1 , 2 , sharey = 'all' , figsize = [ 12 , 6 ], gridspec_kw = { 'wspace' : 0.1 }) fig . suptitle ( title ) axs [ 0 ] . set ( xlabel = xlabel_list [ 0 ], ylabel = ylabel ) axs [ 1 ] . set ( xlabel = xlabel_list [ 1 ]) # plot data axs [ 0 ] . scatter ( x_list [ 0 ], y , color = 'violet' , edgecolors = 'k' , linewidths = 1.0 ) axs [ 1 ] . scatter ( x_list [ 1 ], y , color = 'violet' , edgecolors = 'k' , linewidths = 1.0 ) # label correlation coefficients axs [ 0 ] . text ( 0.1 , 0.95 , str ( np . around ( corr_one [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = axs [ 0 ] . transAxes , weight = 'normal' ) axs [ 1 ] . text ( 0.1 , 0.95 , str ( np . around ( corr_two [ 0 ], 2 )), horizontalalignment = 'left' , verticalalignment = 'top' , transform = axs [ 1 ] . transAxes , weight = 'normal' ) # remove tick marks for ax in axs : ax . tick_params ( labelbottom = True ) # save if needed if save_fig is not None : plt . savefig ( save_fig )","title":"Returns"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.vert_comp_timeseries","text":"function to plot stacked timeseries for comparison","title":"vert_comp_timeseries()"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.vert_comp_timeseries--parameters","text":"x: x data to plot y_list: list of y data to plot title: title of plot xlabel: label for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving","title":"Parameters"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.vert_comp_timeseries--returns","text":"Source code in sdo_hmi_rvs/tools/plotting_funcs.py def vert_comp_timeseries ( x , y_list , title , xlabel = None , ylabel_list = None , save_fig = None ): \"\"\" function to plot stacked timeseries for comparison Parameters ---------- x: x data to plot y_list: list of y data to plot title: title of plot xlabel: label for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'timeseries.mplstyle' ) plt . rcParams [ 'mathtext.fontset' ] = 'custom' plt . rcParams [ 'mathtext.cal' ] = 'Helvetica' plt . style . use ( plot_style ) # set up figure fig , axs = plt . subplots ( len ( y_list ), 1 , sharex = 'all' , figsize = [ 12 , 3 * len ( y_list )], gridspec_kw = { 'hspace' : 0.0 }) if title is not None : fig . suptitle ( title ) # set up axes labels for i in range ( 0 , len ( axs )): if i == len ( axs ) - 1 : axs [ i ] . set ( xlabel = xlabel , ylabel = ylabel_list [ i ]) else : axs [ i ] . set ( ylabel = ylabel_list [ i ]) # plot data color_list = [ 'lightcoral' , 'lightblue' , 'plum' , 'pink' , 'navajowhite' ] for i in range ( 0 , len ( axs )): axs [ i ] . scatter ( x , y_list [ i ], color = color_list [ i ], edgecolors = 'k' , linewidths = 1.0 ) # axs[i].locator_params(axis=\"y\", nbins=5) # axs[1].scatter(x, y_list[1], color='lightblue', edgecolors='k', linewidths=1.0) # set axes tick marks axs [ i ] . tick_params ( labelbottom = True ) # align y axis labels fig . align_ylabels ( axs ) # save if needed if save_fig is not None : plt . savefig ( save_fig )","title":"Returns"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.vert_plot_timeseries","text":"function to plot stacked timeseries for comparison","title":"vert_plot_timeseries()"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.vert_plot_timeseries--parameters","text":"x: x data to plot y_list: list of y data to plot title: title of plot xlabel: label for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving","title":"Parameters"},{"location":"package/plotting_funcs/#sdo_hmi_rvs.tools.plotting_funcs.vert_plot_timeseries--returns","text":"Source code in sdo_hmi_rvs/tools/plotting_funcs.py def vert_plot_timeseries ( x , y_list , title , xlabel = None , ylabel_list = None , save_fig = None ): \"\"\" function to plot stacked timeseries for comparison Parameters ---------- x: x data to plot y_list: list of y data to plot title: title of plot xlabel: label for x axes ylabel_list: list of labels for y axes save_fig: path to save file, None if not saving Returns ------- \"\"\" # plot style plot_style = os . path . join ( PlotDir . MPL , 'timeseries.mplstyle' ) plt . rcParams [ 'mathtext.fontset' ] = 'custom' plt . rcParams [ 'mathtext.cal' ] = 'Helvetica Neue LT Pro' plt . style . use ( plot_style ) # set up figure fig , axs = plt . subplots ( len ( y_list ), 1 , sharex = 'all' , figsize = [ 12 , 2 * len ( y_list )], gridspec_kw = { 'hspace' : 0.0 }) if title is not None : fig . suptitle ( title ) # set up axes labels for i in range ( 0 , len ( axs )): if i == len ( axs ) - 1 : axs [ i ] . set ( xlabel = xlabel , ylabel = ylabel_list [ i ]) else : axs [ i ] . set ( ylabel = ylabel_list [ i ]) # plot data color_list = [ 'lightcoral' , 'lightblue' , 'plum' , 'pink' ] for i in range ( 0 , len ( axs )): axs [ i ] . plot ( x , y_list [ i ], color = color_list [ i ], linewidth = 1.2 ) # axs[1].plot(x, y_list[1], color='steelblue', linewidth=0.8) # set axes tick marks axs [ i ] . tick_params ( labelbottom = True ) # align y axis labels fig . align_ylabels ( axs ) # save if needed if save_fig is not None : plt . savefig ( save_fig )","title":"Returns"},{"location":"package/rvs/","text":"RV Calculation Module Tamar Ervin Date: October 7, 2021 pipeline function to calculate 'sun-as-a-star' RVs rvs ( start_date , end_date , cadence , csv_name = None ) function to calculate rv components using pipeline functions Parameters start_date: start date of RV calculations end_date: end date of RV calculations cadence: how often to calculate RV components csv_name: name of file to store calculations in Returns Source code in sdo_hmi_rvs/tools/rvs.py def rvs ( start_date , end_date , cadence , csv_name = None ): \"\"\" function to calculate rv components using pipeline functions Parameters ---------- start_date: start date of RV calculations end_date: end date of RV calculations cadence: how often to calculate RV components csv_name: name of file to store calculations in Returns ------- \"\"\" # create file names if csv_name is None : csv_name = str ( start_date ) csv_name = os . path . join ( CsvDir . CALC , csv_name + '.csv' ) bad_dates_csv = os . path . join ( CsvDir . CALC , csv_name + '_bad_dates.csv' ) # print out csv title print ( \"Beginning calculation of values for csv file: \" + csv_name ) # List of header strings row_contents = [ 'date_obs' , 'date_jd' , 'v_quiet' , 'v_disc' , 'v_phot' , 'v_conv' , 'f_bright' , 'f_spot' , 'f' , 'Bobs' , 'vphot_bright' , 'vphot_spot' , 'f_small' , 'f_large' , 'f_network' , 'f_plage' , 'f_nonconv' , 'quiet_flux' , 'ar_flux' , 'conv_flux' , 'pol_flux' , 'pol_conv_flux' , 'vconv_quiet' , 'vconv_large' , 'vconv_small' ] # Append a list as new line to an old csv file utils . append_list_as_row ( csv_name , row_contents ) # get hmi data products time_range = datetime . timedelta ( seconds = 22 ) physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] # get dates list xy = ( end_date - start_date ) . seconds + ( end_date - start_date ) . days * 24 * 3600 dates_list = [ start_date + datetime . timedelta ( seconds = cadence * x ) for x in range ( 0 , int ( xy / cadence ))] for i , date in enumerate ( dates_list ): # convert the date to a string -- required for use in csv file date_str , date_obj , date_jd = utils . get_dates ( date ) # pull image within specified time range result = Fido . search ( a . Time ( str ( date_obj - time_range ), str ( date_obj + time_range )), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ]) # add file to list file_download = Fido . fetch ( result ) # remove unusable file types good_files = [] for file in file_download : name , extension = os . path . splitext ( file ) if extension == '.fits' : good_files . append ( file ) if len ( good_files ) != 3 : # append these values to the csv file save_vals = [ date_str , 'not three good files' ] utils . append_list_as_row ( bad_dates_csv , save_vals ) # print that the files are missing print ( ' \\n Not three good files: ' + date_str + ' index: ' + str ( i )) pass else : # convert to map sequence map_seq = sunpy . map . Map ( sorted ( good_files )) # check for missing data types missing_map = False # split into data types for j , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : vmap = map_obj elif map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : mmap = map_obj elif map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : imap = map_obj else : missing_map = True if missing_map : print ( \"Missing a data product for \" + date_str ) # add the data # append these values to the csv file save_vals = [ date_str , 'missing data product' ] utils . append_list_as_row ( bad_dates_csv , save_vals ) pass else : # coordinate transformation for maps x , y , pd , r , d , mu = ctfuncs . coordinates ( vmap ) wij , nij , rij = ctfuncs . vel_coords ( x , y , pd , r , vmap ) # remove bad mu values vmap , mmap , imap = ctfuncs . fix_mu ( mu , [ vmap , mmap , imap ]) # calculate relative positions deltaw , deltan , deltar , dij = sfuncs . rel_positions ( wij , nij , rij , vmap ) # calculate spacecraft velocity vsc = sfuncs . spacecraft_vel ( deltaw , deltan , deltar , dij , vmap ) # optimized solar rotation parameters a1 = 14.713 a2 = - 2.396 a3 = - 1.787 a_parameters = [ a1 , a2 , a3 ] # calculation of solar rotation velocity vrot = sfuncs . solar_rot_vel ( wij , nij , rij , deltaw , deltan , deltar , dij , vmap , a_parameters ) # calculate corrected velocity corrected_vel = vmap . data - np . real ( vsc ) - np . real ( vrot ) # corrected velocity maps map_vel_cor = sfuncs . corrected_map ( corrected_vel , vmap , map_type = 'Corrected-Dopplergram' , frame = frames . HeliographicCarrington ) # limb brightening Lij = lbfuncs . limb_polynomial ( imap ) # calculate corrected data Iflat = imap . data / Lij # corrected intensity maps map_int_cor = sfuncs . corrected_map ( Iflat , imap , map_type = 'Corrected-Intensitygram' , frame = frames . HeliographicCarrington ) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs , Br = sfuncs . mag_field ( mu , mmap , B_noise ) # corrected observed magnetic data map map_mag_obs = sfuncs . corrected_map ( Bobs , mmap , map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active , quiet = sfuncs . mag_thresh ( mu , mmap , Br_cutoff = Br_cutoff , mu_cutoff = mu_cutoff ) # calculate intensity threshold fac_inds , spot_inds = sfuncs . int_thresh ( map_int_cor , active , quiet ) # create threshold array thresh_arr = sfuncs . thresh_map ( fac_inds , spot_inds ) # full threshold maps map_full_thresh = sfuncs . corrected_map ( thresh_arr , mmap , map_type = 'Threshold' , frame = frames . HeliographicCarrington ) ### velocity contribution due to convective motion of quiet-Sun v_quiet = v_quiet ( map_vel_cor , imap , quiet ) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot , vphot_bright , vphot_spot = v_phot ( quiet , active , Lij , vrot , imap , mu , fac_inds , spot_inds ) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = v_disc ( map_vel_cor , imap ) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright , f_spot , f = sfuncs . filling_factor ( mu , mmap , active , fac_inds , spot_inds ) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs . unsigned_flux ( map_mag_obs , imap ) ### calculate the area filling factor pixA_hem = ctfuncs . pix_area_hem ( wij , nij , rij , vmap ) area = sfuncs . area_calc ( active , pixA_hem ) f_small , f_large , f_network , f_plage , f_nonconv = sfuncs . area_filling_factor ( active , area , mu , mmap , fac_inds ) ### get the unsigned flux quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux = sfuncs . area_unsigned_flux ( map_mag_obs , imap , area , active ) ### get area weighted convective velocities vconv_quiet , vconv_large , vconv_small = sfuncs . area_vconv ( map_vel_cor , imap , active , area ) # make array of what we want to save save_vals = [ v_quiet , v_disc , v_phot , v_conv , f_bright , f_spot , f , unsigned_obs_flux , vphot_bright , vphot_spot , f_small , f_large , f_network , f_plage , f_nonconv , quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux , vconv_quiet , vconv_large , vconv_small ] # round stuff rounded = np . around ( save_vals , 3 ) round_vals = [ date_str , date_jd ] for val in rounded : round_vals . append ( val ) # append these values to the csv file utils . append_list_as_row ( csv_name , round_vals ) # print that the date is completed print ( ' \\n Calculations and save to file complete for ' + date_str + ' index: ' + str ( i )) print ( 'Calculation complete for dates:' , start_date , 'to' , end_date )","title":"RV Calculations"},{"location":"package/rvs/#rv-calculation-module","text":"Tamar Ervin Date: October 7, 2021 pipeline function to calculate 'sun-as-a-star' RVs","title":"RV Calculation Module"},{"location":"package/rvs/#sdo_hmi_rvs.tools.rvs.rvs","text":"function to calculate rv components using pipeline functions","title":"rvs()"},{"location":"package/rvs/#sdo_hmi_rvs.tools.rvs.rvs--parameters","text":"start_date: start date of RV calculations end_date: end date of RV calculations cadence: how often to calculate RV components csv_name: name of file to store calculations in","title":"Parameters"},{"location":"package/rvs/#sdo_hmi_rvs.tools.rvs.rvs--returns","text":"Source code in sdo_hmi_rvs/tools/rvs.py def rvs ( start_date , end_date , cadence , csv_name = None ): \"\"\" function to calculate rv components using pipeline functions Parameters ---------- start_date: start date of RV calculations end_date: end date of RV calculations cadence: how often to calculate RV components csv_name: name of file to store calculations in Returns ------- \"\"\" # create file names if csv_name is None : csv_name = str ( start_date ) csv_name = os . path . join ( CsvDir . CALC , csv_name + '.csv' ) bad_dates_csv = os . path . join ( CsvDir . CALC , csv_name + '_bad_dates.csv' ) # print out csv title print ( \"Beginning calculation of values for csv file: \" + csv_name ) # List of header strings row_contents = [ 'date_obs' , 'date_jd' , 'v_quiet' , 'v_disc' , 'v_phot' , 'v_conv' , 'f_bright' , 'f_spot' , 'f' , 'Bobs' , 'vphot_bright' , 'vphot_spot' , 'f_small' , 'f_large' , 'f_network' , 'f_plage' , 'f_nonconv' , 'quiet_flux' , 'ar_flux' , 'conv_flux' , 'pol_flux' , 'pol_conv_flux' , 'vconv_quiet' , 'vconv_large' , 'vconv_small' ] # Append a list as new line to an old csv file utils . append_list_as_row ( csv_name , row_contents ) # get hmi data products time_range = datetime . timedelta ( seconds = 22 ) physobs_list = [ a . Physobs . los_velocity , a . Physobs . los_magnetic_field , a . Physobs . intensity ] # get dates list xy = ( end_date - start_date ) . seconds + ( end_date - start_date ) . days * 24 * 3600 dates_list = [ start_date + datetime . timedelta ( seconds = cadence * x ) for x in range ( 0 , int ( xy / cadence ))] for i , date in enumerate ( dates_list ): # convert the date to a string -- required for use in csv file date_str , date_obj , date_jd = utils . get_dates ( date ) # pull image within specified time range result = Fido . search ( a . Time ( str ( date_obj - time_range ), str ( date_obj + time_range )), a . Instrument . hmi , physobs_list [ 0 ] | physobs_list [ 1 ] | physobs_list [ 2 ]) # add file to list file_download = Fido . fetch ( result ) # remove unusable file types good_files = [] for file in file_download : name , extension = os . path . splitext ( file ) if extension == '.fits' : good_files . append ( file ) if len ( good_files ) != 3 : # append these values to the csv file save_vals = [ date_str , 'not three good files' ] utils . append_list_as_row ( bad_dates_csv , save_vals ) # print that the files are missing print ( ' \\n Not three good files: ' + date_str + ' index: ' + str ( i )) pass else : # convert to map sequence map_seq = sunpy . map . Map ( sorted ( good_files )) # check for missing data types missing_map = False # split into data types for j , map_obj in enumerate ( map_seq ): if map_obj . meta [ 'content' ] == 'DOPPLERGRAM' : vmap = map_obj elif map_obj . meta [ 'content' ] == 'MAGNETOGRAM' : mmap = map_obj elif map_obj . meta [ 'content' ] == 'CONTINUUM INTENSITY' : imap = map_obj else : missing_map = True if missing_map : print ( \"Missing a data product for \" + date_str ) # add the data # append these values to the csv file save_vals = [ date_str , 'missing data product' ] utils . append_list_as_row ( bad_dates_csv , save_vals ) pass else : # coordinate transformation for maps x , y , pd , r , d , mu = ctfuncs . coordinates ( vmap ) wij , nij , rij = ctfuncs . vel_coords ( x , y , pd , r , vmap ) # remove bad mu values vmap , mmap , imap = ctfuncs . fix_mu ( mu , [ vmap , mmap , imap ]) # calculate relative positions deltaw , deltan , deltar , dij = sfuncs . rel_positions ( wij , nij , rij , vmap ) # calculate spacecraft velocity vsc = sfuncs . spacecraft_vel ( deltaw , deltan , deltar , dij , vmap ) # optimized solar rotation parameters a1 = 14.713 a2 = - 2.396 a3 = - 1.787 a_parameters = [ a1 , a2 , a3 ] # calculation of solar rotation velocity vrot = sfuncs . solar_rot_vel ( wij , nij , rij , deltaw , deltan , deltar , dij , vmap , a_parameters ) # calculate corrected velocity corrected_vel = vmap . data - np . real ( vsc ) - np . real ( vrot ) # corrected velocity maps map_vel_cor = sfuncs . corrected_map ( corrected_vel , vmap , map_type = 'Corrected-Dopplergram' , frame = frames . HeliographicCarrington ) # limb brightening Lij = lbfuncs . limb_polynomial ( imap ) # calculate corrected data Iflat = imap . data / Lij # corrected intensity maps map_int_cor = sfuncs . corrected_map ( Iflat , imap , map_type = 'Corrected-Intensitygram' , frame = frames . HeliographicCarrington ) # magnetic noise level B_noise = 8 # calculate unsigned field strength Bobs , Br = sfuncs . mag_field ( mu , mmap , B_noise ) # corrected observed magnetic data map map_mag_obs = sfuncs . corrected_map ( Bobs , mmap , map_type = 'Corrected-Magnetogram' , frame = frames . HeliographicCarrington ) ### calculate magnetic threshold # magnetic threshold value (G) from Yeo et. al. 2013 Br_cutoff = 24 # mu cutoff value mu_cutoff = 0.3 # calculate magnetic threshold active , quiet = sfuncs . mag_thresh ( mu , mmap , Br_cutoff = Br_cutoff , mu_cutoff = mu_cutoff ) # calculate intensity threshold fac_inds , spot_inds = sfuncs . int_thresh ( map_int_cor , active , quiet ) # create threshold array thresh_arr = sfuncs . thresh_map ( fac_inds , spot_inds ) # full threshold maps map_full_thresh = sfuncs . corrected_map ( thresh_arr , mmap , map_type = 'Threshold' , frame = frames . HeliographicCarrington ) ### velocity contribution due to convective motion of quiet-Sun v_quiet = v_quiet ( map_vel_cor , imap , quiet ) ### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots) # calculate photospheric velocity v_phot , vphot_bright , vphot_spot = v_phot ( quiet , active , Lij , vrot , imap , mu , fac_inds , spot_inds ) ### velocity contribution due to suppression of convective blueshift by active regions # calculate disc-averaged velocity v_disc = v_disc ( map_vel_cor , imap ) # calculate convective velocity v_conv = v_disc - v_quiet ### filling factor # calculate filling factor f_bright , f_spot , f = sfuncs . filling_factor ( mu , mmap , active , fac_inds , spot_inds ) ### unsigned magnetic flux # unsigned observed flux unsigned_obs_flux = sfuncs . unsigned_flux ( map_mag_obs , imap ) ### calculate the area filling factor pixA_hem = ctfuncs . pix_area_hem ( wij , nij , rij , vmap ) area = sfuncs . area_calc ( active , pixA_hem ) f_small , f_large , f_network , f_plage , f_nonconv = sfuncs . area_filling_factor ( active , area , mu , mmap , fac_inds ) ### get the unsigned flux quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux = sfuncs . area_unsigned_flux ( map_mag_obs , imap , area , active ) ### get area weighted convective velocities vconv_quiet , vconv_large , vconv_small = sfuncs . area_vconv ( map_vel_cor , imap , active , area ) # make array of what we want to save save_vals = [ v_quiet , v_disc , v_phot , v_conv , f_bright , f_spot , f , unsigned_obs_flux , vphot_bright , vphot_spot , f_small , f_large , f_network , f_plage , f_nonconv , quiet_flux , ar_flux , conv_flux , pol_flux , pol_conv_flux , vconv_quiet , vconv_large , vconv_small ] # round stuff rounded = np . around ( save_vals , 3 ) round_vals = [ date_str , date_jd ] for val in rounded : round_vals . append ( val ) # append these values to the csv file utils . append_list_as_row ( csv_name , round_vals ) # print that the date is completed print ( ' \\n Calculations and save to file complete for ' + date_str + ' index: ' + str ( i )) print ( 'Calculation complete for dates:' , start_date , 'to' , end_date )","title":"Returns"},{"location":"package/settings/","text":"Settings Module Tamar Ervin Date: June 14, 2021 Settings file for paths Update to work with your system. BaseDir Base directory. Update this and follow same file structure and all will be well :) CsvDir CSV directories HARPSN ( Scaling ) Class that holds HARPS-N scaling coefficients for RV calculations. ImgDir Image directories NEID ( Scaling ) Class that holds NEID scaling coefficients for RV calculations. Scaling Class that holds scaling coefficients for RV calculations.","title":"Settings"},{"location":"package/settings/#settings-module","text":"Tamar Ervin Date: June 14, 2021 Settings file for paths Update to work with your system.","title":"Settings Module"},{"location":"package/settings/#sdo_hmi_rvs.tools.settings.BaseDir","text":"Base directory. Update this and follow same file structure and all will be well :)","title":"BaseDir"},{"location":"package/settings/#sdo_hmi_rvs.tools.settings.CsvDir","text":"CSV directories","title":"CsvDir"},{"location":"package/settings/#sdo_hmi_rvs.tools.settings.HARPSN","text":"Class that holds HARPS-N scaling coefficients for RV calculations.","title":"HARPSN"},{"location":"package/settings/#sdo_hmi_rvs.tools.settings.ImgDir","text":"Image directories","title":"ImgDir"},{"location":"package/settings/#sdo_hmi_rvs.tools.settings.NEID","text":"Class that holds NEID scaling coefficients for RV calculations.","title":"NEID"},{"location":"package/settings/#sdo_hmi_rvs.tools.settings.Scaling","text":"Class that holds scaling coefficients for RV calculations.","title":"Scaling"}]}
\ No newline at end of file
diff --git a/mkdocs/site/sitemap.xml b/mkdocs/site/sitemap.xml
index 3559c00..8997744 100644
--- a/mkdocs/site/sitemap.xml
+++ b/mkdocs/site/sitemap.xml
@@ -2,72 +2,102 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2021-10-15</lastmod>
+         <lastmod>2021-10-26</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2021-10-26</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2021-10-26</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2021-10-26</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2021-10-26</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2021-10-26</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2021-10-26</lastmod>
          <changefreq>daily</changefreq>
     </url>
 </urlset>
\ No newline at end of file
diff --git a/mkdocs/site/sitemap.xml.gz b/mkdocs/site/sitemap.xml.gz
index 285c9194ec13b4f34ba18cae46271e88f55892d7..43ee9079971f05f3f1d42625d1e20c36f6f972cf 100644
GIT binary patch
literal 210
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zKq$5(6e^(Ax0imzbalU5IR0Gjx5DaoZ_rEmpq;I7L2`_gm9EjYt?=!7&JMWdJNEP@
zsEE?A4HfQU98Q#u<4A&zsux2b&bKtkE?oj$Lz<H#<~x*XTCj9;G2<k^II9?)y6uAq
zv3CtG^Rmc_JS+EUWX(2XvJ<vdcds725!!qh-6V@he<a|50}eRgfCCOV;D6)uk$!;u
M3%Ko?JuwOZ053UaqW}N^

literal 207
zcmV;=05Ja_iwFph$Y@~#|8r?{Wo=<_E_iKh0PU4c4#FT1h4-F<urEJNOw>@ibLmPC
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zo1h{{VH+yg#W<WW9>);{9aS%efShk}kX;%AUQJBU5%L{KH62*gT+BGhFU~54C$(LO
z5PR3~l9aTdq@ep0S-nk6cEYym?$v`gLYoi68)qTnA2P!;Ji{|Q!!!JE@VTX*$iEVO
J(@aAL008OaUjG09

diff --git a/sdo_hmi_rvs/tools/rvs.py b/sdo_hmi_rvs/tools/rvs.py
index d5f1957..ab2064e 100644
--- a/sdo_hmi_rvs/tools/rvs.py
+++ b/sdo_hmi_rvs/tools/rvs.py
@@ -29,14 +29,19 @@ def rvs(start_date, end_date, cadence, csv_name=None):
 
     Parameters
     ----------
-    start_date: start date of RV calculations (datetime object)
-    end_date: end date of RV calculations (datetime object)
-    cadence: how often to calculate RV components
-    csv_name: name of file to store calculations in
+    start_date: datetime
+        start date of RV calculations (datetime object)
+    end_date: datetime
+        end date of RV calculations (datetime object)
+    cadence: int
+        how often to calculate RV components
+    csv_name: str
+        name of file to store calculations in
 
     Returns
     -------
 
+
     """
 
     # check date formats
diff --git a/sdo_hmi_rvs/tools/utilities.py b/sdo_hmi_rvs/tools/utilities.py
index 3e7819e..18f2c56 100644
--- a/sdo_hmi_rvs/tools/utilities.py
+++ b/sdo_hmi_rvs/tools/utilities.py
@@ -21,11 +21,13 @@ def read_csv(csv_file_path):
 
     Parameters
     ----------
-    csv_file_path: path to csv file (string)
+    csv_file_path : str
+        path to csv file
 
     Returns
     -------
-    csv_list: list (strings) of elements in csv file
+    csv_list : str, list
+        list of elements in csv file
 
     """
 
@@ -42,8 +44,10 @@ def append_list_as_row(file_name, list_of_elem):
 
     Parameters
     ----------
-    file_name: path to csv file
-    list_of_elem: elements as a list to add to file
+    file_name : str
+        path to csv file
+    list_of_elem : list
+        elements as a list to add to file
 
     Returns
     -------
@@ -66,13 +70,17 @@ def get_dates(date):
 
     Parameters
     ----------
-    date: date in any form (JD, string, datetime)
+    date
+        date in any form (JD, string, datetime)
 
     Returns
     -------
-    date_str: UT datetime as string
-    date_obj: UT datetime object
-    date_jd: UT datetime as float (JD)
+    date_str : str
+        UT datetime as string
+    date_obj : datetime
+        UT datetime object
+    date_jd : float
+        UT datetime as float (JD)
 
     """
     if isinstance(date, str):
@@ -101,13 +109,17 @@ def get_neid_struct_dates(date):
 
     Parameters
     ----------
-    date: date in any form (JD, string, datetime)
+    date
+        date in any form (JD, string, datetime)
 
     Returns
     -------
-    date_str: date string
-    date_obj: datetime object
-    date_jd: jd date (float)
+    date_str : str
+        date string
+    date_obj : datetime
+        datetime object
+    date_jd : float
+        jd date
 
     """
 
@@ -139,11 +151,13 @@ def read_sdo_csv(csv_file):
 
     Parameters
     ----------
-    csv_file: path to csv file to read
+    csv_file : str
+        path to csv file to read
 
     Returns
     -------
-    list of SDO derived metrics
+    list : list
+        list of SDO derived metrics
 
     """
 
@@ -192,11 +206,13 @@ def check_date_format(date):
 
     Parameters
     ----------
-    date: date to check
+    date
+        date to check
 
     Returns
     -------
-    date: corrected date
+    date : datetime
+        corrected date
 
     """