refactory v1.30

ovitrac · Mar 7, 2025 · 29fa9b9 · 29fa9b9
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diff --git a/docs/MigrationModeling/index.html b/docs/MigrationModeling/index.html
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  .typora-export li, .typora-export p, .typora-export,  .footnote-line {white-space: normal;} h1,h2,h3,h4,h5 {white-space:pre-line}
 </style><title>layerLinks</title>
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-<body class='typora-export'><div class='typora-export-content'>
-<div id='write'  class=''><h1 id='🔗-layerlink-in-sfppy-dynamic-linking-for-sensitivity-analysis-and-curve-fitting🍏⏩🍎'><span>🔗 </span><strong><span>LayerLink in SFPPy: Dynamic Linking for Sensitivity Analysis and Curve Fitting</span></strong><span>🍏⏩🍎</span></h1><hr /><div class='md-toc' mdtype='toc'><p class="md-toc-content" role="list"><span role="listitem" class="md-toc-item md-toc-h1" data-ref="n107"><a class="md-toc-inner" href="#🔗-layerlink-in-sfppy-dynamic-linking-for-sensitivity-analysis-and-curve-fitting🍏⏩🍎">🔗 <strong>LayerLink in SFPPy: Dynamic Linking for Sensitivity Analysis and Curve Fitting</strong>🍏⏩🍎</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n113"><a class="md-toc-inner" href="#📌-introduction">📌 <strong>Introduction</strong></a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n117"><a class="md-toc-inner" href="#📖-concept-of-layerlink">📖 <strong>Concept of <code>layerLink</code></strong></a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n123"><a class="md-toc-inner" href="#🔬-sensitivity-analysis-exploring-parameter-impact">🔬 <strong>Sensitivity Analysis: Exploring Parameter Impact</strong></a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n130"><a class="md-toc-inner" href="#🎯-fitting-d-and-k-from-experimental-data">🎯 <strong>Fitting D and k from Experimental Data</strong></a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n132"><a class="md-toc-inner" href="#1️⃣-generate-pseudo-experimental-data"><strong>1️⃣ Generate Pseudo-Experimental Data</strong></a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n134"><a class="md-toc-inner" href="#2️⃣-fit-d-and-k-to-minimize-the-difference"><strong>2️⃣ Fit D and k to Minimize the Difference</strong></a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n138"><a class="md-toc-inner" href="#📌-conclusion">📌 <strong>Conclusion</strong></a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n140"><a class="md-toc-inner" href="#🚀-key-takeaways">🚀 <strong>Key Takeaways</strong></a></span></p></div><hr /><h2 id='📌-introduction'><span>📌 </span><strong><span>Introduction</span></strong></h2><p><code>layerLink</code><span> is a powerful tool in </span><strong><span>SFPPy</span></strong><span> that allows dynamic control over </span><strong><span>diffusivity (D)</span></strong><span> and </span><strong><span>partition coefficients (k)</span></strong><span> in </span><strong><span>mass transfer simulations</span></strong><span>. It enables </span><strong><span>sensitivity analysis</span></strong><span>, </span><strong><span>parameter fitting</span></strong><span>, and </span><strong><span>simulation linking</span></strong><span> without modifying the base layer object.</span></p><p><strong><span>Key applications of </span><code>layerLink</code><span>:</span></strong>
+<body class='typora-export typora-export-show-outline typora-export-no-collapse-outline'><div class='typora-export-content'>
+<div class="typora-export-sidebar"><div class="outline-content"><li class="outline-item-wrapper outline-h1"><div class="outline-item"><span class="outline-expander"></span><a class="outline-label" href="#🔗-layerlink-in-sfppy-dynamic-linking-for-sensitivity-analysis-and-curve-fitting🍏⏩🍎">🔗 <strong>LayerLink in SFPPy: Dynamic Linking for Sensitivity Analysis and Curve Fitting</strong>🍏⏩🍎</a></div><ul class="outline-children"><li class="outline-item-wrapper outline-h2"><div class="outline-item"><span class="outline-expander"></span><a class="outline-label" href="#📌-introduction">📌 <strong>Introduction</strong></a></div><ul class="outline-children"></ul></li><li class="outline-item-wrapper outline-h2"><div class="outline-item"><span class="outline-expander"></span><a class="outline-label" href="#📖-concept-of-layerlink">📖 <strong>Concept of <code>layerLink</code></strong></a></div><ul class="outline-children"></ul></li><li class="outline-item-wrapper outline-h2"><div class="outline-item"><span class="outline-expander"></span><a class="outline-label" href="#🔬-sensitivity-analysis-exploring-parameter-impact">🔬 <strong>Sensitivity Analysis: Exploring Parameter Impact</strong></a></div><ul class="outline-children"></ul></li><li class="outline-item-wrapper outline-h2"><div class="outline-item"><span class="outline-expander"></span><a class="outline-label" href="#🎯-fitting-d-and-k-from-experimental-data">🎯 <strong>Fitting D and k from Experimental Data</strong></a></div><ul class="outline-children"><li class="outline-item-wrapper outline-h3"><div class="outline-item"><span class="outline-expander"></span><a class="outline-label" href="#1️⃣-generate-pseudo-experimental-data"><strong>1️⃣ Generate Pseudo-Experimental Data</strong></a></div><ul class="outline-children"></ul></li><li class="outline-item-wrapper outline-h3"><div class="outline-item"><span class="outline-expander"></span><a class="outline-label" href="#2️⃣-fit-d-and-k-to-minimize-the-difference"><strong>2️⃣ Fit D and k to Minimize the Difference</strong></a></div><ul class="outline-children"></ul></li></ul></li><li class="outline-item-wrapper outline-h2"><div class="outline-item"><span class="outline-expander"></span><a class="outline-label" href="#📌-conclusion">📌 <strong>Conclusion</strong></a></div><ul class="outline-children"><li class="outline-item-wrapper outline-h3"><div class="outline-item"><span class="outline-expander"></span><a class="outline-label" href="#🚀-key-takeaways">🚀 <strong>Key Takeaways</strong></a></div><ul class="outline-children"></ul></li></ul></li></ul></li></div></div><div id='write'  class=''><h1 id='🔗-layerlink-in-sfppy-dynamic-linking-for-sensitivity-analysis-and-curve-fitting🍏⏩🍎'><span>🔗 </span><strong><span>LayerLink in SFPPy: Dynamic Linking for Sensitivity Analysis and Curve Fitting</span></strong><span>🍏⏩🍎</span></h1><hr /><div class='md-toc' mdtype='toc'><p class="md-toc-content" role="list"><span role="listitem" class="md-toc-item md-toc-h1" data-ref="n0"><a class="md-toc-inner" href="#🔗-layerlink-in-sfppy-dynamic-linking-for-sensitivity-analysis-and-curve-fitting🍏⏩🍎">🔗 <strong>LayerLink in SFPPy: Dynamic Linking for Sensitivity Analysis and Curve Fitting</strong>🍏⏩🍎</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n6"><a class="md-toc-inner" href="#📌-introduction">📌 <strong>Introduction</strong></a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n10"><a class="md-toc-inner" href="#📖-concept-of-layerlink">📖 <strong>Concept of <code>layerLink</code></strong></a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n16"><a class="md-toc-inner" href="#🔬-sensitivity-analysis-exploring-parameter-impact">🔬 <strong>Sensitivity Analysis: Exploring Parameter Impact</strong></a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n23"><a class="md-toc-inner" href="#🎯-fitting-d-and-k-from-experimental-data">🎯 <strong>Fitting D and k from Experimental Data</strong></a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n25"><a class="md-toc-inner" href="#1️⃣-generate-pseudo-experimental-data"><strong>1️⃣ Generate Pseudo-Experimental Data</strong></a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n27"><a class="md-toc-inner" href="#2️⃣-fit-d-and-k-to-minimize-the-difference"><strong>2️⃣ Fit D and k to Minimize the Difference</strong></a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n31"><a class="md-toc-inner" href="#📌-conclusion">📌 <strong>Conclusion</strong></a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n33"><a class="md-toc-inner" href="#🚀-key-takeaways">🚀 <strong>Key Takeaways</strong></a></span></p></div><hr /><h2 id='📌-introduction'><span>📌 </span><strong><span>Introduction</span></strong></h2><p><code>layerLink</code><span> is a powerful tool in </span><strong><span>SFPPy</span></strong><span> that allows dynamic control over </span><strong><span>diffusivity (D)</span></strong><span> and </span><strong><span>partition coefficients (k)</span></strong><span> in </span><strong><span>mass transfer simulations</span></strong><span>. It enables </span><strong><span>sensitivity analysis</span></strong><span>, </span><strong><span>parameter fitting</span></strong><span>, and </span><strong><span>simulation linking</span></strong><span> without modifying the base layer object.</span></p><p><strong><span>Key applications of </span><code>layerLink</code><span>:</span></strong>
 <span>✅ Sensitivity analysis 📊</span>
 <span>✅ Kinetic data fitting 🔍</span>
 <span>✅ Dynamically linking simulations 🔄</span>
@@ -858,5 +858,6 @@
   <a href="https://github.com/ovitrac/SFPPy" style="color: #fff; text-decoration: underline;">Website</a> |
   <a href="https://ovitrac.github.io/SFPPy/" style="color: #fff; text-decoration: underline;">Documentation</a>
 </div></div></div>
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diff --git a/docs/wikipages/discussion/multiple_inheritance.html b/docs/wikipages/discussion/multiple_inheritance.html