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     <link rel="next" title="How to Cite This Cookbook" href="notebooks/how-to-cite.html" />
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-    <meta name="docbuild:last-update" content="2 August 2024"/>
+    <meta name="docbuild:last-update" content="20 September 2024"/>
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@@ -416,7 +416,7 @@ <h1>Wavelet Cookbook<a class="headerlink" href="#wavelet-cookbook" title="Link t
 <p>This Project Pythia Cookbook covers working with wavelets in Python</p>
 <section id="motivation">
 <h2>Motivation<a class="headerlink" href="#motivation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Wavelets are a tool to analysis time-series data. When data frequencies vary over time, wavelets can be applied to analysis trends and overcome the time vs. frequency limitations of Fourier Transforms</p>
+<p>Wavelets are a powerful tool to analysis time-series data. When data frequencies vary over time, wavelets can be applied to analysis trends and overcome the time vs. frequency limitations of Fourier Transforms</p>
 </section>
 <section id="authors">
 <h2>Authors<a class="headerlink" href="#authors" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
@@ -430,19 +430,21 @@ <h3>Contributors<a class="headerlink" href="#contributors" title="Link to this h
 </section>
 <section id="structure">
 <h2>Structure<a class="headerlink" href="#structure" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>(State one or more sections that will comprise the notebook. E.g., <em>This cookbook is broken up into two main sections - “Foundations” and “Example Workflows.”</em> Then, describe each section below.)</p>
-<section id="foundations">
-<h3>Foundations<a class="headerlink" href="#foundations" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
+<p>This cookbook is broken into two main sections:</p>
 <ul class="simple">
-<li><p>Wavelet Basics</p></li>
+<li><p>Foundations</p></li>
+<li><p>Example Workflow</p></li>
 </ul>
+<section id="foundations">
+<h3>Foundations<a class="headerlink" href="#foundations" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
+<p>“Wavelet Basics” covers the motivation and background for wavelet analysis by review time-series data and the strengths and weaknesses of Fourier transform</p>
 </section>
 <section id="example-workflows">
 <h3>Example Workflows<a class="headerlink" href="#example-workflows" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
 <ul class="simple">
-<li><p>PyWavelets and Jingle Bells</p></li>
-<li><p>Spy Keypad</p></li>
-<li><p>Atmospheric Data: nino3</p></li>
+<li><p>“PyWavelets and Jingle Bells”: Learn how to use <code class="docutils literal notranslate"><span class="pre">PyWavelets</span></code>, a Python implementation of wavelet analysis, to determine the order of notes in a simple musical piece</p></li>
+<li><p>“Spy Keypad”: Learn how to use wavelets to undercover the frequency and order of notes in an unkonwn piece of audio data</p></li>
+<li><p>“Atmospheric Data: nino3”: Replicate the power wavelet scalogram from the original 1999 Torrence and Compo paper in Python</p></li>
 </ul>
 </section>
 </section>
@@ -786,7 +788,7 @@ <h3>Running on Your Own Machine<a class="headerlink" href="#running-on-your-own-
 
   <div class="footer-item">
     <p class="last-updated">
-  Last updated on 2 August 2024.
+  Last updated on 20 September 2024.
   <br/>
 </p>
   </div>
@@ -840,7 +842,7 @@ <h3>Running on Your Own Machine<a class="headerlink" href="#running-on-your-own-
 
           By the <a href="https://projectpythia.org/">Project Pythia</a> Community.
 
-          Last updated on 2 August 2024.
+          Last updated on 20 September 2024.
         </p>
       </div>
     </div>

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@@ -9,7 +9,7 @@
 This Project Pythia Cookbook covers working with wavelets in Python
 
 ## Motivation
-Wavelets are a tool to analysis time-series data. When data frequencies vary over time, wavelets can be applied to analysis trends and overcome the time vs. frequency limitations of Fourier Transforms
+Wavelets are a powerful tool to analysis time-series data. When data frequencies vary over time, wavelets can be applied to analysis trends and overcome the time vs. frequency limitations of Fourier Transforms
 
 ## Authors
 
@@ -23,17 +23,20 @@ Wavelets are a tool to analysis time-series data. When data frequencies vary ove
 
 ## Structure
 
-(State one or more sections that will comprise the notebook. E.g., _This cookbook is broken up into two main sections - "Foundations" and "Example Workflows."_ Then, describe each section below.)
+This cookbook is broken into two main sections:
+
+- Foundations
+- Example Workflow
 
 ### Foundations
 
-- Wavelet Basics
+"Wavelet Basics" covers the motivation and background for wavelet analysis by review time-series data and the strengths and weaknesses of Fourier transform
 
 ### Example Workflows
 
-- PyWavelets and Jingle Bells
-- Spy Keypad
-- Atmospheric Data: nino3
+- "PyWavelets and Jingle Bells": Learn how to use `PyWavelets`, a Python implementation of wavelet analysis, to determine the order of notes in a simple musical piece
+- "Spy Keypad": Learn how to use wavelets to undercover the frequency and order of notes in an unkonwn piece of audio data
+- "Atmospheric Data: nino3": Replicate the power wavelet scalogram from the original 1999 Torrence and Compo paper in Python
 
 ## Running the Notebooks