From d7d4fde3c72774a927188f3a5cfa6a7099c72541 Mon Sep 17 00:00:00 2001
From: dopplershift <dopplershift@users.noreply.github.com>
Date: Fri, 14 Jun 2024 18:11:55 +0000
Subject: [PATCH] deploy: 8674d6fe62f8a20fd4c35617ed75452c8dadfe76

---
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+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/850hPa_Temperature_Advection.html">850 hPa Temperature Advection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/Ageostrophic_Wind_Example.html">Geostrophic and Ageostrophic Wind</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/Analytic_300hPa_Trough.html">Analytic 300-hPa Trough</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/Differential_Temperature_Advection.html">Differential Temperature Advection with NARR Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/Isentropic_Interpolation.html">Isentropic Analysis</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/PV_baroclinic_isobaric.html">Baroclinic Potential Vorticity Analysis, Divergence, and Winds</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/Upperair_Obs.html">DIFAX Replication</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/Wind_Shear_Vectors_Example.html">Calculating Wind Shear</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/xarray_500hPa_map.html">Using Xarray for Data read and selection</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/skewt/index.html">Skew-T Analysis</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/convective/index.html">Convective Calculations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/convective/miller_composite.html">Miller Composite Chart</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/specialty/index.html">Specialty Plotting</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/specialty/Hovmoller_Diagram.html">Hovmoller Diagram Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/specialty/Observational_Data_Cross_Section.html">Real Data Cross-Section Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/specialty/Smoothing_Contours.html">Smoothing Contours</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/declarative/index.html">Simplified Plotting</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
+</ul>
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+<p><a class="reference external" href="https://github.com/ProjectPythia/metpy-cookbook/actions/workflows/nightly-build.yaml"><img alt="nightly-build" src="https://github.com/ProjectPythia/metpy-cookbook/actions/workflows/nightly-build.yaml/badge.svg" /></a>
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+<a class="reference external" href="https://zenodo.org/badge/latestdoi/620944317"><img alt="DOI" src="https://zenodo.org/badge/620944317.svg" /></a></p>
+<p>This Cookbook is the oversized recipe book for all your MetPy needs.
+We provide a gallery of real workflows centered around meteorological data,
+and the building blocks you need to recreate those workflows or cook up brand new ones yourself.
+Create the maps and analyses you’ve seen from class and professional institutions alike!</p>
+<section id="authors">
+<h2>Authors<a class="headerlink" href="#authors" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>MetPy Maintainers and the MetPy Community.</p>
+<section id="contributors">
+<h3>Contributors<a class="headerlink" href="#contributors" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
+<a href="https://github.com/ProjectPythia/metpy-cookbook/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=ProjectPythia/metpy-cookbook" />
+</a>
+</section>
+</section>
+<section id="structure">
+<h2>Structure<a class="headerlink" href="#structure" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<section id="the-metpy-example-gallery">
+<h3>The MetPy Example Gallery<a class="headerlink" href="#the-metpy-example-gallery" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
+</section>
+</section>
+<section id="running-the-notebooks">
+<h2>Running the Notebooks<a class="headerlink" href="#running-the-notebooks" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>You can either run the notebook using <a class="reference external" href="https://mybinder.org/">Binder</a> or on your local machine.</p>
+<section id="running-on-binder">
+<h3>Running on Binder<a class="headerlink" href="#running-on-binder" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
+<p>The simplest way to interact with a Jupyter Notebook is through
+<a class="reference external" href="https://mybinder.org/">Binder</a>, which enables the execution of a
+<a class="reference external" href="https://jupyterbook.org">Jupyter Book</a> in the cloud. The details of how this works are not
+important for now. All you need to know is how to launch a Pythia
+Cookbooks chapter via Binder. Simply navigate your mouse to
+the top right corner of the book chapter you are viewing and click
+on the rocket ship icon, (see figure below), and be sure to select
+“launch Binder”. After a moment you should be presented with a
+notebook that you can interact with. I.e. you’ll be able to execute
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+a live Jupyter notebook are described in <a class="reference external" href="https://foundations.projectpythia.org/foundations/getting-started-jupyter.html">Getting Started with
+Jupyter</a>.</p>
+</section>
+<section id="running-on-your-own-machine">
+<h3>Running on Your Own Machine<a class="headerlink" href="#running-on-your-own-machine" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
+<p>If you are interested in running this material locally on your computer, you will need to follow this workflow:</p>
+<ol class="arabic">
+<li><p>Clone the <code class="docutils literal notranslate"><span class="pre">https://github.com/ProjectPythia/metpy-cookbook</span></code> repository:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="w"> </span>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/ProjectPythia/metpy-cookbook.git
+</pre></div>
+</div>
+</li>
+<li><p>Move into the <code class="docutils literal notranslate"><span class="pre">metpy-cookbook</span></code> directory</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>metpy-cookbook
+</pre></div>
+</div>
+</li>
+<li><p>Create and activate your conda environment from the <code class="docutils literal notranslate"><span class="pre">environment.yml</span></code> file</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>env<span class="w"> </span>create<span class="w"> </span>-f<span class="w"> </span>environment.yml
+conda<span class="w"> </span>activate<span class="w"> </span>metpy-cookbook
+</pre></div>
+</div>
+</li>
+<li><p>Move into the <code class="docutils literal notranslate"><span class="pre">notebooks</span></code> directory and start up Jupyter Lab</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>notebooks/
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AA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkYBTQFo8CLhoSvD459ChK35+pHh35EeqjsH1+1lGANEWORw3V6GvswINpAD3zIGHfxBDCjBBpEYwAM/8O1lcL2QVUDwASfoYBF4UAh6JT5MHeyqfBbwodUUnDnEwMAAAAAA///s3cENgCAMheGauJwTOKOTuANDOAAHNAgPGi+aqLf/uxqQc9O+UhwCAAAAAAC/i2mwKcwW03j/q3WxfQufP+nJent9u+YP+SN9vKyMdbXMoRww1IpAKuLUsGq3zl6dQRov82vrs95B5DeS6Z5z6X3tHlJQ9ovikJkdAAAA///s3bkNgDAQRNEhpTnqIKExmjJtIHEIH6wJbBASRP81YMer2R2GQwAAAAAA4BfT3Kp33aOntnGQ1uXbb9Xaywrporv2snOwk90rSkmi1F5mNfeNT/5YIijeGYrDpLBOZo1kR6OZv3WUrZdd/vyGpB0AAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGAd3AqvcKDCveGBG2DnR72elpVHcW0beXoZ9DhOf2MsSADZbtZeDVRYjDpsG3lyFtI4OtUoIPJCGtJoKtJAKfUQS9wQy2Kgl1exkFw0MMDAwAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkYBXUHmIweGW9/FCVt5YB4Dw8eHtHUajtvLcF1vD2ejbS8DI9hAD2xLGdKtZTD94EEd+EoiyLX3oK1i8AOrkc4YAq1CAg8ogQZ//kMGhUCrh5Cvz4edS0Q2YGBgAAAAAP//7N1BCoAgEIXhF0GXa90Zulp36iBuYgyyUSzIRdHq/9aDisuH4xAOAQAAAACAX22x07hOCjY0t7VllqJ9erxblPI03v5S1mwvy2vV7WWltSy9HvIwyGs8NMp13lbWpw+qjxDorPOpZ2Xs/YvLkLQDAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRgHdwYvf7Axxd4MJWwvaXnZmNtWdR/T2MrStZNi2l4EHcqAakM8XQt9ehnwzGQjAD5RG2jKGur0MbcUSdPAItNIIrB+NTxZgYGAAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkbBgIAdn6QYZr60Imz1vhkMDJ+fUt2J2G4vw7p6CH17GZSHfJsYyvYy2CAP8u1l4NvJENfTw84WQhyAjXTQNHRFEfwWNOSVR+BBItCWM+iKIthB1uQCBgYGAAAAAP//7N3BCYAwEETRKcRGbMkCbMpqLMISvBpZjTpIQDDe/K8ACbkIy86E4RAAAMCP1a6hAwBQq59ajXPz+JVl6KSUPr/v0q+w+HqZF1VH1dAtXnYObrZ42TX08eiYR8/2WFgum46YWPQLxXaRF03beY7uoX1LyQZHeXvoNUkrAAAA///s3TsKgDAQhOFpPZ14RG/kAaxsrewlinksyyI2Ijb/V4VUW0/YCeEQAAAAAAD4TTqkfh607d3zCOuiNI3fjhm+r7dzvLhZL/Pf0Pt+IQt6avl0XC+zTqLrXMupS3jUSqpdN1ELlXJIVNbSXr/zSDoBAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGAgrd/2Bgi7oYSdsKuiQwM315T3anYtpdBmAS2l6HdXoZy2xnkIjP4QA/MHsiqI6hZaLeXwRTBt5fBrr8Hry5C22YGGmRiZoLcXkbJUmAGBgYAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkbBgIMjX0QZ+p47EHTGv1XpNNlehgKwnD+EbXsZRA3SoA5soAe6hQx5uxfy6h+UM4Rg28sYGSCrhpC3jYHPJ0KsXILpQdxehliZRDZgYGAAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkbBoACNz40ZTnxWxO+Ul/cZ/l1eTnXn4hpfwbe9DDZ4hGt7GUwPyvYypDOEYHzkFUHw28qgV+SjX3HPAjqbCH5wNezsIgpGhxgYGAAAAAD//xodHBoFo2AAwegZoKNgFIyCUTAKRsEoGAWjYBSMglGAAKDzh0Lu+jF8/8eGP1S2dzMw/HhP9ZAjensZxkoi1O1l8G1k8GvsYYNCyDeVoZ5NBBsEgq8egsmDxGB6kM86QtpeBrsVjSzAwMAAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYIDA6G1xo2AUjIJRgAk+/mVhiL8bTDBk/q3NZmBgGKDtZegHV6Ndfw85WwjpsGn4IBDyaiPUrWcI9Yhr8GHbyyDnE4G2nSHcA7/GnsKBIQYGBgYAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkbBoALbP0kx3Pwmjt9JT64z/Lu+gerOxre9DH2ACP1watigD0QMdeUR4qwhyFAMYnUR4hp80OAPiA2/sYwZdlMZ6vYy+PlFsEEnSjzMwMAAAAAA//8aHRwaBaNgFIyCUTAKRgHVwehiiFEwCkbBKBgFo2AUUAp874QQNmFrGwPDz49UD2tc28vwqYWtBIIJIq8eQsijbjtDudoeukIIdhYR8jlCMDnQ9jKYKDP4QGroCiKoeWQBBgYGAAAAAP//7N2xDYAwDETRK7Msm7BLtoIhIAgROBRSECkFovivjFyktuwzzSEAAAAAAPA78xI0To3rZWnVFofPvv4WTu3HcpLH2UJn6XMVzdNBPm2fJ4LcUKrCqY/avH525xIV+UNXLlE3STsAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGgBKDby25/F8PvtIeXGf4/2E915+NaPYR8PT2+7WXIg0LwwSK0bWCIlT/Q7WhQOeTVQ+Ar6+GHVzNAVg9B3QBePQQSpORqfwYGBgAAAAD//xodHBoFo2AUjIJRMApGwSgYBUMC/B/dsDgKRsEoGAUjEjjdimT4+Y8Fr9f/r69iYPjzg+rBg/v8IcJ1EmTgCHFWEPLh1LADplGvwofJQVYPwVYLwQZuwCqQtpfBVg9BjKOgjmRgYAAAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGgBR/+sDB434rF77xfPxj+7W+mrRfQbyiDsaECWA+nhoshVhLB9MCutofzoYNF4O1myNaCBomgq4cgW9Gg9oEGjqDnDSGvZiIZMDAwAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwaAGJ78KMWx5p4Pfiee2Mfx/dZnq3sC1vQzbah30w6lhg0KwVUP4Vg+Bt42B9SD4sMEg8FlE0AOoYecQwbaeUQwYGBgAAAAA//8aHRwaBaNgFIyCUUA1QK3KaRSMglEwCkbBKBgFo2AUjAJ0EPvAjeH1b1684fJ/VS4Dw78/dAk7bFfboypAomBqsaweQr5pDHw4Ndr2MsitZ9DVRMgHWUMHkMCDRJRsvWZgYAAAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGgB3/+MzJ434rA78yvHxn+nZhMda/gXj2EqQbb1fbIA0HIq4dgq4nA4kjX2EPOKoJeVQ9dPQQ+iBp2fT30cGrwljJm0KHVFAwOMTAwAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwZAA13/wMbQ+ccbv1MOLGBg+P6W6d4g6nBp5JRFsJRBMHra9DOlwaph+2PYyiB7QIdSQK+7Bg0Sw7WVQfWAx2JY1PO4iGjAwMAAAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGQAR0vDRhOf5bD69x/6/Iovt4dLyCweggZwM4ewra9DHaQNPLqIdRr7pG2nIGuroceTg3Wi3QoNUXbyhgYGAAAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGQAlH3/Ri+/WXD7eQX9xj+XV9LdS8RdTg1ltVDyPrRt5fBbzdDOscItE0MDJmgakGDQMiHU7MwgQeFQFvNQIIUXVbGwMAAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkbBkAIvfrMzxN8Lxu/kza0MDD8/0cVb2K62R5VHbANDVQhZAQQDsMOpwQNIoLOEYLeXwc8wQmxLg5kLPn8I6VBrkgEDAwMAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGQAzs+STEsf2OM19n/NhWA7jCjqtfIvdoeWQvy6iHElfYQRbDtZcjqwYNG4IEg6MHV0NVDYPWUXmnPwMAAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkbBkAQFT2wZnv/ix+30e+cZ/j84SHWvYRsgInS1PcrqIbSrzWCrh5APp0bZXoa2egh07hB8FRHSOUVkAQYGBgAAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGQBN/+MjOYX4/Huzro//pKBoa/v+jiPWzby1CutkcaPEK+ph6yKgh67hDymUTQ7WXoq4dAADSgBD/QmpJDhxgYGAAAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGQBe//sDI0PHbD7fxfPxj+7WukrfdI2NeFvnoI/XBqmFmQ7WKwwSS01UNIW8lgq4fIBgwMDAAAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGQBr2v9BiOf1bE7YVz2xj+v7lKVS/iGo6BreaBq8Gyegh+OxmaYcjisNVA8O1l0NVDID4zdEAIRINXHFEyOMTAwAAAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGQB/H3vRi+/mXH6Y3/q/MZGP79pao3cR1OjTxABJNDVYvQgno4NUQv/HBq5IEl6OohmEaYOvDh1JR4goGBAQAAAP//Gh0cGgWjYBSMglEwCkbBKBjhgNIbTkbBKBgFo2AUjILBAJ7/5mCIuxuC2yWf3jL8Oz2N7i5Fr2YhA0JYbjYDnzkEHS2CrSICrQyCrjRCWT0EHyyCqKFo5RADAwMAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGwALs+SzCsfWuA2ysH5jEwfHlBO6/iGKTBezg1/Cwh2JYyxOHUKPrhcoiBI2RzyAYMDAwAAAAA///s3cEJACEMAMG0ZjF+7M2mDizCjwhCREXFw5/slBGSLMMhAAAAAADwDPsZCXGdt0/ebetmf538Hprp0vaqSdzr2VjdHtJy2TA8KltFN7UyEckAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGwAb//MTG43ozGPQD04j7Dv2trqepdcs4eQrn2Hu3sIZgq5EOrESuNoCuMwAdSQ67Cp2jxEAMDAwAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwbACj39zMhhcycR9QPW2TgaGX5/p6mVs4ze4Vg8hrraHjPwgrx5ihG4rAw88Qe+2x3aGEdGAgYEBAAAA//8aHRwaBaNggMHoGaCjYBSMglEwCkbByAajB4KPglEwCkYBbcDdn9wMpleTGX7/Y8Y0/+8fhn9by6hqL67VQ8iDP9jOHkLWAls9hC6GvnoIYR5k0IiijiUDAwMAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsGwBI//cDJkPAjA7rVbJxj+PzlBc28TOjQa3+ohMI1l9RDo7CEQG7R6CEaTDRgYGAAAAAD//xodHBoFo2AUjIJRMApGAU3A6GKIUTAKRsEoGAWjYBQMOPjPwLD6gzzD1vc6WF3yf3UhA8Pf31RzJe7VQ2hqiFw9BLmungE+KATWh0UvRe0uBgYGAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwbAF//8zMsQ/cGW49k0S04u/fjD8O9hOVa/jGqghevUQbCsZ9EYy+EHU0MEg8EARaLUQ0s1lFAEGBgYAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkbBsAa//jMxhN/1Z/j5jwXTm6fXM/x/d5s23kdbPYQ80INCgweE0OTQVx4hrRiCq4PeVIZv4IkgYGBgAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwbAG//8zMDz8zc1Q9Mgbqzf/r8mDKKISIGqoBm2gCH6+EIyNtnoItEIIJgZfPQQWgx5KTS5gYGAAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAVDBvwfPc1qFIyCUTAKRgGZADT2s+SdMsOmd7qYBrx/wfDv3FzaBC2+1UOEVhah30QGv84eqo4KN5UxMDAwAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwYgA//4zMuQ/dmB4+YsP07t7pjIwfHtDtWDAdTg1IbVMjNChGtjqIdiAEJoZ8JvLQKuHKDl3iIGBAQAAAP//7N2xCQAgDAXRXzmeaziAmzmRA7iCIih2AYtod2+B9OFIWA4BAJ5yFq0AAADAV60HpRrNEaPk/eLso1s9dDZFVj20jlBb9ZCHpAkAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwcgB/xkY9n8RZ5j6wgbTyw8vMfy/t4c2QUGN1UPgq+2hp1Ajn0lEibsYGBgAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkbBiAL//jMwND43Zbj+TQLD2//XVzMw/PlBleDAGLSBDeoQsXoINiCEbhho3Ag2IAQ/iJqS1UMMDAwAAAAA///s3cENABEURdFHfbqYKnSmHF1IZgSxGBJbiY17SrC8+XmIQwAAAAAA4DqpWj3R6V2/t8+fSvDnn2MJPCMNmRmB/uuhvjM0xql3N6klNQAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwcgD/xkYrvzgY2h66oLp9asHGP6/ukSVIMG3eghdDeKcIczVQ+hnC8F4TEwUDu0wMDAAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkbBiAT//zMyTH+jxXDgoxqG9/+vLmBg+P+PKsGCa2UP0YdJwwaKYLeSIY4dAgPwSiJyAQMDAwAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwYgFv/4zMuQ8cmZ494cHNQi+vGf4d7yfNsGCZVAIdvYQ8uoh2AHUsDOG0A+mxjiXiBzAwMAAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkbByAX/GRge/eZkKHroiRkER5YyMHx9SbOgQT6YGrsCTDbshjLwWUTUuM6egYEBAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkbBiAag7WUbPsoyLHtjghoM//8z/FubTZWgIWb4BtfqIZgcbPUQhE+dgSEGBgYGAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwYgHfxkYGSqfWjE8+CGMGhTP7zL8v7OD+sGD5WBqFGkkBnwACG31EEgctHqIouEhBgYGAAAAAP//7N2xDYAwDETRX7AdmzAEOzESE2SAyDJt4ioSKf+rXbk8nWzDIUmSJEmSpIQWB9d7EjnHLfncEP33ilYOU9eZ+tVsbA9tAXwAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsEogG4vO/ZVhKH/uT1qcPz5zfBvXyP1gwjX6iH0a+uhh0/D+WhnD1G0dIiBgQEAAAD//xodHBoFo2AUjIJRMApGwSgYBTiXtI+CUTAKRsEoGAUjDfxjYGToemnAcOWrFKrPz29jYPj0mOLQwFXlYlxHj3b2EJIwlEacPcTERMHwDgMDAwAAAP//7N2xDcAgDETRE2I6BgmzMRZbIAqcAoRERCoo/yvtDU4+mXAIAAAAAABgMJOKOT05qDa/LFqKvX92099b+83wWyeb10MnJL0AAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsEoQAKg7WXXfvAxtD51QpV495zh37W1FAcVMWcPQQUQq4eQdGEoo8QxDAwMAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwShAA6DtZVPeaDOc/KyIKrOljYHh70/qBhfS2UOI7WKYSjDOHaLGvnAGBgYAAAAA//8aHRwaBaNgFIyCUTAKRsEoGAWjYBSMglEwCkbBKEADoO1lv/8zMmQ8dGP48pcDIQvaXrYxn37BhbR6CMItkOr4AAAOIklEQVTFvNaeIsDAwADQ6ODQKBgFgwCMngE6CkbBKBgFo2AUjIJRMApGwSgYBYMPgLaX3f/FzVD7xBXVbXdOMfz/8IAi92L0A4ldPQSC0AEj2OohihYQMTAwAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2CAwehtcaNgFIyCUTB4AWh72YK3qgx7P6gj3Pj/P8P/5SnUP5yaGAAdIAIDatQfDAwMAAAAAP//7N2hDYBADAXQPwBbsQ4DsgsODwJ9BIK6IA5IsO/ppqLyJ22FQwAAAAAN13rZnmSY+6ylq1XbkmMaf43tKdu53RNqvLV/6/FJkhMAAP//7NsxDgAQDIXhN7qa61gdyplMLiGIjbCIWOT/xnbq1jR9HIcAAMAzPEMAAIAf9HhZykYu2mmaGrxU8tWE0740RMt2/SVmtiuektQAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsEoIABA28s2fpRlWPnGCKESdDj1sQk0DzrM84kYGJgYIUM6FI8NMTAwAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwSggAGDbyyqfWjM8+yWAUH1sGQPDz49UDz70q+uRlxOBmShSFAwRMTAwAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFQwr8H92wOApGwSgYBaNggABoe9m7v6wMWQ98EA4ArR5al02Rg7BtLaMbYGBgAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwSggEoC2lx38IsYw+6UVQsfjawz/X12mehBiGydCPpgavmKIkvEkBgYGAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwSggEsC2lzU+N2W4+10Eogt0tf2yDIgkNQCWUSHI4dOo4qDBITCmZHSIgYEBAAAA//8aHRwaBaNgFIyCUUB1MAArYUfBKBgFo2AUjIJRMApGwSigGwBtL/vyj5kh46EPw9//0Mbvz28M/29vIdsJuJrQGOcJYRs4oqT9zcDAAAAAAP//Gh0cGgWjYBSMglEwCkbBKBgFo2AUjIJRMApGwSggEfxlYGQ4802IYeoLW7jO/xvqGRj+/qZOUOK51h55axnFgIGBAQAAAP//7N0xDcAgAATAd1EddYkxTNRBFTARGNqlI2nY7vY38MnnlUMAAAAAq955WbnPXO140mOk1/3X9h9/C6IkEwAA///s3bEJQCEMhOEzTucsDvZWcQ2XsBFEhIeIdtr+X5MuAxwXQjgEAAAAAABwYZyXleYVc5jnZS59Ui1X+46c568GmS1v7Ldpr/UhSR0AAP//7NsxDQAgAAOwKcMbFpCDIjTwwEEwwEfSCtixc8mMQwAAAACP7r2sjXIS1srs9Z86k2wAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsEoIBcgbS+7/k0CbArLlV0Mf76+J8tEotYBUfMGGAYGBgAAAAD//+zbsQkAIRBFwcW+rcvaNJDjMFE00nQmXraAD884BAAAAPDgz8tqT0te1kq+/niafua0bBztqdmViPgAAAD//xodHBoFo2AUjIJRMApGwSgYBaNgFIyCUTAKRsEooBCAtped/y7I0PPcEWwSx92jDD/fP6VJsKKfO0QRYGBgAAAAAP//7NsxDYAwAATAb3BREXWHDdzUSVWwswIJE2Et452AF/D5Vw4BAAAAzLqSMyXb3jKO+q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new file mode 100644
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diff --git a/_preview/13/_sources/README.md b/_preview/13/_sources/README.md
new file mode 100644
index 0000000..e92eeaa
--- /dev/null
+++ b/_preview/13/_sources/README.md
@@ -0,0 +1,79 @@
+<div class="row">
+   <div class="column">
+      <img src="_static/NSF-Unidata_lockup_horizontal_2023_light.png" alt="NSF-Unidata Logo" width="35%"/>
+   </div>
+   <div class="column">
+      <img src="_static/metpy_150x150.png" alt="MetPy Logo" width="100%"/>
+   </div>
+</div>
+
+# MetPy Cookbook
+
+[![nightly-build](https://github.com/ProjectPythia/metpy-cookbook/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/ProjectPythia/metpy-cookbook/actions/workflows/nightly-build.yaml)
+[![Binder](http://binder.projectpythia.org/badge_logo.svg)](http://binder.projectpythia.org/v2/gh/ProjectPythia/metpy-cookbook/main?labpath=notebooks)
+[![DOI](https://zenodo.org/badge/620944317.svg)](https://zenodo.org/badge/latestdoi/620944317)
+
+This Cookbook is the oversized recipe book for all your MetPy needs.
+We provide a gallery of real workflows centered around meteorological data,
+and the building blocks you need to recreate those workflows or cook up brand new ones yourself.
+Create the maps and analyses you've seen from class and professional institutions alike!
+
+## Authors
+
+MetPy Maintainers and the MetPy Community.
+
+### Contributors
+
+<a href="https://github.com/ProjectPythia/metpy-cookbook/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=ProjectPythia/metpy-cookbook" />
+</a>
+
+## Structure
+
+### The MetPy Example Gallery
+
+## Running the Notebooks
+
+You can either run the notebook using [Binder](https://mybinder.org/) or on your local machine.
+
+### Running on Binder
+
+The simplest way to interact with a Jupyter Notebook is through
+[Binder](https://mybinder.org/), which enables the execution of a
+[Jupyter Book](https://jupyterbook.org) in the cloud. The details of how this works are not
+important for now. All you need to know is how to launch a Pythia
+Cookbooks chapter via Binder. Simply navigate your mouse to
+the top right corner of the book chapter you are viewing and click
+on the rocket ship icon, (see figure below), and be sure to select
+“launch Binder”. After a moment you should be presented with a
+notebook that you can interact with. I.e. you’ll be able to execute
+and even change the example programs. You’ll see that the code cells
+have no output at first, until you execute them by pressing
+{kbd}`Shift`\+{kbd}`Enter`. Complete details on how to interact with
+a live Jupyter notebook are described in [Getting Started with
+Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter.html).
+
+### Running on Your Own Machine
+
+If you are interested in running this material locally on your computer, you will need to follow this workflow:
+
+1. Clone the `https://github.com/ProjectPythia/metpy-cookbook` repository:
+
+   ```bash
+    git clone https://github.com/ProjectPythia/metpy-cookbook.git
+   ```
+
+1. Move into the `metpy-cookbook` directory
+   ```bash
+   cd metpy-cookbook
+   ```
+1. Create and activate your conda environment from the `environment.yml` file
+   ```bash
+   conda env create -f environment.yml
+   conda activate metpy-cookbook
+   ```
+1. Move into the `notebooks` directory and start up Jupyter Lab
+   ```bash
+   cd notebooks/
+   jupyter lab
+   ```
diff --git a/_preview/13/_sources/notebooks/convective/MUCAPE.ipynb b/_preview/13/_sources/notebooks/convective/MUCAPE.ipynb
new file mode 100644
index 0000000..f2c2a3a
--- /dev/null
+++ b/_preview/13/_sources/notebooks/convective/MUCAPE.ipynb
@@ -0,0 +1,2573 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3e0347aa-dc94-4383-87f4-25cede999180",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## importing all the packages we might need later for our calculation\n",
+    "import metpy.calc as mpcalc\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "from metpy.calc import cape_cin, surface_based_cape_cin, dewpoint_from_specific_humidity, parcel_profile,relative_humidity_from_specific_humidity,most_unstable_cape_cin, precipitable_water \n",
+    "from metpy.units import units\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ca9c7db-b26c-4fa0-985a-b72780cb31db",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "54f03a63-d5e5-4115-8f8e-ee005918663f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## opening NetCDF file using xarray \n",
+    "\n",
+    "ds = xr.open_mfdataset(\"/home/jovyan/metpy-cookbook/NETCDF_FILE.nc\",combine = \"nested\", concat_dim=\"time\",decode_times=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2f996028-ab1f-44f4-a169-81f9e2fc40a1",
+   "metadata": {},
+   "outputs": [
+    {
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+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2MB\n",
+       "Dimensions:  (time: 1, lev: 23, lat: 41, lon: 71)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 568B -130.5 -129.5 -128.5 ... -62.5 -61.5 -60.5\n",
+       "  * lat      (lat) float64 328B 20.5 21.5 22.5 23.5 24.5 ... 57.5 58.5 59.5 60.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Data variables:\n",
+       "    H        (time, lev, lat, lon) float32 268kB dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;\n",
+       "    OMEGA    (time, lev, lat, lon) float32 268kB dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;\n",
+       "    PS       (time, lat, lon) float32 12kB dask.array&lt;chunksize=(1, 41, 71), meta=np.ndarray&gt;\n",
+       "    QV       (time, lev, lat, lon) float32 268kB dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;\n",
+       "    SLP      (time, lat, lon) float32 12kB dask.array&lt;chunksize=(1, 41, 71), meta=np.ndarray&gt;\n",
+       "    T        (time, lev, lat, lon) float32 268kB dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;\n",
+       "    U        (time, lev, lat, lon) float32 268kB dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;\n",
+       "    V        (time, lev, lat, lon) float32 268kB dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;\n",
+       "Attributes: (12/33)\n",
+       "    CDI:                               Climate Data Interface version 1.9.8 (...\n",
+       "    Conventions:                       CF-1\n",
+       "    History:                           Original file generated: Tue Jun 18 21...\n",
+       "    Comment:                           GMAO filename: d5124_m2_jan10.inst3_3d...\n",
+       "    Filename:                          MERRA2_400.inst3_3d_asm_Np.20190608.nc4\n",
+       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
+       "    ...                                ...\n",
+       "    RangeBeginningTime:                00:00:00.000000\n",
+       "    RangeEndingDate:                   2019-06-08\n",
+       "    RangeEndingTime:                   21:00:00.000000\n",
+       "    history_L34RS:                     &#x27;Created by L34RS v1.4.3 @ NASA GES DI...\n",
+       "    CDO:                               Climate Data Operators version 1.9.8 (...\n",
+       "    cdo_openmp_thread_number:          12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a7916614-3e87-4f82-a2ed-96c06c68daf6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a7916614-3e87-4f82-a2ed-96c06c68daf6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lev</span>: 23</li><li><span class='xr-has-index'>lat</span>: 41</li><li><span class='xr-has-index'>lon</span>: 71</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3e0f982e-9847-477a-8a98-3ce9f8d229f6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3e0f982e-9847-477a-8a98-3ce9f8d229f6' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-06-08</div><input id='attrs-fe42e378-948c-4583-a5bc-3e9abf14daad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe42e378-948c-4583-a5bc-3e9abf14daad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6114851-028f-4905-b760-ab2fd39bb802' class='xr-var-data-in' type='checkbox'><label for='data-a6114851-028f-4905-b760-ab2fd39bb802' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-06-08T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-130.5 -129.5 ... -61.5 -60.5</div><input id='attrs-7ccce5ce-64ca-4b73-b185-a4d6a46f82f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ccce5ce-64ca-4b73-b185-a4d6a46f82f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f013f02-48ed-4aa0-b0dc-d689719770ca' class='xr-var-data-in' type='checkbox'><label for='data-9f013f02-48ed-4aa0-b0dc-d689719770ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-130.5, -129.5, -128.5, -127.5, -126.5, -125.5, -124.5, -123.5, -122.5,\n",
+       "       -121.5, -120.5, -119.5, -118.5, -117.5, -116.5, -115.5, -114.5, -113.5,\n",
+       "       -112.5, -111.5, -110.5, -109.5, -108.5, -107.5, -106.5, -105.5, -104.5,\n",
+       "       -103.5, -102.5, -101.5, -100.5,  -99.5,  -98.5,  -97.5,  -96.5,  -95.5,\n",
+       "        -94.5,  -93.5,  -92.5,  -91.5,  -90.5,  -89.5,  -88.5,  -87.5,  -86.5,\n",
+       "        -85.5,  -84.5,  -83.5,  -82.5,  -81.5,  -80.5,  -79.5,  -78.5,  -77.5,\n",
+       "        -76.5,  -75.5,  -74.5,  -73.5,  -72.5,  -71.5,  -70.5,  -69.5,  -68.5,\n",
+       "        -67.5,  -66.5,  -65.5,  -64.5,  -63.5,  -62.5,  -61.5,  -60.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.5 21.5 22.5 ... 58.5 59.5 60.5</div><input id='attrs-0948982a-4e31-4d33-a3f7-fed8adf56d29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0948982a-4e31-4d33-a3f7-fed8adf56d29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c60c6372-b53b-4ee3-b548-7d9ec95d2b96' class='xr-var-data-in' type='checkbox'><label for='data-c60c6372-b53b-4ee3-b548-7d9ec95d2b96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5,\n",
+       "       32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5,\n",
+       "       44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5,\n",
+       "       56.5, 57.5, 58.5, 59.5, 60.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 975.0 950.0 ... 250.0 200.0</div><input id='attrs-eed49fd7-b373-4aa2-898f-3bd6758724b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eed49fd7-b373-4aa2-898f-3bd6758724b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a572874-6d43-42c6-bd27-e08195c98be3' class='xr-var-data-in' type='checkbox'><label for='data-8a572874-6d43-42c6-bd27-e08195c98be3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>PLE_level</dd><dt><span>long_name :</span></dt><dd>vertical level</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate :</span></dt><dd>PLE</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  975.,  950.,  925.,  900.,  875.,  850.,  825.,  800.,  775.,\n",
+       "        750.,  725.,  700.,  650.,  600.,  550.,  500.,  450.,  400.,  350.,\n",
+       "        300.,  250.,  200.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8768387f-e836-4497-85bc-0d86583d99f6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8768387f-e836-4497-85bc-0d86583d99f6' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>H</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-45d21506-113f-4812-bd69-adcfa82403e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45d21506-113f-4812-bd69-adcfa82403e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42fffa1f-4bf5-433c-a9e3-e2b819ca42c9' class='xr-var-data-in' type='checkbox'><label for='data-42fffa1f-4bf5-433c-a9e3-e2b819ca42c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>edge_heights</dd><dt><span>long_name :</span></dt><dd>edge_heights</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OMEGA</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-1ed3d17e-d70d-426f-8d14-95d5674de50f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ed3d17e-d70d-426f-8d14-95d5674de50f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e1c2a28-4d02-44c2-97c1-8a7c9f2601c7' class='xr-var-data-in' type='checkbox'><label for='data-0e1c2a28-4d02-44c2-97c1-8a7c9f2601c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>vertical_pressure_velocity</dd><dt><span>long_name :</span></dt><dd>vertical_pressure_velocity</dd><dt><span>units :</span></dt><dd>Pa s-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PS</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-fd9df456-1968-4881-a535-e5c0cfe9f98b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd9df456-1968-4881-a535-e5c0cfe9f98b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fbb4166-46d4-4ddb-bce0-3bf0305dff46' class='xr-var-data-in' type='checkbox'><label for='data-4fbb4166-46d4-4ddb-bce0-3bf0305dff46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_pressure</dd><dt><span>long_name :</span></dt><dd>surface_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.37 kiB </td>\n",
+       "                        <td> 11.37 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 41, 71) </td>\n",
+       "                        <td> (1, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-fe68ff2c-3315-40b2-8dc1-6688cb357af4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe68ff2c-3315-40b2-8dc1-6688cb357af4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2169f59a-51e6-4d24-a25d-b6438f471466' class='xr-var-data-in' type='checkbox'><label for='data-2169f59a-51e6-4d24-a25d-b6438f471466' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>long_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SLP</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-46c0d027-30a7-4b26-b61a-897ff460f711' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46c0d027-30a7-4b26-b61a-897ff460f711' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c66ada85-1064-467c-93f4-8be84a53526f' class='xr-var-data-in' type='checkbox'><label for='data-c66ada85-1064-467c-93f4-8be84a53526f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_level_pressure</dd><dt><span>long_name :</span></dt><dd>sea_level_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.37 kiB </td>\n",
+       "                        <td> 11.37 kiB </td>\n",
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+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                <tbody>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 23, 41, 71) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-08999b51-2e9e-450c-90cc-23b42c785e21' class='xr-section-summary-in' type='checkbox'  ><label for='section-08999b51-2e9e-450c-90cc-23b42c785e21' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ffa76ffd-b1ba-49ca-9b42-54ab49ed6dfe' class='xr-index-data-in' type='checkbox'/><label for='index-ffa76ffd-b1ba-49ca-9b42-54ab49ed6dfe' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-06-08&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c76f5f6c-1dc0-45b4-83e5-d9885e7d6197' class='xr-index-data-in' type='checkbox'/><label for='index-c76f5f6c-1dc0-45b4-83e5-d9885e7d6197' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-130.5, -129.5, -128.5, -127.5, -126.5, -125.5, -124.5, -123.5, -122.5,\n",
+       "       -121.5, -120.5, -119.5, -118.5, -117.5, -116.5, -115.5, -114.5, -113.5,\n",
+       "       -112.5, -111.5, -110.5, -109.5, -108.5, -107.5, -106.5, -105.5, -104.5,\n",
+       "       -103.5, -102.5, -101.5, -100.5,  -99.5,  -98.5,  -97.5,  -96.5,  -95.5,\n",
+       "        -94.5,  -93.5,  -92.5,  -91.5,  -90.5,  -89.5,  -88.5,  -87.5,  -86.5,\n",
+       "        -85.5,  -84.5,  -83.5,  -82.5,  -81.5,  -80.5,  -79.5,  -78.5,  -77.5,\n",
+       "        -76.5,  -75.5,  -74.5,  -73.5,  -72.5,  -71.5,  -70.5,  -69.5,  -68.5,\n",
+       "        -67.5,  -66.5,  -65.5,  -64.5,  -63.5,  -62.5,  -61.5,  -60.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-37d568c8-f96e-4b00-b800-a64e39e1e5ed' class='xr-index-data-in' type='checkbox'/><label for='index-37d568c8-f96e-4b00-b800-a64e39e1e5ed' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5,\n",
+       "       32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5,\n",
+       "       44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5,\n",
+       "       56.5, 57.5, 58.5, 59.5, 60.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5da824e5-5b1a-4bee-9813-5c27d8d477ae' class='xr-index-data-in' type='checkbox'/><label for='index-5da824e5-5b1a-4bee-9813-5c27d8d477ae' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0,  975.0,  950.0,  925.0,  900.0,  875.0,  850.0,  825.0,  800.0,\n",
+       "        775.0,  750.0,  725.0,  700.0,  650.0,  600.0,  550.0,  500.0,  450.0,\n",
+       "        400.0,  350.0,  300.0,  250.0,  200.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae225b98-7b24-4f13-a3bd-233bd9c4ce9f' class='xr-section-summary-in' type='checkbox'  ><label for='section-ae225b98-7b24-4f13-a3bd-233bd9c4ce9f' class='xr-section-summary' >Attributes: <span>(33)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1</dd><dt><span>History :</span></dt><dd>Original file generated: Tue Jun 18 21:47:49 2019 GMT</dd><dt><span>Comment :</span></dt><dd>GMAO filename: d5124_m2_jan10.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>Filename :</span></dt><dd>MERRA2_400.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>Institution :</span></dt><dd>NASA Global Modeling and Assimilation Office</dd><dt><span>References :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>Format :</span></dt><dd>NetCDF-4/HDF-5</dd><dt><span>SpatialCoverage :</span></dt><dd>global</dd><dt><span>VersionID :</span></dt><dd>5.12.4</dd><dt><span>TemporalRange :</span></dt><dd>1980-01-01 -&gt; 2016-12-31</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>http://dx.doi.org/</dd><dt><span>ShortName :</span></dt><dd>M2I3NPASM</dd><dt><span>GranuleID :</span></dt><dd>MERRA2_400.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>ProductionDateTime :</span></dt><dd>Original file generated: Tue Jun 18 21:47:49 2019 GMT</dd><dt><span>LongName :</span></dt><dd>MERRA2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields</dd><dt><span>Title :</span></dt><dd>MERRA2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields</dd><dt><span>SouthernmostLatitude :</span></dt><dd>-90.0</dd><dt><span>NorthernmostLatitude :</span></dt><dd>90.0</dd><dt><span>WesternmostLongitude :</span></dt><dd>-180.0</dd><dt><span>EasternmostLongitude :</span></dt><dd>179.375</dd><dt><span>LatitudeResolution :</span></dt><dd>0.5</dd><dt><span>LongitudeResolution :</span></dt><dd>0.625</dd><dt><span>DataResolution :</span></dt><dd>0.5 x 0.625 (42 pressure levels)</dd><dt><span>Contact :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>identifier_product_doi :</span></dt><dd>10.5067/QBZ6MG944HW0</dd><dt><span>RangeBeginningDate :</span></dt><dd>2019-06-08</dd><dt><span>RangeBeginningTime :</span></dt><dd>00:00:00.000000</dd><dt><span>RangeEndingDate :</span></dt><dd>2019-06-08</dd><dt><span>RangeEndingTime :</span></dt><dd>21:00:00.000000</dd><dt><span>history_L34RS :</span></dt><dd>&#x27;Created by L34RS v1.4.3 @ NASA GES DISC on April 29 2023 20:47:34. Regrid: remapcon GLDAS-2_1. Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23. Variables: H OMEGA PS QV SLP T U V&#x27;</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd><dt><span>cdo_openmp_thread_number :</span></dt><dd>12</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 2MB\n",
+       "Dimensions:  (time: 1, lev: 23, lat: 41, lon: 71)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 568B -130.5 -129.5 -128.5 ... -62.5 -61.5 -60.5\n",
+       "  * lat      (lat) float64 328B 20.5 21.5 22.5 23.5 24.5 ... 57.5 58.5 59.5 60.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Data variables:\n",
+       "    H        (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    OMEGA    (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    PS       (time, lat, lon) float32 12kB dask.array<chunksize=(1, 41, 71), meta=np.ndarray>\n",
+       "    QV       (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    SLP      (time, lat, lon) float32 12kB dask.array<chunksize=(1, 41, 71), meta=np.ndarray>\n",
+       "    T        (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    U        (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    V        (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "Attributes: (12/33)\n",
+       "    CDI:                               Climate Data Interface version 1.9.8 (...\n",
+       "    Conventions:                       CF-1\n",
+       "    History:                           Original file generated: Tue Jun 18 21...\n",
+       "    Comment:                           GMAO filename: d5124_m2_jan10.inst3_3d...\n",
+       "    Filename:                          MERRA2_400.inst3_3d_asm_Np.20190608.nc4\n",
+       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
+       "    ...                                ...\n",
+       "    RangeBeginningTime:                00:00:00.000000\n",
+       "    RangeEndingDate:                   2019-06-08\n",
+       "    RangeEndingTime:                   21:00:00.000000\n",
+       "    history_L34RS:                     'Created by L34RS v1.4.3 @ NASA GES DI...\n",
+       "    CDO:                               Climate Data Operators version 1.9.8 (...\n",
+       "    cdo_openmp_thread_number:          12"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bb769f43-fd81-4b56-b8b0-9ecfc0823c33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### making a function to slice the xarray dataset according to our need.\n",
+    "def slicer (data,lat1, lat2, lon1, lon2, time1,time2) :\n",
+    "    sliced_data = data.sel(lat =slice(lat1, lat2), lon = slice(lon1, lon2),time = slice(time1, time2))\n",
+    "    return sliced_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "46dcd279-348d-4932-be71-1d5c9a44fad1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#slicing the data for CONUS only\n",
+    "\n",
+    "new_data = slicer(ds,23.5,50.5,-125.5,-66.5, ds.time[0], ds.time[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4976f026-c468-44db-9011-3bcc238836d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 942kB\n",
+       "Dimensions:  (time: 1, lev: 23, lat: 28, lon: 60)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 480B -125.5 -124.5 -123.5 ... -68.5 -67.5 -66.5\n",
+       "  * lat      (lat) float64 224B 23.5 24.5 25.5 26.5 27.5 ... 47.5 48.5 49.5 50.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Data variables:\n",
+       "    H        (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    OMEGA    (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    PS       (time, lat, lon) float32 7kB dask.array&lt;chunksize=(1, 28, 60), meta=np.ndarray&gt;\n",
+       "    QV       (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    SLP      (time, lat, lon) float32 7kB dask.array&lt;chunksize=(1, 28, 60), meta=np.ndarray&gt;\n",
+       "    T        (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    U        (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    V        (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "Attributes: (12/33)\n",
+       "    CDI:                               Climate Data Interface version 1.9.8 (...\n",
+       "    Conventions:                       CF-1\n",
+       "    History:                           Original file generated: Tue Jun 18 21...\n",
+       "    Comment:                           GMAO filename: d5124_m2_jan10.inst3_3d...\n",
+       "    Filename:                          MERRA2_400.inst3_3d_asm_Np.20190608.nc4\n",
+       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
+       "    ...                                ...\n",
+       "    RangeBeginningTime:                00:00:00.000000\n",
+       "    RangeEndingDate:                   2019-06-08\n",
+       "    RangeEndingTime:                   21:00:00.000000\n",
+       "    history_L34RS:                     &#x27;Created by L34RS v1.4.3 @ NASA GES DI...\n",
+       "    CDO:                               Climate Data Operators version 1.9.8 (...\n",
+       "    cdo_openmp_thread_number:          12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-62441447-c986-45c6-b715-3915307984d7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-62441447-c986-45c6-b715-3915307984d7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lev</span>: 23</li><li><span class='xr-has-index'>lat</span>: 28</li><li><span class='xr-has-index'>lon</span>: 60</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8fd071c8-74b2-4e40-b47f-70ad04a5405b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8fd071c8-74b2-4e40-b47f-70ad04a5405b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-06-08</div><input id='attrs-9cb629ad-f7cd-4116-85ad-472e1aabd4e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9cb629ad-f7cd-4116-85ad-472e1aabd4e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ef7c822-b2f5-491f-baaa-df6ec70c0c9d' class='xr-var-data-in' type='checkbox'><label for='data-9ef7c822-b2f5-491f-baaa-df6ec70c0c9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-06-08T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-125.5 -124.5 ... -67.5 -66.5</div><input id='attrs-b4f5f3a7-a704-4161-a451-fba4c2b2ba2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4f5f3a7-a704-4161-a451-fba4c2b2ba2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40b81ec3-36d9-47a4-83a5-874557380592' class='xr-var-data-in' type='checkbox'><label for='data-40b81ec3-36d9-47a4-83a5-874557380592' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-125.5, -124.5, -123.5, -122.5, -121.5, -120.5, -119.5, -118.5, -117.5,\n",
+       "       -116.5, -115.5, -114.5, -113.5, -112.5, -111.5, -110.5, -109.5, -108.5,\n",
+       "       -107.5, -106.5, -105.5, -104.5, -103.5, -102.5, -101.5, -100.5,  -99.5,\n",
+       "        -98.5,  -97.5,  -96.5,  -95.5,  -94.5,  -93.5,  -92.5,  -91.5,  -90.5,\n",
+       "        -89.5,  -88.5,  -87.5,  -86.5,  -85.5,  -84.5,  -83.5,  -82.5,  -81.5,\n",
+       "        -80.5,  -79.5,  -78.5,  -77.5,  -76.5,  -75.5,  -74.5,  -73.5,  -72.5,\n",
+       "        -71.5,  -70.5,  -69.5,  -68.5,  -67.5,  -66.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>23.5 24.5 25.5 ... 48.5 49.5 50.5</div><input id='attrs-bfe00a43-614f-457c-bf64-515afa29ccb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfe00a43-614f-457c-bf64-515afa29ccb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de4e9fa8-796d-44af-b9ec-0fdd32e11ebb' class='xr-var-data-in' type='checkbox'><label for='data-de4e9fa8-796d-44af-b9ec-0fdd32e11ebb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5,\n",
+       "       35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5,\n",
+       "       47.5, 48.5, 49.5, 50.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 975.0 950.0 ... 250.0 200.0</div><input id='attrs-c6f586d3-8494-42c3-b173-86de815a1a37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6f586d3-8494-42c3-b173-86de815a1a37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a42b2ac5-00b1-4a44-8aff-0a17172b942f' class='xr-var-data-in' type='checkbox'><label for='data-a42b2ac5-00b1-4a44-8aff-0a17172b942f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>PLE_level</dd><dt><span>long_name :</span></dt><dd>vertical level</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate :</span></dt><dd>PLE</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  975.,  950.,  925.,  900.,  875.,  850.,  825.,  800.,  775.,\n",
+       "        750.,  725.,  700.,  650.,  600.,  550.,  500.,  450.,  400.,  350.,\n",
+       "        300.,  250.,  200.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5699edfc-5ebe-4671-b04c-896ec4352478' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5699edfc-5ebe-4671-b04c-896ec4352478' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>H</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-a488c272-f1c4-4c8c-be44-729caa57334a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a488c272-f1c4-4c8c-be44-729caa57334a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eccf244d-e09f-4e20-87ab-c52cd29264d5' class='xr-var-data-in' type='checkbox'><label for='data-eccf244d-e09f-4e20-87ab-c52cd29264d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>edge_heights</dd><dt><span>long_name :</span></dt><dd>edge_heights</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OMEGA</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-a966b35c-4392-4b00-b51b-99f30960cdae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a966b35c-4392-4b00-b51b-99f30960cdae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb5e6605-2fd6-47ad-8c99-174336c59efb' class='xr-var-data-in' type='checkbox'><label for='data-bb5e6605-2fd6-47ad-8c99-174336c59efb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>vertical_pressure_velocity</dd><dt><span>long_name :</span></dt><dd>vertical_pressure_velocity</dd><dt><span>units :</span></dt><dd>Pa s-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PS</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-99576c8c-971c-4ea6-b6fb-d85394f04c44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99576c8c-971c-4ea6-b6fb-d85394f04c44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54b75ec6-dc9b-4da5-a1eb-2150c4f23a81' class='xr-var-data-in' type='checkbox'><label for='data-54b75ec6-dc9b-4da5-a1eb-2150c4f23a81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_pressure</dd><dt><span>long_name :</span></dt><dd>surface_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.56 kiB </td>\n",
+       "                        <td> 6.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 28, 60) </td>\n",
+       "                        <td> (1, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-f63c237d-5537-4e91-89be-059883410e9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f63c237d-5537-4e91-89be-059883410e9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa65974c-ded9-4d3e-a348-9936b094fe6b' class='xr-var-data-in' type='checkbox'><label for='data-fa65974c-ded9-4d3e-a348-9936b094fe6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>long_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SLP</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-89dbf591-ebdc-448e-84eb-af91fec6ef99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89dbf591-ebdc-448e-84eb-af91fec6ef99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c564e3e-115f-49a2-a9df-ec8ab078c3a4' class='xr-var-data-in' type='checkbox'><label for='data-3c564e3e-115f-49a2-a9df-ec8ab078c3a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_level_pressure</dd><dt><span>long_name :</span></dt><dd>sea_level_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.56 kiB </td>\n",
+       "                        <td> 6.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 28, 60) </td>\n",
+       "                        <td> (1, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-cdaaf3c6-6f59-4e51-9e53-a60da162d25a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdaaf3c6-6f59-4e51-9e53-a60da162d25a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-583293b1-f58a-4aaf-aa89-6aa7b8bd3cc9' class='xr-var-data-in' type='checkbox'><label for='data-583293b1-f58a-4aaf-aa89-6aa7b8bd3cc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <th> Array </th>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 23, 28, 60) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-3a98f5e5-9b7a-43db-8cc4-25be3780baef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a98f5e5-9b7a-43db-8cc4-25be3780baef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c8348cc-56ea-4e16-8515-2ce9509a7e49' class='xr-var-data-in' type='checkbox'><label for='data-1c8348cc-56ea-4e16-8515-2ce9509a7e49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>long_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-2b14b460-950e-4267-a1e2-e9dd8b167d04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b14b460-950e-4267-a1e2-e9dd8b167d04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63d53164-2ecb-4bbf-892c-7bfb4911688e' class='xr-var-data-in' type='checkbox'><label for='data-63d53164-2ecb-4bbf-892c-7bfb4911688e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>long_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "  <text x=\"101.529412\" y=\"89.529412\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,101.529412,89.529412)\">23</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7ebfc38-f123-41a0-8c07-3d9b34769886' class='xr-section-summary-in' type='checkbox'  ><label for='section-d7ebfc38-f123-41a0-8c07-3d9b34769886' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2b2629bd-038a-4f62-9848-a71edaa5a76a' class='xr-index-data-in' type='checkbox'/><label for='index-2b2629bd-038a-4f62-9848-a71edaa5a76a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-06-08&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-65a0b80c-4611-4561-b52f-e8f6350bce8d' class='xr-index-data-in' type='checkbox'/><label for='index-65a0b80c-4611-4561-b52f-e8f6350bce8d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-125.5, -124.5, -123.5, -122.5, -121.5, -120.5, -119.5, -118.5, -117.5,\n",
+       "       -116.5, -115.5, -114.5, -113.5, -112.5, -111.5, -110.5, -109.5, -108.5,\n",
+       "       -107.5, -106.5, -105.5, -104.5, -103.5, -102.5, -101.5, -100.5,  -99.5,\n",
+       "        -98.5,  -97.5,  -96.5,  -95.5,  -94.5,  -93.5,  -92.5,  -91.5,  -90.5,\n",
+       "        -89.5,  -88.5,  -87.5,  -86.5,  -85.5,  -84.5,  -83.5,  -82.5,  -81.5,\n",
+       "        -80.5,  -79.5,  -78.5,  -77.5,  -76.5,  -75.5,  -74.5,  -73.5,  -72.5,\n",
+       "        -71.5,  -70.5,  -69.5,  -68.5,  -67.5,  -66.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-85fa8971-cfba-4089-b659-c3df7a691465' class='xr-index-data-in' type='checkbox'/><label for='index-85fa8971-cfba-4089-b659-c3df7a691465' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5,\n",
+       "       35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5,\n",
+       "       47.5, 48.5, 49.5, 50.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7ef7416a-9ad8-42e0-ad4e-b71f0c82963c' class='xr-index-data-in' type='checkbox'/><label for='index-7ef7416a-9ad8-42e0-ad4e-b71f0c82963c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0,  975.0,  950.0,  925.0,  900.0,  875.0,  850.0,  825.0,  800.0,\n",
+       "        775.0,  750.0,  725.0,  700.0,  650.0,  600.0,  550.0,  500.0,  450.0,\n",
+       "        400.0,  350.0,  300.0,  250.0,  200.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-508a3e63-3bd4-416f-8cc8-8784d1d23841' class='xr-section-summary-in' type='checkbox'  ><label for='section-508a3e63-3bd4-416f-8cc8-8784d1d23841' class='xr-section-summary' >Attributes: <span>(33)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1</dd><dt><span>History :</span></dt><dd>Original file generated: Tue Jun 18 21:47:49 2019 GMT</dd><dt><span>Comment :</span></dt><dd>GMAO filename: d5124_m2_jan10.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>Filename :</span></dt><dd>MERRA2_400.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>Institution :</span></dt><dd>NASA Global Modeling and Assimilation Office</dd><dt><span>References :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>Format :</span></dt><dd>NetCDF-4/HDF-5</dd><dt><span>SpatialCoverage :</span></dt><dd>global</dd><dt><span>VersionID :</span></dt><dd>5.12.4</dd><dt><span>TemporalRange :</span></dt><dd>1980-01-01 -&gt; 2016-12-31</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>http://dx.doi.org/</dd><dt><span>ShortName :</span></dt><dd>M2I3NPASM</dd><dt><span>GranuleID :</span></dt><dd>MERRA2_400.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>ProductionDateTime :</span></dt><dd>Original file generated: Tue Jun 18 21:47:49 2019 GMT</dd><dt><span>LongName :</span></dt><dd>MERRA2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields</dd><dt><span>Title :</span></dt><dd>MERRA2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields</dd><dt><span>SouthernmostLatitude :</span></dt><dd>-90.0</dd><dt><span>NorthernmostLatitude :</span></dt><dd>90.0</dd><dt><span>WesternmostLongitude :</span></dt><dd>-180.0</dd><dt><span>EasternmostLongitude :</span></dt><dd>179.375</dd><dt><span>LatitudeResolution :</span></dt><dd>0.5</dd><dt><span>LongitudeResolution :</span></dt><dd>0.625</dd><dt><span>DataResolution :</span></dt><dd>0.5 x 0.625 (42 pressure levels)</dd><dt><span>Contact :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>identifier_product_doi :</span></dt><dd>10.5067/QBZ6MG944HW0</dd><dt><span>RangeBeginningDate :</span></dt><dd>2019-06-08</dd><dt><span>RangeBeginningTime :</span></dt><dd>00:00:00.000000</dd><dt><span>RangeEndingDate :</span></dt><dd>2019-06-08</dd><dt><span>RangeEndingTime :</span></dt><dd>21:00:00.000000</dd><dt><span>history_L34RS :</span></dt><dd>&#x27;Created by L34RS v1.4.3 @ NASA GES DISC on April 29 2023 20:47:34. Regrid: remapcon GLDAS-2_1. Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23. Variables: H OMEGA PS QV SLP T U V&#x27;</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd><dt><span>cdo_openmp_thread_number :</span></dt><dd>12</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 942kB\n",
+       "Dimensions:  (time: 1, lev: 23, lat: 28, lon: 60)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 480B -125.5 -124.5 -123.5 ... -68.5 -67.5 -66.5\n",
+       "  * lat      (lat) float64 224B 23.5 24.5 25.5 26.5 27.5 ... 47.5 48.5 49.5 50.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Data variables:\n",
+       "    H        (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    OMEGA    (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    PS       (time, lat, lon) float32 7kB dask.array<chunksize=(1, 28, 60), meta=np.ndarray>\n",
+       "    QV       (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    SLP      (time, lat, lon) float32 7kB dask.array<chunksize=(1, 28, 60), meta=np.ndarray>\n",
+       "    T        (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    U        (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    V        (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "Attributes: (12/33)\n",
+       "    CDI:                               Climate Data Interface version 1.9.8 (...\n",
+       "    Conventions:                       CF-1\n",
+       "    History:                           Original file generated: Tue Jun 18 21...\n",
+       "    Comment:                           GMAO filename: d5124_m2_jan10.inst3_3d...\n",
+       "    Filename:                          MERRA2_400.inst3_3d_asm_Np.20190608.nc4\n",
+       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
+       "    ...                                ...\n",
+       "    RangeBeginningTime:                00:00:00.000000\n",
+       "    RangeEndingDate:                   2019-06-08\n",
+       "    RangeEndingTime:                   21:00:00.000000\n",
+       "    history_L34RS:                     'Created by L34RS v1.4.3 @ NASA GES DI...\n",
+       "    CDO:                               Climate Data Operators version 1.9.8 (...\n",
+       "    cdo_openmp_thread_number:          12"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fc413f58-8863-4160-bb10-bf62b18da6b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##extracting temperature, pressure  and specific humidity from the dataset\n",
+    "p =new_data.lev*units('hPa')\n",
+    "T = new_data.T\n",
+    "sh =  new_data.QV*units('dimensionless')\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "CAPE = np.zeros((1, 28, 60),dtype=float)\n",
+    "\n",
+    "\n",
+    "###calculating CAPE in multidimension\n",
+    "\n",
+    "for h in range (len(new_data.time)):\n",
+    "    for i in range (len(new_data.lat)):\n",
+    "        for j in range(len(new_data.lon)):\n",
+    "\n",
+    "\n",
+    "            TC = (T[h,:,i,j]-(273.15))*units('degC')\n",
+    "            Td = dewpoint_from_specific_humidity(np.array(p)*units('hPa'),TC, sh[h,:,i,j])\n",
+    "#      \n",
+    "            try: \n",
+    "                MUCAPE = most_unstable_cape_cin(np.array(p)*units('hPa'),np.array(TC)*units('degC'), np.array(Td)*units('degC'))\n",
+    "            except:\n",
+    "                pass\n",
+    "            CAPE[h,i,j] = MUCAPE[0].magnitude\n",
+    "          \n",
+    "        \n",
+    "            \n",
+    "   \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6d7a48a5-ac1d-4070-8583-2b00bd1fd393",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataproj = ccrs. PlateCarree ()\n",
+    "# # Plot projection\n",
+    "# # The look you want for the view.\n",
+    "plotproj = ccrs. PlateCarree ()\n",
+    "fig=plt.figure(1, figsize=(15.,12.))\n",
+    "\n",
+    "ax=plt.subplot(111,projection=plotproj)\n",
+    "\n",
+    "ax.add_feature(cfeature.COASTLINE, linewidth=0.5)\n",
+    "ax.add_feature(cfeature.STATES, linewidth=0.5)\n",
+    "plt.title(\"MUCAPE Calculated Using Metpy (J/Kg)\",size = 30)\n",
+    "plt.contourf (new_data.lon, new_data. lat, CAPE[0,:,:],levels =np.arange(0,2400,100),cmap = \"PuBuGn\", transform=dataproj,extend = \"max\")\n",
+    "plt.colorbar (orientation = \"horizontal\", pad=0.01).ax.tick_params(labelsize=20)\n",
+    "plt. show ()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5866c70c-5c4f-434e-94f2-9d690fb10186",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab54ed07-7368-49ab-934f-f31d61db9a24",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3877aff-e16f-4fbf-8254-1d7263168c7a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dded76a2-14b5-4985-8a10-c00e87e40b35",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_preview/13/_sources/notebooks/convective/TCPW.ipynb b/_preview/13/_sources/notebooks/convective/TCPW.ipynb
new file mode 100644
index 0000000..d1d8e8a
--- /dev/null
+++ b/_preview/13/_sources/notebooks/convective/TCPW.ipynb
@@ -0,0 +1,2567 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "639a6e1d-75e6-4e82-a268-f86b56b50d7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import metpy.calc as mpcalc\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "from metpy.calc import cape_cin, surface_based_cape_cin, dewpoint_from_specific_humidity, parcel_profile,relative_humidity_from_specific_humidity,most_unstable_cape_cin, precipitable_water \n",
+    "from metpy.units import units\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "36cbeee7-8460-4ab8-88f6-fe977ac5a929",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## opening NetCDF file using xarray \n",
+    "\n",
+    "ds = xr.open_mfdataset(\"/home/jovyan/metpy-cookbook/NETCDF_FILE.nc\",combine = \"nested\", concat_dim=\"time\",decode_times=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4ff87fa8-b696-4276-89e9-40e0de7c92d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2MB\n",
+       "Dimensions:  (time: 1, lev: 23, lat: 41, lon: 71)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 568B -130.5 -129.5 -128.5 ... -62.5 -61.5 -60.5\n",
+       "  * lat      (lat) float64 328B 20.5 21.5 22.5 23.5 24.5 ... 57.5 58.5 59.5 60.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Data variables:\n",
+       "    H        (time, lev, lat, lon) float32 268kB dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;\n",
+       "    OMEGA    (time, lev, lat, lon) float32 268kB dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;\n",
+       "    PS       (time, lat, lon) float32 12kB dask.array&lt;chunksize=(1, 41, 71), meta=np.ndarray&gt;\n",
+       "    QV       (time, lev, lat, lon) float32 268kB dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;\n",
+       "    SLP      (time, lat, lon) float32 12kB dask.array&lt;chunksize=(1, 41, 71), meta=np.ndarray&gt;\n",
+       "    T        (time, lev, lat, lon) float32 268kB dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;\n",
+       "    U        (time, lev, lat, lon) float32 268kB dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;\n",
+       "    V        (time, lev, lat, lon) float32 268kB dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;\n",
+       "Attributes: (12/33)\n",
+       "    CDI:                               Climate Data Interface version 1.9.8 (...\n",
+       "    Conventions:                       CF-1\n",
+       "    History:                           Original file generated: Tue Jun 18 21...\n",
+       "    Comment:                           GMAO filename: d5124_m2_jan10.inst3_3d...\n",
+       "    Filename:                          MERRA2_400.inst3_3d_asm_Np.20190608.nc4\n",
+       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
+       "    ...                                ...\n",
+       "    RangeBeginningTime:                00:00:00.000000\n",
+       "    RangeEndingDate:                   2019-06-08\n",
+       "    RangeEndingTime:                   21:00:00.000000\n",
+       "    history_L34RS:                     &#x27;Created by L34RS v1.4.3 @ NASA GES DI...\n",
+       "    CDO:                               Climate Data Operators version 1.9.8 (...\n",
+       "    cdo_openmp_thread_number:          12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-96f1a1ee-1b5d-446e-b2d8-efb249cd8df5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-96f1a1ee-1b5d-446e-b2d8-efb249cd8df5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lev</span>: 23</li><li><span class='xr-has-index'>lat</span>: 41</li><li><span class='xr-has-index'>lon</span>: 71</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5426ffee-c314-4179-ae78-0c1d9e4bc27e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5426ffee-c314-4179-ae78-0c1d9e4bc27e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-06-08</div><input id='attrs-ad8e604f-adcf-4f3b-88bf-88a6d6a5ccd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad8e604f-adcf-4f3b-88bf-88a6d6a5ccd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-021b2057-5f90-42c9-99da-9b9d410f9ccf' class='xr-var-data-in' type='checkbox'><label for='data-021b2057-5f90-42c9-99da-9b9d410f9ccf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-06-08T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-130.5 -129.5 ... -61.5 -60.5</div><input id='attrs-70dfd8be-186e-4d37-b87a-1d21e84584dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70dfd8be-186e-4d37-b87a-1d21e84584dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fcf18e4-5e36-4616-bd5c-c8d8d0dc577f' class='xr-var-data-in' type='checkbox'><label for='data-1fcf18e4-5e36-4616-bd5c-c8d8d0dc577f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-130.5, -129.5, -128.5, -127.5, -126.5, -125.5, -124.5, -123.5, -122.5,\n",
+       "       -121.5, -120.5, -119.5, -118.5, -117.5, -116.5, -115.5, -114.5, -113.5,\n",
+       "       -112.5, -111.5, -110.5, -109.5, -108.5, -107.5, -106.5, -105.5, -104.5,\n",
+       "       -103.5, -102.5, -101.5, -100.5,  -99.5,  -98.5,  -97.5,  -96.5,  -95.5,\n",
+       "        -94.5,  -93.5,  -92.5,  -91.5,  -90.5,  -89.5,  -88.5,  -87.5,  -86.5,\n",
+       "        -85.5,  -84.5,  -83.5,  -82.5,  -81.5,  -80.5,  -79.5,  -78.5,  -77.5,\n",
+       "        -76.5,  -75.5,  -74.5,  -73.5,  -72.5,  -71.5,  -70.5,  -69.5,  -68.5,\n",
+       "        -67.5,  -66.5,  -65.5,  -64.5,  -63.5,  -62.5,  -61.5,  -60.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.5 21.5 22.5 ... 58.5 59.5 60.5</div><input id='attrs-44365616-8c9a-467e-bcf5-411596ca823b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44365616-8c9a-467e-bcf5-411596ca823b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5df829c-2eef-4e5e-9f82-c363aa997e76' class='xr-var-data-in' type='checkbox'><label for='data-a5df829c-2eef-4e5e-9f82-c363aa997e76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5,\n",
+       "       32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5,\n",
+       "       44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5,\n",
+       "       56.5, 57.5, 58.5, 59.5, 60.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 975.0 950.0 ... 250.0 200.0</div><input id='attrs-eef097c2-ae51-4cfe-b636-8784a491ced9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eef097c2-ae51-4cfe-b636-8784a491ced9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ebdd9bc-577d-42be-9dc6-ded5503654c5' class='xr-var-data-in' type='checkbox'><label for='data-6ebdd9bc-577d-42be-9dc6-ded5503654c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>PLE_level</dd><dt><span>long_name :</span></dt><dd>vertical level</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate :</span></dt><dd>PLE</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  975.,  950.,  925.,  900.,  875.,  850.,  825.,  800.,  775.,\n",
+       "        750.,  725.,  700.,  650.,  600.,  550.,  500.,  450.,  400.,  350.,\n",
+       "        300.,  250.,  200.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6f33da20-dcc9-4857-b542-63caeff58a6a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6f33da20-dcc9-4857-b542-63caeff58a6a' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>H</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-fb204561-e85a-456a-992d-8c57075d310e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb204561-e85a-456a-992d-8c57075d310e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd215621-e64b-43ed-817e-d1e79f76306a' class='xr-var-data-in' type='checkbox'><label for='data-fd215621-e64b-43ed-817e-d1e79f76306a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>edge_heights</dd><dt><span>long_name :</span></dt><dd>edge_heights</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OMEGA</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-eb25d134-0b12-468f-b735-c76c22ecb6ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb25d134-0b12-468f-b735-c76c22ecb6ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e37570c-c81a-4775-bf50-687c98faaf21' class='xr-var-data-in' type='checkbox'><label for='data-2e37570c-c81a-4775-bf50-687c98faaf21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>vertical_pressure_velocity</dd><dt><span>long_name :</span></dt><dd>vertical_pressure_velocity</dd><dt><span>units :</span></dt><dd>Pa s-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PS</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-2d22bf55-d211-45f9-88d8-dec827b60204' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d22bf55-d211-45f9-88d8-dec827b60204' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc71e3bc-2dcd-4f25-b389-7b381ab67754' class='xr-var-data-in' type='checkbox'><label for='data-dc71e3bc-2dcd-4f25-b389-7b381ab67754' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_pressure</dd><dt><span>long_name :</span></dt><dd>surface_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.37 kiB </td>\n",
+       "                        <td> 11.37 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 41, 71) </td>\n",
+       "                        <td> (1, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-e71b3baf-ae35-4d8a-ace8-c699a7688b01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e71b3baf-ae35-4d8a-ace8-c699a7688b01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c54d519a-58f8-4b09-b73c-6631e08cb08c' class='xr-var-data-in' type='checkbox'><label for='data-c54d519a-58f8-4b09-b73c-6631e08cb08c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>long_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SLP</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-83d2c84b-6ea1-4b78-98aa-c76ebd81a939' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83d2c84b-6ea1-4b78-98aa-c76ebd81a939' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74396711-14b7-44d0-a914-9e9eecc74066' class='xr-var-data-in' type='checkbox'><label for='data-74396711-14b7-44d0-a914-9e9eecc74066' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_level_pressure</dd><dt><span>long_name :</span></dt><dd>sea_level_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.37 kiB </td>\n",
+       "                        <td> 11.37 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 41, 71) </td>\n",
+       "                        <td> (1, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-772fcd3b-2a96-4065-9a95-577cb8a6c6b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-772fcd3b-2a96-4065-9a95-577cb8a6c6b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43966184-4aab-48da-9c7e-523d8562f3eb' class='xr-var-data-in' type='checkbox'><label for='data-43966184-4aab-48da-9c7e-523d8562f3eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                        <td> 261.54 kiB </td>\n",
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+       "                    \n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-17769393-1d9a-43e5-88d5-d02825a0cb0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17769393-1d9a-43e5-88d5-d02825a0cb0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5cf0bdb-b60e-481a-8628-5209f7409307' class='xr-var-data-in' type='checkbox'><label for='data-b5cf0bdb-b60e-481a-8628-5209f7409307' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>long_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-15d8117a-883b-419b-a989-3a29b0a1548f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15d8117a-883b-419b-a989-3a29b0a1548f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f338b56-a575-42cb-9577-5a90477eb00e' class='xr-var-data-in' type='checkbox'><label for='data-7f338b56-a575-42cb-9577-5a90477eb00e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>long_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
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+       "  <text x=\"99.881074\" y=\"102.176848\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,99.881074,102.176848)\">23</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-08d4b1c1-b920-4026-9a51-0e0d0345a275' class='xr-section-summary-in' type='checkbox'  ><label for='section-08d4b1c1-b920-4026-9a51-0e0d0345a275' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fa9f090d-ad99-4734-a897-f6cf28a63ce1' class='xr-index-data-in' type='checkbox'/><label for='index-fa9f090d-ad99-4734-a897-f6cf28a63ce1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-06-08&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a3c4f17-a7b3-40a9-9954-f978cbcd0b32' class='xr-index-data-in' type='checkbox'/><label for='index-8a3c4f17-a7b3-40a9-9954-f978cbcd0b32' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-130.5, -129.5, -128.5, -127.5, -126.5, -125.5, -124.5, -123.5, -122.5,\n",
+       "       -121.5, -120.5, -119.5, -118.5, -117.5, -116.5, -115.5, -114.5, -113.5,\n",
+       "       -112.5, -111.5, -110.5, -109.5, -108.5, -107.5, -106.5, -105.5, -104.5,\n",
+       "       -103.5, -102.5, -101.5, -100.5,  -99.5,  -98.5,  -97.5,  -96.5,  -95.5,\n",
+       "        -94.5,  -93.5,  -92.5,  -91.5,  -90.5,  -89.5,  -88.5,  -87.5,  -86.5,\n",
+       "        -85.5,  -84.5,  -83.5,  -82.5,  -81.5,  -80.5,  -79.5,  -78.5,  -77.5,\n",
+       "        -76.5,  -75.5,  -74.5,  -73.5,  -72.5,  -71.5,  -70.5,  -69.5,  -68.5,\n",
+       "        -67.5,  -66.5,  -65.5,  -64.5,  -63.5,  -62.5,  -61.5,  -60.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-181b11d2-6d7f-44b0-bd1e-8b0ced8a08a5' class='xr-index-data-in' type='checkbox'/><label for='index-181b11d2-6d7f-44b0-bd1e-8b0ced8a08a5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5,\n",
+       "       32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5,\n",
+       "       44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5,\n",
+       "       56.5, 57.5, 58.5, 59.5, 60.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5ce601c4-50d1-43ce-b86e-0073d084615c' class='xr-index-data-in' type='checkbox'/><label for='index-5ce601c4-50d1-43ce-b86e-0073d084615c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0,  975.0,  950.0,  925.0,  900.0,  875.0,  850.0,  825.0,  800.0,\n",
+       "        775.0,  750.0,  725.0,  700.0,  650.0,  600.0,  550.0,  500.0,  450.0,\n",
+       "        400.0,  350.0,  300.0,  250.0,  200.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0be29175-979a-4566-815f-12291ad5dfa3' class='xr-section-summary-in' type='checkbox'  ><label for='section-0be29175-979a-4566-815f-12291ad5dfa3' class='xr-section-summary' >Attributes: <span>(33)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1</dd><dt><span>History :</span></dt><dd>Original file generated: Tue Jun 18 21:47:49 2019 GMT</dd><dt><span>Comment :</span></dt><dd>GMAO filename: d5124_m2_jan10.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>Filename :</span></dt><dd>MERRA2_400.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>Institution :</span></dt><dd>NASA Global Modeling and Assimilation Office</dd><dt><span>References :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>Format :</span></dt><dd>NetCDF-4/HDF-5</dd><dt><span>SpatialCoverage :</span></dt><dd>global</dd><dt><span>VersionID :</span></dt><dd>5.12.4</dd><dt><span>TemporalRange :</span></dt><dd>1980-01-01 -&gt; 2016-12-31</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>http://dx.doi.org/</dd><dt><span>ShortName :</span></dt><dd>M2I3NPASM</dd><dt><span>GranuleID :</span></dt><dd>MERRA2_400.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>ProductionDateTime :</span></dt><dd>Original file generated: Tue Jun 18 21:47:49 2019 GMT</dd><dt><span>LongName :</span></dt><dd>MERRA2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields</dd><dt><span>Title :</span></dt><dd>MERRA2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields</dd><dt><span>SouthernmostLatitude :</span></dt><dd>-90.0</dd><dt><span>NorthernmostLatitude :</span></dt><dd>90.0</dd><dt><span>WesternmostLongitude :</span></dt><dd>-180.0</dd><dt><span>EasternmostLongitude :</span></dt><dd>179.375</dd><dt><span>LatitudeResolution :</span></dt><dd>0.5</dd><dt><span>LongitudeResolution :</span></dt><dd>0.625</dd><dt><span>DataResolution :</span></dt><dd>0.5 x 0.625 (42 pressure levels)</dd><dt><span>Contact :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>identifier_product_doi :</span></dt><dd>10.5067/QBZ6MG944HW0</dd><dt><span>RangeBeginningDate :</span></dt><dd>2019-06-08</dd><dt><span>RangeBeginningTime :</span></dt><dd>00:00:00.000000</dd><dt><span>RangeEndingDate :</span></dt><dd>2019-06-08</dd><dt><span>RangeEndingTime :</span></dt><dd>21:00:00.000000</dd><dt><span>history_L34RS :</span></dt><dd>&#x27;Created by L34RS v1.4.3 @ NASA GES DISC on April 29 2023 20:47:34. Regrid: remapcon GLDAS-2_1. Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23. Variables: H OMEGA PS QV SLP T U V&#x27;</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd><dt><span>cdo_openmp_thread_number :</span></dt><dd>12</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 2MB\n",
+       "Dimensions:  (time: 1, lev: 23, lat: 41, lon: 71)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 568B -130.5 -129.5 -128.5 ... -62.5 -61.5 -60.5\n",
+       "  * lat      (lat) float64 328B 20.5 21.5 22.5 23.5 24.5 ... 57.5 58.5 59.5 60.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Data variables:\n",
+       "    H        (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    OMEGA    (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    PS       (time, lat, lon) float32 12kB dask.array<chunksize=(1, 41, 71), meta=np.ndarray>\n",
+       "    QV       (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    SLP      (time, lat, lon) float32 12kB dask.array<chunksize=(1, 41, 71), meta=np.ndarray>\n",
+       "    T        (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    U        (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    V        (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "Attributes: (12/33)\n",
+       "    CDI:                               Climate Data Interface version 1.9.8 (...\n",
+       "    Conventions:                       CF-1\n",
+       "    History:                           Original file generated: Tue Jun 18 21...\n",
+       "    Comment:                           GMAO filename: d5124_m2_jan10.inst3_3d...\n",
+       "    Filename:                          MERRA2_400.inst3_3d_asm_Np.20190608.nc4\n",
+       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
+       "    ...                                ...\n",
+       "    RangeBeginningTime:                00:00:00.000000\n",
+       "    RangeEndingDate:                   2019-06-08\n",
+       "    RangeEndingTime:                   21:00:00.000000\n",
+       "    history_L34RS:                     'Created by L34RS v1.4.3 @ NASA GES DI...\n",
+       "    CDO:                               Climate Data Operators version 1.9.8 (...\n",
+       "    cdo_openmp_thread_number:          12"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b866a88e-1036-42b9-90c0-e1ee27e2d8b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### making a function to slice the xarray dataset according to our need.\n",
+    "def slicer (data,lat1, lat2, lon1, lon2, time1,time2) :\n",
+    "    sliced_data = data.sel(lat =slice(lat1, lat2), lon = slice(lon1, lon2),time = slice(time1, time2))\n",
+    "    return sliced_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e85729c9-65b8-4264-8b8f-50bf36068911",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#slicing the data for CONUS only\n",
+    "\n",
+    "new_data = slicer(ds,23.5,50.5,-125.5,-66.5, ds.time[0], ds.time[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2e49ab2c-7ae1-4aed-9338-f29f8d34f783",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 942kB\n",
+       "Dimensions:  (time: 1, lev: 23, lat: 28, lon: 60)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 480B -125.5 -124.5 -123.5 ... -68.5 -67.5 -66.5\n",
+       "  * lat      (lat) float64 224B 23.5 24.5 25.5 26.5 27.5 ... 47.5 48.5 49.5 50.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Data variables:\n",
+       "    H        (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    OMEGA    (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    PS       (time, lat, lon) float32 7kB dask.array&lt;chunksize=(1, 28, 60), meta=np.ndarray&gt;\n",
+       "    QV       (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    SLP      (time, lat, lon) float32 7kB dask.array&lt;chunksize=(1, 28, 60), meta=np.ndarray&gt;\n",
+       "    T        (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    U        (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    V        (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "Attributes: (12/33)\n",
+       "    CDI:                               Climate Data Interface version 1.9.8 (...\n",
+       "    Conventions:                       CF-1\n",
+       "    History:                           Original file generated: Tue Jun 18 21...\n",
+       "    Comment:                           GMAO filename: d5124_m2_jan10.inst3_3d...\n",
+       "    Filename:                          MERRA2_400.inst3_3d_asm_Np.20190608.nc4\n",
+       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
+       "    ...                                ...\n",
+       "    RangeBeginningTime:                00:00:00.000000\n",
+       "    RangeEndingDate:                   2019-06-08\n",
+       "    RangeEndingTime:                   21:00:00.000000\n",
+       "    history_L34RS:                     &#x27;Created by L34RS v1.4.3 @ NASA GES DI...\n",
+       "    CDO:                               Climate Data Operators version 1.9.8 (...\n",
+       "    cdo_openmp_thread_number:          12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4ff7c081-e233-4473-af09-4de731ca46d6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4ff7c081-e233-4473-af09-4de731ca46d6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lev</span>: 23</li><li><span class='xr-has-index'>lat</span>: 28</li><li><span class='xr-has-index'>lon</span>: 60</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f5c064bc-0f69-440e-832d-b96fca5660a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f5c064bc-0f69-440e-832d-b96fca5660a1' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-06-08</div><input id='attrs-90dc57e0-289e-4ff5-bda3-a2f60e40a90b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90dc57e0-289e-4ff5-bda3-a2f60e40a90b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9dabaff-ac3b-4f56-b3b2-1ec2d453f91c' class='xr-var-data-in' type='checkbox'><label for='data-f9dabaff-ac3b-4f56-b3b2-1ec2d453f91c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-06-08T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-125.5 -124.5 ... -67.5 -66.5</div><input id='attrs-7fe5d831-d522-40b5-9459-5df4c77379a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fe5d831-d522-40b5-9459-5df4c77379a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfb001ae-b8f3-4413-ba97-e7fe7b35a60b' class='xr-var-data-in' type='checkbox'><label for='data-dfb001ae-b8f3-4413-ba97-e7fe7b35a60b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-125.5, -124.5, -123.5, -122.5, -121.5, -120.5, -119.5, -118.5, -117.5,\n",
+       "       -116.5, -115.5, -114.5, -113.5, -112.5, -111.5, -110.5, -109.5, -108.5,\n",
+       "       -107.5, -106.5, -105.5, -104.5, -103.5, -102.5, -101.5, -100.5,  -99.5,\n",
+       "        -98.5,  -97.5,  -96.5,  -95.5,  -94.5,  -93.5,  -92.5,  -91.5,  -90.5,\n",
+       "        -89.5,  -88.5,  -87.5,  -86.5,  -85.5,  -84.5,  -83.5,  -82.5,  -81.5,\n",
+       "        -80.5,  -79.5,  -78.5,  -77.5,  -76.5,  -75.5,  -74.5,  -73.5,  -72.5,\n",
+       "        -71.5,  -70.5,  -69.5,  -68.5,  -67.5,  -66.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>23.5 24.5 25.5 ... 48.5 49.5 50.5</div><input id='attrs-5dc34290-beda-4fe1-a199-d36713fff1b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dc34290-beda-4fe1-a199-d36713fff1b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53a8c3b8-1d3d-452f-98e6-a5ea3fd11255' class='xr-var-data-in' type='checkbox'><label for='data-53a8c3b8-1d3d-452f-98e6-a5ea3fd11255' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5,\n",
+       "       35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5,\n",
+       "       47.5, 48.5, 49.5, 50.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 975.0 950.0 ... 250.0 200.0</div><input id='attrs-4337e857-d098-4990-ab48-a37997bc62a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4337e857-d098-4990-ab48-a37997bc62a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90eafa91-3092-4423-b2d4-8b60dec1d7f3' class='xr-var-data-in' type='checkbox'><label for='data-90eafa91-3092-4423-b2d4-8b60dec1d7f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>PLE_level</dd><dt><span>long_name :</span></dt><dd>vertical level</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate :</span></dt><dd>PLE</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  975.,  950.,  925.,  900.,  875.,  850.,  825.,  800.,  775.,\n",
+       "        750.,  725.,  700.,  650.,  600.,  550.,  500.,  450.,  400.,  350.,\n",
+       "        300.,  250.,  200.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-809b7182-385b-4578-99b8-e9dc3722ea97' class='xr-section-summary-in' type='checkbox'  checked><label for='section-809b7182-385b-4578-99b8-e9dc3722ea97' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>H</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-c2644df8-3604-4090-96a1-7f158508cde8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2644df8-3604-4090-96a1-7f158508cde8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1031e38-791c-4673-9256-3a97b1e4ce3a' class='xr-var-data-in' type='checkbox'><label for='data-e1031e38-791c-4673-9256-3a97b1e4ce3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>edge_heights</dd><dt><span>long_name :</span></dt><dd>edge_heights</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"393\" height=\"133\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"28\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"125\" y1=\"27\" x2=\"245\" y2=\"27\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"125\" y1=\"27\" x2=\"125\" y2=\"83\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"245\" y1=\"27\" x2=\"245\" y2=\"83\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"125.05882352941177,27.058823529411764 245.05882352941177,27.058823529411764 245.05882352941177,83.05882352941177 125.05882352941177,83.05882352941177\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
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+       "  <text x=\"101.529412\" y=\"89.529412\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,101.529412,89.529412)\">23</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OMEGA</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-f848327a-ecbd-45f1-a54e-44d54bd3a314' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f848327a-ecbd-45f1-a54e-44d54bd3a314' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e598a68-8bfe-4bab-8636-e95be0a58ea4' class='xr-var-data-in' type='checkbox'><label for='data-8e598a68-8bfe-4bab-8636-e95be0a58ea4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>vertical_pressure_velocity</dd><dt><span>long_name :</span></dt><dd>vertical_pressure_velocity</dd><dt><span>units :</span></dt><dd>Pa s-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"393\" height=\"133\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PS</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-162464be-43df-41b2-9eab-a3a7a324886a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-162464be-43df-41b2-9eab-a3a7a324886a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d03807f-c99f-4c4d-9037-52c8810e2637' class='xr-var-data-in' type='checkbox'><label for='data-3d03807f-c99f-4c4d-9037-52c8810e2637' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_pressure</dd><dt><span>long_name :</span></dt><dd>surface_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.56 kiB </td>\n",
+       "                        <td> 6.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 28, 60) </td>\n",
+       "                        <td> (1, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-5052de02-703b-4353-9e2f-2e35063aaeab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5052de02-703b-4353-9e2f-2e35063aaeab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c47a598-beb0-4c31-8bea-28e556d4b0d8' class='xr-var-data-in' type='checkbox'><label for='data-1c47a598-beb0-4c31-8bea-28e556d4b0d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>long_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SLP</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-4a833062-2d05-4b3f-b199-24197ebb6590' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a833062-2d05-4b3f-b199-24197ebb6590' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b8461e5-2c45-4dc0-a74f-094498ed1d50' class='xr-var-data-in' type='checkbox'><label for='data-9b8461e5-2c45-4dc0-a74f-094498ed1d50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_level_pressure</dd><dt><span>long_name :</span></dt><dd>sea_level_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.56 kiB </td>\n",
+       "                        <td> 6.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 28, 60) </td>\n",
+       "                        <td> (1, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                <tbody>\n",
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+       "                    <tr>\n",
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+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
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+       "                    <tr>\n",
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+       "                        <th> Shape </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "  <line x1=\"245\" y1=\"27\" x2=\"245\" y2=\"83\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <polygon points=\"125.05882352941177,27.058823529411764 245.05882352941177,27.058823529411764 245.05882352941177,83.05882352941177 125.05882352941177,83.05882352941177\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"185.058824\" y=\"103.058824\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >60</text>\n",
+       "  <text x=\"265.058824\" y=\"55.058824\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,265.058824,55.058824)\">28</text>\n",
+       "  <text x=\"101.529412\" y=\"89.529412\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,101.529412,89.529412)\">23</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-677e9956-7b48-47d8-a81a-8c5685000eb0' class='xr-section-summary-in' type='checkbox'  ><label for='section-677e9956-7b48-47d8-a81a-8c5685000eb0' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e8470f46-58b4-4ecf-a6cf-66d054af2345' class='xr-index-data-in' type='checkbox'/><label for='index-e8470f46-58b4-4ecf-a6cf-66d054af2345' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-06-08&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6fa09698-40c5-40fb-b62d-5996fe669c37' class='xr-index-data-in' type='checkbox'/><label for='index-6fa09698-40c5-40fb-b62d-5996fe669c37' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-125.5, -124.5, -123.5, -122.5, -121.5, -120.5, -119.5, -118.5, -117.5,\n",
+       "       -116.5, -115.5, -114.5, -113.5, -112.5, -111.5, -110.5, -109.5, -108.5,\n",
+       "       -107.5, -106.5, -105.5, -104.5, -103.5, -102.5, -101.5, -100.5,  -99.5,\n",
+       "        -98.5,  -97.5,  -96.5,  -95.5,  -94.5,  -93.5,  -92.5,  -91.5,  -90.5,\n",
+       "        -89.5,  -88.5,  -87.5,  -86.5,  -85.5,  -84.5,  -83.5,  -82.5,  -81.5,\n",
+       "        -80.5,  -79.5,  -78.5,  -77.5,  -76.5,  -75.5,  -74.5,  -73.5,  -72.5,\n",
+       "        -71.5,  -70.5,  -69.5,  -68.5,  -67.5,  -66.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a5fe1207-27a3-4f2d-8c8a-05f06f095808' class='xr-index-data-in' type='checkbox'/><label for='index-a5fe1207-27a3-4f2d-8c8a-05f06f095808' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5,\n",
+       "       35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5,\n",
+       "       47.5, 48.5, 49.5, 50.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2743f525-8389-433b-8621-c3e801ad35a7' class='xr-index-data-in' type='checkbox'/><label for='index-2743f525-8389-433b-8621-c3e801ad35a7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0,  975.0,  950.0,  925.0,  900.0,  875.0,  850.0,  825.0,  800.0,\n",
+       "        775.0,  750.0,  725.0,  700.0,  650.0,  600.0,  550.0,  500.0,  450.0,\n",
+       "        400.0,  350.0,  300.0,  250.0,  200.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ec25bbc-db92-4b2d-a97e-27a39716ccb4' class='xr-section-summary-in' type='checkbox'  ><label for='section-3ec25bbc-db92-4b2d-a97e-27a39716ccb4' class='xr-section-summary' >Attributes: <span>(33)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1</dd><dt><span>History :</span></dt><dd>Original file generated: Tue Jun 18 21:47:49 2019 GMT</dd><dt><span>Comment :</span></dt><dd>GMAO filename: d5124_m2_jan10.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>Filename :</span></dt><dd>MERRA2_400.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>Institution :</span></dt><dd>NASA Global Modeling and Assimilation Office</dd><dt><span>References :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>Format :</span></dt><dd>NetCDF-4/HDF-5</dd><dt><span>SpatialCoverage :</span></dt><dd>global</dd><dt><span>VersionID :</span></dt><dd>5.12.4</dd><dt><span>TemporalRange :</span></dt><dd>1980-01-01 -&gt; 2016-12-31</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>http://dx.doi.org/</dd><dt><span>ShortName :</span></dt><dd>M2I3NPASM</dd><dt><span>GranuleID :</span></dt><dd>MERRA2_400.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>ProductionDateTime :</span></dt><dd>Original file generated: Tue Jun 18 21:47:49 2019 GMT</dd><dt><span>LongName :</span></dt><dd>MERRA2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields</dd><dt><span>Title :</span></dt><dd>MERRA2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields</dd><dt><span>SouthernmostLatitude :</span></dt><dd>-90.0</dd><dt><span>NorthernmostLatitude :</span></dt><dd>90.0</dd><dt><span>WesternmostLongitude :</span></dt><dd>-180.0</dd><dt><span>EasternmostLongitude :</span></dt><dd>179.375</dd><dt><span>LatitudeResolution :</span></dt><dd>0.5</dd><dt><span>LongitudeResolution :</span></dt><dd>0.625</dd><dt><span>DataResolution :</span></dt><dd>0.5 x 0.625 (42 pressure levels)</dd><dt><span>Contact :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>identifier_product_doi :</span></dt><dd>10.5067/QBZ6MG944HW0</dd><dt><span>RangeBeginningDate :</span></dt><dd>2019-06-08</dd><dt><span>RangeBeginningTime :</span></dt><dd>00:00:00.000000</dd><dt><span>RangeEndingDate :</span></dt><dd>2019-06-08</dd><dt><span>RangeEndingTime :</span></dt><dd>21:00:00.000000</dd><dt><span>history_L34RS :</span></dt><dd>&#x27;Created by L34RS v1.4.3 @ NASA GES DISC on April 29 2023 20:47:34. Regrid: remapcon GLDAS-2_1. Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23. Variables: H OMEGA PS QV SLP T U V&#x27;</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd><dt><span>cdo_openmp_thread_number :</span></dt><dd>12</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 942kB\n",
+       "Dimensions:  (time: 1, lev: 23, lat: 28, lon: 60)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 480B -125.5 -124.5 -123.5 ... -68.5 -67.5 -66.5\n",
+       "  * lat      (lat) float64 224B 23.5 24.5 25.5 26.5 27.5 ... 47.5 48.5 49.5 50.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Data variables:\n",
+       "    H        (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    OMEGA    (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    PS       (time, lat, lon) float32 7kB dask.array<chunksize=(1, 28, 60), meta=np.ndarray>\n",
+       "    QV       (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    SLP      (time, lat, lon) float32 7kB dask.array<chunksize=(1, 28, 60), meta=np.ndarray>\n",
+       "    T        (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    U        (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    V        (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "Attributes: (12/33)\n",
+       "    CDI:                               Climate Data Interface version 1.9.8 (...\n",
+       "    Conventions:                       CF-1\n",
+       "    History:                           Original file generated: Tue Jun 18 21...\n",
+       "    Comment:                           GMAO filename: d5124_m2_jan10.inst3_3d...\n",
+       "    Filename:                          MERRA2_400.inst3_3d_asm_Np.20190608.nc4\n",
+       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
+       "    ...                                ...\n",
+       "    RangeBeginningTime:                00:00:00.000000\n",
+       "    RangeEndingDate:                   2019-06-08\n",
+       "    RangeEndingTime:                   21:00:00.000000\n",
+       "    history_L34RS:                     'Created by L34RS v1.4.3 @ NASA GES DI...\n",
+       "    CDO:                               Climate Data Operators version 1.9.8 (...\n",
+       "    cdo_openmp_thread_number:          12"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "dbaa4f14-5be0-41f5-b594-4746135a451c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##extracting temperature, pressure  and specific humidity from the dataset\n",
+    "p =new_data.lev*units('hPa')\n",
+    "T = new_data.T\n",
+    "sh =  new_data.QV*units('dimensionless')\n",
+    "\n",
+    "\n",
+    "TCPW = np.zeros((1, 28, 60),dtype=float)\n",
+    "\n",
+    "###calculating TCPW in multidimension\n",
+    "\n",
+    "for h in range (len(new_data.time)):\n",
+    "    for i in range (len(new_data.lat)):\n",
+    "        for j in range(len(new_data.lon)):\n",
+    "\n",
+    "\n",
+    "            TC = (T[h,:,i,j]-(273.15))*units('degC')\n",
+    "            Td = dewpoint_from_specific_humidity(np.array(p)*units('hPa'),TC, sh[h,:,i,j])\n",
+    "\n",
+    "            TPW_S = precipitable_water(np.array(p)*units('hPa'), np.array(Td)*units('degC'))\n",
+    "            TCPW[h,i,j] = TPW_S.magnitude\n",
+    "                \n",
+    "         \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e37662a0-9d9a-4a1e-92c1-b212757da2f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[22.34989166, 22.14568471, 21.66339844, ..., 49.19934755,\n",
+       "         48.19983052, 47.56431138],\n",
+       "        [21.7082633 , 21.17000119, 20.13788047, ..., 45.64615776,\n",
+       "         45.32941988, 44.5238861 ],\n",
+       "        [20.61434703, 20.30073408, 19.39063505, ..., 40.45091568,\n",
+       "         39.51188793, 40.08718096],\n",
+       "        ...,\n",
+       "        [15.20498414, 16.70405236, 16.6958771 , ..., 14.96512996,\n",
+       "         16.09915845, 16.28375345],\n",
+       "        [15.95014737, 15.44401933, 16.36107178, ..., 13.13411618,\n",
+       "         13.86219912, 14.25409531],\n",
+       "        [14.45032494, 14.94088309, 11.27504841, ...,  9.55777922,\n",
+       "         11.2378108 , 13.30571821]]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "TCPW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "fedb10f7-ff76-4c4c-bbc3-7b629fa0b0d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataproj = ccrs. PlateCarree ()\n",
+    "# # Plot projection\n",
+    "# # The look you want for the view.\n",
+    "plotproj = ccrs. PlateCarree ()\n",
+    "fig=plt.figure(1, figsize=(15.,12.))\n",
+    "\n",
+    "ax=plt.subplot(111,projection=plotproj)\n",
+    "\n",
+    "ax.add_feature(cfeature.COASTLINE, linewidth=0.5)\n",
+    "ax.add_feature(cfeature.STATES, linewidth=0.5)\n",
+    "plt.title(\"Total Column Precipitable Water Calculated Using Metpy (mm)\",size = 30)\n",
+    "plt.contourf (new_data.lon, new_data.lat, TCPW[0,:,:],levels =np.arange(0,70,5),cmap = \"PuBuGn\", transform=dataproj,extend = \"max\")\n",
+    "plt.colorbar (orientation = \"horizontal\", pad=0.01).ax.tick_params(labelsize=20)\n",
+    "plt. show ()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fa9c2e1-d66e-4056-a804-9af9997faceb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_preview/13/_sources/notebooks/convective/index.md b/_preview/13/_sources/notebooks/convective/index.md
new file mode 100644
index 0000000..9c97b6f
--- /dev/null
+++ b/_preview/13/_sources/notebooks/convective/index.md
@@ -0,0 +1,3 @@
+# Convective Calculations
+
+This section covers how to perform a variety of calculations around analyzing convection and severe weather.
\ No newline at end of file
diff --git a/_preview/13/_sources/notebooks/convective/miller_composite.ipynb b/_preview/13/_sources/notebooks/convective/miller_composite.ipynb
new file mode 100644
index 0000000..266f7c3
--- /dev/null
+++ b/_preview/13/_sources/notebooks/convective/miller_composite.ipynb
@@ -0,0 +1,470 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Miller Composite Chart\n",
+    "======================\n",
+    "Create a Miller Composite chart based on Miller 1972 in Python with MetPy and\n",
+    "Matplotlib."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.lines as lines\n",
+    "import matplotlib.patches as mpatches\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "import numpy as np\n",
+    "import numpy.ma as ma\n",
+    "\n",
+    "from metpy.units import units\n",
+    "from netCDF4 import num2date\n",
+    "from scipy.ndimage import gaussian_filter\n",
+    "from siphon.catalog import TDSCatalog\n",
+    "from xarray.backends import NetCDF4DataStore\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "###########################"
+   },
+   "source": [
+    "**Get the data**\n",
+    "\n",
+    "This example will use data from the North American Mesoscale Model Analysis for 18 UTC 27 April 2011."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify our date/time of product desired\n",
+    "dt = datetime(2011, 4, 27, 18)\n",
+    "\n",
+    "# Construct the URL for our THREDDS Data Server Catalog,\n",
+    "# and access our desired dataset within via NCSS\n",
+    "base_url = 'https://www.ncei.noaa.gov/thredds/model-namanl-old/'\n",
+    "cat = TDSCatalog(f'{base_url}{dt:%Y%m}/{dt:%Y%m%d}/catalog.xml')\n",
+    "ncss = cat.datasets[f'namanl_218_{dt:%Y%m%d}_{dt:%H}00_000.grb'].subset()\n",
+    "\n",
+    "# Create our NCSS query with desired specifications\n",
+    "query = ncss.query()\n",
+    "query.all_times()\n",
+    "query.add_lonlat()\n",
+    "query.lonlat_box(-135, -60, 15, 65)\n",
+    "query.variables('Geopotential_height_isobaric',\n",
+    "                'u-component_of_wind_isobaric',\n",
+    "                'v-component_of_wind_isobaric',\n",
+    "                'Temperature_isobaric',\n",
+    "                'Relative_humidity_isobaric',\n",
+    "                'Best_4_layer_lifted_index_layer_between_two_pressure_'\n",
+    "                'difference_from_ground_layer',\n",
+    "                'Absolute_vorticity_isobaric',\n",
+    "                'Pressure_reduced_to_MSL_msl',\n",
+    "                'Dew_point_temperature_height_above_ground')\n",
+    "\n",
+    "# Obtain the data we've queried for\n",
+    "data_18z = ncss.get_data(query)\n",
+    "\n",
+    "# Make into an xarray Dataset object\n",
+    "ds_18z = xr.open_dataset(NetCDF4DataStore(data_18z)).metpy.parse_cf()\n",
+    "\n",
+    "# Assign variable names to collected data\n",
+    "lat = ds_18z.lat\n",
+    "lon = ds_18z.lon\n",
+    "\n",
+    "# Create more useable times for output\n",
+    "times = ds_18z.Geopotential_height_isobaric.metpy.time.squeeze()\n",
+    "vtimes = times.values.astype('datetime64[ms]').astype('O')\n",
+    "\n",
+    "temps = ds_18z.Temperature_isobaric.squeeze()\n",
+    "uwnd = ds_18z['u-component_of_wind_isobaric'].squeeze()\n",
+    "vwnd = ds_18z['v-component_of_wind_isobaric'].squeeze()\n",
+    "hgt = ds_18z.Geopotential_height_isobaric.squeeze()\n",
+    "relh = ds_18z.Relative_humidity_isobaric.squeeze()\n",
+    "lifted_index = ds_18z['Best_4_layer_lifted_index_layer_between_two_'\n",
+    "                      'pressure_difference_from_ground_layer'].squeeze()\n",
+    "Td_sfc = ds_18z.Dew_point_temperature_height_above_ground.squeeze()\n",
+    "avor = ds_18z.Absolute_vorticity_isobaric.squeeze()\n",
+    "pmsl = ds_18z.Pressure_reduced_to_MSL_msl.squeeze()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################"
+   },
+   "source": [
+    "Repeat the above process to query for the analysis from 12 hours earlier (06 UTC) to calculate pressure falls and height change."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "td = timedelta(hours=12)\n",
+    "\n",
+    "ncss_06z = cat.datasets[f'namanl_218_{dt:%Y%m%d}_{dt-td:%H}00_000.grb'].subset()\n",
+    "\n",
+    "query = ncss_06z.query()\n",
+    "query.all_times()\n",
+    "query.add_lonlat()\n",
+    "query.lonlat_box(-135, -60, 15, 65)\n",
+    "query.variables('Geopotential_height_isobaric',\n",
+    "                'Pressure_reduced_to_MSL_msl')\n",
+    "\n",
+    "# Actually getting the data\n",
+    "data_06z = ncss_06z.get_data(query)\n",
+    "\n",
+    "# Make into an xarray Dataset object\n",
+    "ds_06z = xr.open_dataset(NetCDF4DataStore(data_06z)).metpy.parse_cf()\n",
+    "\n",
+    "hgt_06z = ds_06z.Geopotential_height_isobaric.squeeze()\n",
+    "pmsl_06z = ds_06z.Pressure_reduced_to_MSL_msl.squeeze()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "###########################"
+   },
+   "source": [
+    "**Subset the Data**\n",
+    "\n",
+    "With the data pulled in, we will now subset to the specific levels desired"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 300 hPa\n",
+    "u_300 = uwnd.metpy.sel(vertical=300*units.hPa).metpy.convert_units('kt').squeeze()\n",
+    "v_300 = vwnd.metpy.sel(vertical=300*units.hPa).metpy.convert_units('kt').squeeze()\n",
+    "\n",
+    "# 500 hPa\n",
+    "avor_500 = avor.metpy.sel(vertical=500*units.hPa)\n",
+    "u_500 = uwnd.metpy.sel(vertical=500*units.hPa).metpy.convert_units('kt').squeeze()\n",
+    "v_500 = vwnd.metpy.sel(vertical=500*units.hPa).metpy.convert_units('kt').squeeze()\n",
+    "hgt_500 = hgt.metpy.sel(vertical=500*units.hPa).squeeze()\n",
+    "hgt_500_06z = hgt_06z.metpy.sel(vertical=500*units.hPa).squeeze()\n",
+    "\n",
+    "# 700 hPa\n",
+    "tmp_700 = temps.metpy.sel(vertical=700*units.hPa).metpy.convert_units('degC').squeeze()\n",
+    "rh_700 = relh.metpy.sel(vertical=700*units.hPa).squeeze()\n",
+    "\n",
+    "# 850 hPa\n",
+    "u_850 = uwnd.metpy.sel(vertical=850*units.hPa).metpy.convert_units('kt').squeeze()\n",
+    "v_850 = vwnd.metpy.sel(vertical=850*units.hPa).metpy.convert_units('kt').squeeze()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################################"
+   },
+   "source": [
+    "**Prepare Variables for Plotting**\n",
+    "\n",
+    "With the data queried and subset, we will make any needed calculations in preparation for\n",
+    "plotting.\n",
+    "\n",
+    "**The following fields should be plotted:**\n",
+    "\n",
+    "  500-hPa cyclonic vorticity advection\n",
+    "\n",
+    "  Surface-based Lifted Index\n",
+    "\n",
+    "  The axis of the 300-hPa, 500-hPa, and 850-hPa jets\n",
+    "\n",
+    "  Surface dewpoint\n",
+    "\n",
+    "  700-hPa dewpoint depression\n",
+    "\n",
+    "  12-hr surface pressure falls and 500-hPa height changes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 500 hPa CVA\n",
+    "vort_adv_500 = mpcalc.advection(avor_500, u_500, v_500,) * 1e9\n",
+    "vort_adv_500_smooth = gaussian_filter(vort_adv_500, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "For the jet axes, we will calculate the windspeed at each level, and plot the highest values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wspd_300 = gaussian_filter(mpcalc.wind_speed(u_300, v_300), 5)\n",
+    "wspd_500 = gaussian_filter(mpcalc.wind_speed(u_500, v_500), 5)\n",
+    "wspd_850 = gaussian_filter(mpcalc.wind_speed(u_850, v_850), 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "700-hPa dewpoint depression will be calculated from Temperature_isobaric and RH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Td_dep_700 = tmp_700 - mpcalc.dewpoint_from_relative_humidity(tmp_700, rh_700)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "12-hr surface pressure falls and 500-hPa height changes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pmsl_change = pmsl.metpy.quantify() - pmsl_06z.metpy.quantify()\n",
+    "hgt_500_change = hgt_500.metpy.quantify() - hgt_500_06z.metpy.quantify()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################"
+   },
+   "source": [
+    "To plot the jet axes, we will mask the wind fields below the upper 1/3 of windspeed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 500 hPa\n",
+    "u_500_masked = u_500.where(wspd_500 > 0.66 * wspd_500.max(), np.nan)\n",
+    "v_500_masked = v_500.where(wspd_500 > 0.66 * wspd_500.max(), np.nan)\n",
+    "\n",
+    "# 300 hPa\n",
+    "u_300_masked = u_300.where(wspd_300 > 0.66 * wspd_300.max(), np.nan)\n",
+    "v_300_masked = v_300.where(wspd_300 > 0.66 * wspd_300.max(), np.nan)\n",
+    "\n",
+    "# 850 hPa\n",
+    "u_850_masked = u_850.where(wspd_850 > 0.66 * wspd_850.max(), np.nan)\n",
+    "v_850_masked = v_850.where(wspd_850 > 0.66 * wspd_850.max(), np.nan)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "################################"
+   },
+   "source": [
+    "**Create the Plot**\n",
+    "\n",
+    "With the data now ready, we will create the plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up our projection\n",
+    "crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)\n",
+    "\n",
+    "# Coordinates to limit map area\n",
+    "bounds = [-122., -75., 25., 50.]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#########################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Plot the composite"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(1, figsize=(17, 12))\n",
+    "ax = fig.add_subplot(1, 1, 1, projection=crs)\n",
+    "ax.set_extent(bounds, crs=ccrs.PlateCarree())\n",
+    "ax.coastlines('50m', edgecolor='black', linewidth=0.75)\n",
+    "ax.add_feature(cfeature.STATES, linewidth=0.25)\n",
+    "\n",
+    "# Plot Lifted Index\n",
+    "cs1 = ax.contour(lon, lat, lifted_index, range(-8, -2, 2), transform=ccrs.PlateCarree(),\n",
+    "                 colors='red', linewidths=0.75, linestyles='solid', zorder=7)\n",
+    "cs1.clabel(fontsize=10, inline=1, inline_spacing=7,\n",
+    "           fmt='%i', rightside_up=True, use_clabeltext=True)\n",
+    "\n",
+    "# Plot Surface pressure falls\n",
+    "cs2 = ax.contour(lon, lat, pmsl_change.metpy.convert_units('hPa'), range(-10, -1, 4),\n",
+    "                 transform=ccrs.PlateCarree(),\n",
+    "                 colors='k', linewidths=0.75, linestyles='dashed', zorder=6)\n",
+    "cs2.clabel(fontsize=10, inline=1, inline_spacing=7,\n",
+    "           fmt='%i', rightside_up=True, use_clabeltext=True)\n",
+    "\n",
+    "# Plot 500-hPa height falls\n",
+    "cs3 = ax.contour(lon, lat, hgt_500_change, range(-60, -29, 15),\n",
+    "                 transform=ccrs.PlateCarree(), colors='k', linewidths=0.75,\n",
+    "                 linestyles='solid', zorder=5)\n",
+    "cs3.clabel(fontsize=10, inline=1, inline_spacing=7,\n",
+    "           fmt='%i', rightside_up=True, use_clabeltext=True)\n",
+    "\n",
+    "# Plot surface pressure\n",
+    "ax.contourf(lon, lat, pmsl.metpy.convert_units('hPa'), range(990, 1011, 20), alpha=0.5,\n",
+    "            transform=ccrs.PlateCarree(),\n",
+    "            colors='yellow', zorder=1)\n",
+    "\n",
+    "# Plot surface dewpoint\n",
+    "ax.contourf(lon, lat, Td_sfc.metpy.convert_units('degF'), range(65, 76, 10), alpha=0.4,\n",
+    "            transform=ccrs.PlateCarree(),\n",
+    "            colors=['green'], zorder=2)\n",
+    "\n",
+    "# Plot 700-hPa dewpoint depression\n",
+    "ax.contourf(lon, lat, Td_dep_700, range(15, 46, 30), alpha=0.5, transform=ccrs.PlateCarree(),\n",
+    "            colors='tan', zorder=3)\n",
+    "\n",
+    "# Plot Vorticity Advection\n",
+    "purple = ax.contourf(lon, lat, vort_adv_500_smooth, range(5, 106, 100), alpha=0.5,\n",
+    "            transform=ccrs.PlateCarree(),\n",
+    "            colors='BlueViolet', zorder=4)\n",
+    "\n",
+    "# Define a skip to reduce the barb point density\n",
+    "skip_300 = (slice(None, None, 12), slice(None, None, 12))\n",
+    "skip_500 = (slice(None, None, 10), slice(None, None, 10))\n",
+    "skip_850 = (slice(None, None, 8), slice(None, None, 8))\n",
+    "\n",
+    "# 300-hPa wind barbs\n",
+    "jet300 = ax.barbs(lon[skip_300].values, lat[skip_300].values,\n",
+    "                  u_300_masked[skip_300].values, v_300_masked[skip_300].values,\n",
+    "                  length=6,\n",
+    "                  transform=ccrs.PlateCarree(),\n",
+    "                  color='green', zorder=10, label='300-hPa Jet Core Winds (kt)')\n",
+    "\n",
+    "\n",
+    "# 500-hPa wind barbs\n",
+    "jet500 = ax.barbs(lon[skip_500].values, lat[skip_500].values,\n",
+    "                  u_500_masked[skip_500].values, v_500_masked[skip_500].values,\n",
+    "                  length=6,\n",
+    "                  transform=ccrs.PlateCarree(),\n",
+    "                  color='blue', zorder=9, label='500-hPa Jet Core Winds (kt)')\n",
+    "\n",
+    "# 850-hPa wind barbs\n",
+    "jet850 = ax.barbs(lon[skip_850].values, lat[skip_850].values,\n",
+    "                  u_850_masked[skip_850].values, v_850_masked[skip_850].values,\n",
+    "                  length=6,\n",
+    "                  transform=ccrs.PlateCarree(),\n",
+    "                  color='k', zorder=8, label='850-hPa Jet Core Winds (kt)')\n",
+    "\n",
+    "# Legend\n",
+    "purple = mpatches.Patch(color='BlueViolet', alpha=0.5, label='Cyclonic Absolute Vorticity Advection')\n",
+    "yellow = mpatches.Patch(color='yellow', alpha=0.5, label='Surface MSLP < 1010 hPa')\n",
+    "green = mpatches.Patch(color='green', alpha=0.5, label='Surface Td > 65 F')\n",
+    "tan = mpatches.Patch(color='tan', alpha=0.5, label='700 hPa Dewpoint Depression > 15 C')\n",
+    "red_line = lines.Line2D([], [], color='red', label='Best Lifted Index (C)')\n",
+    "dashed_black_line = lines.Line2D([], [], linestyle='dashed', color='k',\n",
+    "                                 label='12-hr Surface Pressure Falls (hPa)')\n",
+    "black_line = lines.Line2D([], [], linestyle='solid', color='k',\n",
+    "                          label='12-hr 500-hPa Height Falls (m)')\n",
+    "leg = plt.legend(handles=[jet300, jet500, jet850, dashed_black_line, black_line, red_line,\n",
+    "                          purple, tan, green, yellow], loc=3,\n",
+    "                 title=f'Composite Analysis Valid: {vtimes}',\n",
+    "                 framealpha=1)\n",
+    "leg.set_zorder(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/declarative/index.md b/_preview/13/_sources/notebooks/declarative/index.md
new file mode 100644
index 0000000..b4d0939
--- /dev/null
+++ b/_preview/13/_sources/notebooks/declarative/index.md
@@ -0,0 +1,3 @@
+# MetPy's Simplified Plotting Interface
+
+TBD
\ No newline at end of file
diff --git a/_preview/13/_sources/notebooks/skewt/index.md b/_preview/13/_sources/notebooks/skewt/index.md
new file mode 100644
index 0000000..0287880
--- /dev/null
+++ b/_preview/13/_sources/notebooks/skewt/index.md
@@ -0,0 +1,10 @@
+# Skew-T Analysis
+
+The Skew-T Log-P diagram, or "Skew-T\" for short, is a type of plot specific
+to meteorology that is frequently used in the analysis of vertical profiles of
+temperature and humidity. For instance, the data collected by weather balloons
+in 12-hourly soundings are often shown with such plots, as they foster an
+understanding of e.g. the stability of the atmosphere above a location.
+
+The units in this chapter show ways to use MetPy to generate skew-T plots and
+customize them with various pieces of information.
diff --git a/_preview/13/_sources/notebooks/specialty/Hovmoller_Diagram.ipynb b/_preview/13/_sources/notebooks/specialty/Hovmoller_Diagram.ipynb
new file mode 100644
index 0000000..ae4ed55
--- /dev/null
+++ b/_preview/13/_sources/notebooks/specialty/Hovmoller_Diagram.ipynb
@@ -0,0 +1,200 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Hovmoller Diagram Example\n",
+    "=========================\n",
+    "\n",
+    "By: Kevin Goebbert\n",
+    "\n",
+    "Northern Hemispheric v-wind component over the mid-latitudes in a\n",
+    "Hovmoller diagram. This diagram can be used to illustrate upper-level\n",
+    "wave and energy propogation (e.g., downstream baroclinic development)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.gridspec as gridspec\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "import numpy as np\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Get the data\n",
+    "------------\n",
+    "\n",
+    "Using NCEP/NCAR reanalysis data via xarray remote access using the\n",
+    "OPeNDAP protocol.\n",
+    "\n",
+    "Set the time range, parameter, and level to desired values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create time slice from dates\n",
+    "start_time = '2011-01-20'\n",
+    "end_time = '2011-02-06'\n",
+    "\n",
+    "# Select NCEP/NCAR parameter and level\n",
+    "param = 'vwnd'\n",
+    "level = 250\n",
+    "\n",
+    "# Remote get dataset using OPeNDAP method via xarray\n",
+    "ds = xr.open_dataset('http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/'\n",
+    "                     f'ncep.reanalysis/pressure/{param}.{start_time[:4]}.nc')\n",
+    "\n",
+    "# Create slice variables subset domain\n",
+    "time_slice = slice(start_time, end_time)\n",
+    "lat_slice = slice(60, 40)\n",
+    "lon_slice = slice(0, 360)\n",
+    "\n",
+    "# Get data, selecting time, level, lat/lon slice\n",
+    "data = ds[param].sel(time=time_slice,\n",
+    "                     level=level,\n",
+    "                     lat=lat_slice,\n",
+    "                     lon=lon_slice)\n",
+    "\n",
+    "# Compute weights and take weighted average over latitude dimension\n",
+    "weights = np.cos(np.deg2rad(data.lat.values))\n",
+    "avg_data = (data * weights[None, :, None]).sum(dim='lat') / np.sum(weights)\n",
+    "\n",
+    "# Get times and make array of datetime objects\n",
+    "vtimes = data.time.values.astype('datetime64[ms]').astype('O')\n",
+    "\n",
+    "# Specify longitude values for chosen domain\n",
+    "lons = data.lon.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Make the Hovmoller Plot\n",
+    "-----------------------\n",
+    "\n",
+    "Pretty simple to use common matplotlib/cartopy to create the diagram.\n",
+    "Cartopy is used to create a geographic reference map to highlight the\n",
+    "area being averaged as well as the visual reference for longitude.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Start figure\n",
+    "fig = plt.figure(figsize=(10, 13))\n",
+    "\n",
+    "# Use gridspec to help size elements of plot; small top plot\n",
+    "# and big bottom plot\n",
+    "gs = gridspec.GridSpec(nrows=2, ncols=1, height_ratios=[1, 6], hspace=0.03)\n",
+    "\n",
+    "# Tick labels\n",
+    "x_tick_labels = [u'0\\N{DEGREE SIGN}E', u'90\\N{DEGREE SIGN}E',\n",
+    "                 u'180\\N{DEGREE SIGN}E', u'90\\N{DEGREE SIGN}W',\n",
+    "                 u'0\\N{DEGREE SIGN}E']\n",
+    "\n",
+    "# Top plot for geographic reference (makes small map)\n",
+    "ax1 = fig.add_subplot(gs[0, 0],\n",
+    "                      projection=ccrs.PlateCarree(central_longitude=180))\n",
+    "ax1.set_extent([0, 357.5, 35, 65], ccrs.PlateCarree(central_longitude=180))\n",
+    "ax1.set_yticks([40, 60])\n",
+    "ax1.set_yticklabels([u'40\\N{DEGREE SIGN}N', u'60\\N{DEGREE SIGN}N'])\n",
+    "ax1.set_xticks([-180, -90, 0, 90, 180])\n",
+    "ax1.set_xticklabels(x_tick_labels)\n",
+    "ax1.grid(linestyle='dotted', linewidth=2)\n",
+    "\n",
+    "# Add geopolitical boundaries for map reference\n",
+    "ax1.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
+    "ax1.add_feature(cfeature.LAKES.with_scale('50m'), color='black',\n",
+    "                linewidths=0.5)\n",
+    "\n",
+    "# Set some titles\n",
+    "plt.title('Hovmoller Diagram', loc='left')\n",
+    "plt.title('NCEP/NCAR Reanalysis', loc='right')\n",
+    "\n",
+    "# Bottom plot for Hovmoller diagram\n",
+    "ax2 = fig.add_subplot(gs[1, 0])\n",
+    "ax2.invert_yaxis()  # Reverse the time order to do oldest first\n",
+    "\n",
+    "# Plot of chosen variable averaged over latitude and slightly smoothed\n",
+    "clevs = np.arange(-50, 51, 5)\n",
+    "cf = ax2.contourf(lons, vtimes, mpcalc.smooth_n_point(\n",
+    "    avg_data, 9, 2), clevs, cmap=plt.cm.bwr, extend='both')\n",
+    "cs = ax2.contour(lons, vtimes, mpcalc.smooth_n_point(\n",
+    "    avg_data, 9, 2), clevs, colors='k', linewidths=1)\n",
+    "cbar = plt.colorbar(cf, orientation='horizontal', pad=0.04, aspect=50,\n",
+    "                    extendrect=True)\n",
+    "cbar.set_label('m $s^{-1}$')\n",
+    "\n",
+    "# Make some ticks and tick labels\n",
+    "ax2.set_xticks([0, 90, 180, 270, 357.5])\n",
+    "ax2.set_xticklabels(x_tick_labels)\n",
+    "ax2.set_yticks(vtimes[4::8])\n",
+    "ax2.set_yticklabels(vtimes[4::8])\n",
+    "\n",
+    "# Set some titles\n",
+    "plt.title('250-hPa V-wind', loc='left', fontsize=10)\n",
+    "plt.title(f'Time Range: {vtimes[0]:%Y%m%d %HZ} - {vtimes[-1]:%Y%m%d %HZ}',\n",
+    "          loc='right', fontsize=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/specialty/Observational_Data_Cross_Section.ipynb b/_preview/13/_sources/notebooks/specialty/Observational_Data_Cross_Section.ipynb
new file mode 100644
index 0000000..dacd349
--- /dev/null
+++ b/_preview/13/_sources/notebooks/specialty/Observational_Data_Cross_Section.ipynb
@@ -0,0 +1,430 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Real Data Cross-Section Example\n",
+    "===============================\n",
+    "\n",
+    "Cross-section using real data from soundings.\n",
+    "\n",
+    "This example uses actual soundings to create a cross-section. There are\n",
+    "two functions defined to help interpolate radiosonde observations, which\n",
+    "won’t all be at the same level, to a standard grid. The vertical\n",
+    "interpolation assumes a log-linear relationship. Each radisosonde\n",
+    "vertical profile is interpolated first, then the\n",
+    "``scipy.interpolate.griddata`` function is used to generate a full 2D\n",
+    "(x, p) grid between each station. Pyproj is used to calculate the\n",
+    "distance between each station and the standard atmosphere is used to\n",
+    "convert the elevation of each station to a pressure value for plotting\n",
+    "purposes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "from metpy.units import units\n",
+    "import numpy as np\n",
+    "from siphon.simplewebservice.wyoming import WyomingUpperAir"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Vertical Interpolation Function\n",
+    "-------------------------------\n",
+    "\n",
+    "Function interpolates to given pressure level data to set grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "lines_to_next_cell": 1
+   },
+   "outputs": [],
+   "source": [
+    "def vertical_interpolate(vcoord_data, interp_var, interp_levels):\n",
+    "    \"\"\"A function to interpolate sounding data from each station to\n",
+    "    every millibar. Assumes a log-linear relationship.\n",
+    "\n",
+    "    Input\n",
+    "    -----\n",
+    "    vcoord_data : A 1D array of vertical level values (e.g., pressure from a radiosonde)\n",
+    "    interp_var : A 1D array of the variable to be interpolated to all pressure levels\n",
+    "    vcoord_interp_levels : A 1D array containing veritcal levels to interpolate to\n",
+    "\n",
+    "    Return\n",
+    "    ------\n",
+    "    interp_data : A 1D array that contains the interpolated variable on the interp_levels\n",
+    "    \"\"\"\n",
+    "    # Make veritcal coordinate data and grid level log variables\n",
+    "    lnp = np.log(vcoord_data)\n",
+    "    lnp_intervals = np.log(interp_levels)\n",
+    "\n",
+    "    # Use numpy to interpolate from observed levels to grid levels\n",
+    "    interp_data = np.interp(lnp_intervals[::-1], lnp[::-1], interp_var[::-1])[::-1]\n",
+    "\n",
+    "    # Mask for missing data (generally only near the surface)\n",
+    "    mask_low = interp_levels > vcoord_data[0]\n",
+    "    mask_high = interp_levels < vcoord_data[-1]\n",
+    "    interp_data[mask_low] = interp_var[0]\n",
+    "    interp_data[mask_high] = interp_var[-1]\n",
+    "\n",
+    "    return interp_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Radiosonde Observation Interpolation Function\n",
+    "---------------------------------------------\n",
+    "\n",
+    "This function interpolates given radiosonde data into a 2D array for all\n",
+    "meteorological variables given in dataframe. Returns a dictionary that\n",
+    "will have requesite data for plotting a cross section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "lines_to_next_cell": 1
+   },
+   "outputs": [],
+   "source": [
+    "def radiosonde_cross_section(stns, data, start=1000, end=100, step=10):\n",
+    "    \"\"\"This function takes a list of radiosonde observation sites with a\n",
+    "    dictionary of Pandas Dataframes with the requesite data for each station.\n",
+    "\n",
+    "    Input\n",
+    "    -----\n",
+    "    stns : List of statition three-letter identifiers\n",
+    "    data : A dictionary of Pandas Dataframes containing the radiosonde observations\n",
+    "    for the stations\n",
+    "    start : interpolation start value, optional (default = 1000 hPa)\n",
+    "    end : Interpolation end value, optional (default = 100 hPa)\n",
+    "    step : Interpolation interval, option (default = 10 hPa)\n",
+    "\n",
+    "    Return\n",
+    "    ------\n",
+    "    cross_section : A dictionary that contains the following variables\n",
+    "\n",
+    "        grid_data : An interpolated grid with 100 points between the first and last station,\n",
+    "        with the corresponding number of vertical points based on start, end, and interval\n",
+    "        (default is 90)\n",
+    "        obs_distance : An array of distances between each radiosonde observation location\n",
+    "        x_grid : A 2D array of horizontal direction grid points\n",
+    "        p_grid : A 2D array of vertical pressure levels\n",
+    "        ground_elevation : A representation of the terrain between radiosonde observation sites\n",
+    "        based on the elevation of each station converted to pressure using the standard\n",
+    "        atmosphere\n",
+    "\n",
+    "    \"\"\"\n",
+    "    from pyproj import Geod\n",
+    "    from scipy.interpolate import griddata\n",
+    "    \n",
+    "    data_units = {'pressure': 'hPa',\n",
+    "                  'height': 'meter',\n",
+    "                  'temperature': 'degC',\n",
+    "                  'dewpoint': 'degC',\n",
+    "                  'direction': 'degrees',\n",
+    "                  'speed': 'knot',\n",
+    "                  'u_wind': 'knot',\n",
+    "                  'v_wind': 'knot',\n",
+    "                  'station': None,\n",
+    "                  'station_number': None,\n",
+    "                  'time': None,\n",
+    "                  'latitude': 'degrees',\n",
+    "                  'longitude': 'degrees',\n",
+    "                  'elevation': 'meter',\n",
+    "                  'pw': 'millimeter'}\n",
+    "    # Set up vertical grid, largest value first (high pressure nearest surface)\n",
+    "    vertical_levels = np.arange(start, end-1, -step) * units(data_units['pressure'])\n",
+    "\n",
+    "    # Number of vertical levels and stations\n",
+    "    plevs = len(vertical_levels)\n",
+    "    nstns = len(stns)\n",
+    "\n",
+    "    # Create dictionary of interpolated values and include neccsary attribute data\n",
+    "    # including lat, lon, and elevation of each station\n",
+    "    lats = []\n",
+    "    lons = []\n",
+    "    elev = []\n",
+    "    keys = data[stns[0]].keys()[:8]\n",
+    "    tmp_grid = dict.fromkeys(keys)\n",
+    "\n",
+    "    # Interpolate all variables for each radiosonde observation\n",
+    "    # Temperature, Dewpoint, U-wind, V-wind\n",
+    "    for key in tmp_grid.keys():\n",
+    "        tmp_grid[key] = np.empty((nstns, plevs))\n",
+    "        for station, loc in zip(stns, range(nstns)):\n",
+    "            if key == 'pressure':\n",
+    "                lats.append(data[station].latitude[0])\n",
+    "                lons.append(data[station].longitude[0])\n",
+    "                elev.append(data[station].elevation[0])\n",
+    "                tmp_grid[key][loc, :] = vertical_levels\n",
+    "            else:\n",
+    "                tmp_grid[key][loc, :] = vertical_interpolate(\n",
+    "                    data[station]['pressure'].values, data[station][key].values,\n",
+    "                    vertical_levels.m)\n",
+    "\n",
+    "    # Compute distance between each station using Pyproj\n",
+    "    g = Geod(ellps='sphere')\n",
+    "    _, _, dist = g.inv(nstns*[lons[0]], nstns*[lats[0]], lons[:], lats[:])\n",
+    "\n",
+    "    # Compute sudo ground elevation in pressure from standard atmsophere and the elevation\n",
+    "    # of each station\n",
+    "    ground_elevation = mpcalc.height_to_pressure_std(np.array(elev) * units('meters'))\n",
+    "\n",
+    "    # Set up grid for 2D interpolation\n",
+    "    grid = dict.fromkeys(keys)\n",
+    "    x = np.linspace(dist[0], dist[-1], 100)\n",
+    "    nx = len(x)\n",
+    "\n",
+    "    pp, xx = np.meshgrid(vertical_levels.m, x)\n",
+    "    pdist, ddist = np.meshgrid(vertical_levels.m, dist)\n",
+    "\n",
+    "    # Interpolate to 2D grid using scipy.interpolate.griddata\n",
+    "    for key in grid.keys():\n",
+    "        grid[key] = np.empty((nx, plevs)) * units(data_units[key])\n",
+    "        grid[key][:] = griddata((ddist.flatten(), pdist.flatten()),\n",
+    "                                tmp_grid[key][:].flatten(),\n",
+    "                                (xx, pp),\n",
+    "                                method='cubic') * units(data_units[key])\n",
+    "\n",
+    "    # Gather needed data in dictionary for return\n",
+    "    cross_section = {'grid_data': grid, 'obs_distance': dist * units.meter,\n",
+    "                     'x_grid': xx * units.meter, 'p_grid': pp * units.hPa, 'elevation': ground_elevation}\n",
+    "    return cross_section"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Stations and Time\n",
+    "-----------------\n",
+    "\n",
+    "Select cross section stations by creating a list of three-letter\n",
+    "identifiers and choose a date by creating a datetime object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A roughly east-west cross section\n",
+    "stn_list = ['DNR', 'LBF', 'OAX', 'DVN', 'DTX', 'BUF']\n",
+    "\n",
+    "# Set a date and hour of your choosing\n",
+    "date = datetime(2019, 6, 1, 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Get Radiosonde Data\n",
+    "-------------------\n",
+    "\n",
+    "This example is built around the data from the University of Wyoming\n",
+    "sounding archive and using the Siphon package to remotely access that\n",
+    "data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up empty dictionary to fill with Wyoming Sounding data\n",
+    "df = {}\n",
+    "\n",
+    "# Loop over stations to get data and put into dictionary\n",
+    "for station in stn_list:\n",
+    "    df[station] = WyomingUpperAir.request_data(date, station)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Create Interpolated fields\n",
+    "--------------------------\n",
+    "\n",
+    "Use the function ``radisonde_cross_section`` to generate the 2D grid (x,\n",
+    "p) for all radiosonde variables including, Temperature, Dewpoint,\n",
+    "u-component of the wind, and v-component of the wind."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xsect = radiosonde_cross_section(stn_list, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Calculate Variables for Plotting\n",
+    "--------------------------------\n",
+    "\n",
+    "Use MetPy to calculate common variables for plotting a cross section,\n",
+    "specifically potential temperature and mixing ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "potemp = mpcalc.potential_temperature(\n",
+    "    xsect['p_grid'], xsect['grid_data']['temperature'])\n",
+    "\n",
+    "relhum = mpcalc.relative_humidity_from_dewpoint(\n",
+    "    xsect['grid_data']['temperature'],\n",
+    "    xsect['grid_data']['dewpoint'])\n",
+    "\n",
+    "mixrat = mpcalc.mixing_ratio_from_relative_humidity(xsect['p_grid'],\n",
+    "                                                    xsect['grid_data']['temperature'],\n",
+    "                                                    relhum)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Plot Cross Section\n",
+    "------------------\n",
+    "\n",
+    "Use standard Matplotlib to plot the now 2D cross section grid using the\n",
+    "data from xsect and those calculated above. Additionally, the actualy\n",
+    "radiosonde wind observations are plotted as barbs on this plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Start Figure, set big size for cross section\n",
+    "fig = plt.figure(figsize=(17, 11))\n",
+    "\n",
+    "# Specify plotting axis (single panel)\n",
+    "ax = plt.subplot(111)\n",
+    "\n",
+    "# Set y-scale to be log since pressure decreases exponentially with height\n",
+    "ax.set_yscale('log')\n",
+    "\n",
+    "# Set limits, tickmarks, and ticklabels for y-axis\n",
+    "ax.set_ylim([1030, 101])\n",
+    "ax.set_yticks(range(1000, 101, -100))\n",
+    "ax.set_yticklabels(range(1000, 101, -100))\n",
+    "\n",
+    "# Invert the y-axis since pressure decreases with increasing height\n",
+    "ax.yaxis_inverted()\n",
+    "\n",
+    "# Plot the sudo elevation on the cross section\n",
+    "ax.fill_between(xsect['obs_distance'], xsect['elevation'].m, 1030,\n",
+    "                where=xsect['elevation'].m <= 1030, facecolor='lightgrey',\n",
+    "                interpolate=True, zorder=10)\n",
+    "# Don't plot xticks\n",
+    "plt.xticks([], [])\n",
+    "\n",
+    "# Plot wind barbs for each sounding location\n",
+    "for stn, stn_name in zip(range(len(stn_list)), stn_list):\n",
+    "    ax.axvline(xsect['obs_distance'][stn], ymin=0, ymax=1,\n",
+    "               linewidth=2, color='blue', zorder=11)\n",
+    "    ax.text(xsect['obs_distance'][stn], 1100, stn_name, ha='center', color='blue')\n",
+    "    ax.barbs(xsect['obs_distance'][stn], df[stn_name]['pressure'][::2],\n",
+    "             df[stn_name]['u_wind'].values[::2, None],\n",
+    "             df[stn_name]['v_wind'].values[::2, None], zorder=15)\n",
+    "\n",
+    "# Plot smoothed potential temperature grid (K)\n",
+    "cs = ax.contour(xsect['x_grid'], xsect['p_grid'], mpcalc.smooth_gaussian(potemp, 1),\n",
+    "                range(0, 500, 5), colors='red')\n",
+    "ax.clabel(cs, fmt='%i')\n",
+    "\n",
+    "# Plot smoothed mixing ratio grid (g/kg)\n",
+    "cs = ax.contour(xsect['x_grid'], xsect['p_grid'], mpcalc.smooth_gaussian(mixrat*1000, 2),\n",
+    "                range(0, 41, 2), colors='tab:green', linestyles='dotted')\n",
+    "ax.clabel(cs, fmt='%i')\n",
+    "\n",
+    "# Add some informative titles\n",
+    "plt.title('Cross-Section from DNR to BUF Potential Temp. '\n",
+    "          '(K; red) and Mix. Rat. (g/kg; green)', loc='left')\n",
+    "plt.title(date, loc='right');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/specialty/Smoothing_Contours.ipynb b/_preview/13/_sources/notebooks/specialty/Smoothing_Contours.ipynb
new file mode 100644
index 0000000..7b995ae
--- /dev/null
+++ b/_preview/13/_sources/notebooks/specialty/Smoothing_Contours.ipynb
@@ -0,0 +1,258 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Smoothing Contours\n",
+    "==================\n",
+    "\n",
+    "Demonstrate how to smooth contour values from a higher resolution\n",
+    "model field.\n",
+    "\n",
+    "By: Kevin Goebbert\n",
+    "\n",
+    "Date: 13 April 2017"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "##################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Do the needed imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "import numpy as np\n",
+    "from metpy.units import units\n",
+    "from siphon.catalog import TDSCatalog\n",
+    "from xarray.backends import NetCDF4DataStore\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "##################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Set up netCDF Subset Service link"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify our date/time of product desired\n",
+    "dt = datetime.utcnow()\n",
+    "\n",
+    "# Construct the URL for our THREDDS Data Server Catalog,\n",
+    "# and access our desired dataset within via NCSS\n",
+    "cat = TDSCatalog('https://thredds.ucar.edu/thredds/catalog/grib/'\n",
+    "                 'NCEP/NAM/CONUS_12km/latest.xml')\n",
+    "ncss = cat.datasets[0].subset()\n",
+    "\n",
+    "# Create our NCSS query with desired specifications\n",
+    "query = ncss.query()\n",
+    "query.time(dt)\n",
+    "query.add_lonlat()\n",
+    "query.variables('Geopotential_height_isobaric',\n",
+    "                'u-component_of_wind_isobaric',\n",
+    "                'v-component_of_wind_isobaric')\n",
+    "\n",
+    "# Obtain the data we've queried for\n",
+    "data = ncss.get_data(query)\n",
+    "\n",
+    "# Make into an xarray Dataset object\n",
+    "ds = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "##################################"
+   },
+   "source": [
+    "Pull apart the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get dimension names to pull appropriate variables\n",
+    "# dtime = ds.Geopotential_height_isobaric.dims[0]\n",
+    "# dlev = ds.Geopotential_height_isobaric.dims[1]\n",
+    "# dlat = ds.Geopotential_height_isobaric.dims[2]\n",
+    "# dlon = ds.Geopotential_height_isobaric.dims[3]\n",
+    "\n",
+    "# Get lat and lon data, as well as time data and metadata\n",
+    "lats = ds.lat\n",
+    "lons = ds.lon\n",
+    "\n",
+    "# Need 2D lat/lons for plotting, do so if necessary\n",
+    "if lats.ndim < 2:\n",
+    "    lons, lats = np.meshgrid(lons, lats)\n",
+    "\n",
+    "# Determine the level of 500 hPa\n",
+    "lev_500 = 500 * units.hPa\n",
+    "\n",
+    "# Create more useable times for output\n",
+    "times = ds.Geopotential_height_isobaric.metpy.time.squeeze()\n",
+    "vtimes = times.values.astype('datetime64[ms]').astype('O')\n",
+    "\n",
+    "# Pull out the 500 hPa Heights\n",
+    "hght_500 = ds.Geopotential_height_isobaric.metpy.sel(\n",
+    "    vertical=lev_500).squeeze()\n",
+    "uwnd_500 = ds['u-component_of_wind_isobaric'].metpy.sel(\n",
+    "    vertical=lev_500).squeeze()\n",
+    "vwnd_500 = ds['v-component_of_wind_isobaric'].metpy.sel(\n",
+    "    vertical=lev_500).squeeze()\n",
+    "\n",
+    "# Calculate the magnitude of the wind speed in kts\n",
+    "sped = mpcalc.wind_speed(uwnd_500, vwnd_500).metpy.convert_units('knots')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "##################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Set up the projection for LCC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotcrs = ccrs.LambertConformal(central_longitude=-100.0,\n",
+    "                                central_latitude=45.0)\n",
+    "datacrs = ccrs.PlateCarree(central_longitude=0.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "##################################"
+   },
+   "source": [
+    "Subset and smooth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Smooth the 500-hPa geopotential height field\n",
+    "# Be sure to only smooth the 2D field\n",
+    "Z_500 = mpcalc.smooth_gaussian(hght_500, 50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "##################################"
+   },
+   "source": [
+    "Plot the contours"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Start plot with new figure and axis\n",
+    "fig = plt.figure(figsize=(17., 11.))\n",
+    "ax = plt.subplot(1, 1, 1, projection=plotcrs)\n",
+    "\n",
+    "# Add some titles to make the plot readable by someone else\n",
+    "plt.title('500-hPa Geo Heights (m; black), Smoothed 500-hPa Geo. '\n",
+    "          'Heights (m; red)', loc='left')\n",
+    "plt.title(f'VALID: {vtimes}', loc='right')\n",
+    "\n",
+    "# Set GAREA and add map features\n",
+    "ax.set_extent([-125., -67., 22., 52.], ccrs.PlateCarree())\n",
+    "ax.coastlines('50m', edgecolor='black', linewidth=0.75)\n",
+    "ax.add_feature(cfeature.STATES, linewidth=0.5)\n",
+    "\n",
+    "# Set the CINT\n",
+    "clev500 = np.arange(5100, 6000, 60)\n",
+    "\n",
+    "# Plot smoothed 500-hPa contours\n",
+    "cs2 = ax.contour(lons, lats, Z_500, clev500, colors='black',\n",
+    "                 linewidths=3, linestyles='solid', transform=datacrs)\n",
+    "c2 = plt.clabel(cs2, fontsize=12, colors='black', inline=1, inline_spacing=8,\n",
+    "                fmt='%i', rightside_up=True, use_clabeltext=True)\n",
+    "\n",
+    "# Contour the 500 hPa heights with labels\n",
+    "cs = ax.contour(lons, lats, hght_500, clev500, colors='red',\n",
+    "                linewidths=2.5, linestyles='solid', alpha=0.6,\n",
+    "                transform=datacrs)\n",
+    "cl = plt.clabel(cs, fontsize=12, colors='red', inline=1, inline_spacing=8,\n",
+    "                fmt='%i', rightside_up=True, use_clabeltext=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/specialty/index.md b/_preview/13/_sources/notebooks/specialty/index.md
new file mode 100644
index 0000000..56a86e8
--- /dev/null
+++ b/_preview/13/_sources/notebooks/specialty/index.md
@@ -0,0 +1,3 @@
+# Specialty Plotting
+
+This section shows how to accomplish some specialty plotting, such as cross-sections and smoothing, using MetPy.
\ No newline at end of file
diff --git a/_preview/13/_sources/notebooks/synoptic/250hPa_Hemispheric_Plot.ipynb b/_preview/13/_sources/notebooks/synoptic/250hPa_Hemispheric_Plot.ipynb
new file mode 100644
index 0000000..924fc69
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/250hPa_Hemispheric_Plot.ipynb
@@ -0,0 +1,210 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "A 250-hPa Hemispheric Map using Python\n",
+    "======================================\n",
+    "\n",
+    "This example plots a hemispheric plot of GFS 250-hPa Geopotential Heights and\n",
+    "wind speed in knots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import cartopy.util as cutil\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "import numpy as np\n",
+    "from siphon.catalog import TDSCatalog\n",
+    "from siphon.ncss import NCSS\n",
+    "from xarray.backends import NetCDF4DataStore\n",
+    "import xarray as xr\n",
+    "\n",
+    "# Latest GFS Dataset\n",
+    "cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/'\n",
+    "                 'NCEP/GFS/Global_0p5deg/latest.xml')\n",
+    "best_ds = list(cat.datasets.values())[0]\n",
+    "ncss = NCSS(best_ds.access_urls['NetcdfSubset'])\n",
+    "\n",
+    "# Set the time to current\n",
+    "now = datetime.utcnow()\n",
+    "\n",
+    "# Query for Latest GFS Run\n",
+    "gfsdata_hght = ncss.query().time(now).accept('netcdf4')\n",
+    "gfsdata_hght.variables('Geopotential_height_isobaric')\n",
+    "\n",
+    "# Set the lat/lon box for the data you want to pull in.\n",
+    "# lonlat_box(north_lat,south_lat,east_lon,west_lon)\n",
+    "gfsdata_hght.lonlat_box(0, 359.9, 0, 90)\n",
+    "\n",
+    "# Set desired level 50000 = 50000 Pa = 500 hPa\n",
+    "gfsdata_hght.vertical_level(25000)\n",
+    "\n",
+    "# Actually getting the data\n",
+    "data_hght = ncss.get_data(gfsdata_hght)\n",
+    "\n",
+    "# Make into an xarray Dataset object\n",
+    "ds_hght = xr.open_dataset(NetCDF4DataStore(data_hght))\n",
+    "\n",
+    "# Query for Latest GFS Run\n",
+    "gfsdata_wind = ncss.query().time(now).accept('netcdf4')\n",
+    "gfsdata_wind.variables('u-component_of_wind_isobaric',\n",
+    "                       'v-component_of_wind_isobaric')\n",
+    "\n",
+    "# Set the lat/lon box for the data you want to pull in.\n",
+    "# lonlat_box(north_lat,south_lat,east_lon,west_lon)\n",
+    "gfsdata_wind.lonlat_box(0, 359.9, 0, 90)\n",
+    "\n",
+    "# Set desired level 50000 = 50000 Pa = 500 hPa\n",
+    "gfsdata_wind.vertical_level(25000)\n",
+    "\n",
+    "# Actually getting the data\n",
+    "data_wind = ncss.get_data(gfsdata_wind)\n",
+    "\n",
+    "# Make into an xarray Dataset object\n",
+    "ds_wind = xr.open_dataset(NetCDF4DataStore(data_wind))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#################################################"
+   },
+   "source": [
+    "The next cell will take the downloaded data and parse it to different variables\n",
+    "for use later on. Add a cyclic point using the cartopy utility add_cyclic_point\n",
+    "to the longitudes (the cyclic dimension) as well as any data that is being\n",
+    "contoured or filled."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lat = ds_hght.latitude.values\n",
+    "lon = ds_hght.longitude.values\n",
+    "\n",
+    "# Converting times using the num2date function available through netCDF4\n",
+    "vtimes = ds_hght.Geopotential_height_isobaric.metpy.time.values.astype('datetime64[ms]').astype('O')\n",
+    "\n",
+    "# Smooth the 250-hPa heights using a gaussian filter from scipy.ndimage\n",
+    "hgt = ds_hght.Geopotential_height_isobaric.squeeze()\n",
+    "hgt_250, lon = cutil.add_cyclic_point(hgt, coord=lon)\n",
+    "\n",
+    "Z_250 = mpcalc.smooth_n_point(hgt_250, 9, 2)\n",
+    "\n",
+    "# Calculate wind speed from u and v components, add cyclic point,\n",
+    "# and smooth slightly\n",
+    "u250 = ds_wind['u-component_of_wind_isobaric'].squeeze()\n",
+    "v250 = ds_wind['v-component_of_wind_isobaric'].squeeze()\n",
+    "\n",
+    "wspd250 = mpcalc.wind_speed(u250, v250).metpy.convert_units('knots')\n",
+    "wspd250 = cutil.add_cyclic_point(wspd250)\n",
+    "\n",
+    "smooth_wspd250 = mpcalc.smooth_n_point(wspd250, 9, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#################################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "The next cell sets up the geographic details for the plot that we are going to do later.\n",
+    "This is done using the Cartopy package. We will also bring in some geographic data to\n",
+    "geo-reference the image for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datacrs = ccrs.PlateCarree()\n",
+    "plotcrs = ccrs.NorthPolarStereo(central_longitude=-100.0)\n",
+    "\n",
+    "# Make a grid of lat/lon values to use for plotting.\n",
+    "lons, lats = np.meshgrid(lon, lat)\n",
+    "\n",
+    "fig = plt.figure(1, figsize=(12., 15.))\n",
+    "ax = plt.subplot(111, projection=plotcrs)\n",
+    "\n",
+    "# Set some titles for the plots\n",
+    "ax.set_title('250-hPa Geopotential Heights (m)', loc='left')\n",
+    "ax.set_title(f'VALID: {vtimes[0]}', loc='right')\n",
+    "\n",
+    "# Set the extent of the image for the NH and add\n",
+    "#   ax.set_extent([west long, east long, south lat, north lat])\n",
+    "ax.set_extent([-180, 180, 10, 90], ccrs.PlateCarree())\n",
+    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'), edgecolor='black',\n",
+    "               linewidth=0.5)\n",
+    "ax.add_feature(cfeature.STATES.with_scale('50m'), linewidth=0.5)\n",
+    "\n",
+    "# Add geopotential height contours every 120 m\n",
+    "clev250 = np.arange(9000, 12000, 120)\n",
+    "cs = ax.contour(lons, lats, Z_250, clev250, colors='k',\n",
+    "                linewidths=1.0, linestyles='solid', transform=datacrs)\n",
+    "plt.clabel(cs, fontsize=8, inline=1, inline_spacing=10, fmt='%i',\n",
+    "           rightside_up=True, use_clabeltext=True)\n",
+    "\n",
+    "# Add colorfilled wind speed in knots every 20 kts\n",
+    "clevsped250 = np.arange(50, 200, 20)\n",
+    "cmap = plt.cm.get_cmap('BuPu')\n",
+    "cf = ax.contourf(lons, lats, smooth_wspd250, clevsped250, cmap=cmap,\n",
+    "                 extend='max', transform=datacrs)\n",
+    "cbar = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
+    "                    extendrect=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/500hPa_Absolute_Vorticity_winds.ipynb b/_preview/13/_sources/notebooks/synoptic/500hPa_Absolute_Vorticity_winds.ipynb
new file mode 100644
index 0000000..127d9a7
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/500hPa_Absolute_Vorticity_winds.ipynb
@@ -0,0 +1,325 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "500 hPa Geopotential Heights, Absolute Vorticity, and Winds\n",
+    "===========================================================\n",
+    "\n",
+    "Classic 500-hPa absolute vorticity plot using NAM analysis file.\n",
+    "\n",
+    "This example uses example data from the NAM analysis for 12 UTC 31\n",
+    "October 2016 and uses xarray as the main read source with using MetPy to\n",
+    "calculate absolute vorticity and wind speed with geographic plotting\n",
+    "using Cartopy for a CONUS view of the 500-hPa geopotential heights,\n",
+    "absolute vorticity, and wind barbs.\n",
+    "\n",
+    "Note: NAM winds are grid relative and this example introduces a simple\n",
+    "function to convert the grid-relative wind components to an\n",
+    "earth-relative (north/east oriented winds) to enable easier plotting of\n",
+    "wind barbs and calculation of absolute vorticity.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Import the needed modules\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "from metpy.units import units\n",
+    "import numpy as np\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Function to Compute Earth-Relative Winds\n",
+    "----------------------------------------\n",
+    "\n",
+    "This function takes a data array with relevant information about the\n",
+    "projection of the wind component data, along with the grid-relative\n",
+    "components of the wind. It outputs the earth-relative components of the\n",
+    "wind.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "lines_to_next_cell": 1
+   },
+   "outputs": [],
+   "source": [
+    "def earth_relative_wind_components(ugrd, vgrd):\n",
+    "    \"\"\"Calculate the north-relative components of the\n",
+    "    wind from the grid-relative components using Cartopy\n",
+    "    transform_vectors.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "        ugrd : Xarray DataArray (M, N)\n",
+    "            grid relative u-component of the wind\n",
+    "        vgrd : Xarray DataArray (M, N)\n",
+    "            grid relative v-component of the wind\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "        unr, vnr : tuple of array-like Quantity\n",
+    "            The north-relative wind components in the X (East-West)\n",
+    "            and Y (North-South) directions, respectively.\n",
+    "    \"\"\"\n",
+    "    if 'metpy_crs' not in ugrd.coords:\n",
+    "        raise ValueError('No CRS in coordinate, be sure to'\n",
+    "                         'use the MetPy accessor parse_cf()')\n",
+    "\n",
+    "    data_crs = ugrd.metpy.cartopy_crs\n",
+    "\n",
+    "    x = ugrd.x.values\n",
+    "    y = ugrd.y.values\n",
+    "\n",
+    "    xx, yy = np.meshgrid(x, y)\n",
+    "\n",
+    "    ut, vt = ccrs.PlateCarree().transform_vectors(data_crs, xx, yy,\n",
+    "                                                  ugrd.values, vgrd.values)\n",
+    "\n",
+    "    # Make a copy of u and v component DataArrays\n",
+    "    uer = ugrd.copy()\n",
+    "    ver = vgrd.copy()\n",
+    "\n",
+    "    # Update values with transformed winds\n",
+    "    uer.values = ut\n",
+    "    ver.values = vt\n",
+    "\n",
+    "    return uer, ver\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "The following code reads the example data using the xarray open_dataset\n",
+    "function and prints the coordinate values that are associated with the\n",
+    "various variables contained within the file\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/casestudies/'\n",
+    "                     'python-gallery/NAM_20161031_1200.nc').metpy.parse_cf()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Data Retrieval\n",
+    "--------------\n",
+    "\n",
+    "This code retrieves the necessary data from the file and completes some\n",
+    "smoothing of the geopotential height and wind fields using the SciPy\n",
+    "function gaussian_filter. A nicely formated valid time (vtime) variable\n",
+    "is also created\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Grab lat/lon values (NAM will be 2D)\n",
+    "lats = ds.lat.data\n",
+    "lons = ds.lon.data\n",
+    "\n",
+    "# Grab x, y data and make 2D for wind component plotting because\n",
+    "# u- and v-components are grid relative\n",
+    "x = ds['u-component_of_wind_isobaric'].x\n",
+    "y = ds['u-component_of_wind_isobaric'].y\n",
+    "\n",
+    "xx, yy = np.meshgrid(x, y)\n",
+    "\n",
+    "# Grab Cartopy CRS from metadata for plotting wind barbs\n",
+    "datacrs = ds['u-component_of_wind_isobaric'].metpy.cartopy_crs\n",
+    "\n",
+    "# Select and grab 500-hPa geopotential heights and smooth with n-point smoother\n",
+    "level = 500 * units.hPa\n",
+    "hght_500 = mpcalc.smooth_n_point(ds.Geopotential_height_isobaric.metpy.sel(\n",
+    "    vertical=level).squeeze(), 9, 50)\n",
+    "\n",
+    "# Select and grab 500-hPa wind components\n",
+    "uwnd_500 = ds['u-component_of_wind_isobaric'].metpy.sel(\n",
+    "    vertical=level).squeeze().metpy.assign_latitude_longitude()\n",
+    "vwnd_500 = ds['v-component_of_wind_isobaric'].metpy.sel(\n",
+    "    vertical=level).squeeze().metpy.assign_latitude_longitude()\n",
+    "\n",
+    "# Compute north-relative wind components for plotting purposes\n",
+    "uwnd_er, vwnd_er = earth_relative_wind_components(uwnd_500, vwnd_500)\n",
+    "\n",
+    "# Smooth wind components as desired\n",
+    "uwnd_er = mpcalc.smooth_n_point(uwnd_er, 9, 50)\n",
+    "vwnd_er = mpcalc.smooth_n_point(vwnd_er, 9, 50)\n",
+    "\n",
+    "# Create a clean datetime object for plotting based on time\n",
+    "# of Geopotential heights\n",
+    "vtime = ds.time.data[0].astype('datetime64[ms]').astype('O')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "MetPy Absolute Vorticity Calculation\n",
+    "------------------------------------\n",
+    "\n",
+    "This code calculates ``absolute_vorticity()``\n",
+    "using the wind components and makes use of the\n",
+    "xarray data object features to complete the necessary\n",
+    "grid-aware calculation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate absolute vorticity from MetPy function\n",
+    "avor_500 = mpcalc.absolute_vorticity(uwnd_er, vwnd_er)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Map Creation\n",
+    "------------\n",
+    "\n",
+    "This next set of code creates the plot and draws contours on a Lambert\n",
+    "Conformal map centered on -100 E longitude. The main view is over the\n",
+    "CONUS with geopotential heights contoured every 60 m and absolute\n",
+    "vorticity colorshaded (`*10^5`).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up the projection that will be used for plotting\n",
+    "mapcrs = ccrs.LambertConformal(central_longitude=-100, central_latitude=35,\n",
+    "                               standard_parallels=(30, 60))\n",
+    "\n",
+    "# Set up the projection of the data;\n",
+    "# if lat/lon then PlateCarree is what you want\n",
+    "datacrs = ccrs.PlateCarree()\n",
+    "\n",
+    "# Start the figure and create plot axes with proper projection\n",
+    "fig = plt.figure(1, figsize=(14, 12))\n",
+    "ax = plt.subplot(111, projection=mapcrs)\n",
+    "ax.set_extent([-130, -72, 20, 55], ccrs.PlateCarree())\n",
+    "\n",
+    "# Add geopolitical boundaries for map reference\n",
+    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
+    "ax.add_feature(cfeature.STATES.with_scale('50m'))\n",
+    "\n",
+    "# Absolute Vorticity colors\n",
+    "# Use two different colormaps from matplotlib and combine into one color set\n",
+    "clevs_500_avor = list(range(-8, 1, 1))+list(range(8, 46, 1))\n",
+    "colors1 = plt.cm.YlOrRd(np.linspace(0, 1, 48))\n",
+    "colors2 = plt.cm.BuPu(np.linspace(0.5, 0.75, 8))\n",
+    "colors = np.vstack((colors2, (1, 1, 1, 1), colors1))\n",
+    "\n",
+    "# Plot absolute vorticity values (multiplying by 10^5 to scale appropriately)\n",
+    "cf = ax.contourf(lons, lats, avor_500*1e5, clevs_500_avor, colors=colors,\n",
+    "                 extend='max', transform=datacrs)\n",
+    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
+    "                  extendrect=True)\n",
+    "cb.set_label('Abs. Vorticity ($s^{-1}$)')\n",
+    "\n",
+    "# Plot 500-hPa Geopotential Heights in meters\n",
+    "clevs_500_hght = np.arange(0, 8000, 60)\n",
+    "cs = ax.contour(lons, lats, hght_500, clevs_500_hght, colors='black',\n",
+    "                transform=datacrs)\n",
+    "plt.clabel(cs, fmt='%d')\n",
+    "\n",
+    "# Set up a 2D slice to reduce the number of wind barbs plotted (every 20th)\n",
+    "wind_slice = (slice(None, None, 20), slice(None, None, 20))\n",
+    "ax.barbs(lons[wind_slice], lats[wind_slice],\n",
+    "         uwnd_er.metpy.convert_units('kt')[wind_slice].values,\n",
+    "         vwnd_er[wind_slice].metpy.convert_units('kt').values,\n",
+    "         pivot='middle', color='black', transform=datacrs)\n",
+    "\n",
+    "# Plot two titles, one on right and left side\n",
+    "plt.title('500-hPa NAM Geopotential Heights (m)'\n",
+    "          ' and Wind Barbs (kt)', loc='left')\n",
+    "plt.title(f'Valid Time: {vtime}', loc='right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/500hPa_Vorticity_Advection.ipynb b/_preview/13/_sources/notebooks/synoptic/500hPa_Vorticity_Advection.ipynb
new file mode 100644
index 0000000..3b7312c
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/500hPa_Vorticity_Advection.ipynb
@@ -0,0 +1,349 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "500 hPa Vorticity Advection\n",
+    "===========================\n",
+    "\n",
+    "Plot an 500-hPa map with calculating vorticity advection using MetPy calculations.\n",
+    "\n",
+    "Beyond just plotting 500-hPa level data, this uses calculations from `metpy.calc` to find\n",
+    "the vorticity and vorticity advection. Currently, this needs an extra helper function to\n",
+    "calculate the distance between lat/lon grid points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "from metpy.units import units\n",
+    "import numpy as np\n",
+    "from siphon.catalog import TDSCatalog\n",
+    "from xarray.backends import NetCDF4DataStore\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Function to Compute Earth-Relative Winds\n",
+    "----------------------------------------\n",
+    "\n",
+    "This function takes a data array with relevant information about the\n",
+    "projection of the wind component data, along with the grid-relative\n",
+    "components of the wind. It outputs the earth-relative components of the\n",
+    "wind.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "lines_to_next_cell": 1
+   },
+   "outputs": [],
+   "source": [
+    "def earth_relative_wind_components(ugrd, vgrd):\n",
+    "    \"\"\"Calculate the north-relative components of the\n",
+    "    wind from the grid-relative components using Cartopy\n",
+    "    transform_vectors.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "        ugrd : Xarray DataArray (M, N)\n",
+    "            grid relative u-component of the wind\n",
+    "        vgrd : Xarray DataArray (M, N)\n",
+    "            grid relative v-component of the wind\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "        unr, vnr : tuple of array-like Quantity\n",
+    "            The north-relative wind components in the X (East-West)\n",
+    "            and Y (North-South) directions, respectively.\n",
+    "    \"\"\"\n",
+    "    if 'metpy_crs' not in ugrd.coords:\n",
+    "        raise ValueError('No CRS in coordinate, be sure to use'\n",
+    "                         'the MetPy accessor parse_cf()')\n",
+    "\n",
+    "    data_crs = ugrd.metpy.cartopy_crs\n",
+    "\n",
+    "    x = ugrd.x.values\n",
+    "    y = ugrd.y.values\n",
+    "\n",
+    "    xx, yy = np.meshgrid(x, y)\n",
+    "\n",
+    "    ut, vt = ccrs.PlateCarree().transform_vectors(data_crs, xx, yy,\n",
+    "                                                  ugrd.values, vgrd.values)\n",
+    "\n",
+    "    # Make a copy of u and v component DataArrays\n",
+    "    uer = ugrd.copy()\n",
+    "    ver = vgrd.copy()\n",
+    "\n",
+    "    # Update values with transformed winds\n",
+    "    uer.values = ut\n",
+    "    ver.values = vt\n",
+    "\n",
+    "    return uer, ver\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#######################################"
+   },
+   "source": [
+    "Data Aquisition\n",
+    "---------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt = datetime(2016, 4, 16, 18)\n",
+    "\n",
+    "# Assemble our URL to the THREDDS Data Server catalog,\n",
+    "# and access our desired dataset within via NCSS\n",
+    "base_url = 'https://www.ncei.noaa.gov/thredds/catalog/model-namanl-old/'\n",
+    "cat = TDSCatalog(f'{base_url}{dt:%Y%m}/{dt:%Y%m%d}/catalog.xml')\n",
+    "ncss = cat.datasets[f'namanl_218_{dt:%Y%m%d}_{dt:%H}00_000.grb'].subset()\n",
+    "\n",
+    "# Query for Latest GFS Run\n",
+    "query = ncss.query()\n",
+    "\n",
+    "query.time(dt)\n",
+    "query.accept('netcdf')\n",
+    "query.variables('Geopotential_height_isobaric',\n",
+    "                'u-component_of_wind_isobaric',\n",
+    "                'v-component_of_wind_isobaric')\n",
+    "\n",
+    "# Obtain our queried data\n",
+    "data = ncss.get_data(query)\n",
+    "ds_data = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()\n",
+    "ds = ds_data.metpy.assign_latitude_longitude()\n",
+    "\n",
+    "times = ds.Geopotential_height_isobaric.metpy.time\n",
+    "vtime = times.values.squeeze().astype('datetime64[ms]').astype('O')\n",
+    "\n",
+    "lev_500 = 500 * units.hPa\n",
+    "\n",
+    "hght_500 = ds.Geopotential_height_isobaric.metpy.sel(\n",
+    "    vertical=lev_500).squeeze()\n",
+    "hght_500 = mpcalc.smooth_gaussian(hght_500, 4)\n",
+    "\n",
+    "uwnd_500 = ds['u-component_of_wind_isobaric'].metpy.sel(\n",
+    "    vertical=lev_500).squeeze()\n",
+    "vwnd_500 = ds['v-component_of_wind_isobaric'].metpy.sel(\n",
+    "    vertical=lev_500).squeeze()\n",
+    "\n",
+    "# Compute north-relative wind components for calculation purposes\n",
+    "uwnd_500er, vwnd_500er = earth_relative_wind_components(uwnd_500, vwnd_500)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#######################################"
+   },
+   "source": [
+    "Begin Data Calculations\n",
+    "-----------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/fp/_l45s1m93s5009vb713pyg580000gn/T/ipykernel_51141/3729036084.py:5: UserWarning: Vertical dimension number not found. Defaulting to (..., Z, Y, X) order.\n",
+      "  vort_adv = mpcalc.advection(avor, uwnd_500er, vwnd_500er) * 1e4\n"
+     ]
+    }
+   ],
+   "source": [
+    "avor = mpcalc.vorticity(uwnd_500er, vwnd_500er)\n",
+    "\n",
+    "avor = mpcalc.smooth_n_point(avor, 9, 10) * 1e5\n",
+    "\n",
+    "vort_adv = mpcalc.advection(avor, uwnd_500er, vwnd_500er) * 1e4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#######################################"
+   },
+   "source": [
+    "Map Creation\n",
+    "------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.quiver.Barbs at 0x156fd2810>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n",
+      "  return lib.buffer(\n",
+      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
+      "  return lib.intersects(a, b, **kwargs)\n",
+      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:688: RuntimeWarning: invalid value encountered in covers\n",
+      "  return lib.covers(a, b, **kwargs)\n",
+      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:730: RuntimeWarning: invalid value encountered in disjoint\n",
+      "  return lib.disjoint(a, b, **kwargs)\n",
+      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n",
+      "  return lib.buffer(\n",
+      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
+      "  return lib.intersects(a, b, **kwargs)\n",
+      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n",
+      "  return lib.buffer(\n",
+      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
+      "  return lib.intersects(a, b, **kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x1400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set up Coordinate System for Plot and Transforms\n",
+    "datacrs = ds.Geopotential_height_isobaric.metpy.cartopy_crs\n",
+    "plotcrs = ccrs.LambertConformal(central_latitude=45., central_longitude=-100.,\n",
+    "                                standard_parallels=[30, 60])\n",
+    "\n",
+    "fig = plt.figure(1, figsize=(12., 14.))\n",
+    "ax = plt.subplot(111, projection=plotcrs)\n",
+    "\n",
+    "# Plot Titles\n",
+    "plt.title(r'500-hPa Heights (m), AVOR$*10^5$ ($s^{-1}$),'\n",
+    "          'AVOR Adv$*10^9$ ($s^{-2}$)', loc='left')\n",
+    "plt.title(f'VALID: {vtime}', loc='right')\n",
+    "\n",
+    "# Plot Background\n",
+    "ax.set_extent([235., 290., 20., 58.], ccrs.PlateCarree())\n",
+    "ax.coastlines('50m', edgecolor='black', linewidth=0.75)\n",
+    "ax.add_feature(cfeature.STATES, linewidth=.5)\n",
+    "\n",
+    "# Plot Height Contours\n",
+    "clev500 = np.arange(5100, 6061, 60)\n",
+    "cs = ax.contour(hght_500.longitude, hght_500.latitude,\n",
+    "                hght_500, clev500,\n",
+    "                colors='black', linewidths=1.0,\n",
+    "                linestyles='solid', transform=ccrs.PlateCarree())\n",
+    "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=10, fmt='%i',\n",
+    "           rightside_up=True, use_clabeltext=True)\n",
+    "\n",
+    "# Plot Absolute Vorticity Contours\n",
+    "clevvort500 = np.arange(-9, 50, 5)\n",
+    "cs2 = ax.contour(avor.longitude, avor.latitude,\n",
+    "                 avor, clevvort500,\n",
+    "                 colors='grey', linewidths=1.25, linestyles='dashed',\n",
+    "                 transform=ccrs.PlateCarree())\n",
+    "plt.clabel(cs2, fontsize=10, inline=1, inline_spacing=10, fmt='%i',\n",
+    "           rightside_up=True, use_clabeltext=True)\n",
+    "\n",
+    "# Plot Colorfill of Vorticity Advection\n",
+    "clev_avoradv = np.arange(-30, 31, 5)\n",
+    "cf = ax.contourf(vort_adv.longitude, vort_adv.latitude, vort_adv,\n",
+    "                 clev_avoradv[clev_avoradv != 0], extend='both',\n",
+    "                 cmap='bwr', transform=ccrs.PlateCarree())\n",
+    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
+    "                  extendrect='True', ticks=clev_avoradv)\n",
+    "cb.set_label(r'$1/s^2$', size='large')\n",
+    "\n",
+    "# Plot Wind Barbs\n",
+    "# Transform Vectors and plot wind barbs.\n",
+    "wind_slice = (slice(None, None, 20), slice(None, None, 20))\n",
+    "xx, yy = np.meshgrid(uwnd_500.x.values[wind_slice[0]],\n",
+    "                     uwnd_500.y.values[wind_slice[0]])\n",
+    "ax.barbs(xx, yy, uwnd_500.values[wind_slice], vwnd_500.values[wind_slice],\n",
+    "         length=6, pivot='middle', transform=datacrs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/850hPa_Frontogenesis.ipynb b/_preview/13/_sources/notebooks/synoptic/850hPa_Frontogenesis.ipynb
new file mode 100644
index 0000000..613f29a
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/850hPa_Frontogenesis.ipynb
@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "850-hPa Geopotential Heights, Temperature, Frontogenesis, and Winds\n",
+    "===================================================================\n",
+    "\n",
+    "Frontogenesis at 850-hPa with Geopotential Heights, Temperature, and\n",
+    "Winds\n",
+    "\n",
+    "This example uses example data from the GFS analysis for 12 UTC 26\n",
+    "October 2010 and uses xarray as the main read source with using MetPy to\n",
+    "calculate frontogenesis and wind speed with geographic plotting using\n",
+    "Cartopy for a CONUS view.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Import the needed modules.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "from metpy.units import units\n",
+    "import numpy as np\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Use Xarray to access GFS data from THREDDS resource and uses\n",
+    "metpy accessor to parse file to make it easy to pull data using\n",
+    "common coordinate names (e.g., vertical) and attach units.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/casestudies/'\n",
+    "                     'python-gallery/GFS_20101026_1200.nc').metpy.parse_cf()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Subset data based on latitude and longitude values, calculate potential\n",
+    "temperature for frontogenesis calculation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set subset slice for the geographic extent of data to limit download\n",
+    "lon_slice = slice(200, 350)\n",
+    "lat_slice = slice(85, 10)\n",
+    "\n",
+    "# Grab lat/lon values (GFS will be 1D)\n",
+    "lats = ds.lat.sel(lat=lat_slice).values\n",
+    "lons = ds.lon.sel(lon=lon_slice).values\n",
+    "\n",
+    "level = 850 * units.hPa\n",
+    "hght_850 = ds.Geopotential_height_isobaric.metpy.sel(\n",
+    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze()\n",
+    "tmpk_850 = ds.Temperature_isobaric.metpy.sel(\n",
+    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze()\n",
+    "uwnd_850 = ds['u-component_of_wind_isobaric'].metpy.sel(\n",
+    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze()\n",
+    "vwnd_850 = ds['v-component_of_wind_isobaric'].metpy.sel(\n",
+    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze()\n",
+    "\n",
+    "# Convert temperatures to degree Celsius for plotting purposes\n",
+    "tmpc_850 = tmpk_850.metpy.convert_units('degC')\n",
+    "\n",
+    "# Calculate potential temperature for frontogenesis calculation\n",
+    "thta_850 = mpcalc.potential_temperature(level, tmpk_850)\n",
+    "\n",
+    "# Get a sensible datetime format\n",
+    "vtime = ds.time.data[0].astype('datetime64[ms]').astype('O')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Calculate frontogenesis\n",
+    "-----------------------\n",
+    "\n",
+    "Frontogenesis calculation in MetPy requires temperature, wind\n",
+    "components, and grid spacings. First compute the grid deltas using MetPy\n",
+    "functionality, then put it all together in the frontogenesis function.\n",
+    "\n",
+    "Note: MetPy will give the output with SI units, but typically\n",
+    "frontogenesis (read: GEMPAK) output this variable with units of K per\n",
+    "100 km per 3 h; a conversion factor is included here to use at plot time\n",
+    "to reflect those units.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fronto_850 = mpcalc.frontogenesis(thta_850, uwnd_850, vwnd_850)\n",
+    "\n",
+    "# A conversion factor to get frontogensis units of K per 100 km per 3 h\n",
+    "convert_to_per_100km_3h = 1000*100*3600*3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Plotting Frontogenesis\n",
+    "----------------------\n",
+    "\n",
+    "Using a Lambert Conformal projection from Cartopy to plot 850-hPa\n",
+    "variables including frontogenesis.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set map projection\n",
+    "mapcrs = ccrs.LambertConformal(central_longitude=-100, central_latitude=35,\n",
+    "                               standard_parallels=(30, 60))\n",
+    "\n",
+    "# Set projection of the data (GFS is lat/lon)\n",
+    "datacrs = ccrs.PlateCarree()\n",
+    "\n",
+    "# Start figure and limit the graphical area extent\n",
+    "fig = plt.figure(1, figsize=(14, 12))\n",
+    "ax = plt.subplot(111, projection=mapcrs)\n",
+    "ax.set_extent([-130, -72, 20, 55], ccrs.PlateCarree())\n",
+    "\n",
+    "# Add map features of Coastlines and States\n",
+    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
+    "ax.add_feature(cfeature.STATES.with_scale('50m'))\n",
+    "\n",
+    "# Plot 850-hPa Frontogenesis\n",
+    "clevs_tmpc = np.arange(-40, 41, 2)\n",
+    "cf = ax.contourf(lons, lats, fronto_850*convert_to_per_100km_3h,\n",
+    "                 np.arange(-8, 8.5, 0.5), cmap=plt.cm.bwr, extend='both',\n",
+    "                 transform=datacrs)\n",
+    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
+    "                  extendrect=True)\n",
+    "cb.set_label('Frontogenesis K / 100 km / 3 h')\n",
+    "\n",
+    "# Plot 850-hPa Temperature in Celsius\n",
+    "csf = ax.contour(lons, lats, tmpc_850, clevs_tmpc, colors='grey',\n",
+    "                 linestyles='dashed', transform=datacrs)\n",
+    "plt.clabel(csf, fmt='%d')\n",
+    "\n",
+    "# Plot 850-hPa Geopotential Heights\n",
+    "clevs_850_hght = np.arange(0, 8000, 30)\n",
+    "cs = ax.contour(lons, lats, hght_850, clevs_850_hght, colors='black',\n",
+    "                transform=datacrs)\n",
+    "plt.clabel(cs, fmt='%d')\n",
+    "\n",
+    "# Plot 850-hPa Wind Barbs only plotting every fifth barb\n",
+    "wind_slice = (slice(None, None, 5), slice(None, None, 5))\n",
+    "ax.barbs(lons[wind_slice[0]], lats[wind_slice[1]],\n",
+    "         uwnd_850[wind_slice].metpy.convert_units('kt').values,\n",
+    "         vwnd_850[wind_slice].metpy.convert_units('kt').values,\n",
+    "         color='black', transform=datacrs)\n",
+    "\n",
+    "# Plot some titles\n",
+    "plt.title('GFS 850-hPa Geopotential Heights (m), Temp (C), and Winds',\n",
+    "          loc='left')\n",
+    "plt.title(f'Valid Time: {vtime}', loc='right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/850hPa_QVectors.ipynb b/_preview/13/_sources/notebooks/synoptic/850hPa_QVectors.ipynb
new file mode 100644
index 0000000..60aa1a7
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/850hPa_QVectors.ipynb
@@ -0,0 +1,240 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "QVector Example\n",
+    "===============\n",
+    "\n",
+    "Computing Q-vectors and Q-vector divergence for a real case.\n",
+    "\n",
+    "By: Kevin Goebbert\n",
+    "\n",
+    "This example uses GFS output to compute the 850-hPa Q-vectors and\n",
+    "Q-vector divergence for 12 UTC 26 October 2010."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Import needed modules\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "from metpy.plots import named_areas\n",
+    "from metpy.units import units\n",
+    "import numpy as np\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Use Xarray to access GFS data from THREDDS resource and uses\n",
+    "metpy accessor to parse file to make it easy to pull data using\n",
+    "common coordinate names (e.g., vertical) and attach units.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/casestudies/'\n",
+    "                     'python-gallery/GFS_20101026_1200.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Subset data based on latitude and longitude values and select only data\n",
+    "from 850 hPa\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set subset slice for the geographic extent of data to limit download\n",
+    "lon_slice = slice(200, 350)\n",
+    "lat_slice = slice(85, 10)\n",
+    "\n",
+    "# Grab data and smooth using a nine-point filter applied 50 times to grab\n",
+    "# the synoptic signal\n",
+    "level = 850 * units.hPa\n",
+    "hght_850 = mpcalc.smooth_n_point(ds.Geopotential_height_isobaric.metpy.sel(\n",
+    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze(), 9, 50)\n",
+    "tmpk_850 = mpcalc.smooth_n_point(ds.Temperature_isobaric.metpy.sel(\n",
+    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze(), 9, 25)\n",
+    "uwnd_850 = mpcalc.smooth_n_point(ds['u-component_of_wind_isobaric'].metpy.sel(\n",
+    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze(), 9, 50)\n",
+    "vwnd_850 = mpcalc.smooth_n_point(ds['v-component_of_wind_isobaric'].metpy.sel(\n",
+    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze(), 9, 50)\n",
+    "\n",
+    "# Grab lat/lon values (GFS will be 1D) from a subset variable\n",
+    "lons, lats = hght_850.lon, hght_850.lat\n",
+    "\n",
+    "# Convert temperatures to degree Celsius for plotting purposes\n",
+    "tmpc_850 = tmpk_850.metpy.convert_units('degC')\n",
+    "\n",
+    "# Get a sensible datetime format\n",
+    "vtime = ds.time.data[0].astype('datetime64[ms]').astype('O')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Compute Q-vectors\n",
+    "-----------------\n",
+    "\n",
+    "Use the MetPy module to compute Q-vectors from requisite data and\n",
+    "additionally compute the Q-vector divergence (and multiply by -2) to\n",
+    "calculate the right hand side forcing of the Q-G Omega equation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute grid spacings for data\n",
+    "#dx, dy = mpcalc.lat_lon_grid_deltas(lons, lats)\n",
+    "\n",
+    "# Compute the Q-vector components\n",
+    "uqvect, vqvect = mpcalc.q_vector(uwnd_850, vwnd_850, tmpk_850, 850*units.hPa)\n",
+    "\n",
+    "# Compute the divergence of the Q-vectors calculated above\n",
+    "q_div = -2*mpcalc.divergence(uqvect, vqvect)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Plot Data\n",
+    "---------\n",
+    "\n",
+    "Use Cartopy to plot data on a map using a Lambert Conformal projection.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the map projection (how the data will be displayed)\n",
+    "mapcrs = named_areas['uslcc'].projection\n",
+    "\n",
+    "# Set the data project (GFS is lat/lon format)\n",
+    "datacrs = ccrs.PlateCarree()\n",
+    "\n",
+    "# Start the figure and set an extent to only display a smaller graphics area\n",
+    "fig = plt.figure(1, figsize=(14, 12))\n",
+    "ax = plt.subplot(111, projection=mapcrs)\n",
+    "ax.set_extent(named_areas['uslcc'].bounds, ccrs.PlateCarree())\n",
+    "\n",
+    "# Add map features to plot coastlines and state boundaries\n",
+    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
+    "ax.add_feature(cfeature.STATES.with_scale('50m'))\n",
+    "\n",
+    "# Plot 850-hPa Q-Vector Divergence and scale\n",
+    "clevs_850_tmpc = np.arange(-40, 41, 2)\n",
+    "clevs_qdiv = list(range(-35, -4, 5))+list(range(5, 36, 5))\n",
+    "cf = ax.contourf(lons, lats, q_div*1e18, clevs_qdiv, cmap=plt.cm.bwr,\n",
+    "                 extend='both', transform=datacrs)\n",
+    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
+    "                  extendrect=True, ticks=clevs_qdiv)\n",
+    "cb.set_label('Q-Vector Div. (*10$^{18}$ m s$^{-1}$ kg$^{-1}$)')\n",
+    "\n",
+    "# Plot 850-hPa Temperatures\n",
+    "csf = ax.contour(lons, lats, tmpc_850, clevs_850_tmpc, colors='grey',\n",
+    "                 linestyles='dashed', transform=datacrs)\n",
+    "plt.clabel(csf, fmt='%d')\n",
+    "\n",
+    "# Plot 850-hPa Geopotential Heights\n",
+    "clevs_850_hght = np.arange(0, 8000, 30)\n",
+    "cs = ax.contour(lons, lats, hght_850, clevs_850_hght, colors='black',\n",
+    "                transform=datacrs)\n",
+    "plt.clabel(cs, fmt='%d')\n",
+    "\n",
+    "# Plot 850-hPa Q-vectors, scale to get nice sized arrows\n",
+    "wind_slice = (slice(None, None, 5), slice(None, None, 5))\n",
+    "ax.quiver(lons[wind_slice[0]], lats[wind_slice[1]],\n",
+    "          uqvect[wind_slice].values,\n",
+    "          vqvect[wind_slice].values,\n",
+    "          pivot='mid', color='black',\n",
+    "          scale=5e-12, scale_units='inches',\n",
+    "          transform=datacrs)\n",
+    "\n",
+    "# Add some titles\n",
+    "plt.title('850-hPa GFS Geo. Heights (m), Temp (C),'\n",
+    "          ' and Q-Vectors (m$^2$ kg$^{-1}$ s$^{-1}$)', loc='left')\n",
+    "plt.title(f'Valid Time: {vtime}', loc='right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/850hPa_Temperature_Advection.ipynb b/_preview/13/_sources/notebooks/synoptic/850hPa_Temperature_Advection.ipynb
new file mode 100644
index 0000000..b044ea6
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/850hPa_Temperature_Advection.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "850 hPa Temperature Advection\n",
+    "=============================\n",
+    "\n",
+    "Plot an 850 hPa map with calculating advection using MetPy.\n",
+    "\n",
+    "Beyond just plotting 850-hPa level data, this uses calculations from `metpy.calc` to find\n",
+    "the temperature advection. Currently, this needs an extra helper function to calculate\n",
+    "the distance between lat/lon grid points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "###############################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "import numpy as np\n",
+    "from metpy.units import units\n",
+    "from siphon.catalog import TDSCatalog\n",
+    "from xarray.backends import NetCDF4DataStore\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "###############################################"
+   },
+   "source": [
+    "Create NCSS object to access the NetcdfSubset\n",
+    "---------------------------------------------\n",
+    "Data from NCEI GFS 0.5 deg Analysis Archive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt = datetime(2017, 4, 5, 12)\n",
+    "\n",
+    "# Assemble our URL to the THREDDS Data Server catalog,\n",
+    "# and access our desired dataset within via NCSS\n",
+    "base_url = 'https://www.ncei.noaa.gov/thredds/model-gfs-g4-anl-files-old/'\n",
+    "cat = TDSCatalog(f'{base_url}{dt:%Y%m}/{dt:%Y%m%d}/catalog.xml')\n",
+    "ncss = cat.datasets[f'gfsanl_4_{dt:%Y%m%d}_{dt:%H}00_000.grb2'].subset()\n",
+    "\n",
+    "# Create NCSS query for our desired time, region, and data variables\n",
+    "query = ncss.query()\n",
+    "\n",
+    "query.time(dt)\n",
+    "query.lonlat_box(north=65, south=15, east=310, west=220)\n",
+    "query.accept('netcdf')\n",
+    "query.variables('Geopotential_height_isobaric',\n",
+    "                'Temperature_isobaric',\n",
+    "                'u-component_of_wind_isobaric',\n",
+    "                'v-component_of_wind_isobaric')\n",
+    "\n",
+    "# Obtain the queried data\n",
+    "data = ncss.get_data(query)\n",
+    "\n",
+    "# Make into an xarray Dataset object\n",
+    "ds = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()\n",
+    "\n",
+    "# Pull out variables you want to use\n",
+    "level = 850 * units.hPa\n",
+    "hght_850 = ds.Geopotential_height_isobaric.metpy.sel(\n",
+    "    vertical=level).squeeze()\n",
+    "temp_850 = ds.Temperature_isobaric.metpy.sel(\n",
+    "    vertical=level).squeeze()\n",
+    "u_wind_850 = ds['u-component_of_wind_isobaric'].metpy.sel(\n",
+    "    vertical=level).squeeze()\n",
+    "v_wind_850 = ds['v-component_of_wind_isobaric'].metpy.sel(\n",
+    "    vertical=level).squeeze()\n",
+    "time = hght_850.metpy.time\n",
+    "lat = ds.lat.values\n",
+    "lon = ds.lon.values\n",
+    "\n",
+    "# Convert number of hours since the reference time into an actual date\n",
+    "vtime = time.values.astype('datetime64[ms]').astype('O')\n",
+    "\n",
+    "# Combine 1D latitude and longitudes into a 2D grid of locations\n",
+    "lon_2d, lat_2d = np.meshgrid(lon, lat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "###############################################"
+   },
+   "source": [
+    "Begin data calculations\n",
+    "-----------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate temperature advection using metpy function\n",
+    "adv = mpcalc.advection(temp_850, u_wind_850, v_wind_850)\n",
+    "\n",
+    "# Smooth heights and advection a little\n",
+    "# Be sure to only put in a 2D lat/lon or Y/X array for smoothing\n",
+    "Z_850 = mpcalc.smooth_gaussian(hght_850, 2)\n",
+    "adv = mpcalc.smooth_gaussian(adv, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "###############################################"
+   },
+   "source": [
+    "Begin map creation\n",
+    "------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set Projection of Data\n",
+    "datacrs = ccrs.PlateCarree()\n",
+    "\n",
+    "# Set Projection of Plot\n",
+    "plotcrs = ccrs.LambertConformal(central_latitude=45,\n",
+    "                                central_longitude=-100, standard_parallels=[30, 60])\n",
+    "\n",
+    "# Create new figure\n",
+    "fig = plt.figure(figsize=(14, 12))\n",
+    "\n",
+    "# Add the map and set the extent\n",
+    "ax = plt.subplot(111, projection=plotcrs)\n",
+    "plt.title(f'850mb Temperature Advection for {vtime:%d %B %Y %H:%MZ}',\n",
+    "          fontsize=16)\n",
+    "ax.set_extent([235., 290., 20., 55.])\n",
+    "\n",
+    "# Add state/country boundaries to plot\n",
+    "ax.add_feature(cfeature.STATES)\n",
+    "ax.add_feature(cfeature.BORDERS)\n",
+    "\n",
+    "# Plot Height Contours\n",
+    "clev850 = np.arange(900, 3000, 30)\n",
+    "cs = ax.contour(lon_2d, lat_2d, Z_850, clev850, colors='black', linewidths=1.5,\n",
+    "                linestyles='solid', transform=datacrs)\n",
+    "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=10, fmt='%i',\n",
+    "           rightside_up=True, use_clabeltext=True)\n",
+    "\n",
+    "# Plot Temperature Contours\n",
+    "clevtemp850 = np.arange(-20, 20, 2)\n",
+    "cs2 = ax.contour(lon_2d, lat_2d, temp_850.metpy.convert_units('degC'),\n",
+    "                 clevtemp850, colors='grey', linewidths=1.25,\n",
+    "                 linestyles='dashed', transform=datacrs)\n",
+    "plt.clabel(cs2, fontsize=10, inline=1, inline_spacing=10, fmt='%i',\n",
+    "           rightside_up=True, use_clabeltext=True)\n",
+    "\n",
+    "# Plot Colorfill of Temperature Advection\n",
+    "cint = np.arange(-8, 9)\n",
+    "cf = ax.contourf(lon_2d, lat_2d, 3*adv.metpy.convert_units('delta_degC/hour'),\n",
+    "                 cint[cint != 0],\n",
+    "                 extend='both', cmap='bwr', transform=datacrs)\n",
+    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
+    "                  extendrect=True, ticks=cint)\n",
+    "cb.set_label(r'$^{o}C/3h$', size='large')\n",
+    "\n",
+    "wind_slice = (slice(None, None, 10), slice(None, None, 10))\n",
+    "# Plot Wind Barbs\n",
+    "ax.barbs(lon_2d[wind_slice], lat_2d[wind_slice],\n",
+    "         u_wind_850.metpy.convert_units('kt').values[wind_slice],\n",
+    "         v_wind_850.metpy.convert_units('kt').values[wind_slice],\n",
+    "         length=6, pivot='middle', transform=datacrs);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/Ageostrophic_Wind_Example.ipynb b/_preview/13/_sources/notebooks/synoptic/Ageostrophic_Wind_Example.ipynb
new file mode 100644
index 0000000..05efaa7
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/Ageostrophic_Wind_Example.ipynb
@@ -0,0 +1,216 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Geostrophic and Ageostrophic Wind\n",
+    "=================================\n",
+    "\n",
+    "Plot a 1000-hPa map calculating the geostrophic from MetPy and finding the\n",
+    "ageostrophic wind from the total wind and the geostrophic wind.\n",
+    "\n",
+    "This uses the geostrophic wind calculation from `metpy.calc` to find\n",
+    "the geostrophic wind, then performs the simple subtraction to find the ageostrophic\n",
+    "wind. Currently, this needs an extra helper function to calculate\n",
+    "the distance between lat/lon grid points.\n",
+    "\n",
+    "Additionally, we utilize the `ndimage.zoom` method for smoothing the 1000-hPa\n",
+    "height contours without smoothing the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "import numpy as np\n",
+    "from scipy import ndimage\n",
+    "from siphon.catalog import TDSCatalog\n",
+    "from xarray.backends import NetCDF4DataStore\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################################"
+   },
+   "source": [
+    "Set up access to the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt = datetime(2016, 8, 22, 18)\n",
+    "forecast_hour = 3\n",
+    "h = timedelta(hours=forecast_hour)\n",
+    "\n",
+    "# Assemble our URL to the THREDDS Data Server catalog,\n",
+    "# and access our desired dataset within via NCSS\n",
+    "base_url = 'https://www.ncei.noaa.gov/thredds/model-gfs-g4-anl-files-old/'\n",
+    "cat = TDSCatalog(f'{base_url}{dt:%Y%m}/{dt:%Y%m%d}/catalog.xml')\n",
+    "ncss = cat.datasets[f'gfsanl_4_{dt:%Y%m%d}_{dt:%H}'\n",
+    "                    f'00_00{forecast_hour}.grb2'].subset()\n",
+    "\n",
+    "# Create NCSS query for our desired time, region, and data variables\n",
+    "query = ncss.query()\n",
+    "\n",
+    "query.lonlat_box(north=50, south=30, east=-80, west=-115)\n",
+    "query.time(dt + h)\n",
+    "query.variables('Geopotential_height_isobaric',\n",
+    "                'u-component_of_wind_isobaric',\n",
+    "                'v-component_of_wind_isobaric')\n",
+    "query.vertical_level(100000)\n",
+    "\n",
+    "data = ncss.get_data(query)\n",
+    "\n",
+    "ds = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pull out variables you want to use\n",
+    "height = ds.Geopotential_height_isobaric.squeeze()\n",
+    "u_wind = ds['u-component_of_wind_isobaric'].squeeze().metpy.quantify()\n",
+    "v_wind = ds['v-component_of_wind_isobaric'].squeeze().metpy.quantify()\n",
+    "\n",
+    "vtime = height.time.values.squeeze().astype('datetime64[ms]').astype('O')\n",
+    "\n",
+    "lat = ds.lat\n",
+    "lon = ds.lon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combine 1D latitude and longitudes into a 2D grid of locations\n",
+    "lon_2d, lat_2d = np.meshgrid(lon, lat)\n",
+    "\n",
+    "# Smooth height data\n",
+    "height = mpcalc.smooth_n_point(height, 9, 3)\n",
+    "\n",
+    "# Compute the geostrophic wind\n",
+    "geo_wind_u, geo_wind_v = mpcalc.geostrophic_wind(height)\n",
+    "\n",
+    "# Calculate ageostrophic wind components\n",
+    "ageo_wind_u = u_wind - geo_wind_u\n",
+    "ageo_wind_v = v_wind - geo_wind_v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create new figure\n",
+    "fig = plt.figure(figsize=(15, 10), facecolor='black')\n",
+    "\n",
+    "# Add the map and set the extent\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "ax.set_extent([-105., -93., 35., 43.])\n",
+    "ax.patch.set_fill(False)\n",
+    "\n",
+    "# Add state boundaries to plot\n",
+    "ax.add_feature(cfeature.STATES, edgecolor='white', linewidth=2)\n",
+    "\n",
+    "# Contour the heights every 10 m\n",
+    "contours = np.arange(10, 200, 10)\n",
+    "# Because we have a very local graphics area, the contours have joints\n",
+    "# to smooth those out we can use `ndimage.zoom`\n",
+    "zoom_500 = mpcalc.zoom_xarray(height, 5)\n",
+    "c = ax.contour(zoom_500.lon, zoom_500.lat, zoom_500, levels=contours,\n",
+    "               colors='red', linewidths=4)\n",
+    "ax.clabel(c, fontsize=12, inline=1, inline_spacing=3, fmt='%i')\n",
+    "\n",
+    "# Set up parameters for quiver plot. The slices below are used to\n",
+    "# subset the data (here taking every 4th point in x and y). The\n",
+    "# quiver_kwargs are parameters to control the appearance of the\n",
+    "# quiver so that they stay consistent between the calls.\n",
+    "quiver_slices = (slice(None, None, 2), slice(None, None, 2))\n",
+    "quiver_kwargs = {'headlength': 4, 'headwidth': 3, 'angles': 'uv',\n",
+    "                 'scale_units': 'xy', 'scale': 20}\n",
+    "\n",
+    "# Plot the wind vectors\n",
+    "wind = ax.quiver(lon_2d[quiver_slices], lat_2d[quiver_slices],\n",
+    "                 u_wind[quiver_slices], v_wind[quiver_slices],\n",
+    "                 color='blue', **quiver_kwargs)\n",
+    "geo = ax.quiver(lon_2d[quiver_slices], lat_2d[quiver_slices],\n",
+    "                geo_wind_u[quiver_slices], geo_wind_v[quiver_slices],\n",
+    "                color='darkorchid', **quiver_kwargs)\n",
+    "ageo = ax.quiver(lon_2d[quiver_slices], lat_2d[quiver_slices],\n",
+    "                 ageo_wind_u[quiver_slices], ageo_wind_v[quiver_slices],\n",
+    "                 color='lime', **quiver_kwargs)\n",
+    "\n",
+    "# Add a title to the plot\n",
+    "plt.title('1000mb Geopotential Heights(m), Wind(blue), '\n",
+    "          'Geostrophic Wind(purple), and \\n Ageostrophic Wind(green) '\n",
+    "          f'for {vtime:%d %B %Y %H:%MZ}', color='white', size=14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/Analytic_300hPa_Trough.ipynb b/_preview/13/_sources/notebooks/synoptic/Analytic_300hPa_Trough.ipynb
new file mode 100644
index 0000000..a563ab1
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/Analytic_300hPa_Trough.ipynb
@@ -0,0 +1,275 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Analytic 300-hPa Trough\n",
+    "======================="
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "from metpy.units import units\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Below are three definitions to create an analytic 300-hPa trough roughly\n",
+    "based on the Sanders Analytic Model with modified coefficients to create\n",
+    "different style waves.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "lines_to_next_cell": 1
+   },
+   "outputs": [],
+   "source": [
+    "def single_300hPa_trough(parameter='hght'):\n",
+    "    \"\"\" Single trough with heights and Temperatures based on Sanders Analytic Model\n",
+    "    \"\"\"\n",
+    "    X = np.linspace(.25, .75, 101)\n",
+    "    Y = np.linspace(.25, .75, 101)\n",
+    "\n",
+    "    x, y = np.meshgrid(X, Y)\n",
+    "\n",
+    "    p = 4\n",
+    "    q = 2\n",
+    "\n",
+    "    if parameter == 'hght':\n",
+    "        return (9240 + 100 * np.cos(p * x * np.pi) * np.cos(q * y * np.pi)\n",
+    "                + 200 * np.cos(y * np.pi) + 300 * y * np.cos(x * np.pi + np.pi / 2))\n",
+    "    elif parameter == 'temp':\n",
+    "        return (-50 + 2 * np.cos(p * x * np.pi) * np.cos(q * y * np.pi)\n",
+    "                + 2 * np.cos(y * np.pi) + 0.5 * y * np.cos(x * np.pi + np.pi / 2))\n",
+    "\n",
+    "\n",
+    "def lifting_300hPa_trough(parameter='hght'):\n",
+    "    \"\"\" Lifting trough with heights and Temperatures based on Sanders Analytic Model\n",
+    "    \"\"\"\n",
+    "    X = np.linspace(.25, .75, 101)\n",
+    "    Y = np.linspace(.25, .75, 101)\n",
+    "\n",
+    "    x, y = np.meshgrid(X, Y)\n",
+    "\n",
+    "    p = 4\n",
+    "    q = 2\n",
+    "\n",
+    "    if parameter == 'hght':\n",
+    "        return (9240 + 150 * np.cos(p * x * np.pi) * np.cos(q * y * np.pi)\n",
+    "                + 200 * np.cos(y * np.pi) + 400 * y * np.cos(x * np.pi + np.pi))\n",
+    "    elif parameter == 'temp':\n",
+    "        return (-50 + 2 * np.cos(p * x * np.pi) * np.cos(q * y * np.pi)\n",
+    "                + 2 * np.cos(y * np.pi) + 5 * y * np.cos(x * np.pi + np.pi))\n",
+    "\n",
+    "\n",
+    "def digging_300hPa_trough(parameter='hght'):\n",
+    "    \"\"\" Digging trough with heights and Temperatures based on Sanders Analytic Model\n",
+    "    \"\"\"\n",
+    "    X = np.linspace(.25, .75, 101)\n",
+    "    Y = np.linspace(.25, .75, 101)\n",
+    "\n",
+    "    x, y = np.meshgrid(X, Y)\n",
+    "\n",
+    "    p = 4\n",
+    "    q = 2\n",
+    "\n",
+    "    if parameter == 'hght':\n",
+    "        return (9240 + 150 * np.cos(p * x * np.pi) * np.cos(q * y * np.pi)\n",
+    "                + 200 * np.cos(y * np.pi) + 400 * y * np.sin(x * np.pi + 5 * np.pi / 2))\n",
+    "    elif parameter == 'temp':\n",
+    "        return (-50 + 2 * np.cos(p * x * np.pi) * np.cos(q * y * np.pi)\n",
+    "                + 2 * np.cos(y * np.pi) + 5 * y * np.sin(x * np.pi + np.pi / 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Call the appropriate definition to develop the desired wave.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Single Trough\n",
+    "Z = single_300hPa_trough(parameter='hght')\n",
+    "T = single_300hPa_trough(parameter='temp')\n",
+    "\n",
+    "# Lifting Trough\n",
+    "# Z = lifting_300hPa_trough(parameter='hght')\n",
+    "# T = lifting_300hPa_trough(parameter='temp')\n",
+    "\n",
+    "# Digging Trough\n",
+    "# Z = digging_300hPa_trough(parameter='hght')\n",
+    "# T = digging_300hPa_trough(parameter='temp')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Set geographic parameters for analytic grid to then\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lats = np.linspace(35, 50, 101)\n",
+    "lons = np.linspace(260, 290, 101)\n",
+    "lon, lat = np.meshgrid(lons, lats)\n",
+    "\n",
+    "# Coriolis Parameter\n",
+    "f = mpcalc.coriolis_parameter(lat * units.degrees)\n",
+    "\n",
+    "# Calculate Geostrophic Wind from Analytic Heights\n",
+    "dx, dy = mpcalc.lat_lon_grid_deltas(lons, lats)\n",
+    "ugeo, vgeo = mpcalc.geostrophic_wind(Z*units.meter, dx, dy, latitude=lat * units.degrees)\n",
+    "\n",
+    "# Get the wind direction for each point\n",
+    "wdir = mpcalc.wind_direction(ugeo, vgeo)\n",
+    "\n",
+    "# Compute the Gradient Wind via an approximation\n",
+    "dydx = mpcalc.first_derivative(Z, delta=dx, axis=1)\n",
+    "d2ydx2 = mpcalc.first_derivative(dydx, delta=dx, axis=1)\n",
+    "R = ((1 + dydx.m**2)**(3. / 2.)) / d2ydx2.m\n",
+    "\n",
+    "geo_mag = mpcalc.wind_speed(ugeo, vgeo)\n",
+    "grad_mag = geo_mag.m - (geo_mag.m**2) / (f.magnitude * R)\n",
+    "\n",
+    "ugrad, vgrad = mpcalc.wind_components(grad_mag * units('m/s'), wdir)\n",
+    "\n",
+    "# Calculate Ageostrophic wind\n",
+    "uageo = ugrad - ugeo\n",
+    "vageo = vgrad - vgeo\n",
+    "\n",
+    "# Compute QVectors\n",
+    "uqvect, vqvect = mpcalc.q_vector(ugeo, vgeo, T * units.degC, 500 * units.hPa, dx, dy)\n",
+    "\n",
+    "# Calculate divergence of the ageostrophic wind\n",
+    "div = mpcalc.divergence(uageo, vageo, dx=dx, dy=dy)\n",
+    "\n",
+    "# Calculate Relative Vorticity Advection\n",
+    "relvor = mpcalc.vorticity(ugeo, vgeo, dx=dx, dy=dy)\n",
+    "adv = mpcalc.advection(relvor, ugeo, vgeo, dx=dx, dy=dy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Create figure containing Geopotential Heights, Temperature, Divergence\n",
+    "of the Ageostrophic Wind, Relative Vorticity Advection (shaded),\n",
+    "geostrphic wind barbs, and Q-vectors.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "ax = plt.subplot(111)\n",
+    "\n",
+    "# Plot Geopotential Height Contours\n",
+    "cs = ax.contour(lons, lats, Z, range(0, 12000, 120), colors='k')\n",
+    "plt.clabel(cs, fmt='%d')\n",
+    "\n",
+    "# Plot Temperature Contours\n",
+    "cs2 = ax.contour(lons, lats, T, range(-50, 50, 2), colors='r', linestyles='dashed')\n",
+    "plt.clabel(cs2, fmt='%d')\n",
+    "\n",
+    "# Plot Divergence of Ageo Wind Contours\n",
+    "cs3 = ax.contour(lons, lats, div*10**9, np.arange(-25, 26, 3), colors='grey',\n",
+    "                 linestyles='dotted')\n",
+    "plt.clabel(cs3, fmt='%d')\n",
+    "\n",
+    "# Plot Rel. Vor. Adv. colorfilled\n",
+    "cf = ax.contourf(lons, lats, adv*10**9, np.arange(-20, 21, 1), cmap=plt.cm.bwr)\n",
+    "cbar = plt.colorbar(cf, orientation='horizontal', pad=0.05, aspect=50)\n",
+    "cbar.set_label('Rel. Vor. Adv.')\n",
+    "\n",
+    "# Plot Geostrophic Wind Barbs\n",
+    "wind_slice = slice(5, None, 10)\n",
+    "ax.barbs(lons[wind_slice], lats[wind_slice],\n",
+    "         ugeo[wind_slice, wind_slice].to('kt').m, vgeo[wind_slice, wind_slice].to('kt').m)\n",
+    "\n",
+    "# Plot Ageostrophic Wind Vectors\n",
+    "# ageo_slice = slice(None, None, 10)\n",
+    "# ax.quiver(lons[ageo_slice], lats[ageo_slice],\n",
+    "#           uageo[ageo_slice, ageo_slice].m, vageo[ageo_slice, ageo_slice].m,\n",
+    "#           color='blue', pivot='mid')\n",
+    "\n",
+    "# Plot QVectors\n",
+    "qvec_slice = slice(None, None, 10)\n",
+    "ax.quiver(lons[qvec_slice], lats[qvec_slice],\n",
+    "          uqvect[qvec_slice, qvec_slice].m, vqvect[qvec_slice, qvec_slice].m,\n",
+    "          color='darkcyan', pivot='mid')\n",
+    "\n",
+    "plt.title('300-hPa Geo Heights (black), Q-Vector (dark cyan), Divergence (grey; dashed)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/Differential_Temperature_Advection.ipynb b/_preview/13/_sources/notebooks/synoptic/Differential_Temperature_Advection.ipynb
new file mode 100644
index 0000000..cea2fdb
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/Differential_Temperature_Advection.ipynb
@@ -0,0 +1,325 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Differential Temperature Advection with NARR Data\n",
+    "=================================================\n",
+    "\n",
+    "By: Kevin Goebbert\n",
+    "\n",
+    "This example creates a four-panel plot to illustrate the difference\n",
+    "between single level temperature advection and a computed differential\n",
+    "temperature advection between two layers. This example makes use of NARR\n",
+    "output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.gridspec as gridspec\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "from metpy.units import units\n",
+    "import numpy as np\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Data Input\n",
+    "----------\n",
+    "\n",
+    "Use Xarray to access GFS data from THREDDS resource and uses\n",
+    "metpy accessor to parse file to make it easy to pull data using\n",
+    "common coordinate names (e.g., vertical) and attach units.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/casestudies/'\n",
+    "                     'python-gallery/NARR_19930313_1800.nc').metpy.parse_cf()\n",
+    "ds = ds.metpy.assign_latitude_longitude()\n",
+    "\n",
+    "# Get lat/lon data from file\n",
+    "lats = ds.lat\n",
+    "lons = ds.lon\n",
+    "\n",
+    "# Get 700-hPa data and smooth\n",
+    "level = 700 * units.hPa\n",
+    "hght_700 = mpcalc.smooth_n_point(ds['Geopotential_height_isobaric'].metpy.sel(\n",
+    "    vertical=level).squeeze(), 9)\n",
+    "tmpk_700 = mpcalc.smooth_n_point(ds['Temperature_isobaric'].metpy.sel(\n",
+    "    vertical=level).squeeze(), 9)\n",
+    "uwnd_700 = mpcalc.smooth_n_point(\n",
+    "    ds['u-component_of_wind_isobaric'].metpy.sel(vertical=level).squeeze(), 9)\n",
+    "vwnd_700 = mpcalc.smooth_n_point(\n",
+    "    ds['v-component_of_wind_isobaric'].metpy.sel(vertical=level).squeeze(), 9)\n",
+    "\n",
+    "# Get 300-hPa data and\n",
+    "level = 300 * units.hPa\n",
+    "hght_300 = mpcalc.smooth_n_point(ds['Geopotential_height_isobaric'].metpy.sel(\n",
+    "    vertical=level).squeeze(), 9)\n",
+    "tmpk_300 = mpcalc.smooth_n_point(ds['Temperature_isobaric'].metpy.sel(\n",
+    "    vertical=level).squeeze(), 9)\n",
+    "uwnd_300 = mpcalc.smooth_n_point(\n",
+    "    ds['u-component_of_wind_isobaric'].metpy.sel(vertical=level).squeeze(), 9)\n",
+    "vwnd_300 = mpcalc.smooth_n_point(\n",
+    "    ds['v-component_of_wind_isobaric'].metpy.sel(vertical=level).squeeze(), 9)\n",
+    "\n",
+    "# Convert Temperatures to degC\n",
+    "tmpc_700 = tmpk_700.metpy.convert_units('degC')\n",
+    "tmpc_300 = tmpk_300.metpy.convert_units('degC')\n",
+    "\n",
+    "# Get time in a nice datetime object format\n",
+    "vtime = ds.time.values.astype('datetime64[ms]').astype('O')[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Differential Temperature Advection Calculation\n",
+    "----------------------------------------------\n",
+    "\n",
+    "Use MetPy advection funtion to calculate temperature advection at 700\n",
+    "and 300 hPa, then manually compute the differential between those two\n",
+    "layers. The differential temperature advection is then valid at 500 hPa\n",
+    "(due to centered differencing) and is the same level that height changes\n",
+    "due to absolute vorticity advection is commonly assessed.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use MetPy advection function to calculate temperature advection at two levels\n",
+    "tadv_700 = mpcalc.advection(tmpk_700, uwnd_700, vwnd_700)\n",
+    "tadv_300 = mpcalc.advection(tmpk_300, uwnd_300, vwnd_300)\n",
+    "\n",
+    "# Centered finite difference to calculate differential temperature advection\n",
+    "diff_tadv = ((tadv_700.data - tadv_300.data)/(400 * units.hPa)).to_base_units()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Make Four Panel Plot\n",
+    "--------------------\n",
+    "\n",
+    "A four panel plot is produced to illustrate the temperature fields at\n",
+    "two levels (700 and 300 hPa), which are common fields to be plotted.\n",
+    "Then a panel containing the evaluated temperature advection at 700 hPa\n",
+    "and differential temperature advection between 700 and 300 hPa. Of\n",
+    "meteorological significance is the difference between these two\n",
+    "advection plots. For the QG Height Tendency equation, the forcing term\n",
+    "is proportional to the differential temperature advection, which paints\n",
+    "a slightly different picture than just the 700-hPa temperature advection\n",
+    "alone.\n",
+    "\n",
+    "To create the four panel plot it takes a bit of code at this point. The\n",
+    "following code is segmented into Upper-left, Lower-left, Upper-right,\n",
+    "Lower-right panels using matplotlib’s gridspec to help with spacing.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up plot crs (mapcrs) and the data crs, will need to\n",
+    "# transform all variables\n",
+    "mapcrs = ccrs.LambertConformal(central_longitude=-100,\n",
+    "                               central_latitude=35,\n",
+    "                               standard_parallels=(30, 60))\n",
+    "datacrs = ccrs.PlateCarree()\n",
+    "\n",
+    "# Set some common contour interval levels\n",
+    "clevs_700_tmpc = np.arange(-40, 41, 2)\n",
+    "clevs_700_hght = np.arange(0, 8000, 30)\n",
+    "clevs_300_hght = np.arange(0, 10000, 120)\n",
+    "\n",
+    "# Create slice to reduce number of wind barbs at plot time\n",
+    "wind_slice = (slice(None, None, 10), slice(None, None, 10))\n",
+    "\n",
+    "# Start figure\n",
+    "fig = plt.figure(1, figsize=(22, 15))\n",
+    "\n",
+    "# Use gridspec to help size elements of plot; small top plot\n",
+    "# and big bottom plot\n",
+    "gs = gridspec.GridSpec(nrows=2, ncols=2, height_ratios=[1, 1],\n",
+    "                       hspace=0.03, wspace=0.03)\n",
+    "\n",
+    "\n",
+    "# Upper-left panel (700-hPa TMPC)\n",
+    "ax1 = plt.subplot(gs[0, 0], projection=mapcrs)\n",
+    "ax1.set_extent([-130, -72, 25, 49], ccrs.PlateCarree())\n",
+    "ax1.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
+    "ax1.add_feature(cfeature.STATES.with_scale('50m'))\n",
+    "\n",
+    "cf = ax1.contourf(lons, lats, tmpc_700, clevs_700_tmpc,\n",
+    "                  cmap=plt.cm.coolwarm, transform=datacrs)\n",
+    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50)\n",
+    "cb.set_label(r'$^{\\circ}$C')\n",
+    "\n",
+    "csf = ax1.contour(lons, lats, tmpc_700, clevs_700_tmpc, colors='grey',\n",
+    "                  linestyles='dashed', transform=datacrs)\n",
+    "plt.clabel(csf, fmt='%d')\n",
+    "\n",
+    "cs = ax1.contour(lons, lats, hght_700, clevs_700_hght, colors='black',\n",
+    "                 transform=datacrs)\n",
+    "plt.clabel(cs, fmt='%d')\n",
+    "\n",
+    "ax1.barbs(lons[wind_slice].values, lats[wind_slice].values,\n",
+    "          uwnd_700.metpy.convert_units('kt')[wind_slice].values,\n",
+    "          vwnd_700[wind_slice].metpy.convert_units('kt').values,\n",
+    "          pivot='middle', color='black', transform=datacrs)\n",
+    "\n",
+    "plt.title('700-hPa NARR HGHT (m), TMPC, and Wind (kt)', loc='left')\n",
+    "plt.title(f'Valid: {vtime}', loc='right')\n",
+    "\n",
+    "\n",
+    "# Lower-left panel (300-hPa TMPC)\n",
+    "ax2 = plt.subplot(gs[1, 0], projection=mapcrs)\n",
+    "ax2.set_extent([-130, -72, 25, 49], ccrs.PlateCarree())\n",
+    "ax2.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
+    "ax2.add_feature(cfeature.STATES.with_scale('50m'))\n",
+    "\n",
+    "cf = ax2.contourf(lons, lats, tmpc_300, range(-60, -24, 2),\n",
+    "                  cmap=plt.cm.BuPu_r, transform=datacrs)\n",
+    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50)\n",
+    "cb.set_label(r'$^{\\circ}$C')\n",
+    "\n",
+    "csf = ax2.contour(lons, lats, tmpc_300, range(-60, 0, 2),\n",
+    "                  colors='grey', linestyles='dashed', transform=datacrs)\n",
+    "plt.clabel(csf, fmt='%d')\n",
+    "\n",
+    "cs = ax2.contour(lons, lats, hght_300, clevs_300_hght, colors='black',\n",
+    "                 transform=datacrs)\n",
+    "plt.clabel(cs, fmt='%d')\n",
+    "\n",
+    "ax2.barbs(lons[wind_slice].values, lats[wind_slice].values,\n",
+    "          uwnd_300.metpy.convert_units('kt')[wind_slice].values,\n",
+    "          vwnd_300[wind_slice].metpy.convert_units('kt').values,\n",
+    "          pivot='middle', color='black', transform=datacrs)\n",
+    "\n",
+    "plt.title('300-hPa NARR HGHT (m), TMPC, and Wind (kt)', loc='left')\n",
+    "plt.title(f'Valid: {vtime}', loc='right')\n",
+    "\n",
+    "\n",
+    "# Upper-right panel (700-hPa TMPC Adv)\n",
+    "ax3 = plt.subplot(gs[0, 1], projection=mapcrs)\n",
+    "ax3.set_extent([-130, -72, 25, 49], ccrs.PlateCarree())\n",
+    "ax3.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
+    "ax3.add_feature(cfeature.STATES.with_scale('50m'))\n",
+    "\n",
+    "cf = ax3.contourf(lons, lats, tadv_700*3600, range(-8, 9, 1),\n",
+    "                  cmap=plt.cm.coolwarm, transform=datacrs)\n",
+    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50)\n",
+    "cb.set_label(r'TMPC ADV ($^{\\circ}$C h$^{-1}$)')\n",
+    "\n",
+    "csf = ax3.contour(lons, lats, tmpc_700, clevs_700_tmpc, colors='grey',\n",
+    "                  linestyles='dashed', transform=datacrs)\n",
+    "plt.clabel(csf, fmt='%d')\n",
+    "\n",
+    "cs = ax3.contour(lons, lats, hght_700, clevs_700_hght, colors='black',\n",
+    "                 transform=datacrs)\n",
+    "plt.clabel(cs, fmt='%d')\n",
+    "\n",
+    "ax3.barbs(lons[wind_slice].values, lats[wind_slice].values,\n",
+    "          uwnd_700.metpy.convert_units('kt')[wind_slice].values,\n",
+    "          vwnd_700[wind_slice].metpy.convert_units('kt').values,\n",
+    "          pivot='middle', color='black', transform=datacrs)\n",
+    "\n",
+    "plt.title('700-hPa NARR HGHT (m), TMP ADV (C/h), and Wind (kt)', loc='left')\n",
+    "plt.title(f'Valid: {vtime}', loc='right')\n",
+    "\n",
+    "\n",
+    "# Lower-right panel (diff TMPC)\n",
+    "ax4 = plt.subplot(gs[1, 1], projection=mapcrs)\n",
+    "ax4.set_extent([-130, -72, 25, 49], ccrs.PlateCarree())\n",
+    "ax4.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
+    "ax4.add_feature(cfeature.STATES.with_scale('50m'))\n",
+    "\n",
+    "cf = ax4.contourf(lons, lats, diff_tadv*1e9, clevs_700_tmpc,\n",
+    "                  cmap=plt.cm.coolwarm, extend='both', transform=datacrs)\n",
+    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
+    "                  extendrect=True)\n",
+    "cb.set_label(r'dTMPC ($10^9$ $^{\\circ}$C s$^{-1}$ Pa$^{-1}$)')\n",
+    "\n",
+    "csf = ax4.contour(lons, lats, tmpc_700, clevs_700_tmpc, colors='grey',\n",
+    "                  linestyles='dashed', transform=datacrs)\n",
+    "plt.clabel(csf, fmt='%d')\n",
+    "\n",
+    "cs = ax4.contour(lons, lats, hght_700, clevs_700_hght, colors='black',\n",
+    "                 transform=datacrs)\n",
+    "plt.clabel(cs, fmt='%d')\n",
+    "\n",
+    "ax4.barbs(lons[wind_slice].values, lats[wind_slice].values,\n",
+    "          uwnd_700.metpy.convert_units('kt')[wind_slice].values,\n",
+    "          vwnd_700[wind_slice].metpy.convert_units('kt').values,\n",
+    "          pivot='middle', color='black', transform=datacrs)\n",
+    "\n",
+    "plt.title('700-hPa NARR HGHT (m), Diff. TMP ADV (C/s/Pa), and Wind (kt)', loc='left')\n",
+    "plt.title(f'Valid: {vtime}', loc='right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
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diff --git a/_preview/13/_sources/notebooks/synoptic/Geostrophic_Wind_And_Few_More.ipynb b/_preview/13/_sources/notebooks/synoptic/Geostrophic_Wind_And_Few_More.ipynb
new file mode 100644
index 0000000..36fdf0c
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/Geostrophic_Wind_And_Few_More.ipynb
@@ -0,0 +1,3154 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "75321594-8039-4f1a-85f3-b292d69be83a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_459/2639708157.py:10: DeprecationWarning: Please import `gaussian_filter` from the `scipy.ndimage` namespace; the `scipy.ndimage.filters` namespace is deprecated and will be removed in SciPy 2.0.0.\n",
+      "  from scipy.ndimage.filters import gaussian_filter\n"
+     ]
+    }
+   ],
+   "source": [
+    "import metpy.calc as mpcalc\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "from metpy.calc import geostrophic_wind\n",
+    "from metpy.calc import q_vector\n",
+    "from metpy.units import units\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "from scipy.ndimage.filters import gaussian_filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6a902c37-5034-4908-8fc2-501727d1357c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## opening NetCDF file using xarray \n",
+    "\n",
+    "ds = xr.open_mfdataset(\"/home/jovyan/metpy-cookbook/NETCDF_FILE.nc\",combine = \"nested\", concat_dim=\"time\",decode_times=True)"
+   ]
+  },
+  {
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+   "execution_count": 3,
+   "id": "6ed072ca-b162-4d57-a223-de1ee39f3f6d",
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+       "    history_L34RS:                     &#x27;Created by L34RS v1.4.3 @ NASA GES DI...\n",
+       "    CDO:                               Climate Data Operators version 1.9.8 (...\n",
+       "    cdo_openmp_thread_number:          12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d4cde7b2-7236-476c-9843-81836703fe8a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d4cde7b2-7236-476c-9843-81836703fe8a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lev</span>: 23</li><li><span class='xr-has-index'>lat</span>: 41</li><li><span class='xr-has-index'>lon</span>: 71</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d88b8127-8089-4950-8fd3-47cc6304a1fa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d88b8127-8089-4950-8fd3-47cc6304a1fa' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-06-08</div><input id='attrs-f052bc1b-81c5-4c87-b77e-def79f1a19a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f052bc1b-81c5-4c87-b77e-def79f1a19a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d85b138d-01f4-47bf-9c7b-6cbf0636c9a8' class='xr-var-data-in' type='checkbox'><label for='data-d85b138d-01f4-47bf-9c7b-6cbf0636c9a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-06-08T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-130.5 -129.5 ... -61.5 -60.5</div><input id='attrs-9dcb316f-042f-4192-bc29-f490808dd4c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9dcb316f-042f-4192-bc29-f490808dd4c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7feb47d4-63e2-4c94-8c2d-c59e153d3acb' class='xr-var-data-in' type='checkbox'><label for='data-7feb47d4-63e2-4c94-8c2d-c59e153d3acb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-130.5, -129.5, -128.5, -127.5, -126.5, -125.5, -124.5, -123.5, -122.5,\n",
+       "       -121.5, -120.5, -119.5, -118.5, -117.5, -116.5, -115.5, -114.5, -113.5,\n",
+       "       -112.5, -111.5, -110.5, -109.5, -108.5, -107.5, -106.5, -105.5, -104.5,\n",
+       "       -103.5, -102.5, -101.5, -100.5,  -99.5,  -98.5,  -97.5,  -96.5,  -95.5,\n",
+       "        -94.5,  -93.5,  -92.5,  -91.5,  -90.5,  -89.5,  -88.5,  -87.5,  -86.5,\n",
+       "        -85.5,  -84.5,  -83.5,  -82.5,  -81.5,  -80.5,  -79.5,  -78.5,  -77.5,\n",
+       "        -76.5,  -75.5,  -74.5,  -73.5,  -72.5,  -71.5,  -70.5,  -69.5,  -68.5,\n",
+       "        -67.5,  -66.5,  -65.5,  -64.5,  -63.5,  -62.5,  -61.5,  -60.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.5 21.5 22.5 ... 58.5 59.5 60.5</div><input id='attrs-4ff061d5-8059-47b3-8d04-eba1562a60f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ff061d5-8059-47b3-8d04-eba1562a60f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e449fdcd-85dd-4c0e-bc41-0de37601d2b8' class='xr-var-data-in' type='checkbox'><label for='data-e449fdcd-85dd-4c0e-bc41-0de37601d2b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5,\n",
+       "       32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5,\n",
+       "       44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5,\n",
+       "       56.5, 57.5, 58.5, 59.5, 60.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 975.0 950.0 ... 250.0 200.0</div><input id='attrs-f64b3a67-d699-4d2c-a4cb-a2656eede978' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f64b3a67-d699-4d2c-a4cb-a2656eede978' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddb2689b-a1a0-40ad-b3c7-d04808bd16b8' class='xr-var-data-in' type='checkbox'><label for='data-ddb2689b-a1a0-40ad-b3c7-d04808bd16b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>PLE_level</dd><dt><span>long_name :</span></dt><dd>vertical level</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate :</span></dt><dd>PLE</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  975.,  950.,  925.,  900.,  875.,  850.,  825.,  800.,  775.,\n",
+       "        750.,  725.,  700.,  650.,  600.,  550.,  500.,  450.,  400.,  350.,\n",
+       "        300.,  250.,  200.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-569524b5-bd24-464f-a499-2c7d55aa64d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-569524b5-bd24-464f-a499-2c7d55aa64d0' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>H</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-98b23db3-8238-4ba2-843c-6635cd88fdb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98b23db3-8238-4ba2-843c-6635cd88fdb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e998268b-7fa9-45b3-9b5d-4647879291b7' class='xr-var-data-in' type='checkbox'><label for='data-e998268b-7fa9-45b3-9b5d-4647879291b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>edge_heights</dd><dt><span>long_name :</span></dt><dd>edge_heights</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OMEGA</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-b7a0c2dc-9e6a-42ef-a683-c6e6f8122e96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7a0c2dc-9e6a-42ef-a683-c6e6f8122e96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9840f17-efc6-44c7-93cb-e41028390ea7' class='xr-var-data-in' type='checkbox'><label for='data-f9840f17-efc6-44c7-93cb-e41028390ea7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>vertical_pressure_velocity</dd><dt><span>long_name :</span></dt><dd>vertical_pressure_velocity</dd><dt><span>units :</span></dt><dd>Pa s-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PS</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-3c7db185-5e43-4801-acb7-448413c9fb4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c7db185-5e43-4801-acb7-448413c9fb4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2f0565a-7c1d-46b1-9aba-b4c8ba5882d8' class='xr-var-data-in' type='checkbox'><label for='data-a2f0565a-7c1d-46b1-9aba-b4c8ba5882d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_pressure</dd><dt><span>long_name :</span></dt><dd>surface_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.37 kiB </td>\n",
+       "                        <td> 11.37 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 41, 71) </td>\n",
+       "                        <td> (1, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-fc1d15e7-5b78-42f3-bd61-b039fc8934a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc1d15e7-5b78-42f3-bd61-b039fc8934a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0005798e-0f4e-4467-b03e-313eb06264e9' class='xr-var-data-in' type='checkbox'><label for='data-0005798e-0f4e-4467-b03e-313eb06264e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>long_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                        <td> 261.54 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SLP</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 41, 71), meta=np.ndarray&gt;</div><input id='attrs-7f20f0aa-9ee9-498b-8e8f-815ee5d73ade' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f20f0aa-9ee9-498b-8e8f-815ee5d73ade' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56824334-f826-439e-a22c-e6952287ce14' class='xr-var-data-in' type='checkbox'><label for='data-56824334-f826-439e-a22c-e6952287ce14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_level_pressure</dd><dt><span>long_name :</span></dt><dd>sea_level_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.37 kiB </td>\n",
+       "                        <td> 11.37 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 41, 71) </td>\n",
+       "                        <td> (1, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "    <tr>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
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+       "                        <td> 261.54 kiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 23, 41, 71) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-3742f126-709b-4d5e-8b62-6408babdc3cd' class='xr-section-summary-in' type='checkbox'  ><label for='section-3742f126-709b-4d5e-8b62-6408babdc3cd' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-359e36ad-d72b-4e71-b1e4-519ba798db3b' class='xr-index-data-in' type='checkbox'/><label for='index-359e36ad-d72b-4e71-b1e4-519ba798db3b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-06-08&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-daa2e969-c9ef-4aee-8b08-056595520ee2' class='xr-index-data-in' type='checkbox'/><label for='index-daa2e969-c9ef-4aee-8b08-056595520ee2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-130.5, -129.5, -128.5, -127.5, -126.5, -125.5, -124.5, -123.5, -122.5,\n",
+       "       -121.5, -120.5, -119.5, -118.5, -117.5, -116.5, -115.5, -114.5, -113.5,\n",
+       "       -112.5, -111.5, -110.5, -109.5, -108.5, -107.5, -106.5, -105.5, -104.5,\n",
+       "       -103.5, -102.5, -101.5, -100.5,  -99.5,  -98.5,  -97.5,  -96.5,  -95.5,\n",
+       "        -94.5,  -93.5,  -92.5,  -91.5,  -90.5,  -89.5,  -88.5,  -87.5,  -86.5,\n",
+       "        -85.5,  -84.5,  -83.5,  -82.5,  -81.5,  -80.5,  -79.5,  -78.5,  -77.5,\n",
+       "        -76.5,  -75.5,  -74.5,  -73.5,  -72.5,  -71.5,  -70.5,  -69.5,  -68.5,\n",
+       "        -67.5,  -66.5,  -65.5,  -64.5,  -63.5,  -62.5,  -61.5,  -60.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d2307b0d-b98b-49dc-87ce-6d5d13451a72' class='xr-index-data-in' type='checkbox'/><label for='index-d2307b0d-b98b-49dc-87ce-6d5d13451a72' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5,\n",
+       "       32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5,\n",
+       "       44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5,\n",
+       "       56.5, 57.5, 58.5, 59.5, 60.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a36d63f5-5447-47c1-9651-19ca596a9a83' class='xr-index-data-in' type='checkbox'/><label for='index-a36d63f5-5447-47c1-9651-19ca596a9a83' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0,  975.0,  950.0,  925.0,  900.0,  875.0,  850.0,  825.0,  800.0,\n",
+       "        775.0,  750.0,  725.0,  700.0,  650.0,  600.0,  550.0,  500.0,  450.0,\n",
+       "        400.0,  350.0,  300.0,  250.0,  200.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-df63035a-f923-42d0-ad57-d8458386ef71' class='xr-section-summary-in' type='checkbox'  ><label for='section-df63035a-f923-42d0-ad57-d8458386ef71' class='xr-section-summary' >Attributes: <span>(33)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1</dd><dt><span>History :</span></dt><dd>Original file generated: Tue Jun 18 21:47:49 2019 GMT</dd><dt><span>Comment :</span></dt><dd>GMAO filename: d5124_m2_jan10.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>Filename :</span></dt><dd>MERRA2_400.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>Institution :</span></dt><dd>NASA Global Modeling and Assimilation Office</dd><dt><span>References :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>Format :</span></dt><dd>NetCDF-4/HDF-5</dd><dt><span>SpatialCoverage :</span></dt><dd>global</dd><dt><span>VersionID :</span></dt><dd>5.12.4</dd><dt><span>TemporalRange :</span></dt><dd>1980-01-01 -&gt; 2016-12-31</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>http://dx.doi.org/</dd><dt><span>ShortName :</span></dt><dd>M2I3NPASM</dd><dt><span>GranuleID :</span></dt><dd>MERRA2_400.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>ProductionDateTime :</span></dt><dd>Original file generated: Tue Jun 18 21:47:49 2019 GMT</dd><dt><span>LongName :</span></dt><dd>MERRA2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields</dd><dt><span>Title :</span></dt><dd>MERRA2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields</dd><dt><span>SouthernmostLatitude :</span></dt><dd>-90.0</dd><dt><span>NorthernmostLatitude :</span></dt><dd>90.0</dd><dt><span>WesternmostLongitude :</span></dt><dd>-180.0</dd><dt><span>EasternmostLongitude :</span></dt><dd>179.375</dd><dt><span>LatitudeResolution :</span></dt><dd>0.5</dd><dt><span>LongitudeResolution :</span></dt><dd>0.625</dd><dt><span>DataResolution :</span></dt><dd>0.5 x 0.625 (42 pressure levels)</dd><dt><span>Contact :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>identifier_product_doi :</span></dt><dd>10.5067/QBZ6MG944HW0</dd><dt><span>RangeBeginningDate :</span></dt><dd>2019-06-08</dd><dt><span>RangeBeginningTime :</span></dt><dd>00:00:00.000000</dd><dt><span>RangeEndingDate :</span></dt><dd>2019-06-08</dd><dt><span>RangeEndingTime :</span></dt><dd>21:00:00.000000</dd><dt><span>history_L34RS :</span></dt><dd>&#x27;Created by L34RS v1.4.3 @ NASA GES DISC on April 29 2023 20:47:34. Regrid: remapcon GLDAS-2_1. Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23. Variables: H OMEGA PS QV SLP T U V&#x27;</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd><dt><span>cdo_openmp_thread_number :</span></dt><dd>12</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 2MB\n",
+       "Dimensions:  (time: 1, lev: 23, lat: 41, lon: 71)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 568B -130.5 -129.5 -128.5 ... -62.5 -61.5 -60.5\n",
+       "  * lat      (lat) float64 328B 20.5 21.5 22.5 23.5 24.5 ... 57.5 58.5 59.5 60.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Data variables:\n",
+       "    H        (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    OMEGA    (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    PS       (time, lat, lon) float32 12kB dask.array<chunksize=(1, 41, 71), meta=np.ndarray>\n",
+       "    QV       (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    SLP      (time, lat, lon) float32 12kB dask.array<chunksize=(1, 41, 71), meta=np.ndarray>\n",
+       "    T        (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    U        (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "    V        (time, lev, lat, lon) float32 268kB dask.array<chunksize=(1, 23, 41, 71), meta=np.ndarray>\n",
+       "Attributes: (12/33)\n",
+       "    CDI:                               Climate Data Interface version 1.9.8 (...\n",
+       "    Conventions:                       CF-1\n",
+       "    History:                           Original file generated: Tue Jun 18 21...\n",
+       "    Comment:                           GMAO filename: d5124_m2_jan10.inst3_3d...\n",
+       "    Filename:                          MERRA2_400.inst3_3d_asm_Np.20190608.nc4\n",
+       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
+       "    ...                                ...\n",
+       "    RangeBeginningTime:                00:00:00.000000\n",
+       "    RangeEndingDate:                   2019-06-08\n",
+       "    RangeEndingTime:                   21:00:00.000000\n",
+       "    history_L34RS:                     'Created by L34RS v1.4.3 @ NASA GES DI...\n",
+       "    CDO:                               Climate Data Operators version 1.9.8 (...\n",
+       "    cdo_openmp_thread_number:          12"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "358c2770-ce37-4742-94e1-1da433d35e36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### making a function to slice the xarray dataset according to our need.\n",
+    "def slicer (data,lat1, lat2, lon1, lon2, time1,time2) :\n",
+    "    sliced_data = data.sel(lat =slice(lat1, lat2), lon = slice(lon1, lon2),time = slice(time1, time2))\n",
+    "    return sliced_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "30bf5f26-a094-4f58-a88a-0b1760cd2713",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#slicing the data for CONUS only\n",
+    "\n",
+    "new_data = slicer(ds,23.5,50.5,-125.5,-66.5, ds.time[0], ds.time[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6afb5649-5d46-4546-98b0-d06627ef19bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 942kB\n",
+       "Dimensions:  (time: 1, lev: 23, lat: 28, lon: 60)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 480B -125.5 -124.5 -123.5 ... -68.5 -67.5 -66.5\n",
+       "  * lat      (lat) float64 224B 23.5 24.5 25.5 26.5 27.5 ... 47.5 48.5 49.5 50.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Data variables:\n",
+       "    H        (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    OMEGA    (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    PS       (time, lat, lon) float32 7kB dask.array&lt;chunksize=(1, 28, 60), meta=np.ndarray&gt;\n",
+       "    QV       (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    SLP      (time, lat, lon) float32 7kB dask.array&lt;chunksize=(1, 28, 60), meta=np.ndarray&gt;\n",
+       "    T        (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    U        (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "    V        (time, lev, lat, lon) float32 155kB dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;\n",
+       "Attributes: (12/33)\n",
+       "    CDI:                               Climate Data Interface version 1.9.8 (...\n",
+       "    Conventions:                       CF-1\n",
+       "    History:                           Original file generated: Tue Jun 18 21...\n",
+       "    Comment:                           GMAO filename: d5124_m2_jan10.inst3_3d...\n",
+       "    Filename:                          MERRA2_400.inst3_3d_asm_Np.20190608.nc4\n",
+       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
+       "    ...                                ...\n",
+       "    RangeBeginningTime:                00:00:00.000000\n",
+       "    RangeEndingDate:                   2019-06-08\n",
+       "    RangeEndingTime:                   21:00:00.000000\n",
+       "    history_L34RS:                     &#x27;Created by L34RS v1.4.3 @ NASA GES DI...\n",
+       "    CDO:                               Climate Data Operators version 1.9.8 (...\n",
+       "    cdo_openmp_thread_number:          12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-591e499c-f502-4730-9336-01ec8f7606c1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-591e499c-f502-4730-9336-01ec8f7606c1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lev</span>: 23</li><li><span class='xr-has-index'>lat</span>: 28</li><li><span class='xr-has-index'>lon</span>: 60</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1d8c3b2d-71d4-4d37-9bbd-20cefa5ebc63' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d8c3b2d-71d4-4d37-9bbd-20cefa5ebc63' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-06-08</div><input id='attrs-92af7be8-3bf8-4c22-9796-4da5eefb472c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92af7be8-3bf8-4c22-9796-4da5eefb472c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6262b65-9ae7-48f8-adc5-e5c48e472ee3' class='xr-var-data-in' type='checkbox'><label for='data-c6262b65-9ae7-48f8-adc5-e5c48e472ee3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-06-08T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-125.5 -124.5 ... -67.5 -66.5</div><input id='attrs-078639e2-39c7-4b9a-abb4-d8124ebf90b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-078639e2-39c7-4b9a-abb4-d8124ebf90b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51c01692-aa29-4412-9f68-109fa7f27246' class='xr-var-data-in' type='checkbox'><label for='data-51c01692-aa29-4412-9f68-109fa7f27246' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-125.5, -124.5, -123.5, -122.5, -121.5, -120.5, -119.5, -118.5, -117.5,\n",
+       "       -116.5, -115.5, -114.5, -113.5, -112.5, -111.5, -110.5, -109.5, -108.5,\n",
+       "       -107.5, -106.5, -105.5, -104.5, -103.5, -102.5, -101.5, -100.5,  -99.5,\n",
+       "        -98.5,  -97.5,  -96.5,  -95.5,  -94.5,  -93.5,  -92.5,  -91.5,  -90.5,\n",
+       "        -89.5,  -88.5,  -87.5,  -86.5,  -85.5,  -84.5,  -83.5,  -82.5,  -81.5,\n",
+       "        -80.5,  -79.5,  -78.5,  -77.5,  -76.5,  -75.5,  -74.5,  -73.5,  -72.5,\n",
+       "        -71.5,  -70.5,  -69.5,  -68.5,  -67.5,  -66.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>23.5 24.5 25.5 ... 48.5 49.5 50.5</div><input id='attrs-66ef27fc-2c98-45f7-9bf9-55d531c9a060' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66ef27fc-2c98-45f7-9bf9-55d531c9a060' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6572c62-a76c-4e77-9f41-992638945145' class='xr-var-data-in' type='checkbox'><label for='data-f6572c62-a76c-4e77-9f41-992638945145' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5,\n",
+       "       35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5,\n",
+       "       47.5, 48.5, 49.5, 50.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 975.0 950.0 ... 250.0 200.0</div><input id='attrs-17c8af52-c01f-4c6b-adff-8ce570e80a0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17c8af52-c01f-4c6b-adff-8ce570e80a0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-def6f6dc-17eb-432c-93c5-2d25e0424110' class='xr-var-data-in' type='checkbox'><label for='data-def6f6dc-17eb-432c-93c5-2d25e0424110' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>PLE_level</dd><dt><span>long_name :</span></dt><dd>vertical level</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate :</span></dt><dd>PLE</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  975.,  950.,  925.,  900.,  875.,  850.,  825.,  800.,  775.,\n",
+       "        750.,  725.,  700.,  650.,  600.,  550.,  500.,  450.,  400.,  350.,\n",
+       "        300.,  250.,  200.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7a344e5-f2fd-4545-9f86-b95ca1f2de55' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f7a344e5-f2fd-4545-9f86-b95ca1f2de55' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>H</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-82b3216e-a249-4618-9511-be398534e3f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82b3216e-a249-4618-9511-be398534e3f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2c7853c-375e-4399-8326-690ec5fbcab8' class='xr-var-data-in' type='checkbox'><label for='data-c2c7853c-375e-4399-8326-690ec5fbcab8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>edge_heights</dd><dt><span>long_name :</span></dt><dd>edge_heights</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"393\" height=\"133\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"28\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"28\" x2=\"28\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"28\" y1=\"0\" x2=\"28\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 28.02293761910704,0.0 28.02293761910704,28.02293761910704 0.0,28.02293761910704\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"14.011469\" y=\"48.022938\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"48.022938\" y=\"14.011469\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,48.022938,14.011469)\">1</text>\n",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"98\" y1=\"0\" x2=\"125\" y2=\"27\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"98\" y1=\"56\" x2=\"125\" y2=\"83\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"98\" y1=\"0\" x2=\"98\" y2=\"56\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"125\" y1=\"27\" x2=\"125\" y2=\"83\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"98.0,0.0 125.05882352941177,27.058823529411764 125.05882352941177,83.05882352941177 98.0,56.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"98\" y1=\"0\" x2=\"218\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"125\" y1=\"27\" x2=\"245\" y2=\"27\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"98\" y1=\"0\" x2=\"125\" y2=\"27\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"218\" y1=\"0\" x2=\"245\" y2=\"27\" style=\"stroke-width:2\" />\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OMEGA</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-ab562541-3308-4ebf-bcf0-7d2ff024007a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab562541-3308-4ebf-bcf0-7d2ff024007a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52478862-b1c6-4b2c-9d93-65552272b995' class='xr-var-data-in' type='checkbox'><label for='data-52478862-b1c6-4b2c-9d93-65552272b995' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>vertical_pressure_velocity</dd><dt><span>long_name :</span></dt><dd>vertical_pressure_velocity</dd><dt><span>units :</span></dt><dd>Pa s-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PS</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-6612099a-6e52-4d5c-9cca-6743f6ac0992' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6612099a-6e52-4d5c-9cca-6743f6ac0992' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f08d115f-a2c9-432e-8bb3-4f2274527c22' class='xr-var-data-in' type='checkbox'><label for='data-f08d115f-a2c9-432e-8bb3-4f2274527c22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_pressure</dd><dt><span>long_name :</span></dt><dd>surface_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.56 kiB </td>\n",
+       "                        <td> 6.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 28, 60) </td>\n",
+       "                        <td> (1, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-0157b482-042d-4b30-8dda-b67a53227be4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0157b482-042d-4b30-8dda-b67a53227be4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a21177c-5bc6-48a1-8d2d-35c1121b89d5' class='xr-var-data-in' type='checkbox'><label for='data-4a21177c-5bc6-48a1-8d2d-35c1121b89d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>long_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SLP</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-39bc69d4-dc14-43e2-b7e3-c5d3cc19ac09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39bc69d4-dc14-43e2-b7e3-c5d3cc19ac09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bc0a535-1571-40d2-a28b-91b475c88991' class='xr-var-data-in' type='checkbox'><label for='data-6bc0a535-1571-40d2-a28b-91b475c88991' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_level_pressure</dd><dt><span>long_name :</span></dt><dd>sea_level_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.56 kiB </td>\n",
+       "                        <td> 6.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 28, 60) </td>\n",
+       "                        <td> (1, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-2dad713b-96ae-41ff-b47b-8d0bce44cc63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2dad713b-96ae-41ff-b47b-8d0bce44cc63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5837792-db68-497e-8b58-df51da6ca822' class='xr-var-data-in' type='checkbox'><label for='data-f5837792-db68-497e-8b58-df51da6ca822' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</div><input id='attrs-d3c31df4-571c-4c1d-90b9-cea915d1b4dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3c31df4-571c-4c1d-90b9-cea915d1b4dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1023e63a-7152-4fa5-a6f1-b0e39e9d44c5' class='xr-var-data-in' type='checkbox'><label for='data-1023e63a-7152-4fa5-a6f1-b0e39e9d44c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>long_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3e228a7-9d4b-49da-a2a4-9427152e9623' class='xr-section-summary-in' type='checkbox'  ><label for='section-b3e228a7-9d4b-49da-a2a4-9427152e9623' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2b757675-1b55-4460-af5e-089614440d08' class='xr-index-data-in' type='checkbox'/><label for='index-2b757675-1b55-4460-af5e-089614440d08' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-06-08&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-82800555-66c5-4d92-9114-9433b7a03c0c' class='xr-index-data-in' type='checkbox'/><label for='index-82800555-66c5-4d92-9114-9433b7a03c0c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-125.5, -124.5, -123.5, -122.5, -121.5, -120.5, -119.5, -118.5, -117.5,\n",
+       "       -116.5, -115.5, -114.5, -113.5, -112.5, -111.5, -110.5, -109.5, -108.5,\n",
+       "       -107.5, -106.5, -105.5, -104.5, -103.5, -102.5, -101.5, -100.5,  -99.5,\n",
+       "        -98.5,  -97.5,  -96.5,  -95.5,  -94.5,  -93.5,  -92.5,  -91.5,  -90.5,\n",
+       "        -89.5,  -88.5,  -87.5,  -86.5,  -85.5,  -84.5,  -83.5,  -82.5,  -81.5,\n",
+       "        -80.5,  -79.5,  -78.5,  -77.5,  -76.5,  -75.5,  -74.5,  -73.5,  -72.5,\n",
+       "        -71.5,  -70.5,  -69.5,  -68.5,  -67.5,  -66.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-938f1959-558b-4502-84ba-10bd64c70488' class='xr-index-data-in' type='checkbox'/><label for='index-938f1959-558b-4502-84ba-10bd64c70488' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5,\n",
+       "       35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5,\n",
+       "       47.5, 48.5, 49.5, 50.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d2b8a7c2-9b55-4d10-a1ab-c15f1199dc3c' class='xr-index-data-in' type='checkbox'/><label for='index-d2b8a7c2-9b55-4d10-a1ab-c15f1199dc3c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0,  975.0,  950.0,  925.0,  900.0,  875.0,  850.0,  825.0,  800.0,\n",
+       "        775.0,  750.0,  725.0,  700.0,  650.0,  600.0,  550.0,  500.0,  450.0,\n",
+       "        400.0,  350.0,  300.0,  250.0,  200.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c4fb7ac6-c004-4669-92dd-c0a76a46d0b6' class='xr-section-summary-in' type='checkbox'  ><label for='section-c4fb7ac6-c004-4669-92dd-c0a76a46d0b6' class='xr-section-summary' >Attributes: <span>(33)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1</dd><dt><span>History :</span></dt><dd>Original file generated: Tue Jun 18 21:47:49 2019 GMT</dd><dt><span>Comment :</span></dt><dd>GMAO filename: d5124_m2_jan10.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>Filename :</span></dt><dd>MERRA2_400.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>Institution :</span></dt><dd>NASA Global Modeling and Assimilation Office</dd><dt><span>References :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>Format :</span></dt><dd>NetCDF-4/HDF-5</dd><dt><span>SpatialCoverage :</span></dt><dd>global</dd><dt><span>VersionID :</span></dt><dd>5.12.4</dd><dt><span>TemporalRange :</span></dt><dd>1980-01-01 -&gt; 2016-12-31</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>http://dx.doi.org/</dd><dt><span>ShortName :</span></dt><dd>M2I3NPASM</dd><dt><span>GranuleID :</span></dt><dd>MERRA2_400.inst3_3d_asm_Np.20190608.nc4</dd><dt><span>ProductionDateTime :</span></dt><dd>Original file generated: Tue Jun 18 21:47:49 2019 GMT</dd><dt><span>LongName :</span></dt><dd>MERRA2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields</dd><dt><span>Title :</span></dt><dd>MERRA2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields</dd><dt><span>SouthernmostLatitude :</span></dt><dd>-90.0</dd><dt><span>NorthernmostLatitude :</span></dt><dd>90.0</dd><dt><span>WesternmostLongitude :</span></dt><dd>-180.0</dd><dt><span>EasternmostLongitude :</span></dt><dd>179.375</dd><dt><span>LatitudeResolution :</span></dt><dd>0.5</dd><dt><span>LongitudeResolution :</span></dt><dd>0.625</dd><dt><span>DataResolution :</span></dt><dd>0.5 x 0.625 (42 pressure levels)</dd><dt><span>Contact :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>identifier_product_doi :</span></dt><dd>10.5067/QBZ6MG944HW0</dd><dt><span>RangeBeginningDate :</span></dt><dd>2019-06-08</dd><dt><span>RangeBeginningTime :</span></dt><dd>00:00:00.000000</dd><dt><span>RangeEndingDate :</span></dt><dd>2019-06-08</dd><dt><span>RangeEndingTime :</span></dt><dd>21:00:00.000000</dd><dt><span>history_L34RS :</span></dt><dd>&#x27;Created by L34RS v1.4.3 @ NASA GES DISC on April 29 2023 20:47:34. Regrid: remapcon GLDAS-2_1. Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23. Variables: H OMEGA PS QV SLP T U V&#x27;</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd><dt><span>cdo_openmp_thread_number :</span></dt><dd>12</dd></dl></div></li></ul></div></div>"
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+      "text/plain": [
+       "<xarray.Dataset> Size: 942kB\n",
+       "Dimensions:  (time: 1, lev: 23, lat: 28, lon: 60)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 480B -125.5 -124.5 -123.5 ... -68.5 -67.5 -66.5\n",
+       "  * lat      (lat) float64 224B 23.5 24.5 25.5 26.5 27.5 ... 47.5 48.5 49.5 50.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Data variables:\n",
+       "    H        (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    OMEGA    (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    PS       (time, lat, lon) float32 7kB dask.array<chunksize=(1, 28, 60), meta=np.ndarray>\n",
+       "    QV       (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    SLP      (time, lat, lon) float32 7kB dask.array<chunksize=(1, 28, 60), meta=np.ndarray>\n",
+       "    T        (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    U        (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "    V        (time, lev, lat, lon) float32 155kB dask.array<chunksize=(1, 23, 28, 60), meta=np.ndarray>\n",
+       "Attributes: (12/33)\n",
+       "    CDI:                               Climate Data Interface version 1.9.8 (...\n",
+       "    Conventions:                       CF-1\n",
+       "    History:                           Original file generated: Tue Jun 18 21...\n",
+       "    Comment:                           GMAO filename: d5124_m2_jan10.inst3_3d...\n",
+       "    Filename:                          MERRA2_400.inst3_3d_asm_Np.20190608.nc4\n",
+       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
+       "    ...                                ...\n",
+       "    RangeBeginningTime:                00:00:00.000000\n",
+       "    RangeEndingDate:                   2019-06-08\n",
+       "    RangeEndingTime:                   21:00:00.000000\n",
+       "    history_L34RS:                     'Created by L34RS v1.4.3 @ NASA GES DI...\n",
+       "    CDO:                               Climate Data Operators version 1.9.8 (...\n",
+       "    cdo_openmp_thread_number:          12"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8578a22a-b045-4421-be7a-f0ed9bf22411",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "###extracting temperature, pressure, and geopotential from the dataset\n",
+    "gph = new_data.H\n",
+    "p =new_data.lev\n",
+    "T = new_data.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c20a158f-e6b0-41be-8187-f89aa9231f24",
+   "metadata": {},
+   "outputs": [
+    {
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+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;H&#x27; (time: 1, lev: 23, lat: 28, lon: 60)&gt; Size: 155kB\n",
+       "dask.array&lt;getitem, shape=(1, 23, 28, 60), dtype=float32, chunksize=(1, 23, 28, 60), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 480B -125.5 -124.5 -123.5 ... -68.5 -67.5 -66.5\n",
+       "  * lat      (lat) float64 224B 23.5 24.5 25.5 26.5 27.5 ... 47.5 48.5 49.5 50.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Attributes:\n",
+       "    standard_name:   edge_heights\n",
+       "    long_name:       edge_heights\n",
+       "    units:           m\n",
+       "    fmissing_value:  1000000000000000.0\n",
+       "    vmax:            1000000000000000.0\n",
+       "    vmin:            -1000000000000000.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'H'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lev</span>: 23</li><li><span class='xr-has-index'>lat</span>: 28</li><li><span class='xr-has-index'>lon</span>: 60</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6a54f31b-4544-4f4b-acfd-ff865ea2f6c9' class='xr-array-in' type='checkbox' checked><label for='section-6a54f31b-4544-4f4b-acfd-ff865ea2f6c9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 23, 28, 60), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                        <td> 150.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                        <td> (1, 23, 28, 60) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-8ac7ba76-10b6-40e8-96f4-48bc88a56408' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8ac7ba76-10b6-40e8-96f4-48bc88a56408' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-06-08</div><input id='attrs-92dff5af-8d07-4237-8aa7-cc4168260cd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92dff5af-8d07-4237-8aa7-cc4168260cd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-443c1bd1-e56e-43b6-aa69-2b2303640723' class='xr-var-data-in' type='checkbox'><label for='data-443c1bd1-e56e-43b6-aa69-2b2303640723' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-06-08T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-125.5 -124.5 ... -67.5 -66.5</div><input id='attrs-e071b0e2-6f12-4939-bb91-22f11a86d715' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e071b0e2-6f12-4939-bb91-22f11a86d715' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3494873c-4748-4944-a9e7-38ee2815f298' class='xr-var-data-in' type='checkbox'><label for='data-3494873c-4748-4944-a9e7-38ee2815f298' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-125.5, -124.5, -123.5, -122.5, -121.5, -120.5, -119.5, -118.5, -117.5,\n",
+       "       -116.5, -115.5, -114.5, -113.5, -112.5, -111.5, -110.5, -109.5, -108.5,\n",
+       "       -107.5, -106.5, -105.5, -104.5, -103.5, -102.5, -101.5, -100.5,  -99.5,\n",
+       "        -98.5,  -97.5,  -96.5,  -95.5,  -94.5,  -93.5,  -92.5,  -91.5,  -90.5,\n",
+       "        -89.5,  -88.5,  -87.5,  -86.5,  -85.5,  -84.5,  -83.5,  -82.5,  -81.5,\n",
+       "        -80.5,  -79.5,  -78.5,  -77.5,  -76.5,  -75.5,  -74.5,  -73.5,  -72.5,\n",
+       "        -71.5,  -70.5,  -69.5,  -68.5,  -67.5,  -66.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>23.5 24.5 25.5 ... 48.5 49.5 50.5</div><input id='attrs-81561680-3093-4025-a3e3-21b4c4ccd8d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81561680-3093-4025-a3e3-21b4c4ccd8d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3467749-2e08-42b7-ac94-8fda77c32b9f' class='xr-var-data-in' type='checkbox'><label for='data-e3467749-2e08-42b7-ac94-8fda77c32b9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5,\n",
+       "       35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5,\n",
+       "       47.5, 48.5, 49.5, 50.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 975.0 950.0 ... 250.0 200.0</div><input id='attrs-d3738ab9-70bb-485c-8fc7-0d84dc062138' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3738ab9-70bb-485c-8fc7-0d84dc062138' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dcac355-cfa3-404b-8a31-1ef7d0acc71b' class='xr-var-data-in' type='checkbox'><label for='data-7dcac355-cfa3-404b-8a31-1ef7d0acc71b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>PLE_level</dd><dt><span>long_name :</span></dt><dd>vertical level</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate :</span></dt><dd>PLE</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  975.,  950.,  925.,  900.,  875.,  850.,  825.,  800.,  775.,\n",
+       "        750.,  725.,  700.,  650.,  600.,  550.,  500.,  450.,  400.,  350.,\n",
+       "        300.,  250.,  200.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a707597c-89de-44bf-9e62-8d98ea65f9c0' class='xr-section-summary-in' type='checkbox'  ><label for='section-a707597c-89de-44bf-9e62-8d98ea65f9c0' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-da47dff4-a3ef-4d7e-a235-c277466343c5' class='xr-index-data-in' type='checkbox'/><label for='index-da47dff4-a3ef-4d7e-a235-c277466343c5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-06-08&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9118e349-3a8c-43e6-ab1c-00a7155d14c1' class='xr-index-data-in' type='checkbox'/><label for='index-9118e349-3a8c-43e6-ab1c-00a7155d14c1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-125.5, -124.5, -123.5, -122.5, -121.5, -120.5, -119.5, -118.5, -117.5,\n",
+       "       -116.5, -115.5, -114.5, -113.5, -112.5, -111.5, -110.5, -109.5, -108.5,\n",
+       "       -107.5, -106.5, -105.5, -104.5, -103.5, -102.5, -101.5, -100.5,  -99.5,\n",
+       "        -98.5,  -97.5,  -96.5,  -95.5,  -94.5,  -93.5,  -92.5,  -91.5,  -90.5,\n",
+       "        -89.5,  -88.5,  -87.5,  -86.5,  -85.5,  -84.5,  -83.5,  -82.5,  -81.5,\n",
+       "        -80.5,  -79.5,  -78.5,  -77.5,  -76.5,  -75.5,  -74.5,  -73.5,  -72.5,\n",
+       "        -71.5,  -70.5,  -69.5,  -68.5,  -67.5,  -66.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-887874dd-2dc1-4345-8dc4-947875a46217' class='xr-index-data-in' type='checkbox'/><label for='index-887874dd-2dc1-4345-8dc4-947875a46217' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5,\n",
+       "       35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5,\n",
+       "       47.5, 48.5, 49.5, 50.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a5073ca3-fb5f-4962-a682-1bd546385c2a' class='xr-index-data-in' type='checkbox'/><label for='index-a5073ca3-fb5f-4962-a682-1bd546385c2a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0,  975.0,  950.0,  925.0,  900.0,  875.0,  850.0,  825.0,  800.0,\n",
+       "        775.0,  750.0,  725.0,  700.0,  650.0,  600.0,  550.0,  500.0,  450.0,\n",
+       "        400.0,  350.0,  300.0,  250.0,  200.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e9a29ad-4415-4f2b-96b2-2cc00a8073a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2e9a29ad-4415-4f2b-96b2-2cc00a8073a7' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>edge_heights</dd><dt><span>long_name :</span></dt><dd>edge_heights</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>fmissing_value :</span></dt><dd>1000000000000000.0</dd><dt><span>vmax :</span></dt><dd>1000000000000000.0</dd><dt><span>vmin :</span></dt><dd>-1000000000000000.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'H' (time: 1, lev: 23, lat: 28, lon: 60)> Size: 155kB\n",
+       "dask.array<getitem, shape=(1, 23, 28, 60), dtype=float32, chunksize=(1, 23, 28, 60), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2019-06-08\n",
+       "  * lon      (lon) float64 480B -125.5 -124.5 -123.5 ... -68.5 -67.5 -66.5\n",
+       "  * lat      (lat) float64 224B 23.5 24.5 25.5 26.5 27.5 ... 47.5 48.5 49.5 50.5\n",
+       "  * lev      (lev) float64 184B 1e+03 975.0 950.0 925.0 ... 300.0 250.0 200.0\n",
+       "Attributes:\n",
+       "    standard_name:   edge_heights\n",
+       "    long_name:       edge_heights\n",
+       "    units:           m\n",
+       "    fmissing_value:  1000000000000000.0\n",
+       "    vmax:            1000000000000000.0\n",
+       "    vmin:            -1000000000000000.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "96df4b51-1cd3-43a8-8e85-0090023cce2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "U,V = geostrophic_wind(gph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2ca7f254-6f68-443f-a4e8-b1c31d7180d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 23, 28, 60)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.shape(U)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f296eb3-dca0-4680-9d82-01a2b61184a2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b3dde0b5-2394-4cf7-a090-77d81a2dccbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataproj = ccrs. PlateCarree ()\n",
+    "# # Plot projection\n",
+    "# # The look you want for the view.\n",
+    "plotproj = ccrs. PlateCarree ()\n",
+    "fig=plt.figure(1, figsize=(15.,12.))\n",
+    "\n",
+    "ax=plt.subplot(111,projection=plotproj)\n",
+    "\n",
+    "ax.add_feature(cfeature.COASTLINE, linewidth=0.5)\n",
+    "ax.add_feature(cfeature.STATES, linewidth=0.5)\n",
+    "plt.title(\"Geostrophic Wind Calculated Using Metpy\",size = 30)\n",
+    "plt.quiver (new_data.lon, new_data.lat, U[0,12,:,:],V[0,12,:,:],minlength = 0.5,units='width')\n",
+    "# plt.colorbar (orientation = \"horizontal\", pad=0.01).ax.tick_params(labelsize=20)\n",
+    "plt. show ()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1f8df97c-b97e-498f-985f-92aa2b4897d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qx, qy = q_vector(U,V,T,p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b9d73bb-04c8-4de6-8967-c32e9efee22e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0e1331cc-8244-4b8d-a882-aa2ca89bf888",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataproj = ccrs. PlateCarree ()\n",
+    "# # Plot projection\n",
+    "# # The look you want for the view.\n",
+    "plotproj = ccrs. PlateCarree ()\n",
+    "fig=plt.figure(1, figsize=(15.,12.))\n",
+    "\n",
+    "ax=plt.subplot(111,projection=plotproj)\n",
+    "\n",
+    "ax.add_feature(cfeature.COASTLINE, linewidth=0.5)\n",
+    "ax.add_feature(cfeature.STATES, linewidth=0.5)\n",
+    "plt.title(\"Q-vector Calculated Using Metpy\",size = 30)\n",
+    "\n",
+    "plt.contour(new_data.lon, new_data.lat,gaussian_filter(gph[0,12,:,:],1), colors = \"black\")\n",
+    "# plt.contourf(new_data.lon, new_data.lat, new_data.OMEGA[0,12,:,:],levels =np.arange(-2,2,0.2),cmap = \"RdBu\", transform=dataproj,extend = \"both\" )\n",
+    "plt.colorbar (orientation = \"horizontal\", pad=0.01).ax.tick_params(labelsize=20)\n",
+    "# plt.colorbar (orientation = \"horizontal\", pad=0.01).ax.tick_params(labelsize=20)\n",
+    "plt.quiver (new_data.lon, new_data.lat, qx[0,12,:,:],gaussian_filter(qy[0,12,:,:],0.7), color='blue',pivot='mid', \n",
+    "          scale=1e-11, scale_units='inches',\n",
+    "          transform=dataproj)\n",
+    "\n",
+    "# gaussian_filter(data, sigma)\n",
+    "plt. show ()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24c290ac-9aba-4f12-a835-242581fb90c7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/Isentropic_Interpolation.ipynb b/_preview/13/_sources/notebooks/synoptic/Isentropic_Interpolation.ipynb
new file mode 100644
index 0000000..815e4fe
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/Isentropic_Interpolation.ipynb
@@ -0,0 +1,306 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Isentropic Analysis\n",
+    "===================\n",
+    "\n",
+    "The MetPy function `metpy.calc.isentropic_interpolation` allows for isentropic analysis from\n",
+    "model analysis data in isobaric coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "from metpy.units import units\n",
+    "import numpy as np\n",
+    "from siphon.catalog import TDSCatalog\n",
+    "from xarray.backends import NetCDF4DataStore\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "###########################"
+   },
+   "source": [
+    "Getting the data\n",
+    "\n",
+    "In this example, the latest GFS forecasts data from the National Centers for\n",
+    "Environmental Information (https://nomads.ncdc.noaa.gov) will be used, courtesy of the\n",
+    "Univeristy Corporation for Atmospheric Research Thredds Data Server."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Latest GFS Dataset\n",
+    "cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/'\n",
+    "                 'NCEP/GFS/Global_0p5deg/catalog.xml')\n",
+    "ncss = cat.latest.subset()\n",
+    "\n",
+    "# Find the start of the model run and define time range\n",
+    "start_time = ncss.metadata.time_span['begin']\n",
+    "start = datetime.strptime(start_time, '%Y-%m-%dT%H:%M:%Sz')\n",
+    "end = start + timedelta(hours=9)\n",
+    "\n",
+    "# Query for Latest GFS Run\n",
+    "gfsdata = ncss.query().time_range(start, end).accept('netcdf4')\n",
+    "\n",
+    "gfsdata.variables('Temperature_isobaric',\n",
+    "                  'u-component_of_wind_isobaric',\n",
+    "                  'v-component_of_wind_isobaric',\n",
+    "                  'Relative_humidity_isobaric').add_lonlat()\n",
+    "\n",
+    "# Set the lat/lon box for the data you want to pull in.\n",
+    "# lonlat_box(north_lat,south_lat,east_lon,west_lon)\n",
+    "gfsdata.lonlat_box(-150, -50, 15, 65)\n",
+    "\n",
+    "# Actually getting the data\n",
+    "data = ncss.get_data(gfsdata)\n",
+    "\n",
+    "# Make into an xarray Dataset object\n",
+    "ds = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()\n",
+    "\n",
+    "dlev_hght = ds.Temperature_isobaric.dims[1]\n",
+    "dlev_uwnd = ds['u-component_of_wind_isobaric'].dims[1]\n",
+    "lat = ds.latitude\n",
+    "lon = ds.longitude\n",
+    "\n",
+    "lev_hght = ds[dlev_hght]\n",
+    "lev_uwnd = ds[dlev_uwnd]\n",
+    "\n",
+    "# Due to a different number of vertical levels find where they are common\n",
+    "_, _, common_ind = np.intersect1d(lev_uwnd, lev_hght, return_indices=True)\n",
+    "\n",
+    "common_levels = lev_hght[common_ind].metpy.unit_array\n",
+    "times = ds.Temperature_isobaric.metpy.time\n",
+    "vtimes = times.values.astype('datetime64[ms]').astype('O')\n",
+    "temps = ds.Temperature_isobaric.metpy.unit_array\n",
+    "uwnd = ds['u-component_of_wind_isobaric'].metpy.unit_array\n",
+    "vwnd = ds['v-component_of_wind_isobaric'].metpy.unit_array\n",
+    "relh = ds.Relative_humidity_isobaric.metpy.unit_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################"
+   },
+   "source": [
+    "To properly interpolate to isentropic coordinates, the function must know the desired output\n",
+    "isentropic levels. An array with these levels will be created below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isentlevs = np.arange(310, 316, 5) * units.kelvin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################"
+   },
+   "source": [
+    "Conversion to Isentropic Coordinates\n",
+    "\n",
+    "Once model data in isobaric coordinates has been pulled and the desired isentropic levels\n",
+    "created, the conversion to isentropic coordinates can begin. Data will be passed to the\n",
+    "function as below. The function requires that isentropic levels, isobaric levels, and\n",
+    "temperature be input. Any additional inputs (in this case relative humidity, u, and v wind\n",
+    "components) will be linearly interpolated to isentropic space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isent_anal = mpcalc.isentropic_interpolation(isentlevs,\n",
+    "                                             common_levels,\n",
+    "                                             temps[:, common_ind, :, :],\n",
+    "                                             relh,\n",
+    "                                             uwnd,\n",
+    "                                             vwnd,\n",
+    "                                             vertical_dim=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "####################"
+   },
+   "source": [
+    "The output is a list, so now we will separate the variables to different names before\n",
+    "plotting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isentprs, isentrh, isentu, isentv = isent_anal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "##########################"
+   },
+   "source": [
+    "A quick look at the shape of these variables will show that the data is now in isentropic\n",
+    "coordinates, with the number of vertical levels as specified above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(isentprs.shape)\n",
+    "print(isentrh.shape)\n",
+    "print(isentu.shape)\n",
+    "print(isentv.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "####################"
+   },
+   "source": [
+    "**Plotting the Isentropic Analysis**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#####################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Set up our projection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)\n",
+    "\n",
+    "# Set up our array of latitude and longitude values and transform to\n",
+    "# the desired projection.\n",
+    "clons, clats = np.meshgrid(lon, lat)\n",
+    "\n",
+    "# Get data to plot state and province boundaries\n",
+    "states_provinces = cfeature.NaturalEarthFeature(\n",
+    "    category='cultural',\n",
+    "    name='admin_1_states_provinces_lakes',\n",
+    "    scale='50m',\n",
+    "    facecolor='none')\n",
+    "\n",
+    "# Choose Isentropic Level index value\n",
+    "level = 0 # [310, 315]\n",
+    "\n",
+    "# Choose forecast hour index value\n",
+    "FH = 0 # [0, 3, 6, 9]\n",
+    "\n",
+    "# Start Figure\n",
+    "fig = plt.figure(1, figsize=(14., 12.))\n",
+    "ax = plt.subplot(111, projection=crs)\n",
+    "\n",
+    "# Set plot extent\n",
+    "ax.set_extent((-121., -74., 25., 50.), crs=ccrs.PlateCarree())\n",
+    "ax.coastlines('50m', edgecolor='black', linewidth=0.75)\n",
+    "ax.add_feature(states_provinces, edgecolor='black', linewidth=0.5)\n",
+    "\n",
+    "# Plot the 300K surface\n",
+    "clevisent = np.arange(0, 1000, 25)\n",
+    "cs = ax.contour(clons, clats,\n",
+    "                mpcalc.smooth_n_point(isentprs[FH, level, :, :], 9, 4),\n",
+    "                clevisent,\n",
+    "                transform=ccrs.PlateCarree(),\n",
+    "                colors='k', linewidths=1.5, linestyles='solid')\n",
+    "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=7,\n",
+    "           fmt='%i', rightside_up=True, use_clabeltext=True)\n",
+    "\n",
+    "cf = ax.contourf(clons, clats,\n",
+    "                 mpcalc.smooth_n_point(isentrh[FH, level, :, :], 9, 4),\n",
+    "                 np.arange(50, 101, 1),\n",
+    "                 transform=ccrs.PlateCarree(),\n",
+    "                 cmap=plt.cm.YlGn)\n",
+    "plt.colorbar(cf, orientation='horizontal', aspect=65, pad=0, extendrect='True')\n",
+    "\n",
+    "wind_slice = (FH, level, slice(None, None, 5), slice(None, None, 5))\n",
+    "ax.barbs(clons[wind_slice[2:]], clats[wind_slice[2:]],\n",
+    "         isentu[wind_slice].m, isentv[wind_slice].m, length=6,\n",
+    "         transform=ccrs.PlateCarree())\n",
+    "\n",
+    "# Make some titles\n",
+    "plt.title(f'{isentlevs[level].m:.0f} K Isentropic Level', loc='left')\n",
+    "plt.title(f'VALID: {vtimes[FH]} UTC', loc='right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/PV_baroclinic_isobaric.ipynb b/_preview/13/_sources/notebooks/synoptic/PV_baroclinic_isobaric.ipynb
new file mode 100644
index 0000000..83a7d74
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/PV_baroclinic_isobaric.ipynb
@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Baroclinic Potential Vorticity Analysis, Divergence, and Winds\n",
+    "==============================================================\n",
+    "\n",
+    "Classic baroclinic potential vorticity plot at 250 hPa using GFS\n",
+    "analysis file.\n",
+    "\n",
+    "This example uses example data from the GFS analysis for 12 UTC 31\n",
+    "October 2016 and uses xarray as the main read source with using MetPy to\n",
+    "calculate the baroclinic potential vorticity, divergence and wind speed\n",
+    "with geographic plotting using Cartopy for a CONUS view of the 250-hPa\n",
+    "surface with divergence and wind barbs.\n",
+    "\n",
+    "By: Kevin Goebbert"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "from metpy.units import units\n",
+    "import numpy as np\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "The following code reads the example data using the xarray open_dataset\n",
+    "function and prints the coordinate values that are associated with the\n",
+    "various variables contained within the file.\n",
+    "\n",
+    "This code uses the metpy accessor to parse file to make it easy to pull\n",
+    "data using common coordinate names (e.g., vertical) and attach units.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/casestudies'\n",
+    "                     '/python-gallery/GFS_20101026_1200.nc').metpy.parse_cf()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Data Retrieval\n",
+    "--------------\n",
+    "\n",
+    "This code retrieves the necessary data from the file and completes some\n",
+    "smoothing of the temperature, and wind fields using the MetPy function\n",
+    "smooth_n_point. A nicely formated valid time (vtime) variable is also\n",
+    "created.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set subset slice for the geographic extent of data to limit download\n",
+    "lon_slice = slice(200, 350)\n",
+    "lat_slice = slice(85, 10)\n",
+    "\n",
+    "# Grab lat/lon values (GFS will be 1D)\n",
+    "lats = ds.lat.sel(lat=lat_slice).values\n",
+    "lons = ds.lon.sel(lon=lon_slice).values\n",
+    "\n",
+    "\n",
+    "# Grab the pressure levels and select the data to be imported\n",
+    "# Need all pressure levels for Temperatures, U and V Wind,\n",
+    "# and Rel. Humidity\n",
+    "# Smooth with the gaussian filter from scipy\n",
+    "pres = ds.Temperature_isobaric.metpy.vertical\n",
+    "\n",
+    "tmpk_var = ds['Temperature_isobaric'].metpy.sel(\n",
+    "    lat=lat_slice, lon=lon_slice).squeeze()\n",
+    "tmpk = mpcalc.smooth_n_point(tmpk_var, 9, 2)\n",
+    "thta = mpcalc.potential_temperature(pres, tmpk)\n",
+    "\n",
+    "uwnd_var = ds['u-component_of_wind_isobaric'].metpy.sel(\n",
+    "    lat=lat_slice, lon=lon_slice).squeeze()\n",
+    "vwnd_var = ds['v-component_of_wind_isobaric'].metpy.sel(\n",
+    "    lat=lat_slice, lon=lon_slice).squeeze()\n",
+    "uwnd = mpcalc.smooth_n_point(uwnd_var, 9, 2)\n",
+    "vwnd = mpcalc.smooth_n_point(vwnd_var, 9, 2)\n",
+    "\n",
+    "# Create a clean datetime object for plotting based on time of\n",
+    "# Geopotential heights\n",
+    "vtime = ds.time.data[0].astype('datetime64[ms]').astype('O')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Use MetPy to compute the baroclinic potential vorticity on all isobaric\n",
+    "levels and other variables\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Comput the PV on all isobaric surfaces\n",
+    "pv = mpcalc.potential_vorticity_baroclinic(thta, pres, uwnd, vwnd)\n",
+    "\n",
+    "# Use MetPy to compute the divergence on the pressure surfaces\n",
+    "div = mpcalc.divergence(uwnd, vwnd)\n",
+    "\n",
+    "# Select the 250-hPa surface\n",
+    "pv_250 = pv.metpy.sel(vertical=250*units.hPa)\n",
+    "div_250 = div.metpy.sel(vertical=250*units.hPa)\n",
+    "uwnd_250 = uwnd.metpy.sel(vertical=250*units.hPa)\n",
+    "vwnd_250 = vwnd.metpy.sel(vertical=250*units.hPa)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Map Creation\n",
+    "------------\n",
+    "\n",
+    "This next set of code creates the plot and draws contours on a Lambert\n",
+    "Conformal map centered on -100 E longitude. The main view is over the\n",
+    "CONUS with isobaric PV map with PV contoured every 1 PVU and divergence\n",
+    "colorshaded.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up the projection that will be used for plotting\n",
+    "mapcrs = ccrs.LambertConformal(central_longitude=-100,\n",
+    "                               central_latitude=35,\n",
+    "                               standard_parallels=(30, 60))\n",
+    "\n",
+    "# Set up the projection of the data;\n",
+    "# if lat/lon then PlateCarree is what you want\n",
+    "datacrs = ccrs.PlateCarree()\n",
+    "\n",
+    "# Start the figure and create plot axes with proper projection\n",
+    "fig = plt.figure(1, figsize=(14, 12))\n",
+    "ax = plt.subplot(111, projection=mapcrs)\n",
+    "ax.set_extent([-130, -72, 20, 55], ccrs.PlateCarree())\n",
+    "\n",
+    "# Add geopolitical boundaries for map reference\n",
+    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
+    "ax.add_feature(cfeature.STATES.with_scale('50m'))\n",
+    "\n",
+    "# Plot the contours of PV at 250 hPa, scaling 10^6 every 1 PVU\n",
+    "clevs_pv = np.arange(0, 25, 1)\n",
+    "cs1 = ax.contour(lons, lats, pv_250*1e6, clevs_pv, colors='black',\n",
+    "                 transform=datacrs)\n",
+    "plt.clabel(cs1, fmt='%d', fontsize='large')\n",
+    "\n",
+    "# Plot the colorfill of divergence, scaled 10^5 every 1 s^1\n",
+    "clevs_div = np.arange(-15, 16, 1)\n",
+    "cs1 = ax.contourf(lons, lats, div_250*1e5, clevs_div, cmap=plt.cm.PuOr,\n",
+    "                  extend='both', transform=datacrs)\n",
+    "plt.colorbar(cs1, orientation='horizontal', pad=0, aspect=50, extendrect=True)\n",
+    "\n",
+    "# Plot the wind barbs at 250 hPa\n",
+    "wind_slice = slice(None, None, 6)\n",
+    "ax.barbs(lons[wind_slice], lats[wind_slice],\n",
+    "         uwnd_250[wind_slice, wind_slice].metpy.convert_units('kt').values,\n",
+    "         vwnd_250[wind_slice, wind_slice].metpy.convert_units('kt').values,\n",
+    "         pivot='middle', color='black', length=6.5, transform=datacrs)\n",
+    "\n",
+    "# Plot some titles to tell people what is on the map\n",
+    "plt.title('250-hPa GFS PV (PVU), Divergence ($10^5$ s$^{-1}$),'\n",
+    "          ' and Wind Barbs (kt)', loc='left')\n",
+    "plt.title(f'Valid Time: {vtime}', loc='right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/Upperair_Obs.ipynb b/_preview/13/_sources/notebooks/synoptic/Upperair_Obs.ipynb
new file mode 100644
index 0000000..b7147a6
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/Upperair_Obs.ipynb
@@ -0,0 +1,507 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "DIFAX Replication\n",
+    "=================\n",
+    "\n",
+    "This example replicates the traditional DIFAX images for upper-level\n",
+    "observations.\n",
+    "\n",
+    "By: Kevin Goebbert\n",
+    "\n",
+    "Observation data comes from Iowa State Archive, accessed through the\n",
+    "Siphon package. Contour data comes from the GFS 0.5 degree analysis.\n",
+    "Classic upper-level data of Geopotential Height and Temperature are\n",
+    "plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import urllib.request\n",
+    "\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FixedLocator\n",
+    "import metpy.calc as mpcalc\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "\n",
+    "from metpy.plots import StationPlot\n",
+    "from metpy.units import units\n",
+    "from siphon.simplewebservice.iastate import IAStateUpperAir"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Plotting High/Low Symbols\n",
+    "-------------------------\n",
+    "\n",
+    "A helper function to plot a text symbol (e.g., H, L) for relative\n",
+    "maximum/minimum for a given field (e.g., geopotential height)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_maxmin_points(lon, lat, data, extrema, nsize, symbol, color='k',\n",
+    "                       plotValue=True, transform=None):\n",
+    "    \"\"\"\n",
+    "    This function will find and plot relative maximum and minimum for a 2D grid. The function\n",
+    "    can be used to plot an H for maximum values (e.g., High pressure) and an L for minimum\n",
+    "    values (e.g., low pressue). It is best to used filetered data to obtain  a synoptic scale\n",
+    "    max/min value. The symbol text can be set to a string value and optionally the color of the\n",
+    "    symbol and any plotted value can be set with the parameter color.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "        lon : 2D array\n",
+    "            Plotting longitude values\n",
+    "        lat : 2D array\n",
+    "            Plotting latitude values\n",
+    "        data : 2D array\n",
+    "            Data that you wish to plot the max/min symbol placement\n",
+    "        extrema : str\n",
+    "            Either a value of max for Maximum Values or min for Minimum Values\n",
+    "        nsize : int\n",
+    "            Size of the grid box to filter the max and min values to plot a reasonable number\n",
+    "        symbol : str\n",
+    "            Text to be placed at location of max/min value\n",
+    "        color : str\n",
+    "            Name of matplotlib colorname to plot the symbol (and numerical value, if plotted)\n",
+    "        plot_value : Boolean (True/False)\n",
+    "            Whether to plot the numeric value of max/min point\n",
+    "\n",
+    "    Return\n",
+    "    ------\n",
+    "        The max/min symbol will be plotted on the current axes within the bounding frame\n",
+    "        (e.g., clip_on=True)\n",
+    "    \"\"\"\n",
+    "    from scipy.ndimage import maximum_filter, minimum_filter\n",
+    "\n",
+    "    if (extrema == 'max'):\n",
+    "        data_ext = maximum_filter(data, nsize, mode='nearest')\n",
+    "    elif (extrema == 'min'):\n",
+    "        data_ext = minimum_filter(data, nsize, mode='nearest')\n",
+    "    else:\n",
+    "        raise ValueError('Value for hilo must be either max or min')\n",
+    "\n",
+    "    if lon.ndim == 1:\n",
+    "        lon, lat = np.meshgrid(lon, lat)\n",
+    "\n",
+    "    mxx, mxy = np.where(data_ext == data)\n",
+    "\n",
+    "    for i in range(len(mxy)):\n",
+    "        ax.text(lon[mxx[i], mxy[i]], lat[mxx[i], mxy[i]], symbol, color=color, size=36,\n",
+    "                clip_on=True, horizontalalignment='center', verticalalignment='center',\n",
+    "                transform=transform)\n",
+    "        ax.text(lon[mxx[i], mxy[i]], lat[mxx[i], mxy[i]],\n",
+    "                '\\n' + str(np.int64(data[mxx[i], mxy[i]])),\n",
+    "                color=color, size=12, clip_on=True, fontweight='bold',\n",
+    "                horizontalalignment='center', verticalalignment='top', transform=transform)\n",
+    "        ax.plot(lon[mxx[i], mxy[i]], lat[mxx[i], mxy[i]], marker='o', markeredgecolor='black',\n",
+    "                markerfacecolor='white', transform=transform)\n",
+    "        ax.plot(lon[mxx[i], mxy[i]], lat[mxx[i], mxy[i]],\n",
+    "                marker='x', color='black', transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Station Information\n",
+    "-------------------\n",
+    "\n",
+    "A helper function for obtaining radiosonde station information (e.g.,\n",
+    "latitude/longitude) requried to plot data obtained from each station.\n",
+    "Original code by github user sgdecker."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "lines_to_next_cell": 1
+   },
+   "outputs": [],
+   "source": [
+    "def station_info(stid):\n",
+    "    r\"\"\"Provide information about weather stations.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    stid: str or iterable object containing strs\n",
+    "         The ICAO or IATA code(s) for which station information is requested.\n",
+    "    with_units: bool\n",
+    "         Whether to include units for values that have them. Default True.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    info: dict\n",
+    "         Information about the station(s) within a dictionary with these keys:\n",
+    "             'state': Two-character ID of the state/province where the station is located,\n",
+    "                       if applicable\n",
+    "             'name': The name of the station\n",
+    "             'lat': The latitude of the station [deg]\n",
+    "             'lon': The longitude of the station [deg]\n",
+    "             'elevation': The elevation of the station [m]\n",
+    "             'country': Two-character ID of the country where the station is located\n",
+    "\n",
+    "    Modified code from Steven Decker, Rutgers University\n",
+    "\n",
+    "    \"\"\"\n",
+    "    # Provide a helper function for later usage\n",
+    "    def str2latlon(s):\n",
+    "        deg = float(s[:3])\n",
+    "        mn = float(s[-3:-1])\n",
+    "        if s[-1] == 'S' or s[-1] == 'W':\n",
+    "            deg = -deg\n",
+    "            mn = -mn\n",
+    "        return deg + mn / 60.\n",
+    "\n",
+    "    # Various constants describing the underlying data\n",
+    "    url = 'https://www.aviationweather.gov/docs/metar/stations.txt'\n",
+    "    # file = 'stations.txt'\n",
+    "    state_bnds = slice(0, 2)\n",
+    "    name_bnds = slice(3, 19)\n",
+    "    icao_bnds = slice(20, 24)\n",
+    "    iata_bnds = slice(26, 29)\n",
+    "    lat_bnds = slice(39, 45)\n",
+    "    lon_bnds = slice(47, 54)\n",
+    "    z_bnds = slice(55, 59)\n",
+    "    cntry_bnds = slice(81, 83)\n",
+    "\n",
+    "    # Generalize to any number of IDs\n",
+    "    if isinstance(stid, str):\n",
+    "        stid = [stid]\n",
+    "\n",
+    "    # Get the station dataset\n",
+    "    infile = urllib.request.urlopen(url)\n",
+    "    data = infile.readlines()\n",
+    "\n",
+    "    state = []\n",
+    "    name = []\n",
+    "    lat = []\n",
+    "    lon = []\n",
+    "    z = []\n",
+    "    cntry = []\n",
+    "\n",
+    "    for s in stid:\n",
+    "        s = s.upper()\n",
+    "        for line_bytes in data:\n",
+    "            line = line_bytes.decode('UTF-8')\n",
+    "            icao = line[icao_bnds]\n",
+    "            iata = line[iata_bnds]\n",
+    "            if len(s) == 3 and s in iata or len(s) == 4 and s in icao:\n",
+    "                state.append(line[state_bnds].strip())\n",
+    "                name.append(line[name_bnds].strip())\n",
+    "                lat.append(str2latlon(line[lat_bnds]))\n",
+    "                lon.append(str2latlon(line[lon_bnds]))\n",
+    "                z.append(float(line[z_bnds]))\n",
+    "                cntry.append(line[cntry_bnds])\n",
+    "\n",
+    "                break\n",
+    "        else:\n",
+    "            state.append('NA')\n",
+    "            name.append('NA')\n",
+    "            lat.append(np.nan)\n",
+    "            lon.append(np.nan)\n",
+    "            z.append(np.nan)\n",
+    "            cntry.append('NA')\n",
+    "\n",
+    "    infile.close()\n",
+    "\n",
+    "    return {'state': np.array(state), 'name': np.array(name), 'lat': np.array(lat),\n",
+    "            'lon': np.array(lon), 'elevation': np.array(z), 'country': np.array(cntry),\n",
+    "            'units': {'lat': 'deg', 'lon': 'deg', 'z': 'm'}}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Observation Data\n",
+    "----------------\n",
+    "\n",
+    "Set a date and time for upper-air observations (should only be 00 or 12\n",
+    "UTC for the hour).\n",
+    "\n",
+    "Request all data from Iowa State using the Siphon package. The result is\n",
+    "a pandas DataFrame containing all of the sounding data from all\n",
+    "available stations.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set date for desired UPA data\n",
+    "today = datetime.utcnow()\n",
+    "\n",
+    "# Go back one day to ensure data availability\n",
+    "date = datetime(today.year, today.month, today.day, 0) - timedelta(days=1)\n",
+    "\n",
+    "# Request data using Siphon request for data from Iowa State Archive\n",
+    "data = IAStateUpperAir.request_all_data(date)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Subset Observational Data\n",
+    "-------------------------\n",
+    "\n",
+    "From the request above will give all levels from all radisonde sites\n",
+    "available through the service. For plotting a pressure surface map there\n",
+    "is only need to have the data from that level. Below the data is subset\n",
+    "and a few parameters set based on the level chosen. Additionally, the\n",
+    "station information is obtained and latitude and longitude data is added\n",
+    "to the DataFrame.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "level = 500\n",
+    "\n",
+    "if (level == 925) | (level == 850) | (level == 700):\n",
+    "    cint = 30\n",
+    "    def hght_format(v): return format(v, '.0f')[1:]\n",
+    "elif level == 500:\n",
+    "    cint = 60\n",
+    "    def hght_format(v): return format(v, '.0f')[:3]\n",
+    "elif level == 300:\n",
+    "    cint = 120\n",
+    "    def hght_format(v): return format(v, '.0f')[:3]\n",
+    "elif level < 300:\n",
+    "    cint = 120\n",
+    "    def hght_format(v): return format(v, '.0f')[1:4]\n",
+    "\n",
+    "# Create subset of all data for a given level\n",
+    "data_subset = data.pressure == level\n",
+    "df = data[data_subset]\n",
+    "\n",
+    "# Get station lat/lon from look-up file; add to Dataframe\n",
+    "stn_info = station_info(list(df.station.values))\n",
+    "df.insert(10, 'latitude', stn_info['lat'])\n",
+    "df.insert(11, 'longitude', stn_info['lon'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Gridded Data\n",
+    "------------\n",
+    "\n",
+    "Obtain GFS gridded output for contour plotting. Specifically,\n",
+    "geopotential height and temperature data for the given level and subset\n",
+    "for over North America. Data are smoothed for aesthetic reasons.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get GFS data and subset to North America for Geopotential Height and Temperature\n",
+    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/grib/NCEP/GFS/Global_0p5deg_ana/'\n",
+    "                     'GFS_Global_0p5deg_ana_{0:%Y%m%d}_{0:%H}00.grib2'.format(\n",
+    "                         date)).metpy.parse_cf()\n",
+    "\n",
+    "# Geopotential height and smooth\n",
+    "hght = ds.Geopotential_height_isobaric.metpy.sel(\n",
+    "    vertical=level*units.hPa, time=date, lat=slice(70, 15), lon=slice(360-145, 360-50))\n",
+    "smooth_hght = mpcalc.smooth_n_point(hght, 9, 10)\n",
+    "\n",
+    "# Temperature, smooth, and convert to Celsius\n",
+    "tmpk = ds.Temperature_isobaric.metpy.sel(\n",
+    "    vertical=level*units.hPa, time=date, lat=slice(70, 15), lon=slice(360-145, 360-50))\n",
+    "smooth_tmpc = (mpcalc.smooth_n_point(tmpk, 9, 10)).metpy.convert_units('degC')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "######################################################################"
+   },
+   "source": [
+    "Create DIFAX Replication\n",
+    "------------------------\n",
+    "\n",
+    "Plot the observational data and contours on a Lambert Conformal map and\n",
+    "add features that resemble the historic DIFAX maps.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up map coordinate reference system\n",
+    "mapcrs = ccrs.LambertConformal(\n",
+    "    central_latitude=45, central_longitude=-100, standard_parallels=(30, 60))\n",
+    "\n",
+    "# Set up station locations for plotting observations\n",
+    "point_locs = mapcrs.transform_points(\n",
+    "    ccrs.PlateCarree(), df['longitude'].values, df['latitude'].values)\n",
+    "\n",
+    "# Start figure and set graphics extent\n",
+    "fig = plt.figure(1, figsize=(17, 15))\n",
+    "ax = plt.subplot(111, projection=mapcrs)\n",
+    "ax.set_extent([-125, -70, 20, 55])\n",
+    "\n",
+    "# Add map features for geographic reference\n",
+    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'), edgecolor='grey')\n",
+    "ax.add_feature(cfeature.LAND.with_scale('50m'), facecolor='white')\n",
+    "ax.add_feature(cfeature.STATES.with_scale('50m'), edgecolor='grey')\n",
+    "\n",
+    "# Plot plus signs every degree lat/lon\n",
+    "plus_lat = []\n",
+    "plus_lon = []\n",
+    "other_lat = []\n",
+    "other_lon = []\n",
+    "\n",
+    "for x in hght.lon.values[::2]:\n",
+    "    for y in hght.lat.values[::2]:\n",
+    "        if (x % 5 == 0) | (y % 5 == 0):\n",
+    "            plus_lon.append(x)\n",
+    "            plus_lat.append(y)\n",
+    "        else:\n",
+    "            other_lon.append(x)\n",
+    "            other_lat.append(y)\n",
+    "ax.scatter(other_lon, other_lat, s=2, marker='o',\n",
+    "           transform=ccrs.PlateCarree(), color='lightgrey', zorder=-1)\n",
+    "ax.scatter(plus_lon, plus_lat, s=30, marker='+', transform=ccrs.PlateCarree(),\n",
+    "           color='lightgrey')\n",
+    "\n",
+    "# Add gridlines for every 5 degree lat/lon\n",
+    "ax.gridlines(linestyle='solid', ylocs=range(15, 71, 5), xlocs=range(-150, -49, 5))\n",
+    "\n",
+    "# Start the station plot by specifying the axes to draw on, as well as the\n",
+    "# lon/lat of the stations (with transform). We also the fontsize to 10 pt.\n",
+    "stationplot = StationPlot(ax, df['longitude'].values, df['latitude'].values, clip_on=True,\n",
+    "                          transform=ccrs.PlateCarree(), fontsize=10)\n",
+    "\n",
+    "# Plot the temperature and dew point to the upper and lower left, respectively, of\n",
+    "# the center point.\n",
+    "stationplot.plot_parameter('NW', df['temperature'], color='black')\n",
+    "stationplot.plot_parameter('SW', df['dewpoint'], color='black')\n",
+    "\n",
+    "# A more complex example uses a custom formatter to control how the geopotential height\n",
+    "# values are plotted. This is set in an earlier if-statement to work appropriate for\n",
+    "# different levels.\n",
+    "stationplot.plot_parameter('NE', df['height'], formatter=hght_format)\n",
+    "\n",
+    "# Add wind barbs\n",
+    "stationplot.plot_barb(df['u_wind'], df['v_wind'], length=7, pivot='tip')\n",
+    "\n",
+    "# Plot Solid Contours of Geopotential Height\n",
+    "cs = ax.contour(hght.lon, hght.lat, smooth_hght,\n",
+    "                range(0, 20000, cint), colors='black', transform=ccrs.PlateCarree())\n",
+    "clabels = plt.clabel(cs, fmt='%d', colors='white', inline_spacing=5, use_clabeltext=True)\n",
+    "\n",
+    "# Contour labels with black boxes and white text\n",
+    "for t in cs.labelTexts:\n",
+    "    t.set_bbox({'facecolor': 'black', 'pad': 4})\n",
+    "    t.set_fontweight('heavy')\n",
+    "\n",
+    "# Plot Dashed Contours of Temperature\n",
+    "cs2 = ax.contour(hght.lon, hght.lat, smooth_tmpc, range(-60, 51, 5),\n",
+    "                 colors='black', transform=ccrs.PlateCarree())\n",
+    "clabels = plt.clabel(cs2, fmt='%d', colors='black', inline_spacing=5, use_clabeltext=True)\n",
+    "\n",
+    "# Set longer dashes than default\n",
+    "for c in cs2.collections:\n",
+    "    c.set_dashes([(0, (5.0, 3.0))])\n",
+    "\n",
+    "# Contour labels with black boxes and white text\n",
+    "for t in cs.labelTexts:\n",
+    "    t.set_bbox({'facecolor': 'black', 'pad': 4})\n",
+    "    t.set_fontweight('heavy')\n",
+    "\n",
+    "# Plot filled circles for Radiosonde Obs\n",
+    "ax.scatter(df['longitude'].values, df['latitude'].values, s=10,\n",
+    "           marker='o', color='black', transform=ccrs.PlateCarree())\n",
+    "\n",
+    "# Use definition to plot H/L symbols\n",
+    "plot_maxmin_points(hght.lon, hght.lat, smooth_hght.values, 'max', 50,\n",
+    "                   symbol='H', color='black', transform=ccrs.PlateCarree())\n",
+    "plot_maxmin_points(hght.lon, hght.lat, smooth_hght.values, 'min', 25,\n",
+    "                   symbol='L', color='black', transform=ccrs.PlateCarree())\n",
+    "\n",
+    "# Add titles\n",
+    "plt.title(f'Upper-air Observations at {level}-hPa Analysis Heights/Temperature',\n",
+    "          loc='left')\n",
+    "plt.title(f'Valid: {date}', loc='right');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/Wind_Shear_Vectors_Example.ipynb b/_preview/13/_sources/notebooks/synoptic/Wind_Shear_Vectors_Example.ipynb
new file mode 100644
index 0000000..92d133c
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/Wind_Shear_Vectors_Example.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Calculating Wind Shear\n",
+    "======================\n",
+    "\n",
+    "This example plots calculates the 850-500 hPa Bulk Wind Shear\n",
+    "\n",
+    "Plotting over a regional domain, accessing the Best Collection of GFS from the\n",
+    "Unidata Thredds server, plots MSLP (hPa), 850-hPa Wind Vector (m/s), 500-hPa\n",
+    "Wind Vector (m/s), and the Wind Shear between the two layers (m/s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "## Import necessary packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.pyplot as plt\n",
+    "from metpy.units import units\n",
+    "import numpy as np\n",
+    "import scipy.ndimage as ndimage\n",
+    "from siphon.catalog import TDSCatalog\n",
+    "from xarray.backends import NetCDF4DataStore\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#####################################"
+   },
+   "source": [
+    "## Obtain data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Construct a TDSCatalog instance pointing to the gfs dataset\n",
+    "best_gfs = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/'\n",
+    "                      'NCEP/GFS/Global_0p5deg/catalog.xml')\n",
+    "\n",
+    "# Interface with the Best GFS Half Degree Forecast Time Series dataset\n",
+    "# via NCSS directly\n",
+    "ncss = best_gfs.datasets['Best GFS Half Degree Forecast Time Series'].subset()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#####################################"
+   },
+   "source": [
+    "## First Query for MSLP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create our NCSS query with desired specifications\n",
+    "query = ncss.query()\n",
+    "query.lonlat_box(north=50, south=30, east=-80, west=-115)\n",
+    "query.time(datetime.utcnow())\n",
+    "query.accept('netcdf4')\n",
+    "query.variables('MSLP_Eta_model_reduction_msl')\n",
+    "\n",
+    "# Obtain the data we've queried for as a netcdf4-python dataset\n",
+    "data = ncss.get_data(query)\n",
+    "\n",
+    "# Make into an xarray Dataset object\n",
+    "ds = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()\n",
+    "\n",
+    "# Pull out the variables you want to use\n",
+    "mslp_var = ds.MSLP_Eta_model_reduction_msl\n",
+    "time_var = ds.MSLP_Eta_model_reduction_msl.metpy.time\n",
+    "lat_var = ds.latitude\n",
+    "lon_var = ds.longitude"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#####################################"
+   },
+   "source": [
+    "## Second Query for 850-hPa data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Request data for 850-hPa winds\n",
+    "# First clear the query's variables from previous query for MSLP\n",
+    "query.var = set()\n",
+    "query.vertical_level(85000)\n",
+    "query.variables('u-component_of_wind_isobaric',\n",
+    "                'v-component_of_wind_isobaric')\n",
+    "\n",
+    "data = ncss.get_data(query)\n",
+    "\n",
+    "# Make into an xarray Dataset object\n",
+    "ds_wind = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()\n",
+    "\n",
+    "u_wind_var850 = ds_wind['u-component_of_wind_isobaric']\n",
+    "v_wind_var850 = ds_wind['v-component_of_wind_isobaric']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#####################################"
+   },
+   "source": [
+    "Third Query for 500-hPa data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Request data for 500-hPa winds\n",
+    "# First clear the query's variables from previous query for 850-hPa data\n",
+    "query.var = set()\n",
+    "query.vertical_level(50000)\n",
+    "query.variables('u-component_of_wind_isobaric',\n",
+    "                'v-component_of_wind_isobaric')\n",
+    "\n",
+    "data = ncss.get_data(query)\n",
+    "\n",
+    "# Make into an xarray Dataset object\n",
+    "ds_wind = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()\n",
+    "\n",
+    "u_wind_var500 = ds_wind['u-component_of_wind_isobaric']\n",
+    "v_wind_var500 = ds_wind['v-component_of_wind_isobaric']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#####################################"
+   },
+   "source": [
+    "## Data Manipulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get actual data values and remove any size 1 dimensions\n",
+    "lat = lat_var.values.squeeze()\n",
+    "lon = lon_var.values.squeeze()\n",
+    "mslp = mslp_var.metpy.convert_units('hPa').squeeze()\n",
+    "u_wind850 = u_wind_var850.squeeze()\n",
+    "v_wind850 = v_wind_var850.squeeze()\n",
+    "u_wind500 = u_wind_var500.squeeze()\n",
+    "v_wind500 = v_wind_var500.squeeze()\n",
+    "\n",
+    "# Convert number of hours since the reference time into an actual date\n",
+    "time = time_var.values.astype('datetime64[ms]').astype('O').squeeze()\n",
+    "\n",
+    "# Combine 1D latitude and longitudes into a 2D grid of locations\n",
+    "lon_2d, lat_2d = np.meshgrid(lon, lat)\n",
+    "\n",
+    "# Smooth mslp data\n",
+    "mslp = ndimage.gaussian_filter(mslp, sigma=3, order=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "#####################################"
+   },
+   "source": [
+    "Begin making figure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/fp/_l45s1m93s5009vb713pyg580000gn/T/ipykernel_52398/1356670571.py:43: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.\n",
+      "  legend.legendHandles[0].set_color('gold')\n",
+      "/var/folders/fp/_l45s1m93s5009vb713pyg580000gn/T/ipykernel_52398/1356670571.py:44: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.\n",
+      "  legend.legendHandles[1].set_color('cornflowerblue')\n",
+      "/var/folders/fp/_l45s1m93s5009vb713pyg580000gn/T/ipykernel_52398/1356670571.py:45: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.\n",
+      "  legend.legendHandles[2].set_color('deeppink')\n",
+      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
+      "  return lib.intersects(a, b, **kwargs)\n",
+      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
+      "  return lib.intersects(a, b, **kwargs)\n",
+      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
+      "  return lib.intersects(a, b, **kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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zOOpc1aELKS9dujSLFi0iICCATp06sWvXLqpUqcKRI0fIlSsX8+fP5+TJk/Tu3Ztly5aZ1KFMmTIcOnQIJycn/vjjD3bu3EnHjh05dOgQlSpVMrt/UrNgwQI0Gg0DBgww2ubs7Ezfvn2JjY1lxYoV+jVPnTrFF198wfXr15k5cyZbt27Vh4K/8sorRvO4urqyd+9eOnTowObNm/ntt9949OgR48aN4/bt24ASeq5r6f2uuLq6smvXLqZPn46Pjw8LFizgzz//5Nq1a3z00UcGuRuHDx9Os2bNOHHiBL/99htLly4lX758zJ49mylTpli0j8yR1nXr8uXLnD9/nitXrmT4uuXj48OhQ4eoUaMGy5YtY/369dSrV48dO3YYGZSqVq3K4cOH6dChA9u3b+f333/HxcWFQ4cOWZWTKTQ0FMDq/GrWXGNdXFzYsWMH06ZNAxTj5tKlSylZsiSbNm3i008/NZDv0aMH7733Hjdv3mTRokXMmjWL0NBQRowYwa5du3ByMp3RZf369bz77rvs27ePX3/9VYYHSiQvEVnudiWbbLLZr+lc/rdt2yYAceHCBXH27Fn99iJFioikpCTx888/CzAdIjdt2jQhhBBBQUFi6NChol69esLV1TXNdU3NY6qlFWq3ePFiIUTGQu0A8ffffwshhDh58qSYOnWqmDp1qjh16pSIiIgQH374oZG8LrTgeY4dOyZKlSplIFulShUhhBCbNm0yufYnn3wihBDi448/1vfpQkYSEhIMQpe8vLxEcHCwiI6OFg8ePBClS5fWbytWrJiIj483+O7gWUhRbGysqFKlir5fo9GIHTt2CCHMh0g+37Zs2SKEEKJ+/fr6vr/++ktERkYKjUajD0/o3r27fvvo0aOFEEIMGTLE6Pt/PuxHF0Zw48YNUahQIX1/3rx5RWhoqIiIiBDOzs5G+2nr1q0GYVrVqlUT8fHxQgj7hdo9T1hYmOjXr5+R/PHjx4UQQuTJk8dom4eHh/44ef77HzZsmMn1V69eLYQQonLlykb76erVqyJfvnz6/ldeeUUIIURoaKjYv3+/8PDw0G/ThZQ+HwqpO5YvXrwovLy89P0FCxYU9+/fF4mJiQbHmbmmVqtFRESEiIiIEBqNRt9/9epVsXXrVgFKuKZWqxV58+bVb9+zZ48QwjCUxVyY5K1bt4QQQmzYsMHgOGjVqpUQ4tk17Pn9NGrUKIP+QYMG6b/HjIbamdo3np6e4ty5cyIsLEy4u7vr+3XXWiGE+OabbwzGTJgwQQghxPDhw/V9KpVKXL9+XWi1WvHqq68ayC9dulQ/l6W6mkKr1YrFixcb6GnLcak7ts+fP29SvmPHjkIIIX788UeL9e3Tp49ITEwUISEhYs6cOWLSpElizZo1Ij4+Xqxbt07kypUrU8/V1Ptw6tSpBv1//PGH/tz7/PPPDbb9888/Rsd46mNhwoQJBvJvv/22EEKI3bt3W7SfdL/RN27cMNqmO+8XL16s7zt48KBITEwUbdq0MZAtX768iIiIEOfOnTN53m3fvl24ubkZrZFeqJ0Qxtf6H3/8UQghxKJFi4xCbL29vYWnp6f+7+d/U+HZ71dSUpIoXry4kb6Whovlz59fCCFEYGCgvs/JyUlERUWJP/74QwBi1qxZIjQ0VKhUKr3MjRs3hFarNTh2zIV66vjtt98M5nj33XeFEMb3Nrr92bdvX4P+iRMn6ueyJNTu9ddfF0IIER4eLiZPnixatmyZbuiotdfY77//XgghxJgxYwz6vby8xPHjx0V8fLwoXLiwwbGael5d090nPP+Zdfvi9OnTwtfX16LvVDbZZHuhWpYrIJtsstmxPW94GjZsmBBCiDp16ghAjBo1Sgjx7AFDCGODkZubm1i4cKFITk7W3xglJSWJkydPim+//dYov5O5eUw13UPoiRMnxNixY8XYsWPFtGnTxJkzZ4QQQoSEhIiyZctmaB+o1Wrx008/Ca1WK1KzaNEikzd4X3zxhWjfvr0oWLCgcHd3FzVq1NDnQ7l+/brBQ1yjRo2EEEIsWbLE5NpvvfWWEEKIESNG6Pt0N7CmDGpz584VQggxevRoo227d+8WSUlJBg/+OsPTX3/9ZSRft25dIYQQu3btsmg/ffXVV0IIw4fmq1ev6o8dJycnER0dLWbMmKHfrjMs1KxZ0+j7N2d4MpXTSLetWrVqRnPXrl3bSH7OnDlCiIwbnpo2bSo++ugjUaZMGeHm5iZKlCghPv74Y/H06VOh1WrFa6+9ZiB/9epVIYQw+A5St6SkJHHlyhX93yNHjhRCCPHee++ZlNd93w0bNjTaF++8846R/PXr14UQQjRt2tToGE9ISDDa57obez8/P6O5vvzySyGEEN9++61F+0r3gP3KK68IQBQuXFgI8cyYojsXevToIeBZfqenT58aPJClZ3gy9SB669YtERISov/b2dlZxMbGiuDgYCMjuEql0ud2cVSOp6FDhwohhGjWrJm+T3etvXHjhsHnTb0tda65pk2bCiGE+Pvvv43mL1GihEhKShJCWG54+umnn0T9+vWFj4+PyJ07t2jRooU4fPiwEMI4/5y1x6Xuuz5w4IBJ+SZNmgghhJg1a5ZV+7F169YiODhYpObixYuiS5cumX6ugnLdioyMNDDqpv58QUFBRvPorvGpDRK67/vp06dGcwHi/PnzQghhlN/KXNO9FGjRooVB/7Zt2wyO81q1agkhzOf9mTJlihBCiKpVqxqdd9WrVzc5xlrDk1qtFuHh4SIsLCxDObS6d+9u8jpobZ6iixcvCiGEKFiwoIBn16k+ffoIQPj5+Qkhnt0TFStWTAghxJkzZwzmScvwFBUVZWBMA8V4lpiYKE6ePKnvK168uBBCGL1AAsWgHRoaKoSwzPAEym92ZGSkwfkTFBQkZs6cKcqVK2ckb801VqVSiadPn4pr166ZXLtz585CCCE+/fTTdPX09fUVQhjf8+iOLVPnu2yyyfbiNxlqJ5G84CxZsoTExER9FZwBAwZw9OjRNKsRxcfHM2DAAEqWLMkHH3zA3LlzuXTpEnXr1uX777/nwoUL+vwXtlKvXj29G/8nn3yCt7c3s2fPpk6dOhmqlufm5sb69et5++236du3L3nz5iVPnjz07t2btm3bcuLECSP39unTp7Njxw4ePXqkz4czYMAAli1bpq9WZw9S50TSoauYY6pq08OHD3FycjIZ8njgwAGjvlOnThEbG0utWrUs0uf5sITChQtToUIFfShicnIyhw8f1m93cXGhYcOGPH36lHPnzlm0BsDp06eN+nQ5WFLn/qhZsyYxMTEm95Opz2sLBw4cYNasWdy8eZP4+Hju3LnDn3/+yZtvvolardbnAMkKrDk+UlJSePz4MUWLFjU5l6n9peuz9fh4vurTiRMniImJ0W/XhWEGBARYXLUvLCxMH9qTmnv37hkcGxUrVsTd3Z2TJ0+SkJBgICuE4PDhwxatlx758+dn6tSpXLp0iZiYGH2OG13oyfNhYgDnzp0z+rzmjm8w/d3cuXOHu3fvWqXrN998w4kTJ/S5hAICAmjdujVBQUH4+flRpUoVq+bLKGPHjjVquXPn1m8fMGAAW7ZsYfny5ZQpUwZ3d3dq1arFnTt32LRpE5999pnBfJl1rgYFBRlVadOdd+fPnzeS120zde6dOXPGZMU3XZVB3TGQHrr8Tal/e4oWLUrbtm25ceMG+/btA9CHmBYqVMjk/teF9z0f5qfLn2cPKlWqRO7cufXHYnp4eXkxbtw4zp49S1RUlP4cW79+PWD6HLOG5/PTPX/d0u073XVL96+5UE9T6HLApUar1fLo0SOT57ypKpMxMTEmf/fTYsqUKRQpUoTevXvzyy+/cODAAUqUKMHgwYM5f/68yTxM1lxj8+TJQ0JCgsljSZe+4PljaeDAgezbt4+nT5+SnJyMEEIfFmjuuzx+/LhVn1sikbwYmA6+lUgkLwxPnjxh69at+Pn5sWnTJsqXL29xHoX79+8zZ84c5syZAyi5JObPn0/z5s355Zdf6Natm816zZo1i48//tjm8eYYOXIkXbt25fXXX2fz5s36/rVr1xIVFcX27dsZM2aMyfwZzzNv3jz69evHq6++yh9//AGgT6qe+oEqNd7e3gZyqYmMjDTqS05OTnebs7Oz0bbHjx+bXD8tY8TznD59mrCwMF599VU0Go1Bficd+/btY8KECeTLl48qVarg4eFhMnF7WpjaF7rPptFo9H25c+c2+/D96NEjq9a0ll27dnHnzh3q1q2Li4uLPply6u9bdzOtw8PDAycnJ4PP56jjIyoqyuQ2U8cGmD4+dPvQnG7Po3sQa9GiBT/++CMtW7YkOjqakydP6tc/cuSI/sHO2jwpYHo/6OZ+/tgA88e9PY4PX19fTpw4QcmSJTl48CC7d+8mPDwcrVZLrVq16Natm8ncXKY+g1arBYyP7/Q+Q0YN+nFxcaxYsYIxY8bw6quvcunSJQMdLT0ubTmOx40bZyS3cOFCIiIiqFChAn/99Rf//PMPw4YN028/d+4c3bt358qVK0yaNIn58+cbPdA/j73OVR2ZcV229tzbtGkTjx8/pmfPngwePJioqCgGDBiARqNh/vz5ejld/r3OnTvTuXNns/M9n+jZnJ62oDNe3L9/P11ZZ2dnAgICqFu3LqdPn2bJkiV6g0WpUqUYMGCAxQUyzLF3714GDx5MixYtWLVqFS1btuTq1av67+DBgwdcv36dFi1aMHXq1Bx33YqOjmbt2rWsXbsWUM7FSZMm8emnnzJv3jyKFi1KUlKS1brqjqVq1apRrVo1s+unPpZmzJjBZ599pjceP3z4UP9iYNy4cWa/S0f/nkskkuyJ9HiSSF4C5s+fT548eZg3b55BUlJruXnzpt5g06pVKztqaD86deoEmL6J9Pf3JyUlhbp161o0V0hICKA8tOi4ceMGWq3WbDUnXX9QUJBVeluLuQpQBQoUsLjioBCC/fv3kytXLurXr69PJqwzLIDyllitVtOiRQu9gcGaG3RriIiIMPu5MiPRfUhICGq1Gnd3d32f7ns09X2b+q7Tktf1a7VahydTNbUfdfvQ0uPj7NmzhIaG0qRJEzQaDS1atODQoUN6owoox0e1atXInz+/Q48Pnc6OPD7ee+89SpYsybfffkvTpk35/PPPGTNmjD4xfkbJjM8Apq9b1h6XsbGxPHjwgNKlSxsUH0gtn3peUIoDPN/+/fdfANq1a4eLi4vJYyMhIYHDhw/j5eVlcQJue5yrjiC979bScy85OZklS5bg6empr2w5YMAAkpOTWbhwoV5OZxgbPHiwyf2va4sXLzaY31KPREvQeTlZ8sKja9eu1K1blzlz5lC3bl0++eQTRo8ezfjx49m+fbtd9Nm3bx8pKSm0bNkSJycnGjdubFRQRFcNU/fbptVq2b9/v13WT01mnPORkZEMHjyY27dvkz9/fqpXr27zPKC8pEvrWNJ5z+fPn59PP/2Uc+fOUalSJQYOHMioUaMYP368UeVMiUQiAWl4kkheCrZu3crDhw8pVqwY69atM+k9YSnpvY3OalxcXABMlrvOly8farXaKFTHHA0aNAAwcFNPSEjg+PHjVKpUiRIlShiNadeuHfHx8Rw7dswG7S3HVAn5unXr4uHhYZX7fuqwhBYtWnD48GH9G31QXOJjY2MzxfB07tw5PD09qV27ttE2U5/XnuTKlYtKlSoRFhZm8HCo8/5q166d0Zj27dsbyAAcPXqU+Ph4o2pBoITDVK9enWPHjll8DNqKqf2l67P0+NAZJr28vOjWrRvly5c3+KxguH8aNmzI48eP9ZUl7cnVq1eJi4ujXr16Rm/RVSoVjRs3zvAaZcuWBRRvk+exx/GnC081NVeJEiUoXrx4htcA09ctW47Lffv24eXlxauvvmo0xtSxnxZpXZdT91tyXtjrXHUEtWvXNjD46dDtQ2tClFOH2zVr1oxy5cqxY8cOHjx4oJfR/c40atQoI2oboDMsmzI4muLq1atERERQv359g9AtUzj6HAOl+tv58+epWLEi3bp1w9PT0+R1K3fu3HTv3p3SpUtz+vRpk95tGUX3fTdp0sRom6enp8Vhz5ZgKsTTGi5fvkxERAT16tUzW40uNWXKlEGtVrN7927i4uIMtjn691oikeRMpOFJInkJ0Gq1vP7663Tr1o1vv/02XfnRo0dTrFgxk9tGjhwJmM5Z4AiaN2+OEMJiY4eu9PfYsWMNbpxVKpU+J0jquUqXLk2hQoWM5qlUqRITJ04EYOXKlQbbZs+eDcAPP/xg0D9gwACqVKnCqlWrMmTcs4S3337bIIeLRqNh0qRJACxatMjieXT7om/fvpQvX97ozXBSUhJHjx6lffv2NGzYkEePHunDd+zNkiVLAJg4caLBd1etWjXefvttk2PGjh2LEIKxY8datIYpA4Wbmxtz5szBw8PD6LtevXo14eHhfPbZZwbnRKFChRgyZAhhYWGsWbNG3x8VFcWqVasoW7YsH330kcFckydPRqPR6ENXHcm3335rUHa+QIECDBs2jKSkJJYvX27xPLrjQ7d/nz8+jh8/TlxcHMOHD8fd3d1ou71ISkpi9erVFCxYkC+//NJg2/vvv0/FihVNjvP390cIQfPmzdNdQ+ed8/xDop+fn96TMiMcPHiQmzdv0rlzZyNjzqRJkyx62NNRq1YtA28fHb169cLPz48nT56we/dufb8tx6XuOvf9998bhJW1atWK9u3bs2/fPos9iHTX5Q8++MDIM6Zly5a0bNmS4OBgg2uLo89VR5AnTx5GjBhh0Pf2229To0YN9uzZo8/9ZQmXL1/myJEjNG7cWP9bpDNG6Thx4gRHjx7Fz8+PN954w2gOlUpFs2bNrPoMujBFc/cAz6PVavnrr7/w8fFh+vTpRgYrb29vfXiWuXOsWbNmDBo0yCo900J33RozZgxgfN3SGaJ01zVHvUy5e/cu+/bto2bNmvTt29dg26hRo/D19bV4rg8++IB69eqZ3Na9e3e9MfbixYs26arVavnzzz8pVaoUU6ZMMXk9qlq1qt5IrPsuGzdujEql0ssULVrU6N5IIpFIQOZ4kkheGk6ePGkQQpUWw4YNY9y4cZw8eZJTp04RGhpK3rx5adWqFRUqVCAkJMTo4Q+U5NQLFiwwOeedO3csNg6kRncTm9oLJy0mTpzI66+/Tv/+/albty579+5FCEHLli2pUaMGt27d4scff9TLN2vWjDlz5uDv78+NGzeIioqifPnydOrUCRcXF8aPH2/kvbR48WL69OmDn58fpUuXJiAggDJlytCzZ0/u3LnD8OHDrf6c1rJ7926OHj3KypUrCQ0NpWPHjlSvXp3t27ezdOlSi+c5f/48ISEhevd8Ux4B+/btY/z48YDpN9X2YtGiRfTt25cOHTpw5swZtm3bRp48efDz82Pnzp0mE6dae3wcOnSIwMBATp06xYMHDyhQoABt2rShRIkSnD17llGjRhnIh4eHM3jwYJYuXcrp06dZuXIlKSkp9OnTh0KFCvHWW28ZJdQdMWIELVu25Pfff6dNmzZcu3aNpk2b0qRJE7Zv326VYdBWbt68ycWLF1m3bh3Ozs688cYbFCxYkFGjRnHr1i2L59E9kFWvXp2YmBhOnDhhsD0xMZGjR4/alCfFWkaMGEHr1q2ZOHEiTZo04cyZM1SuXJmOHTuyY8cOvVdLaqw5PpYsWcLw4cOZOXMmLVu25N9//6VGjRq0adOGdevW0bNnzwzpL4Tggw8+YOvWrezevZtVq1bx4MEDWrVqReHChTl37pzFyae/+OILunXrxp49e7hz5w4qlYo6derQrFkz4uLi6N+/v5F3qrXHZUBAAHPmzGHQoEGcOXOGLVu2ULBgQfr06UNkZKRVOfqOHTvGkiVLePvtt7l06RIbNmwgODiYihUr6s/rzz//nJSUFP2YzDhX7c3+/fv5/PPPadiwISdOnKBChQp0795dr5u1zJs3j0aNGtGkSRMeP35skLdQh5+fH/7+/qxatYohQ4Zw6tQp4uPjKVGiBI0aNSJ//vwmjZTm2Lt3L71792bNmjVs3bqV+Ph4Lly4wNatW82OGTNmDA0bNuSdd96hYcOGbNu2jYSEBMqUKcNrr71GkyZNOHfuHJs3b+bWrVsMHz6catWqcfHiRSpWrEjnzp3ZuHEjvXr1snofmcLf35+hQ4dSvXp1goKC9Anhddy9e5dbt27pf/cced369NNPOXToEIsXL6Zbt24EBQVRv359XnnlFfbv32+xYbBDhw789ddfBAUFcejQIR48eICXlxe1atWiWbNmaLVaPvnkE33OM1sYO3YsderU4YsvvqBTp07s27ePJ0+eULRoUapXr06tWrVo2LAhT548ITg4mLVr19KrVy9OnjzJnj17KFiwIJ07d2bv3r167zaJRCJJTZaX1pNNNtns13Rlnbdt22aRvBBCXL582aCvSZMmYtKkSeLQoUPi3r17IiEhQURGRoqzZ8+Kn376SRQqVMjkPGmhK1WsK63+559/WqTfZ599JoQwXwbcVCtYsKCYPn26uHbtmoiPjxdxcXHiypUrYsqUKSJPnjwGstWrVxeLFi0SgYGBIiwsTCQmJoqHDx+KjRs3irZt25pdw8XFRYwePVq/xsOHD8XcuXNN7htzZZkBMXbsWCGE6TLwCxYsMCq1nFp+0KBB4uLFiyIuLk7cuXNHTJo0Sbi5uVl9zKxdu1YIIUR0dLRwcnIy2q77zoQQ4sMPPzR7HKUusW1O//Q+t7u7u/jhhx/E3bt3RVxcnLh48aIYNGiQXoexY8cayK9bt04kJyeL8uXLW/RZp0yZIg4ePCiCg4P1x/WxY8fE119/nea+a9++vQgICBCRkZEiMjJSBAQEiHbt2pmVL1SokJg7d654+PChiI+PF9euXROjR48WLi4uFn3PupZWaXNTZcZ18m5ubuKnn34Sd+/eFfHx8eLChQvi3XfftfrYAMTjx4+FEELs3LnT5HbddymEEBUrVjR7/Dz/3aVVJt3c5y5evLhYsWKFCA0NFdHR0WLfvn2iadOmZo+nJ0+eiJs3bwqNRmPRZ61Ro4bYvn27ePr0qYiIiBD+/v6iVatWJs9h3bV2wYIFFp8ToFxfAwICRExMjAgJCRGrVq0SxYsXT7eMferWrVs3sWHDBnHz5k0RHR0t4uPjxY0bN8ScOXNMfge2HJeglFgfPHiwuHDhgoiLixNPnjwRq1evtvh8e36uQYMGiYMHD4qIiAiRlJQkgoODxfr160Xjxo2z5Fw19x2l9d2aOp5Ty1erVk1s3bpVREREiMjISLF582ZRpUoVm849Ly8vERUVJYQQYsqUKWblfHx8xIQJE8T58+dFTEyMiIyMFFevXhVLly4V3bp1s/i8A4RGoxE//PCDuH37tkhMTDTaD+b2mYuLixg2bJg4ffq0XoeLFy+Kn3/+WeTOnVsvV6pUKbFmzRrx6NEjER0dLY4dOybeeOMNm64T5pq3t7dISkoSQggxe/ZskzK6625iYqLw9PQ02m7ud9vc509L16pVq4p//vlHREZGioiICLFlyxZRtWrVNK/9z7cKFSqIr776SuzYsUPcuHFDxMbGitjYWBEUFCQWLFgg6tSpY7E+YP4aq1arxaBBg8SBAwdEeHi4iIuLE7dv3xZbt24VH374ofDw8NDLenp6ip9//lncvHlTxMXFiatXr4pvv/1WODk5mdxP1lzjZJNNthevqf77j0QikWRL1qxZQ8OGDSlTpoxBpRaJBJTqOAEBAfoEvBKJjqpVq3Lx4kU++eQT/vzzz6xWRyKRSCQSieSlReZ4kkgk2ZomTZowZcoUaXSSGFGpUiUKFCjA5MmTs1oVSTakadOmBAcHG5Sfl0gkEolEIpFkPtLjSSKRSCQSiUQikUgkEolE4hCkx5NEIpFIJBKJRCKRSCQSicQhSMOTRCKRSCQSiUQikUgkEonEIUjDk0QikUgkEolEIpFIJBKJxCFIw5NEIpFIJBKJRCKRSCQSicQhSMOTRCKRSCQZYMiQIVy6dInY2FiEEPTv3z+rVZJIJBKJRCKRSLIN0vAkkUgkEomN+Pn58csvvxAbG8svv/zCuHHjOHv2bKat7+HhQb9+/Vi1ahVXr14lNjaWsLAwAgICePPNN02OyZ8/PyNGjGDNmjXcvHkTIQRCZLzA7fjx4xFCEB8fT548eTI8X3bk1q1b3Lp1y6oxefLkYdCgQfz999/cuHGD+Ph4njx5wtatW2nXrp3ZcS4uLowePZqrV68SFxfHgwcPmDNnDgULFjSSLVeuHCNHjmTfvn3cv3+fhIQE7ty5w6JFi6hYsaLJ+YcPH86OHTu4c+cOsbGxhISEcOLECYYOHYq7u7vFn8/d3Z1hw4axbNkyLl++jFarRQhByZIl0xzXokULtmzZol//+vXrLFu2jBo1ali8to569eqxZcsWQkNDiY6O5tixY/j5+ZmVz5UrF1OnTuX27dvEx8dz+/Ztpk6dSq5cuQzkSpYsqT8/LGkSiUQikUhMowLkL6VEIpFIJDawZMkS3nrrLQoVKsSjR48yff327duzfft2QkJC2LNnDzdv3qRAgQL06NEDX19fZs6cyeeff24wpnnz5gQEBJCSkkJQUBDFihXD09MTlUplsx4qlYpbt25RvHhx1Go1X3zxBTNmzMjox8t26IxOpUuXtnjMhx9+yKxZs7h37x579+7l/v37FCtWjJ49e+Lh4cFXX33F1KlTDcaoVCq2bt3Ka6+9xtGjRwkICKBs2bL06NGDe/fu0aBBA4PjbcWKFbz55ptcuHCBgwcPEhkZSfXq1enYsSOxsbG0b9+egwcPGqxx8+ZNQkJCuHDhAo8fP8bLy4sWLVpQrVo1zp49S+PGjYmLi0v385UsWZLbt28DcPv2bXLlykXevHkpVaoU//77r8kxgwcPZubMmYSFhbF+/XqePHlChQoV6NKlC0IIOnbsyJ49eyzav82bN2fHjh0kJiaycuVKIiIi6NGjB2XKlGHUqFFMnjzZQN7Dw4ODBw9Su3Ztdu7cyenTp6lZsyYdOnTgzJkzNGnShNjYWABy587NkCFD0ly/c+fO1KtXj8DAQKpVq2aRzhKJRCKRvIwI2WSTTTbZZJPN+rZnzx4hFFeHLGk1atQQfn5+wsnJyaC/QIEC4tatW0IIIerVq2e0rWnTpsLLy0sA4vLlyxn+DO3atRNCCPH777+LqKgoce7cuSz/bhzRbt26JW7dumXVmJYtW4qOHTsKlUpl0F+hQgURFhYmEhISROHChQ22DRgwQAghxPLly032L1y40KC/f//+okaNGkZr9+nTRwghxMWLF422ubq6mtR30aJFQgghPvnkE4s+n6enp2jTpo3w9fUVgNi2bZsQQoiSJUualHdychLh4eEiPDxcFCtWzGBb165dhRBC7Nmzx6K1NRqNCAoKEnFxcaJWrVr6fi8vL3HhwgWRmJgoypUrZzBm3LhxQgghfvjhB5P948aNs/i7bdiwoYiPjxehoaFG68gmm2yyySabbAYtyxWQTTbZZJNNthzV+vfvL0zxvFHinXfeEUeOHBFRUVEiKipKHDlyRLzzzjtG8zVv3lwIIcTYsWNFw4YNxfbt20VYWFiGDEIjRowQQgjx5ZdfpilnD8PTqlWrhBBCVK1aVSxevNikwUvXhBDC39/f5DZzhp2SJUuKlStXiqdPn4qoqCgREBAgmjZtKsaOHSuEEKJ58+ZG303//v3T3M+p+2vXri3WrFkj/v33XxEfHy+Cg4PF4cOHxfDhw/Xrm+P5uaxps2bNEkII0bNnT4P+Q4cOCSGEKFGihNGYwMBAERcXpzccpteuXLkihBAib968Fsl36dJFCCHEtGnTbPpM6RmeChYsKIQQ4sCBA0bbnJ2dhVarFRcuXLBorbZt2wohhJg3b57RtjfeeEMIIcTEiRMN+u/duyciIyOFh4eHQb+rq6t4+vSpuHv3rkVrFy5cWNy/f18kJyeLDh06ZOj8kU022WSTTbYXvckcTxKJRCKRWMnZs2cZN26cPsRo3LhxjBs3jl9//VUvM23aNBYtWkSxYsWYN28ec+fOpWjRoixatMgotEpH48aN2bdvHwCzZ89m5cqVNuuYlJQEQHJyss1zWEKePHno2rUrp0+fJjAwkMWLFwPw3nvv2WX+IkWKcPjwYfr06cORI0eYMWMGISEh7Ny5kwYNGmR4/po1a3L48GE6dOjAwYMHmTZtGuvXrycpKYlBgwYBEB4ezrhx4wgPD9f/X9cCAgJsXtvUd+Tq6kqDBg24cuUKd+7cMRqzc+dO3NzcaNiwoc1rpEWnTp0AuHjxokXy1vLo0SOePHlC9erVKVKkiMG2Dh06oFar2bt3r0F/8+bNEULg7+9v0N+iRQtA2SfPo+tr3ry5vq98+fIULVqUQ4cO6cPpdCQkJLB//36KFStGuXLl0vwMLi4urF+/niJFivDdd9+xbdu2tD+0RCKRSCSSrLd+ySabbLLJJltObP7+/ia9hZo0aSKEECIwMFB4e3vr+729vcWlS5eEEEK8+uqr+n6dJ44QQgwcODDDeqnVanHu3Dmh1WpF1apV05TNqMfTF198IYQQYsiQIQIQKpVK3LlzR4SHhwt3d3cjeWs9nnQeVM97bqX2OsuIx9OUKVOEEEJ06dLFSD5Pnjzp6mdr8/LyEg8fPhSxsbEG61SpUkUIIcSmTZtMjvvkk0+EEEJ8/PHH6a5Rv359IYQQx44dS/P7Gzt2rPjll1/E8ePHhRBCbN++3Sh809KWnscTKCGAiYmJIiQkRMyZM0dMmjRJrFmzRsTHx4t169aJXLlymfzenj9uVq9eLYQQok6dOibXefz4sXj06JH+744dOwohhJgxY4ZJ+Z9++kkIIdL1YJo7d64QQojVq1fb5ViQTTbZZJNNthe9OSGRSCQSicSuDBgwAFA8oSIjI/X9kZGRjB8/npUrVzJgwAAOHTpkMO706dMsWLAgw+v/73//o0aNGsybN4/AwMAMz5cW7777LsnJyaxYsQIAIQTLli1jxIgR9OrViyVLltg8t4uLC7179yY4ONgoWfmiRYsYPnw4lStXzpD+Okwl0g4NDbXL3KaYNWsWhQoVYvTo0Qbr5M6dG4CIiAiT43THk07OHN7e3ixatAitVss333xjVm7IkCGUKlVK//eSJUv4+OOPHeopt2rVKkJCQli2bBnvv/++vj8wMJCFCxcSFRVlIH/8+HEqVapk5KVkyb4qVqyYVfKp5UwxePBg3nvvPc6fP68/zyUSiUQikaSNDLWTSCQSicTO1K5dG8BkGJaur1atWkbbjh8/nuG1Bw0axKhRozh9+jRffPFFhudLi/r161OjRg127dplUGVt0aJFgGKUyggVK1bEzc2NkydP6kPGUnPkyJEMzQ+wdu1atFotGzduZP78+fj5+VG8ePEMz5sWEydOpF+/fmzbto1JkybZfX5XV1fWr19P5cqVGT16tD580xSlS5dGpVJRqFAh/Pz8aNGiBceOHaNo0aJ210vHgAED2LJlC8uXL6dMmTK4u7tTq1Yt7ty5w6ZNm/jss88M5OPi4rh69Sp37951mE6W0KxZM6ZNm8bTp0/p1q2bkSFMIpFIJBKJaaThSSKRSCQSO+Pt7Y1Wq+XJkydG2x49eoRWqzXpVZHaeGMLAwYMYNasWZw/f562bdsSExOTofnSQ2dYet6r6cqVK5w4cYIWLVpQtmxZm+f39vYGMLkfIeP7C+Do0aO0atWKAwcO4Ofnx/Lly7lz545ef3szZswYRo0axZ49e+jRowcpKSkG23XeOOa8bnT7xJzXjouLCxs2bKB169ZMmjSJyZMnW6TXo0ePWLlyJT169KBq1apm85BllAoVKvDXX3+xZcsWhg0bxq1bt4iPj+fcuXN0796d27dvM2nSJDw9PdOdy5J9lXo/ZWTfFi9enDVr1qBSqXjzzTe5detWuvpJJBKJRCJRkIYniUQikUjsTGRkJBqNhvz58xttK1CgABqNxiAET4eSask2Bg4cyNy5c7l06RKtW7d2aJgYgLu7O35+fgAsX74cIYRBq1+/PmDs9ZSSkoKTk+lI/+cNArp9ZGo/AhQsWNCoT2fIMbWGOYPD/v376dChA76+vrRo0YKpU6dStWpVtmzZQpkyZUyOsYUxY8Ywfvx4/P396dKlC/Hx8UYyN27cQKvVUr58eZNz6PqDgoKMtrm6uvL333/ToUMHfvzxR7799lurdTx58iShoaEOMboBtGvXDhcXF6NE4aAk+D58+DBeXl5UqlQp3bl0+8DUvvLx8SF//vwG+ykt+dT9z+9bNzc3Nm7cSIECBRg5ciS7d+9OVzeJRCKRSCTPkIYniUQikUjszJkzZwBMPrzrqmydPXvWbuvpjE5XrlyhVatWhISE2G1uc/Tq1YvcuXNz5swZ5s6da7IlJibSv39/1OpntxthYWEmw7hKliyJr6+vQd/Vq1eJj4+nbt26ODs7G40xVdktLCwMwOQauhBIc8THx7Nv3z6++uorJk2ahIeHB23atNFv12q1aDSaNOcwx9ixYxk/fjwBAQF06tTJZE4pUIwvupxGJUqUMNrerl074uPjOXbsmEG/q6srGzdu5LXXXuPnn39mxIgRNunp6elJ7ty5HZbjycXFBTBvTNT1JyQkpDuXLoSwXbt2Rtt0fanDDIOCgrh//z6vvvoqHh4eBvKurq40a9aM+/fvc/36dYNtc+fOpU6dOixfvpwpU6akq5dEIpFIJBJjsjzDuWyyySabbLLlxGauql3Tpk2FEEJcvHjRoEJXrly5xMWLF4UQQjRt2lTfb6ramqXt3XffFVqtVgQGBooCBQpYPd7WqnYBAQFGFeWeb+vWrRNCCNGpUyd93/bt243GOTs762Wfrxq3ZMkSk1Xt3nnnHZNV7YoUKSK0Wq24dOmScHV11feXK1dOhIaGGu3nJk2aGFVRA8TMmTOFEEK8/fbb+r7jx4+L2NhYg3ktaePGjRNCCLFv3z7h4eGRrvyAAQOEEEIsX77cZP/ChQsN+l1dXfX7dcqUKenOX6JECZNV55ycnMScOXOEEELMmTPHpnMivap2DRo0EEII8fDhQ1G0aFGDbS1bthRJSUni4cOHQq1W6/vd3d1FxYoVRfHixQ3kNRqNuH79uoiLixM1a9bU93t5eYkLFy6IxMREUb58eZPfxQ8//GCyf9y4cQb9X331lRBCiNOnT5us0iibbLLJJptssqXfVP/9RyKRSCQSiZX4+/vTokULVCqV0bbp06fz+eefc+fOHdatW4dKpaJHjx6UKFGC6dOnM2TIEL1s8+bNCQgIYNy4cYwfP97i9Vu2bMnu3btRq9XMmjWL4OBgI5mzZ8/y999/G/SlrpzXvXt3cufOzcKFC/V9X331FU+fPjW7btmyZbl+/To3btygXLlyZuU6d+7M5s2b2bBhAz169ACgffv2bN++nZiYGFasWEFsbCxt27YlPDyckiVLkpiYSOnSpfVzFCtWjBMnTlCoUCG2bNnC2bNnqVixIp07d8bf358OHTrQrFkzDhw4oB+zfPly/Pz8CAwMZPv27RQoUIDu3buzfft2evXqZbCfN2zYQNu2bfH39+fmzZvEx8dTp04d2rRpQ1BQEHXq1CE6OhqAH374geHDh7Nr1y4OHDhAYmIiBw8eNKpOmJr+/fuzcOFCkpKSmD59un6u1AQEBBh45qjVarZs2cJrr73G0aNHCQgIoEyZMvTs2ZP79+/zyiuvGOS3WrBgAQMGDODhw4f89ddfJvVYuHAh//77LwBdu3Zl3bp1HDhwgKCgIEJCQihYsCBt2rShRIkSXLlyhebNm/P48WOznys1P//8M/ny5QOgbdu2FC1alLVr1xrst6tXr+rlFy9ezNtvv01kZCQbNmwgODiYihUr0qVLFwD8/PxYs2aNXl53fgQEBNCyZUuDtVu0aMGOHTtISEhgxYoVREZG0qNHD8qUKcO3335rlLzdw8ODgwcPUrt2bXbu3MmpU6eoWbMmHTt25MyZMzRp0kSfNLxBgwYcPHgQJycnFixYoN9/5ki9jyUSiUQikRiS5dYv2WSTTTbZZMuJzZzHk64NGDBAHDt2TERHR4vo6Ghx7NgxMWDAACM5Wz2e+vfvL9JjwYIFRuPSw5y3iq5NmjRJCCHEt99+m6acRqMRDx8+FImJiQbeWL179xbnzp0T8fHx4sGDB2L69OnC09NT3Lp1y8jjCRClSpUSq1atEmFhYSI6Olrs27dPNG3aVMyYMUMIIQy8XUDxkJk+fbp4+PChiIuLE2fPnhV+fn4m93O7du3EwoULxeXLl0VERISIjIwUFy9eFBMmTBB58uQxmNfT01P89ddf4v79+yI5Odmi72zs2LHp7m9Tc7i4uIjRo0eLa9euifj4ePHw4UMxd+5cUahQIbPHYVqk9gorXry4mDp1qjhx4oR48uSJSEpKEmFhYeLw4cPi66+/tsgrK3W7deuWxWsDQqVSiUGDBomDBw+KiIgIkZSUJIKDg8X69etF48aNzZ4f/v7+JtevX7++2Lp1qwgLCxMxMTHi+PHjom/fvmb19fb2FlOnThX//vuvSEhIEP/++6+YOnWq8Pb2tvr8SutzyiabbLLJJptsSpMeTxKJRCKRSHIkBw4coFGjRuTOndvhFfwkEolEIpFIJLYhk4tLJBKJRCLJ1hQqVMior2/fvjRp0oTdu3dLo5NEIpFIJBJJNkZ6PEkkEolEIsnWhISEcObMGS5duoRWq6VWrVq0bNmSyMhIXn31VS5evJjVKkokEolEIpFIzCANTxKJRCKRSLI133//PV26dKFEiRJ4enry5MkT/P39+d///meQtFoikUgkEolEkv2QhieJRCKRSCQSiUQikUgkEolDkDmeJBKJRCKRSCQSiUQikUgkDkEaniQSiUQikUgkEolEIpFIJA7BYsOTqYoyEolEIpFIJBKJRCKRSCQSiTmsyvF09epVcuXKZXKbxtkNlcpyByptcgIiRWuxvEQiyX5cU11jostEdjntsm2CBGDafy3RjopJJJLsSR6gF9AHqGfnubcAvwLH7TyvROJAqqoLM82rOw2dSlk1Lt41mf8N285v7++3Ln7hBso5eNOq5exGcHAw7u7uCCHw8fHJGiXswFsu9fifZ2fyqDzMymiFYFbCQSbG7iBG3uRkb8oCC4EaVoyJBQYB/1g+xBUnPnZryjD3luRWuaUp+zAlkomxO1iaeCJLEzKr1E70nRSIxsnFoD8xIYb4qCfERYXo/42Lekxc5BPio58SF/WEo3vXUaBAfgC6deuGv79/VnyELMcZDcPd2zDMrRUalcqs3MbE8wyL3sBTYhyqj2+RypR7pQ9l6nbD1T23xePio0O5cWod14+tJuLxdavXtVty8dwFy9P16124uJs2TD3P1hndeXB1vz2WlkgkWU0zYApQ38bxQcCngI32K4lEkgOpCLwNvAWUtOO8h4CfgM3I8imSHME4j46M9ehg09j9ja7z9szF3CkZZvmgp0A34KBNS2aI2NhYveFJrc7ZGT/yqbz4ybMrA90apil3XxvOFzHrWJd4NnMUk9iGKzAV5X7UGr4BfrZuSD6VF2M8XuMjtyY4qzRpyl5IfsDXMRvZkXTZSsXsR5m63UlJTiI28hFxUU+Ii3pCckL6xpGdO3fStm1bAKZPn86QIUMcrGn2pr5TSRZ5vUVlJ/ORZI9Tovg4ehXrE885XB+NsxulanaiYuO3KVKxqVVjH904xtUjS7l5+m+LjgWwc1W75u/8QfkGfSySXT+pGaH3A+21tEQiyWpUKF4Mk4DSNs6xChgGPLCXUhKJJNujApoC7wC9AW87zXsF5WFgKdKjUpKt+cytOTO8etk8Psoznk+/X8OSt6xw90sABgIrbF7WJl4kw5OOpk5l+dOrD1WdCqcptzUxkMHRa7iV8jSTNJPYRA9gAdb9Fs0BPgGSrVuqvDo/P3h2pYdrzXRldyVe4euYjZzT3rdukSzk119/5YsvvgBg06ZNdO3aNYs1ynrccOZ/Hp0Y5t4SdRrRYsvjTzI4Zg1hIjZT9PLOX5oKjfpRvoEfnj6Wp1hKjI/m2pFlHF07Kl1Zu13x3bzyUbr26xbLx0eH2mtpSQ5EpVLj5GLePVmSAxHASqAyfBnzJc44Wz9HH+Ay8DmQ9gsgiUTyoiCA/cD7QEHAD9hrh3krAfOA2yhvpC33JpdIMpUQEZ2h8bli3Fg89G1+H9YbVZKFg1yB5cC3GVpaAhxIvkHt8B8ZEbOJWGHeyt3RpSqBvqMY5d4OF5wyUUOJVawHXgX+tWLMIGA74GPdUkEpT+gZNZcm4b9wLOl2mrJtXSpx2ucb5nv1o6jayoWyiOvXn4VjFSlSJAs1yT7Ek8TXsRtpFjGd69onZuX6utUj0HcUnZyrZopekU9ucXLT96wcXYOdf/bl3/PbSNGmb0l1cfOiQkM/i9awm8dT3c6jqN3hS6P+cztnEB/9BG1yEtrkBFKSE9EmJ3LrzCZEipVmYckLgUqtofk7v3Ny8ySin97JanUkDuDx48c8zf+Uj/iIfeyzbZIzwEfIfC0SyctKeRRj1EAgvx3miwT+AqYDOeeFseQloJ1zJXbktja+xzQBDa/RYf6fxOe34h57AfAhYKnRKgO8iB5PqSmlzstMr150dqmWptzl5GA+jlnFviTr86RIMomCwN9AAyvGXAY6Y3MOtTdc6vCD5+uU1uRNUy5WJDItbi8/xu0mWiTYtlgmsH37dtq3bw/ApUuXqFo1c4woOQUPXPjB83U+c2+eptz8+CMMjVlPpIjPJM0U3L0LUr7Bm1R89S1y5y9jVi4xLpLFX6Uf7mIXw5Ozqxdvfn8OVw8fg/6H1w+z5Zcu6Y7XoCafypNHIiqjqkiyOWqNMy0HzqZ07ddZOLQ4yYmZ4z4oyVwePXpEgQIFEAgKfFmAkOEhUMCGiVKA2cBIINyuKkokkpyCC9AT+AqoY4f5EoFlKHnpLtlhPokkg9TRFOeU7zd2m+928afUW/YzTytbkaB2D8p5FmE3NUzyohuedHRzqcEMz14U1/imKbco/hhfx2zkSQa93iQOwh1YhBIGbilPUHKoHbZtSRec+NStKaM9XsNXnXZ0yKOUSMbFbmNO/GG0pNi2oINo374927ZtQ/VfMu0xY8bwv//9L4u1yp60dK7AAq9+lNTkMStzVxvGe9HL2ZV0JRM1+w+VisLlGlOx8duUqtUZJxd3g82Zaniq3voTGvQwPpB2/PEmdwPTzhZcQJWLVd4DGRK9LkfFrEqsR+PkSuv351Oi+mskJ8aycGjxrFZJ4iB0hieAcuXKcSP0hpL76QNsC/B9jPLQucR+OkokkhxIa5SwuXZ2mu8flETkB+w0n0RiA7lUbnRzqYEbTriq/ms44aZyxpXn/v7v/64qp2fyur9Tycd7J9FqwUxutbAin9AloCPWhRhZyctieALwUrkyzqMjX7g1xymNBNKhKTGMiNnE3IQjCFkRIfuhAr4H0k9h8ww75FDzVXnwrUd7PnNrhosq7dDMy8nBDI/9m82JF21f0M5s3ryZzp07AxAYGEi1aml7Ab7s5FK5MdWzO4PcGqcp92fcAb6O2ZhllTJd3HNTtn4vKr36NnmLVQcy0fCkdnKhz/jTePoYJtQLvX+J9ZPSzo7ewKkUa3O9SwF1LryefkUS2oyoIsnGaJzdafvBYopVaQVA1NM7rBpTO4u1kjiKhw8fUqiQkpiuUqVKXL16VdnQAPgTsPWrD0BJ3ph1hT0kEkl2oBaKMboP2CVVylEUA9TfkM1eGksktuOMEl460Ioxj4AuwAmHaPRSGZ501NAUZZZXHxo5p/1gdjjpJh9Hr+a8fBGfPRmAcj65WDFmLDAhY8uWVudlsufr9HFN3+U3ICmIr2I2cCr5bsYWtQOrV6+md2/FVWzlypX4+VmWB+hl5zXnKsz18qOoxseszE1tCAOjlrE/OWtDdfMWr0nFxm9Ronp7Vn5XI135DF/xy9XvZWR0Aji3a3qa4z50e5X9ub+gmMaXQO1DaXR6gXFy9aT9Jyv0RieA+GhZ0eOl5BhQHxgC2BJZ2wI4h+I95Z62qEQieYE5C7wFlEPJ2WRFRJFJGqIklL2MkiTWNYPzSSTZgSTgXeA7K8YURHnJ080B+ryknNfe59WIX/ggagVhKeZTTDR2LsMpn6/52aMbnlZZNySZwkIUb1tr6mONR/HWz8Bvyq2Up7wZtYCG4VM5mHQjTdkWzuU56fMNS73eoYQ67TBPHXlVnqhR2a6gGfbs2aP/f82a6VftkyhsT7pEtfBJLIo/ZlamjCYf+3y+4BfPHrjbUszJTjy9e47Dq75m9bhXLJLPmOFJpaJGm8+NuqOe3uHmqQ0mh7jhzHyvfszyelPvNng2WVr2X1Sc3XLR4dM1FKlg6P0WF2U+i7/kxcLojaYW5UGxErDahgmdUXI+XQI6ZVA5iUSSs/kXxZBdAhiNklsjI1RAySt3G+U645PB+SSS7MBEoC9K+I8leADrgKEO0+ilQyCYk3CYimH/Y3G8+aopTioNX3m05rLvd3RzSd+DQJLJ7AMaAdY4mrwF7AbyZWzpY8m3aRrxKz0i5xKkfZymbD+3+lz1Hc0PHq+TW2X+TW03lxpc9v2WqZ7dM6acCQ4dOqT/v6+vZUYwiUK4iGNA9FK6Rs7mUUqkWbkh7i056zuChk6lMk85E6QkW5b0PEOGp5I1OuJTqLxR/4U9vyNSjD2YSqrzcNBnCAPdGhr0n0m+lxE1JNkUVw8fOn6+gYJljctBSI+nFxshLIjgfYASJvMa1v2A6yiFkp9lPSDThUkkLzehKDk4SgIfA2m/FE6fQiielXeBqchrjCTnswJoA1h6+6UGpgG/AebTE0ms5ImIpn/0ElpGzOBK8iOzcsU1vmzwHsQm7w8oqTafcFiFikGujfnYLe30JhI7cg3FS3a/FWOaoIR0V8z48hsSz1E1bBKfRa8hJMV8Uno3lTPDPdpy3XcMn7k1xznViZxb5c5Cr7fY4D2I/OpcDHFvySd2PoZ01ewAQkOtcROT6NiUeIGqYZNYlXDarEwFTQEO5h7KDx6v42qX3AOOI0OGp5rtvjDqi4sK4erhZUb9bZ0rccrnG+o6lTDadlYanl443Lzy0vGLjeQvaTqZT7z0eHppSDeHww6gOoo7si0VYbujhMd8hX1yvUgkkpxLHDALxXOpNxnPU+MFDEMxZC0CZG5USU7mINZ7a3wKbAQ8HaHQy0tAUhA1w3/g25jNxAnzSYK7uFTnku+3fOPexsBwAFBNU5iDuYcwO5cfMzx70tSprKPVluh4CrQFFlsxpixwBGiZ8eWT0PJb/H7Kho3nh9hdxIsks7L51F7M8OpFoO8oerjUpLVzRS74jKS/m6FjwAzPXrzmXCXjyv3H558/i4ry9/e327wvG09FDG9GLeCNyPlmDY0alZrhHm055fMNdTTZ902ZzYanwuWbUKBUXaP+wIDZaJPi9H+rUDHSvR3bvT8mr9r0r5asZvdi4e5dgE5DNukz3ZsiLiokEzWSZHvigXFADRU+FypYP94T+Bk4DbxqT8UkEkmOJAVYC7yCcpO/LYPzOQPvABeALSj55iSSnEgQivHpUHqCqeiMUvmxiEM0emlJJJlJcTupFjaZbYmXzMp5qFz40bMrZ3yG08SpLB648KOH8ndj5zKAEqK3ynsghVTemaW+JBHoj3U51HxRXri+ax8VIkU8I2M3UTHsfyxJI4QToLymAOu832d37sEU1xiHvmlUalbnGkh1TcZP9MmTJ1OihOJskpKSwtSpUzM858vOmsQzVA2bxMaE82ZlqjoV5pjPl4zz6GhkqM4O2Gx4qtHOOLdTUkI0l/fP0/+dS+XGulzvMcmzC2qV6aVuaJ8QKSyLC5Rkfzx8itBpyGZ8C1dKU06G2r08qFSWJSxUO7nQquk8eh44jO+XleGhDYtVR3mjOw/Ia8N4iUTy4hGAUiK+BkqSV/Mvhi2jI+APHEd5IJdIchohQGtglRVjaqMUCJFph+zOzZQQOkb+Sa/IedzXhpuVq+pUmAM+Q7idZxzfeLTBSWX4YFlYnZtV3gNxynjtKIk1TATeRHmJagnOKPepk8FeOb3vpITxTvQS6ob9hH/iNZvnyaV24x/vDzNkwCxbtixff/21/u+//vqLW7du2Tyf5BmPRRTdo+bwdtRiws0UKnBSaRjr0YFjPl/axYhoT2y6MuUtVp3iVVob9V85uJiE2HAAKmsKcSL3V3R3TTuLvUws/uLglac4nYduxqdguXRlZXLxF5vUOZ4sKZfs5OJBuw+XUaZOV1SoECtSlOTjM8CmgpfvAleB97Dbj7pEIsnhXEDxWiqLkrvGfGoMy6gPbEYxbNXP4FwSSWaTAPih5DKzlGIoL3fapycosYV1iWepHD6RX+P80YoUs3L51bnMbmvmXI4fPLs6Qj1JWqxC8a5NO+e3ISOANdi1SvNp7V1aRc6kc8QsLicH2zRHCU0eNnl/gIeNlRV37dqFRqMYRYODg/nkk09smkdinqUJJ6gaPilNT8naTsU56fM1o9zbockmxmibtKjR9jOjPm1yIhf2/glAL5daHPf5iopOBdOdSyYWfzHwzl+azkP/wTtfKYvkpcfTi41FycX/w8U9Nx0+W0exKq30fbHhDyES+AIlVCZt72HT5AXmooQHmI/6lEgkLxt3gS9REoaPAszn97WM5ijXqFUoRi2JJKcggG+B94FkC8fkQins8YGjlHq5iRLxDI1ZT73wnzme9K9Nc3zp3oqeLrXsq5gkfY4CDVCqLltKT5RKeYXsq8qWpECqh0/mo+iVaVZFM0d955IsyfUOKivf3rZv357SpUvr/540yRrLtsQaHqRE0DHyT96PWk5kSpxJGReVExM9u3A49zAqadK3yzgaqw1PufKVonSdbkb910+sISE8mJ88urLG+z28VK4WzScTi+d8fApWoNOQf/DKU8ziMdLj6eUhLY8nd++CdB66mYJlXtH3JSfGkhiX6kfyNEo+ik+AcBsUePW/OX5GJkeVSCTPCEcJdSiJ8hBte3SCwhsohQ6mk+Gy2RJJpjIP6ABEWCjvBPwF/Ij0KnYQZ7X3aBQxlY+jV5kNqUmLBV79qJgNHjRfOm4DjYFdVoypjxLGaueXpFpS+Cv+EOXCJrA8/qTV43u41uQHj9etGpOUlGTw8nn69OkEBQXRubOMS3cU8xKOUD18MnsSr5qVecW5JGd8hjPMvRXqLLxoW2d4coIabQajVhsnq3q4cx47vT/la482Vk15RisNTzmZPEWq0GnoJjx9rDPVS48nSa68JekybAt5ilY16I8JN5HcKQX4EyX8bqkNizmhVL27jFIFTyKRSHQkAHOAykAPlAcAW3EGPkepgjcK8MiwdhJJ5rAb5UXNHSvGfAOsBtwcotFLTwqCWfEHqRT2PXutzNuTS63k2fW0MVxKkgEiUHIB/mXFmBIoCf872F+daJHAKe1dm8Z+49GG910bWyy/d+9evv76a5KSlGSKKpWKcuXKsXnzZh48eMDAgQNt0kOSNndSwmgb+TufRq8mRpguEe6mcmaqZ3f25f6CsuqseTtmneHpLtz8aQOn6/3EgyIHSHZSLPBue/9mb1JPWrlYV43qSUoUD1Isfb0iyW7kLV6Djl/8jXuu/FaNS4yPRpskE8q/zPgWrkTnYVvxzl/aaFusKcOTjkfA20Ar4IoNCxcH1qOECRgvLZFIXmZSgA1AQ6AZynXCVrxREs4GoeSay37FZSQSYwJRQoVOWTGmF7AXsO5WUGIFj0SUTS/qqzoVZk6uvg7QSJIuycBHKGHd5tN1GZILJW/gp/ZXJyMGyD+93qC1c0WL5adOnUqRIkUICAggJeXZhy9cuDDz58/nzJkzeHvL6ov2RiD4I/4ANcN+4EDSDbNyTZzLcs53BJ+6NbM6lDKjWGd48oCHxQ9x+pUf2dqtG4vfK8Pmbh0QTRYT2PYhkV6m4wvNIfM75WyKVGiCm1ceq8fFR4c4QBtJTiF/qbp0GvqPWS+5mAgLkiH6AzVRclNYd9lR6IRyg/0/wMuG8RKJ5MXmANAFqAosQCmbbQtFUHLNnUNWwJPkDIJRDK+brBjTCCW/jeXPphIrKagyn1A8Lfxc6/KZW3M7ayOxmGkonrQxFsprgN9QQrbt+MLCQ2W74clJpWFtrneprLE8uiUkJISWLVtStGhR/v77b7TaZ5WCatWqRXBwMK+99prNOknMcyMlhBYR0xkWvZ54YbqMr6fKld+8erPL+1NKqq1/lreVDKU4T9Ek8ajIcX4fEkCHVX/ie3049Xb9xJfj1/P3a+d56pv2WSYr2uVsLuz5g1tnNls9Lj5KGp5yCm+7vpK+kBUUqdicjp+vx83T16xMbEQaHk+pSUSpxlMV2GKDMu7AdygeCe+TwauhRCJ5IbmEUiWzDEqeOOtztCpU5VkFPPteViUS+xOLEpY+3YoxZYAjKMn2JXanYBqV7NJjqmd3GjlJN+8s428UY+4DK8Z8/t84O70c9cyA4QnAR+3BFu+PyK+yTqHg4GC6deuGj48P69ev1+d/cnd3Z+vWrcybNy9DeklMk4Lgl3h/aof/mGaRgtYuFbngM9KqcMqMYNdHrRSN4FStu0z7xJ9uS+aQ79oIqu+bxKc/rmZ119M8LGgYVndW5nfK8exf+hnhwUFWjZGJxXMGzZ3LWZ1UMC1K1exM+49X4Oya9o+WyRxPaXELxZOgB0q1KmsphJLf5SzQ1obxEonkxec+Sj6bMsCv2O4B1Rwlh5SsgCfJ7qQAQ1AegC0NFfIFdgJvOUinl5gCGTA8Oas0rMn1LgVs9JqS2IHTKC8dzloxphNwECVNRAbJqOEJoLQmL397f4AbzlaPjY6OpmfPnvTs2ZOEBCUHkUql4t133+XOnTu0bStvwB3BFe0jGkdMY1TMZhKF6dKludRuzMnlx1bvjymizu1QfRz+jv9ilYf88e4B+sxdQJGL31Hh6ATe/2U5i984xvEitpUJlWQfkuKj2D2nP0kJ0RaPkYnFsz9OqPnd8w2KaHKTV5XxUnAVGvWl1fvz0TinX+0y1pJQO1NsQEkOPAXLy0KnpjrKDfNWoIptKkgkkhecp8BQlEIHKzIwj6yAJ8kpzAS6YXmokAuwBBjrKIVeTjLi8QRQVOPDylwD0Ej37qzjPtAExfvVUmqivKyom7GlVySc4qfY3WxMOM+l5IdmjRDp0ci5NAty9bM5N9CGDRsoUqQIly9f1vcVL16cHTt2MG7cOJvmlKSNlhQmx+2kbvhPnE42/4a+g0sVLvqM4i3X+g7TRQWIdKV0RKAkzLQn/wL7U7WMljOWZAkVGvWj2VszLJI9u+NXTm76n4M1kmSEL91bMcVTKf/WMmIGAUnWebXdu3ePokWLAtD7w0n41PzI4rGbp3bg0c3jVq1nRHWUKniv2jhei+IFNRZ4nDFVJBLJC0xd4CeUgge2EolSlv5XlBAniSQ7Ugcl4X5hK8YsRgllT5VmJDY2Fnd3d4QQqNXSCGIpP3i8Tn2nktRxKoaP2vZymT/E7mJkrDUJvCR2R43yknSoFWNigb4o4Xd2UUFFCXUeKmjyU0FTwKCVVPuiVqV9bn4fu53RsbbkuXjGlClTGDp0qP46kJKSQoMGDTh58mSG5pWYxwk133q05zv39jipzCcR25hwno+iV/JIRNl1/aw3PD1PMIaGqItYo6Eki2jQ439Ub/2JRbJH133Hxb1/Olgjia0UVftwxfc7vFSKd9Ln0WuZGb/PqjlSG57+tyacO08s9dOHlaNrER1qW9lXA1TAQJSHwrw2zhEJTEZ5IJSFGCUSiTleQ7nWVM/AHA+AMcBCFOO3RJLdKI7iFVzNijH+KKHw4cqf0vCUccqo81HHqRh1nIpTx6k4dZ2Kk09tee6dbpFz+DvxvAM1lFjExygehZYmEU8BvgJ+cZhGALjiRBlNvv8MUYphqvx//xZOFYo1IGopixKOZWitIkWKcPHiRXx9ldyvQgguXbrEuHHj2L59O9HRlkfUSCynjqY4i3K9RTWnImZlQlKi+SR6NWsSz9ht3exneHqeUJTqMjpD1BnkDVk2w9UzD2/+7yzOroYhWcmJsSTGR5GSnESKNokUbTIp2kRObprInYs7skhbSXqsyjWQN1zr6P+eE3+YD6KtiynJiOFp/heFSUm2NYGKCfKieBO8l4E5/gVGAiuRhnCJRGIaNfAOSrXMYhmYJxAYgeJdIpFkN7yBNUA7K8ZcAToCt6ThyVEUU/vojVB1nIpTR1OcIhrT+VoiUuKoF/4z11NkztUspz2wGuuer/8EPiNLnodzqdwor1aMUCU1efgj/gBRImNvZlu1asWuXbuMrgdCCFJSUoiNjSUsLIyHDx9y+/Ztrly5wvnz5zl+/Dj37sl80bbighPjPTrytXtrNGl4uK1KOM2n0at5KiyNtzaPVYan8kf78qT8ScLzZGE8XBRK1YwD/7VjSE+ELKZOp+HU6fiNUf+WX1/nYdChLNBIYittnCuyK/dgg76jSbdoFDHNqnkehURQIK/yKzphVTh3n1pmeIqLCmHZCAfVY26M8pYoIxWljgPDAHlYSyQSc7gBX6AYqzOSp3MfSkLzDEYeSyR2xwn4AxhkxZjHwOsQ6y8NT5lFIZU3tZ/zjCqpUUqnn0++T6PwacTaXClBYjeqobxoKGnFmG1AH5Tn4heAevXqMWvWLOrUqYNKZXn+KCEEERERHD16lHnz5rF27VoHavli0sCpFIu83qKiU0GzMo9SIvkgeiWbEi8YbSulzssYj9d4N3pZumtZZXh6Z8otXNy9ObLvWwKTZymlIZuhJD7Lqt+OROAUihHq4H8tLIt0eQlxdvXize/P4erhY9D/6OYJNk99LWuUktiEC06c9xlhdOGJFgl4P/0aYYWrz4OHwRQupMxjjeHp6b0LbJjcwuJ1rEYF+KGEz5XIwDxrgeHATXsoJZFIXkjyAt8Cn6IkXLaV1cAo4IY9lJJI7MgIlN9TS4mDZepl9HXtKw1PWURelafeGHVN+5iNMuQue1AQJX9TAyvGnEepfPcCOf0UK1aMGTNmULduXXx9ffHw8ECjsTQWER4+fEjfvn0JCAhwnJIvIO44M9GzC1+4NU8zv9fi+ON8EbOWcBGHt8qNUe7tGOLeggSRTO5QYyeU57Ha8IRKzcrvapAYF6Hvn160J2Wb5WNfo+vsb3SDk7XuoHWyPLTG7lxEMUDpvKLskC5GYprqrT+lQY8JRv07/+wrw+lyGCPc2zLZ83WT28qEjuNWiuXVCFOH2o1fFc49Cw1Pdy7uZOeffhavYzNuKGWiRwG2FopJRInN/x597gqJRCIxojTKdaJvBuZIAmahhPHJ6BhJduINYBHK76qFTGEKQ8VQNGrLHyglkhceN5SE/L2tGPMA6AKcdohG2QIvLy9eeeUVateuTZUqVShTpgxFixYlX7585MqVCycnJwN5IQSbN2+ma9euWaRxzqWpU1kW5nqLMhrz5Xbva8OZm3CEj92aUOC/apuRKXGOMTxdObSI4xvG6fsaOpXiiM+XBnLRngmsrnWaQXVWkNJcKNZbK36Q7M4dFAPUPhRXxodZqMsLhMbJlT4TTuORu5BBf+j9S6yf3AyETIaTUyih9uWy73d4qEy/lu8aOduke6U5bDU8XTm0mIPLrSnzkUEKABNQqu7Yev/79L85/sSgco9EIpEYYK8KeD+hhA3LCniS7EJjFG8N888qRnwuPmeG0wwlYbJEIlFQARNRQrUtJQblxcZLWqywRYsWfPbZZ3Tt2tXAO+r48eM0aGCNC5kEwBMXfvLsxifuTS0eY6nhySofV602iYt7/9L/rUHNn159jOS8YlxZtvUkKWMFtAB8gKYoLuc7gMxOUF8C6AfMRrEMH0M5oatksh4vGOUbvmlkdAI4t/NXaXTKYfzq2dOs0QmgusZ81QNTpI7PjnhseXxIbHgmW4UfAx+hhAtvt3GOvMB04BxK6LFEIpGY4hTQGugAWG7HN8QbxXvqEtDGTnpJJBnlMNAQsCIF7AzVDFiBki9KIpEoCBRv/Pew/GWmJ7ABJaz7JSQgIICePXtSrFgxTp48qe9/5ZVXOHHiRBZqljOJIZFPY1bTJuI37mhD7Tq3VYanmyfXExvx7MHwE7em1HIyLt2yIuEUe5NS/fokoIS+TUIpOeyLkuD3KxTrbGbnZHrlP10CgSBgCophTHr8WoxKraFGm8+M+iOf3OLm6Y2Zr5DEZjo4V6G7a800ZaqnUW4zPW6ft9yiE5PZhicdgSgPg+1RQnVtoTKKV+UCrHrrK5FIXjK2A7WAAdiem6MksAsl/M7WcGGJxJ7cABqhVKC2lDeAOY5RRyLJ0cxHuS+NSE/wP9TAb8DnDtMo2xMcHEz9+vVZsmQJ4j8HiLp169K/f/8s1ixnsifpKtXDJzMv/ojd5rTK8BToP1v//0Iqb7736GQkE5kSx5cx69OeKBk4AUwFuqJ4DNRAKQ25BnhkjVYZpBzwJcoPZTDKQ2M3wCMTdciBlKnTDe/8pY36z+/+DZGSBfU9JTbhihMzvHqlK1ddU9jmNcrVtzxYPSYii+Ngd6I8EH6A7dehAcBVlLdVlhfmkEgkLxMpKHlxyqMkaLb04eJ5PkTxnmptJ70kkowQCrQF0i9u9IwBWJegXCJ5WdiDEsZ624ox03lpPZ90vPPOO/zzzz+AEoHx+++/G+WBklhGpIjn/ejldIqYxQOtrTcqz7DK8BQZ8qyE0xTPbnir3Y1kxsRu5WFKpHVaCJQbp99Q3n4UAiqilGldSuYlB8+H8gO4AQhB8cZ6FyUPTBbimaFyOA5ApaJmuyFG3bERwQQdXZH5+khsZrh7W8pp8qcrV0FTABcb/eE9vI1PoNtntxAYMNuohT+8YtMadkWL8ga2HEqcfZwNc+QB5qIYtKvZTzWJRPKCEQ/8CJRFydtkS2XzksBuFE9u6bktyWoSgbdQEuFbygheak8NicQsl1ByJR+zYsxvwCeOUSen8MYbbxAfHw+Ap6cny5ZZYw2XPM/WpEC7VMC06UmypXMF+rnVN+o/l3yf3+Kt8bFNg2v/tbn//V0SJX9KU6AJSliLI3FHqRLQBeXN5BGUxIl/Y1UMuz342qMNS+NPcD0le5SyKVG1HXmKGifIurD3T7TJCVmgkcQWSqvzMtKjrUWyTioNlTUFOae9n+F1o0PvsXf+e6Ros3km7mjgO+AvFAPU2zbM0QSl0sg0lATkMhmwRCIxxVNgGM8qZdpSAW8kSioBP2TlO0nWMwa4iZJf1dkC+ekonsarHKmURJIDeQy0RKl4l36QgsLvKM+vsxylVPYmPj6er7/+mpkzZwLQu3dvypQpw4EDBzh37hwnT54kMDAwi7XMWZTR5M3wHFZ5PAE4o+F3T9OhM59Er0LrqPIU/wJLUEJgqgD5ge4o4XrHUcL3HIUaeBWlksxV4DLwA0oixUwIpWnvXIlvPdo7fiELqdl+iFFfQmw4Vw4szHRdJLYzw6sXbipL7gYVajgVtWkd1XPnyNkdv2R/o1Nq7gLvAPWxLneFDmdgOMpbq8521Esikbx43EIphlIX2GvD+NYoScxfsadSEomNLET53bO0qNBiZNioRGKKOJSooB+tGPMnynPzS8pvv/3G2bNnASXkrl69egwdOpSFCxdy8eJFUlJSiI+P5/HjxwQGBrJlyxY+/PBD3NzcslbxbEppdRYYnoa5t6Kyk3Els/nxRzicfCvDCllMCLARJUF5AyA3yo/VOBSX8xgHrl0J5UHyCIpBbAoOu8nzVrlR36kkb7nWo4w66zMWFyrXmIJljD9sYMAckhIyu1yhxFZed6lOZxfrYsAykudJR3ToPa4dXZ7hebKEk0BzoAdw3YbxJYHNwDrAuCaDRCKRPOM0zyrgWevdXhw4gFKxUyLJanaiRCw8tUDWBSXdRW2HaiSR5EwESljqB2Cxn8dfwPsO0yjb06BBA44ePapPNp4alUqFq6sr+fPnp0qVKnTs2JFZs2YRGxvLgwcPmD9/vjRC/YcaFaU0eewwjxUUU/kw2oTnTWhKDMNjNmVYmQwRi/J2cDxKYkMfFGPQMOAflDwKjqA4SnLyYyguxT8Adew3fQvn8mhUapxUGkZ5tLPfxDZSq/1Qo76khBgCA2abkJZkR9xxZrpnT6vHWVPZTuP8LP9baoenszt+ISXZliQm2YgNKF6XI7AtdK4HitfkUGQ+FolEkjbbUR7CB2PdCzUXlLfdi1BSB0gkWckZFM8nS34zcwHbgDIO1UgiybnMQclJbKnxaQ5KzuKXkMTERBo1akTVqlX59ddf2bp1K4GBgTx+/JiEhASzBqnChQszcOBAnj59Sq9elsY3vrgUUefG1YooGXNYZXj6wfN1PFWuRv0jYzcTIrKZt4uuct4vKHma8qE88C3CsrcutlAaxRPqFEoeqO9RqvVlgDbOFfX/f8f1FUrZwc3NVvIWr0mxKq2M+q8cWkxCTGgWaCSxhVEe7ShlQ5xudY1lhie1xhlnVy/936r/rjLRYfdzrrfT8yShuDtXQTFsW4sXSt6nkygemxKJxCReHvBmx6zWIotJQcnXUR0IsHLsOyje2WXtrJNEYi1HgTdAIyx441IQ2EGWF/eRSLItS1CMSdYYnwY4TJtsz+XLlxk6dCidOnWiWrVqFCxYEDc3N9RqNZUqVaJ///7MmzeP27dvk5LybKd6eHiwevVq9u7di4fHy1vyPrfKnWBri8eZwCrDUxcToTnHkm4zN/5whhVxODEongoDUH7QmqM8+N1w0HrlgW+BcyjeDeOwKSF6asOTs0pjcTJoR1DLRG4nbXIiF/b8kfnKSGzmePK/hKVY76pTVOODryr9i26FRn1RqY1vLM+9CN5Oz/MvimG7O3DPhvG1UG7GlyLf7kokz5HHB/YsgIqls1qTbMItoBVKqWxrLuE1UYzcMsecJKvZAr8n/W6ZbDlgK8qLGolEYswiLA+jUwPzUF5GSAy4evUqixcv5v3336d06dI4Ozvz6aef6qviqVQqWrZsSUhICH369MlibbOGQO1DCod+S4XQCbwXtYxF8ce4qQ2xeh6rczylJkWk8En0alIwdlPL1mhRkgR/ifLDVg2letUJB61XCRiLklz4wn9rlU9/WBF1bqN8WgNcG1BC7Wt/HdMhd8HylKppfNd6/dgqYsMfZLo+EtvZnHiROuE/cSLpX6vHpuf1pNY4U/O5cEw1irfT1SMvcCnTjSiG5anYVuigH3AFxavBOIWeRPLSUaQA7F8Cr9SA+4+zWptshAD+ABphXa45H5Qcc/8jg3d+EknGeEf7DhOZaJlwXWA9llXFk0heRhZgnfFpAfCW49R5EUhJSeGPP/4gf/78HDhwQB+O5+7uzooVK9i3b99L6/0UlPKE+QlHGRC9lLJh4ykWOhq/yIXMjT9i0fgM3X78EX+Q09q7GZkiexCIUi79FaAo8DFKXgVHOGdUQ7nxu4YS8z4Cs54OrVN5O+lwUTkxwj3zvZ5qtP0MldrwcBEpKZzbPTPTdZFknNspT2kS8Ssz4gKsGlfZqWCa28s39CNXnuJGpuhzO3598bydnicapdhBPZScb9biDHyC4oU5GeVBUSJ5CSlTHA4ug6rllL8fSMOTMedRrjXWptf8DiV/TtZF7UskjGQkn4pPLRNui1IdLxOqSEskOZJ5WF69To3iKdXPceq8KERHR9OsWTP69etHXFwcoHg/NWvWjJCQEPz8/LJYw6znfko4KxNP8WXsBovkbTY8PUqJ5LtYW5KbZHMeALNQKsnkQylduQwId8BatVAeMG+geFt9BZR4trmNCcMTwHtujSim9nGAQqbx9C1K+VfeMOq/deZvIh87KlZR4mgSSeaLmHV8Hr3WrEyy0HIg6QajYjZTO+xH/oo/ZFZWrXGm1mvDjPrjop5y9chSu+icIzgHNEapKBVuw3gPFIP0LWDkf39LJC8J1corRqfSqSo/3n+UdfpkayKAbsAoLM/zAdAOpWpefQfoJJFYgAoVv/IrrLZwQF+UCtISicQ0c4APLZTVGZ+k3cQiVqxYQb58+QgICDDwflq2bBkHDhzAy0vGA1uKzYanr2I2EiHi7KlL9iMKWIPikpgfaAPMxLZcLulRD/gZJWfMEWAIVClpOubGReXE8Ez0eqre+lPUGmM/53M7p2eaDhLHESMSjPpCU2LoHTmP/KEjaRbxK5PjdnJWm/aBX77Bm+TKU1z5I1WViKBjy198b6fnSUEpYVsRJX+TLfgAk1AM058iQw1M4JG7MIUrNM1qNSR2okFN2LcYCuc37JceT2kgUF5gtQesSbdQAjiA5W/JJRI7o0EDb6NUpLaEYSgvaCUSiWlmo3jOW4IGJUH5y5myyGpiY2Np2bIlb775poH3U5MmTXjy5ImsfGchNhme9iUFsTTBUQmRsinJwB7gc5QbtleBGcBDB6zVEPgF6l/8mSb//MLM9/fxsGCEgcggt0YUUed2wOKGuHnlpdKrbxv13w3czdN7Fxy+vsTx1HIqZtR3PPlf1iaeJdxC47I5byeAu4E7M6RfjuYxyo11a5TwWlsoBPyGkgPqLWR+FkDt5EKNtl/Qe+xRUrQvmVHzBaVNY9gzX0konprERAgJyxKVcha7gTrAcSvGuKIYyBcAbo5QSiJJh0SU4hxnLZT/GZmfRiJJiz+BwRbKalBejvZ2nDovGqtXryZPnjzs3btX7/3k5ubGqlWrePXVV7NYu+yP1Y8wyULLp9FrHKFLzkEAh4EvgGJAC5Rknw54K3uowU0+n7yWoudH03LDDP4ccIDH+aJwVTnzjXsb+y/4HFVbfIiTi3Gsz9kdvzh8bUnmYMrwdDb5vlVzlG/wJrnyljC5LSU5ySa9Xij2AjWAMUC8jXOUQXk7dQ543U565UCKV21Lz28P8kq3MSTGRfHopjVP2pLsSPe28M+f4GkirPRhiIEDpSQt7gJNUdIFWMMAlHsaWT1QkhVEoqS3uGWh/HwUDz+JRGKa31EcJSzBCVgO9HScOi8a8fHxtG7dml69ehEbq5SYVavV7Nq1i3z58mWxdtkbqw1Pv8T5E6h1hJtPDiUF2IcSClMEJRxvNvDUvssItSCgSRCf/Lyawhe/pe2a33AapKZAXsfFlTq75aJKc+NSCcE3jvLoxlGHrSvJPFSoqGmiUt3ZZMvjSdPydgLlYiwBElAKC1QHzKfVSp9qwN8oD4otMq5WTsE7fxnafbSc9p+sJHeBsgDcOrNJWiVyOAN7wJpfwNXF9HYZZmcliSgFUgYA1mRDqA2cAjo5QCeJJD2CUYxJTyyQdQbWIXOUSSRpMRMYYqGsE7AS6OEwbV5I1q9fT+XKlUlMVDzv3d3dOX36dBZrlb2x6onwfko442O3OUqXnI8WJRzvQ5TwmNdQ3szYOUwgRSPY3eIqg39ZQ8jDGNiKEqNr5sbdVio3HYirh3E4n/R2enEopc6Dt9rdqP+c1nKPp/IN+hh5O0lTQBpcR3FrrgdkJAqxEeAP7EApOf2C4uTqSf2uY+j53SFKVDd8zX3rzN9ZpJXEHgztD/MngkZjXkYmFreRRSjXiJtWjPEF/gHGI0N6JZlPENARpUJsengCW4DyDtVIIsnZTAeGWiirMz51c5g2LyR37tyha9eu+rC74sWLs2GDZRXeXkasurUYEbOZGGQ+DYtIRnkgfA8oCHQGFqO4FNuRFGehuCivRHGzn4xd3OU1zm5Ua/WRUf/Texe4F7g74wtIsgWmwuxiRSLXtJa5Gag1ztRqb+ztJFK0z2Skx5NpTqG84W0FHMvAPO2AkyiFEEwXwsyxlK3Xk95jjlGz3RdonAwt6zHhD2WYXQ5mwucwbUT6ctLjKQOcQzFKW1uAeAzKC608dtdIIkmbkyghP5ZE6OdHuc82XYdHIpEA/Ap8aaGsM0qlyZc4nYMtbN++nQkTJuiNT127dqV79+5ZrFX2xKonwk1JMpm0TSShvJnpDxRAsSYvx7K3OtZQAKUM+01g+3/rpPEmOS0qNOyLh3dBo/5zO361VTtJNqSWU1GjvgvJD0ix0GepfIM+5MpX0qg/MS4qw7q9NPijFBToClzMwDy9gEBgHlDcDnplIXmLVafzsC20HDgbT5/CJmVkmF3ORKWCmd/B6I8tk78vDU8ZIxzlIeI7lNQAltIeOM0L7U0pyabsBAZaKFsa2AZ4O04diSTHMw34xkJZZ5QXmZ0dp86LyLhx4zhxQim8plKpWLZsGd7e8sL0PNIVIbNJQMnP0g/FUNQL5QSPtfM67YENwL/AOJQk6BaiUjtRo41xSYSIxzeUhz3JC0MtjanE4pbld1KpnUx6O8WEPyQpIeaZnEplu4IvE5uAmihV8CxNsvo8GuBdlJCFaUAOy3Ho6unLq29OoevwPRQq2zBNWRlml/NwcoLFP8DgfpaPkR5PdkAAE1G8o63JP1kSOAQYp3rMFrR4BVo2yGotJA5hGfCVhbK1gI0oVRolEolpfkZxTrAEF5Q8ajLnn1W0bt2auDgluaK7uzt79uzJYo2yH9LwlJXEoZzYb6AYod4EzQYVrvFO9lujKDAWuI3yw/wa6X7rZev1MOnFcn7XTISw5pWpJLtT04TH01kL8zuZ83Y6t3M6KTLUzjZSUErbVkQphxts4zyuKHH9N1EMz7nsoZzjUKk1VG76Lr3HnqBy04Go1Wm7asowu5yHmyusmw5vWenCL3M82ZGdKB5MJ60Y4wrMAeYCbo5QyjY+7AM758IjOxdykWQjpv7XLKElStVXebshkZjnR2CUhbI641MHx6nzohEdHY2fn58+5K5evXp8+aWlcY4vB/ISnV2IAVZB6z4VeVx5Eks+focu26vhnGhjrNzzaFBCebahJDcegWLseh6ViprtvjBWL/whQcdX2UcXSbbAV+VBSY1xEg9LPJ5Uaidqv2Z8MY0JD+bqocUGfU5OdjSkviwkoZTDLYtykxBu4zy5UAzPN4FhZKsHRx2FyjWi2/A9vPrmz7h5+lo0RobZ5SxyecK22fB6K+vHSo8nO/Mv0ASl+q41vIfi/VTK3gpZh5OTEqo5a5wSthn0b9bqI3EwX6O8jLGE3ijJlCUSiXkmo4ReW4IrSvRM+/QEJTr+/vtvg+TiP/zwA6VL2yH58guCNDxlM9o4V8Q72p231tZn09sf8rjyJBZ81o8Ou6vglGSnr6s0yoXnHkpS8hbPNpWo/hq+hSsZDbmw53dSkmVi+RcJU95OKSKFC8kP0h1bvsEbZrydfkWbnEBKyjPPOE1aJaskaROLcq6WAX7A9pDcfChvjoNQwmaywVfi4VOElgNn03noP+QtVt2qsTLMLmfRpaUSFmUL0vDkABJQqu++C8RbMa4OSlGELHoD7ptbMWDqQjWv34EkS5JQS3IuAuU43WGh/GAs9+iQSF5WJqIUkbAEV5SImXYO0+aFo3fv3jx9qrjjOjk5sW/fvizWKPsgDU/ZjDYuhmWpfCI9GLCyIVv9Pia46iTmDPWjrX8lNMl2+OqcgT4oyY2vAEOgSrf3jMTio0O58pwXiyTnYyq/0/WUkHQrV6rUTtRKx9tJpPJGkaF2diAMGAmUA/7Asoo/piiGEjZzCeXtcBal3ypSsRm9xxylbL2eVo+VYXY5j+X/wMqt1o+LilGaxEEsABpjXU65PChV8saRqXeQlcrAsVXQptGzvks3Mm99SRaShJIP1dIQ0Ykoxqr/cHKCHm3tr5ZEkqP5HzDeQlk3lPzE8jyyiJSUFNq1a6d/CV+8eHFWrFiRxVplD+QTYTZjdMwW1iecI0lojbblDfPk/aWN2fnGpzys9j1TRnUj32VP+yxcEfgFdo3uR0Drj3lU6Djiv8pmgftmk5wg7/5fNExVtLMkzK58gzfwzlfKqP/8rulokxMAQ8OT9HiyIw+BT4FKKMlXbU25VgGlZO5JssSFOuzhVdRq20IwZZhdzuSDMXDttnVjpLdTJnAGJe+TNYZBNUoI7z8ohigH074JHF0J5Z9zsr0sDU8vD9FARxSvXUuYDXSG0sXg4FJoWNNxqkkkOZZxKAYoS9AZn1o7TJsXitOnTzNt2jT932+++SbHjx+nS5cuWahV1iMNT9mMLUmB9IyaS9HQ7xgavZ7zyaYTPT99FMvsnw4TUiUGmgHLIR1HFYvQOiVwveJqNvfowPo+zbhQ8Q8CT1mbDEKSE6jlZKqiXdqJxdPydkrtFSdD7RzMTeAtlGo+mzMwTx1gOxAANEpb1J7ERT7i+ok1No2VYXY5k6gYeGMoxCdYPkYmFs8kwlBKZ4/FOmN2B5TQuzqOUErhi3dgyyzIbaJAgvR4esl4gvKixJKiGxrQrIW/dkKDmnDojIN1k0hyKmNQvAQtwR2lArMN+RpfRr7++muOHj2q/7t+/fps2rSJuLg4xo4dm4WaZR3S8JRNeSKi+TXen5rhPzA1bq/R9tspT7mm/e918AGgH0oYzTcoycPtQFjeSxxrPZrE6xHwF1DbPvNKsh5nNFTWFDTqT8/jqdwraXg7JZlOFiKTizuQC8DrwKtARkLImwOHgb0o5XMzIQTvwp4/rB4jw+xyNueuwJDJlstLj6dMRAATUM7/UCvGlUJJOv5uOnJW4uwMc/4Hv44Ec+8upOHpJeQWisEzMn1RrSv0qQCXXODIWQfrJZHkZL5DySdqCR4oLzxbOk6dF4lGjRrh7+9vEAni5ubGuHHjuHHjxkuXeFwannIAQVrju+8i6tzGgk+An1HCaNqilMFMtoMCXsAHwGngGDAQ5cIjybFU0RTCRWVsEDqnNe/xpFSyG2bUHxsRbJQDLLXHk0qVRYmEXiYOoxQJeA3lPLWVlijhM5dQznn3DGtmlvDgq9y5YGnGWAUZZvdycV8anjKf7Sihd6esGOMGzEPJH+eacRXy+cLu+fB+L/MyKSlw1ZrcVJIXh7NANyzy8g/TQOvC8DgbVnSVSLIVo4AfLZT1QLlXlMYni2jVqhX16tVj7dq1REdH6/vLlClDUFAQkydb8UYuhyMNTzmABykRRn2F1d7mBwhgN0oyxhLAaOCunZR5BZgP3AdmAFXtNK8kUzEVZvckJcrksaaj3Ctv4J3f2DJ/bqext1Nqy770eMpEdgD1UIoGXMvAPJVQvBzvoHhBGDvH2YXAANNhvB6xCahSjA1MMswuZ+PiDKM+sFxeejxlEbdRvCjnWTnufRTvpxK2L129ApxYA83qpS13+z7EWVORT/Ji4Y8Sbm5BaGiwO4pB1cehGkkkOZ8RKA4MlqAzPrVwmDYvFKdPn6Z3797kypWLuXPn6p+TNBoNI0aM4Pbt21SsWDGdWXI+0vCUA3iYYuxTnF+dCxcseKB/CHwPlAa6AFuwPSFxanyAz4CLwH6gL3Z50ynJHEwnFk/L20lDrdeGGvWb8nZ6ntTeT5JMQKAkDq8KDALSzxdvnnwohut/UR5Cq2VYOwMqNOpr8LdLYjId9gcycONRhNrQU06G2eV8BnSHEkWM+7+bDnV7Kq3Of612D1iakfxlkoyRgGJIeh+wxsBTFziKkn/OSl5vBYeXQynjnycjZJidhDXAFxbKVkMJD5KeTxJJ2nwDTEtXSsED5bmyuePUeREZNGgQderUITj4WcK6kiVLcunSJYOE5C8i0vCUAzDnhVJIbSLbpjm0KJbpzkAZlERyliRotISmKBW27qHECOez07wSh2E6sbh5C0W5V94gd/4yRv3nds0wm9tJksUkA3OBcsAwICQDc7mi5HC5gPLmuF2GtaNGm88oW68nAGptCk1PXuf7GZvpseccZysZH58yzC5n4+wMoz407r/zAH6aB6cvKe3Mf+3sZQgNz3Q1Jc8zD2iC4gVlKYVRXkhZcZ0YMQg2zAQvCwv1SsOTBKDlCRh8x0LhJsBKQNY7kUjS5kvgVwtlPVCqokrjk1WcPXuWwoUL8/vvv+tf0KvVaoYOHcq9e/d4//33qVatGlWrVtW3ypUrU7lyZUqWLJnO7NkXaXjKATxKiSJFGHuNmMzzZAn/oiSSKwEVfulHkXtNM6Sfnnwobpq3gElkSplliW3U1JjweDKT30nxdjKT2+ngIrvrJrEzCcAvKAbn8UBUBudrjxLSdwEl35uL9VMUq9Ka+l3HgBDUvnyXcX9s5Z3Nx/GNiiPBWcPJqsaxOjLMLmczoBuUNOHtNGk2JCVlujoSaziF4sm03YoxuVBedvVPW8zVBZb8CJOHgdqKO9JLdiqiIsmZODnB918oucBmxMLAcAsHdgX+dKBiEsmLwlBguoWyOs+nZo5T50Vl8ODB1KxZk/v3nz2DFS1alDlz5nDhwgUuXryob5cuXeLSpUvcvn2b6OhoPvzQxNu8bI40POUAtKTwSBg/LRa21fD0H+5u+Wms+pGOmzbSe9kxqp/9FNd43wzNCSjJyEeivCGdgIyrz2aUUPviqzbODm/O46lc/d7S2+lFIAoYh2KA+gXFIJURqqHke9MZsvNaNsy7QFlaDpxDhTshjJi7k09WHqBwyLNw4o3iKgGbx3Lz1AaiQpXkdDLMLmdjztvp7kNYsD7z9ZHYQChKxbsJVoxxBhYC35reXCg/7FsMb71uvTqXb1o/RvJiULKIctx8+5FirFQBs4OhU3S6QxUGobyEkUgkaTMEy41PniieT9L4ZDUXL16kWLFiTJ061eL0JJ6ensyaNYugoCAqV67sYA3thwolI4gkm3PK5xvqOBU36BscvYbf4/fbPGe910dTq/0Qg75kTTzrrzQhsuctxS3ZHoShxAzPtdN8kgzxukt1/vY2zPAbL5LwevoV2ucSgKnUGnqNOWpkeIqNCGbV2LpmDU+BgYFUqVIFgJ49e7J+vXy6zHYUR6li0h/7VK+LRXHN/hGzpa6d3XLx2bsb6X82nBrXHpiUaRkxg4CkIP3fHrkL4e5dgKd3z9tBSUlW8H5vmGPCYPHJBPhzRebrI8kgHYGlgDXvqWYBn2BwxzlsAEwdbpsK3vUgKsa2sZKsJTY2Fnd3d4QQqK1xcwN6tVeuJT4m6uvEqqB1CThq6e/ZxyjHpUQiSZvpwOcWysYAHYADjlPnRaZQoUJMmjSJpk2b4ulpOvY8T548uLo+S6yckpLCtm3b2LVrF8HBwdy4cYOTJ09mlspWIT2ecgim8jzZHGoHuLh7U6XZu0b9T66cIvLXW0repurAb5h9iLQYX5Qyy1sBE6EWksyllsY4f87F5IdGRicw7+10ftdM6e2U07mLcuOtq3z5KIPzeaAYsm6ivCV7LgSvuCYP/1Sdws9rLpk1Ot3WPmVfkmEMTWxEsDQ65WCcnExXsrsXDPPWZr4+EjuwFSX07qwVYz4Cphh2TV8CJy9av/zdh9Lo9LLh7gazJ8CaX00bnQA8BPxzFypZ6s37G7Iil0RiCV8AMy2U9UQJs7ZzMZqXheDgYN59913Kly9PkSJFTDYvLy9WrlxpkBuqU6dO/Prrr6xcuZITJ04QExPDu+8aP+dnNdLwlEMwZXgqrDbz62sBlZu9h4u78fizO3559sdFlMp1RVAq22TUeNrhvzn7ZXAeSYYwWdFOaxxmp1JrqNXhS6P+2MhHXJa5nV4cQlAqX5ZESSBuw4OgAXlRQvmuAv3AV+3Ozx7dCMozjnb/JqJOw8d2UcJxhHTCfaF4pyuUNrZ1M3kOJMrcTjmXW0AjwBrj4TCUvHD/odXCwFGQmGjd0jKx+MtF9Qpwcg0M6p2+bN4U2HEXilhybdGghILafistkbw8fI5irLUEbxTjU0HHqfMyk5ycjJ+fH9WqVePatWsmZTw8PJg7dy41atTIZO3SRhqecggPU4zdjmz1eNI4u1OtpXHCjZA757h/2d94QAxKZZv6QD0U7yVb3zb6orjorwcK2DiHJEOYrmhnnFi8bL1epr2dds5AmxSX5hoqlcp2BSVZQwKwAMXTsT2wM4PzlQKWQtGzPlTvVAQXC+xJi+KPZXBRSXbCyQm++8i4//4j6e30QhAP9MHyHCCghDY1fvbnxSCYYGWyZ5nf6eXByQm2z4Eq5SwfUyIZtt8DL0uMTyWx3JNDInnZ+Qz43ULZksDf2CeVg8Qkly9fpmLFinTo0IENGzZw9OhRAgMDSUhQ3D5VKhWTJ0/OYi0NkYanHII9Q+0qNn4L91z5jfrP7fw1/cGngA9QvKA+xXbviO7/je1p43iJTeRWuVNaY5wF+vnE4iq1htoZ8HZKbXgSQnqw5Dh2ohifqqMkEM9AIvKL1R/y2uo/aLPuN07WNF/3OiApiFspT21fSJLtePt1095OP8yBBCu9XCTZlBSU0FrjnwvTuKC8eEqVsvLHuXAq0PIlZUW7l4fkZOV6YS3VE2DQUhTjaHq8A7xh/RoSyUvJYCw3PjUAFqNklJY4jO3bt9OjRw8aNWpEtWrV6NKli35b+/btadYs+2R8l4anHIK9Qu1Uaieqt/nUqD/80XVun/3H8okigT9QHkxfA2wpOJUfxU1/OZDHhvESq6mhMZ1k67zWMOdO2Xq9yF2grLHcrpnpejuBoeEpOTnZSi0l2YaLwHsob67+B2TALrS32TXq7/6ZPnPmc730E6PtC6W30wuFkxN8a6KS3YPHMGdN5utjMdJb0zamAW9imZG6IMqb8P+KqyYnQ3Ss5UvJULuXiz9XQqANxsYnGwE/QGuB8CzAhJFcIpGYYDDKM6Al9AImOlAXiRG7du3i8ePHAGg0Gvbu3Uv79u2zWCsFaXjKITw0YXjKp/bCBSer5ilXvxe58hQ36j+/azpCWFbC0YgdKFbtzsBpG8b7oTzgdrJteYnlmAqzu6F9QpR49lpQpdZQ+7VhRnJxkY+5fGChRetIj6cXjEfAGBQvhY9Q8jfZyOpuZ6h86HsG/7CaR/mVEOJokcDahDN2UFSSXXirC5QtYdyfHbyd1BpnvAuUpVjVNlRpPoiGPSfS7qNl9Bp9hEa9spdbeo5iFYqnpCVl7Wuj5NdRQc920Ly+5cvIULuXi+RkGGLDaXn4DLAR+NoCYV8Uzwz5VCSRWMZg4C8LZUdikN9P4njatm1LfLzybKfRaNi2bRsrVqygRYsWWaqXdVYLSZZhyuMJFK+nf1NCLZtEpaJmuy+MuqPD7nP9uB1eQW/5r/VAiZm3poJdYZREdAtQ3PYzWklPYhKTicWfy+9Utl5Pchc0Tqhwfrdl3k5gaHjSVV2QvADEodxozEYxFI9Byf1mJcnOKfz+3gEWvnmMr35vTcFfvIlBxl69KGg0pr2dHj7JPG8nJ1dPvPOVwjtfabzzlyZX/mf/9/QtilqtMRpz99Iejq77LnMUfFHZh1JAZAPpP8T3Bs1l+Lmx8ab4BFi1Dby9wNsTcudS/p+UDKHhdtdaks3ZfRj+3gNdW1sm/ygEbt79749fUX6v0hvbEiUB/pR05CQSCQiUlCvFgY4WyP+FUpQiwIE6SfScP3+eGjVqcPHiRVxcXFCpVLz55pu8+eabxMTEMGnSJCZNmpTpeknDUw7hcUo0WpGCRmV4J1dEndtiw1OpGp3wKVTBqP/C7t9I0dqxvNB6wB+YAbxl5diB4NMdwnsDu+2nkkTBdGLxZ/mdFG8n42Qd1ng7gaHhSau1xM9dkqMQKIbif1DcqCcB5a2fJsYzkfHfbMO5vwYmoBQukJXOcjz9OkO5ksb9P8xRDAr2wtUzD975y+Cdv5RiZMpfBu98pciVvxQe3taV03l67yJ7572HSJGhwRlmE/AdynUhHbRj4PR9KB1l2D9tIXz7q/1Vk+Rc/t5rueHpUGoHWgH0B86TflqHiSj3nmet108ieenQohSYOASkVzzNGeX5sBEZ8pqXWE5QUBD9+vVj+fLlODs76/s9PT2ZOHEiFStWpH///pmqk3QqzSFoSeFRSpRRvzV5nmq2H2rUFxcVwtXDSzOkm0nCgLeBHqAyTueSJuE+wC6U5HWe9lbs5cUJNVU1hYz6z2qfeTyVrdvDrLdTcqLlSTikx9NLxFqgCvAxaIJt+0lJKqhVzvdLKEleZZqdHItGA6M/Nu5/+ARmr7bvWj1GHaDr1ztoOeAv6nYeSfkGfShYtoHVRqeYsAfs+NOPpHjj31iJjUwGVlgm+k5hOOv67O+HT2DybIdoJcmh+OaGiUMslz/8fOT2fcCEF6YRLsAyZCUuicRSolFSrTy0QNYXJTLGuMaRxEGsXbsWNzc3+vXrx/bt24mJeVaW/u2336Zq1aqZqo80POUgHgrbK9sVrdSC/CVrGfUHBsy2yqBgDicnqFAKurSEr96Fud/DgaXw+CsIDocettzPfwKcA5pmWD0JUElTEFeVs1H/uf9C7VRqDbU6fGW0PS7qiVXeTiA9nl46koFZ4FwnL5/+2QOvaNd0h5ikHEqemFNAV/upJ8k8+prxdvppnn29nQC0yZaUrEqbxLgodvzRh9jwB+kLS6zjPeBk+mKxauhaDB7/F/046hfrko1LXnx+GQGFjYsxm8XI8ATKS5L0i/IqL1J+tHwtSfbFxfiWV+II7gJdAEuu22VRcq+5OFIhSWpSUlJYvnw5HTp0wMvLi2vXrgHKs9q3336bqbpIw1MOwlSeJ0sNTzXbDTHqS4yP5tK+uRnSae73cHkLxJ6Gq9tg0x/w89fwXk9oUhfy54ECWlh7H5Y9AF9rbRBlUeKBpwJuGVL1pcdUmF1oSgx3U8IAxdvJx5S30y7rvJ1Aejy9jKidXGjbdwlNDn/JjfpjGTy3GU5JNv7E1Ea5MTkDdEN6QOUQNBr47iPj/uAn8Ncq+6+nTcqYJStFm8yeuQMIfXDJThpJDIhDMSBb8Cb8jjP0KAqHL8GijQ7WS5Kj6NAM+ncz7r/7EFoNgLe+UcIy/1oF2w/A+atw2twp/RlgSXL6z1AqNktyJE5OMPx9eK9XVmvyEnEKJb+fJbf8TYD5jlVHYp4ffvhB//+6detm6trS8JSDeKA1NjwVtsDwlL9UPYpUNHYburx/PolxppOWW0rjWlCpDDin81ZBBfSNhIu3oKMlFW9So0ZJ+HgGeMUWLSVgLr/Tf95OKnUa3k4LMrSuNDy9HLz6xk/UzFWBLvsuUiAkFzNH9uZK4+94c30d2yethZKk+AxK0QJpgMrW+HVSPF+f56d5EJdx5yQjtEkZm/TgimHcvxJgH2UkpnmAYjy24Ks65AHd1SALoUp0eHvB7PGmt30wFvyPwbLNMOkv+GgcdPgAanZLo3JmFEoaCEtegi4ArPCykmQP6leHk2vghy9hz5Gs1uYlYyMw3ELZfigFaiSZzoYNG/T/L1TIOAWLI5GGpxzEQ2Fc6q2IBTmearUfYtSXnBTPRf8/M6yTte7wRZLhn3sw7yHkstb7qRJwGCX5o3TRtJpaGhMV7bRKYvEy9ezn7SR5+ajc7D3q1ezF58sC8Ix7dsdf9nZ+Vnw4kJOtv6b1PuPCBhZTE1iHkvC1J9IAlQ1Rq017Oz0KgVkO8HaCjBmezmybyrUjy+yojcQsx4FBlok+7gYYF9+VvKT8/DUUM/FctHCD4t1kE4exKPE9hVAKXkhyBF4e8MtIOLoSalaCoH/h2u2s1uolZAqWnzfjgb4O1EVikvDwcMR/b3ic0/McsTPS8JSDMOXxlF6onW/hSpSs0cGo/9qR5cRFPs6wTrbkYVAB70bAhVtQ4YaVgzXAKOAEijeExGJqOpkwPCXfR6VSU7vD10bb7OHtBKBWy8vMi0zh8q/S/PUJfLZ8H/nDYkzK1D1fgl29BrOj9yfUumDseWcxNVDydJxDqaYnDVDZhjc7QsXSxv2O8nYCQGXbteX68TWc+ifzywi/1CwFfrJQdirQ1oG6SHIErRvBB28Y9z98AkN/MO63ignAMQvkumKx0VSSdXRsDoGbYcg7yksQgC37slanl5pPsLwy+XzgVQfqIjHCyclJ///MjkqRT4Q5CFM5ntILtTOV2ylFm8z53TP/z955h0dRdXH43Zbe6L33Kr2KIkW6YAEbYm8oNmzYQEFRPgQVEFQQUFQEFEGkSJGi9N5774SQXje73x8DgbCbndnN1uS8PvO4e+fcmZOwmZ0595zfcYtP+REA3b8SDtdGuUDZf17Nm4Yoq6jvA0YVW4Fy+hiK6yNsxrebT+ed7bRsvGQ7CQ6JKFqejo9P4dm5G6hyJs5mf7o1i44J47g9/kuGpy4mfGkw6zu8xk/PPkrx4/loWdkAmI3SHrsvEoDyMXq9/U52Fy97JtupTI223PXGUkpVbe703LMH17D6p5fc75SgzhCovkuDnQGlyUAND/sj+C3hYTB5uP19zw2DeNsCAOcwA/1ROnKpMRb5LPopJYvBL5/DX5OgYtnc+xas9IlLAih/X/cB+zTYBqOU6FX1pEPCjVSrVi1Hi/fGLnfeQAJPAcQ5i+03bTF9OMF5RF4ii1WiarN7bMaPbplL8uWTbvHJ1cDTnsPQ71WwmIGJKKU0zqZNm1BWrdahdCER8sRemV2m1cwByyUad7Wn7RTLvtXuUf6TjKeCicEUSqenf+Dxf4/T6MAZuzYDkn5kRdZBVpsPMzR1IbcmjKX45SHMmLqJonXDlZKa2Hw4UR+YBewC7ke+0XzE/d0Urb+b+d/3kJrmvvMULVuXLgNn0uOV+ZSs7Lx22JVzB1j27aNYzHkJwAiepEwxWJ0NdbRowhcB/gS09U8RChifvgaVbW9b+HkBzF/hppMcBl7RYBcO/IQscvoZT9wL+/9Ssm1vJikFVmvoqCl4kASgB6CluKY48BcQ40mHBHvcmP3kDeQ2PYCwl/EEeWc9New8CL3eYDO+4+8v3OLPfV3gtmbOz7sUB72eh8QbV5qOAO1RRMSdLctoBmwF3kA+0XlgT1h8T/Y5KjS9i5jStkt5u/KZ7WQVddgCz239v+LBU0F02HjI7v43Uv5gdqZtT+tkawaLsvZyMO0ifIXSufJDlJsUV6kHzEQJQD2AXAe8SF7ZTpfi4Otf3HOO8CLluO2RCdw9ZBUV6rlWg5WWeJElX9+f74YaguuMfA3KhMD80xo73NZC+bu2vY0RCjDtmsGLD9uOX7wML33s5pNNQcm2UKM5MNTN5xZcokZlWDENpoyAInkEpv/+D7KyvOmVYJdjaG4uQW0ULU/vSg4VSg4cOIDZbAagSJEiTJo0yWvnltvzAOKiNYlsq20tpj2B8dCoUtRo9aDN+Imdi7hybn++/Gh5C/z7E8z+AsqVcm5uRib0eRGOnbaz04KS0twYpYzOGYJR9CPWICnRdmhkV9/pbB7aTrHsXeO+PqfX0jmFgkPDzi/TL7QZ/f62DSwBTEhbzei05doOlggMAyqT/wBUXeAXYDfwIPIN5wX6dYU61WzH3ZHtFBwWQ4u7P6Tv0I3UbPUAOhezJ7MyUlgy8SGS407lzyHBZZo3gEf7KK+rZ8HsM2DQsj7RFe3aUELAExoC34+wv++F4XA53gMnfQo4p8FuCKJF40NMJnjnWdj5B9zR0rGtlNn5EeuAxzXadkCpghE8zh9//AEoz2jPPvssX331lVfOK7flAYQFK+ftlNvZy3hq0OE5jKYQm/HtS75w+fyVysLPo5WOEW1d7JD+5Huw1v6z6nX2A21QRMSdrYhog9L5ahCi+3ID9jKeTpcpmne2U0b+an4l46ngUr5eJx5s9CxP/r7W7v75Gbt4OeU35w8cz/UA1LCr712lDvAzsAelY4p803kET2U7GUwhNOz8Ev0+3ErDTi/a/S67RlZGMjuXfkX8hcN291ss2fwz9RliT6p98Qie5Ishud93TIUvLmic/BrwmJsdEvyS4S9B9Uq247/9DXOWeOikl9H2+TKgiOSrN5MW3EzLW2DLHPj4FQgJVrdf5GrHQ8EzzETR5NXCk8CbHvRFAOD+++9n586dOe8HDhzoFWkUuR0PMOzpPN3c2S4oNJra7Z6wsTt7YA2Xjjtf9BwVoaTI718ID/ZwenoOwyfCT39qNM4GRqKkN2938kRhKCU8S4ASTs4tgETogqlusP1FmG/raTPm7mwnEI2ngkR0yeo8eNcYXpy5BpPZNvtyU9YJHkyaRjb56JIRj5L5VBmltOGK64eiNoo2x14UIVkp2XEr93WBurZ9Cfh8KqS40vFUp6dm64foN3QTLfoMJTgsb4EfS3YWe1ZNZtbQZmz840P++PQOu1041895h5O7FjvvjOA2HugObRrbjheZAXyj8SCTgNZudErwO1o1glcftR2/HK9kO3mUv1HuG9WoDOTRm6dc7faUqtYKU7BtIxfBNSLC4Kt3Ye3P0KCmtjkbd8KF/GhHCp5hBPCDRtvPgHs96IuAxWLhlltu4fLlywAYDAbat2/v8fOKVF6AYU/n6ebAU93bnyIoxE4HsyVjnTqXwQBP94UPX1Q6Rzji/CUlQBUWan//rEUw1JVGejuBFiiR8iE494ntDGwD+gH2kzMKBQ0NZe2Ox9dpYDO2a/mEfGc7gWQ8FURMIZHc//hUXp+zhYg021TEY9mX6Zn4DalOpynmQQJK84AvgJdQsh6KuHisWsCPKNeRESjZUFo0ZoQ80engg4G247FXYMLPzh+vYv0uNO/zAUXK1Fa1PbplLpv//ITES0dzxsyZqfw383VO7v6b2x7+ktCokuxaPoG9qyY774zgNkJDYJRt/wpS0+Dtz1GyTWoDt6scKBiYi7IYlY+KyaLl61OyclPizx8k/vxB0pMvu34wwW0EBykldvbWqV7+xEuBhLdQSn3qq9gNQBFCnnXDmE7HbY9MIDymNAAJFw5z+fQuYk/t4vKpHVw+vUs+a07S6w6Y8D5UKOPcvAWrPOOP4AaeRgne3qbB9kfgJLBJ++EfuxumzXXFscLLgQMHaNOmDQDNmjVjxQp3dW+wjwSeAgx7gacyN2g8GYPCqH/HszY2l05s4+wB7VfjbrfB6Dfsr2bfSGoajJ4Ko6ZAmRIwfaTtyubGnfDYO+ByLCIL+ACYD0zHuQ525YBVKDcUY1w8f4Bjr8zuYkQQqaFBucbSky+zd/UUb7klBBCmIB2vDJ3IczNPUfKKbf/pOGsa3RMnctGa5P6TJ6IEi75CKaF9DSjq4rFqoqy4XQtA/YQEoFzkvi5QL49sJ2e6nZas0ozmfYZSpnobVduzB9ewce6HDsvmTu3+m98+bkfN1g+zc5l3NAuEvHn9CfsPjqOmwOnzV9/ch6LrWEXlYKWAecCtgIu9L2q17k+99k/nvE9PvpwThIo/f5D4C8r/k6+cycdNi+AsQ1+wrxX35z9OZMrnl3TgYZTPolo51ySUBc2reqXFKzTKCToBRJeqTnSp6lRtenfOWMqVs1w+vZPYUzu5fGoXl0/vJDnOnuBp4Uavh19GQ79urs3/a6Vb3RHcSSZwN7AedT3eUJTnvpYoASgVBj8OLzwkgSdnycy8vlgcHKyhjjWfSOApwFArtavVpj8hEbbpSTs0ajs1qAmj34Q7NQgoTv8D3v0CzlzVaTh8Atr1V/74h7+krGCdOge9X4Q0ZzvV2WMz0AQYDgxGe6GoEfgcRRTycZQH2UKEvcDTmfIlbcZ2ukHbyR5SahfYVCkPrw8bQutp4VQ9Y3uTnEE2fRK+YX+2VsEWF0kEPiZ3AEolEzNPaqAEsa8FoGYgASgnyCvb6XI8jP9J2zGiS1an2V3vUaVxL1XbuDN72PjHh5zeq02wPj05lp1Lv9TmiOAxypWCt560HT91Tgk85RAL9EZ5kFerUmoMTAPuB1yIC5WrnTu1KiSiGKWrt6Z09dx1fFkZKSRcOET8hUPEnz+UE5hKvHQUS7a0y3InTevBG7bqEMQnwnPDvOzMThR90c9V7IqgLGJ0AixQqWFX1UOHFylLeJGyVGxw3TY9OY7Lp5UgVOzJnVw+vYvEi0ew2mkkVFiwWJRMSVc4exG27XOvP4KbiQN6oASf1BYRSwMLUJ7fHKxrvj8QPhoEG3a4ycdCRLVq1yP++/Z5/o9HAk8BhqNSO73BRINOL9rsjz9/kOM7/3J43FLFlWDRE/coJXaOWLkRBn8GW/fa7rNY4H9TYOFqJUX25U+UMjy3kYEiOvcHys2nMx3s7gEaoqyuFqKLUyODbUe7U6Vjcr2XbCfBHvfeCS+9ehdZ42+n8f6Ddm0eTfyBNeYj3nMqCfgERWfjRZQgtKsBqOoo15EbA1Dm/LtY0Ln3Tqhv59qrJdspNKoUTbq/Qa02j6A3OL4FSYo7xZY/R3Jk0+xC/SAWqHz6GoSH2Y6/PcbOYtQuFB22PzQcuC9K58qPnPMnLKYsMaW1CcWYgsMpXrERxSs2yjVuyc4i8dLxXNlR8ecPEX/hoEcWbgo6JhN8/zEY7VwKXvtMCSR4nbFAd6Cjit0dKAsgo8kVTHKGkIiilKt9e66AaFZGCnFn9hB7aiexJ7ZxeNNsrJbCtTLy4QSl1M5Z/loliYoBwSGUzKelQJCKbQPgV6AXdhcIP3kVhjyjvL4Y50YfCwllyigpyVarNafTnSeRVIQAw1GpXfUWfYkoYhtk2PH3l3leiUND4L3n4fBiRc/JUdDp4HHo/QLc8aj9oNON7DkE7QfAjv2O7VxmLdCIPEUe86Q6SpTdzupaQcSAnvpG2zqHU6Vzi+XsXOYebSehYBAcBOPfh7Ej6nHx51fpuMF+0OmtlHn8mrnVy95dJQmlAUFlFP23/GiAVAOmAgdQrg2yJJMneWU7xcU7znYyhUTStOc79Bu2iTrtHncYdEpPucKG399nzoctObzxVwk6BSAtb4H+d9mOr9sOPy/IY9I84F2NJ/gQZTHJCcrVbu/cBDvoDSZiStegcqMeNOryKu0fnUjjboMJDovJ97ELI+88Aw1r2Y4v+Rem/u59fwAlk+5RlMwMNT6G0NtLUay8mjCUdkzB4ZSq2oKY0jWJPbm90AWdALbsUYJIzuLKHMFHrEbRfNJCN2zE/3U6pVvqtaATwAWRUXOKZs2aYbwa9b9y5Qpms+dXXuX2OsA4ZyfwVFQfTqgumFs6v2yzLynuFIc3zbEZ1+ng4V4w8lUoX9pmdy7i4uHDr2HiTMjypwzzVBTR4bkoD412WvDaJQSYgqIT8QKQ5hHv/IKahpKE6myXE24MPKUnX2afB7OddDqdx44tuJ/qlWDWGKhevSjzvh/LQ4t327WblP4fo9KWedk7OyQDnwLjgYHAG0BxF49VFeXa8B5KWd90JAPqJu7pbL+70JjpkGQndq03BlHn1sdo1HUwoZGO/2HMmWnsWfktO/7+gsy0QlYTXYC49kBgj1dGqkz+BEXc+UENJ/oBOILmDOaby+zyS8qVs6yd/TYndjjOKBfy5l1bSVKSUuCZod73JRdngGeB2Sp2QWCaHY55bipGs530PhdIij3B+t/fL/Sfqw8nQA8n/mQzMmHZOs/5I3iAH1AqV97TYDsQZXHwK0UHbNIwJWHiRi5K4MkpunW7LqR24MABr5xTAk8Bhr2MJ4Dm9fsQXcpW6XXXsvFYLbmfnG5rDp+/Cc1UFmgyM2H8zzBiElyxf1r/4B+U7KdpKDoRWnkcaIpSenfI7V75BY2MthlwqSEmLseE57zPPPo1TWoncylO6UiVaKsd7TTS1S4weaA7fPsRhIYamTV3Ivf/egi9nX/KhdkHeDF5lu0OX5IMjAImcD0AVcLFY1UBJqNkX1xrASwBKIfZTuNm2BpXa3oPTXu9Q1Txyg6Pa7Fkc2j9L2z56zNS48+6y13BRzzUE1rdYjv+43yl2YgqT6I8jDRTsQtHyZJqDtxU0q/T6YkuWZ3ilZRyuRIVG1GichMNJ1fHarGwZ9V3bFkwkqx0DzRUKESYTLZjb46Gkz6+DOj0BqLWVMX8ZxopvRwLgCeWOMrGVh/S5t/P8nXOrIwUdiz5gl3LJ5BtzsjXsQoCm3bB4jXQtZ02+382QIqLTQcEH/IByvX+fg22Y0F/Aqa1gUfsZNRKqZ1zXLlyJed1iRKu3jA7hwSeAoxL1mTM1myMutw1cW2b9L/5vou0pEscWHu99qF6JaWt8d2d1M8zZ4miw3BEQycBvyAe6AO8jlJ+o/WT3RBFtPxJwDYxLOBpZLAVFj9VqojyBAlkpsYx/cXJhLxyfX9mJsTGK0GoS3HK62tBqdgrcOnKDfuuKPv9KhNOcJrQEPjyneurRwvWDKfXtwmYzLYlTtt0F+l35Vuy8dPypxTgf8DXwPMoAShbLX1tVEHJgHoHJQD1I4VahLxPJ/tlMWN/yB2wLlvrdlr0+cBGH8ceJ3YuYtP84cSf885qm+BZwkIVbaebSUmFt9UEm6+RhvJ9vglQa6VeCfgdIh+oQonSjSlxNdBUrMItBIWoKZU7T+ypnfz782sOOysK2rCXC/3PBvjmV286oSOyWCWKlKlNkbJ1KFKmFkXK1CGmVA0MpmAyzycyN6E9SdEnHB5mb8PJVDjZmQonNdxg2+Hwpjls/ONDCbzfQJFo+50O80LK7AIUK/AYUBFo7dgUPZhmQf0zKJq/N3EhP5ILhZBp06bx1VdfodPpqFJFra2se5DAU4Bhwcp5SyLlDbk1eiqGl7cJPO1eMYnsrDSKRCur1C88aH916UY27VIEHf/d4l6/vcZoYAMwEyircU4USjr1FyjC5QUoiGIv4+lGYfGMIxMIMeZOcQoKgrIllU0rCUm5g1JFrzdalK52fk7tqjBr7PXyqQ37B9BidGkiU21T304b0uh28QtSyLTZ53ekoFwPbgxAlXLxWNc0oN5F6ar5E4UuAKXTwVA72U5XEuCrH5XXxSo0pHnvDyhfR10V9sLRTWz8YxgXjqx3s6eCL3nzSfvl+59OdlIo+gyK+OxKlPJ4R9wKZZa2od0/X6KzG87IP1kZKWxZMJI9K78tlJo77iSv6vuUVHjqfc+JQ4fHlFWCS2XrKIGmMnWIKV0DU3B4nnOCsqK4fflE/urTE6ve8WLL6g6DuOfX1YSmac8ciD25g3VzhnDhyAbNcwoDOh1MHwmVtN7HI4GngCYdZbFhPcqCnwMygqBXedhwAsrdlIkuGU/OkZycTHJyMpGRkRgMBoxGo8d1niTwFICcsxN4iknMnV+amZbIoXVTeOVR+OB5ZeXAESfPwpCx8MtfBaAjxBqUlsu/AB2cmPcK0BLoBzjOrA4YGhntZDyVUT476clxVDdOdst5oiOVrVpF+O1vuJJopZirWSaC1xjQG77+4HrnqaMXWlLywy6UjLP99k40WLgzdgwXrAFWWpKK0hp7IvAcSnDZ1QBUdRTdp2sBqJ/BXxO/3E3vjnBLbdvxsT+AJbgi7e97h+rN+9oa3ET8hcNsnjec4zvyUpgWApUKZZTA082cPKt0PHSaDRD0SgyZk+JVTQ/W+Ymil+tRf6cd0aB8cnLXYtbOeovkuAJyY+BD9Pq8F0Df+QKOnsr/OUKjSlKkzNXspZwgU22CQqNcOl7p8y1ptOU1tjUf7dAuLewia9q/QudFM1QDoGlJsWyeP4KD636S5gl2GPy4c13t9h6GY/LnGdhcBHqiNI9SeWY9Y1KCT6tPQMQNz6wiLu48ISHKyo7FYhFxccE+9nSeYpJyK2SbT05h++wkaqgIbielwMhvYex0SC9IJeUXgc4onW+0iNZdozWwDXgY+NsDfnmR0rooSuojbcavCYvvWvE1d/dwg6DTVTIy4dVPYeIvcMC1zsKClwgPgwnvw6N9ro/Fp5YhZeirNDh9c+4kmA3QK248+7LPe89Jd5MKjCF3AEqlsUKe1EQpu3sPpaX7TAp8AOqD523HTl8uxqorr9H3gycwGB33RE5NOM/WhaM4sHaGZIwUUD59TSnbvZk3P4e0dPX5oVGlKFGxUS5dptCokmzYNoxdjdVb2G5o8x4xV2pS/pQLfdjtkBJ/nnWz3+b49j/dcjwBXhkABjsxmf+2Ou6KaY/g8CJXA0y5y+RCIoq6x9kbaLzldU5XXMGlUo67uJ6sspgDdX+g9t5H7e63ZGexZ9Vkti0cJQ0U8qBNY6XxkT3uf02RALmlNtxS6/r/F0i2U8FgL4ru7iJUIxTbQqBfOZh/+rqpiIs7h9FozOlql56u4UvaHef0ylkEt2Iv8BSdfD3wZCCNiU9OIsrBc0B2NkyeAx+MK8B/qBbgfZTo+QxA671IcZSL3nCUh8oAfaC0l+1k1us4VyKa9OQ49q6aTKPB7jnXwePQ71XYsd89xxM8R4Oa8OuY3NoJWZYgDn48ihZ7bINOAF9V+JXVFwqIAn8aMBaYBDwDvIW6jkxe1EIpu3sPJcg9m4C9Xjiid0doXPf6+4zsMJadeo55h16i5q22we0byUxLYueycexeMRFzpii/FlRaN1JExW/m3y3w60Lb8ZCI4hSveIsSYKrUmOIVGxEeY/8Psfn694kvup9TlZY69MGqt7Ci85P0/u1vohOUZisZqQnEntxO7MntXDq5jVJVW9Kgg50o6o3HsVjYu3oKm//8WMTD3UiNyjDCtvkyaenwxLtgyePaaQqJzAkq5QSZytYmLMrV1FXtZKYnc+XsPuLP7Sf6v5rETtqONdzxRX592/coc6ZtzmfwGqf3rmD9nHeJv3DQky4HNMViYObnYLTzdDpmGsxapLzesif3viAVGREhgFiG0iDmW3XTRRHwXGn47jxYsuFyvId9K4Bc6zzuraZQEngKQM5ZbFdJom/IeLqt/AyigvJWWFu8Bl7/H+wpIM+RqixCKb2bDbTQOEcPDAXaAA8BAShYZ0/f6XyJaMxGA3G7v+atx5OoWiH/55kxH57/EJLzeKYUjSf/4em+ioj4jVkJVitsmzCaFqvj7c5Jevw/Rk391zsOepM04EvgG5QA1Nu4HoCqg5L19D5KAGoOimBmAeFaJ7tsi4H/zj3M/GNvkZBZGgx5z8k2Z7JvzVS2L/6c9OSCurohgKLH8uU79ve9MhKCw2IoXrHR9a1SIyKLav/y0VsN3LH0W+bf04X4oo4f2jNDEvizfXdKPdyKK3v3khh7PJd+wIntf1G0TB3K1Wlvd/7l07v49+fXuHTCcWaL4Bw6HUwZbj8j7oNxyuKVMSiMmNI1c2UvFSlTm4iitoto7sacmUr8+YPEnd1P/Ln9xJ1Tgk025ZXBKB1PHR3LlMo/nZ/lrt8Xo7eYSLx0jPW/v8/JnYs85n9BQKeDHz5TSnZvZv0OpeFRXmQWIG1WAfgOQhtC2ovqplNioEIWPHcg7+C1YB+z2UxmZiZBQUGEhYURERFBcrL7KmHsIYGnAMRRqZ1Bl0WXCuPtztt9CF4fBUsK4DOkKieBdihiw4OcmNcZpfTuPhTR8gDCrr5T6RhCDVeY8+ZkQvP515+aBi8Mh2lzbfd5K3IuOMejfWxv/Hf9Pphms+3rUcTdeYzEO2dy4X+e981npANfoayuPQ0MwfUAVD1gFrDz6rE2usNB39LrDmhcB7Ze6sHcI+9zPrWG6pzDm2az5c+RJF123AlKKBj0vwuaN1Bep5ojOZl0CycSG/HPvkZU69uIxiXy3y0nKDOKzgt/Yv59nckIiXdom17uMife/kvRC7npq8hqtfDPtGfo89aKXAGNrIwUtv71Kbv/+UZKQT3ACw9Bu2bKaytKRzsr8PW699gZVJt+w2oTWawSOg8vVGWbM0m4cIi4s/uIP3cgJ8CUFHtCm9bSFJTPVR/HZrElt7Op8Qh07xvY/c9ELOYAaMjhY958ErrfZjseF6+U2En35MJD6RLwd2V4PwnmOU6qBmBYCZSGFILTnD59mqpVq6LT6ejRowe//urZtqISeApA7JbaXQ08tSj1G8VCc6/QXIiF97+C739XSuwKLZnAS8B/KCtWWjstlwdWAF1RhMsDhObhteAmnbhTpYvQpdLXhBrzVz6w+5BSWrfvSL4OI3iZF0fAljmKwCvA8XW9qT2+Gnqr7YXh4i3JlBzyBX8HuNaZZtKBccB3wLMoGVCuakA1RLlWvAZMcIt3PuO15yry2ZZvOJKoni56Zt9KNs77kMundnrBM8EfCA9TtJ1mHfqInZe7cCH1hvKiIIjS3uArT8xZ6cSd2UPsie0U39CEM2NXqN+9diVPjcf05Mss++4xer32FwZTMCd3/83aX98kOc4NytaCDZXLwSev6pi0ezJnE+vyXvUQgq5+B21Pe5WKDdx/Tku2mcRLR7lybj9Xzu5TAkxnD5Bw6ShWSz4FdJ9CaUSjskCxq9l4SMbmPkyw5damShnmhvP3cOVidcKLn6FI8Dligs/x/AfnOHk23tcuCl7EYoGikfDTWbitEmxV62wKfNQQ6Ags97R3BYsLFy5QtWpVAMqX93x2qQSeAhB7pXbh6VkEZ2bSrdKXOWPpGUonmc8mKyLiwlV+BXaglMPU0zgnDPgL5aK2yUN+uZEwgqhstm0RfLGciSfLf5evY383G17+RJtYrOBfbN8Hk36FgQ8qq4dxSztQ2WwbdIqtoKPEZ0PBaGFDYYshpKOU4H2LIkL+Fq51wQsCxqM0LHgGRdw8wOjQClrXi2XBuooAtNt8mD3VyxAXk/vaEntyB5vmfcSZ/St94KXgSx7uCWVLwuULFbicWIUe/+1mSZs6mE0O6jAdYMnOIu7MHi6d3E7sCUWb6cq5/Viyb0h3sKL8banxFjANOGy7K/bkNtb89DJmczrHt4l4uLvo378/TZo0Qa/XYzQa0ev1tGpkILKageZRd1HyrBmT7lrOEzzd2fY+xSmsVrIyU8hKSyIzLZGsjGv/T8Za0gL1QoEmV7f8cU0LBWD7we2MLDPS8QQDVFtQjU+Wf6La5S6QuPH3oIZWqYWm9cBYBkpGNqNxRjbJphDOFo/hoknPh5NgmMVCdlY65qx0sq9utq8z8HaNuzO/C3u2eoMRgynUL7TknPlZ8jtfi+1xHbQuA39mX6a15Q1O6u1rkF7DoofIhZGM+XcMpVNdXTXMP87+Ht39e3OW1q1b57zes2ePA0v3cC3bVQggSuoiuVDsEwCy9ToSI0JIiAjln0GZPH7r44Ciu/POF3DqnA8d9XfCUASGH3FizhWgPUopjR9TShfJF0UepHlEXSqnGzFYlD/zJRNO0qWha3VTSSnw7FD45S912/3791OrVi0A7rrrLv78U27s/YUi0XBwEUycCZ9P0zGr84fcuapIzv74IiZCvxtKcKkLALR+ENZv95Gz/kAY8DxKF7ySLh5jF3AvEIC6er3ugPsfG0CpWQ/TacNBTpeMZtSTnUkLCSIp9gSb//yYI1t+z6WjIxQu+naFvg+8SsNxrah14iKb6lXku/vaYtU7vkm2ZJu5cm7/dfHvE9u5cnYv2WYNLXYnoWQmqvEjMEDLTyHkl99++4177rnH1254jZd5ma/4StVuPvPpRS8veCQIBZO97KUtbYknXtW2AQ1YxzrCyWdQu5BhtVoxGo1YPCyUJYGnAESHjlsM5ThnSaBE9WzGjnuIpadf5LkGj3P64DYGj4JNu3ztZQDxNEqJTbBG+4vAbcABj3nkVoa/XI7mVV/k8oEW9Hm+D2FXy+wuxcHifxWtpmfvd3yMrXuVGvvDGiVbJPDk33S7DVash4yrshMj2z3B4INNMZv0ZIybREztHQBkZkJU8+t2hZpw4AXgDZTOl86SADwKzHOnU54njCB+jnqM3kHX62F2VSrGIxEr2fXfNNEuEaikL8rSEoOpkR2VM7akTR3mdGmc895qsRB/4SCXrmYxxZ7cxuXTe8jOSrN3SHVMwFLgdhW7bKA2drOeBPdy7NgxKleu7Gs3vEYaaTSnOXtwnCXQhCZsZnOBynoSBG+zmtV0pjOZqN9z9KMfM5kpf3NOkJaWRlhYmMfPI4GnAsD496FzGwNDxmTzu+Nuw0JeNEHpeldVo/0ZFLHyYx7zyG20aQz//QwZ5iC27clk0WpYtAY2776epPDlO/BSHplfX/0Ib/zPua4hEngKPB6s3p5PB0dQsf2CnLGNO6GlSlCy0BEBvAi8DhRzYf6nKLozAaC3p0fHf9Gv0spkKw79Xfpankn+xQdeCf5EWX00m2PeoIw+2mbfl/X0TNbv4tLJ7Vw+vQtzhptr/oujCPiraZdPAx5376kFW8aMGcOrr76a83727NlYLBYsFgu366pSliiwWJWnjhKhcCkVzFbOhVjZEHyFpPQ4MlLjr5ZMuY4zzU2cbYRyczbA6WKnGd13NGaDYyGnZxY+Q8NjDd3mh69xxl+tGRT3BDeigj4mz/0Xi0ZwqGIJ0kOCNJ/7Rpz5FVutFsyZaWRlpmBOT1H+n5FCVsbV/2emYk5PJjs7y6XfhV5voHqLfgSHF7Frl3DxCBePbQ64jrCOfhdqv6e8Pic6nfJvF6YLoljDTB7u+rYmXx47NYw7d97FlXP7Sb5yGqsXW945+/fsib8nLZQoUYJnnnkGgLi4OIoVc+Wm1jkk8FQAiAxXMhKknWg+iUG5Qe2t0f4YSvDJzzspGAzQryssXQuxV+zb6HTw42fw8A3Z4FcS4In34I9lzp/zxsBTnz59mDcvwNI8CiFTRsAT9+Ye++pHRc9LsEMkSofMwUBRJ+cuBx4EHEsW+AXPhLTlm4gH7O57K2Ueo9JcuEAIBYopEQ/xREhrm3GL1cI9SVOYl+nB2vRmqOsumoFawFHPuSHAe++9x/DhwwHYuHEjLVu2BMCInlHhfXg19I48557OvsKDSdP51xyAHUs+Bt5RsdmOssApT1x2CcbIpOiHeczUzKFdptHA4lvrsvjWOmSZfC9TnJmWRGrCOVLile3a69T4c6TEnyUl/hzpSZdsOiY26fEWTbq/qXr8Y9vms23h/4g7u9dTP0LAUEVfjLmV32X5Q2sYPNxOO+2bserosuBXKpzqSGrCeY5s/p3Dm+Zw+dQOzzsbQJjNZgwGA1arlRYtWrB582aPnk8CT4JwM1+jaLpo4QBK2d1Fz7njLUwmmDdeKcNatx0eHAwnzrp2rH379lG7dm1AAk+BQJBJKbcsXxrKl7r+/yFjYdYiX3vn50ShlN8NAZzRUz4N3Ads8IRT7uWzsN68GdbJ7r5+id8zO3Oblz0S/AkjehZEPUeXoDo2+9KsmdyRMI4N5uOec+B34G4Vm8koZfWCx/jqq68YNGgQAP/++y/t2rXLtf+uoAZMi+hPEb39cg6zNZsPUhfyadpSrIH0aFIUZSEySsXuHkDD83JhpqWxMuPD+9LMVNGh3fkwPVNbl2V3wxqERpVCp1HE3BdYss2kJl64Gow6R1riRWq1fQSDUXvmlgSgFHToeCKsDWmTM/n5QfVOT8Hp0dw1ZxnRidfLWeLPH+Twxtkc3jyH5MsnPeluQLB+/fqcRYJLly5RsqSrYqbakMCTINyMDpiKoseihZ0oguN5ZBMFEmGh8HRfmPAzmPPRAlgCT0KhowMwE3CmfXwm8Aow0RMOuQ8dOmZFPs59wY1t9qVbs7gj4SvWezKwIPg9kboQVke/TCOjbTvmS5YkWseP4Ygl1jMnbwxsVbHJAmoAGnUKBefp2bNnTlm91Wrlp59+4pFHctfwV9QXYWbk47S2U757jb8z9/FI0o9ctPq+05dmPgLeV7HZCTRCnrpU0KPjieDWjAzvRXF9hEPbxZl7eSX1D05H6gmPKUNYTBnCo8vkeh0Wo7w3Bnlev8bTHN06j22L/seVs/t87YpPKRoSRvSaEI41i1O1LXu2Gp0WLCPIbBsZPn9kPYc3zubYtnlkpBSAhzgXqFSpEkeOHMFgUFZOhw0bxocffuix80ngSRDsYQB+AfpqtN8IdAIC6D7Jk9wYeLrnnnuYO1eW+YRCQHkUrbhWTs77EaVDl4s6y94gBBP/RA+yq/d0yZJEq/gxHPVUYEEICMroo1gfPZiKBtva00PZF2kTP5ZYa7JnTj4fVBuHTUJ7NrPgErt376ZevXo575cvX06nTrmzJY3oGRHWk7fCOud5nHOWBB5Kms7KrABpBRoDHAdspc5ycx/wm6edKRgU0YUxPKwHz4XcikGXd0ZTptXMmLR/GJG6mBQHwtNBodHXA1IxZQiPKWsTnAqNdGblyHdIAAooB6YtBrJKqQtmdvqnBS3WzORicft/oNnmTE7vXc7hjbM4uftvsrPS3e2tXzNq1CjeeOMNAE6dOkXFio4zDvODBJ4EIS9MKCn8PTXarwG6Aqke8yhg2Lt3L3XqKGUXEngSChVBwBiUDnjOsBOlFMOPJU5K6CJYHzOYqgbbtn77zRdokzCGK1a5ABZm6hnK8G/0K8TYKadal3WMjgnjSMMDgpRatJ4ygWooZa6CRzAajaxbt45mza5r9Rw4cCBnIepGupnq8kPkI3lmtWRbLXyUupgRaYuxBMKjyjBgqIrNbqAh8uTlBI0M5Rkf0Ze2Jsfdf05nX+H1lD/4NVMt/TFv9MYgwqJK2c2YuvG90RTi8jncSaEPQN0KrEB5XlPhw5G9aLzlNRa1q+tQHywzLZFj2//k8MbZnD/0n40+V0EkKCiI9PR0dDodWVlZBAW5JuCvBQk8CYIjQoAFQEeN9n8DdwH5a8oS8EjgSSj09Ae+AZzJ7o8HBgB+3ASytqEUa6Nfs6vTsirrEHcmfE0m+ajTFQKe9qYaLIkaSJDO9uZ+bsYO7kua4plAwl9AdxWb8ShNAQSPMn/+fHr1up6C9v777zNixAgbu3L6GH6JfIx2pmp5HmtF5kEeTprOeWuiR3x1G9EoWU8xKnb3A7M87UzBo39wc0aF97bbQfNGVmYdYlDybHZnn/OYL8HhRe2X9sWUIezq+5AIz3cIu8bRrX+wbeH/uHJuv9fO6Tc8hya5Ap1Fx5/9n6H5llb83L0Ze2qUVZ2TcuUsR7b8xuGNs4k7syf/vvoxSUlJREQoiwCDBg1i/PjxHjmPBJ4EQY1wlIBSG43281DSqQvxs9eNgad7772X33//3cceCYIPaIhSVlHdyXkfAx8AfrrQ5iiw8EP6Rh5N/tEHXgn+xEPBzfgp0r5Q4ldpK3k5xQP1Rq2AdSo2GUBVwMXGGYJ2/vzzT3r2VFLGV61aRfv27e3aGdAzLKwb74TeiT6PkqqLliT6J/3A0iw/f7B+H0XvyRF7gQb47fXdn4nUhfBBaFdeDm2PSZd3Nw+zNZsJ6WsYmrqQBKtvatgNxmDCokvblPZVrH8n0SXzDrS6SmLscdb++ian9y53+7H9nm/R1DwiKjGETZ3foObRkmyuV5FfuzYhPkrb6mDcmb0c3jSbI5vmkBJf8L5A1q5dS+vW17vTZmZmsmvXLmbMmMGkSZNIT3dP+aEEngRBC9EoLdCbarSfCTxMob2xuFHnoW/fvsyZM8fHHgmCj4gGfkDJhHSGpcBDgJ/KJg0IbsH0yEfs7huWupAPU6UdYmHn7dDOjAy3/8EfnDKXMWkr3H/SJcCdKjZfooj6Cx5l8ODBjB49GrDf5e5mOplqMSNyAKX09tvDWawWRqYtZWjqQrL99eYqCiXrqYiK3YMo94mCS9QxlOar8PvoFFTLod1FSxJvpcxjesZGv+mUWK3Zvdzx+Lf5Pk5q4gXOHljDuYNrOHNgdeHu0BYErARaq9gBdQ6UZn3X14hKDiXVqGN+p8b806ImFoP2zojnDv7L4U2zObZtPplpfp6JqZHSpUtz+PBhwsPDbfZZLBZOnTrF5MmT7WauOoMEngRBK8WAVUA9NcOrfA88RaH8C7sx8PTAAw/w66+/+tgjQfAhOuBtYDhK4wKtnETJnlTvGuwThoV1Z2hYN7v7BiT9yI8ZG73skeBvTAy/n+dCb7W7r1/i98zO3ObeE7YF/lWxSUPJejrv3lMLuXnzzTf57LPPAFi9ejW333676pzSuih+inyUDkE187RZnXWYh5Kmc8YS7y5X3cu7gNqz2X6Ue0k/jZ8FCvcGNWJM+N12GxrcyPqsY7yYMpst5lNe8ixvDKZQHh65j6DQSKfmZaTGc+7gf5w9uIazB1cTf+6AhzwMUMoAW67+X4XeCxvw+2NPobcqwab9oRlM79qQ2EZaswsUsrMyOLn7bw5vms2pPUuxmPMWtw8EwsLCmDx5Ml27diUmJgadTmdjs3LlSu644w6XzyGBJ0FwhtLAapS2zFoYB7zkOXf8lZ07d9KgQQMAHnroIX755RcfeyQIfkAnlG6ZttrceZMBvIyiF+WH/BgxgP4hzW3GM61m7kycwKqswxjR08xYkfamGtxhqkGMLoyWCaN94K3gbQzo+SPqaXoG1bfZl2HNolPCBP41u1lRfxnquoyfA6+797RCbt555x0+/vhjwLmHFT063g3twtCwbnl2M4u1JDMg6UcWZe11m79uIxI4hrJY6Yj+wE+ed6egE0YQQ8I680ZoR4J1eatMW6wWvktfy7upC7hsTfGih7bc+tBYarcd4NAmKyOF80fWc+7AGs4eXMPlUzsLhdB1vmiNkvmkQRt72GfdGDr6uiigxWrhR+MBfmpRhuKt7iE8prRTp85IjefY1nkc3jSb80fWgzWwwyuVKlVi8ODB9OrVi4oVK6LXX78W2+tWqhUJPAmCs1RA6WBXSaP9p8AQz7njj+zYsYOGDRsC8MgjjzBjxgwfeyQIfkIFYA7Qwsl53wIDAfXOwV4lCCN/Rw/kdpNtNP6KJZUN5uPcaqpGhC44176mV0axNdv3q8+C5wkniJXRL9PMZNuiOc6SQpuEsRzIvuC+E96Gkp3siFSgMnDJfacVrhMWFsbo0aN5/vnnAUVsvHfv3k4d43ZTdX6JfMyhmPSo1GW8m/onZn9LHRoCfKJicxCoi99d0wOVavrijI24h15BDRzaxVlSeC/1L75J/9dn3RJLVW1Br8G5y9GzzZlcPLZJyWg6sIZLx7dgyfZAB9CCzlPAd9pM5/V/hruW5P68XLQk8UbKPJZVCqF6y75UbtSLoBD7nTfzIinuFEc2/cbhTbMKRGZaSEgIs2bNymkYYbVacwWinEECT4LgCtVQgk8aUjoB5SbkU8+5429s27aNRo0aAfDoo4/yww8/+NYhQfAnglB0Zp5zct584AGUUiE/oogujHXRr1HLWErznC/TVvKKJwSmBb+klC6SdTGDqWKwTQM5ln2Z1vGfc8Ga5L4TrgTUKrs+QymBFdxGyZIlOXDgADExMbnGP/30U4YMcX4FroQugh8jB9AlqE6eNuuyjvFA0lROWq44fXyPEYGS9aSW3ToAkF4MbqWHqR5fRNxLdUMJh3bbzKd4MXk2a83HvORZbu57fz2Z6UmcO7iGswdWc+HoRsyZqT7xpcDxNfC8ullkUggb7xxM7cO22U0rsw4xMHkWB/UJVGrQheot+lK+bkf0hryz6uwRe2onRzbN5sjm30lNCOz67oSEBKKiFA2+8PBwUlOd/7xK4EkQXKUuyqqq1rKZp4ApnnPHn9i6dSuNGzcG4IknnmDq1Kk+9kgQ/JABwCQg1Ik5a4FeQJxHPHKZavrirIt5jRJ6bboVlyxJlI17z/8yFQSPUctQirXRr1JUbyteujnrJO0TviQFN2lkdEBpCOKIZJSsp8vuOaUAo0ePZvDgwbnGLBYLVatW5cSJEy4dU4eOt0I7MTysB8Y8OpldsaTyWPIM5mfucukcHuFNlOCmIw4DtZGsJzcTjJHBoR14N6wLYTrHdVc/pG/krZR5nLd6VyRapzdgtcg/vEcwASsA+/KCuah5uCQb73yd6CTbG7Esazaj05YzPHUxaWQRHF6Uqk36UL1FX0pVdS5t3WqxcPbgGg5vms3x7QvISnfjQouXuHLlSs6iQtWqVTl2zPmgrWt5UoIgKC1x7wTiNdp/AziXaR6wWG+obbYnTicIAkq3u9bAUSfmtEERT67gEY9c5ogllt6J32HRqEFRQh9J16C6HvZK8CcOZF/grsRvSbfalo80M1Xk16gnMLjrtnQF8J+KTQTwmntOJyjEx8fbjPXp08floBOAFSufpi2lfcJXnM62n9VURB/GvKhnGBt+DyaVDg6hOJex4DITUC/lrA7Ybw4q5IMMzHyS9je1r4xgdobjBgYDQlpwoMh7vBpyB0YvPhZL0MmDZKE0Zjmjbnqw+kUenjQdi8723sWkMzAk7E72FnmXHqZ6ZKTEsW/N9/z5eTd+HdqULQtGEn/hsCaXdHo95Wrfzu2PjOfhkfvo8MRkKtbv4nQGla946qmncoJOFouFM2c0/HLtIBlPgpBfWgN/o9zEqpGOEqxa41GPfM7mzZtp2lTpDvHMM8/w3XcaC64FoTASg1Ju0dOJOWeArsBuTzjkGq2MlVkXM1jd8CpzMrbRN+l7D3ok+CP3BTVidtSTdvd9k/Yvz6W4qQtqZ5TvZkckoeg1+lGVViCj1+vJysrK0f/IysoiKEiD0q9GiunCmRbZ365Y/TU2ZZ3g/qSpHLPkTmUroYvgzdBOPB3ShtsSvmRntmsPTk7xOvA/FZujQC3A7Hl3CisdTDUZF34fdY2O9TH2ms8xKGUOK7IOeskzwaO0QGkIFaxmCC+Mbsf4z/o5tJmbsYOXU37j1E1lvcUrNqZ6i75Ua3o3oVElnXIxPTmOo1v/4PDGWVw85lwL47DoMtTv8BxbFowkOyvdqbnOUKtWLXbs2EFwsPKLnDNnDn379nXpWBJ4EgR3cAewEAjRYJuAIn6606Me+ZRNmzbRrFkzAJ577jm++cZPW3IJgr+gA94BPkJ7LnI8cBd+E8jOq8NdXmRYsygT9x5XrKJrUdh4LbQDn4ffbXffkJT5fJq21D0nWge0UrH5CBjqntMJkJqaSmjo9bKVkSNHsn37dsxmM/Hx8axYsSJfx9eh49XQO/g07C5MeZTeJVjSeDL5Z37L3E4xXThvhHbkxdDbCL/a5GBz1klaJXxOtqdLfcNQAktq8ndPAhKD9yhG9AwKuZ1hYd2I0juub5+VsZXXU/6wCTAIAchjgEa1j/sfbML0pf0ddkdMsWYwLHURX6T9YyMVoNMbKFf7dqo370elW7pjCrYtK3dE4qVjHNn8G4c3zSHhwiGHthFFK9D9pblElajCxWObWfpNf9KS3N8to0aNGuzYsSPnmp6amkqRIkXIzHStLF4CT4LgLroBf6CpjSfngLYo4pMFkI0bN9K8ufIAOnDgQCZOnOhjjwQhQLgTmAkU0WifDjwEzPWYR5oooYvgVNGPHN6w2eO55Jl8k65WEyUURL4Mv5eXQtvb3fdw0nR+ztic876OoTQdTTXpZKrFyym/ccKiUeSsG8qikCMSULKeErQdUnBMRkaGwyyn2NhYatWqRVxc/oTqWhorMzPyMSrbEay/xqLMvbSz01UT4PWUuXyelr8gmCZeBcao2BxDyXqSJmYep7Quis/CezMgxLFGT4o1g09S/+bztBVkOEhHK6YLp3tQPX7M2OhuVwV38RUwSINdMlRqU5TJJx6iU1Ath6a7zGd5PvlX/jPb10owBodTqWE3qjfvS7k6d6DXOy4BvplLJ7ZxeOMsjm6ZaxNQiipZje4vzSWiSLmcsaS4U/w98UGunN3n1HkcUaVKFXbv3k1YWBgAZrOZjh07snr1apePKYEnQXAnDwC/aLQ9jBJ8uug5d3zFhg0baNFC+VJ/4YUX+Prrr33skSAEEPWAxUB5jfYW4AUUoXIfMST0Tj4J7+X0vLVZR2mbMNYDHgn+jh4dsyOf5J7gW2z2ZVrNDE1dSD1DGTqYalLWEJ2zb2jKQj5KW2QzJ082Ac1UbD4Ahms/pJA333zzDU8//bRDfccFCxbktObODzG6UL6PeJi77XyG1Ei1ZtLgykiOWmLz7YdDQlGynmwbZ+XmWeBbz7oiXKetsSrjI/rSyOj4i/Zw9iVeTv6NhVl7bPY9GNyUL8PvpYQ+kkeSfmBGhnOlUoKXMALLUO90CnAIaAEPpjVlTPg9lNZHOTSfkr6Ot1LmcdmakqdNaGQJqja9m2rN+1KychNnPMdiyebM/pUc2Tib4zsXElmsIt0G/UZYlG0aZWZaEiu+f5LTe9U6a6hTvnx59u/fT3i4krWVnZ1Nly5dWL48f8eWwJMguJtBKNF1LWwF2qPoTBQg1q9fT8uWLQEYNGgQ48eP97FHghBgVEAJPjmjvz0c5QHayxjQc7TIUCoairo0v0bcRxy2uD9FXPB/QjCxInoQrU1VNM85mh1L9SsfYdV6+9oLmK9icwUl66mAfRf7ip49ezJgwABCQkIICwujfPnyBAcHU7lyZUBpy31NqNYdDAq5ndHhfQjSGZ2atzzzAJ0SvXB/8jLwhYrNGaAGkOZxb4Sr6NHxbMitjAjrYbfb5o38mbmLV5J/56gllgr6IkyMuJ8eQfVy9qdZM7k1/gu2Zp/ytNuCK5QAtqCtMcsioCdEW0MZEdaTgSG3otflrYFw2ZLCmyl/MDVjg+r3UnTJ6lRrfh/Vm99HVAnt33sAWRkpWC3ZBIXmHQyzWLJZP+dd9q7Kn7bujc9x2dnZ9OjRgyVLluTrmCCBJ0HwDMOB9zTargC6Axmec8fbrFu3jlatFGGNl156iXHjxvnYI0EIQIoAf6JkRmplMvAcXm3PHYKJYWHdeDm0PSFOltoBDE9dzAepf3nAMyEQKK6LYG3Mq9QwaBdlbZ/wJauytHUTApRFnsYqNu8Cn2g/pOA8mZmZmEwmrFYrQUFBmM3uU9RuaqzAr5GPU81Qwql5Tyb9xPcZ693mh11CgCNAWRW7IcCnnnVFsKWYLpyPw3rydEgbhwGGDGsWv2Rs5d6gW4jU24q6nsyOo1n8/7hkTfaku4KrNEXpCqxFj/cTlO8EoJmxIhPD76eZqaLDKf9lHeW55Jnszj6nyZ2SVZpRvXk/qjbtQ0hE3iXDrrBn1WTWz3nH5e6JWVlZGI1GrFYrvXv35s8//3SLX97rGykIhYn30Z4y3QGYQYH6a7Rar8ezr3W3EQTBSa4AnYB5Tsx5CvgdpbzDS6STxdup86l5ZTjT0zdgsTon2PtIcHN05F2WIxRsYq3JdEuYyCWL9nSjx4PVFMNv4iMNNq+hrTut4DIXLyraAjqdjk6dOrn12FvMp2gSP4q1WfY1V/JidPjdlNY5LqfJN+nASA12bwPuff4UNHDZmsJzKb/SImE067PyFl8N1pl4LKSl3aATQEVDUWZFPYGxIN3QFyS2AM9otH0HuNq4bbP5JC0TRvNi8mwSLHmnJLY1VWVbzFv8L6wP4RoEfy8e28zaWW/y05C6LJn4IEc2/4450z0pj/Vuf4o7n/sZU0ik03P79++P0ahkj8bGxrot6AQF6lFXEPyM51EeALVwHzDBg754mRsDT450HgRBUCEduBfntD/uQtEzcK3yzWVOWa7wWPIMmsSP4u9M7QKXlQ3FaGes5kHPBH/niCWWTeaTmu3vC25kVzA6T+ah3km2GDBQ+yEF59m7d2/O63vvvdftx0+0ppNgda6teBF9GOMi7nO7LzZ8B6hVYUWjPPAKPmGL+RRtEsbyeNIMLjoRCL+R9qYaeXbsFPyAHwGtspJTgQbKSwtWJqSvpvaVEfycvjnPKUadgdfDOrKvyHv0CWqo6TRWi5lTu//mn6lP89OQOqz64QXO7FuJ1ZK/rpsV6nWi1+BFRBTVUl94naZNm+a83rJlS758uBmnA0+hUdpToQWhUGNB6Ta1UqP9c8AwTznjXSTjSRDcSDaK8OyHTsxpg5JS7tz9hlvYkX2GLolfc2fCBLabT2uao9ZhSCjYVNQXoaupjmb7cF0w/YLUauduwIo28fDXgTDthxWcY+7c6+03e/bs6ZFzNDSo1bPZcl9wY80PiS6TgTYNvhdQ9MYEn2DFyrSMDdS8Mpwv0v7BbHW+VOml0PY8GtzSA94JbuENFJkTNcJRupXf0GX4vDWRh5On0ylhPAez8+4OVcFQhLlRT/Nn1LNU1mtPY8xKT+LQhpksGn8vv7zXgPW/vUfsyR2a599M0bJ16P3mUkpWUeuwcZ1z566XCkZGOp8x5Qinngj1hiA6P/MjOidbAgpCoSUD6A1s12g/lAK34ioZT4LgJobhnH5THWAdUN9TDjlmadZ+msaPYkDSj5zMdtw6vW9QI0JxXh9KKBioaavY4/EQJ8vtfgNsG1PlpgTK35jgEb755puchakiRYqoWDtPUV0Y5QwxLs2dEN6PaJ2Ha5R/AHar2AQjHRb9gARrGq+m/E7j+M9YmXXI6fmTIu6nmdGxJpDgI7KB+4HjGmyrAjOBm0Ify7MO0PDKSD5I+Yt0a1ae03sG1WdPkXcYEnonppsPokJqwnl2r5jIH591YM+qyU7NvZHQyBJ0f3keVZveo8n+xIkTOa/dfZ126lu+5b3DKVmlGUaTF8UjBCHQSQS6oghLamEcOXXFgcqNGU+CILiRb1BKc7VWk5QD1gDtPOaRQyxY+TFjI7WujODNlD+It6TatYvSh9Lb0xkHgl9iRM9TIW2cnnerqRo19E4ISVuBERrs3sCrGmmFie7du+csRiUkJLj9+A2Mzmc7XaOsIZr/hfdxnzP2sKDoOKnxMHCLZ10RtLE7+xyPJP3g9LwQnYm5kU9RUufejBHBTcQCdwP2b0lycyd2G09kYGZ42mLqX/mExZl7bQ2uEqYL4pPwXuyIeZvbTdVdcjfxknPadTdjNIXQ4YnvaNz9DVXbo0evnysqyr36d04Fnmq06AeAMUi+kQXBKS6gXLjOa7DVo4iNd/SoR15DSu0Ewc38AXRGER/XQgzwN6BtscsjpJPF/9KWU+3Kh4xJW0Gm1bab1aNSblco6R3UkNJ6125uH3M262kWsF/FpjTaBWgFp3j00UdzXq9bt87tx29oKJev+U+HtKG9qYabvMmDv4DVKjZ6pLudH1FUF+7SvPKGIsyJetLpTBfBS2xHaciihTeBB+zvOmKJpVviRPomTuFMdnyeh6hjLM3K6JeZHvEIJXTOdbJIjtMmW6BG0x5v0/6xSRiMeWskHjlyPVPCp6V21zBI4EkQnOco0A0lA0qNIGAuSuvPAETExQXBw/yLksWk9V4kBJiNz8uI4qypDE6Za1egs7OpNmVcDEAIgUsFfRGXdFQABgQ3R+9MR0QL2rKe3kJby21BM7feeit33XVXzvsZM2a4/RzbzKf5JWMLV/LIrNTCdxEPer7s900NNl1Ruh4LPqeY3rXAE0A7UzXGhvtw1UdwzC/A/zTaTsFhJuKczO3Uif+YL9L+IdtBd98BIS04UOR9ng1pq7mjb3KcWmcC7VRv3pfuL/9BSERxu/vj4uKwXBU2j4yMdGsCgUtHkownQXCR7SiaTxkabCOBRYCHF988gYiLC4IX2AO0BvLO8M6NHpiIttbyHuaY5TIPJ0+nWfwo/sk8CIBBp+ehYO0CmELB4Iv0f6gX/wmzM7Y5Pbe8oQidTbWdmzQTUJNsKQM86bQ7Qh6MGzeO1atXExSktBhPSUlhzpw5bj/Pv+YjPJQ0jRJxQ2gbP4aPU5ewzezcA1t1QwmGhnVzu2+52ICiOabGZ+BMXFXwDEV1+es48ELobTwR7GR2puA9hgBLNdiFoWScO9AKT7Km82rK7zSL/x/rs47laVdEH8akiAdYF/0ajQzlVU+dHHcKXT673N1Iqaot6P3G38SUqWV3/4ULFwDlGa5r165uO69rgSeTtPwQBJdZCTyINoHgEiglMmU86ZD7EY0nQfASp4Fbgf+cmPM+SmtvP8j+32I+RYfEcfRImMQe8znpBFRIOZh9kX5J39M8/n8szzzg1FynRcazgY812L2Nkn0s5ItPP/2UF198MSf7OSsri7vv9my7+WwsrDUf473UBTSJH0W5uPd4MuknfsvYTqIlTXX+4NAONNbwMJgv3gFsK45z0wzo51k3BHXyk/F0ja8j+tHSWDn/zgjuJxuljE6LjFJl4FdU75+2Z5+mTcJYnk2e6TADs6WpMptj3uCL8HuJ1NlPs9Wh4/Hsenz0xVyCjx8iMfY4CRcOc+XcAeLO7CH25A4undjGhaObOH94HWcPruHMvpWc2ruck7uWcHzHQo5tm8/RLXM5vGk2B9fP5MDaGZze/w/Vm/fDGGQb19m79/qK5q233qrhF6MNo0uTJONJEPLHXOB54FsNtpVRMp9aA+r3S36HlNoJgoe5AnRCyeTorXHOU0AK8IqHfHKShVl7WBK/j0eDW1BWH81Zi/uFhwX/Z7P5JJ0Sx9PZVJtPw++iibGC6pw+QQ0oogvjitWJ8qqfUFrbV3VgUx54HEXQX3CZPn365Ly2Wq1UrlyZs2fPetWHs5YEvs9Yz/cZ6zFhoK2pKt1NdekeVI96RtuVPaPOwJTIh2gRPxoz7ssyyMVBYDLq5c8fA78DeTfOEjyMxWolzZpJqM71SHSwzsTvkU/RNH4U561aNDcErxIH9EHpBKwWZ+wIjAIGOzazYuXb9P+Ym7GD/4X34dEQ+wtrBp2el0Pb0zeoMa+k/MbszOvZvw0MZfkm4gFam6pAQgaPfjuHdglfkKEatc4fKSkpOa9NJveVHutQ+nxoYsDoYwSFRrF4Qj9O713uNicEodDyLtr0JkCpQdaiC+AH/PPPP7Rv3x6AP//8k8WLF/vWoUKIZJ0VPix6C6sfWM2+W/dpntPri16UP1g+p55f87ncmPItFAwyMzP57bffSE5OdsvxdOjoF9SYEeE9qW5w3L3uheRZfJ2+xrkTPIGi2eGIY0A1nLhTFm5m0qRJPPvsswAkJSW5vUtSfqmkL0q3oLp0D6pLR1Mtwm4ILrydMp/P0rTU4LhIaeAw6g+6T6MEqQSfYUBPdUMJGhrK0sBYloaGsjQ0lqOKwUHdlR3WZh3ljoRxZHo4cCC4SF+UJhRaeAAl+0kjt5uq83V4P+raCXbfyJLMfbyZMo+Hg5vxWugdGHW506u+SfuX51KcOLEL/P777zmZqePGjeOll15yy3FdCjwt++4xjm//0y0OCEKh5ytgkAa7LKAxiq6Ln7N582aaNg1QZXRBCGCsWPnw6n9auJM7WcISD3slFBbMZjO9e/dm4cKFbjumET1PhbRhaFi3PLvfbco6QYuE0c4d2ISSdVJZxa45sFnFRsiTDz/8kA8++ACAy5cvU7y4fUFbfyAYI7ebqtM9qB7dg+pSQV+EhldGcshyyXMn/Qil/NkRmwBp+umXROpCqG8oQ0NjWRoayuUEpaL1eVcHeSNwIOSDT1B0n9RIQfm71KqzCZgwMDi0A++Hdc0V5HaWx5NmMC1jg8vz1fjiiy94+eWXAVi/fj2tW7d2y3FdCjytnP48hzdqDQcKguAQHUra/4MabFcDt3vWHXewa9cu6tev72s3BKHQMolJvMALWDSUiexjH7VxUqBZEPLAYrEwfPhwhg0b5tbjhhPEy6HteTO0k92HuvpXPmFP9jnnDvoM6qV0bwBOxrSE6yQnJxMerqT0jB07ltdee83HHmmnur4ExfThbDAf99xJIoEjKJqejqhPQCw8CgoV9UVoaCxHA0PZq0GpstQylMKgU+SVn02eybfpzogzCl5DDyxA6USuxgGUxYkk505RWV+MryLupVdQA6fdA0izZtImfizbs7W2NnaOevXqsXv3bsC9maouBZ7+/eU19v873S0OCIKAsvK6ALhTg+2jwA+edSe/rFixgjvuuAOA06dPc+bMGR97VLgQXS0BYGWRlbxf7X0y9ZkO7fpe6MsbJ9/QfFz5fAk3o9PpaNiwYS4tiOTkZMaMGcPQoUPdeq5iunCGhN3JiyHtCNZdP9/naSt4PWWucwcLAo7juIHHAqCX834KCmlpaYSEKKK5BoNBynTtMQgl+90RASS3INgnGCN1DKVpaCxLbUMpRqctJ84ZbTrBe8SgZBpW12C7GOU7woXqyd5BDRkXfh8VDEWcnns0O5Zm8f9zTt/QCSwWCzqdjrS0NMLC3NNYzqXA0/o577L7n0lucUAQhKuEo2Q0NVGxuwjURhEU9lOWL19Ohw4dAPjggw8YPny4jz0ShEJKW2A5EOzAJhkoB4jeqZAPSpcuzY4dOyhZsmSu8bS0NCZMmMBbb73l1qBDBX0RPgzrzoDgFhh0ei5YEikf977zYtA/AI842J8AFAVPaUwXZIxGI6mpqTkBSZPJhNks2jY2hAHnAEdJBeeBCrj0cCsIggvUBTYAERpsp6LoBrpAOEEMDevGq3b0nNT4K3MPvRK/weoBIcLs7Gz0ej2ZmZkEBzu6idSO3pVJ9truCYKQT1JQLlrZKnYl0dYKWhAE4T+UbneOiEDp3iUI+eD8+fOUKlWK0aNHk5Z2vQVraGgor7/+OqmpqYwfP56gINd1LW7klOUKTyT/RMP4kczL2EkpfRTdg+o5f6CVKvujgUbOH7awU7RoUU6dOpUTdEpKSpKgU16koi5oXBro4gVfBEFQ2Iv2YNLjKHptLpBCJm+mzuO2hC+dntsjqB7vhXrmwnCtSZHRaHTbMV0MPOUtmCYIQj7YAYzTYPcsSk2xIAiCGlquKYNw8Y5AEHLzxhtvEBYWxkcffZSrw11wcDAvvPACaWlppKWlsXz5csqXL5/v8+3NPk+fpO9oGz9GtfudXVZpsGnv/GELO/PmzaN06dKA8gAzYoTWFr6FlKkabB7ztBOCIORiNvC5Rtv3UZ7PXCTemqZuZIdhYd3oYqrj+onzID4+HgC9Xp/T4S6/uHSbaZDAkyB4jg+Asyo2emAifvugeC1KLgiCH7AFWKtiUw1tQpqCoJGhQ4cSGRnJkCFDSEhIyBnX6/WEhITQoUMHTpw4wbJly2zK81xhrfkYY9JWOD/xCKCmzxoATT38jTp1rj8IDR06lFGjRvnQmwBgLXBIxeYuoJgXfBEE4Tpvo22BAmACyt+pC5TUa6nps0Wv0/NT5KNU0hd17cR5sHHjxpzXP/zwAxUrVsz3MSXjSRD8jSTgVQ12TYHnPOyLIAgFAzXhWlCyngTBzXz66afExMTw0ksvcf78ecxmc87ihF6vp2PHjpw+fZpXXnnFd06qPVS0w2MLPbVq1eKJJ55g/PjxLFy4kBkzZvD222+7rYuQr7gmRmu1WkXnUSvTVPYHoa0DsiAI7sMM3A+c0GBrQJE3aOX8aUrqIp2fdJVi+nDmRD5JMO4ri3vsscdyyqMjIiLYv39/vjuWuxZ4MkngSRA8yixgqQa7j4FSHvZFEITA5zdArblkF5TGBYLgAcaNG0eZMmUwmUy0b9+egwcP5gSgTCYTY8eOZdeuXXTu3NmjfsTExPDEE08wefJkxowZQ69evTD+p3KzXgRoqLyMiIhgyJAhLFiwgDVr1jBu3Dh69eqVp3aV0Wikbdu2DB48mB9++IF169Zx6tQpUlJSsFgs7N+/nylTpvDCCy/QrVs3Hn74YUaOHMmVK1dYs2YNERGurYL7kkGDBhEaqjwrZGerCVcKOfyAuoj9Y17wQxCE3FwAugJxGmxDUbqh1nTuFKX0rgeeAJqZKjIuom++jnEjFy9epH///jlNQUJDQ9m4cWO+vpNc6mp3bNt8lk8WJVJ/IbpkdRIuHva1G4K7qQHswnE3KoAfgQGed8cZli1bRseOHQEYNmwYH374oY89EgSBdwE1mZUJwIte8EUQgM6dOzNr1ixiYmJyjaenp7Np0yYmTJjAr7/+6pZz1a9fn5kzZ1K3bl10Ol2ufQesB6itcxx1bT2rNV9U/oJmzZqh19uu21qtVlJTU7l8+TJms5nIyEiio6MxmUw253OGtLQ02rRpw/bt210+hrdJTU3NCTxt27aNJk3U2vUKOSwB7lSxaYhyfygIgndpAyxDCS6pcQxojRK00sBHYT14P6yry65d48mkn/g+Y32+j3ONLl26sGDBghyR8b/++ouePXu6dCwptQtwSlZpxi1dtNRlCQHHIUCLJMIj+J3wqWg8CYIf8i2QoWLzKEoXL0HwAkuXLqVYsWLMnj071/dGSEgI7dq1Y+bMmWRlZbF582YGDhzocnedihUrsnXrVurVq2c3CFRTV5MylHF4jJL9StKiRQu7QScAnU5HeHg4FStWpGrVqpQoUYKgoKA8g05Wq5XMzExiY2PZvXs38+fP5/PPP2f69OkcPXo05/cRGhrKhg0baNmypZM/tW/o2LFjTtDJbDbToUMHH3sUYEzTYPOYh30QBME+a4GHUM9MBKgCLETpHKyBGRmbeCF5FlPS17HVfIpMq2tdQCdE9KOJoYJLc+2xZMkS+vfvn/P+WmKBK7iU8XTu4L/89WVvl08quI9bHxpLtWb38NOQupgzUnztjuBuQoA9QFUVu33ALUCWxz3SxNKlS+nUqRMAH374IcOGDfOtQ4IgKExDCS454lXgC497Igi5aNKkCUOHDqV9+/ZERkbaDdhkZ2dz8OBBhg0bxqxZav3nrzN79mzuu+8+QAn4HDlyhF27dhEREUHNmjUpVaoUjwc/zkzdzDyPUYQixBKLzqrj5MmTLFy4kEuXLtGyZUvq1KlDqVKlbAJNVquV9PR04uLiOHPmDIcOHWLnzp2sX7+e9evXk5mZmef57rnnHmbOnInJZAKUIM4zzzzD1Kla2p95n6CgILZt20bdunVzxk6dOuUWQdpCRQhwHscLABeBcijaM4IgeJ+BKBniWlgC9MLpZ7QgjNQzlKaxsTxNjBVoYqzALcZyhOnsl3XfyLHsyzSNH8UVa6pzJ3VAcnIy4eHhAISHh5Oa6tqxrVq3AaOPWZ+acNl61xtLNc+RzXObMSjMOuDzE9anJly2Vm/Rz+f+yOahrQdWTf+95Qe+Xt3+/vtv6zWGDRvmc39kk022q1sT1K8lh7Gi9wNfZSu0W8OGDa0zZsywXrp0yWqxWKw3Y7FYrB9++KHm46WmpubMff755+3amAaZVP82Plv8mbVGjRp5nqdo0aLWtm3bWjt27GitX79+vn8PzZo1s2ZkZOT6uf/55x9rUFCQz/+Nbt4WLVpk8280aNAgn/sVkNsk1K/Td/mBn7LJVpi3T9D2fGbFynT3nNOA3lrHUNr6cHAz6+fhd1v/iX7JGl90lNVafJzN9lfUc1YdOrf9vOfOncu5vterV8/V42g3HjD6mLX/Zwetdzz+re//sWWz1mj5gPWpCZetT024bO324m8+90c2D26/o35RS8FKRT/wldyBJ2ceDmSTTTYvbP+ifj3p4Qd+yiYbWCtVqmSdOHGi9cyZMzZBqKVLl1qfffZZa0hIiN25QUFB1n///TfHPikpKe9z1UL972KQ93/+hg0bWhMTE3P93GlpadZRo0bl+XN7e6tWrZrVbDbnBJyWLVtmve2223zuV8BurVD/LP7uB37KJlth36ajPfj0sWd80KGzVtUXt94X1Mj6cVgv66Ko560Xin5itRYfZ/0gtJvbznPo0KGc76CePXu6ehztxqaQKN//A8uWs/V4ZX5O4OnJcZesYTFlfe6TbB7aKmIlGfWL2lw/8BWsixcvlsCTbLL569YP9WvJEj/wUzbZbtpKlixpTU5OtpsBdfHiRevixYutkydPtn799dfW33//3ZqSkpLL7pNPPnF8jnM4/ruY45ufOygoyLpixQqbwJvZbLaePn3aun79euuqVausf//9t3X+/PnW4cOHW2NiYrziW9GiRXMFxk6ePOnzz0mB2Pbh+LOYiZXifuCnbLIV5s2Ecr+k9b+B3vOtnD7G2t1UzxqM0S3H+++//3Ku859++qmrx/GDfzTZnN6iSlTNCTpd2xp2fsnnfsnmwe1NtF3UevreVwk8ySabH29GrJxG/VpSxw98lU22m7ZGjRpZ4+PjbYJPjrBYLNatW7eqH38mjv8mLmFF57uffcCAAdaEhARNP3N2drb10KFD1r/++st69913e8ynGzPKzGaztWXLlj7/jBSI7S3Ur9Ev+YGfsslW2LcIrGxB2zNaNlb6+IHPLmyjRo3KudYnJye7lHHrUlc7wffUbPWgzViNlvf7wBPBa4xFERpX4yu0tfn0Enl1ABIEwUeYgYka7F70tCOC4Dzbt28nJiaGdu3a8f3333Ps2DGys7PztE9LS+Pxxx+nSZMm6gdfpbK/OFBXxcaD/PDDD0RHR/POO+9w6tQphx1k9Xo91atXp3v37vz++++sWrXK5a6AeVGlShXatGkDgNVq5YEHHmDDhg1uPUeh5Ucg74+1wuPecEQQBIckA92BYxps9cDPQBuPeuQRRowYQUaG0ho5PDycv/76y+ljONXVTvAPdDo9D4zYSXiMbevfuZ924PKpHT7wSvAKt6F+YwwwAnjfw744YNGiRXTt2lVxZcQI3n/fh84IgmBLCeAUEOzAJgWlc1KCVzwShHzRpUsXunbtSqlSpQgODiY2NpatW7fy3XffYbFo6X0N1AH2qti8iPZuRh7GaDRyxx13ULt2bcLDwwkNDaVIkSJ0796dqlWr2nQGtFgsJCYmkp6eTlpaGsnJySQlJZGYmMi2bdv46KOPSE9P13z+kSNH8vbbbwOwa9cuGjZs6Nafr9CzEOimYtMIkNt+QfA9NYG1QDENtnFAW2C/Rz1yOwMGDGDatGnodDqsViv9+/fn559/duoYPk/dks25rXzdjjZldte2Vvep6BfIFvibFiG7DKzU9J2PN3a3GTFihO9/Z7LJJpvtNhX1a8krfuCnbLJ5c7uA47+JWX7go4atePHi1gcffNC6atUqu50B7ZGVlWV97bXXNJ9j/PjxOXP37Nnj85+5wG1a9PjG+oGfsskmm7K1wkoq2srujmOljB/47OS2atWqXN8ZAwYM0DxXamACkJqtH85zX7Vm96LTuzeVWvAz3gDiVWyCgK8970peOEr/FwTBTxinweZFlNRwQSgsrFbZf7tXvMg3sbGx/PLLL9x+++288MILnDt3TvW72Wg0Mnr0aMaMGaPpHBMmTMg5Zt26dfnqq6/y7bdwA/OAKyo2DwMmL/giCII664EHUC+TBaiEktUY6VGP3E63bt1ITk4GlO+MadOmsXevWqqwgtxOBhjB4UWp1DDvvNvQyOJUqNvRix4JXuci8I4Gu44oFz9BEAR7bAX+U7GphqJdIAiFhZUq+0uilOQFEBMnTqRs2bLo9Xqio6OpX78+nTp14uGHH2bw4MGMGzeOK1eUCIdOp+OVV17hwQdttURvZt++fSxcuDDn/YsvvsiqVasoWrSox36WQkUG8IuKTQmghxd8EQRBG/OBFzTaNgJ+J6CCx6mpqTRr1ozU1FRA+c4oV66cprmi8RRg1Gv/DK37jnRoc3TrPFZMecJLHgk+QQ9sAJqp2J0DagOJHvcoFwsXLqRbNyVAOnLkSN55R0ukTBAEr9MP+FXFZilwpxd8EQR/oD6wS8VmINoE+gMIo9HI7t27qVWrFgCZmZl89913WK1WYmJiCAkJ4fjx4/z444/s3Lkz19w9e/ZQt+511XWz2cz333/P888/r11fS7BPc2Cjis08oI/nXREEwQmGA+9ptJ0BDCCgojJhYWHMmDGD3r17k5ycTHR0tOocCTwFGPe8s5qi5eo5tMnOyuCnIXXITBNF2AJNM5Tgk1re4pfAKx73Jhd//fUX3bsraRISeBIEP8YIHEcREXdEXWCfx70RBN+jQ8ksLu7A5lcKZEZxWFgYcXFxBAfn3XXAarVy5MgRmjdvTnx8PKB00Fu2bBnt27fPJWiemprKwIEDmT59uqddL9jswXE3RTPKNfyid9wRBEEjU4HHNNp+BrztOVc8RUhICAMHDtRUoi2ldgFEsQq3qAadAAymYKo27eN5hwTfshmYpMHuRZRUTh9xc1cdQRD8CDPa9OAGedoRQfATrBQYnSdnSU1N5dFHH8VsNudpo9PpqF69Or/99lvOmMVioUOHDvTs2ZPz58/njIeFhTF16lTGjx/vUb8LPFNV9htRtJ4EQfAvngYWa7R9C+WZLcBIT0/XrAsogacAopYDUfGbqdHifg96IvgN76K+wmVAKQnwYvxHxMUFIYD4DkVLxBEDAPUsakEoGKxU2V8aqOUFP3zAr7/+SnR0NB988AHz5s1jzpw5TJkyhYkTc9cWlipVymbuwoULKVOmDIMHDyY9PR1QAlUDBw6kWrVqXvG/QDIDdbHix73hiCAITmEG7kNJFtDCl8A9nnPH10jgKUAwmEKo1vxezfalqrUkqkQVD3ok+AXxwOsa7FoBT3rWFUEQApRLqAvYhgMiHSgUFlZpsCmgWU+gZD4NHz6cPn360LdvX5566ilee+21XItKjla4x4wZw44dO3Le63Q6ihd3VLsoOOQ86lkTDYAmXvBFEATnSEFpAHBUg60e+Am41aMe+QwJPAUIlRp2Jzgsxqk51Vv084wzgn/xI9pukj/DsWaFh5BSO0EIAMZpsHkRuWsQCge7gDgVm/Ze8MOPCAkJyfW+UaNGDu1vFBsHchqOCC4yTYPNYx72QRAE17gIdEFZ6FMjBKUzXoB1T9WC3EIGCM6U2V2jRov7QR76CwcDgSwVm6IowScvcGOwScruBCEA2Ar8q2JTFWnbLRQOCrHOU17Ex8dz+vTpnPeDBg0iISGBAQMG2LWfMWNGru//Dz74gM6dO3vczwLLfNSDoQ8BQV7wRRAE5zkM9ARSNdgWQclyLOtRj7yOBJ4CgIiiFShb6zan50UWr0Spqi094JHgd+wFtOi6PQG09rAvSOBJEAKSrzTYvORxLwTBP1DLJC4L1PCGI/5DkyZNOHfuXM77qKgopk2bxtNPP21jO3DgQNq3b8/Zs2cB5b7g66+1dDIQ7JIJ/KxiUwzo5QVfBEFwjY1AP9Q12wAqAouAKI965FUk8BQA1Gj1ADq9a/9UNVqKyHihYThwUoPd1yiC4/nAFBxhfxwDNQ0lc41ZLJb8nUwQBO8wFzitYtMJx229BaGgsFKDTSHLeoqNjaVs2bK8//77ZGZmAkpA6fPPP7drv3r1am655Zac91WqVMFoNHrF1wLJNA02j3nYB0EQ8sdfwHMabRui3JsVkExGCTwFAKf3LOfMvpVOz0uKPcH5w+vd75Dgn6QAL2uwawQ87/ppytRsR8029ks/p0Q8RBNDBdF1EoRAxIzSAVONQZ52RBD8gJ0oDTwcUcgCT9cYMWIEJUqUICtLqfGPjIykYcOGdm1jY2PJzlaW9w0GAytXrvSWmwWPLSj6Y47oitJ1URAE/2Uy8KFG2w7AVLzandxTSOApALh0YiuLxt/LgrG9OHfoP1X7hEtHWfXjIGZ92ILDG3/1goeC3/AHSiRdjRFASVUrG0IiinPHY5MIi7Jto/xRWA8eCWkBSKmdIAQs3wLpKjYPAaFe8EUQfIkFdZ2n9l7ww09JTExk586dOe9XrVrF8OHD6dWrFyVL5r7BmDRpUs7rNm3aeM3HAsk0lf1GoL8X/BAEIX8MA6ZotH0Ir+n0ehIJPAUQ5w+v5a8v7uKvL/tw5dx+m/0p8edZOf155nzUikPrf8ZqMfvAS8HnDEL9wTEaGOXkcXU6bn/0a8KiSxMSUSzXrieDW/N+WNec9/obSkOl1E4QAohY4BcVmyjgLi/4Igi+Rk3nqTyK6H4h5b333stZXIqJieG9995j/vz5XLhwgezsbJKTkzl16hQtWrTIyXrS6XSULi0pOS7zE0p2qiMe84IfgiDkn+fQljAA8Abwmgd98QISeApAzh1cw+m9y23HD/3L4Y2zsFq0KJYJBZZjwEgNdo8C9bUftmHHF6lQtyNArsBTF1MdJkVc1xKzYpWMJ0EIZMZpsLHfyMrnvPAQlC7hay+EAsNKDTbtPeyDH7N48WKGDh2aE1S6Eb1eT3h4OOXLl6d58+YYDNfFJc+dO0d2djYXL15k7ty5lC1bwFo3eZILwEIVm3pAcy/4IghC/jCjiI1v1Gj/OUon8wBFAk8+oJGhPJ1NtfN3EJ29fzp5wBeuMgo4osHuCW2HK1G5Gc3uejfnfUhEcUD5LM+OfAKjLrdauQSeBCGA2QZsVbHxs2qZsFD48TP4cBCcv+Rrb4QCw3YgQcXGz/4WvM3w4cMpWbIk48ePZ/Xq1Rw5coSEhATMZrPD73+9Xk+JEiXo06cPJ0+e5IknNN6QCNpExu/ztBOCILiFVKAncFij/QTgGc+540kk8OQDyumjmR7Zn+I6+53BtKCzozAmD/hCDuloa3uuQRg1KDSKDo9/i95gyhkLiSxGBX0R/op6jkh9SC77mz+FUmonCAHIWpX9MUCkF/zQQPVKsH4m9L8Ltu71tTdCgcIC/KtiU9Ebjvg3cXFxDBo0iNtvv53q1asTExODyWQiLCyMdu3a8eqrrzJlyhS2bt3KiRMniI+Pz5UlZTAY+Prrr334EwQYC1DKoh3RwRuOCILgFi6hNAa4qNH+GzQnD/gTEnjyAWX00ZTRRzMl4iGXj6HT2/7TWeUBX7iRhShi4464BdWHx3YPf0lk8Uq5xooZo1kY9RxlDdH5cFAQBL/lqAabDI97ocpdHWDzbGhQU3m/bZ9v/REKIGp/C2Fe8SIgSU9P599//+WLL77gqaeeomnTplSuXJkiRYpgNBrp06dPzuJUcHAw99xzj489DhCygJkqNo1RFggEQQgMjgA9ULqUa+E7/Fb2IC8k8OQDyuijALgruAHPhdzq2kHsldpZJfAk3MQ3KvsNQKu8d9dp9zhVGudWETaYs3l53nbqG7VpMujtBEkFQfBzwlX2nwMyveGIffR6+PgVmDcBom8InkvGk+B2UlX2S4dHl5k3bx6zZs3Kef/aawGunOtNFqvsNwC3ecMRQRDcxmagL+oNBECJ4kwFHvaoR25Fngh9wLXAE8CY8LupY3C+u4fOTuDJKhpPws2sQykVcEQesc+i5erR8t4RuQetVh6dt4Haxy7keTgr1lxlnzfqPQmCECBUUNl/0ite2KV4EVgyGd551nbfNgk8Ce4mTWW/ZDzli9mzZ+e8rly5su8cCTTWAGq9hO7whiOCILiVRcDjqD+/gRLJmQ7cr2boH0jgyQeUviHwFKoL4ufIRwnC6NQx7D7MS6mdcDMJwC4Vm3a2Q8bgcDo8MQWjKbd+U58VO2m987jqaUVvTBACHDXdGh8Fnpo3gK2/QafWtvuSUuDQCe/7JBRwJOPJozz44IM5r3fv3u1DTwKMRJTsCEeIzpMgBCYzgKc02hqu2j/qOXfchQSefMCNGU8AjYzlGRney7mD2Ak8WaXUTrDHGpX9LQFT7qE2fT8lpnSNXGPtNh+mx+o9Tp9eMp4EIQDxw8DTs/fDmhlQoYz9/Tv2g8S8BbcjGU8epWPHjjmvJ0yY4ENPApB/VPY3BIp7wxFBENzOVLR3rzOidLv8FOz0H/MbJPDkA8robQWZXwvtQGdTbc3HsFtqJ4EnwR5qHXnCUEQor1K9eV9qts4tfF//0Fke/muTptNZsUonO0EIdPwo8BQaAlM/gUnDIDgobzvRdxI8glrgSTKeXEav1xMTEwNAVlYWf/75p28dCjRWaLBp72knBEHwGN8BA52wfwv4HXWdTh8hgScfUFpvv43Y9Mj+FNdFaDqGvcCTINhFLfAEOTpPUSWr0fbB0bl2VTwbx7Oz/sVgcS2VQMTFBSHAKAKofRWd8oYjULUCrP0ZHrtb3VY62gkeQUrtPEbDhg1zsqIvX77sY28CkP9Qb/IgOk+CENhMBF5ywr4PyrOfmlanD5AnQi9TVBdGsM5kd18ZfTTfRz5kd58N9krtJMtEsMcZ4LiKza1gMAbT4YnJmIKvP3EWjU9h0E8rCcnU0l7hOiIuLggBjFq2E3gl46n77bB5NjSqo81eAk+CR1DLeDIADjLxhLxp0qRJzuuEhAQfehKgpAIbVWwk8CQIgc84wJmmn41Qrg0tPOKNy0jgycvYK7O7kV5BDXg+xI7a801IqZ3gFGpZT7dC87uHUbxCw5yh0LRMXp6xkpjkdKdOZUXExQUhoPFx4Emvhw8HwV+ToIjjr8wcMjJh7xHP+SQUYtQynkCynlwkLOy6QFbNmjWZNGmSD70JUNTK7eoAeejiCYIQQIxFKaXTSmlgFfCAZ9xxBQk8eZmbhcXt8Xl4H+oaSju0kcCT4BRqgacSUL5P+5y3RnM2A2eupuwlWYEUhEKHWuApDbjkmVMXjVECTh84o2kA7D4EWVkecUko7KhlPIEIjLvI119/zYkTSitKnU7Hs88+y9ixY33sVYChJjAOovMkCAWFUcAbTtiHAL8Aw/AL0XEJPHkZtYwngFBdED9HPkYwxjxt7Go8SZaJkBcadJ7Ol9mQ89qUlU1kaoYHHRIEhXB5YPM/fCQsXrYkbJkDXdWTfm0QYXHBY2gJPEnGk0tYLBaqV6/Ozp07c8ZefvnlXJlQggrrALXE9A7ecEQQBK8wGrgXbdm41xiKEoDy8XeVBJ68TF7C4jdzi7EcI8PvytvATtRSMp6EPNkLxDk2uVBmfc7rtNAgxj5yB7ExzrdFsCIBUEEb93SGwY/52gvBhkoq+z0UeDofC5dUrlN5IfpOgseQUjuPYjabueWWW3JK9HU6HUePHsVozHvxVbiBDGCtio3oPAlCweJ3lMZQp52Ycz+wEp+W3krgyctoyXi6xquhd9DFZF9VVUrtBKewonQ/ccCFGzKeABKiwhgzoAMJESGe80sotLzxJPz2FZy+4GtPBBt8lPFkscAzQyE72/m5kvEkeAwptfMKc+bMyQk+lSpVip9//tnHHgUQajpP1dCm3ScIQuCwDUU8fJMTc1qgiI439ohHqkjgycto0Xi6kWmR/Smhs+1rbb/UTgJPggNUyu0So4+RGnY+19ilYpF88cgdpIbY78RoD8l3EhxhNMJ3w2HU68r7g8d96o5gDx8FngC274MvfnBuTnY27DzgGX+EwMBkgmhtCeXOIxlPXqFfv3588MEHOe979OjhQ28CDC06T5L1JAgFj3PA7cAsJ+aUB9YAd3vEI4dI4MnLOBt4Kq2PYkrkQ7Y77GY8ueqVUCjQoPO08dJHNmOnSxdh0a11PeCQUNiIjoTF38JT910fk8CTn2FCPQ37hGddGDoeTp7Vbr//GKQ513xTKECULgErpkKop5JzJePJa4wYMYL0dOWPOTQ0FL1eHlM0sQlIUbGRwJMgFEzSUDrXfejEnHCUcr23PeJRnsgV3cs4U2p3jV5BDRgYklttVaezFXmSUjvBIZtRFaA8bPqVo1vn2Yw3PJj3U2Cm1czx7MuszTrK7IxtHM++nE9HhYJIlfKw7hfo2Pr6WEISXJSPi39RHvU7Aw9mPAGkpMIIJ7qqb5Myu0JLm8aw9TeoWRnOe6jTooiLe5e0NOUXrtPpKF++vI+9CRCyUDIYHCEC44JQcLGidK57EPVmAzcyEpgOBHnAJzuIcp+XKaNzLuMJINGSxsHsi7nGpNROcJpMlFUxRx2j2kH0xaq5hkpeTqTGSds7+vdTFjAp/T8uW1NEUFxwSOtG8Md4KFks9/ghD2fOCC6gRQfEw4EnnQ4e7aPdXvSdCifPPwhfDlHK7P5W0TDMF1mAGcd3zJLx5BZCQkKIjlYWaK1WK2fPOpH6WNj5B+jqYH8FFK2nI95xRxAEHzATOAb8AZTWOGcAyrXhbsBTCzhXkYwnLxJOEJF653LBz2YncFvClyzLuknAQsTFBVdQWxFrBJFVKucaarP9mI1ZhjWLCelriLUmS9BJcMj93WHFNNugE0iZnV+iJfDkTBcVF3j8HmjbRLu9dLQrXIQEw/cfw9cfKEEngB37PXxStawnyXhyC1988UVOed25c+cwm80+9iiAUBMYB8l6EoTCwAYUEfHtTsxpiyI6Xt8TDl1HMp68iLNldvvM5+ma+DUnLVds9tkvtZMAgKCCms6TAS6W2kz504oYgM5iofX2ozZm8zJ3ccWqRXFVKMy8+xyMeDnv/RJ48kMqqew/h9K+20MUi7kuPH8jaenw0icQEQYliyqBzJJFoVQx2O7poIPgN1QsC799Cc1uujn2+GcgFXAkXi6BJ7fQrVu3nNeffvqpDz0JQLYBCYCjR407gO+8444gCD7kFHAr8BPQW+OcysBaFL2ohZ5xSwJPXsQZYfF1WcfokTgpz4d7e4EnKbUTVFkHWHCY63ihzIacwFPtYxcommi71DstfYPmU4o4aOEjyATffqReLiWBJz/Ehx3tAD57XQk+3cwn38Lk2Z49t+DfdGgFv46B4kVs93k88KSW8SSldm4hJiYm5/U//2hp1SbkkA2sAu5yYCMC44JQeEgB7kHRcXpT45xI4E/gdWCs+12SJ0Ivst58nKZXRvFs8kwmp69lu/k0Zmu2Xdud5jOOM0qk1E5whXhgt2OTC2XW57xuu8022+lsdgJ/Zzm+y5fsu8KJwRhM8+7P8vcUbRo9ovHkh/gw8NSmMTx5r+34gWMwarLnziv4P68/AX9Pth90Ss9QPiMeRS3BVzKe3ML+/dfvLSZNcqLDgKCgFqsrDdTxhiOCIPgFFuAt4HEUrV8t6IExwLconY7diASevEgW2WzNPsW36f/xdPIvNI7/jMjLbzA+bbWNbaegWg6PZU9cXB72BU2olNtdLLUFiz6L0LRMGu+zFXP5MWMj2UiQU8iNTm+g5wvf0bdfK25vrm2OBJ78ELXAk4f+zYxGmDTM/r6BH0FmlmfOK/g34WFKltP/3gCDwb7N7kOQbX8Nz31IxpNXGD58eM69bNu2bTl58iQ9e/b0sVcBhBadJ8l6EoTCxzSgIxDrxJyngSVAUfe5IYEnH5NOFnMyt9mMVzOUoIrejhrvVex3tZPAk6ABlcCT2ZRKbPGdNNtzkiCz7d38tAztZXaQR1moUOC4b9BYStTsQZEIbQJAF2IhIcnDTgnOo6bx5KGMp5cfgQY1bcd/XgAr1tuOCwWf6pVg/Uzo182x3Y4Djve7BREX9woLFixg0aJFOe8rVKjA/Pnz2blzJ8WLF/ehZwHCLtQfLEVgXBAKJ/+iiI470wn4DmA94OiwywABAABJREFU4DgfRjMSePID1mUdJ8Vq+7DWOah23pPsiotLFoqgATWBcRSdp7bbbHvurs86xv7sC6rzJfuucPHYGx8RXfNhAEz6dE1zRN/JDymGeuaGBwJP5UvDsBdsxxOS4LXP3H8+wf/p0R42zYL6NdRtt3ujq6GU2nmNHj16MGPGDLKvprHpdDoaNGjA8ePHiYiI8LF3fo4VRefJEe0BWQ8UhMLJMaA1sNiJOTVQgk+d8n96CTz5AZmYWZ1l+5DfyZR3eNF+qZ0EngQNnEK1XCa+6Bqqnb5sM+5sthNIxlNBZ/CwVzFWvh41MOm1ZTwdlDI7/0OtzA48Enj68h2ICLcdf/cLJTNOKDzodDD0BVgwEWI09mPxi4wnKbVzK4888gilS5dm4cKFOQtZ4eHhzJ0718eeBQBq5XbFgIbecEQQBL8kEegJfOnEnBhgEfB8/k4tgSc/YWmmrVhzR1NN9HksS0jgScgXKllP58tuxErurKV0axYzM7Z60Ckh0Gjc6XESSryXa0xrxtOh4x5wSMgfamV24HaNp+63wz2dbcc374aJM917LsG/iY6EeRNg2IvOzdvh6Y52IBlPPiA2NpYePXrw5JNP5oy1bdvWhx4FCFqaAYrOkyAUbrKBV4DnALPGOUbga+ArIA/NRTUk8OQnLLXTJayoPpwmxgp27e1mkUh5k6AVlcBTQnQ8h6peyjU2N3MnCVa1ZV9bpOyuYFK16T006T3KZtwoGU+Bi1rGUwoQ577ThYbAuHdtxy0WeG6Y8n+hcFCvBmyaDb2cfCA+egoSkz3jUy4k48lnTJ06FbNZeTIKDg72sTcBwD7gvIqNBJ4EQQD4BugKXHFiziDgLyDa+dNJ4MlP2J19jvOWRJvxPMvtRONJyA8adJ7+bZW7/HNquij8Cgrl63Wi/aNfo9PbfoVoLrU77manhPyjFnhyc5ndO89CVTtrKxNnwpY97j2X4L8EmWD591BDS8bdTXilzA5EXNzHyAKWk6hlPd2OyxkLgiAUMJYDrYCDTszpAqwDqjp3Kgk8+RHLMm3voDqb7AuMS6mdkC/2APGOTdbcEHg6nX2F5VneusMX/JlS1VrS6amp6A0mu/tNBvVSO4sFjnioO5qQD7wYeKpVBd58wnb8Qqyi7SQUHjKzYNQU1+Z6RVgc1EvtJOPJo1zLeNLpdCIwrgU1nadooLE3HBEEISA4iBJ8Urt23EgdYCNwm/YpEnjyI5bZebBva6pCKLYPePYCT1JqJ2jGCvzn2OTflkdzXv+QsQkL2j9fIiheMClavj53PvcLxqC8n7K0ZDydOg/p2hKjBG+ilnHixvLIrz+AoCDb8cGjlG52QuHiyx9hqzMtnq8iGU+Fg/PnldoxnU7Hli1b0NvJthVuQIvOUwePeyEIQiBxBSWT6Vsn5hQDlgIPazOXK7cfYU/nKVhnop2pmq2x3YwnCTwJTqBSbne46iXOl1TKP6flo8xOPpcFg6gSVen2wmyCwxwXdWsRF5cyOz/FSxlPD/WEDq1sx1esh5/+dM85hMAiOxuefl/5vzP4TcaTBJ48ysiRI3PuJWrWrMnRo0fp2LGjj73yY46gfr0WnSdBEG7GDDyLIjyu9fs4CEV0XAMSePIjzloS2Gs+ZzNur9zOXkaJlNoJTrFG3eS/Fkf5L+sohyyX1I2FAktYdBm6DfqN0KiSqrZaMp4k8OSHBAOlVWzcEHiKjoQxb9mOZ2bCwI/yf3whcNm6F774Qbt9fCKcOOs5f3Ih4uI+5bvvvuPzzz/PCT5VqlSJpUuXcubMGR588EEfe+enqGU9tQM7BRWCIAjwJdALsJWfzhcSePIz7JXbdQqyIzBut9ROAk+CE2wGMh1fAtY1Oyai4oWc4PCidBv0G5HF1NJhFG7MeJq3HN7+HIZPhDHTYNJM+HE+/K1S5in4gFs02Lih1O7jV6BUcdvx/30PB47l//hCYDN7iXZbr5XZgZTa+QFvvPEGX331VU7wSafTUbZsWX766Se2bdtG+fLlfeyhn6EWeAoHmnnDEUEQApJFQBvAjfdmRvcdSnAHS7MO8FJo+1xjjYzlKamL5KL1uvCFiIsL+SYDok5WIrF63leUc8US+TNzt9OHFo2ngoEpOIIuA3+lSJk8umvawXg14ykhCZ58Dy7He8g5wb28qcEmnxlPzerD8w/Yjh87DR9/k79jCwWD/72u3dZrZXYAal9pdvTKBPfzyiuvMHbsWL7++mu6dOmCwWBAp9PRqFEjjh07RqNGjdizR1piAtp0npqidKYSBEGwxx6gBTAXuDX/h5OMJz9jZdYhzFbbosqOQTVzvbdfaidaOoIT6HQYIkMcmpzJiCfJqq7Z4wiLRQKigYjBGEynZ3+kZOUmTs27Vmr38TcSdAoY6gH3qtjEkq/Ak14PE4cq/7+ZF0dAWv4uM0IBoP9d0M6JDAyvZjxVV9kf6xUvBODEiRP06NGDqKgoFi5cmHPvazQamThxoo+98yNOop6poCXTVRCEwk0s0BGYnv9DScaTn5FszWC9+Ti33iQo3slUi18ytlwfsJdRIhlPghMUr9iIhCKHHNqcOhTv0rEl4ymw0ekN3PH4d5Sr5USP1KsY9RmcOAtf/egBxwTP8I4Gm7lAPr5inntAyXi6md+XwsJVrh9XKBhER8LoN2zH0zOgQW+IS4DiMVC8yPXNqyW7akmf3gyCCQCkpqbSo0cPunbtyqJFiwBo0sS5hZICz3agioP9EngSBEELmcBjwF5gJC6nLkngyQ9ZmnnAJvB0s8C4lNoJ+aV0q9bEBm1zaHP0oGvLuDcGniQTL/CILlmd8nVd67Vs0mfwzljIyHSzU4JnqAHcr2KTDYxy/RSlisMnr9iOJ6fAy5+4flyh4PDRIOVzcjq5LilZRahVRIkqffodHL6qLRYX78PGBBJ48luWLl1Meno6ISEhhIeHU6NGDQ4dcryoVmjYAdztYH99wID27lWCIBRuRqF8380AIpyfLqV2fsiyrP02YxUMRahlKJXz3n7gyaNuCQWMmDY11I2O5P88UmoXeMSfP8DK6c9jdeHf7uDRdH75ywNOCZ5hCMqDhyNmAoddP8XnbyoZLTczbAKcPu/6cYWCwS214YWHYNOF3ozcvJgQQzIAR0/BZ5N97BwoS7RVVWwOesMR4WY6tYGPXzFiNF5fRw8NFaX3HHao7A9FWXwQBEHQyjwUfbitzk+VwJMfstF8gkSLbQuVTqbrS246e0IZkvEkaCQ4vAiG2o71nQCXA096e59PIaA4vv1PNvz+vtPzRn+XIUHwQKEy8IgGu49dP0WHVvBwL9vxXQfhSynHLPTodDD+fT1/HPuAb/d8T5YlhDLhSvrQS58opXY+pwrqbecl48mrREXAtx/B0ilQr83wnMBTQkICO3fu9LF3foRa4Amk3E4QBOc5CLQGRqCU4WlEng79EDMW/smyTRPubLox19ueuLgEngRtlKt9B0kxKr3RY4GE/J9LSu0Cl93/TGLPym+dmrNuq6hEBwxvoV5wPwdwsXtYkAm+/sD+vuc/BLPZteMKBYdH74ths34mi0++DECpsCMEGdKZvwL+Wulb33KoqW4igSfv0bUd7J4PT/dV3pcs1y5n32+//eYjr/yU40Ciio0EngRBcIVM4H2gAdq6aCKBJ79lWZbtXcwdphoYr/6TicaTkB/K1+1IUpRKu5N8lNmJuHjBYf2cd7lwZINm+2yzP6QoCKqUAx7XYDdC+yGDw4tSo9VDOe/feBJq2RG2/f43+M+FFG2hYFGxRh1K3rmMPXEdc8bKR+whLd3PtL/U9J0yAJV1HCH/xETB1E9g0bdQoYwytusgHDx0/V6me/fuPvLOT7ECaglgEngSBCE/HAT6aDOVwJOfstRO4ClKH0pzYyUgjwd7ySwRtKDTUaFuBxKjjzu2c4O+E0jGU6Cj0xuIKuGoLU5usrMk8BQQvAEEq9j8ibZSDcBgCuXO534m+upnpUp5ePdZW7vL8fDmaCf8FAokVRrfRdeXFhOXmfvaUj5iL598C8fP+Mgxe6gFng6Tr46Pgjo928OeP+Gxm4Syl/wLgwcPJjtbUccuXbo0Y8aM8b6D/ozaNVwCT4IgeAnpauenHMi+wKnsK1QwFMk13tlUm3XmY2BHQ0cyngQtFCvfkNCokh7NeBIKDpUb9SQ0qqTNePOSv1Ei9AThpjiMliu88lEcF87HkZWZ4gMvBacoBTyjwU5jtpNOp+eOx7+lVNXmnD2wCoDx70OoHRm5tz5Xgk9C4USn09O017s06vKK3ViNIXUP/5vidbcco1ZqJ2V2HqNoDHw5BPrfZX//kv/g4sWLTJw4kRdffBGAV155hRo1atCrlx1xucKIWuCpHFAMuOwFXwRBKNRIxpMfY6/crlOQsvSmc7PGU2Q4PHmfy9OFAKJC3Y5kGZNJDb/o2FACTwJQp51tPVbxkOM8Ve9Z7q72MXdWnMiKeTPZte5vLh7bLJmXgcBrKN2MHPE3sFHb4Vr3+4zKtyglLlarhXs6Q/fbbO3WblPK7ITCSVBoNHc+/wuNurySp824b/aQ4YRQqVdQy3iSjnYe4e7OsGd+3kGntHT4d4vyetCgQRw7piym6XQ6evbsyfHjxylevLiXvPVjRGBcEAQ/QQJPfszSrP02Y62MlYnQBbtV4+nBHnBgEVQo7dJ0IcAoX68jidEaBCkk8FToiSlTizI12tqM31ZuOnqdEmA6cwHGTPOyY4LrFAMGarAbru1wt9z5CnVveyLnvclo4ct3bO3MZnhumMQlCytFytSm91vLqFCvU542RmsiC/8+7UWvNBAJlFGxkYwnt1K8CPzyOfz+FZQukbfdqk25ux5Wr16dVatW5byvVKkSJ0+epG1b2++wQsVu1EtBJfAkCIIXkMCTH7M803YZzaQz0N5Uw26pHU7e0NeuCsunws+joUwJmLvMRUeFgCEoNJqSVZpjDtKwRCuBp0JPnVtts52MugxuLfNTzvv3vlRWnoUA4WUgQsVmFfCv+qGqt7if5r3fzzXWvrmF8nYWMb78URECFgoflRv14q43lhBdoqpDu0un9njJIyeQjnZepW9XRcvpAQ0a4Uv+y/3eYrHQvn17Pv744xxtydDQUJYuXYre3j1zYSEVsG2UnRsJPAmC4AUK8ZXY/7loTWKH2VZhs5OpVr4ynsJCYeRrsPMP6NBKGTt6CnbYJlgJBYxytW9HrzcQlb7NsWEqcM4955QOd4GJMTicGi3vtxlvWnI+kUGKGMSO/fDDPG97JrhMNPCSBjsN2U7larfntv5f2oy3bWy7AnL6PAwbr+G8QoFCp9PTrNe7dHp6GqZgtWgnnDu21wteOYlamR1IqZ0bKFkM5nwJs8Yqr7Xw93/2x9977z26d+9OZqZSsxkaGsqbb77pJk8DFBEYFwTBD5DAk5+zNNM2GtTZVMtljae7O8O+BfD202AyXR+XbKfCQfl6ncBqRaff59jwaP7OI53sAp9qTe8hKDTKZrx9ue9zXr/xP7BIT4PA4UWU4JMj1gPLHZsUK9+Ajk9PR28w2ewz6G0/EC9/Asmpmr0UCgBBoVHc+fzPNOr6muY5cWcDMOPpMiLKnE8e7AF7F8C9d2qfc/o87D1sO35tUXbx4sX89tt1QbmePXvm183ARi3wVBewvZwLgiC4FQk8+Tn2BMbrGstQNNmO+qaDJ8BqFWHhN0rNfMWytvt/X5ofL4VAoXydjlQ4f4ULJc86NpQyu0JPnRt0e65RLnwP1aIVxenFa2DpWm97JbhMOPCqBjuVbKeIohXoMnAmQSH2M1h0utzfQ4tWy/dLYSOmTC16v7mMCvU6OzUv7owfBp5EWNxjlC4Bf4xX5B6KxTg398Yyu9DIEtRqO4AuA3+l0i09csajo69H2U+dOpVPbwMctcBTEFDbG44IglCYMfraAcExa7KOkGHNIliXeymi/ul4tlTIbWu1I/IUEgxvPaVkOIUE2z/H+UuwbrubHBb8lqLl6xMeU5q2azcz/YlYx8YSeCrUlKjcjOIVGtqMty83FZ1OiXG/OdoHjgmu8zyKsLgjtgEL894dHBZD1xdmERaddycK/Q0qtmnp8OIIp7wUApzKt/TktgET8gxMOuLKWT+s91cLPIm+k8s8dR/07uja3KXbKtOgYw8q39KDklWao9PrOXNgNce3/wmA0Wjkzjuvp1BNmzbNDR4HMFo72+3ytCOCIBRmJPDk56SSydqsY9wRlDvfu8GJOLa0zm17c6ldt9tg3LtKtpMj/lgunYYKA+XrdMRozqbJ3iOcqBDn2NiNgSfReAo86rSzFRUPNiTTqvRsAKbOFaHogCIEeF2DnYMgkcEUQufnfiKmtOPaoxsznj75VtEPFAoPpau3dinolHjpGFkZyR7wKJ+oldpJ4MllvvwBXhmgLdvJaoVTyfXZdqkH2y71ILJDPVresD/bnMnaX6/rOPXr1w+jUXnEuXjxIkuWLHGv84HGaSAOKOrA5hZghnfcEQShcCKBpwBgadYBm8BTvRNxyjfxjQ/1VwNPFcvCF0Pg7ry7FudCyiAKBxXqdaTBwbPEFTtPtlFFmMeNgSfRewosgsOLULVpH5vxVqVnE2JMJiUVPvjK+34J+eBpoJSKzR5grv1dOp2e9o9OonS1Vqqn0l3NeDpwDEZNdspLoQCwecEnVLqlO5HFVFa8biLujB8Ki5dDKVF1hATgXSYpBT6bDKPyCIpbrHoOJ7TICTZdTq+U57F2LRtPwoXrrdtatbp+rdq6davbfA5odgB3ONgvAuOCIHgY0XgKAJZm2aafx6RmUu5iQq4xg97CW08p4uFag07xibBykzu8FPwZU0gkpaq2pO22IxyprFJmB1JqV4ip2epBjKYQm/FrouKfT4OzF73slOA6QYCWhk4fg51qbQBa3fcxVRr30nS6axlPAz+CzCxNU4QChDkjhf9mDnZ6nl8Ki2vpaCcZT/li/E9w7tL191mWIHbGdmb6vi94/d+9/G/rXyw7NdBh0Cnp8km2LR6Ta6xbt245r0NCbL/PCiXS2U4QBB8jGU8BwFbzKeIsKRTV5156q3vkHGdKxeS8nzQUutZ37th/roQseTgo8JSrdTsxqVnUP3yOtW1UAk/ZwAmvuCX4GzodtW99zGa4evR6ykfs5fwl+N8U77sl5IPHgPIqNgeBX+3vatDpReq1f0bz6XRY+XkBrFiveYpQwDi9dwWHNs6iRot+muf4ZcaTWpmdBbDTWU3QTlo6jPgukkef7MS2Sz3YdbkzGdnOlWqum/U22VlpOe+HDBlC9erVc94vX67SprOwoBZ4KgmUBs57wRdBEAolkvEUAFiwsiLLNp+7ztHc3w6Vyjrf13zuMpfdEgKI8vU60mrncQwWq3rG0wlAgpGFknK1bie6ZDWb8dvLTQVg6HhITvW2V4LLGIG3NdiNBOx8fVRrdi8t7/7QqVNmZGbz2mdOTREKIOvnvAtZKlqCNxCQHe1OABnecKRgUrxiY7oMnEl68wN8t2cymy/e7XTQ6cTORZzcnVu/6fnnn895/c8//zBihHQ4ALQLjAuCIHgICTwFCEuzbPO5ax6/iNGcnfP+5jbWaqSmwZJ/8+2aEABUqNORNtuPAqgHnqTMrtBiT1Q8whRL05Lz2XsYpvzmA6cE13kYqKJicxy7grJla93GbY+Md/qUf66wcEFDNa9QsClbNhpDkJpAkkJWRgpJscc965ArSEc7jxISUZQK9TqjN+TRclkFc2Yq62YPsRkPCwvLef3QQw+57F+BYy9gVrGRwJMgCB5EAk8BwrJM2zuc4Kxsqp66foevs7dk7YAl/ynBJ6FgU6RsHeqlheRogkngSbBHWHQZKjboajPetszPmPSZvPk5ZGfbmSj4J3rgHQ12n2LzMGIMDqfDE1MwGIOcPu2qjc5n3goFj/te+Jhsq7aAwpVz+2268voF0tHOo8Se3J6v+dsWfU5ynG3bTIPBkPM6OdkPOyX6igzAVjI2NxJ4EgTBg0jgKUA4aonlaLZtwKDuDeV2uryUYfNAutkVDsrX7UibbUq2kxUNpXYSeCqU1G77CHpDbtk/HRZuLzeNFevhr5W+8UtwkX6oPzifAabaDpszUtj4xzCXTmux+GEAQfAqXXrfyRVTF5vxYqY9HFg7nWPb/uTswTXEndlDSvw5Yk9s976TagQBlVVspKNdvri/02WKhZx0aW78+YPsWj7B7j7dDd2e5Xp0EyIwLgiCDxFx8QBiWdYBnjEUzzVW58h5/uiofFM4U2qXlQULVrrTO8FfqVqrIy3mK2rh50slkhqe6XiCBJ4KHTq9kVptH7UZr1dsOSVCT/D6/3zglOA6OuBdDXajgDwuBwfX/URIRHFa9PnAqVP7ZeaK4DWCgoOp1+0TEm/KjtTrzGyZ/Sxrlu7zjWPOUg0wqNhIxpPL1KwM49+DH49u53J6Rafn//frm1iy7YtRJicnEx0dDUDjxo3577//8uNqwWIHSgl2XtQCghHtMkEQPIJkPAUQSzNtc2Qrn40jLE15cjh0XPsN/z8bIT7Rba4JfoopOIIuWeUIv/oZUc12Agk8FUIqNexGeExpm/H25aby43zY5ocNpwQH9AHUOpxeAL5zbLJz6ZfsWv61U6e2Wp3LvBUKFk+++iKJ2bbCYmUyJrMsUIJOoK7vBJLx5CJBJvjlc4gIh8qR252ef3jTbM4dXJPn/uPHj+e8/uWXXwgJCXHBywKKWsaTEajrDUcEQSiMSOApgFiRdRDLTavJequVWscuAFCqmPYbfulmVzgoU6sdt+66nsquKfB0NP/nvTG9/ca0d8E/qXubrah40eBT1IhYyrtfeN8fIZ+8p8Hmc0CDxt+GuR9weNNszae2SmlLoaVKjQpYK75qMx5hvMiUMQHW6lAt8JQKnPaGIwWPka9Bk7pgthi5kGbbRdURmWmJbPh9qEObTz/9NOd1hQoVWLJkiQPrQsZODTZ+Um5XVBdGB1PuevGqTe+hXvtnCAqN8pFXgiDkBwk8BRBx1lS2mm3vdOoeOQdAkShtgSeLBf5Y7lbXBD+lcdWO1Dt8XQdMNfB0AXCDFueNWQ83Cn0K/kdUyWqUrXW7zfht5X7gqx8tnDrnA6cE1+kONFGxuQxM1Hg8q5Xky7YCvnnbS+CpsHL/i8MxW0NtxjMPfMTpUwGWYq2mj3YQnJTVFIBut8Frj0FCRknGbJvLf+cc1X3ZsvnPT0hLvODQZsGCBRw9en0FrVKlSq64WjA5D1xUsfGTwNOUiIdZHj2IPyKfprq+BKbgCFr3/YTWfUfy4Me7ufWhsRQtr5baKwiCPyEaTwHG0qz9NDPlroe/JjCuVeNp3XY4f8ndngn+SD9rLfTW4znvvSUsfmPgSa+X+LY/c8sdj9mMGXRZ1A2bwUPfet8fIZ+8r8HmCzQHmHU6PbVaP2gzXizkJBUjtrJgyQXOnLpAWtJFUhPOE3tSrZZDKIh07d2eWGMvm/HSQRsZ+dVMH3iUT9QynqTMzmlKl4Bpn8Dh+BZM2j2VhEzb8m5HxJ7cwb4132uy3bJlC1WrVgVg504lzSckJISoqCguXlSLvBRwdgCdHez3g8DTsyFt6RPcEIDewQ3pFlSXWWXS2GiMJg0wBYdTu+0AarcdwIWjG9m3+nuObZtPtlnEqQTBn5HAU4CxLOsAQ7gz11jJuGSKX0lGz/XAk9kMA96G4CBo3gCa14dbakFQkJTZFRZiStXijgNxucZ8EXiSUjv/45mQtpy3JLLQepg6bR/k5pB14xIL+PybiyRKJ+rAoiPQSsUmARin/ZDl6txBaHQZm/H+tQYzb/YKFml7DhQKMEHBJup3/4x4c+5xHRbWzXqLbHMApgapBZ5EWNwpdDqYPhJ2pT7N7MPDybaanJpvtVj479c3sFqy1Y2ByMjInNe33XYbW7ZsoXHjxuh0OlJSUvj4448ZOXKkUz4UGPw88FTXUJqx4ffkGgvSGel/PpLeX/3JvA4NWdOkGhaDsqhZqmoLSlVtQct7R3Bg7Qz2/zuN5DgnsnQFQfAaEngKMP7LOkqaNZNQXVCu8TpHzqO78/rj45uj4Ze/lNfT5ir/DzJBw1pw/Iy3vBV8SffKPShzJHd5gwSehNqGUowLv48gnZHDISn8syeBzfWic27iAKoZpvLiLB86KbiGFm2ncSjBJ4207NrfZiwm+CyhqSv5+BvtxxEKLk+/+hzx5uo246UypvPdUi2iMn5GUaC4io0EnpzitSdCORUzhvWH+uVpc3LXYsxZ6VRpdBe6mzKl96/9gUvHt2g+38mT17Uto6OjadLkev1xeHg4n3zyCdWqVeOpp55y4qcoIKglpRYFKgEnvODLTQRj5JfIx2yeca4RmZpB/wWbaL/xIL92a8r+qtez5kIji9Ooyyvc0vklTu35m72rp3B63z8gDS8CHoMBnumndMN8tZDGiwsKUgMTYGRgZrXZVv25ztHzXBMcmLkQxk63nZuZBZt3Q+wVDzsp+AUDjPVyvU+MSCO2uEoKi5TaFXi+Du9HkE5Zc6ieHk7ndfuJSknP2V8m7AATvv4PszmvIwh+yR1AexWbZJQyO42ERRWjWLWuNuNtSv/CSx9bSEu3M0koVFSpVgZrpddtxsMMcUwd87EPPHIDavpOIKV2TtCiUTgR7Raz/oL9oJPVYmHz/I/5+5v+rJjyJHOGt+bA2hlkm5VuvGlJsWyeP8Kpcw4ZMiRX8AlsO27ec0/urJpCw3YNNs097YQtLw+A2X2/pKGxnKpt+YsJDJ6+ghd+XkXJy7kXWHV6PRUbdKXrC7PpN3QTDTq+QHB4EU+5LXiYLrfCjj/g6w/glQHwUDc990e3Q4csagci/2fvvuObKrsAjv8yujejQNlbhuwlQxFQhmwFQUVkKKCiiILiwoGKioLKUl4UUFEQBUUQ2SCK7CUie+9CS/dK8v5x29CStLlpm32+fu6H9N5zb06FNsm5z3Me+UTogdZlWr7jue3kZbQmIweOwDA1d72FV9JqodtdsHxGGK3P5R2SfqCuii7RxVR4klXt3NPDAc242z/vp6pvujcnPjzY/HVF41csk+m4niUS+EpF3CyUxuIqvVlzMPUPWzYETDqxkJWb1F9HeK/nKt5HQJrl9KeMw29z/pyH3uWyNc0OZMSTHWqUyKJKyHYA2m87wvi5a+i/ahdVzl8jLek6q2b2Z+/vH5tHpty4cow/vn2WxRObcmD9LLb99Brpyfb9W7p+/TqVK1fmySefZMuWLSxdupR69eqRnJxsjlm2bFmxfY8e5RDKqowFaemMRPIaPwyqJITZDsyl0eHzvDljJf1W7SYoNcPieHjpqrTs+xYDJx3gzkc+o1SlxsWVrnCw26rBis9h1Ryolz2g9twluDuxJ9/79edglY+421/NL2vhTjTIuhwep5FfJfZEjMOggbMV/DlfL4iTtYK5484X6DL4FMdcMDxWuFZUBAztC6MGQPVKYDRpOXW2NUkruxC5OppKV9Pp/78v+aHXnoIvVAbbK56osGzZMnr16gXA1KlTGTt2bNEvKookUhPE4ajXiNbmfWN3OTyTT57tyFV9Bfy1yeyeW4+/dya6KEtRKD8AD9iISQWqoqxcqUIbfTU2RjyDXqNjXXMjP3fvSSrh1Aj7g+nje3Puku1rCO/Wya82ayKe5pImkQ33l2J9/b6g0VBav5cPn7qHrCwPXeHwG6CgxdYuAZZtz4QVnf3q8G5ID4ZGzeC5+xdQe7c/rfafAmB+izI8/ffzTuvH06xZM3bs2AFARkYGAQEBTnlet7QZaFfA8XVAJyflkq1eTfg9+H+UP59aqPMTgwMs+j9Zc/X0Hv7dPJcTu5ZiyJRhu+6mZCS88TSMfBD0tzQEOnE8ksgnJ1MiJdO8b6XhCC8k/MAhg7wp8QTS48kD7cs8S/cbs9mcdZyk2DQmtIA3+0Cf0UjRyUfpdZCYDCfPQ5XyoNMZqVZpC4zcwtzS8MXvNdjR41jBF7lMsRSdbpV79JNwnXeDe1gUnQDKJPhxx5vT2T/kGvccvJ2ZUnTyLMOxXXQCmIPqolMpTSiLwoag1+gA6LhDS8Sezzg6/hrbjpyWopMP0aDhjeCuXDUmccWYxBVTIleMicSZUpgZqkydKmsKY+CSdGqvm8raoR1ZteFFzy06+QPdbcT864xEPN8jAc35KvRh9Bod2xMm4T/vQp7jqzd+TFKK85pAR0REmB/7+/tz5coVTp06xfHjx/n777/55JNPnJaLy+2g4MLT7c5K5KYTZzSUKlH4VekK6v+UW+nKjblr0HRa9n2bQ5vnsue3KRgNmVZjhfP4+cHTD8HrT0JkuOVxkwmuLZlMtZS8f1fddLXoHDWBuWlbmZi8kkumBMuThduQEU9eomoFOHnO1VkId1AqCnp1hH6doWMrOHwK6l8HHrVx4hfAiOLJIfeIp48++ogXXrDsASKcp4W+MlsjxqLV5H8X8IYxlTBNAOHXxpGM5ZB14YbqADuBYBtxqUBNQMXCElo0/BY+inv961gc+yX9AH2Sv0Bqyb6jli6aw1GvqY7/PH0LIxMXOTAjB+sGrLAR8xLwvhNy8XCd/eqwKuLJfI/fFf8Jm7Ns3BArZjdu3CA83MqnWmDDhg106NDBqfm4zGBgno2YYhoBr1ZlbQlOlXiz2K63t3Z5fujcmCsl8/59Z2Wmce7gWk7sXsaZf1aTlZ6czxWEM7VooHxuaVYfmtaDsJC8x/86OIwmzzQjMCP/BqTJpnSmpK7nw5S18j7WTcmIJy8hRSeRIzYO5i5RthKRENkIWGPjJCPwscNTEy6gQ8vs0AcLLDoBRGiDAGisr8iWrGJq9iUcJxD4HttFJ1BWu1O5mumrQV2sFp2uGZN5OvkHKTr5mCa6iqpjrxoTeTlpuQOzcYL7VcT85PAsvMLvmYf4LHUTo4Pusnr8vDHeuQkBTZo04eeff6ZOnToWC5+0b9+ewMBA0tJ8YPqVmsUmGwAe3O+x0eHz1D92kfUtavFzm5ocOfEHJ3cv48yB38lMt7HQjnC67fuVDZR+tbWqQPPboVk95c9a5X9h9z1taLKGfItPIZoAJgZ3ZURgGyamrGRu2lYMyJsWZwgICCA93faIRWkuLoQXux4PJwZiu8T8A9Is1Us9FdiOxnr1Hx6b2hErXOhDlA8GtqwCpqq7ZCe/2kwMtlzFDuCRxAWcNXpos2hRaI31FVTHvpeyhusmW12L3ZgO6GUj5gBw1Am5eInxyT/zb5b1hU0uGG84ORs4fvw49evXR6fT0bhxYyZMmJDneGhoqNNzcolDgOWaAHk5ebrdaeN19mYV7110vcFInR17uP29V/jri2Ec3/mjFJ08gNEI/52Ar3+GZ9+F1gOhXIerjNo2nGF3nWFD85oYtPkvXFRWG87noQPYH/kS3f3rWxzX+QU6Mn2fEh0dzcqVK7l0SV0PBik8CeHNKgCPqYgr5lWvZSU79xCjjWBSsK2GJYpThmtMS93AeiurZgo30xN4WkXcZZQpFSom1MdoI/g2bLDVkXHvpPzOqkxpbOOLmthRiO4f0BitJy9xfRdQ0kbMj85IxHukkclDifPJMOUdoRBnTCEV1/bV2bt3L4sW3ZwWmpKSQmxsrAszcqI0wNZLvZobG8VsWYaaoVj5uxoZzB/l/fiwxEm6pn5JyWsvUunSeEbd+JYUmXrl0bKyYP9/RhZ+9x6Dz37MhKF3sue2gm+M1NWXY3n4CDZEPENTfUWCI8rSos+bPPTuv5Ss2NAi3g8djwQ0R+PJr2NONH36dC5evEjXrl0tRpDmR6baCeHNxqE0Sy3Izyh3cR3EZJI2cq4yNaQvYdr87+zsyTrLsvQDLMvYz36DyrlYwrXKA1+qjH0UVT069Gj5PmyI1ebzGzOPMjFlpT0ZCi/SxI4RT638qvJEYBtmp21xYEYOpGaanRSe7LbPcJ5XUn7lw5De5n2umGZnTb9+/cyPDx/2sWHfB1D6BObHBQ3Gl6Xv543gbqpiYyNDOF2uBKfKRbAz8wzrzq5h38FfyEiV5tLe7uw/q/n8ci+ODZ9Hq9a38cDqPVQ7dy3f+PZ+NdkZOZ5tdSuwrGkTYoNDaTNgCsundMZkUqbi1dWV5euwR2mir0htXTSvpdhq9ufbxo0bx5NPPmkeaKD2s54UnoTwVmWAx1XEFfNoJ+EeOvvVoX9Akzz7skwGNmceZ1nGfn7JOMBp43UXZScKRYuy1LutURmgTMVbre6y7wT3oJ1fdYv9l4wJDEyYJz0SfFRlbQlKaENsB+byXnAPlqbv47LJw1bH1AB9bMQcAf5xQi5e6OPU9XTzq8vd/rUA10yzs6Zt27bmx6dOnXJdIq5wAOhfwPG6KNNPbU3JK0b7DOc5ZbhGFV3eF7nThuscjdKR2Kwdp8uX4GSZcA6f+ZsTuxdz5teVpKfEOy9J4RYSrp7klyldiHtoKkeHP0Czg2fos3Yf0XH5T6Vs+e85mhy+wIYWtVhxZz1qtxnE4S0LeC7obt4J7k6gxg+AV4O7cNwQy7z0bc76djxK8+bNmTx5srnodP78eQYPHqzqXCk8CeGtxgJBNmJ+R1lWV3iVQPyYEarcyU02pbMq4xDLMvazIuMgcZ7cg8XXvQy0VxG3E3hF3SV7+NdnfHAni/0Gk5GBifNkaWIfZk9/pxyR2mCmht7PQ4nzij8hR7oDKGcjRpqKF5oRE48mfc3+yAlEaYM57yaFp44dO5ofz5kzx4WZuICtWW1BQA2c3v9zbtpWmugrsivrLDuzzrAr6yyxpiT0ycG07zibM39/wen9K0lPlp6Dvs6QmcrG+SO5cmoXxvvfZs9tFWi/4yjdN/1DaKr1qZV+BiP3bv2PNntP0Kb5Q5QtUYo7tVUs4r4IHchpYxwbpP2EhWXLlpmn1l25coVKlSphVLnyjBSehPBGJYD8VzG+aZJjnl56PLnWsMA72Jh5jDHJP7E24zBpLu6lIYpBa+ANFXGJwABQ81deRVuS+aGDrB57PWUFGzOli7Ivs6e/U24P+jfmNe2vHDd6UL8cmWbncOeM8YxMWsSi8CFuMeKpb9++BAcry4ImJSWxatUqF2fkZGpaLNyO0wtPk1J/t7o/KyOFtXMedW4ywiP8u2kO187uo8Owr1h3x2381aga3f44SMdth/HLsl4QCUnNoP/mI2Cl6ATgp9HxY9gw7rjxMYcNlx2YvWcZPHgwMTExABgMBlq1aqW66ATSXFwI7/QsYGtxlk2Ag1px5C48SY8n55uRtpnhSQv5NeMfKTp5g0hgIcq0B1tGAcdth/mjZ3HYEKK0wRbHVmYc5L3UNfblKLxOYQpP14zJdE2Y5VlFJ4C+No6fRhlJKIpkccZuvk7b7hYjnl588UXz499++82FmbjIKcDWAm8u6PMkRGFcPrGdZe934NKxraQG+fPjvY15dXQPtjasUuhrRmmDWRk+klIaH1ntUoWPPvrI/Pj777/n5MmTdp0vhSchvE048IyKuLcdl0LuwpM9lXAhhBVzgMoq4hYA36q75MchfWjuZ3nRM4brDEpcgEnNUnjCqzXW2TfVbkfmaZrEv8/qzP8clJGDNAWq2IiRaXbF5unkH9iedcrVaXD77TerKv369ePcuXN8//33jB49mhIlSrgwMycxYXvUkwtWthOisFITLrPik97EntkHwPXIEL7s25q3Rnbh32plCnXNarpS/Bz+OIH4FWeqHqlLly6ULKn0X8vMzOSJJ56w+xpSeBLC24xDGSFRkL+BdY5LQUY8CVFMHgceUBF3FHhK3SUf9G/CU0F3WuzPNBnon/gV16UPmM8rqwknRhehOv7z1C20uzGNM0YP7Ltia7QTSOGpGCWY0tiVddbVaVg0Ey9fvjwPPvggn376KVeuXOHBBx90TWLOZKvwJCOehIcxGbMICo/Os+9suRJMfbQDnzzSnnPR6l/XcrT2q8a8sEfQ4NttRF5++WXz4xUrVpCSYv97RSk8CeFNaqAUnmxxUG+nHFJ4EqIY1AU+URGXCQzE9rQJoLauDP8Le8jqsXHJy9jmBiMRhOupbSyeZspkSOI3jExeRDpZDs7KQWz1d7oE/OWMRIQzNWjQgMmTJ3P+/HmLkdk6nY5Zs2a5KDMnstVgvDq22zYI4UaCI8oSEmllpQiNhn9qxvDWqK4crVTa7us+GNCEt4PvK4YMPVerVq0A5XPdxIkTC3UNKTwJ4U0+AQJsxOwFVjg2DWkuLkQRBQLfYXtlSoAJwC7bYUH48UPYUEI1lr8klqTv4ZO0jfblKLyWmv5OJw3XaB3/sWcvOV0PqG0jZikgM8a9TlZWFhMmTKBChQoEBATw5ptv5jkeG+thfcoKQ02D8XoOz0KIYlOqYsMCj5u0Wgzawn1GeSW4M48FtCzUuZ6uc+fO+Pkp0w2vX7/O/v22qtbWyap2QniLnkA3FXEOHu0E0uNJiCL7EHX9NVYBH6u75MzQB7ldH2Ox/6jhCsOSFtqTnfBytkY8rcg4yKDEBcR5+rRMWc1OALNmzWLo0KHmr7OysrjvPh8Y3aB2ZTsPri0L31KqciObMf6ZhkJf/4vQgZw2xrEh80ihr+GJunbtan68Z8+eQl9HRjwJ4Q0CgWkq4nah3L11MK325q8WmWonhJ16AU+riLsMDAY1fcCHBLTisUDLO3Vppkz6JXxJginNziSFN8tvxJPRZOT15BX0SPjc84tOYLu/03WUFWCF12rQoAHDhg0zv2/Jysqib9++HD161MWZOUEccM5GjDQYFx6kVKVGNmP8Mws/LdxPo+OnsGHcpitcs3JP1bLlzfePf/75Z6GvI4UnIbzBS0BVFXFP4fQpAzLiSQg7VAC+VBn7KHDFdlgDXXlmhPazemx00hL2Gc6rzU74gChNMFV1JS32XzMm0y1hNm+nrvKOVQ+rAwXPyoCfwVNbVwl1+vbtax6lbTKZaNCgAcuXL3dxVk4kDcaFF9mz8kP2r/2MG1dP5BtTlBFPAJHaYFaEj6S0xncaoJUvX978eP369YW+jky1E8LTVQNeVBE3FxkuLYQ70wJfA2pW8v4QWG07LEwTyA/hQwnS+FscW5C2nf+lS9dkkVcjK9Psdmae4YHEuZw2XndBRg4i0+wE0Lp1a/Pj6dOnc+jQIRdm4wIHgK4FHJfCk/AgV0/v5urp3Wxf+gZRMXWo0vA+Kje8j1IVbw7dCyjCiKcc1XSl+Dn8CTrc+Iw0Mot8PXeXe/ZKUQYUSOFJCE83DWWqXUHiUEZFCSHc1xigvYq4ncAr6i45N/QhaumiLfb/k3WBUUmL1OcmfEaTWwpPc9L+YnTSD567al1+bBWeEoG1zkhEuFK1atXMjzds2ODCTFzEVo/gkkAMcMEJuQhRjOIuHCLuwiH2/DaF0JKVqNKgG5Ub3cf7j3WkbFwS0dcSKZO9RV9PpMSNZLR2DOa9w68q88IeYWDiPO8YBVyAK1euUKlSJQDat2/Pli1bCnUdKTwJ4cm6Az1UxL0K+MACLUJ4rHrAuyriEoEBoOYG29OBd9IvoLHF/iRTOv0SvySFDDuTFL6giU7p75RmyuTJpMV8lf63izNygIpACxsxvwLpTshFuFRm5s1fpl9//TU//vgjCxYsYN26dS7MyonUNhiXwpPwYEnXzvDPhtn8s2E260JLUblBFyo36Eb5Zu3R+Skr/eozDZSOS6LMtQSiryWS9edyysanUlNXmgq6KKvXfTCgCccNsbyS4t3Tc7du3UqzZs0AePzxx5k0qXArVWlQ1ZZUCOF2AoGDKFPtCrIHaIZTezutXr2ae+65B4C33nqLiRMnOu/JhfA0fijTYC1rRJYeAb61HdZCX5k/Isbgr7G8v/RQ4jy+S99lZ5LCVxyKfJUAjZ77E/7HHoOtzsMe6hngExsxDyBT7XzA2LFj+eijjyz2Z2Rk8PDDD7NkyRIXZOVE/kASyutQfsajTO8Wwsv4BYRSoV5HqjS8j4r17sU/KMx8LCnuPEvevoOs9GSC8aeGrjQ1s7daumjz4zLacIYmfuudN2myxcTEcPbsWfMiDOnp6WzdupXhw4dz/Phxu65lkk022Txwex2Tqv/ucH5uq1evNuV48803Xf//SjbZ3Hl7G3U/y/PznneP322m4QGtTc30lUyB+Jn3l9AEm05FvWkylfrMYpsZ0t/1369sbruF4G9aHj7CFKUJdnkuDt02UfDPWgomgt0gT9mcsn322Wcmg8FgulVmZqbLc3PKdoCCfx4WuEGOssnm4E2r9zdVqNvR1Hbgx6aH3ztkGj7jmqlF74k2zwvXBJpu18W4PH9Hb/PmzbP4HWkwGEzffPONSafTqbqGjHgSwhNVAf4FgmzEfQUMdXg2FmTEkxAqtQK2ADobcSdQVuBKurlrQ8QztPerCUCWycB/hsvszTpPFV0J2vpVt7jErqwztImf6n29ekSx8UdPJga8ul9FGZRpQwWt67wU6OucdIR7CAwM5Pvvv6dXr17mfcnJyYSG+sDKVQuBgQUc34u6EblCeAmNRkt0teZUqt+Z/Wunk57sRQtrFMGbb77Js88+S0RERJ79Fy9eJCYmxub5Bb3sCiHc1TRsF53iUbfanRDCNYKBBdguOhmBweQpOkVqgmirvznPVq/RUV8fwyOBza0WneKNKfRL+FKKTqJAGWR5d9EJoBe23/3KFDufk5aWxosvvphn9aa6deu6MCMnstVgvC7SFVj4FJPJyOXj29jx81tSdMpl4sSJREZG0qpVK06cOGHeHxISoup8KTwJ4Wm6orxxtuU14KqDcxFCFN6HQE2VcbcsINLFry56ja2K1U2PJX3LSeM1e7ITwjvZWs0uA6WxuPA5hw8fNheeMjMzOXPmjIszchJbDcb9gVrOSEQI4Qm2bdtG9erVeeqpp0hNTVV9nhSehPAkAcCnKuL2AbMcnEsBNBqN655cCE9wL/CkirgDwOuWu3v411f9VF+l/c3PGbZuaQvhA6KAu23ErAVuOCEX4ZZyVrnT6/VER0e7OBsnUfPycLvDsxBCeJiZM2dSqlQpYmPVLZ0uhSchPMkLQA0VcU8BBgfnUoDchSej0YnL6QnhCaKAL1XEZaCsYpeRd7ceLV391U8BaaGvjAYpBgtBTwpevQvgJ2ckItxVVpYyHVmj0fDCCy+4OBsnOYvSnqEgDZyQhxDC46SkpNCsWTNVsVJ4EsJTVAZeURG3APjTwbnYkLPcJoDB4MIKmBDuaAZQXkXc61i9E93GrxpR2mDVT1dPX46+/g1VxwvhtWw1DDcAPzsjEeGOoqOjCQpSGmiaTCbWrVvn4oyc6B8bx2XEkxAiH3FxcaripPAkhKeYiu2G4jeA8U7IxYbcI56k8CRELg9S8OpBOf5E6e1kRQ9/+z8BvBbcRUY9Cd8WijLFtSCbAHUzBoQX+umnn8w3zi5dusTvv//u4oycyNZ0OxnxJIQoIik8CeEJOgN9VMRNBC47OBcVco94kql2QmSLAWaqiEsCHkVZzc6K7n717H7qhvrydvWFEsLr3AcE2oiR1ex8Wq1aNzto5/R68hm2GoxXBsKdkYgQwltJ4UkId+cPfKYi7gAw3cG5qCSFJyGsmAuUUBH3PHDC+qGa2tLU1pcp1NO/EtS5UOcJ4RVsTbMDWOboJIQ7mzFjhvlxxYoVadDAh4b52Co8Aci9CyFEEUjhSQh3Nwl1S667uKF4bjLVTohbPAN0URG3Evgi/8M9AgrXaGNX1hlGJS0q1LlCeLxAoJuNmL+AC07IRbitLVu2mB9rNJo8N9G8nprCkw/V4YQQxU9vT/A333xDcLD6hqZCiKJZUmsJC+sttBl355k7GfPcGHjO8Tmpcdttt5kfDx48mLZt29p9DZPJlOdrXxg5dev37I5s5WjP95CVlcWMGTPYunVrUdNyb52Aj1TEXQOGFRxi73S5ZFM6ryav4LO0TRjym7snhLfrgdLjqSAyzc7n1a5dO8/XvvC+wywBOI0ypS4/0mBcCFEEGkD1p4QbN24QHi4TfIVwhqlMZSxjbcaFEcZhDlOOck7ISojiZTQaiYqKIiEhwdWpOEYtYBsQqSK2P/BD/ocjNUFcLfEeeo1O1VP/mvEPTyUt5oxR3WojQnitbUALGzFVgVOOT0W4r/DwcK5fv45Op/yONRqNDB06lPnz57s4Myf5BaVIm5+/gDZOykUI4XV8aAypEJ5jNrNVFZ0A3uANKToJj6XVamnWrJmr03CMSGA56opO31Jg0Qmgq39dVUWni8Yb9EuYS4+Ez6XoJMTd2C467UaKToKEhASGDh1qbhGg1WqZPXu2i7NyIlvT7Rpj51wZIYS4ya5fHzNnziQw0NaSIEKIotheZzvfdf5OVWzZ2LKYFpqYZpzm2KQKoVSpUqSmppKcnGz3ubl7RAFe0Wfh1u/JE9n6Huz5e3r44Ye9ewStHliMMuLJlnPA07bDuvvZnmY3K/UPJqQs54YpVcUTC+EDXlQRo+4lV/iABQsW8Oeff3L48GF0Oh2BgYGULVuWS5cuuTo1x9tl43gQUBfY74RchBBex66pdkIIB+sPLATUzKRJB9oCOx2akRAOcfz4capVqwZAx44dWb9+vYszKmafoaqYhAG4F7Dx7evRcqXEe0RprfdZ/CfrAk8kfc/WrJN2JiqEF2uMMpqpIIlAJSDe4dkID5KZmYler8dkMqHX632j31NF4IyNmOEoK7QKIYSdPH8YgRDeoifwDeqKTpnAA0jRSQh3NBJ1RSdQFgRQUXNr41fNatEpzZTJy8nLaRL/gRSdhLjVeBUxs5Gik7CQ0+fJYDD4RtEJ4CxwxUZMc2ckIoTwJKNGjVIVJ4UnIdzBvSjTcvxUxBqAh4BfHZqREE7jDdMQzTqgjHZS4wv1sT38LZcTWptxmPpx7/Je6moyMajNUAjfUA3oZyMmA5jm+FSE58lpE6DX6xk7Vl3PTa+ww8ZxL23JKIQonE8//ZT33ntPVawUnoRwtbuAZUCAilgj8BiwxIH5COEEJpMXzvKugdIgXE33xI2oHxUF9PC/2d8p1pjEo4lfc0/CdI4bY+3LUQhf8QK2RxAvAC44IRfhcebNm2d+/MILL7guEWezVXhqgLr3q0IIr7d8+XJGjx6t+gayFJ6EcKVWKCOXglTGj0SZjieEcC8RKCvYlVARexy4H2XKrAo1taWppYsGYF7aNm6Lm8TX6dsLl6cQviAaGGIjxgh86IRchEd67rnnzDdISpYs6eJsnMhWCwc/oKEzEhFCuCu9Xs/evXvp3r27fec5KB8hhC1NgFVAqMr4Z4E5jktHCFFIOmARcJuK2BtAD+C6+sv3CLidI4YrjExaxIbMI4VKUQif8ixgaxHmpYD8OIl8ZGVlYTKZ0Gg06PU+9HHJ1ognUKbbyb0PIXxSZGQkBw8eJCYmxrxP7QJBPvSbVAg3Uh9YjTJKQo2XgE8dl44QrqTVevjg24+BziriDMAA4JB9l/8v6zIN4t4jnSz7cxPC14QBT6qIe9/RiQhP9sADD5hfm+Lj412bjDNdQVnZrlIBMdJgXAifVKFCBQ4ePEh4eDigtM2YM2cOI0aMUHW+h7/bF8ID1QLWAmpHbr+FvEEWXsdrejw9ATyjMvYFlFGOdlqZeVCKTkKo9QQQaSNmA+pGdgif9dprr5kfr1mzxoWZuICt6XZSeBLC54SHh7N///48RaeXX35ZddEJpPAkhHNVBdYBZVTGTwEmOi4dIdxViwauzkCF9sB0lbH/Q1bPEsLR/AE1C5BNdnQiwtPVrFkTUD5c+dSqdmC7KFsHCHFGIkIId7F582aioqIAMBqNDBw4kMmT7XsxlcKTEM5SAaXoVEFl/AxgnOPSEcJdjRoIzw12dRY2VAd+RGm0assm1E39EUIUzSNAjI2YPShT3YUoQGCg0iTMYDBw4YKPLX1oq/CkRelTKoTwCT169KBhQ2VVAZPJxPDhw1m0aJHd15HCkxDOUBZYjzLiSY25wGjHpSOEuxrQDaa/CukZrs6kAPasYHcCu1awE0IUkhYYryJOpq4LFbKylOnNer2e0aN97A3ZLhUxMt1OCJ/x7LPPmh+vXr2ar776qlDXkcKTEI5WCqWnU02V8QtRelR4SQscIdTq0g4WTAat1o0LTzrge5SpBrYkoKxgd82hGQkhAHoBtW3EnACWOCEX4fH2799vfvzCCy+4MBMXiAeO2oiRwpMQPqNatWrmx59+WvjVrqTwJIQjRaIM6a+nMv4nYDBgdFRCQrinOxrBj5+AX/bUtbR0l6aTvylAFxVxRmAg8K9j0xFCZHtRRcyHKKtLClGAqlWrUq/ezTduqampLszGRWxNt2vmlCyEEG4gNDTU/PjQITuXZs5FCk9COEoYygpWjVXGr0T5oCqLVwkfU7UCrJgNwUE397nliKfhwBiVseNQfqaFEI7XHmhpI+YyMM/hmQgP17JlSw4fPmzu8ZSZmUmPHj1cnJUL2FrZrgYQ5YxEhBCuFhwcDCj9nU6fPl3o60jhSQhHCAJ+xfYb4RzrUPrAuOOHbSEc7N3nICoi7750d+uJdBcwU2Xsl8DHDsxFCJGXmtFOnwBpjk5EeLrvvvsOv+yht0ajkd69e3P0qK15Z17I1ognkFFPQviAyMhIc+HJYDBgNBZ+Wo4UnoQobgHAMuBOlfFbUHpTyBti4aNKRljuc6sRT9VQv4LdH8Aox6YjhMilEbanvyYCsxyfivB8FStWBJQ7+61atWLlSh8duroH29NSpfAkhNebPHkyGo0GgGPHjhXpWlJ4EqI4+QE/APeqjN8B3AckOywjIdySzsarj9v0eApHWcGupIrYU8jIRSGcTc1Kdp+jNEwWwgaDQam2aDQaWrdu7eJsXCgZsNXKRRqMC+HV9Ho9jz32mPnrzz77rEjXk8KTEMUlDFiMsoqVGvuAzigrXwnhQ4ICoWzpgmPcYsSTFvgOqKsiNhHlZ/+qQzMSQuRWF+hvIyYDmOqEXIRXWLdunfnxW2+95cJM3ICt6XZSeBLCq82YMYOAgAAA4uPjmTlTbc8J66TwJERxaATsAnqrjD8E3APEOSgfIdyUXg9LPoGggILj3KLw9CHQTUWcEXgI+Mex6QghbvEeoLMR8zVwwQm5CK/Qq1cvsrKUVV7Cw8NJTU3ll19+oW3bti7OzAVsFZ4qAGWdkYgQwtlGjBjB448/bv76/fffL/I1pfAkRFGNBLYCNVXGHwM6IiMjhM/RaGDeu9Dt1v5nGsuXIpcXnoYCY1XGvoSymIAQwnnaAj1txBhRCshCqJSVlcW7775r/jowMJAePXrwxx9/EBcXx/Dhw12YnZPZWtkOpM+TEF7G39+fb775hpkzZ+bp7TR58uQiX1sKT0IUVjiwCKVhaaDKc06jFJ0uOiopIdzXJy/Dwyqnoqa5svDUDvWNiOcjH2yFcIUPVMQsAw47OA/hdSZOnMjs2bNJTU3Nsz8yMpIvvviCCRMmuCgzJ9uH7Z6FMt1OCI/n7+/PkCFDWL16NQkJCTz88MNotUqZ6Nq1azRs2LDYnsskm2yy2bk1wcRRTHb9dx4T1d0gd9lkc8E28SlMpkO5tvSjJrMznfMeO4SpdycX5VoFE1dQ9zO9BRP+rv9/K5tsPrf1wfbPZxYm6rlBrrJ59HbPPfeYfvvtN1N6err5JSspKcnleTlt20HBP2cr3CBH2WSTTdWm1WpNo0aNMq1evdp04cIFU2pqqikrK8tkNBpN1pw+fdoUExNTbM+vRwhhn6eAjwAbPWryuIIy0um4QzISwq09/TC88bR957hkql0Yygp2NhqfA8roxT7ICnZCOJsepbeTLfOBgw7ORXi9NWvWsGbNGvz9/YmLiyM4OJiQkBDatm3Lli1bXJ2e4+2k4Ol0MuJJiAIFBgZSrlw5ypYtS9myZSlVqhSlS5emRIkS+Pv7M3XqVE6ePOnQHPr378+4ceNo1KgRer3t8s+NGzcYN24cc+bMKdY8pPAkhFrhwFzgATvPu4Syet1/xZ6REG7voe7w2av2n+f0wpMWWAjUVxGbhKxgJ4SrDANq24hJBSY6IRfhMzIyMrhy5QpVqlQBoGbNmpw9e5bU1FRSUlJISUnBaDS6NklH2IHSyzQ/pYHKKDdjhHARrVZLcHAw4eHhhISEEBISQkBAAH5+fhZ/+vv7W/0zZ9Pr9eY//f39zV/rdDrz/tybTqcjPDycyMhIwsPDCQ4OJigoCH9/f3Q6nblPUn6eeOIJateuzenTxf9DNGbMGF599VVKlixp9bjJZMJgMJCenk5sbCybNm1izpw5DiuqS+FJCDWaAouBanaetxFltSvp6SR80G3VlGbihZGWXry52DQZ6K4izgg8DBxwbDpCCCtCgDdUxH0KnHNsKsL3xMTEmB9/+eWXFsdNJlOexzmb0Wg0/5mzGQwGDAaD+XFWVhYGg4HMzEzz1xkZGWRmZpKZmUl6ejrp6elkZGSQnp5OWloaaWlp5sepqamkpaWZi2C5C2IpKSkkJSWRnJxMcnIySUlJJCUlmVfvK5Ctle1AGRElhSe7+fv7ExoaSmhoqLlQkvPvICMjw/w4599CVlaWecvIcP5w65xcw8LCCAkJMeedMwowKCjI/GdwcDCBgYEEBgYSFBREQEAAgYGB5j9zij25i0G5Cz65izo5m1arNW8ajca8ATaLO+4sICCA48ePM3nyZF59tRB3aq2Ijo7mwIEDREdH59lvMpm4fPkyv/76K0uWLGHNmjVOLZhrUObcCSHyMxqYAvjbcY4RmAS8BRgckZQQnmHO2zDc2ijBakfBv4by+GwXSP49z+FmD8AuZ02T6Y+yUIAaLwFFX1FWCFEYrwJv24i5DlQH4h2ejfAhwcHBJCUlefQH3FvdWiizViDLMGZQPrQ8qZrUfK8z/NpwxlwaYy6aZWZmkpWVRWZmprl4llMsyb2lp6fnKarlFNYAc3Eh58+ckSO37s85lvvrW4/dui938SL3nzlbQEBAnlEyuUfG5BRHrBVI9Ho9Wq3WokhirVACxVMsyf13WNDf7a1/WtuX8//d2wo7rpDzc5SVlWX+t52amkpqairJyckkJiYSERFBo0aN8pzTpEkT9u7dW6TnDgwM5OLFi0RGRpr3ZWZm8uOPP/Lyyy87fFpfQWTEkxD5iUSZWtfXzvMuo4yIWFfcCQnheUa9CRXKQJd29p3ntKl2dVF+ztVYgBSdhHCV0sB4FXHvIEUnUex69OiR58P3pUuXzAWG/EZkOLLgUBxuzSlnFavcQgihMY35i7/yvc7xksepV7KeQ3IUBcvv35K7/BsrLrcWzXIXSK2NHMzKyrI4lvvPnLjcsdZGHeYupOY8zl1YzRmBFh8fz9mzZzlz5gzHjx/n0qVLqr6vzp0788svv+Dv749Go6FPnz5FLjwtW7bMXHQyGo3MnDmT559/3iWj5G4lhSch8tMA6G3nOetRik7qft8I4fWysqDfGNj8NTSum1+U5Ruk9ExHZpUtHPgJCFURuxV4wrHpCCEK8CrKAgAFOQ3McEIuwucEBwebH58+fdrc66kwcvrR5PSCyZmqFBgYaP4z9+OgoCCCgoLM+3NvOaNz/P39CQwMNI/MyRmdk/On2ulLOX/m0Gg0NKd5gYWnXezCiBEtloUrX1VQoeTW6Za5ix/WCpa5/26APMfy226Ny/k6R36P88s3d545f+YUX3JvOaPYMjIy8kwFzT09NGcaaM7on5yvc08FzRkVlJSU5L3904Dff/+db775hqFDhwIwatQoYmNj+e6774iNjbX7epUrV+bee+81fz1x4kQmTZpUbPkWlUy1E6IgE1HXT8KIMq3u7ezHQog8ypWGrd9D5ZwWGVWPQEBN5fHZrpC8Kk98pQ5w1tG90X5CWZnOljNAC5TRjEII56sGHML2lPdHga8dn47wPdHR0Vy6dMn8IT05OZnjx4+zaNEiJk+e7LUfjLVaLfrH9GTMLXi0RNfnuhIdF20ugOUuiuUUxqxtOVPXcqaz6XQ6IG/hJmcKoLXNYDDke+zW86x9fes+g8FgtX9WzpaWlkZycrK5UJK7YJLTS8sdRpYIz1GnTh0OHjxoUQDcsmULAwYM4MKFC6qv1atXL5YtW2b++rHHHmP+/PnFmW6RmWSTzWe3Opi4CxMxmAiwclyLiXWYCvzvIiY6uMH3Iptsbr5t+hqT6VD2lnbEZHamy8392Vt0SQfn8zIF/1zn/JeEiUau/38nm2w+vX2H7Z/VvSiv2a7OVTav3U6cOGGy5vr166Y6deq4PD+HbbWw/fP3sBvkKZtsHrq9+uqrJqPRaPG7JTEx0aTValVfJzAw0JSenm4+32g0mt577z2Xf3+5NpcnIJtszt9ux8RaTFzGxElMxGJiSj6xZTFxCesvtGswUcYNvh/ZZHPzrW6NvIWl3IWnczu7mLZ+j2n5LExfvYtpynhMgdYKwcW1PYi6opMJE/1c//9ONtl8euuEup/VLm6Qq2xevYWGhpo2bdpkSk9Pt/iQeOrUKZfn57BNg4l4Cv75m+YGecommwdvNWvWNM2bN8909OjRPL9bdu7caXr00UdVX+fOO+80JSQkmM/PysoyRUZGuvz7y95cnoBssjl3uxcTJzCxABP3YKI7JlZgwoCJVvmc0yn7eM5/WZh4Fbm7KptsKrdPXs6/8NSlSxfn5dIWE2mo+yCbXzFaNtlkc84WhonT2P5ZXecGucrmU1tMTIxp7ty55texpKQkl+fk0M3W6P+dbpCjbLJ5ybZw4cI8xSej0WiaOXOm6vMDAwNN8fHx5vOfffZZl39PgEm6wAnfEgCMBi4AE1Cagf+KsgpOHFA/n/PWZscAXAQ6AZOQfk5CqBAcBIN7uzoLoBawDOX3gC2bgJccmo0QwpYpQCUVcWpWuxOiGF24cIHffvvN/HV8fLzrknGGHTaONwXKOCMRIbzf8OHDSUhIMH+t0WgYOXIkf//9Nx07drR5flpaGvv27TN/HRUV5ZA87SWFJ+FbSgLNgP3AefKu65gJbCzg3DeByUAjG3FCiDwGdIMIW6tROVopYCXK7wBbLgAPAlkOzUgIUZB7ULeS5PfALgfnIoQVJ06cMD8uW7YszZo1c2E2DvaHipguDs9CCJ+QkpJCuXLleP3118nKUt6MajQaWrZsyZo1a/j0009tXqN69ermxzt37nRYrvaQVe2Ed4pGWYXqP5QRSsnZ+8sCPwMVUd7U/gvUBOaj/CSsA7ahjIISQhSL7Yuh+e1596VXOEJAqLKqXdeuXVm1apWVM4tJIMroxjtUxGYAd0MBK0cLIRwtHDiA7dFOySgjlU85OiEhrLt48SJly5YFIDMzk2bNmrF//34XZ+UAwcA1lNfT/CxGuWkjhCg2NWvWZM2aNVSqVMm88p3JZKJSpUqcO3cu3/MyMjLw8/PDZDLh7+9vLmC5kox4Et4lBJgFHEcZor8b+AEon338EvAhkASsyD5+EEgHtqIsrf4j8DLqpuMIIQrUtJ5l0QngRpKTEtAA36Cu6AQwHCk6CeFqH6F+it0px6YiREHuu+8+MjIyAPDz8+Obb75xcUYOkoIyBb0g95J3JoEQosiOHj1KlSpVqF27NsnJykiKnKl3BdFqlTKPwWBwi6ITSOFJeJuXgTbAYKAf8BrQBOUuTOvsmCVAK2AgEAY8A9wHPA+0RRn9NAao5sS8hfBSIwdY7ktKhsRchaecF0eH+BC4X2Xs68DXjktFCKFCZ5QCsC0bUG40CeFCu3fvpm3btphMygSS+vXzaxbqBVbaOB6J+ps8Qgi7HD16NM8Ip5dffpmhQ4fmG59TENfpdERGRjo6PVWk8CS8RzlgCLAK+AllmP4nKAWoZihNxUOzY69n709FeeOaMxUvHqX5cEmgjnPSFsJbRYTBQ/dZ7v/2VzAYnTDL+ymUgrIa84C3HZeKEEKFcGCOirgkYCjSLEK4hR07dpg/5Hk1W4UngK4Oz0IInzVo0KA8PZ/+97//sWzZMoKDgy1ic4pUGo2GXr16OTXP/EjhyQdFxXhpRaUqEEXelTe0KA0R56AMAc4ZfRGM0hfiGjeHBWuy/2yU/WeKA3MVwgcM6qmsaHer2Yuc8OTdUQrPaqxDXRNjIYRjfYzSg9GWccgUO+FWckY8aTQaRo0a5eJsHOQYcNRGTDdnJCKEb9qxYwdNmzblxo0bwM2iUlJSEqdOnWLSpElMmTKFnTt3UrNmTfN5VapUcVHGlkyy+c4WHBlj6jNho8vzKNI2GBMPYKLxLfujMZGGiRHZX2uyNzBRGRMnMfEbJiKz932LiThM9M/+OgwTd2FiCya+dIPvsxi30pWbmPT+wS7PQzbf2v5Zjsl0KO/29/fKscOHD5tydOvWrXifuykmkjCp+u8AJiJc//9KNttbaDCmpvVcn4dsDtq6oO5ndi03X9tlk81NtlWrVplf0wwGg6lJkyYuz8kh2zRs/4yWd4M8ZZPNi7fAwEDTjh07TGoYjUZT1apVXZ4zYJIRTz6mdf/3KRFTD51fQctSuKmhKCOUXgWmATuB94AK2ceDgD3ZcXDzn7kGOI2yUl0zbs4/fx1lit13wEaUBsTLUKbhveXA78MF6t39BJVul3VuhfO0awb1aljun/W95b5i7fFUCeVnPURF7EWU/m43iu/phWNUioEt30JMtKszEQ4RAfxPRVwiMAzltV0IN9K7d29iY2MB5TVNzXLnHuk3FTEy3U4Ih0pLS6N58+YF9ngCSE1N5dlnn+XkyZNOyqxgUnjyIVUa9aBKw25otFrCS1d1dTr2qY6yes0slAbgrYFJwJMoq98AnEGZMtMMuCt7X+5/4TOAEig9JEBZ+a4fMDn7cRzK9JyeeNUQ/oCQElRt1JPqzfq6OhXhQ0ZaWVI57gYsXuXAJ41A6UFRVkVsEkrR6YwD8xHFolUj2L4IGt4Gh064OhvhEFO5ufpsQcah3EgSws2kpaVx9913m6fctWrVisBAD7zJa8smbLeikOl2QjjFrb9jjEYju3bt4o033qBGjRoEBwfz2WefuSg7S7LopY/wDwqndf/J5q8jytQg7sIhF2Zkp0dRPkx+AlzN3vcGyqiG51B6N32P0lh8IPAOSoHKmOsal4ErKEWsHFuzNz8g02HZu1StVgPR+QVQoW5H/IMiyEiV4R3CsUqXgAfutdw//2dITXPQk/qhLCpQT0WsAeV3xh4H5SKKzUPdYe4kCAyAjAw4ec72OcLDdENZGMSWtcDnDs5FiCKIjo5Go1Eahup0Op588kk+/vhjF2dVzNKA9Sg3avPTCa9+Xy2Eu3j33XfNj48cOUK7du24cuWKCzMqmH0jnmrafYZwE817TyQ44uYwgMjomgVEu5i1G0RRKFPgcopOepSh9nOBP1GmxmmALSirU90BPH7LNRpmX8faCAdvfXHUaLitzaMA6PT+VGlU0DsFIYrHkL7g72+5f3auaXY5d4WLzRygg8rY0cCK4n16Ubw0Gnj7Wfj2Q6XoBHD0DBgMrs1LFLNI4AsVcTlT7IRwUw0aNGDNmjV59rVr144xY8bgb+0F0ZPZWt0uDOXmrxDCYcaPH09kZCQAGRkZ3H777W5ddAJ7y0g7UV78/0a56zQK5QN+aEEnCVcrW+MO6rR9LM++iDJWmq+42gModzQfsnLMH+XuSZPsr7Oy/zwEzEcZxfRI9r7vgIXAp8AL2cdqZh/fBfzugNzdVEytdnn+rqs3lel2wrE0GhjR33L/hm1wOJ8p5jl3iAvtdWCwytgPUabsCrcVHASLp8KrI/Pu/0+m2XmfaaibYvc8Mi1WuLWqVata9Cvs3bs3U6dOJSEhgREjRrgoMwdQ0+dJptsJ4VAvvvii+fGsWbPIyMhwYTbq2D9+KRhoibL09EzgL5Ri1BFgMfAySt8MNW8khMPp9AG0HTjVYr9bFZ6qofxbmgfcDdwDxGQfy5kM+n32vgYoI5vI9ecfwF6UQigoy70OBZYDE1Gm321G6fv0OjdHTfmA29rm/TRernY7gsKlO69wnHtaQzUry6HPXuSgJ3wUeFNl7A/AizajhAvFRMPmr+GBzpbHpL+Tl+mOuoLxapQRjUK4sZ9//pnExESrxwICApg1axbdu3vJqPNTKDd+CyKFJyEcpmrVqkRFRQHKaKexY8e6OCN1iq/HU83srV+ufbEoBYF92X/uBf7j5mgV4XANOz9HZFnLaXUR0W5UeHoKpb/Du0AplALSLygjl3L+rewC9qOMhloNXODmqjZHUP6N3Ykyuuk4ytS5x7K/boiyatVyh38nbiUoPJoqDe/Ls0+r1VG1cS/+3STv4oVjjBpgue9yLCxd64An64C6lbBAuUnyKLIalhtrWg9+mZn/ynUy4smLRKKuX1MCMNyxqQhRXJo3b867775LxYoViY+PJy0tjY4dOxIcHIxGo2HhwoWULVuWlBRb3bk9wEqgTgHH6wKVkcUAhHCA5557zjxbYP/+/RiNRhtnuAfHNhcvhdJgrlOufenAQW4WovaiFBSk33GxiyxXm4b3Pmv1WEBwBEFhpUlNdMLwHz+gC0rT749Qiki5bUUZQv9J9tcDUApMfwA5jWQTgekob1S7AAtQilL67D/3Az1QGojnSAEOZG8+qFarh9Dq/Cz2V2/aRwpPwiHKl4Eed1vun/sjZBZ3H7W6wI8ov19sOQb0QmmKKtzSA51h/nvKNLv8SOHJi3zCzZHNBXkeOOvgXIQoJocPH+b+++/Ps69UqVKcO3eOgIAAwsLCuHr1Kv/++y8JCQmcPHmS6dOns3fvXtckXBQrUX4+C9IVmO2EXITwMblHT3777bcuzMQ+zm8VHoDSp2coSg+ezUA8cAJlRaLXUT4gVHZ6Zt5Fo6HdQ9PQ6fNvaBgeXT3fY0WmBdqjNA29hDKCaSDwsJXYJdwsOoFSnOqCZaPgxSjNw1/iZjEzC6UZeUvgYvGk7hVyNRW/VZnqLQktUcHJCQlf8Hg/0Ony7jMa4YvFxfxEZVHe9EaqiL2GMuQ/tphzEMXm1VHww7SCi06Qf48w4WF6oIw+tOV31I9oFMJNxcbGMmrUKPOCGsHBwTRr1owOHTowbNgwdu/ezY8//ujiLAthC8pN4YLIdDshHKJiRaWnhclkYvZsz6nuus8adVWBPii9OpahzB+OAzagNJ98DGiE0mRa2FSn3VDKVGtRYEykI/o8NUMpHJ1F+bt7HCiR6/gAbP+r+xS4gjLqKacAqUF5gRsBRKA0B+6f/XwPoayeMQ1IKobvwQtUqHM3YaXyr95Wkybjopjp9Urh6Va//QGnLxTjE4UAv6Lu5kQa0BM4WozPL4pNYICyat3bz9iOPXsRkrxgdorPi0LdFLsbyBQ74TW++uorxo4da7X5r0ajoW/fvjz++K1LMbu5DJQFgQrSEWXAgRCi2FSqVAm9Xpm0lpCQQFqa5wznd5/CkzWRKKNmngW+AvagFBYOo9zt/gwYg9Kgsg7KyBdBcGQMzXu+ZjOuWBqMR6GMNnoTpdfSDmAs+Q+hL4fS5LsgacD7KE3GO2bvM6EUnw6jFK8OotwJXQpMAWYAXxb2m/A+t7UdUuDxak37OCkT4Su6t7fem2f298X4JDqU3m9NVcYPRuntJNxOmVKwYT48pLLX7n8y2sk7fIryPsCWsdycai+EF5g2bRoBAQF07dqVp556irfeeivP8YYNG7oosyKwtbpdMLbf8wsh7NK1a1fz43PnPOuF0rE9nhzBD6iVvVlzDqW59LHsP3Nv8U7Izw20efAD/IPCbMapbjAeCdTgZgP5nK0GULIQCT6EMhqqILNQ3ng+DKxDaU7oh3KHZQPwJ8rKiTWA9YChEHl4qeDIGCrVv7fAmFIVGxBRpiY3LstQEFE8rDUVP3MBVm4uxif5BGWajhrjUabnCrfT8Db4ZQZUUtPjJ5v0d/ICPYFHVMT9htxIEl5r1apVgNKjxWQyodFoMJlMLFmyxMWZFYKtwhMofZ5WOzoRIXxHzZo3Fw07edKz7srZV3h6CGiOMuWtEe7Zh6lC9matwn4Ny2JUToHKS/oDVWncg8oNutoOBCLK5FrtLhzLolLO41LFnOT9KCvZWY44VmhRVqV7F6UA1RXlxa0PyoiqP7PPPZm9iTxqt34Yrc72j3b1Zn3ZveJ9J2QkvF31SnBvG8v9X/yg9HgqFs+h/N5QYzbwYTE9ryhWPTvAtx9AaIh958mIJw9XAvVT7DxsxpEQhTF27FjzqlRpaWns3r3bxRkVwjmUxX0aFBDTDeX1WwhRLPz9b/Yd8qRpdmBv4WlF9pYjCuWXTaPsrSFQD/ftw1Qye7PW+igFpcG5tdFSp1GaWLs5/6AIWvebnO/xDL8EEiJPcCPiJAkRx7kRfgw6oxSZ8lm+2iGiUJ53eT7Hcz6ozgPGZW9jgeooL15/Ojg/D6bR6qjdepCq2GpNpfAkiscT/S33ZWYqq9mpodXamPXdF2VKrRorgadVxgqn6nonLP0MbP11W3PoePHnI5zoU5RFAWx5Djjv4FyEcANz5syhffv2aDQagoKCOH78OBUrVvS4D5KspODCUy2UzxnHnJOOEN7Oz+/mcs5ZWR5QoMilaFPt4oBN2VsOP5R+S41QClGNsrcSuLdgoH72dqsslOJT7mLUCSABSEcZfZOR67G1fU6YCtai90T0pUKIjdhPQsQJbkQeJyH8ZHax6QRpwVcdn4RaD5F/4QmgEtAapZF4SZTV8VojK1PZULHePYRGlVcVG1mmBiUrNuDa2f0Ozkp4swB/GGqlV/2ydXCpgF85OSv82NQK+AZ1HQn3AA8iU2/d1OadcOo8VKto/7ky1c6D9cb6ira3WonSz1MIH/Ddd99Rrlw5pkyZgkajoVSpUmzbts3zej2tRFltuiBdUfryCiGKzGC4+SY3JMTO4eMuVvw9njJRhl3e+lm2InkLUY1QRrB4Aj1KrkXJ14j6IlV+j63t0yl5+d0exq56k9kSPLYISTrBP8BCoKCGwyHAXKADSiPh5wA3qpm5s9vaDrYrvnqzvlJ4EkVy/71QKspy/+xFxXDxasAvQJCK2LPAfcjKlm4sOQWGvAKbFth3XkISXJTXAM9UEmXqqy3xyBQ74XM+/vhjUlNTmTFjBhqNhgYNGjBkyBC++sqDKrBbUabIRhQQI4UnIYrNrl27zI9z93vyBM5rLn42e/s1174w8k7Va4Qy4sgbV6fTonx4UvMBqhAySSSTRMdcvKhOoRSQFqIUnmxJBr5FeRN6ymFZeZ3QEhWpWLeTXedUa9KH7cveBJMJfz/IyHRQcsJrjXzQct+RU7BhWxEvXAKlt1tpFbEJKH0kvKRXnzfbvAM+WQDPPqr+HBnt5ME+A8qoiBsDXHBsKkK4o1mzZtGnTx/uueceAObOnUuVKlWYOHGiizNTKQuleXi/AmLuRvn8k+qUjITwamvWrDE/LldOzTKx7qMQnRaKUSJKv54ZKEWG5kAoSp+oh1Gaw65GRrt4oisobzjvAKoCL6Ou6JRjHlJ0slPt1oPQ2Nk8JbREBcpUa0FgAHw4zkGJCa9Vvya0a2a5f/b3oHYmHVjp8RQALCP/1Utzy0RZsMCe3y/CpSZMhaOn1ccfksKTZ+oLDFQR9ysw38G5COHGHn30UXNvJ41Gw+uvv86HH3rQChkrbRwPRCk+CSGK7Ny5c+bpdmFhtlexdyeuLTxZYwD+RRkdMx6lCXU0EIMyVHM0MA3ljcohlClnwj1cRCkY3Yvy9/UM8LcrE/IdGq2e2q3VNNGwVL3p/bw1GrrdWcxJCa83wspop7R0mP9zES6qQfk90k5tEsDaIjyfcLrUNKU4qZaMePJApVBWpbUlHuVnWAgfdunSJSpXrkxS0s254o888ogLM7LTKhUx3RyehRA+48aNG4By47Z27douzkY95021K6qLWJ9GoQHKo6yYUP2WrQYFzzkW9rsGHEVZneJoru0YyhtI4RLlarYhOELNkkGWarXoyZjOEzAZDWi1YDTaPkeIkGB4tJfl/kW/wfX4Ilz4HWCAyti3kWbEHigoEMbYM9XupONy8XUlK9xO/OVjGDKLeQ7MdNStlvsMMsVOCODKlSucO3eO2267DYADBw64OCM7XAJ2AU0LiJHCkxDF5ty5c5Qooazcds8993D48GEXZ6SO5xSe8mMCzmVvG60cL4n1glR1wLOmRTpPHJZFpZzHcS7MS+TrwuFN/PbZ/TTv9TqlKqlfESUt8SqdKn0BGj3+/gZiouHcJQcmKrzGwPsgPNRyf5Gaij8OTFAZ+w3wehGeS7jM2MegopXX34wM8Pe33C8jnhwnslxtmvd6nTWfP4Ihq5iGkN+PsrqkLcuBr4vnKYXwdIGBgeaRCyaTiREjPGwo4EoKLjxVBWoDnvH5WAi3dujQIRo0aABAy5YtmT59uoszUsfzC0+2XMvetls5FoyyalLuYlTOVhnv/r8TT/4jl665Li1ReOf/28j5w5uo2rgnzXq8QkR0/sswJsaeZv/a6QxpvZDBvdLM+6tWkMKTUGeUlVFJ+/6Dv/cW8oJdgJkqYzcCwwr5PMKlypWGl4Zb7j97EWp3UxY4CAvJu9nTD0rYJzM1kQp1O9Bx+FesnTMYo6GIK0yUQt3PcRwyxU6IXHr27IlGowHg9OnTnDzpYUM9fwNesxHTDSk8CVEMtm/fzoMPKnd46tWr5+Js1PPm0optKSgNaa01pdWjFJ+sjZaqhlK0cnMBaSHUO1yCmidKU/NEaYyn/Xl353KlwBTr6uyEQ5hMnNz9M6f2rqBZj5dpeO+zeQ5nZaTwx7djOLF7Ga0bGXj+lukuVcvDHzudmK/wSM1vhyZ1LffPsqNvT27xVeLhTdS9Ih0C+gAZhXsu4VqTnoXQEMv9E6YqvZ8A4hOUTTheRpqyGm6l2zvTYej/WDd3GCZjVuEvOAP1U+xkFUohzMLDw82Py5YtS48ePdi0aRMJCR7yy3Abyo3rkgXEdAOmOicdIbzZ2rU3m5tWrFjRhZnYx7cLTwXJAo5nb9aEAP4oqy/l/jO/x4U5ruYcPUoRKdeopVKZjendZh4fTNlOVOLN0SzHjLG8e3150f6/CI9gMmZx9cwei/2pibEc3/kjQYHw1btw62JiVSs4KUHh0UZamUaTmAzfFuLXyxnOsPWVraBmYY7LKG9c4+1/HuF6jevCY30s92/fDwt/dX4+AjKzC08AVRp1p/3gWWycPxKT0WD/xfoB/VXE/YIyVVYIYbZmzRpMJhMajYbAwEB++eUXTCYTmZmZbNiwgeHDh3Pu3DlXp5k/I/A78FABMXeirF6eVECMEMKm/fv3m39fREZGujod1aTwVFjJ2ZsbqtClA4//cDpP0QmgoiYCDRpM2LHOufBYOn2AxT5jljJM5J0xULOy5TlSeBK2RIbDACtNQr9dDkkp9l3rGtfoTGfSSqbZjNWkaogZFUOgLlAZfSo8zsxJoLXSw+mjhVCtmvPzERBasiSlw2/egSjd4QHKlQxi+7I3MZnUrzSRWS6Tc1+cw0jB52jjtVR8ryL66vm//dTp4JlHYOp81U8vhFdYs2YN9957r/lrjUaDv78/nTt35vTp04wfP56PPvrIhRnasJKCC0/+QEegKCvfCiEASEpKIiwsDL1eT3R0NFeuXHF1SjZJ4ckL9dPVo8U/lk0xAjR+lNGEccnkIcN2RZFodZaf8AxZGbRtCs8Osn6OFJ6ELY/2guAgy/12T7MLhu505z/+sxmqQcNPQT/R+6fedj6J8ASLlrk6A5HHIw/Dhw+rDs8kk/a05wxnbMYuiFzAw1vVXfspW/1ihPAhWq2WKVOmMGzYMJo0aUJamu0bNk73O8rIJ20BMQ8jhSchisHFixcJC1OmCwwcOJBPPvnExRnZVtCvBuGBymrCGb0v/+YnlXRRTsxGuJJOb2VogSmDr96xnGKXo2p5x+YkPJ+1aXZb98J+exqG6uDZMs/yN3+rCp/KVHrT244nEEI4y2u8xl/8ZTOuF714qMDhEEIIW+rUqcPVq1fp2LGjq1OxFAvssBHTG1lVXIhisG7dOvPjd955h1KlSrkwG3VkxJOXmVdiKGFp+a9KU0kbxXZkiSBfYK3wVL50BjWsTLEzHy8Dfn6QWcSFjYR3urM51LEyzW22vaOdZsPqgNWqQh9PeZyBSQO5gvsPIXY2rc4/exUy954+HRQIYVYW5EhOVTbhWoGhJQGN1WNZmalkpeffV2C9/3rej3zf5nNEGaOYdH0SV41XrR7300N4KOiyb4okpUCKGw7oEMJVoqKi0Ov1aDQaQkNDWbNmDV999RXDhrnZEq+/Ai0LOO4H2uEajG+79+uWEO7u1VdfZfjw4fj5+RESEsKxY8eoW7cuFy5ccHVq+ZLCkxcZFnAHnTUFNz+ppCvhpGyEq2mtFJ4qlil4KTCtFiqVg+O2Z0wIHzRqgOW+6/GweJUdF3kTGK4ydinMeWAOc4xz7HgC31G79SM07fEy25e+wbHti12djlUlI+HY70B43v2XY6FmF6UpvXCtR94/kl18sm7f6hns+PktywPlgH3qniPu4Thu//52q8cG3gdfvgO6XG0JH3sWflRXmxbCZzzyyCPMnTsXf39/NBoNQ4cOpV69erRv3959pt59jfI6X8CcmrARAdx4Nw0KsYaBEEJx/fp1+vXrx9KlS9FoNERERLBv3z7KlCmD0ai+R6MzyVQ7L1FFW5KpIX1txlXWylQ7X6Gz0uNJr7W9Br30eRLWRJeEvp0s989bBmnpKi8yEnhdZexfKL0g3PO10y2c2reSwJCStB88i+5jV1CiQn1Xp2ThjaeVhvS3eu1TKTq5i4xcK9tZ0/DeZ2ly34t5d/oB3wOlVTzBV9mxVkx8ChZOgcBb1sI4fFLFdYXwMd988w0VK1bkv/9u9kZs2bIlKSkpxMXF0a2blZU/nO00sKLgkBvl02jRp4Dh90IIVX7++WceeeQRc6GpVKlSfPDBBy7OKn9SePICGjR8FfYwYdpAm7GVtDLiyVdYG/Gk16goPEmfJ2HF0L7gb6Vt2OeLVF6gDzBDZey/QHdApmEVKD35OheP/glA2eqt6P3ielr3/4CA4EjXJpatTnXrPcEOHIEvf3J+PsK6zDTba5s36Taehp2fu7ljNsrS6LYcAp623B3gD99+qBQmb2U0wjEZdSuEVVeuXKFOnTqsXLnSvC9nSfVly5YRHm6l0u9ss2yHhDzpTzWt+/ekEcLdLVy4kLfeujkquVevXi7MpmBSePICzwa2p71fTVWx0lzcd1jr8SQjnkRhaLXwRH/L/eu2wpFTKi7QDliIulecc0AXIM6OBH3YyT2/mB9rtTrq3jWMfhO3U7v1INBY79vjLB+OA72VCf1j3weDTLFwG5k2RjzlaN7zVW7v+CSMB4aqOCEV6A+k5N1dugSsnwcPdbd+2pmLdoyiFMJH3XfffUyZMoXTp0+bRzv4+flx9OhRli9fzhtvvGGzCFWqVClmzpzJrl27OHHiBKdPn+bHH3+kRIki3qT+HSqdLvjzxoa7j/Jxo94E4le05xJCsHjxzXYLoaGhLsykYFJ48nC36crwXkgP1fGVZcSTz7A64kkKT6IQ7m1j/d/FbDWjneoDvwC2B2QqxaYuwFl7svNtp/etwGjMW8UJDC1Ju4en0fOF1ZSu3MQled3TGu67y3L/rxthre0F0IQT2Zpql1v0Cy3Adi9xxTPAP3l31asJ2xZB68b5nybT7IRQZ9y4cVSpUoWBAwdiMinNuqOjo+nevTsTJ07k2rVrvPrqq1bPnTt3LpcvX2bUqFE0adKEqlWrUqlSJfr27cuxY8fQ5rf8sQo6o5Yn57ezGffH0BN8Enp/oZ9HCKEoU6aM+XFWVpYLMymYFJ48mB4tC0IHEahRf7egpDaEEKzMlxFex2qPJ5lqJwrBWlPxi1dh2TrL/XlUBH4DIlU8SRrQEzhoZ3I+LjXxKpeOWa/kRFdpQq/xa2j30LQCm0cXN50OPn7Jcn9mJrzgvq0HfJbaEU+xpfeyseModRf9Dvhf3l2d28JfC23f3JDCkxD2Wbx4MV9++aW5+JRDr9fz1ltvMXfuXPO+Dh06cOnSJYYOHZpvcSkqKooxY8YUOp8ITSBDF7bCP73gNay+fOhvBkU2Z1BAi0I/lxACBg0aZH584sQJF2ZSMCk8ebAJQffS3M/+5nwVZbqdT6gQY1mQVDPiqYoUnkQuFctZH7ky90co8KZKCeB3QMUIOo1RAwOBLYXL0ded3LO8wOO12wyi38Tt1L1rOBqtzuH5DH8A6luZ/T3reykquCM1I56SQy6wuuvDGPxUNF47DIzIu+vJh2DFbAhXMQNA1fRdIUQew4cPJyYmhtGjR/PZZ59x9epVAPPqd7GxscTGxrJ27do8oyNOnz7Niy++SMeOHcnMzDTvr1KlSqFzidIEU/paGA8sb1RgXFxUCot77WF26IPU05Ur9PMJ4evuvPNm08UffvjBhZkUTApPHqqOriyvBXcp1LmVZGU7nxAUFGCxT03hqUwpCA5yREbCUYLCox127eEPKCNYcjMaYU5Br2tBwHKgjrrnaPl1S1hWuPyEMt3OZGPp3IDgSFr3f5/eL66nTPVWDsslPBTeGm25/3o8vDnTYU8risDWiKdMfTKruz1MSugl2xe7BtwHZF9Sp4NPXoYZr1n+HsnP4VPq4oQQeV26dInp06fzzDPPULZsWXbv3m0+VrJkSUqWLIkmu/efwWDgzTffpEqVKnzwwQds3LgRfa6mfD179qRRo0aFyiNKGwzAk1/Znm4367EtBGv8WRI2jFCN5ftWIYRtuYvGzz33XJGmyjqSe2YlbDpiuMKstD8Kda70efINZy5ZGfGkYqodyKgnT9N+8Gwq1OtU7NfV65XC061WbIIzF/I5SYcyzaa1uud4jdeos0llhUpYlXLjEpdPbFMVW7JCfXqMXUH7wbMJjihb7Lm8MhKirczqe2uWUnwS7icjNf/CkwkjGzuN5Frp/SouBPQFjitfhoXALzPhmUEFnWRJRsW5r0adx6LRFjx9SrgHo9FI06ZNWbBgQZ4PpQaDgbVr11KpUiXeeOONPPG5C1WVK1dm9+7d/Pfff9SrV8+u547SKIWn1turcvvBmAJjtzU7xe4GZ7lNX4Y5oQPteh4hhGL69OnmqbbVqlXjxx9/dHFG1knhyUMZMPJs8o88nfQDBlPBd7pvJSvb+QadvnAjnkAajHuahNiTdHlyES36vFGsHwp63g0xVgZTFdhUfBagciXX4QznTd4034EVhZd7dTs1arToxwOvb+P2jk+h1RXPqkJVK8CzVooMR07BzO+K5SmEA2SmJ+V7bEertzldbWW+x/MYAWxWHlaKgT8XQrc7CzzDQkoqnFMxsEq4Rrlabek+5heCIwsuJgj3MXjwYAIDA7nvvvto3rw5er2ee+65hwsXLO8etWjRgi1btpg/wGo0GmrXrs2uXbuoUEH9G8OcwpMGjcpRT8qN9M5+dSivjVT9PEIIxcyZM5k9e7b56169erFlyxb8/d2rr7MUnjzcjLTNdE/4nCSt+rWpK8mIJ5+g1Vt+mNSpKDylZ0BosCMyEo5y5eQuABp0Gk2PsSsILVGxWK470kpT8VPnYVV+gy3fAB5Xd+074+9kFrPQoHHbIcGe5NTeX+0+xz8wlJZ936Lvy5sJKYYPku8/DwFW3uOM+1BpLC7cU35T7Y7c9i37m3yq7iKTgXk3v5w9EW6vZX8uR0/DLf2RhRuJPbOXMtVb0uelDZS/rb2r0xEqGY1GVq5cyc6dO23GtWvXjkaNGvHXXzcXrQgICOCVV15R/XyR2pv9Gh5e0ozQpIKn0C3su4ujoVe4+8annDfGq34eIcRNTz75JMeOHQOUonGbNm3Ytk3daHhnkXf7XmBV5r+MaWt7rXKjycj+rPPsN5x3QlbC1ayNePKzMtXuRiKs3AwTPoZ2j0BEc1ik8ga3cA9XT958MxldtRl9JmyiSsPuRbpmjcpwj5Xpcl8sVno8WRgBTFR58b/g3RPvokcZnSUjnoouOf4Cl0/sKNS5ev9gUhKuFOn52zaFflbaDq7/G35ZX6RLCwez1lz8QswW/rhrrLoL/AS8nHfX+CnKTQx7yTQ793b19B4AgsJK0eWpH2hy30toNPJRwtvs37+fNm3acPToUfO+2NhY1efnjHgCCEsOZNDi5gXGp4Rk0KbXVPbJ5xMhiqR27dqsWLHCPGqxYcOGdk+VdSR5tfASJcpZ75GyLuMwb6Ss5N4bM4i6/iIN4yfzUap8CvAFakY8rfkLSrSC+0bA5DmwZVfhPiwI14q/cpT0lBvmrwOCI+j0xHzu6DfZagFSjRH9LfdlZsKXP1kJ7g3MUHnhf4EeEGC8mZeMeCoep/baN90ux4H1MzEZC1qisGAaDUx9yXK/0Qhj3y/0ZYWTZN7S4+lGxHHWdXkMk07Fv4ldwCDgllFK/xyF1z+zP5cjp+0/RzjP1dM3ewBptFqadBtHl9E/EhRW2oVZCUcpXfrm3+vChQtVnxelybtCzah5tqfbXR2R/5RfIYQ6RqOR7t27s3+/0pdRo9HQs2dPF2d1k7zb9wIBISVocsOyr8s1TRqdEqbzZspvrMn8jwRTmguyE65idcTTLYWnDdvyGb0iPIvJxNVTuyx212v/OD1eWEV4dHW7LhfgD0P6Wu5fuhYu33rTsy1KM3E1K1adB7oA1zHfjQEpPBWXk3uW231Oeko8h//6pkjP+0hPaFbfcv+XP8G+/4p0aeEE8ZeOsOe3Kfyzfjb/7P+CFR17kB4YZ/vE80BPIMX64Slfwl977MtFRjy5t+S486TeMjqyfO076TNhI2VrqFxRQngMP7+bNzBr1Kih+jy9Ju8bgtsPxdD272oFn1QHaG9HckKIfB05csT8uEqVKq5L5Bbybt8LlK7cmFqnLadJbMo4aiVa+AprDYN1t0y127LbIkR4qCunrPduKFWxAb1fXE/1Zvervla/LlAy0nL/rO9v2VEP+AWwPdMX4lGKTmeVL3MXnmSqXfFIun7WPBVGrUObvyQrPbnQzxkcBO+OsZJLMrz6SaEvK5woOf4Cu359j79/foW/+08gpexlFScBPYD8VrdEuakxeAIk51OYskYKT+7P2u+Y4IiydHt2GQ3vHaMMgRQep379+kyfPp1x48ZRtqyy4um///5rPr548WLVN4leSF5K5LXxtIn/mBFJ3/NZ6ibumqOicDWqUKkLIW6RM+IJ4N5773VhJnlJ4ckLVKndhJqnr1rs35h+2AXZCGfq5lePdnrro1lsrWqXkQE7DjgsNeFkV07m3zTUPzCUu4d8QbuHpqHzC8o3LsfIBy33/XcCNm7PtaMCsApQs0hmGsrIiH9u7pIRT45hz+p2hqwMDm6aU6Tne2EIVChruf+9OVZGxwn3NhPooCLOpEE/LBhU1DhPnYfzdrQPO3JKfaxwjatnrP/Fa7U6mvd6jXtHLiQgRFZP9iTdu3dn3759PPXUU3zwwQdcuHCB5OTkPKOcAgICCAxUc5dJccOUyl9ZJ/ki7U+eSV7CO9+sJuKqjfP7AFZeT4QQ9vn4448xZk9pqVy5MtWr2zfzwVHk3b4X6F6rAWEp6Rb7N2Uec0E2wpmSTOn8HvEk3fwsG8dZ6/GUu/C08yCkWf6zER4qd++N/NRuM4jeL64lslztfGNurwVtmljun70o1xdRwO8oxSdbjMBDwC0r4cmIJ8c4tVf9dDud3p/yte8q9HPFRMP4YZb7T1+Aj+cV+rLCFZ5D9YqUzbe+Tp3YIapiH+8Htaqou+7lWGWxC+HebI2qrFT/Xnq/tIHSVZo6KSNRVDNnzsxzA0ij0RAcHExU1M0CYmxsLCkpdgxfvFUGVJlXsuAYP2B44Z9CCKFISUnhn3+Uu70ajYa//voLvd6yLY+zSeHJw1WOgepXLG8P3NBm8Y+hgDHwwiucN8YTpPFnWfjjDAzI+ybP6ognzc01zf+wbAkkPFh6chzxl20Xm6PK3Ubv8WupdcfDVo+PHGC5LzUNFvyc/UUgyvS6uioTewpYarlbRjw5RsLVk8Se3W87MFvbh6ZSsmKDQj3Xu89BSLDl/pc+kqK2R+kBTFEXWuvQQzTYO5q67R9Hoy24sVt4KLz5tOX++ARYsUkZQbnzHzh0HM5eVG6GCPcXq2I6b1iJinR/7lfqtX/CCRmJooqJiTE/Pnz4MKmpqXleo2NjY2nXznaDcFvOz7yBxmjjRtMTqOsZKYQo0LBhw8yjnqKjozl79iy1a+d/49kZ5N2+h3v52UpUPmF5B+IvzmG8dZkZ4XUuGJWVzPw0Or4JfZSRgW3Nx3RWejzptTc/DW6RwpPXKWi6XW56/2DufORT2j82G7+AUPP+0GAYZGXxi0W/QdwNlDeD36M0FFfjbWC29UMy4slx7Jlup/cPotMTCwgMtXEn+hZN68Hg3pb7t+6F71fadSnhSg2Bhah6N1j2fGvabPoIDRrCSlSkcsNuBca/9DiULmG5/7VPoftIuHswNO8HdbtDpQ7KPuE6dXVlCdfYnkqVlnSNxGtnbMbp9P7c0e89Og6fh19gWHGkKIqZXq9nwYIF6HRKpefGjRvcdtttBAcHExMTQ6dOnbjrrrsoXbo0hw8XvX1H7KkkWq+uWnBQReC+Ij+VED5v586dPP/88+b322XLluXgwYO8/PLLLstJCk8e7PZa0LB5E6uNxdcl7XV+QsLpUskkzqgUHrUaLbNCH2RCkNJETmu1x9PNEU9/7XVKisKJrubTYDw/NZr3o/dL680jXh7qDmEhlnHmpuIzgF4qL/4/4PX8D8uIJ8fJXXhKjD3N8Z0/8feSV1gxrafVUXFhJSrSYdhcNFr1w7CnvmR9/3OT7U5XuEpZYDkQaisQQq9XotOq+eiM/uZ99e/Ov1JUKQaeG2y5//DJW6btCrdRUxdNXIn3OR81iXXho5ke0o+nA++kk19tymsj88Tas4hB1cY9lNeZCrcXc8aiKOrXr8/Vq1cZNGiQed/x48fNjy9dusS6devYvHlzsT5vhS8ibQc9WaxPKYTPmjZtGq+//rp55JNOp+Odd95hz549REZGOj0f10/2E4X2zhi4frQZEUlpFsc2ZhyxPEF4pQvGG0Rpb853eTekB1GaIP7VWf546zXKiKeDx+B6vLMyFM6idsRTbhHR1en5/Cr+WvwiowZ8bXF8z7+wfT8wERih8qLLATtGL8iIp+KVcOU4v312P9fPHyQ1Me/CE2s/H0TPcavxD8o7AiGmVjta9nmTv398xeb1+94D7ZpZ7l/4K2zbV6TUhbMEoUyZragiNg5iXmhPYLO8w5fKVm9FqUqNibXSbPrdMRBoee+D8VMgK6swCQtH+zljPxNSlvN+SC9idBF0oFae44nGNP4zXOY/w2Xitp9GH3iWi6XDuRoVikFf8NyoiNLV6PHCKrb+MIHDfy5w5LchVHj88ceZNWuWeaQTQFxcHP3793f4c/+38iqVz5TgdKXr+Qd1BqoDx/MPEUKoM2nSJJYuXcr69euJjo4GoFGjRly8eJFBgwaxZMkSp+Uit5k9VJsm0ONu0OyrZnEsWWdib9Z5F2QlXOG8Md5i37jgTgz5bT+a7Ap3Dl32iCeZZuedrl/4l6wM+5t/6vwCqFYliEZ1LI/NXoTSc+ENlRfbCjwIGAoOkxFPjnX+v40WRSeA+MtH2LjA+prV9TuMpEYLK0sa5uLvBx+8YLk/NQ1e+rhQqQpn0wDzgeYqYjOBB+D04uVWf7fU72BZjW5+Ozzcw/JSG7fDL+vtTVY40wepa5mfts3qsTBtIM39KjMosAXPXCzFk4v+4O3pK5jxzmLe/nQ5T363mb5r9nLH3hNUPRdLUFpGnvP1foG0e2gqde/Kv3u0Tnr7ONzjjz/O559/bi46ZWVl8dxzz1GiRIk8I54cJen2ljy0oKXtQLU3uoQQNh08eJAyZcqwePFi8/vvwMBAFi9ezNKlS532Plze7Xuo958Hg1FHyf8sl0Y/GH4NA0YrZwlvdMGYYHX/nXtOMuKHP9Fn3awA+GX3eJLG4t7JZDTYNQUix/ULh3ig/pcW+xOSYAEoy6yrcQjoDqTaDpUeT65zZv9v7F7xvtVjbR/6mFKVGuV77jODoHoly/0fz1MaRAsP8BbQT2Xsk8B6ZfGCo9ss58hVa9Kb4IhyefZ9NN76pV74wK4shYs8kfQ9WzLVFyB0RhNlryXS+L9zdN3yL0OX/s3Lc1bz6XtLeGda3n5zGakJnNxjufJm9Uqw5BMoY1+rOWGn6OhoZs+ebX7NjYuLo0GDBkybNs1pOSReO02zg4Pxy7BRZRwGhDslJSF8xoMPPkivXr1ITVXeqGs0Gnr37s2VK1do1KiRw59fCk8eqMfdyoinC0m3UePUNYvjV8rL0jC+xNqIpxxN/z3L0ws34Z+hzG3QaZQ7kDLiyXtdOWn/X+4/K19iYFfL+S9v7IO0eahbYeY80AUoYPR8bjLiybV2//Yhp/f/ZrFf7xeY3Wy8lMWx0iXgVStTKC9ehclzHJGlKG4lBgfDqyqDp6D0ast2cKPlX7JW50fdO4eav+7dyfo0zK9/gV3y1sQjZJBF34T/ccpg+f7SXoEZeV9Xdv36HqkJl81fR4TBlPHw73KICocLli1LRTF65513zK+38fHxVKpUiUOHDjk1h/hLRzhTth79fmlccGAJYIJTUhLCpyxfvpzo6Gi2b99u3leyZEl27drF5MmObdQp7/Y9jFarLGENcPHoHUQlWA4tCGlpf58X4blyVrbLT73jl3huwXqCUzPw02Zw7hKcvuCk5ITTXbGzwfiJXUvpevsWgm5ZzOigP3zyAEovGFviUYpOthc6MpMRTy5mMrFx/ijiLx21OBQaVZ6Ow7+0aDb+5mjlg+KtXv0Ekuyf4SmcoKa2NMMC7mB+6CAWdR1C4hfptk8Cpf/Ti3l3xV86zNl/11mE3tZ2MDq/IPwKmIb58lT7cxeuc9WURI+Ez0k0WvYQtUdawM3fIdfO/cO/m+cCypS6Jx+CY7/D80PA3x8WrijSUwkV6tevb3786aefkpSU5PQcMlJvsLckDP3mLtvBzwFVHJ2REL4nKSmJli1bMn78eAwGZWaMVqvlxRdf5L///jP3gipuUnjyMI/0gPo1lcdZu+tbHM/wh3pd7fj0JzzeeRuFJ4AaZ2MZ99Va9PF+bNnthKSEy1w9pX7EkyErg20/vc7IW9r6nNVDhzJgjFBxkXSUle7+sSdLKTy5g8y0RNZ8/ggZqZbTdcvVbEOr+yeZv65bA56wMj1rz78wb6kjsxRqadBwuy6GpwLvZFHYEC6WeIcjJV7nf2EP0ea2qjz59WIy/W00XwPYCzwE1mbsH9ww22JfYGhJajR/gFEDoGZly3M+ngfnLtn5zQiX+8dwkYGJ8zCaCt+6ITXAz/z4r0XjMBkNdL0T9i+DGa9BqSjlWEYG/Li6iAkLm4y5+n5mZmYWEOlYV2OPE3G9LbcfjCk4MAB4zykpCeGTPvzwQ2rXrs25c+fM+2rXrs3Zs2cZPNjK0rRFJIUnDxLgD289c/Pr4H8tp0LE10oluoyRmlWcl5dwLVsjnnJUuBxP5HMP8+8f0kTBm6XcuETi9bOqYnV6f9p3bMhtudYoiNNCl4pwJUTFBYzAw0AhVluWqXbu4caVY2ycZ30JwnrtH6dmq4GA0rfHWuPfse+DUVoKuoQeLc31lXk+qAM/hz3BtRKT2R81gemh/egf0ISyWqVBSnx4Cj2++YJrJZNtX/Qi0APIJ/TcoQ3EX7JcNbdBp5G8ZqVn/eVYeP9/lvuFZ1iReZBxKT8X+vw0f6XwFHtmH6V021k1B1Z+rhSyc1u5GeKtt6sURTRixAiSkpIwGo20bt3avP/SJddVg+MvHeVA7Qq8Mu1e28EDgFYOT0kIn3X8+HEqVqzIl19+aX5v7u/vz1dffcWaNWvQ6y1XSS8sebfvQUYNhMrZNwfSDcFUOGY5ZN7Y4CQAbWxMnRbeo6AeT7fSXijB03+OoY6urOMSEi539aT66XZV7v6QlEzlA2qqBnpWgH+tLINu1dPAj/bnBzLiyZ2c+ed3di5/1+qxNgOm0KNXE7q0szy2bK2yUplwnoa68rwS1Jnfw58kruQHbI98gSkhfegZcDtR2mCL+Ey9gf7/+4pDtW1/yNSnaqEncK6AIJOJfzZ8brE7osxtXDG1t9g/cTokqqh3Cff1cep65qZtLdS56dlT7UpVasj8OV3p3NZ6nEyzc5ypU6cSEhKS53U2LS2NBQsWuCynG5ePcqBmDA/83IRWO6rYPmEqymqcwme8OFwpUg/oBoFq35OKIhk2bBidOnUyT8HVaDR06tSJ2NhY2rRpUyzPIYUnDxEeCq/kWlr0/PE7KBVv2VQjvLny5qBNE2dlJlztsjERgx1D4aNNkfwRMYZmeitLUwmvcCVX4cloyCTx+lkuHf+b2DN7LWLTNeVYcvwNsoCHYmCL5WdX6yYBswqfo4x4ci97f/+YU3stP/3p/QKp3nU+N9LzzvfPzITxHzkrO5HjlPE6/hodd+irEqop+N24CRPPvLuENXf/p+raC54axIFjE/g1fCQzQvozPqgT/f2b0FJfhbKacDTZn/yObV9MWnKcxflrz+Zd//zgMfjfEpXfmHBro5IWsSnTsh+cLXWPX6Lfqt0EpWaw6Nj7pGWFWsQkJsOvG4shSWFVYODNBo7JyckcOHCAli1bkpGR4bKc4i8dITE0kJMVSvPx631tn9AKeNBmlPASocEwfhh0vRO++wgu/QFz3ra+cIUoXuvXr6d06dJs3rzZ/D49IiKCP/74g+nTpxf5+sU3dko41AtDb86FB0je0dwiJlOvIbiB0mhFRjz5DgNGLhsTidGpacijKKkN4aWge3ggca4DMxOucmzHEi6f2E5y/AVSE65gyi5M+gWG8cCrfxESlbevwuYLj7Ky1QcsC1M59H4u8FrRcpQRT27GZGLTgieJKLOaqHK18xxKMsQw+58veb5xH/RapS/I9IVw9JQL8vRxN0ypTExZyWepm3kp+B6eCmxHoMbPauynT2xi9pAtqq476d3uDFzeDPRQH+t9V9JNmZwxxnHaEEfyvJ8w1WvB9cgQrkWGEBsZygHu5VJyDcqGHANg3IdgUNFSSri/TAzcnzCXbZHPU11XWvV5eoORe7f+xx37TvJL+9v5ucTLPFjn5TwxS9cqDeiFY2RkZBAQoBSpZ82axbhx41ycEcRlT9fdV7s896+N5cGlTVjUx0YD0snAMkD+rXi9px6CEpE3v44Ig+EPwJFT8Iesn+VwaWlp3HXXXYwYMYLPPvsMPz8/NBoNTz31FO3ataNx48Z5+sXZQ24ze4AypWDsLf299PstR6tcrQb4K0vX1qme94dWeDe1fZ5ynDPEMTJpkYOyEa6WlhTL1dO7SblxyVx0AqWZ9J/fZy87ZTKhyX7h2NnyHRaVVVl0Wg6MsBllkxSe3E9mehJrvhhEesoNMJmoc/wSIxZvwS8zi2M37uCHY28BcC0e3prp2lx9XawpiReSl1I97k1mpf5BpilvhWdZ13089/ZPqq41aFFzXp5qu9dKgMaPmrpoOvnXpteVIHpvOMDQpX8z7qt1jPtqLQDrzim/HNb8Bb8VovebcF/XTMn0SPiCG0bL1ZRtCUtJ5+GVO9EvbsvJhLxD8hf+WlwZCmvmzZtnfvz888/z33//0b59e5flA5ASf4GM1ET21y4PwOS3exKQZmMsRGXgdcfnJlwrJFhZ6TI3o0nDG9PhQ7lX7lSff/451apV4/jx4+Z9DRo0YMeOHYW+phSePMB9dyk/iLmVOOZvEZda/3Ker1s3cmBSwq3Y6vMUHxrI3jrlAMgyGRiQOI9Yk/OX0RWud+af3zm+80fa7zjK2PnrOVxnGvuaqlzrfCvKcPdiGMWQ+26JTLVzHylXThIx6y1em/0bYxesp9nBM7Tee5II/0s0L6MUMt6YLo2A3cUF4w2eTF5M7bi3WZC2HaPJyNZmJxn4+XxMWpPN89tsq8acsQPN0+gK61pECBqMJGdGYDDACx8U6XLCTR0yXOLBxK/smt6fIyEkgOPt09FrbvYnvXIN1v1dnBmKWz355JOcOnUKUG7y1K5dm3Xr1tGyZUuX5hV/+SgXSkdwJSqUKmdL8tznd9s+aQKgoh+58FwjH4TSJfLu+2nX+9wROJXGZUuiQYNOyhdOc+7cOWrUqMHcuXPNN4ybNGnCqFFWVhNRQf7mPMCXP0Kf0coLdI4Xqr7M5DZb+L35DY5X9iNDryWw6d4850mfJ9+R34inuMAslrdP4NuXznPkqQNcNN7glZRf+TPrhJMzFO5Eu2Q2D/62iz/b/Mwfd7+t7qT/UFa7sv9mt1Uy4sm9hGoCGBN4N8eiJvJZQlMqXYo3H+u0ZS8PRXeiRsQODh2H2TJY0u2cNF5jcNLX1Cr1Np2/mUFakO2l0queKsnSwcMJyLA+Vc8e8QHneaFuS56o/wTzl8H+w0W+pHBTv2ce4rnk/EfTnTRcY12G5T+AP1qf4vl2XagYdtC8b/EqyMpySJoim9FopHr16mzYsMH8uqvValm2bJlL84q/dAQ0GvbXLk+mTkuPn/sRcSPQ9onfQD6zgYUHu2fEN/R5aR017/2Izecf5XRiA7KMfmy92J8S3zWg83w9cxpN5Z1yffktfBQlNSGUVT/rVxTR8OHD8/zOmDBhQqGuIz2ePMSytbBlF8yaCH06wfe/XyEraxGgfAKoUU7P4TtufpA7f1nZhG84b6Xw9E7K70yK/Z20JZmwBPz9oLmpGn9lnXRBhsJdlNdG8q3f/Sy7bzdPfPSdupMuAJ2Ba7YC1ZPm4u6hnDacZwLbMzKwDZFWVkUDKBufyegBpSnf7yKHjssHRbdVGo7/GgslbYeGJwTy68MjKH0tDICzhjieTF5MBW0klbUlqKIrQWWtsqnpH7h67zqm9j7By0/Ax/OK+H0It/dZ2ibq6soyMijvMnWzUv9gfMrPJJnS6eZXj4/D+1FbU5J/TVfp9d1kPqpi5NlHb8Z/K9PsnMJoNNKhQwfq16/P3r170el0lC1blsmTJ/PSSy+5JKf47D5PK+6sx/ouZXijfUveT01jpK1fN6WBhUBHimX0tXA9v8AwKtTtiE7vz87rjdh5Xdmv16RjQsNdJZXPLU133qAp7cEf3uwZTZ3eJ+k4JP/riuI1YMAA0tLS0Gg0xMTEoNfrybLzDaEUnjxIbBz0GwOd21q+8a9YKYtrifDD7/D9SqVIZbI9yl54CWsjnkpogknj5l3vjEz4Exnp5Mv80fNj2DD2dDrHQ7PnY9Sp+CVxA+gCnCneXGTEk2vV0ZXlhaAOPBLQHH+N7bcCT/u3p/2C/U7ITBRKEEr/tRq2Q3VZWn4YNpS6R8qZ9w1PWsjqTOur3/mjp6I2kiq6klTWRlE5pyiV/WdFbSSnDNeIS1EaigvfMDr5B2rqStPRvzZnDNcZlrSQtZk3RzqtzDzI6muHGBXYjn8MF4kubaRfl5vnnzwHf+91ft6+7J9//mHq1Km88ILS63HcuHFkZWUxadIk0tKc27U7/vIRDJnpXCOJCkGZnEhoxjDdFmZEwgFbA5/uAiYiPZ+8RMXsotOtskxKU/xLpcLz7N9/h46nJp8kK0tpPH4j0Slp+ryMjAyuXLlCmTJl0Ol0HDt2jLvuuovTp0+rvoYGkPKEFwgLgZQ0WUHGV7XWV2VayP0A5l4dGWTRNWEWCSZZAkQo5oQOpHrHUnT7braqqTikoYx0ckCT4F9//ZX77rsPgPfff99ld119zZ36GowL7kh3//qq4m8YU/k87U8+Sdto9yIGwkm0wI9Ab3Xhs59/kBELbo5UmZu2leFJCwv99Dq06NCSgQyF8zVRmmDGB3Xi3dTVJNp4r6HRQL0aypLo7ZrCroPw0VdOSlTkceHCBcqVu1l4NhgMLFiwgCeeeMLuEQzF4eJmzNOm9gVAy8qQbmsgtBHl/claBycnHO7uIXOo3qxvvsdLxCfz/tSfAUgI98P/21cIjFSG4A94HhatdEqaAnj22WeZNm2a+Wuj0cjixYt59NFHycy0/blCRjx5icRkV2cgXOmvrJO0uDHF1WkINzYisA117ixL52/V9X8hC+iHQ4pOIFPtnEmHlj7+DRgX1IkWfpVVnXPWEMe0tI3MSfvL5gdK4WKfoLroVOv9aNp+Wd387u+8IZ7nk5cW6ekNGDFQuKWVhWeLM6UwIeUXVbEmE/xzVNlmqZzlLRyjVatWHDx4kNDQUAB0Oh1DhgxhwIABjB8/nunTpzs1n92HoFt24alhOnxyBUaWtXGSFvgWaARcdGh6woG0en8q1r+nwJi48GDS/XQEZBo4O/wQ9SJv9n3o0V4KT870ySefULlyZcaMGYNGo0Gr1TJgwAA6dOhAmTJlbJ4v7/aFEMLLtdZXZXCbFnT7bhbJIRnqTnoMcGD/DZlq53jB+PNU4J0ciXqNH8KHqSo67c86z6DEBVSPe5OPU9dL0cndvQA8rTL2Gzjy0hUaxL/HI4nzOW64yhNJ33PDVEwrBgghPMKZM2eIiopi0qRJXLp0ybw/KCiITz/9lCFDnNs4Z8+/eb9+Ih7uUTN7Jxql+CSfZj1WTK22+AeGFRhj0mq4UjKMPbdV4KfyPfK0kunaDnQ6Bycp8hg7diwtW7bk5MmbPYMDA1UsDID8qAohhFcrpw3nrVb3cd8Pn5MQrrKIMBrlzZwDSeHJcUprQnkzuBtnSrzF9NB+VNOVsnnOmoz/6HxjBg3jJ/NN+g4ypWur+xsAqO2ptA4Yqjw0YuLb9J3UinublZkHCzxNCOGdsrKyeO211yhXrhyjR48mNVUpQGs0GmbNmsX48eNp1KiRU3LZfUvhSQN8nwxl41ScfDfS68mDVW3UTVXcyZiSfNetGWeSGnPw+t0YDLB0LfQfK21mXGHHjh1Uq1aNxx9/nKSkJNXnSY8nIYTwUv7omdfsEcas+JEr0Sq7L74OvO3QtABYtmwZvXr1AmDq1KmMHTvW8U/q5WpqSzM2qAODA1sQpLFs1HmrLJOBRem7mZK6nr2Gc07IUBSbO4HVQICK2ANAWyDBoRkJITxYZGQk169ft7gRFB8fT5kyZcjIUDla2k4B/sqK3UOstPjZEwB3qO33dA+w3gEJCsfRaBj20T9oAmzNq4Tg1AxSgpT3NQEpf/LzRz05d8nGScIptFotH374Ic8//7ztWCfkI4QQwgVeq9+ZF35Zqr7o9CFOKTpB3hFPomju0Fflp7Dh/Bf1KiOD2tosOiWZ0pmauoHqcW/ySNICKTp5mjrAMtQVnc4B3ZCikxCiQPHx8SxZssTitTkyMpK2bdvmc1bRVCkPfy60XnQCaJwOU6+ouFBOvyfbLWaEGylXramqohNgLjoBpAe3ITOkpaPSEnYyGo2qik4ghSchhPBKA2s05X8rtnKhnMqVyKYD4x2aUr5kqp39NGjo5d+ALRHP8VfkWPoENESrKfgl/aLxBhOSf6Hi9dcYm/wTZ4xq5jEIt1IJWAVEqYhNQCk6SV1RCKFC//796datG7///rt5X1ZWFhs3biz257qvPez+EZrWKzhuZDz0U1M4L4v0e/IwXfqom2ZnTcV6BTckF+5JVrUTQggv06RCBbb/fprTla6rO2Eu8IxDU7IgPZ4KJwA9jwa04PmgDtTWq7u9+2/WRaakrufb9J2y5L0nK4OydHglFbGZwP0o0+yEEEKlVatWsX37dmJjY9FoNGRmZmI0Fu+qlc8PgSkqb3RpgDmXYHcgHLc1g7wj8CrwVtHyE46n00HpWt24rmKR5dyS4y+x7cdXOLF7mUPyEo4lhSchhPAi0aVDSVqTwfFqsepOWAg8gdO7/eUuPGm1covSlhKaYJ4MbMfTQXdSRhuu6pxNmUf5MGUdKzP/xSTtHD1bFLAGqKkyfhhKkUoIIex0/fp1MjIyCAgIIDAwkNDQULsaCNty/Kx98RFGWHxe6feUYevtwkTgD2BDIZMTTjGwX02uZ6p9QQOj0cC/m/7Hrl/fIzNNZfsI4Xbk3b4QQngJfaSWiLVBHLlNTVME4CdgMEpjTifLfQfVl0Y8RWqC7Iqvoi3JpyEPcKbEW7wd0t1m0clgMvJD+h5axE+h/Y1PWZF5UIpOni4U+A24XWX8q8DXjktHCOH9zp1T5uhqNBrmzJlTrNdethaW21kYapIOw/5SEZjT7ym6EIkJp9BqoVOP+1THXzm1i58/6MTfS16WopOHk8KTEEJ4gzCosDqSow2uqotfgbIcu4tmXvli4enloHs5FjWRDn61bMY21Vfk+7AhHIt6ndFBdxGiKbiTdIopgxmpm6kV9zb9E79kR9bp4kpbuFIg8Augto/qHOAdx6UjhPANb799c6WR/v37U6FChWK9/uhJkJJq3zlVfgZ+UBFYDvgG+ZTrpvp3gcNJtvs7pafEs+W751k+pQvXzu53QmbC0eRHUgghPF0QlFkZxqnmKns6rQMeQOkD4yK5p9rpdDrXJeIkE4Lu5Z2QHpTUhrA6/CnGBN5tEaNBQze/emyIeIadkeN5MKAJOhsNw68aE3k9eQWVrr/O08k/cMKocoqlcH9+KB+yLP+pWLcCGOW4dIQQvmP+/PmcPq3cwNBqtRw7doz33nuv2K5/+gK8NdO+c/49BgwHjqsIvgeYYH9ewvH++rccV7OaFhhzdNsilrzViv+2zMNkcsGwfOEQUngSQghPFgDhvwRyua3K4cdbgF5AmiOTss2Xmou/FHQP74b0MH+t02iZGtqX+aGDCMQPf/Q8FtCSA5ETWBExkvZ+tvseHDVcYWTS91S6PpG3U1dxzZTsyG9BOJsWZbpcd5Xxm4F+gMFhGQkhfMyjjz5qfq0OCAjgpZde4uLFi4SHq+szaMvH8+HgMfXxB4+hrNb5IJCh4oQ3gTsLlZpwoJCKXfI9FnfxMCum9WTTgidJTVQ5gl94DGkuLoQQnsoP/JfoSOiksoq0A7gPcIMaha8Unl4Muof3QnpaPfZoYAsa6ytQUhNCjC5C1fW2Zp7kw9R1/JyxH6P0bvJen6N8uFJjJ9ADsHPaihBCFGTz5s089thjfPbZZ+ZiU9myZVm4cCHdu6utiucvMxNGvQmbVfakO3Qi+8Eu4HngMxsn6IDvgEaA1DDcRuUGltPssjJS2PPbRxxYNwOjwYXD8YVDyYgnIYTwRDrQfAsZ3VUOcdgHdEa5W+gGcvd48tZV7cYHdWJyPkWnHLfrY1QVnZal76dN/Me0vvExSzP2SdHJm32MMp1EjYNAF9zm51oI4V0WLFhAREQE3377rXlfw4YNi+36f+yEeUttx506D8kpuXZMB35U8QQxKKNHvff+lkfxDwonpna7PPvOHFjFkklt2Ld6mhSdvJx3vtsXQghvpgG+AlM/lfGHUPodxDkuJXt5+4in8UGdeD+kV5GukWbK5Iu0P6kd9zZ9EufwV9bJYspOuK2JwHMqY4+j/Fxfc1w6QggBMHbsWPPjMmXKFOu1x30I1+MLjrE6JW8YoOZlsTPwkt1pCQeoULcTWp0fAEnXz7Hm80Gsnv0wSdfOuDgz4QxSeBJCCE8zGxikMvYY0BG3G2buzYWncUEdi1R0um5MZlLKKipfn8iIpO85YrhSjNkJtzUWeENl7DmgE3DRYdkIIYTZlStXyMxURqP4+fmRnJzM1q1badu2bZGvHRsHL35ccIzVwtMNoD/q+j29DbSzGSUcrErDbhgNmexf8ylLJrXm9P6Vrk5JOJEUnoQQwpNMA55QGXsapejkhh9OvbXw9EJQRz4I6V2oc08arjE66QcqXX+d11JWcMWksmG88HzDgY9Uxl5FGel0ymHZCCGEhZ07d5ofBwcH06pVKzZv3syBAwdo0qRJka49dwn8tSf/4weP5pcUMF7FE+T0eypld2qimGj1/ugDQlj6Xnu2L3uTrHQ3aDgqnEqaiwshhKd4D3hWZewFlKKTm45ezl148pYeT88HdeDDQhadEoyp1Ix7CwOybLDPeRClmbga8cC9wH8Oy0YIIaxq27YtH330EU888QTBwcGAcuOofv367Ny5k4sXL3Lq1CkCAwNJS0vDaDRy8OBBnn76abKysgq8tskEI9+A3T+C3sqn0wJXv/sEaA/0tvENlAcWoCyyIm0Snc5kNLJ69kPKX7bwSd7xbl8IIbzdq6jvUXAFpeh03HHpFFXu5uLeMOLp+aAOTAnpU+jzk0wZUnTyRd1RGt+qeTeWjPKBaa8jExJCCOuMRiPPPfccISEh1K1bl02bNplvImk0GmJiYmjdujVNmjShdevWtG3blhEjRpCUlMTSpUt54IEHCrz+gSMwbYH1Y+YV7fIzBHWjQLuiboSUKHYmY5YUnXycFJ6EEMLdPY/Sn0CNOJRpOG4+IsKbptqNLWLRCSBVVZMK4VXuBn4A/FTEpqPczf/LkQkJIYQ6hw4don379tSuXZtt27ZhMOS/wm5AQAC9e/fmhx9+YOvWrQVe940ZcPaW9gAnzkJKqo2E4lFGj6pZFG0S0EZFnBCiWMlUOyGEcGejgCkqYxNQpuHsd1w6xcVbCk/PBd7NR0UsOgGkmmQJYZ/SEvgFCFQRmwUMANY6NCMhhLDb0aNHadWqFXq9ngEDBlCyZEmSk5OJiIhg9OjRVK5cOU98ixYtCrxecgo88y4s/ezmvgKn2eW2HXgRsNGoHD3wPdAIWRVUCCeSwpMQQrirx4CZKmOTgW4ojTY9gDcUnsYE3s3HoX2L5VpSePIhtwO/AaEq4x8DljkqGSGEKLqsrCy++eabPPs++ugj7rnnHn777Td0Oh2g7vV+2Vr4dSN0b698rbrwBDAVpd9TTxtxFYD5QA+k35MQTiJT7YQQwh0NAOaqjE1DeZP1p+PSKW65ezx5YnPxMYF3M7WYik4ACaa0YruWcGM1gTVAlMr4UcC3jktHCCEcac2aNVy5csX8de6V8QoyetLN6XV2FZ5AKdafVhF3H0orAyGEU8iIJyGEcDe9Ud9wOAPoC6x3ZELFz5NHPBVUdIozpnDdlMx1YwpxphSum1K4rmJfOgWv+CO8QCWU6XJlVMa/CMx2XDpCCOEM3377LS+88AIAycnJqs45dR7engXvjS1E4SkO5ebdZmz30HsP5aZdwa2nhBDFQApPQgjhTroAi1D32zmn98tvDs3IITy18FRKE0oGWTyUOC+7eJTCdWMy100p3DClYpQx+8KaMihFp0oq498BPnBcOkII4SzXr18v1HkfzYOHe8B/tla0s+ZvYAK2e2Tm9HtqDBQuTSGESlJ4EkIId3E38BPgryLWCDwKLHVoRg7jqYWnWFMSM9P+cHUawpNEAatRptmp8RnwquPSEUIIT5CZCQ88C6mFnYn+MXAXSh+nglQC5mG7L5QQokg8r7GGEEJ4o9Yoq1wFqYwfDnznuHQcLXePJ08qPAlhl1CUEYkNVMbPA551WDZCCOFRDp8swskmlH5PZ1TE9gDGFuG5hBA2SeFJCCFcrSmwEvWrXD0NfOW4dJzBU0c8CaFaIEoxuaXK+CUoBWWZrSmEEMXjOkpLAjVtFCej/ve1EMJuUngSQghXaoyyylWEyvhxwAzHpeMsuQtPnriqnRAF0qH0DblbZfwq4GHA4LCMhBDCN20FXlYR5wf8CFR0bDpC+Cp5ty+EEK7SAPuWVn8d240yPYRMtRNe7XOgl8rYzSgrU2Y4Lh0hhPBpU1BGlttSHmV6dKRDsxHCJ0nhSQghXKE+sA4oqTJ+MvC249JxNplqJ7zWO8AwlbE7UXqLpDouHSGE8HkmlAVZzqmIrQf8DAQ4NCMhfI4UnoQQwtnqohSdSqmM/xRlWWAvIoUn4ZWeQd2UDoCDQBcgwXHpCCGEyHYN9f2e7gS+BuTtiRDFRgpPQgjhTLcB64FolfFzgDEOy8ZlZKqd8DoDgU9Uxh4H7kH5ICSEEMI5/gReVRnbD2XRh3DHpSOEL5HCkxBCOEtdlKJTGXXhft/oYCReucqVjHgSXqUzMF9l7AWgE3DRcekIIYTIxwfAUpWxfVGmRN/uuHSE8BVSeBJCCGdogdJEuJy68PDvA8l8zABG27GeSApPwmu0QFkJyU9FbBxKkeqUIxMSQgiRLxPKKqJ/qYyvCfyN0iNKCFFoUngSQghH64hdjcTLL4kg4ZE0r15aXabaCa9QG1gBhKiITUVpJP6PQzMSQghhS87v48Mq44NRRrV+jjQdF6KQpPAkhBCO1Aflg2mouvC6y8pyfuANry463UoKT8IjlQdWo26RgCygP0p/ESGE8CFu+xp/HWWBh0t2nPMEyu/xKo5ISAjvJoUnIYRwlCHAD6i+O9bq1yoc6n9J3YorHk6m2gmPFgX8DlRSGf848Kvj0hFCCE+Q+7XfLZwCugGJdpzTFNgN3OeIhITwXlJ4EkIIRxgLfAno1IV3+K0m/95/CVOmI5NyH1J4Eh4rCKWIVE9l/IvAPIdlI4QQoij2oCz4cMGOc6JQXgfeQfX7PCF8nRSehBCiuE0CPlIf3nVVXS7en0hCRprDUnI30uNJeCQ9yijG1irjP0JZQUkIIYT72g40ATbYed7LKFOuo4s9IyG8jhSehBCiuGiBWcAr6k8ZtKg5AffrOZRqT5MBz5d7xJNWKy9FwgNogP+hfnrF18A4x6UjhBCiGF0G7gHes/O8DihT79oUe0ZCeBV5ty+EEMXBD/gWGKn+lNFz7qLWsDIsS9nvqKzclox4Eh7nfWCwytiVwFCUZbuFEEJ4BgPKKKaeQLwd55UHNgLPFX9KQngLKTwJIURRBUHYLwEwQP0pb7zflS4v1GFi8grH5eXGpPAkPMo41I9e2gr0wycWCRBCCK+0HGXq3W47ztEDH6NMxw5zRFJCeDYpPAkhRFFEgnaNhsQu6apP+XTCAzzyfnMeTpyP0UeHREjhSXiMYajv0/Qv0B1IcVw6QgghnOAkSj+/OXae9wCwE6hf7BkJ4dGk8CSEEIVVBtgIxjbqike6LC1fj3qUoXNa0TthDvGmVIem585kVTvhEfoAn6uMPQt0Bq47Lh0hhBBOlA48ATwG2POWrRawDXjEATkJ4aGk8CSEEIVRBdgCNFQXHpjqx9LBw3lkSXOGJH7LP4aLDkzO/cmIJ+H27ga+Q91S2deAe4FzDs1ICCE8jlcsIDIfaAUcs+OcYJRFJmYBAY5ISgjP4gW/CYQQwsnqAn8CNdSFhyUGsmrAKHqsvp33U9bwQ8YeR2bnEWTEk3BrTYGfUfdhIRllpbv/HJqREEJ4vNyv/R5nP8prw1I7zxuJcqOySnEnJIRnkcKTEELYowWwGYhRF176aigbez/DXX/VBJCiUzYpPAm3VRv4DXXNYTNR+nlsc2hGQggh3EEC0Bd4AfsWkGgG7AK6OSIpITyDFJ6EEEKtjsA6oKS68Irnovijxxia7K9o3vd8UEeHpOZpZKqdcEsVgNVAaRWxRuBRYJVDMxJCCOFuPgI6APZ0TSgBrADeRj6BC58k/+yFEEKNvihvGELVhdc+Gs2W7mOofbxMnv39/RtTVauycuXFZMSTcDslUYpOlVTGPwN877h0hBBCuLE/gMbARjvPexX1NziE8CJSeBJCCFuGAItR3Ryyyb6K/NFjDJXOl7A4ptNoeT6oQ7Gm54lkxJNwK6HASqCOyviJwAzHpSOEEMIDXAY6AZPtPK8jsAdoXewZCeG2pPAkhBAFeR74EnUrWwF3/VmDDb1HU/pa/g1ihga2opRG5dApL+XRDUaFd/FHaRbbQmX8Z8BbjktHCCGEBzEAE4BeQLwd55VHGS31bPGnJIQ7ksKTEELk5x1givrwHqvq89uAUYQnBRUYl2zKoLquVNFy83Ay4km4BS3wLcodazUWIh8ShBBCWPoFZdU7e9aQ8QOmoYyqV7OghRAeTApPQghxKy0wC3hZ/SmDFjXnxyHDCUrzzzfGaDIyK/UPasW9zbasU0XN0qNJjyfhFmahrEqnxkrgMUAG6wkhhLDmBMr0uf/ZeV4/YAdQr9gzEsJtSOFJCCFy80MZ1TBS/SnPfHEX80Y/gl9W/vPx/s48SfP4KTyZvJg4U0qR0/R0MuJJuNw7wBMqY/9EKVBlOi4dIYQQXiANeBwYCqTacV5tYBvwsCOSEsL1pPDk5SrUlSbGQqgWDPwMPKj+lDcnd2PaK/ejNVn/dXrVmMiQxG9ofWMquw1niyVNbyAjnoRLjUX9iMYDQHfs+wAhhBDCt30F3AEct+OcEOAbYCZK/0EhvIgUnrxYhbodaHjvGFenIYRniERZ3rar+lM+nfAAr3/UFQ2WhRODychnqZuoFfc289K3YZL5OXnIiCfhMoOBj1TGngA6Y1/DWCGEEAJgH0rfp2V2njcK2AJULu6EhHAdKTx5scZdxxNdtTl6/2BXpyKEeysLbALaqAvXZmn4etSjjP7fXVaPb8k8TtP4D3gmeQnxJhkmYY2MeBIu0RP1vTcuAfcCFx2XjhBCCC93A+gDjAey7DivObAbu26ICuHOpPDkpWJq30WZas3R6f0pW6OVq9MRwn1VAf4AGqiMT4XPBw3gkSXNLQ5dMibwaOLXtLsxjX2G88WYpPeREU/C6e4EFgF6FbHxKCOd7JkiIYQQQuTnQ6Ajyk0NtUqgLGzxPVDNEUkJ4TxSePJSTbqNMz+OqW19VIYQPq8eStPgGirjb0CtbqUZvrZ1nt1ZJgPTUjdQO+5tvk7fXtxZeiUpPAmnagQsBwJVxKYCPYD9jkxICCGEz9kMNEYZZW+PB4FDwDSgZDHnJISTSOHJC5Wr2ZayNe4wf13+Nik8CWGhJcobgBiV8VeAu+Gx7XlHEG7OPEbj+Pd5LvknEkxpxZyk95KpdsJpagK/A+EqYrNQlrXe4tCMhBBC+KpLKCOfPrDzPH/gWZSRuC8BQcWclxAOJoUnL9S42wt5vi5Z4XYCQ0u5KBsh3FBHYC3KEGY1zgDtgD3wQEAjAC4ab/Bw4nzuuvEJ/xikCYy9ZMSTcIoYlEUDolXGDwFWOC4dIYQQAgPwItAbpQeUPSKA94AjwIDiTUsIR5LCk5cpW+MOYmq1s9gfU9tynxA+6X6U+fKhKuMPoTQdPwK362Kooi3JlJR11I6bxML0nQ5L09vlHvEkhEOEAr+h9HFT41mUZayFEEIIZ/gZZdW7vYU4twLwHbARqF98KQnhKFJ48jKNu46zur+89HkSAp4AFqMMV1ZjJ0pD4nPKl2W0YTSMn8y4lGUkyrS6IpERT8KhtMBC1C8a8DbwqePSEUIIIaw6DtwBfFnI8+8C9qD0f4ooppyEcAApPHmRMtVa5NvPKUb6PAlf9zLwOep/620EOgCxN3etzTzMIYM9y5GI/OQe8aTVykuRKGbvoTQIV2MW8LoDcxFCCCEKkvZ/9u46zqpqC+D479Z0093dXdKtlKhIKiqioBiooKiEomJgN/gwQVFRTLqkEaS7e6jpvvH+OMwww70zt3PW933m48w5++y7eAwz566z9trAA9c/0h24XotStXsYGA3I8zzhg+RuP4AUVu0EEFmiMpElq3ouGCF8RTDKU6RX7LhmMdAXSHFLRAKpeBJudC8wycaxPwCPujEWIYQQwlb/Q9lxeb6D15cG5gEbUZbwCeFDJPEUIEpVbUnF+t2KHCO724lipwLKznX32XHNVyh9oGQlnVvlTzwJ4TIdgM9tHLsMuAeQb0UhhBC+4iQwAmgJrHZwjrbAVuBToIRrwhLCWZJ4ChDNb9rJzpLy0udJFCcdgO1AazuueQ8lSWVwS0Qin/xL7aTiSbhEFeAXbOvhthUYDGS7NSIhhBDCMdtRWj7cBuxz4Ho18BBw6Pp/5V2/8DL5FgwAJSs3o1KDnlbHla/dEeQNnigOHkZ5SlTGjmteBJ4AZLM1j5CldsKlIoHfgVI2jD0DDATS3BqREEII4by/gCbAGOCSA9eXQKl82oZSCSWEl0jiKQDYUu0EEBIRR4mKjdwcjfCkuPL1iS1X19th+I4gYA5Ks2CdjdcYgXHATHcFJSzJX/EkhFNyd7Cz5ddbGjAAkD0ChBBC+AsD8AVQG2X3Or0DczQHNqH0gCrtssiEsJkknvxciUqNqdyoj83jK8hyu4BStlZ7Wg54wdth+IZyKDvRjbHjmmxgKMqTIOFRUvEkXGYW0M/GsaOAne4LRQghhHCbJOBJlCTSWgfnGI2y+93jKLvhCeEhknjyc8362FbtlKu8NBgPKGWqt6FK476UrtbK26F4VzuUtfDt7LjmKsrOdT+6JSJhhSSehEuMBgrf0LWgKSg9oIQQQgh/tgfoAgwDzjlwfTRK5dQOQN4aCg+RxJMfi6vQgKpNb7PrmrI12qLRBrspIuFpZWsoi7VbDZrq5Ui8aCxKpVM5O67ZBbQCVrkjIGELaS4unHYL8JmNY78FXnNjLEIIIYSnfQ/UBV7Hsc0yGqHcQ89H2QlaCDeSxJMfa9bXvmonAG1QKKWr27PNl/BVEXEVCY8tD0C5mu2p2KCHlyPysCCUJXKfYdsuVrm+B9oDJ9wRlLCVVDwJp1QFFmHbv/3N2LcEVwghhPAXqcCzQGNgmYNzDAMOApOw755aCDtI4slPxZavR7VmAxy6toIstwsIZWoU3Jqi1YAXi8+uhWVRdq17yI5rDCi/UIcB6e4IStjDYDB4OwThr+zZwe40MAjIcmdAQgghhJcdAnoDtwMnHbg+AqVyajfQy3VhCZFLEk9+KiP5Mke3/eTQteXrdHJxNMIbylRvU+DrEhUbUqPFYC9F40FtUPo5tbfjmgTgVuBNt0QkHJC/4kmtll9FwkZqYAHQ0IaxuTvYxbs1IiGECGhxqjBvhyDs8StQH5gBZDpwfR1gKUpVcVWXRSWEJJ78VWbqFdZ8+RBLPrqLlCun7LpWow1Gpda4KTLhKWVrtDE71qLfc6g1Oi9E4yEPoOziUd6Oa/YALXG8/LiY69Iawt1wzylL7YRDXgdsbW04AqWfmxBCCIe9FX47bbRVvR2GsEcGMB0lAbXYwTluB/YDU4EQ14QlijdJPPm5s/tX8fMrtxD646cYDfpCx2WlJ3Hgn3ksfqMnv7zWGZNRlrn4s6DQKGLL1TM7HlWqGnU63OOFiNxMB3wEzAXs6Y3/I8pOd8fdEVTgU6ngrUnQupHr587fXFwIm9wP2Nra8Dkcv9kWQgiRx4iJpVHjaaWt4u1QhL1OoCw3vxU44sD1oSiVU/uBga4LSxRPkngKAOE5Jl7creLk2q/MziVdPs7qeWOZP6U+G75/msundnghQuFqpau1QlXI8qRmfZ9CGxzu4YjcqAywEhhvxzVGlDeeQ1CW2wiHDOkLLRpAu6aunzt/xZMQVnUEPrFx7DfALDfGIoQQxUiiKYNodSjLosbTQlvJ2+EIR/yNskT9ORy7L66GsoRvA9DVdWGJ4kUSTwFgdHAbSqkj6ZwaZXbu7L4VHPv3Zww5jizyFb7q5v5O+YVFlaFhF3u6bvuwVsC/KG86bZWIshRH3ng6RaeDVx5XPm/fzL2vJUvtRJGqYfsOdhuBB90bjhBCFCcJRmVHlhh1GMujHqW5RpJPfikb5d64LvCDg3O0B1Zd/7Cn16oQSOLJ76lQMT5UeVfe/bJ5b5+0xIueDkl4gKX+Tvk17jmB4PBYD0XjJqOBf4CKdlyzDyVZtcQdARUvY++CGpWVz9s1cf2GidLjSdgkdwe7kjaMPYXSk0J2sBNCCJdJNGXkfR6rDmN59CM01dh+cya/433MWWAo0A3lvtkRXVGqn/4CWrgoLhHwJPHk57rralNbUxqAVucyCM7KKXA+PUkST4FGrdFRqmrzIscEhUbRpNcTngnI1bTAB8A87Ovn9DPQFjjqjqCKl8hwmJpvaWNcDNSp5trXyN/jSW5KhUVqYD7QwIaxqSg72F1ya0RCCFHsJJrSC3wdpw5nRfSjNNFUsHsu6e/oQ1YDTYGJQLKDc/RFWZmwCHBDP1ARWCTx5OfGh9xYgxRkMNH48LkC5zOSZR/pQFOiUmO0Qda3GavfeQzhMfZs/+YDSgErgEftuMYIPA/chfLmUzjtqfugdImCx1y93E56PAmrZgL9bBhnRNnBbrd7wxGiuFKj4tGQTt4OQ3hJojHD7FgJdTgroyfQ2IHkk/AheuAdoA7wtRPz3I7yO3jB9bmEsEAST36sojqGAUEF08st950u8HVa0gVPhiQ8oEz11jaN0+pCaH7rJDdH40ItgO1AZzuuSQL6A68C8hDNJcqUhKdGmx+XxJPwqGEoTVBt8RzwmxtjEaKYi1AF83b4YDpoq3s7FOEF+Zfa5acknx6loaachyMSLncRuBe4BdjpxDxDUZbvfYnSn1GIfCTx5MceCumARlXwr7DRkfMFltulS4+ngFO2Rlubx9ZqN5zoMrXcGI2LjALWA/b0qzwAtEZZXy5c5sVxEGFhU8T2Td33mrLUThTQAvjCxrFfAW+4MRYhBJGqEHQqDT9G3U85tflGNiKwJdy01C6/kuoIVkVPoIEknwLDBpTfweOBBAfn0KAksQ4Bn2Jfr1YR0CTx5Kd0aHgwxHw7AZ3eSJPry+30OZlkZyR5OjThZmWsNBbPT63W0LL/8zaPDw+DAd0cicpBWpQS36+BEDuu+xVoAxx2Q0zFWM0qSlNxS+rVUHo9uYrBYMj7XBJPIk9ZlH/foTaM3QCMdWs0QgggUqU0XCynjubHyAfQofFyRMKTCqt4ylVKHcmq6AnU05T1UETCrYzAJ0AtlAc7hecdi6YDHkLpvfouUMYVwQl/JoknP3VHUFPKFPLUqeVeZbmdNBYPPFGlqhMaWcqua6o160+pKkU3I8/11jPQ1fa8lnNKAsuAJ+y8bhowGEhxdUDilcdBZ745Zp62TVz3WrKrnTAThNKg1Jano6dRfg5kuzUin1K/8xiqNRvg7TBEMRShurHTRwdddd4OH+zFaISnWerxdLPS15NPdTWSXQgYV4HJQA3gfRzfMTYYeBw4DrwOlCh6uAhcknjyU+NDOxZ6ruHR84Rk5pCRJI3FA00ZO5bZ5ddq4FSrY/p2goeHQiVPPLBqhrILRlc7rklG2bXqJaSfkxu0agRD+hY9pl1T172eJJ6EmU+BdjaMSwcGUux2sIs/vo3uY+bR78k/KFHJhVlgIayIVBUsSX40tBP3BNvWb1L4v3SyyTEZrI4rq45idfRj1JHkU2C5iJI4qgV8BuQUPbxQYcAk4AQwA4h2SXTCj0jiyQ810pSno65GoeeV5XZnSZOKp4BT1sbG4jcrV6sDMWVqF3o+Lga+mKl87vbE0wiUJTJV7LjmIEo/p9/dEpEAXn/K+hhX9nmS5uKigCeA+2wcOxrnmp/6qatndpEUf5SyNdsxaNIKOo38gLBoWdoiXKdExUaoVOZvDSLzVTzl+jTibppppHlLcZFYRJ+n/Mqqo1gVNYFaavuq84UfOAM8DNRF6a9oPRdpWSQwFSUBNQWIcEl0wg9I4skPjQ8pvNopV8u9p2WpXQBypOIp6fJx/ni3H4nxhTdE+mQqlLt+j1DJXf0hNcBs4Fts69+S63eUfk6H3BGUAOh9i21LLNs0Bo2LWnuYTDfK1qTiqZjrCbxl49iXgR/dGIuPO7Z9EQAqtZra7YZz17QtNO09EY3OniZ5QlhWtmY7ut4/F7U2qMBxS4mnUFUQi6LGEKcK81R4wosSrPR5yq+8JprV0Y9RU5JPgek4ygOghsAPTswTC7xyfb6J2PfeQPglSTz5mShVCCNDWlkd1+DoBdTXLnsgIuEpGm0w2iD7firvWzOHX17tTPyxLYWOGd6v4BKrcqUgqIg+Pw4pASxF+cVijxkoS2qSXRyPyKNS2VbtBErz+cZ1XPO6stROAErp/g9gU6/iX1F6vBVjx/5dVOBrXXAELQc8z51TN1O9+SDvBCUCRuq1s1RvPpDe439AF3yjDOHmpXa5qmpKsCByNGrkZ3igs6XPU34VNDGsjp5ADXVJN0UkvO4gMBRogvL72VGlUB5MHwMeQen3KAKSJJ78zD3BrQs0eSyMzmCk3QVHF+EKX2TQZ/H91Kb8/vat7F/7BRkphScWU6+d5c/3BrHpx2fRZxdeHl2hDHz0ouXjLtMEpZ9TdzuuSQEGAdORfk5uNqI/NKlr+3hXLbeTpXaCKOA3lKee1uwFRlHsfx4kxR/hypndZscj4yrR7YEv6D/xL0pWbuaFyEQgSE1QdkWuUKcTtz2xmJAIJWlQ1H1nr6B6vBzWzyPxCe+xdaldfhU1scwKlw0RAt5u4HagFfC3E/OUAz4EjgAPoOx8LQKKJJ78jC3L7HL1Sox0YyTCK0wm4o9tYePCScyf0oC/P7iDK6d3mQ07d3ANFw7/U+RUKhXMexViLGyO6JLldipgHLARqGrHdYdRltYtdkEMokjBQTDzcfuuae+i97RS8VTMqYH5KL0irLmKsrFAqlsj8hvHb6p6yq9MjTYMmryCzvd8TFi0u9ZNi0CVdj3xBFCyclP6P/UXESUqW1xql9+UsF7cHiQN7wNZoh1L7XKdNSTwaGoxXhtd3PwL3ArcAqxxYp7KwFyUiqqRSLYigMhfpR/poqtFPa3tjUQ755QhWiULZgOVyWjg3ME1nN6zxOxcZAnrnbvHD4Oe7S2fc7rBeBVgOfAxyi4WtvoTpYn4ASdfX9hk3DCoUt6+ayTxJFziVeA2G8bpgTtRmpAK4Eafp6LUanM3d03bQrO+T6PRyX2AsE1m6hX0OZl5X0eXrkH/iX9TKsL6L4qvIkbKbmYBzN7EU4Ypm0Epc4g3pbgpIuGzNqDsWt0d2OTEPDWAb4A9wF0gK3r9nySe/MgjdlQ7AQSjYUBQIzdFI3xF8pVTZsciSxadeKpdFd54uvDzlZ15UD4G5ZeEPUvrAGaiVDUkOfHawmZREfDCw/ZfV7UClC/t/Ovnby4u/FusvVsijwAm2zj2cZx7chqA0hLOcfGo9bt5XXA4Lfo9x13TtlCj5R0eiEwEgrTE8wW+Do8pS72G1pfSRapD+CVyTKH9oIR/SzDat9Tu/pT5bNefcVM0wi+sAtqjVEHtcGKe+sBC4D+gvwviEl4jiSc/UV4dzaCgxnZfNyRYej0EupSr5omn8NgKqNSWF0drtfDN6xBWxENwh5baVUBZ2z0HZatUW6UCdwAvAtL2x2OG3QYlYhy7tl1T519fKp4CQ5fW8OBddlzQEqWE3hafoVRNCjM3NxkvSkRsBbre9zn9n1pCqaot3RiVCARp186ZHQsz2vYzup62LF9GjHR1SMIH2FPx9Gb6Cr7P3u7GaIRf+RtoAQxG6dfoqCYofSE3A/L21i9J4slPjA3pgFZl/z7mvXR1iZHldgEtxULFk1qtISKuosXxz42F1lZymHYvtRuGUuXUx87rjgJtAdvfQwkX+XwhPPIypNvftsEliSe9Xp/3uSSe/JNOBx9PhYa1bLygFMrON7YURKwDJjgaWeA78d9ijAa99YH5lKneioHPLKXLvZ8SHmPnGltRbKQmmCeeQrJs/14bHNyEyaE9XRmS8AH2JJ7SyHZjJMJv/YKSPBqO0s/VUW1QlvCNdkFMwqMk8eQneuns2HYqnyCVloEOVEoJ/5GefLFAT4ZcUSWrmh1r0QCmjrM+p82JpziUrdDnY9vOVPn9jbIDxj47rxMuYTLBx/Oh6WDYtNO+a+tUc/71ZVc7//fUaKhXAxrWtPGCL1AqI605hdLXSTZmLVRm6lXOHVrr0LU1W99FqWotXByRCBRplhJP2fb9Y3wlrB89dHVcFZLwAccMV9iuP80B/UVOG65x1ZhGhslygumRkI6EoPNwhMIvGIEFKMvn7gNOOjhPMDAPeA/Z/c6PSOLJT3RNep+n036xe43179l72KM/b32g8F8mE6lXzdfRR5SoXODrkGBliZ3Whh/QNvV46oNSMjvEpigLeg3oByQ6cK1wqSMnoeNIeO5tyCnivcXFy/DBt3DLCBgw3rUxSMWT/6laAV68nsSuVwPU1u4mxmJbb4Y0YCBw2anwioWidrcrSsrV05za9ZeLoxGBIi3RuYonAI1KzYLI0VRRx7kqLOFly3MO0jLxTeonvkKVhGmUvPYsYVefYkDyZ2ZjS6kjGRXcygtRCr9hAL4EaqPsgG3+Y8c2jwFLgRKuCUu4lySe/EQWemZnrKJGwgzeTF9Blqnwd4g5GjW/l82mfsIrDEj+nB0Gae4X6FKunjQ7dnPF02sTlTeItoiNhvDCdqMLBz5BqViytxfUaaAnMAXp5+RDDAaYNQcuXjE/9+sK6HovVOgCj70CG3Yo1VLOkoonx5So1BiV2v5l1672/vM3+sSFBEONykUMrgW8bePE9wK7nIutuDi560+L1a7W7F87B5PR4IaIRCBIvXbW7FiwnRVPACXVETwccosrQhI+7I/sfRw2XDI7PjG0GyrZhkxYkwN8CtQEngDiHZijG/AvyjI+4dMk8eRnEkzpTEpfTO2Ely1WP21sUo3nnhzIC+VOccBw0QsRCm9IuXLa7Fhkvoqnbm3hiXvsm9Picrt2wE7Agd3Q+AJoBKxw4FrhdiHBlpvKv/4FrNkKrs4TSXNxx8SWq8cdz2+gahPru0y5y4Bu0L9rwWOF9nnSAt+iJKytmQH87FRoxUpOZgpn9i6z75qsVA5t/NZNEYlAYHmpnX0VTwALsrbzQvofrghJ+DATJt7JWG12vK62DLfq6nshIuGXMlGWzdUAngWu2Xl9VWADYM9mJ8LjJPHkp04bE9Bj/sRyW8PKJEWGkp7kSMpY+CtLFU+R1yueoiPhy1ftn7NA4kkHvAL8g/JUwh7nULZSHQMk2x+H8IxqlnvRc8w8p+kSJleUTRVDJ3f+TlhMOXqM/YoBTy+lXK0OHn39sFCl2ulmDQr7ufAi0NqGiRejJJ6EXezZ3Q7g8KYFZGfID2JROMvNxe2rePo6cysjU77CIKXNxcJXmVu4Ykw1O/5UaDcvRCP8WhrwOlANmIZ97xvCgYUo71ckw+GT5K/Fj4WqgsyO5eiUBj7pSVLtVJwUVfH0/vOWK1msybumIbAVZXmcvSt8vr1+/d/2v77wrBqVzI+lpMFle5862Ugqnhyjz07nxI5fAShdrSW3PfEbvcd/T1yFBh55/RfHQRULG6JZrHhqB1hIUpk5D9wPSC7Sbmf2LSc7I8Xm8ZaqWYTILyczpcD3lMpoJDjH9qWZczM3cl/qtxjlH3SxkUEOn2SuNzveNag2zTSFPNUSoijJwEsoCai37Lx2CsrDrChXByWcJYknPxZqYceIHK2SGUhPlsRTcZJsoeIpJKIEsXGRdGrp2JwVywNPo6ybbmrnxVdQdqUahTQQ9xOWevS4q9oJpMeTMw5vnl/g60oNenL7s2vofO8nRMRZyCC6SP2ayk52lpglniKAb7AtWX0f9pfVCwAMOZmc3PWnzeNbDniBcrU7ujEiEQhSE270eQrOtj3ptCcGxqZ+L0mnYuijjHUW+89K1ZNwyjXgGWAA9lU/9QO2oDQvFz5DEk9+SocGjcr8ry9bdz3xlCiJp+Ik5copi8dVoZVpczdssbNZ7wkdLHgaeBNly1J7/InSy0l6tfgVSxVPx9y4L4FUPDku/tgWki4dK3BMpVZTq/UQ7pq6hbZ3vEJIhOu3ePl4KugK2SG7dpWbzr2L0qvBmvcA+9oUFVtNNBXoqqtN0E17Rx/fbvsPW7VGS/cx84gqVd3V4YkAkr8yLlun4a3R3fn2tlasaFObfTXKciXGctO28gnpqKWhdLEUb0rh26x/zY7fHdycKHWoFyISAeV3oC1wxI5r6qKs2OjrloiEAyTx5KdCVJbv/nO0GvTZGWRnJHk4IuFNOZkpZKaalwxElqzKpavQdTT8tNT6PCbgi2hoXBWOFLVLlSWpwIMoTxkk7+l3LFY8eSjxJOx3ZPP3Fo9rdME07PYwQ6Zvp2mfp9AG29LV27pRA6FzEbtj63RQu+r1L24HHrBh0n0oTUSFTY4YLjMnYihXS8xiceRYHg65harqEpw7uI6MlMsFxhoNOVw6ud0sQQkQEh5Lr4fnExQq6xCEZfkTT0aNmkPVyrC2dS1+uLUl797TjeeeHMirD/Yyu66EKoxOOnsbQYpA8XbGKrNjWpWGttqqng9GBJ4DKD0jl9hxTTTwB3Kv4SMk8eSnLC2zA+XJVHqyNBYvjlKumq+LiipZBYCMTBjyJMyaU/j18RoYWAHGlINUe3s5rUfZxnSundcJn2Gp4umo5UI6l5CKJ+cc2fI9piKSd0GhkbTsP4Uh07dRr9MDqDWFlCrZICYK3nrG+riGtYCywOc2TJoNjETZyUbYJJ1sHkz9nghVMAOCG/FJxN2ciJvOgahnue273yiz8m/2LH6NP98dyNdPV+e3N3vx9weDyUg23+o8pmwtut3/BSq1vT/sRXFgqcH4zU5UKMHV6DCz47cHNXZHSMIP7Ddc5O/s/WbHW2jctwRcFDOJwG3AG3ZcowZeAxYA5j+yhAdJ4slPhRZR8SSNxYsnSzvbRZSokve5yQTPvQ0XL5sN45cIaFgNfo+080WzgclAZ+C4ndcKn6FWQ7UK5sdlqZ3vSks8z7lDa62OC4sqQ4e73+COFzdSvcXt4MD/168+CaVtWLlXvyYwDyhpw6QvADvtDqXYW51zmM8zNxQ4VkdbhmEXQpm5LoHV28vxRXwjxqtbUVtTmtRrZ1n++T0YcrLM5qpYvxttBr/sqdCFH0lLPG99kErFf/XMEwq3BzVGJcvtiq3ZFqqeQtWOP/gQwowR5b3HcCDDjuuGAhuAKtYGCneRxJOfKizxlK3TSuKpmLLU5ym34ilXzSpQttSNr5PUcG85GFwRrmixz26gFcpTB1k15dcqlYUg800ypbm4jzu8ab71QddFl6pOt/vnMmjSCirU7WLzda0awUNDbBt7ZjDQx4aBa4HZNocgbvJM2q+cNSRYPBeqCqJvUH3ei7iTQ7Evcix2GtMuVUM352WCs8wb/zbs+hB1Otzj7pCFn0m9dtb6IGCHhcRTRU0srbT2rtUXgWJlziF26WX3TOEBC4BbAHvuVZsC21AemAuPk8STnypsqZ1S8SRL7YqjlKvmiafIEgUTT11a3/h8VRg0qgZfR9v5QkZgFkrSabe9UQpfZKm/U3Y2nHFjDlsqnpx3avdfZKXb18+vZOWm9J3wM30nLKJEpSZFjlWr4ZNpyn+tORAE39xiQwBJwD1IstoJyaZMHk79waax1TUlGR/akc/i6/POawuZ+OVKeq3fT/n4RKUMFuhw9xuUq2XLX54oLtJsWGoHcLRySZLDzXcgGRxU9M8W4buqNulHaFRpp+awVPUkhFvsAFoC6+y4phSwAnjELRGJIkjiyU9ZqnjK0agxqVVS8VRMWap4iihRucDSmi6tIEMFT5aG7pXhjL3Vz8eBTsBzKMvsRECw1N/p5HlwZ1GSVDw5z5CTyfHtixy6tkLdztS75d4ix4wbCi0aWJ8rGxhRHrJsaRf0CPY9nRQW/Zmzj+8yt9l1jc6kot6JeO5avpMZH//FG2//St91+1BrdNd3uqvmpmiFv0lNOMvGhZPZ8P0zrF/wFP/Mf5J13z7Gph+fK9BbzqRWs7NORbPrBwdL4slfBYfHMuK1Awx4ZhlN+zxFXPn6ds/xfdZ2zhkSXR+cEJZcBnoAH9txjRb4EJgDWKj4F+4hiSc/Faoy/1eSo1Pu+jOSLtKyoacjEt5mqeJJqwshLKpM3tdx3aBFVXg3zoEX+BylgfgGawOFv7G4o52bkwNS8eQahwvZ3c4WB9Z/Wei5sqXglSdsm2daKfgvxIaB3wPf2TansO7xtJ+5ZExx+PrY5AxKJKYBEBIRR8+HvkMXYm+jPxGIjPps9q+dy4F//sfB9V9yaMPXHN70HfvWfM7xHb8UGPtfffMnF7U0pWmgKeepcIULHdr0LRePbaZ01Ra07D+Fwc//w90v76TdkNepUK8raq31d+k5GPgg03IJSrQq1NUhCwE5KA+2HsS+B+NjgNUoG6MIt5PEk5+yVPGUrVUST33bxjPElj4bIqCkXjuL0WgwOx5ZsgpoIO4tGNAcDphXxRftItAPeAhIdUGgwudYqnhyZ2NxkIonV7l88l8SLx62+7pLJ7dz9Uzha2XfegaibchB/BMKr9uSyD4DjLM5PGGDq6Y0JqT+5NQc50vfWGsdW64O3e6fKzvdiSLt+OvNAvcaB6uVIT3Y/J5Ultv5KZOJ9QsmYtDfePceGVeJBp3H0PfRnxj5+hG6j5lHrTZDCQ4v/If/Z5nrSTWZb2pQWR3rlrCFAJTdtbuivHexVXvgX5QWIsKtJPHkpyz1eMqteJo47CLlSpmdFgHOaMixuBNNcLNY2ADXngK9vYUlPwONgD9dEaHwVZJ48m+HNy+w+5oD/3xZ6LnSJWBQd+tzJKlhVHkw2fJzZTTKNsjCpRZm7+DXLMeb7Z0rE1Pg60oNetD69hlORiUCWVL8EY5tu5Hw1Gs17Kld3mycLLfzX4kXDrF7+QcWzwWFRFCt2QA63/MRI2YdpN/EP2nc8zFiytQuOIcpg/9lbjK7vqQ6nJpqeZMi3GgjSt8ne1ajV0DpE+XIXhsRQJQD1xVDknjyUxZ7PGmVbclKhl+kvHN9AYWfSrlyMu9zE0b2NZzDmW9WQBs7J0oERgJ3AldcFp6wQ/k6najWfCAhETbsY+8kWWrn345uXWix2rEwWemJHN/+S6HnL12FNkPh4PGi55lQBk7Z0iduNiC9Zt1mfNoPJBrTHbr2XGnz3SUadRtHnfYj7ZpHI0VSxcp/f7+J0aDP+9rS7nZNtRWppnb/7y/hHjuXvk3SpWNFjlGrNZSt0ZbWg6Zx59RN3DV9G23umEm5WregUmt5L2MtRlPBh0xqVDwR2sWNkQsBnEPpSfuNHdeEAG8BMXZcUxvYCkyHQvb9EvlI4slPWVxqp9OgU2cQpk2SiqdiKuWqki1Iij7Gkv53sanTs5hC9FauuslKlCon6cXiVZdP/Ueb219i5OuHuf25NbQZ/BIVG/RAGxzu0tcpFQdREebHj3ow8SSck550kXMHbM/spF47g9GQU+SYfUeg1V3ww9+Wzy+MhG9s2RFzN/C8zaEJB1wwJjMxrfBEYmHijcls3DTH4rkOQ9+ibM32Ns81/VG7X174seTLJziSr9JyX81yee0e8rtdqp78liEnkw3fP2PXNdGlqtOo2zhue2IxI18/RJV7X2VZ+BW46dnSfSFtiVWFuTBaISzIRKlgmgjY8mxOD9yFfdXZh4EDwADgTTvjK4Yk8eSnQi204M/RaogJuohKhVQ8FVMJmQfZ1GEKPw1tz7lKa+y7OAN4DOgJnHV9bMI+OZkprPv2MQBKVGxEo+6P0Gf8D9zz5jH6TfyT5rdNpmzN9jY1+iyKpWongBNu/h6QiifXOrxpvs1jS1RsRMfh71gdl5oOQyfCoy9Ddr5mnWe18LAtjTizgBHX/yvcal7WZpZlH7DrmjCCKPHnj5zes8TsnFqjo8eDXxJZoorVeapVhCljoVZVu15e+Ln/lryV1wcoK1jH/hrmPxSkz5N/O39oLUe2LnTo2uCwGGq0vIOdw0YUPGGCMFUQD4fc4oIIhbDBO0Af4JqVcU8Aa+2YV3v9v3cCK4AJQH97gyteJPHkpwqreIoOjgeUprChtuwyJHyKBjWTQnvQTlsN1c2PiIqiAx6H/XPnsK/JZ5g0dlY5/Qs0Bz4ATPZdKtzn/KG17Fs7t8AxtUZH2RptaX7rJPo9+Tv3vHmMPo/8SOMeEyhRqQkqlX0/1i31dzp7ETIlWeBXTu9ZSmZags3ja7cbTvNbJ9k09qP5sHGn8rkRGF0OEmxZWvUcsNfmkISTxqZ+b7GZb2Ei1SH8FfUwvecvJvnMPrPzIREl6DVuvtWd7kYOALUaRg+yN2Lhz1KvneXQxm/zvt5hYXe7DrrqlFVJ8xN/tuXnF8hMtfaOvXC7o/ScMSSaHZ8Q2omgvHfuQrjZCpTm4YXdk3wBfGTnnLlvtQYCg4HLgOwqXyRJPPkpyz2eNEQH32jjL8vt/I8BI1eMqWyMmcj5uJl8Gn43fXT1i/7lPAjYB7wLxuiil8+Y0aOsS24HHHQsZuFe236dQdLlwpvtaIPCqFi/G61vn87tz65ixOuH6T7mS+p1vJ/o0jWtzu+NxuK5TCYlyykVT84z6LM49u/PFs+lJZhvOgDQ/LbJ1Go73Kb5m9ZV/vt+LKy0ZbXnSuBdm6YWLnLKeI1n036z+7qJQR2ZNGcJkWfN/+HHlqtL1/s+LzKhfc8A5b+jb5deT8XNzqXvoM/JBGBX7QoY1OY/ywcGN/J0WMKFMlOvsvXXaQ5ff/XMbjbqT5gdL6eOZlhwC2dCE8I+x1He79y8Mn0TMN6B+UoA81E2Ytp3fY6PnQkw8EniyU9Z2tUuW6cstcsly+3807ysLWzIOU5ZdRQPhd7C39HjuBz3KvMjRzMkqDkRqmBlYAuUktBfgFr2v07cBZQfwDO4kbUXPkefnc66rx/BZGNPpJDwWKo160+HoW9y17QtDJu5h06jPqJm67sJiy5nNt4bjcVvJokn18jtuZJ67SyHN81nzZcPM39KAxa82Jhj/y6yeE3H4W9ToW6XIuetVhFiomBPMDxrywONBOBepHrSCz7O/Id/copuCGxJC1U5ZsxZRfNd5tdWbtiLVoMsv/Fs2xRqXl+NV7409JbVM8VKeuJ5Dq7/Svk8LJhDVcuYjZHldv7v8Kb5XDiywaFrr57dw0GD5b3tJ4Z2dSYsIeyXCtwB5P5KO4dSrZRd6BXQGMw63NyJUj3VF3gdeARYBCS5MtjAI4knP1XYrna5S+1AKp78lQkT41J/QG+60QkvSh3KsOAW/BB1H/81nETXBbWU5XGdHHuNx65B1YEocwifF398K3tWfujQteGx5anddihd7v2Y9kNmmZ33ZsWTcK0rp3fyw7QWfP9iE9Z9O4Gj234kPekimEys++ZRLhzdaHaNWqOj+5gviavQoNB5m9WDLBWMKAdZttw1PIxyMyc8zoSJManzyTSZV78mGTN4K30l5ywsewGIRMe4RVsYtXgLQdkFn0Y07vGoxeq4ewYW/Pr+wQ6HLvzUrmXvos9WdlX8r15Fs/NddbWJUYV6OizhYusXPIUhx/41+FfP7i30GURjbQV66uo6F5gQ9jIBL6GsGBkMWM6LKu4G1qE8qAeIAr4GfgCOAGOB14D9189LZqVI8n+Pn4rQWko8qaXiKUDsMZzn/cyCHe5SwjN58dk/aLRlFquHHnFo3oo5sPw0zDwBu3a4IlLhKdv/mEXCBefWQybGHzU7JomnwJJy5aTF4wZ9Fis+G0XiRfOfHUGhkfQe/wPhMeUtXtusPrxQEvbY0jfwG8CxXrTCRQ4bLjEt/a+8r1NNWbySvpRqCdN5Jv1XmiTO4o/swptvddpxjOc/X0r5+MQCx28ZNpsyNdrmfR2kg6F9C147oCuUjHXJH0P4iYzkS+xf9wUA/9U1TzzpVBr6BUnjE3+XFH+EXcvetfu67IzkIs8/FdrNwYiEcNJiYKuVMT8AJ4DJwH0oS+oGAbNRqpx+BFKuj9WgNMIUhZLEk59qW9fCrnb5mouDVDz5u2npf3HOkIhBbWTOyA3U2voSM59aSmaonX2crhudCHtOQI90+Gc7GGzZWlT4DIM+izVfjcdocOzvHyDpUsFlNBFhUNbCzwlZaheYstITWfLxENKT483OhceUo/f4Hyw2klZ3h9lxNrzAKeBR5+MUzpudsYp1OUd5M30F1a5N54X0P0gwKVUpV01p9E/+jCdSfybbZHmddfnLSTz/+VI6/nsUrvdi02iD6PHgV0SUUNbn9usCsdEFr9PplGbjonjZvfwDcrJSSYoK42ilkmbnZbldYNi17D2LD7CK0uvhb4usqO0dVI+GGvM2AEL4jLuBXsBclPucB4BXgT03jTOg9H0aBQwHOl//WuSRxJMf6tsJapQ1L1vO1mqJCZaKp0CRaspiaLt5NFv1OmPf+Z740inWL7Kg2vGWTF4+lXkXIeZ6Jn6NtQy/8ElXz+zivyWzHb4+Kb5gtUt1C9VOIBVPgSz16mmWfTKcnKw0s3NxFerT48GvUGvyVdTGwIe9wGQtN2gE7gGKfrgtPMSAkc5J7zEpfTFXTKkWx7yXuYb2SW9z1HDZ4vkgvYF7ft/KQz9uIDRDaYARGlmSXg99hy44wmyZXa4H7nDJH0H4kczUq+xbMwewXPXUJ6geYWZNUoS/Meiz2LBgol3XaIPCqNr0tryvjRYW3k2Uqifhyw4DT1z/fB5KlVMiFNh8vDJKMuo08BlK9ffPwG+AvB/PI4knP1MqDua9AvqMCLNzOToNMUEX8r6Wiic/Vg/4E9b/eZw9DSzvSGVNzeOl+OWeMezqMobw850LnJPEk//aueQdrpze6dC1SZcKPqm01Fg8IUn58CSpePKsK6d3sup/YzAazcseK9TtzC3D3837OmQuXAi2YdI3UfogCL+yXX+G5olv8F3mtkLHtNx3mqmf/k21s1cAJUHZe+xn9Olo+RayYS1oJRuZFTt7VnxIdkYy/9Uzf6IRqgqiT1A9L0QlXO3CkQ0c3jTf4esvYf7QY0RwS8qqopwJSwSgSJUt6/s95ENgCzAGqIiSdMrNofYF/gQmAAuA+4FYlKqnNJTlegKQxJPf+d8rUKYkGDPNK54MQUZCtTceN0viyQ+VQtmKczdwq2NTxCaE8e7zd7DvlikM+rsJp8uX4HJm9dzVEqSkwY79Rc8hfJfJqGfN1+PtbvKZk5WGPjujwDFL/Z2OeniZnfCOM3uXsfGHSRbP1W47lOa3PQvDINOW6pX/gKkuDU94UIopk5GpX3NfyrekmSz/XCmZmMakL5bTe/1+VEYTZev24ffTLxY6pzQZL36y0hPZu+oTLpeI5EyZGLPzt8tyu4Cx5ZdpZKZetXguPTneYkVtrrRy5hVxQSotj4R2dFl8wv9oUdNKW4UnQrryU+QDXIx7hb+jxnk7rILuBSoBcdxIOj2Msrt4MjAUeAb4/vrXy4AXgDo4vBlUoJHEkx8ZN0zpqQBgzDLPAmtD0shfOFBeEk/+Ixilcd1RYBygtX8KXbaGJz7twtHWU3n88y4E5SiTnCxfggxDFOn6GED6OwWCxAuH+PePV+26RhcczsBJKwr0WpDG4sXbwfVfsnPpuxbP1R4yDPVn5ptYmMkARlD0VsTCL3yZtYWWiW+yW295S0Kt0cSdy3fy2HdriEzNZOnpx9hwYZjFscNug1AfelgtPGPPqk/ITEuwWPXUP6ghOjReiEq4WlbaNTb/XDDxnJZwno0Ln+WHqc1Z/GZPEi8etnxtaDD7q5cxOz4upKMsxyxGIlUh9NTVZUbYrayMmkBiiTfYGvM070QM5o7gppRRR9FBV53Kah/areII0AClOACUiqb3UBJPD6NUPSVcP5d7+2RAeY9XeC62WJHEk5+oXxNm53s4bcoy/+GsCy3YAyg2GkJsWSIhvGsocBCYhbJNpwNu/6Mx+zpO4Z0X7yAuMbzAuVMVlK7AVzKqALLMLlDsXfkxF49ttuuauPL1GPjMchp1Hw8qlcWldp5uLA6y1M5dVKisLl/49/eZHN32U4FjJoys7f4IxkgbGtlPBg44EaTwKQcN8bRJnM3HGf8UOqbh0QtM/eQv6h6/yLcHZ3M0sbXZmOhIGNzTnZEKX5STmcKelR+xo555VUu0OpRuutpeiEq4w9GtP3Du0DpSrp5m/YKn+GF6C/avnYMhJ5PEC4f49Y0eZr9bci1vb77ssoQ6nHtD2rg7bOEl5dXR3BXUjPfC72B7zCQS4l5nWfQjTA3rS7eg2oSrLL9hHRrcwsORWpHbikKF0kT8MPAKNxqN597O5t4+DQSikcTTdZJ48gNBOpj/ZsGnh6os8yfRulDzrq6y3M6HtQM2oawHrurYFC12VmLtgMdZdN+D1DpuuXvdyfJK4ulypiSeAonJZGTdN48WWdJuiUYXTJvBL9P30Z8pVba82XmpeAoMPXR12Br9NJtjniq6T4LJxLpvJ3Dh8Pq8Q3ubfMqFCusLvybXUpS+ByKgZJLDI2kLuSN5LonGdItjYlIzefLrVfRbcZBPd37JlQzzChdZblc87V8zh2PhBi7FmfcivSO4qecDEm6z6ov7WTi9FQfXf4lRX7DsVZ+VxpovH2LD909jvKnMfl+NcpwvZf5Q5MnQLqiRB1H+ToWK+pqyjA3pwNcRozgeO51zcTNZGHU/j4V2obm2EhqVbSmIYb6WeMpVEmgJvA/svX5MzY0leJEoPZ/Go7RQOejpAH2TJJ78QNN6ULd6wWOqbPNy5ZCwRLNjknjyQVVQGs1tBNo6NkXFczF8PX4UW3s9TadNNQsdl63VkBGsVMddyagi/Z0CTPLlE2z9ZbpD11ao25l5Z9ex/VL/Ascl8eTfWmorszzqUZZHP0pLXWWqaOJ4O/z2Iq8x6rNZ/vk9JFw4xLW4A2xr+7L1F7oK3AcWNigSAWJR9i6aJr7OppwTFs+rTXDbun08NGc336ydR6a+YKKhW1uoZl74IgJcTlYqu1Z8wA4Ly+1uD28piYUAkpWWgMmoL3LMgX/mcXjTdwWOmdQqlreraza2lqY0/YNkZwJ/1F5bjUmhPfgtaixX4l5jX+zzfBYxlFEhrammKeHwvE21FamnKevCSF0kGqWPU/5NY6/vHE5t4CngNeAkNx7QSdZF/i/wB1t3Q+8xkJivoEmTbf6LOyQ8wexYednC0XcEAc8B+4Ehjk2hSoUXX+3DoXYvMurH1qhNRf8TDtIbmDJnKeUuJXE5o4r0dwpAB9bP49yBNWbHT+9ZyqJXOvLPd48XWhWVro/l071fMm//B2ToIwHvLLUTzqutKc3CyPvZFvMMPYLqFDg3JqQ9fXX1i7w+OyOJJXPuYlXXBzBqbGjYNBa4YHWU8HOnjNfolPQur6UvK3RMrdOXGfv2MVbNfx+jqeC9yX1S9VQs7V/7BZsqh5sdL2kI4pbgWl6ISHhTetJFs2ObG1cjOdy8GveZiF6eCEm4mAETfYPq0z+oEXFq83/7zvhfxHBmRdzOg8HtKaFy7dwOO3r9YxhKogmgLMoDuY+BF4E/gPYoy/FASUxVB5p7NFKfIoknP7F2G3QYAafPK19rcswfM4eGXTE7JhVPPqIbsAt4FQhz4HoDMAdMtSBtZjZhGbY3YCx3JZmBq3ZzJbOKLLMLRCYT6757jOwMJTMdf3wbf7zTj2WfDufa+f0c2vgtv7zWhUsntxe4TJejp9IFJVm98eJw9l3rSmYWnL/k8T+B9HhyQnl1NJ9FDGVfzBTuCm5W6Lg5EcOIURXcDVWFigrqGDpqa3BvcBvajo0gscwh6y/6JbDIubiF/9BjZEr67/RK+oiMMPMl/QDhGdm0/hE2nRpe4PjoQaCWO81ix5CTwaLd80iMNN+B+f7KDj55EwEh+fJxjAY9ep2G1a3Nk5Ad1FVopa3ihciEM7boT9I16X16J33EDr1rS+fb6qoxOaQbn0cOo6RaqawN9oU+9PcA9YC/UFaxbEFZetfg+rmhKBuv5P4O1KLcO30C9PF0sL5Bbgf8yP6j0G4Y/LdXhU5vnngKD79sdkwST15WFvgOWAmYVxXbZjnQDKXC4CK8n7GGPfrzNl9+JSacbwa05lpmJUk8Bai0hHOs+2YCyz8bxe+z+3Dx6KYC55MvH+f32bey4683MRoNqA1Gxv64gclfLKPh4XO0Kv0zLUr9xvEzYJKlU34hVhXGrLABHI2dytiQDmhVRe8WVUETw4+RD/Bh+F38FTWOgzEvkFFiNmfjXmZdzBO8VWUQ254+ZfV1SyUBj7noDyH8ygbtQQxzZ0Er827yBrWKneMv0q7KggLHK5WDHu08FaHwJQc2fMWeenEFjiUGq7icYnnXRFE8pF47y1/vDyI96SJrWtUiW3vjd5cR+Dl7F2mmLO8FKJyyLOcgLRPfZEjy/zhscP2TzJOGqwBs/h7aNHH59PaJB24HZgNXgN9RKp0qo7z3y2VEaTquR1mClwF8hFL9VMw4sGm78Kbzl6DDSGim+5RS0aVpVLM0tSuVoHxcHC1r7AJAr4d9R2HbXlhp36ZXwlU0KA3lXkZZB+yI/cDTwN8FD+sxMi71B9bHPGnxsuOGK1TXlAQgS2XkzTq/UjF5Gu0rnuc16e8UsE7u+qPI8yajnh1/zuLsvpUsqPw0TQ8pNwSPzl/LJ5t/YkpjSLXcR1j4kFB0PBbahcmhPYhV21c+2SOoDj2oY/HczIlLSY7KLPJ6lQmazYJlKUUOEwEqOgLWn0yhz1ufwPweGOf0R21UqhXn1zzF1z+9yQd/wOKPblyzYQdcMe8CIIqBrq2ziOy5hquHmnCqqYqjpRfxybY/2X9Cnm4UdxePbuKXWV3pOvpzNjWtRttdJ9jSvBwJLZ5lf+Ihyv8L+zd6O0rhKBMmfsz+j1+yd3FfcFumhfWlgibG6XkTI4OZPSGU4ydSaFoP1n4N41+C//3sfMwO23v945Objqu50fMp92sDSiFCQ+BtYC7KiphiRIW0Bg0IajWMHwbb98HOg5BR9PsH4U5tUH4AFb7qpWiXgGnAHJQfUoX4ImI494fceJS8NucIr6YvY1nOQUYGt+KziKE8mvoj87Ik+yhumBnWj+fDepsdfzrtF2ZnrPJoLAaDAbVaTXZ2NsHBlrfSFQotau4Pace00L6U1ziazbbseJUr1N04k5wgKw3gZqH0qRPFWuvG8OI46BtXg2uTn2B/5gW6XnwNEyYiwiBlO2zZBS++D8vlzWOxtXIedGwZzInk5tSOU6pwjUZ4aDrM/dG7sQnPmjZtGtOnTwdgxYoV9OzZEwCVSs07M6dwKngsD7UfSp1Y5QfG5wvhoWneila4WnRYSd655T2GHEglPMOGHpKFOFqpJGfeWEHXil8UOP7AC15OPuXKTTYVlV2ZAExEKVBYBzxMwQblAU4qngKE0Qgffmd9nHCjesB0HG4cThbwDsouCJbbaBQwOe03BgY1ZqP+OK+lL2eT/sbOQ99mbWNNzhHOGhMdDEYEogkhnS0mnQBqqWVdri9SoeLOoKbMDO9HbY17dot44bk/rCaddPs15EyTnQmEsuFJ/3HQvP4xQjSTOBKvwXT9Ljs7B257GP5a6+UghVe1bqzsaghZeUknUB6S3tJcEk9CYTIZ6dpgJuUrf0XJ0Bt9gQ4e92JQwqXCosvR/ZGFbKxQn/96ZdN7wwF6bD5IcI5t9xOny8ZyrFJJSiakcq5MDMeudS6QeLqSAL959plp4XIrnHKTThpuFBA0AF4BugCHgHeBgivTiwVJPAnhrFooFUrDcLxr2vfAs4D1Fit5rphSqZEwgyRThsXzknQS+d0d1Jx3wy1vMbUoaxfj0xZ6OCJhTQ9dHV4LG0BLXWW3vca/TU6z4I7tVsflTDIoTTKFuG7HflCaVdyQnSNJJwHPPWh+bNVmePdr+FO+P8R1ajXUrgohwQWbUR+QxFNAiCpVnb4TfiayhHIPkxEaxK89mrC6TW1uW7uXjtuPojUWvvAqS6fh87s6cKVUCM1L/UGnCu/SO2Z9gTGPveJDy7nDgYHAeuA0N5JOjwJPXj//PfA6kFsrcPOSvAAniSchHFUNmAqMQslqO2If8Ajg4I1YYUknIfLrrqvD15GjUKvMM6Nrc44wPOVLjLLq2me01FbmtbAB9Aiy3I/JGWtyjrA55yQnjFc5ZrjMhudOWL9oLfCny0MRQgSg+jVhUA/l88wsmP8HvPcN7LZhw0xRvFStACEWVtlLxZP/K1GpMX0eWUhopHk1fVJkKPP7tWJ5+7oMWL2H1ntOorZwC7qsTyU6tHmHDuXmExVkvnP7n2thgS/dmxhQVr78iZJoqgG8AXQHjlw/981N1xSjpBNI4kkI+1UGXgBGAzoH50gDZqCUWua4JCohLGqhrcQvkWMIUpn/uN+lP8fA5DlkofdCZDeoVCqvvr4v0aCmijqOvYbzxOSE0lRbweqOdbbKMRm4M/kLrprSlAO9UW6IrHnGJS8vhCgGJo+BC5fh4wXw2Q9w+Zq3IxK+qq6FXb0yMuH0Bc/HIlynXK1b6PnQtwSFRhY57nJcJF/c0Z6lHepx+8pdND58Y8fuzdrL1Br8OHWrWM7MpKTBw9NdGbULZAKvovToDUG5v4oGfkKpcjpyfZylKqdKKEv0znokUq+RxJMQtioPPA+MAYKcmOdnlEz4GWsDhXBOTXUp/ooaR6Q6xOzcCcNV+iZ/LFVzPsaAkZ+zd/Jz9k4Awgmita4qHbTVuEVXg3baqkSpQx2ae3nOwRtJJzXKkzhrFgLbHHo5IUQxE6SDv9bBmBchRx6qCSvqVjM/dvik0rdW+KeqTfrR9b7P0ehs3zDmbNlYPhjRhVqnLnH7il2UO32e4Zc+4v2zRurWtHzN5Nlw9qKLgnalL4FewFiU3e5evX4sP0vf3yOvfzRwY2w+QBJPQlhTBmUnp4dQMtiOOoaym8HfrghKiKKVVUWxNHo8pdXmT5wuG1PonfQRF4w2dLF3I5NJlvdZk0Y2q3MOszrnMGSAGhUNNeXpoKvOLdrqdNBVp4omzqa5vs/aceOLkUBjKxfkAFMcjVwIUdxk58APf3k7CuEvLFU8SX8n/1Wn/Sg6DJuNWu1YlfaRKqV54/4ebJramRPGqxwppO/tP//Cp987Eai7PQD0QGmn8uX1Y/kbjVvyDcoOd+8CT7gvNG+TxJMQhSkJTELpwRTmxDxZKNuQz0IpwxTCzaJUIfwdPY7qmpJm51JNWdya/ClHjJe9EJllstTOdkZM7DacY7fhHJ/wDwAV1DF00FbnFp2SiGqiqYDmpn5emaYcfs3erXwRAsy04cU+RUmYCyGEEC5Wz0LiSfo7+acmvZ6g1cAXnZ9IpeJgivJNYCnxlJmlVFT69HPLDJSG4guAH4HFFJ10AmWJ3QTgF5QG5JvdGaD3SOJJiJvFAU8BjwERTsxjBH5AaUB+1AVxCWGDYLT8GvkgTbUVzc5lm/QMTp7Lv/rTXohMuMs5YyILs3ewMFupaIpQBdNGW1VJRGmr01ZblWU5B0kxXc98T0DpJ1CUFOBld0YthBCiOLNU8XTQhv0uhA9RqWhz+0s06j7eJdPps9Mx5CgtICwlnmZ8pCzH9HkLgRZAMGDrs9U/gS9Q3jtWcVNcXiaJJyFyRaP0XnoSiHJyrp9Qdi/Y5+Q8QthBjYpvI++ha1Bti+fvTfmW5TkHPRyV8LRUUxYrcw6xMkfZRkqDmgjV9X4Lcdi2fO51wHeK4oQQQgQIrRZ6dYCSsebnpOLJf6jUWjqNfJ9abe522ZwZqVfzPj9ysuC5//bDW/Nc9lLuNxmlwrywaicVSkPx3P8aUDZz6YKySuZZ94foaZJ4EiISpbrpKcDCL0G7LEZJOO10ch4hHPBh+F3cGdzM4rnHU3/i++ztHo6oaNLjyTMMGG80kZ8CxFi54DzwjntjEkIIUfzUrwnx/0BcjOXzFcrALnk+5vM0ulC6P/AFlRv1LnBcn5OJUZ+DQZ+F0ZCDUZ+NQZ+N0ZCNQZ+DUZ+FwZBdYIxBn62MM2STevXGzktnLkJWNgQHgV4PD7yo/NevFNVixYSy0Uswyo7pnYAKKPdozwCLgK1ujs/DJPEkiq8wlDW4k4ASTs71FzAN+NfZoIRwzNTQvowL7Wjx3Gvpy3g/c62HIxI+pwrKzzxrpgPp7g1FCCFE8dCq0Y3Py5eiyDfjf34Kb34BL7ynNKsXvuuf+U/kJZYM+mxMRtdmhYxGOHZaSVbO/lKpeAoIFYHaQA1gIMomVs2vn0sHDgC7sd4Xyg9J4kkUPyHAOJQSxtJOzrUcJeG0ydmghHDcQyEdmBF+q8Vz/8vcxJT03z0ckX2kubiHzER5slaUA8D/PBCLEEKIYsFoafv4IjzzAHRvB8OfhkPS88knGXIyyLjei8mdjpwCnQ6mf+j2l3KvrsAIlGqmbijtXfRAGrAHeAWlwfhvKEvvLnglSreTxJMoPoKBB1GWmpRzcq61KE3D1zkblBDOGRzUhI/Dh1g893v2Hsam+vKes8JjmgIjbRj3LAH5lE0IIYR3nL9k/zXN68OOn+Gh6fDtby4PSfiJwyfh7a+U3ez8mgq4D+Xh3lJgP0oz8WxgbxHXBFhHCkk8icCnA+4Hnsf6Tk7WbAReBFY5G5QQzuusq8n8yHtRq9Rm5zbkHOfu5HkYsPNRowhMr9swZj3K0zYhhBDCReKvWh9jSVgoaMxvb0Qx8vZXcDEQNjpZBTQADgKhwM3FYpaSTAGWdAJJPIlApgXuQUkUVXVyrm3X51nq5DxCuEhjTQUWR44lWKUzO7dPf4H+yZ+RgTRIEEBPoJcN455xdyBCCCGKG3uX2uXavg++XuzaWIR/CYikU67cpvmWVigGYJLJEskji8CjBkahlDN+gXNJp/+A/kBrJOkkPCoU84RSrmrqEiyJHke0OtTs3GnDNXonf0yCyfe7Q8uudh6gAt6wYdzPwGY3xyKEEELY6MlZILcJQgQOqXgSgUMFDEHZkamuk3PtQWka/ivFJgstfMvHEXfTU1eHJTkHWJp9gBU5h0gwpVNKFcHS6PGUU0ebXXPVmEbv5I85Z0z0fMBOkObibjQCpb9TUfTAc+4PRQghhLDFz8vgH9kpWoiAIokn4f+CgKEoy0QaOjnXAZTE1Y9Iwkl4TYwqlLuDmxGqCuIBTTseCGmHwWRki/4k0apQamnMt2NMN2XTL/lTDhrivRCx8EnBKDvZWfM5cMTNsQghhBA2yM6GSW95OwohPKAqMBk4BLzr1Ug8QhJPwn+VBh4GxgNlnJzrKDADmA/Si1l424jgVoSqggoc06jUtNdVtzhebzJwV/L/2Kw/6YHoXEeW2rnZI0AVK2NSUX72CSGEED7g/W/h+BlvRyGEG9VC2WV9JEo25hLwGZb7PwUQ6fEk/E8T4H/AaZQ3TM4knU6g7HhXF/gWSToJnzA2pL1d4x9Inc9fOfvcFI3wSzEoO3la8ybKDY8QQgjhZZevwcxPvR2FEG7SAKXI4QAwmhslQKWBsV6KyYMk8ST8gxoYgLId5U7gPpRlJI46AzwE1AHmAQYn4xPCRVprq9BYW8Hm8ZPTFvN11lY3RiT80nNAnJUxF4HZHohFCCGEsMG0DyEpxdtRCOFicSibuOwFhgEaC2Oewbn3tn5AEk/Ct0UAE1DWvi4Gujo533ngUaAmSl8T2W1e+JixIR3sGr9Pf8FNkQi/VQl4zIZxM4A0N8cihBBC2GDfUfh8obejEMINElGqnYpSAaWwIoBJ4kn4pqooT+LPAu+jJIqcEQ88CdQAPgKynZxPCDeIVIUwNLi5Xdc8ENLOTdG4X26PJ9nVzsVeBkKsjDkEzPVALEIIIYQNnnodDLICQQQiI/CqDeOeJaA7cEviSfiWjiiliEeBiYD5jvH2uQJMAqqj7BaQ6eR8QrjR8OAWhKvsq7PtH9SQMqpIN0Uk/E5jYJQN454D9G6ORQghRLGX/+FSTiG/d5b8A0vXeyggIbxhPkpv4aJUQWk4HqAk8SS8LwjljdJ2YB0wGMtrX+1xDaWxbjWU5rnpTs4nhAc8aGdTcQCtSsM9Ia3dEI3wS69j/Tf7RuAXD8QihBBC5JOcar6brV4PT73hhWCE8CQ98JoN46bg/PtgHyWJJ+E9pYAXgVPA14B9K4wsOwSMQ+lx8irKVuFC+IHmmkq00FZ26NoHgv1zuV3uUjvhIt2BPjaMm+TuQIQQQghzyRbuyz//EfYf9XwsQnjcVyhtZIpSCxjigVi8QBJPwvMaofQWOQ28BJR1wZxLgb5APeBTpMJJ+J2xDlQ75TpoiCecIBdGI/yOCrDlifGvwAb3hiKEEEJYknRT4ikpBaZ94J1YhPC4bGy7V3se5b4uwEjiSXiGCugHrAB2Aw9gvfmtNRkoO9M1QHnKvwSQAgrhh8IJYnhwS7uvW5K9n9aJbzEoZQ5p0jG/eBuK9apRPUrjSiGEEMILbq54mvkpXEnwTixCeMUclE2vitIAuN0DsXhYAPdNFz4hBCXJ9DhK6aArnAc+REk6XXXRnEJ40dDgFkSqbc/Ers05wgtpf7Jef8yNUbmfLLVzkSDgFRvGfYGyHFkIIYTwgqxsiL8CZUrCsdPw/jfejkgID8sE3kLpQVyUF4BF7g/Hk6TiSbiHBrgfOIySJHJF0mkbMAKoitKcTZJOIkCMDelg07jNOSfokfQhXZLe9/ukk3Ch8SgbKRQlDZju/lCEEEKIomzbq/x30luQnePdWITwik+x/j62GXCbB2LxIKl4Eq43COXpe30XzGVAyfa+i7ITkxABpommAq11VYoc85/+DC+k/clfOfs8FJXwGxEoT8WsmQ1cdHMsQgghhBXb9kBkOCxa7u1IhPCSVOAdYKaVcVOAP90fjqeosKMrzrVr14iKinJjOMKfrWUtU9RT2KLa4vRc0aZoHjA9wCOmR6hC0W/KhfBHavX1glOjqdD+gaa8j8BckqZWq1GplD+9wWDwcjT+6VPVpzyqfrTIMaVMpThsPEwkkR6KSgghhFCoVKq8e541a9bw+tSuXLoKO/Z7OTAhvCkaZWf3aCvjKgNn3B+OJ9hV8aTRaNBoNO6KRfip3ezmOZ7jL/5yeq5a1OJxHude1b1EqCJcEJ0QPk5T+LYVKgJyUwuL5HeLY/7H/6yOmaqaSowmxv3BCCGEEEUoWbIkS9eDtHgUxV4S8D7wopVxLSmeiaecnBxycmQxrlDsYQ+zNLP4SfUTJpVzv0G6GbsxwTiBvqa+qK+3HstBvtdEYNJqtXmVPhiMeXWnJkwYTSaMAVrhdLPcp6Amkwmj0ejtcPzOf/zHDt2OIsfUNNXkPv198vNUCCGEV6jV6ryHS5cvX5akkxC53gOeoeid3mt4KBYPMcmHfNj10QITv2By+n8ZmJiDiYY+8GeSD/nw4MfGjRtNeZ5bazob+7JpXEhHUxBar8cmH3708RHWf87e6wNxyod8yId8yEex/ZgxY0beLc+SJUu8Ho98yIdPfeym6Pu4T3wgRhd9SHNxYbv2KE1s+zo5zwXgY5SO/lecDUoI/7YoaxcjEl4iUypShD3CUHb5LEoSsNADsQghhBBCCPsdAxoVcT6AKp4k8SSs64qy/rSrk/OcA2YAXwHZzgYlRGBYqz8qSSdhv7uw3pDyOyDDA7EIIYQQQgj7HbNyXhJPoljoi1Lh1N7JeRKAWcAHyJsgIW6S1+tJCHuMsWHMHLdHIYQQQgghHHXUyvnKKBkbvQdicTNJPImCVMAglIRTcyfnykBJNs1CST4JIQAwSWdN4Yx6wC1WxmwHdro/FCGEEEII4SBrFU9aoIoN4/yA2tsBCB+hBoYCu4FFOJd0MqA8aa8FTEaSTkII4UoP2DBGqp2EEEIIIXybLQmlAFluJxVPxZ0WGAk8B9R2ci4j8DMwFTjo5FxCCCHMBQH3WhmTBsz3QCxCCCGEEMJxp1GW0RWVlZHEk/BrwcBo4FmgqpNzGVDe5LwGHHByLiGKGenxJOwyCChpZcxCIMX9oQghhBDWqNWywEaIQumBUxSdXJLEk/BLocBY4BmggpNz5aDsUDeLgFh3KoSnSI8n4TBpKi6EEMJPyf2PEBYcQxJPIoBEAuOBiUBpJ+fKBOYCb6KUBwohHCZPAoXNqgE9rYzZB2zyQCxCCCGEEMJ51go4anokCreTxFOgiwEeAx4H4pycKw34FJgNXHByLiGEEPa534Yxc90ehRBCCCGEcBVriafqHonC7Yrfo3aVihb9pqBSa7wdiXuVBF5FWTM6A+eSTsnAKyi9oJ5Gkk5CCOFpGuA+K2OygG88EIsQQghhI+llKYQV1hJPYUA5TwTiXsUv8WQyUalBd3qNW0BQaJS3o3G9cigVSadQdqpz5o94DXgRqAK8AFxxOjohxE3khkzY5Fas9+X7BbjqgViEEEIIB0iPJyEssKVXcgD0eSp+iScg4eJhKtXvzoCnlxFVKkBq1yoDHwEnUPo4hTkxVzwwCSXhNBNIdDY4IUR+cuMl7CZNxYUQQgghAs9xG8ZI4sk/JV44BEBM2VoMnLSc8nU6ezkiJ9QEvgCOojQPD3ZirrMo/aCqoTQOT3U6OiGEEM4qD9xmZcwxYLUHYhFCCCGEEK6TBly0MkYST/4p4eKhvM+Dw2Lo88hC6nV6wIsROaAB8C1wEKXhrM6JuU4AY1G+oT8AMpyOTghhI9nVTlg1GqXHU1G+AKSQTgghhI+R+xwhbGBtuZ0knvxTbsVTLrVGS4e736DD0LdQqX18o7+OwO/AXmAE1t+MFOUQcC9QG2WJRrbT0QkhbCBL7YTNVIC15yJ6YJ4HYhFCCCGcIPc/QhTiqJXzknjyTylXT2HIyTI7Xq/jffSd8BPB4bFeiKoIKmAQsAlYB/Rzcr49wN1AfeBrlDctQgghfE93rG+j+wfWS7SFEEIIIYRvkoqnwGQyGki6ZDmtWL52RwY+s5yYcnU8HJUFwShPug+g7FbU1sn5/kVJYDUBFgJGJ+cTQjhNdrUTRbKlqfhct0chhBBCCCHcxVriqSTO7VbvA4pl4gkg4abldvlFlarGgKeWUqlBTw9GlE80MBml99JcwNkc2AagD9AKWIz0ARHCy6TUXNikJHC7lTFngSUeiEUIIYQQQriHtcQT+H3VU7FNPCVeLDzxBBAUGkmvh+fTqPsjHooIZeeiN4DTwCygnJPzrQS6ALcAS52cSwghhGeNAoKsjPkfYPBALEIIIYQQwj0k8RS4Ei8etjpGpVbTZvBLdBr1IWqttbt/J9RD2ZHoBPAMzpfR/Qm0B3oAa52cSwjhVrLbiyjUg1bOG1EST0IIIYQQwn9dAZKtjJHEk39KsFLxlF/ttsO47bFfCY0s5dog2qMsfdsP3I/1J9vW/Aw0R2k+vsnJuYQQbiNL7YRVHVAeShRlOXDKA7EIIYQQQgj3CvAG48U28ZR86ThGg+3buZWp0YaBk1YQV7Ghcy+sAgYA61F6Lw1wbjpygK+AhsCdwH9OzieEEML7pKm4EEIIIUTxIYmnwGQ05JB8+bhd10TEVaT/xL+o2qSf/S8YBNwH7EOpcupg/xQFpAJvo2yzPfr6vEIIvyO72gkz0cAQK2MuofwuEUIIIXyY3OcIYSNriaeaHonCbYpt4gmK3tmuMLrgcJr2fQps/SEaBTwNHEfpxWFt6YQ18cAUoBLwFMqORkIIvyJL7USRhgFhVsZ8hVLxKoQQQvgJuf8RogjWEk8Vcb41jxdpvR2AN9nSYPxmRoOef759DKz94CwLPA6MQ3l67awjwFvA10CmC+YTQgjhm6w1FQdlQwohhBBCCBEYrCWe1EA1wP7aGZ9QzBNP9v+t7Vz6DlfP7il8QG2UCqd7gGBHI8tnG/A68AvKDkZCCCECV/PrH0VZh9/edAghhBBCCAusJZ5A6fPkp/eAxTrxZM/OdgDXzu1j55LZlk+2Bp4FBuKaBYx/A28Aa1wwlxDCZ0nvA1GALU3F57g9CiGEEMIl5D5HCBudAbIpejmdHzcYL9aJp6T4o5iMRlRq65kio0HP2m8mYDTc1FQjFvgAGOGCgPTA98CbwG4XzCeE8EnS40BYFIb13yWJwM/uD0UIIYQQQniQETgB1ClijB8nnop1c3FDTibJV0/aONqEPju94KHewF6cTzqlAe+hfG+CZnoAANJkSURBVCONQpJOQghRHA1F2ZCiKN8CGR6IRQghhBBCeJa15XZ+nHhSAcX60Xuvh7+jcqM+No09vXcZyz4ZBuEoVUnjnHzxy8D7wMfANSfnEkL4NK1Wyx9//EGzZs2IiYkhKEipo83IyCAz0zs7BkjllW8ZEDmADboNRY5Zk7SGRoZGds0bKH/PJpOJzZs3079/f2+HIoQQwkazZs1i8uTJAGRnZ5OUlOTliITwXc9FPMcXoYXvIFNLX4sNCUXfK3paSkoKNWpYz4gV66V2AJdO7iC6bG1yMpLJzkghOzOF7IxkSlRsQImKBW/uKzfsRakRzbk8fQfUdOJFj6PsUPcl8uRaiGLi0UcfpXfv3mbHQ0NDCQ0N9UJEwpdkk82//FvkmJa0pHN0Zw9F5Jv69evHsGHDWLBggbdDEUIIYYPy5cvnfR4UFESpUqW8GI0Qvq0hDYs8H6+N97l/Q8HBtu2oVuwTTzuXzLbYMDymTG0GP/8Pao3yf1G2LpntbV7j8sM7HV+guANlh7qfAYODcwgh/FLFihW9HYLwYfvYRxZZRY4Zy1gPRePbbHmqJoQQwjesW7eOUaNGeTsMIfxCOOHeDsFtin3iqTCJ8YfZv+4LGnZ9iJPV/mBjx2dJj7jg2GTLUHaoW+nKCIUQ/sRoNOZ9/s8//zB48GD0er3X4lHbsKmC8JzMUZnwbtFjJjWcxLMXnrVrXk//Pbvr9X777TfatGnjlrmFEEK4zx9//JH3eWpqKg0aNPBiNEL4tpRRKTCz8POp11Kp0qyK5wKyQf73OEWRxFMRduyexdlXV3K2lgMZIwOwECXhtNO1cQkh/E/+7YQTExO5cuWKF6MRPqeelfMXIHFfoici8UlpaWneDkEIIYQDLl68iF6vR6vVEhYWxunTp70dkhC+K6Xo08Yso9/+G5JH3pZogCcge3uyY0mnv4FawHAk6SSEAAomngKl2bNwoVZWzhfd/kkIIYTwWYmJiYBSFSsVT0IUQWflfI5HonALSTzdrDmwBXgHiLDz2lRgLHArcMLFcQkh/JpGo8n73NaSVFFMBAPWNqqTxJMQQgg/debMmbzPe/bs6cVIhPBxkngqBiJQkk1bgRYOXL8eaALMcWVQQohAkb/iSRJPooDGWL/RkMRTnvz/loQQQvi+ffv25X0u/fqEKIIkngLcQGA/8ATKMjt7ZKtgEtAZOO7iuIQQAUMqnkShWtowZrvbo/BpsjxVCCH815YtW/I+r1fPWlNDIYoxSTwFqIrAL8CvQCX7Ly9zvi29Pp+P+m0dyPtIIUQRpMeTKJS1xNMZIN4TgQghhBCut3z58rzPK1as6MVIhPBxQVbOS+LJz6iBx1CqnAbZf3lwZgwdV79Hv19/p7K6F/U63e/a+IQQASf/NvNS8SQKsJZ4kmV2BeT/tySEEML3HTp0KO/eJyYmxrvBCOHLpOIpgOQ2D38PiLT/8qr7BnDngs3UOTAS1fX/+5rfOong8DhXRimECDD53yxLxZPIEwpY2+BHEk/yb0YIIfxcSoqyT7xGo5GqJyEKI4mnABABvI3SPNyWfho3Owr0hNBxJQjNKFXgVHBYDC36Ped8jEKIgCVL7YRFTbHeW1ASTwVIc3EhhPA/586dy/u8V69eXoxECB8miSc/NwBlWd2T2N88PAeYibLV9Qo4uOFrrp3bbzas7i33EltemuUJISzLX/FkMBi8GInwKdJY3CaSrBVCCP926NChvM/bt2/vxUiE8GGSePJTFYBFwGIcah7OepSn0S8Cmcohk9HA5p+fNxuqVmtoe8crDgYqhAh0stROWGQt8XQCuOqJQPyHVDwJIYT/2bp1a97nDRs29GIkQvgwa4mnbI9E4RaBmXhSAxOAA8DtDlyfAIwBOqFUSt3k/KF1nNr9t9nxCnU7U7lRHwdeUAgR6GSpnbBIGovbRBryCyGEf1u5cmXe51WrVvVeIEL4Mql48iPNgM3A+zjUPJzvgLrAF0AR7w23LJqKQW+ecmwz+CVidVGMC+nowIsLIQKVLLUTZiJQft8URRJPZmRXOyGE8D/btm3Le/AWFyebMglhkSSe/EA4MBvYBrRy4PpjQC9gJHDJ+vDky8fZt+Yzs+PRpWtQseMo3g6/nWdDezoQiBAiEMlSO2GmGdZ/C0viCZB/M0IIEQjS09MB0Gq1xMTEeDcYIXyRJJ58XH+UJXETcax5+CtAQ2C5fZf+9/dsMlIumx1vduvTXDSl8lr4AF4LG2BnQEKIQJR/qZ0sGxKAbY3Fd7g9Cr8jPZ6EEMI/XbhwAVB+jsvOdkJYIIknH1Ue+An4DajswPUbUJqHv0Be83B75GSm8O/vr5odDwqNIjk2GoBnw3rySfjdqJEbZSGKM43mRlZcEk8CsJ54OgIkeiAOPyAVT0II4f+OHDmS9/ktt9zixUiE8FGSePIxauBRlObhdzhwfQIwFuiIxebh9ji88Vuunt1jdjy7wo1M2MOht/BNxD1o/fT/biGE86S5uDAjjcUdIj2ehBDCP+3YcaOMt0mTJl6MRAgfJYknH9IUpXn4B0CUA9fPB+oBcyiyebitTCYjm396wex4UmRYga+Hh7Tkl6gHCbH63SSECESy1E4UEA3UtjJGEk95JFkrhBD+b+3atXmfV6tWzYuRCOGjgqycl8STB4QDb6HciDvSPPw40BsYAcS7MC7gwpH1nNz5R4FjiZGhZuP6BTXk7+hxRKpCXBuAEMLn5a/SkMSToIUNYyTxJIQQIoCsXbs270FCqVKlvByNED5IKp68SAc8gLIk7ikcax7+Kkrz8GWuDS2/LYumYsjJyvs6yULiCaCLrhYrox6lhCrcfcEIIXyOLLUTBVhbZmdEGosXQpqLCyGEf8rOziYrS3m/FBwcTFCQtfIOIYoZSTx5QRDwMEpz1bk43jy8GfA8kOG60CxJuXqKvas/zfs68aaldvm10lVhbfTjlFdHuzcoIYTPyF/xJIknYTXxdBBI9UQg/kH+zQghRGC4fFnZEVylUtG1a1cvRyOEj5HEkweFABOAY8AnQBUH5kgEHkJpHr7PZZFZtXPpO6QnK+v4kiKLXk7XQFuOf6KfoJq6hCdCE0J4Wf7Ek8Fg8GIkwidIY3GHScWTEEL4r+PHj+d93rlzZy9GIoQPksSTB4QBE4ETwPtARQfnWQDUBT7HJc3D7ZGTmcK/v70CFF3xlKu6piTro5+kgaacu0MTQnhZ/jfLkngq5koA1nqqSuKpAKl4EkKIwLBz5868z5s1a+a9QITwRZJ4cqMIYBJwEpgNlHVwnuNAH2A4Lm8ebo8jmxdw5fQu0kN0ZGutN6Qqr4lmbfTjtNI6Utol/F1YdFlCIkp6OwzhAbLUTuSRxuJCCCGKqfXr1+d9XqtWLS9GIoQPksSTG0Sh9F46CbwOOLqxQQ7wGkrz8KUuicwpJpORTT9NAZXK6nK7XCXU4ayMepQuOvnhW9wYDXoGTl5BXMWG3g5FuJlUPIk81pbZGYCdHojDT+VP4gohhPAvK1asyHsAV7asoxUHQgQoSTy5UCwwHTgFzERZcuCojUBzYApubx5uj/hjmzm+41ebltvlilSH8HfUOPoFSQKiOMlMvYIuKIz+E/+iatP+3g5HuJFUPIk81hJP+/Cp32m+QP7NCCFEYEhMTCQnR3n3HBYWJg8ThMhPEk8uUAJ4BaXCaRoQ48RcF1Cah98C7HU2MPfY+st0EsLt2yI0RKXjl8gxDAu2ZR2GCBQJFw6iCw6nx4Nf0uzWZ0Aa5wYkqXgSeaSxuFOkubgQnqNC/r0J17t27Rqg/Dxv1aqVl6MRwodI4skJpYE3UBJOU1CW2DnqLPAoUB2vNA+3R+q1MxxPOGz3dVqVhkmhPeQXfTGSePHG90mL256l2/1foA2yvVpO+AepeBIAlAEqWRkjiScz8m9GCO+YHtaXOJXckwjXOnXqVN7n3bp182IkQvgYa4mnbI9E4RbuTTzdh9L0+xmUJuKOOolS4VQD+AjIdDoyj9h1ap3d16QYM7k7ZR4mX86qCZdKuHCwwNfVmw+k38Q/CY+t4KWIhDvkTzxJxVMxJo3FhYuERJSgcc/H0AaHezsUEcCqqONYGT2BEir5PhOus3fvjSUrUvEkRD5S8eSA54H/Ac78njoG3A/UQqlw8rMM39nsy3ZfMyZ1AYcNl9wQjfBVCRcOmR0rWakxgyatoEz11l6ISLiDVDwJwPoyuxxgtycC8V+y1E6RmXqVCnW7cPeMHTTuMUEqZYVbXDGl0VRbkVXREyilcuYpshA3bNiwIe/zunXrejESIXyIGtBYGSOJp5s8gNI43FGHgFFAHWAeoHdFUJ4XUiHJrvEfZ/zDwuwdbopG+KrEmyqecoVGlebWxxdTu91wD0ck3CH/m2Wj0ejFSIRXWUs87QGyPBGIf5FkrWUH1s8jNLIkrW+fzt0zttOo+3g0ulBvhyUCyFVjGgCNtRVYHf0YpVWRXo5IuFIttaPbijtn+fLleZ+XL1/eKzEI4XOsVTuBJJ4K6Ad85uC1+4ChQH3gW5Qtpf3Y1nPJNo/drT/HxLRFboxG+KqMlMtkpl6zeE6jDaLTyA9oc8dMVGprKXDhy6S5uACgtpXzssxO2OHUrr9JT7oIKA8r2gx+mbtf2k6Drg+h0YV4OTrhTvU63kf15oPQBbu3CumqKS3v8wbacqyJfoyyKmcatgpf0j+4EVuin2ZwUBPUHuwve/bs2bx7ochISWYKAUjiyS5tgR+wXiJ2s13AnUCj69cHSDHAqWzbK56iVaEYpa9TsZVw0XLVU65G3cbRe9z3BIVGeygi4Wqy1E4AEGzl/AmPROHXZOvtG0xGPYc2flfgWFhUGdrd+SpDpm+nQZexaLTWvumEP7pwdCNdRn/KyNcP0+vh+dRuN4KQiBIuf53ciqdc9bRlWRP9GOXVcj8SCOZmbqSupjQ/R43hYOwLjA3pQDBaj7x2YmIioPxMr1OnjkdeUwifZkviyc9aD+Xnuru3OsAfgD0tBv4FBgLNgJ/x6V3qHJFkyiBbY9t3RxVNHPcGt3FzRMJXJVro83SzivW7MeCZZUSXrumBiISrScWTAKzfMHjmft/vSLK2cIc2fo3JwvLd8JiytLvrNYbM2E69Tg+g1gZ5ITrhLokXDrF31SdodMFUbtSbTiPfZ/hrB7jtid9o0GUsEXEVXfI6V0ypZsfqaMuwJvoxKqhjXPIawnuSTZnMzdoEQC1NaT6LGMqpuBk8F9qLGJV7l+2ePXs27/OePXu69bWE8Au2/Jou9omncsBSwNYHLZuBW4FWwG8EXMIpvztLz4CfpsKPU1n21kWee3wAzz/Wj2tR5hm6KWG90Lp5o0Hhmyw1GLckpkxNBjyzjAr1uro5IuFqUvEkAOv9m6Q4xSppLl5Q6rWznNm3vNDz4THl6HD3GwyZ/i/1Ot4nCagAsuPvt0hNOJf3tVqtoVytDrS76zWGvryLQZNX0rTPU8SUdbya5OaKp1y1NKVZG/0YldSxDs8tfMN7GWswmG4kr8uoo3g1vD9n4l5mdvjtVHRTgvHAgQN5n7dpIw/fhZDEkzXRwBKgig1jjcC9QDvgb6df2S8sOZhMWmQClE2gc8t30JdL5lKJKP6+pb7Z2GqaEowKll3MiqPEi7YlngCCw6LpPf4HGnYb58aIhKtJc3EBWL9hkJyARZKsLdrB9V9ZHRMRW4EOQ9/irmlbqdPhHtQaW2r6hS/TZ6Wx+afnCz1fsnJTWvafwp0vbuTOqVtoNXAqpaq2ADuSt/l7PN2shqYUa6Mfp4o6zq64hW85bUzgp+ydZscjVMFMDO3G8djpfBkxkgaaci593c2bN+d93qBBA5fOLYRfsuXhY7FNPAUDvwKNbRz/BPC1U6/od3JyYP31jeqCNencUeMlANY3r0FCpHkJ6/NhvdBI1VOxk1DIznaFUas1tL1jJp1GfiBPr/2QLLUrxqTiyWlS8WTuzL7lpF47a30gEBlXiY7D3+GuqVuo3W4EKrXt6zvl/sT3nNz5O2f2rbA6LqZMTZr0epyBzyxj2Mu7aTfkdcrX6WT17/+qMb3I89U0JVgb/TjV1K7vLyU8Z3bGqkLP6VQa7g1pw97YKfwe9RAdtTVc8porVtz4vq1UqZJL5hTCr0nFUxFXfgN0sXH8LOADh1/Nr63ZeuPz1mV+olrUdvQ6DUssVD3V0JRiRLC1/bZFoMlIvkRmWoLd19VuN5xOI993Q0TC1WSpnQDcWvHUqhHc2dvx6/2FNBc3ZzIZObjBvid7kSWr0Gnk+9w1dTO12g63aefU18MHOhqicKNNPz6LPifT5vHhseVp0HkMtz72CyNmHaTTqI+o3LivxQdZ2ehJMRY9dxVNHGuiH6OGuqTdsQvfsE1/ivU5x6yO6xfUkHUxT7ApeiK3O7kT3r59+/IqwGNiYhyeR4iAYcs9oLUHmD7M8bu3d4G7bBz7FfCcw6/k91bnSzypVSburjUFgH9a1CAxwny74+fDestTxWIo0c6qp1y2LLEQ3idL7QRg/YbBicTTvqPw9Sz48zOoWsHxeXyRJGutO7zpO4wGvd3XRZWqRudRH1Crzd1FjlOhYlzILYyWjVB8TvLlE+xe9p5D14aEx1K77VB6PfQtYdFlLY4parldrsrXk0+11KUcikN4X1FVTzdrq6vGoqgx7I95njHB7R3eCS81VWler9VqKVvW8vefEMWGVDxZMBmYYOPYJcAYh14lYGzfB6n5fmfXiP6XNmV+JEenZWkH86qn2prSDA1u7sEIhS9IsKPPU659a+Zw8egmN0QjXC1/4smVb6Kb14fRt7tsOuFu1m4YnFhql54BKzbBrZ1g3+8weQxoA3CXPFlqZ1l60kVO7XasgaY+O4OTO/8ockwFdTRhqiDeDh9MGVWkQ68j3GfX8vdJunzc4eszU6+SevW0xXO2JJ4AKmpiWRP9OHU0ZRyOQ3jPb9l7OGq4bNc1dbRlmBM5jBNx05kc2pMIlX2/xC5cuJD3ea9evey6VoiAI4mnm9yLsmzOFtuAOwH7H8AFFL3+Rp+nXINrvESQOp11LWuSHG5e9fRx1T6UjJGb6+Ik0cad7XIlXznJtt9edlM0wtXyLw9yZY+nwyfhi5nw8TQIkl7Bvs+NFU8Ai68/sA4LhVlPwX+LoH0z5+b0BVIlaJuD67906LqTu/4gOyO5yDG1NKUBiFWH8UGErSXvwlMMOZlsWvisw9dfPr2z0HNXCtnZzpLymmjWRD9GPY1Ur/gbIybezVjj0LXl1NHMCh9AjMq8f21RDh68Ue3foUMHh15biIAhiad8egBzbRx7BLgNsP13VUDLv9wOIC7kPL2rvE92kJalHeqZjY+6WobDzzSnT0cPBSi8LsHOxNM/3z2BPkv+gfkLdy21S02Ho6dh3FBY+w1UkAfNvs2NFU8Af6yB/N9eDWvBhvnw+UsQG+3c3ML3nTu01qGql8ObFlgdU1tzYwnVXcHNGBhk684ywlPO7l/Jif9+d+jaK6f+K/TcVTsSTwBl1VGsjp7g8l3QhPt9mbmZBCsN5QuzIec4Z42Jdl3z77//5n3eqFEjh15XiIAhiad8vgablvDGA70B+6o1A9qarebHelf+kLjgs6xpVYuUMPN3G7GLevP3pypem+iBAIXX2bOzXVD2YS4e+ceN0QhXy594Co+rjEZnXunoqJ0HlP+2bQLbf4LOrVw2tXA1N1c8xV+BLbvNjz94Fxz8E0YOcG5+4eNMJrv7/qVcO8P5w+usjsuteMr1cfgQou2sbhDut/mnKeQ48FCqqIonW5fa5VdGHcWPkfejcqL5tPC8NLL5NHO9Q9f+kLXD+qCbrF69Ou/zqlWrOvS6QgQMWx4+FpvEU7gNY1KAvsAJR8IJXNv3QcpNv7eDNRkMrjGD7CAty9qbVz1xqhysacrla56JUXhXRnI8WemJNo3NDqrNm2/2D8j+LYEq/1K7ep3GcO/skwyavIoOQ9+iVtvhxJSrg0rlWNu9nflylmVKwor/wZP3OhuxcAs3VzwB/FZIf9jSJeCb15Xvj9pVnX8dT8rfF016PBXtyOYFGHJs3/bmwqF/wIa+czc3jS6vieYN2eXO56Qlnue/v960+7r0xIuFnnMk8WQwGZmQ9hMmZGMAf/Nh5jpyTPa1BDCajPyYVXjVXGE2bdqU9/O9RIkSdl8vRECRiic7ZAODAft/7gQ8gwH+2W5+vHWZRdSI2srq1rVIDbWwje3cPnz0ndxkFxf2VD1djn2JRR+GECYPnP3OgQ3fotboKFm5CfU63kfnUR9w5wsbGfXmcW597FdaDZxK1Sb9CIu2bZnCfwcKfq3VwtvPwoLZEB7mhj+AcJybK57gRp+nwnRvB7sXw7RHINgFryd8S2bqVU7s/M3m8bXaDKVaM+ulcLVvqngCGBvSgc66mnbFJ9xv7+pP7V6+3+eRhVSo28XiOXt6POWanL6YlTn2b5oivO+8MYnvsyy8aSnC2pyjXDQV3SfOEqPRSEZGBgA6nY6IiAi75xAiYFi7J/PjpBO4OvE0Gljh0hkDyuot5sdUKri79hSygnUst1D1FHSmPLeqpI9CcbHpxyn8+e4AFr3Wme+nNuObZ2rwxYTS7FzyttnYq5mVUVV+hFXzoESM52MV9slf8XTl9C72rPzYbExQaCTl63SkSa/H6TH2K4a/updhr+ylx4Nf07jn45Sr3RFdiPluUjsLyVcOvRU2fw81q7jsjyGc5YGKpwPH4OgpKy8TBNMfVRJQXds4/5rCt9iz3E6lVtNl9GeFJh0ANKiprrFcjTAnYhghyM4GvsRoyGHjD8/YdU1IRBy9H1lIk15PmJ2zt8fT91nbmZ1hJQMufNrbGautD8rnnDHJ4deKj48HlGrWnj17OjyPcL/+QQ3Nql+FC0niyUYTAeu9KYs1S32eAKpF/Ue7st+zqnVt0ixUPU0N6yNr5IuJq2d2ceHIBq6d3Uvq1dNkpSdiMhrYuexd0iyUwf996nFq1y3H+u+gcnkvBCxsdnNz8W2LX+LSSetPFMNjylG16W20HjSV2x7/lXvePM4dL2yg08gPqNfxPkpUakz8VS3xVyxf37AW/Psj9O/qqj+JcIoHKp7AetVTrtpVYdWX8PXrUCrONa/tDiYbloKJGy4e3WRXxYtGG0SPsV9RqmoLi+erqOMIUlle211LU5rpYX0dilO4z4UjGzi69Ue7rlGrNbQa+CI9Hvy6wEMOe5ba7dKf44GU+Xa9rvA9Ow1nWZV92ObxI0Na8XLYbQ691tGjR/M+79hRdlXyZaEEcThuKlfjZvF31Dimh91KX1194lRSXu8SAZ54ck2HmLeAd1wyU0D77wAkp0KUhSrSwTVeZvul/ixvW4dBq/cUONdUW5H+QQ35LXuP+YWiWNBnpbFt8Ut0ubdglUy2MZxFR6fyQINxbJwPvR+EfUe8FKQoUv7Ek8FgwGjIYdUXD3D7c2sIDouxfR61mthydYktV5fa7YYDkBh/lP8OtCl0F8zoSPjtY3j5E5j+YcFdz4SHeaDiCeC31fDUfbaPHzUAbusEk9+GL36yqeWP1+SvHhSFO7j+S9rd9RoAhpwsLhzZwJn9K7hyeiddRn9GZFylAuN1wRH0Hvc9f7x9G4nxBd9w1tIU/YT7qdBu/JC1g/8MZ137hxBO2fLLVCo36k1QaJTZudRrZwmLLoNaY16tVrXpbcSUq8OKz+8h8eIhrhhTbX7Nw4ZLpPv7uyM/ERweR1hUadTaINQaHWqNFrUm6Pp/dag1OjTX/6u6fl6jDUKtvn5em39s7rU6rp3bx8H1XzI7YxXdgmrbHM8LYX3QY2RG+t92/Tl27NiRV+nUtGlTu64VnrUwewddMmoyLrQjfYLq0yeoft65o4bLbMk5yRb9KbboT7JTf45s9F6M1g9J4smK74BJzgdSHBgMsO5f6NfF/FxM8EX6VnmPZW2eptemg4Rl5hQ4Py2srySeirmj2xbSoPMDZk+kN8cPoUvFL6hR5l/++QYGPALr7VuaLzwg/5tl4/XMT+q1M6z9+hF6PfydU3NnpV5l50EKTTzlenEctGoIw5+BBMer4oUzPFTxtGEHXE20bxluXAzMeQnuHQj3PAsnfCiHIBVP9juy5Xuiy9Ti7L4VnD/8D/rsG1ukL/ngTvpN/JPQyJIFrgmJiKPvhJ/5/e2+pF678Q1gLfGkVWn4InI4rRPfQo9ktn1FRvIl/v39FdoPeT3v2LVz+9m1/D2Ob/+F0tVa0v2B/xEWXdbs2pgyNRk4aRnrvpnA1V2276J7R1ATaqhLcsxYSBmucBmT0UDbO16hQr0uLp03/vhWDq7/kr9z9nNQH09dbRmbr50edisGk5GZGUttvmbdunVMnjwZgOrVq9sdr/CsJ9MW0U5XjabaigWO19SUoqamFCNQtlbOMuWwU3+OLfrryaick/JzwZoATzw599hwOXAfyIYVtitsuR1Ar8ofERZ7kRVt65ida66txG26Bm6MTPg8k4mNP04BoPWek6iMN/7hfX/4NYwmFbHRsGwuDOjmrSCFLfK/iT69Zwl7Vn7k1HxpSRcK7fN0sz4dlaV3TS1spCk8wEMVTwYD/LHGsWvbNAaD5A78XnZGMht/eIbTe5cWSDoBJF06ypKPhpCdaV7JEh5bnj6P/kxIxI2eTpYai9+smbYST4V2dz5w4VIH1v2PK6d3EX98K8s+Gc6i1zpxbNtPmIwG4o9t4ZdZ3bh4bLPFa3XBEXQfM4+yAx4n8tokSl59lorXXqTGtRk0SHiF3kkfYTQV/GGhVql5Wr4PPCI7I4klH9/N/rVfuHTeyBKVATBh4u2benVtyznF5LTFNE2YxaYcy1uYvxzej+dCe9n8eqtWrcq7Lypd2vrPGuFdWegZkvw/UoyZRY4LVuloo6vKY6Fd+C7yXo7GTeNy3Gv8GfUwU0P70ltXj1hZoleQtXvAYpt42o6yg12OtYEiP0sNxhOSYNdBCNJkckeN6axsW5eMYPNitGnSQ6HYu3zyX6rpFtJs/xke/3Y10SnKTiAnU5qz+eIQAEJDYNH78MCd3oxU3Cx/xZNeX7D0eNvil7l04l+H505LvMB/+20fX70SbJwPo2QndM/zUMUTwG8O9vb9+jc4fd51cbha/mWrwnFXz+xi+acjMOSYf1PGlKlJ7/EL8/r8WKt4yjU9rK80nvUxJpORP98byO+z+3J671KzdbQZyfH89d4g9q35vNA5GvecQKdHF5AWHso5YyLHjVfYb7jIspyDLMreZTZ+dEgbyqjMN8IQrmcy6tm4cBIbF07GaDS4ZM6w6LJodCEAfJO1jb+z9zMxdRFVr02jddJbvJGxgl2Gc/RJ/pitOZZ3sng1vD+TQnvY9HqZmZlkZyvvqENCQtBqXdMJRrjPEeNlxqZ+b/d1JdUR3BrUgBnht7IkejzXSrzO62FyM5rH2j2gtXtIH+dY4ukYcCtg+5Jvcd3OgzD1Axg5CdrcDSXaQVxbaHo7fP8XtCy9mPJlt7OqjXnVUytdFfro6luYVRQnZRJf4kTVaBocu8i0j/+iyUFlOcSiY1PJ1CsNxDQamPsyTHnIm5GK/PK/Wb552ZDRkMOq/z1AZlqCQ3OnJ13k6GlIS7c+NldoCHw9Cz58EXSyIZXneKjiCWDpBsiy8+mYwQCz5rguBleRpXbuceHIelbNe9DiG9ZSVZrSc+w3aLTBNlU8AYSodMyJHCYboviYnMyUIs8bDTls+vE51nz5sFl1XK7ydToy6NlVZsv938hYaTY2RKVjQmhnxwMWdtu/di5LPxpCVrpr1tFHxCrLqDLJ4dbkT3gnczWnjNcKjEk2ZdIr+SP+zTltcY7XwwfyVKhtJfhXrihLsFQqFZ06dXIicuEp32dv5/PMDU7PkylVLDfIUrubHAZ6A5dcHkuxYDTCyx/Dd7/D1t1wLfHGufEvwflLMLTW86xoV5vMIKl6Eubiz1+gbDtl7XxkehaPLljH8D+2kZFakr9OPVFg7CtPwPvPg/Ti9b78iaebK55AafS67utHHJo7I/kSRiPstn0DmjyPDIfVX0ryyWM8WPGUlg4rLa+gKdT3f8FRyw+wfYZUPLnWqV1/sn7+kxbPla/TkZ73fkYVte1bHnbW1eLBkPauCk940NFtP/LbW31Ivmx5CVVEbAX6PfE7dTrck3dsm/4Uqy3sfjY+pCMRKhdm0oVV5w6u4fe3+pB0+bjTc0WUqGR9EJBkyqBX8kfs0J+xeP6t8Nt5IsT6tronTtz4nuvcWZKW/uLx1J/ZrT/n1BznjNJ0NI8knvL5FGiJUvEkXC4hCe5/HqpE7aJJjUWsam2+k0RbXVV66up6ITrhK05fgDbt3yMzWJN3rOu2Izz/+RIObB3G5YwqBcZPGAnz34IgSSx4VVEVT7lO713K7hUf2j13m8EvU6PVnew44FhsR05Bjjxw8gxrNw0hrn25xebFCEV6tfDVNl4lFU/udXjTd2z9ZbrFcy0rdUKjsu928Y2wgZRXR7sgMuFp187t49fXu3N67zKL5zW6YDoOf4eOI95Do1USS5aqnmLVYTwYLAlIT0uMP8xvb/biwuH1Ts3TZvDLNOz6sMXG8zdLMKXTM+lDduot70jxTsRgJoQUnUzatevGks0WLVoUMVL4kkxyGJIyj1ST42vAzhoSXReQv5PEUz6TgaKrdYWTlm2AD7+DQdVfYW3HSharnqZH3OaFyISvOH0BgoIyyKoXX+B4+cvJTPpsDYc/ex6MBSsC7u4Lf30GkeGejFTklz/xlLurnSXbFr9M/PFtds0dGlmSrqM/Y6duIVcybHtKmetKAjz9hl2XCGfYcm/mwvYWv6+2fazRCOWlr2uxtXvFB+xe/r7Z8TJX7b/xi1aH8nH4EFeEJbwgOyOJZZ8OZ8efrxc6pk77kfSb+BcRcRVZkrOfXRaqHiaGdkWHxsLVwp2y0hL4+8M7Objha4fniCtfj7Z3vsKwmXvo+9gv1G43gqDQqELHXzOl0yPpw0KrX96PuJPxIYVvvbthw40lW7Vrmz94F77rkCGehx3o95TrnDHRdcH4O0k8CU+b9BbEn4+nc/2PWdOqltn59pqqdNPJD+Xi6tT1pr9RLTeZndMZjHT4OYvUx6fAlYI3CF1aQ0PzbyfhIfkTTwZD4Q1ATUY9q+eNcazfU1x3pm3ZwLLT4zAYbbvZn/g6XE20/6WEg2y5aXDh6pQLl5Vl3bZQq2H+m1DR+gNur5Kldu6z9dcZHNr4bYFjpR1IPAF0D6pDJXWsK8IS3mAyseOvN1j6ybBC+waVqtKUQZNXUaFuF97IWGF2vqImluHBLd0dqbDAaMhh/fwn2fzT85iKeNhljUqtpkKdTnQa+T4jXjtIjwe/plqzAXnNx/O7akqje9KH7NVb3p3io4ghPBTSweK5pUuX5n1etqyP/xISZr7L+pcvMs3fl9hCltrlI4kn4WkZmTBqMnQt/wn/dgsnS2f+BnJG1CDPByZ8wpkLyn9VjQtfwx+xsyym0c/B+oZ5x17+BDbtdHNwolC2VjyBc/2eso3h/Hh0Jq9tX8qZlAZFjl2xCb5Z7NDLCEfZUvHkZJ+nmq3u4u4ZOxjwzDJ6PvQt3x6azeLjk1l99n52XOrH0aRWXM6oQpbBfBvjUnGw8B3f6/ll7d+MsE1oCJQrBfVrQvtmcFsXGNEfHh0BL4yD2ZPh3noTKWP6M+8aRyqejhku0y5xNmeMjm2YIHzHmb3LWPxGd66d22fxfEhECXo/spCDHTtwynDN7Pwzod2l2bwX7V39Kcs+HU52pvM7Qml0wVRtehvdx8xjxGsH6TTqIyrU7YJKfeN9yhVTKt2TPmS//oLFOT6NGGpxCea1a9fIub7mPyIiwulYhedNSP2x0KRjURZEjqa6uqQbIvJD1h48+vmudipAGif4qOmPQr+7+nP1rcfotemg2fkuSe+xNueoFyIT3nZtM8SG6KDPG6C3si5n0D+sb/cLXcbmUEShjXCz48ePU61aNQB69OjBypXWm++0vn06jXtMMDueFH+UfWvn0rDrQ0SVqlbo9WqVnt6VP6Rf1TcJ0mSanW9xB+zYb8cfQjivI7DOyphywEXnXqZB14dod+erVscFqVOJCY4nKugSUUGXiQ66RFTQJbZtj+fLHy6TnnyJjOR4MlIuYzR4rxHYt99+y4gRIwCYO3cuDz74oNdi8QcjB8Dw2yA2GmIiITYKYqIg2MakZo4hmPd2/cChxI489eVK6p6It37RdcuyDzA05UsSTHZssyl8njYojFuGv03NVncVOqbpzz/zyG7zd0b9kz/jj+y97gxPWBFbri69Hp5PZMkq1gfbKSP5Esd3/MrRbT9z+eS/AJRRRbI6+jHqaS1XL92f8h3zsgrufhEfH0/p0sp67xYtWrBjxw6Xxyrcq56mLNtinibczo0F0k3ZTE3/k3cz1mCgGD9o+hG4s4jzC4G7PRSLG0jFkw+b+SkYLv7O0VtTyNaaVz3NLDHUC1EJX3D6AhCcA3Utb2FbwK8dKT35GRpQwe1xCdvY2ih52+KZBfo95WSlsW3xy/z8akf2r53Dz690ZNey9zEazHfJAzCatPx96glmbF3HgWs39VbI0jK7RmfCXLmNmrDOAxVPAPtWf8a/v1tPPGUbI7iUUYOjSe3YcXkAq8+NYfGJKZyPe49e4+YzaPIKhr2yh/vfv0i5WpaXSHiCNBe3z3e/w/od0Koh1K0OZUrannQC0GmyeKTxKCpH7iQo2/LPF0veSF/BrcmfStIpAOmz01nz5cNs+vG5QpPQ+/sPJCXY/H51UmgPd4cnrEi4cJDFb/bi4rHCtzrNzkxlycd3s+ar8ZzZvxKj0banlaFRpWnQZSwDn1nKkBnbadFvClllytMt6QMO6S0nredGDOOe4NYFjp0+feOetnv37ja9tvAtBwwXGZ+60O7rwlRBvBV+O1tinqKJphi/X5GldsJb9Hq451no0XAa61rWMDt/i74MHXU1vRCZ8LbTuRXMTWzbYrI25dga8xRPhHSVkncvUatv/LjV6217I2cy6ln1vwfITL3Gif9+46eX27Fr2bsY9cpvHkNOBtsWz2DxGz24cnpnofNcyqjB2zt/5csD75OWHaMswbxnCl023cl3FYp6tCJczoM9nnYumc2uZe+5ZjJArfGN9XfS48k6kwle/Qx6PgDxVxybI1SbwuNN7uaLx6oz5bH+fN2/NVsbViEpwvLWi6cN15icvrh4P60uBvat+Zw/3xtEerJ5QiE7SMuatvXMjnfU1aCdtvDqXOEZmalX+Ov92zm82bwRdHZGMks+vIOz+1ZwdOsPLP1oCAumNGDjwsnEH99q82tElaxKs75PceeLG2n37G882jiNoybzJZhqlZp5ESMYGdwq79iePXvyPm/durXZNcJHqVRUqNuFHg9+RZfRn/F11la+zNzi0FQttJX5N+YZXg3rTwi+cc/hUZJ4Et506AR88OkeEgbvJ0dr/tf1evnRng9KeF1e4qmRbYkngGCVjnciBvN31DjKqgrfmUS4nz39atISzvHjS21YOfc+0hIs7xZz9eweFr/Ziy2LXsSoL7zS4Njufpx5+CN47iE4XwqAQVntGCHNXz3HQxVPubYtfol9a+e6ZK78fTw8TSqeHLN6CzQbDP/869j1UUFXmNjsTnLKp/BPy5rMuasDTz99Ox8PNd+dqrImjtKqSCcjFv4g/thmfp3Vjfhj5m8uV7WpbbE36WSpevIJRn026755hK2/zshrOp6ZlsCf7w3i0omCPygyUi6zf+1cfp/dlx+mNmfbbzNJuGDe+qMwJSo2ourdL/D5U/dzIcK8LYRapebLiJEMC24BwKZNN5pT16tnnsAUviU4PJZG3cdz14ub6TvhZ6o27UfNVndSqkpzHkldWGifr/U5x5idsYock+WKOq1Kw3Nhvdgd+yydi1uBhSSehLd9NB9CjK+ysWVls3Pt0qLpGFZ0A2ERePIST42Pg8q+p8u9g+qxO/ZZuuvquD4wUShbd7WzJCvN/GnhzUxGA3tWfsyPL93C2QOrC5wLzcxmyJLtTPv4L+oeMZ9rbszd1JDGjp7h4V3tADb9+CyHNy9weh5vJp7yk4on+1y4DN3ugze+cOz6EqFnebLpnYRrr//sUKnYV6McerX530Mbnev7xwjflJ12kapXBtK+VMHEdmp4CBuamVfpDwxuTF1NGU+FJ6zYvfx9Vsy5l+QrJ/nr3QFcPbOryPEpV0+xa+k7/DyzA4te6ciuZe+Reu2sTa+VEB3Guw/exuXYcLNzGpWabyLu4e6g5ixbtizveIUKxXi5lY8rVbUlnUZ9xLBX9tJm8MtElymYHGo1aCrpZDMkZR7pphs3PReNyYxK+ZqOSe/ydNovtEx8k205pwp9nVqa0qyJfpzPI4YRrQp125/Hp0jiSXibyQTjnr9M+uCV5GjM/8rer36/F6IS3jT/D+j1AHR/LINjWstPFIpSSh2JHuk07kn27GrnjJSrp1jy4Z2s+WocWSlX6LDjGDPf/4Oemw6hNVquGgkxhPBT7H0EYaVRvXCehyueADCZ+Oe7xzm+41enppGKJ/+l18Pkt2DQo5CYbP/15cMP8ViToQRrlJ2xsoO0nC0bazaurSynKhb6dITdv8L7z+VwX6PJ3F9vHDp1Rt755e3rYrCQmHwmVPr2+JJTu//ixxmtuXbevl1Grp3fz7bFL/H91Kb8/vat7F/3PzJTrxZ9TUw4b43uwZUYy8mnbyPvodWF2LwHc1FRUpnvS7RBYdRpP4pBk1cx8Jml1G47FK3O8rLr8rU7UrF+N/YZLvBo6o/oTQZmZ6yidsLLfJt1o2/pbsM52ibN5snURaSZCr85ejCkPQdin+f2oCYu/3P5HGv3f36+q50knvzEuXj45peP2d66lNm5pheD6F+5nReiEt5y9iIs3wirNsOSZNuX2+X6JWuX7IjoYc5UPDmixI6tPP7GPEYv3kJUmvmOdrkulIzil3vKsWdUMd9JxFO8UPEESkXcmi8f5vSepTZfozbc+H5Qq3KoUS6Z/kEN0eHdyiepeHLc4pXQ4k7HdrOsHr2dcQ3vRaNSvolPVChhNqZtUHVnQxQ+rHZV+ONT+PtzqJevqKlduYU826IPJUNOAnAlNoJ/G5hX6Y8MaUN5dbRnghU2MdnYQNzyxSbij21h4w/P8N1z9Vny8d0c3fojOVmpFodfiwln9ujuXI0OMzunVWlYEH0/GclpgNIXs0YN88o54VkxZevQ7q5ZDH91Hx1HvEvJyrYlf1oNnAYqFfOyNlMjYQaTMv8gM1hHSERJwmMrEFW6BrHl6xFXuSkLKmTQI24V64IuFzpfOXU0i6LG8HPkGEqozJOXAcPa/Z+fVzzJ420/suD3bHrO+J6WW7ujNRR8gzizzDB+P72pkCtFIPsn5xiPhHayOu5yZAalUkLJNumZlLbYA5GJ/DxV8VRWFcWs8AHcG9LGpvEmFayo1ZoXW8RTYaGR0+fdFpoA71Q8XWc05LDyi/vpPe57ytcx79GTKzI1g7uW/ce+muXY0lipYGlX9ge69tpA+60zCI7QMi9zC3MzN3LM6GDnajtJxZPrHD8D7YfB+8/D2CH2XdugxBoeqD+OOfvmcLxSSbpuO1LgfGttFdSoMCJ/X4EkOhKmjocJI0BXSL/fypF7eaFVd+bu+4y913qwtEM92uwpuIwmCDVPhHRhUrrcgwQak1HP2X0rOLtvBdqgMCo36k3NVndSsX73AhtTXImN4K3RPXhm3grikgv2pNSaVBjOJ0OsUu3Uq1cvPvnkE4/+OYSykUjVpv2o1/E+h3ezLVGxIfe8eRy1RotGG4JKbb3W5RuTiaO7TjBkyQ4iMixnWJprK5JpsryrZkCQpXbClzz19iL2tLqxzjVHo2Z13VQG7ZnlxaiEN63XHy/yfEJ0MB/23EvTCzO4aEzmw8x1HDUW/lRBuJ87Ek9BaHkmtDuHY1+0OekEUOZqCi11r9P//tOSdPIEW24azAtbXcaQk8myz0ZY3KVIZTTSZcthZn7wB+12nSQyVcmSqTAQv+19nn42klRTFmXUUTwb1pOjcdP4IfI+9wVbCLUNN7CiaFnZ8NA0Zefc9Azr4/NrVeZXhteeZFbxdEKdyi+Z/xGpsrz8QvgftVpJTh5ZAhNHF550yhWuS2RCk2H0q/omZ8rFsa9G2bxz6UFq3shcxTuZq4uYQQQCfXY6x7f/wrJPR/Ddc/VYv2Ai5XUb8s5fiYvgrdHdSYgs2LdnRdgVlu/bnPd127ZtPRazgIi4irQc8ALDZu6m2/1zHU465QoKjUIbFGZT0gkAlYpNTasz9dF+bGlkuV/gQ6nfk+bv2ZeiWEs8+XnOTSqe/ExCEnyQ+RkfB41mb3MTk459wOr1B7wdlvCic8ZEThiuUk1jvuxhY70M7t47nbMLlLX39xi+YZu+8EZ+wn3yVzzp9XqXzt1XV593I+6gtqa03ddqjCbeH7+YnQH8e9ynZALpgPlKgxt6A/PdF4I+K42lH9/NrY8vpmSlxgCEZWQz8auVVLmQkDeu16YDdNp+lMiUZCKynmCyhRUynqprcWeVYHH2zWL4bz/89B7UsdKe6cgpCA+F8qWhS8V5pLaM49duA4ivqCM+6ymmfSRV14GkS2t49zloUte+69QqIwOrz6Jq1H+s6vwyFS4lsa5DJbaFjGPm3KIflInAk5WWQKnMr5jR8SuuZZZnW/xgtsTfwRkaM3t0d57+ciUxKRnsrFeO0z2fZ//WMdw55E4AGjSQzZPcTaVSU6F+N+p3vI9KDXrZniRyo5SIEObe2YGtjaoy4o9teZVxW5tWYFTPgxx8j8B9UGrt7UGER6JwG0k8+aF5K7aQ3OMqv/59FA+0ihF+4J+cY2aJpzd165n0zw8Fji3PsX0bXOFa+RNPrlo2VEtdinci7uC2IOduzuqpyrAT23anEU4yATuAW4oYcxugAXf2/8/OSGbJh3dy2xO/E1uuDukhOtQ3fV/GJmcARZfD/J3tQLMgJ0mPJ9faewRa3QVzXoa7+1oes+cwdLlXefjVvD707wr9u8zm/GgYUPp3bh112KMxC/epVhHefAbu6OXcPE1KLqXckMN8WOcLBtedQdJ6SToVV0/co/w3LuQ8vat8SO8qH3I+rQ5b4wczL+hWmq1N5eh9pxjTOJk/N63Iu65yZfM+YcJ1VCo1TftMpFH3RwgK9b1m7rvrVOBQ1dIMXrGLFvtPs7B3M97qB/26QKWukJpudQr/Ew8Utel4OU8F4h6SePJTP6+QxtDihn9yjnFPSOsCx+7OrM/LqhBSTIU3lhae48oeTxGqYF4I7c2ToV0JUjn/Y3xkcCtaa6tQQ1OSwclz0UuTcff6g6ITTyWAdsB694aRmXqVvz8YTL8n/yCqVDX+vqU+Y3/aaNccS7M9U3ErPZ7cKyUNhk6E9dth9iT+z959xzdVdgEc/2V2D0qBsjdlCLI3yB6yEQR5EQUExIWioAIqCoIKKkNFQVyAMgRlKHsKInvvUcpe3btZ7x+BtKEjSds0HefrJx/T5Lk3p6VN7j33POdBm6rc/0IodBwO4ZHmrw+dMt8mfwVlS35OpxZwVAqv8z1vT3h3pHlKnXsOLXBQ3CuEtxp04OJVI9ccX4BXFAAVSkOvdmkfL+V1jt6VpmOqOJ2Qtg2oojJnEVT645hMJhQKBUWKpF05U+Qck8nIkfUzOb55LqVrtqNS/d6Ur9MFjVvuldWYjEYM+kSUJFHEOwm1MgmNMgmNMtHy/xPPJnEpSUs1r82YTPDb3wU06QRg630yyMbzeZwknoQoAHbrU1a2SzAlE2III8QYTgt1JTbocr8iQWQuO1PtfBXuzPXqTzdtrRxJOgE8qa3Fk9RibfIJSTrlhrWArbZ8PXB64gkgPuo2f8/pQ/exf3GoZjnuBhyneHj6KxI96qj+OrdN0U6OMC2peHKer5bAgZOw/AsoV8o8naHDMLiTQQ/5a7dg4e+5G6PIWQoFPNsTpo81T6PMSYvWwLCJRnJ4drnIR175H6gyWQhVoTCvmPlQ64YQGxuLj48ParWa4sWLc/fu3VyItPAy6JO4enw9V4+vR6XxoOxjHahUvw/lHuuEWuthewcZ2PHzS8SEhWLQJWHQJz74f1Kq/ydiNJibFo0bDp+9Zd9+e7WDibPMFbgFjq3Ek1Q8CSFc7azhDs0iPyfEEMYdU4yrwxHpSN0QOTsVT9GmRJ6LXQRAGaU/9dRlqKcqa/6/ugzlVQFZ3vfapJNZ3lY44DRwGchs5fkewNu5E05s+DXWz+lL9zfWsqFFTYasTdt4PD25Oc0udcWTJJ6ca98xqP+Uub/PR/MKcC8NAUDzetC2CdyPgOIBoM7BM4Pl65GkUyHm5QnDn3JsG08PuBV9Cx8fHwA6dOjAr786semhsGLQJXDlyFquHFmL2s2Lco91plKDPpSt2R6Vxv5SyBtndnBx/zLbAx+oVsH+GEsVh6/fg0F2JqrylXyYeFIo1VRv+Rxndi20OVYST0IUEP/pr7g6BGGnnGqUfN0YyfXkSNaSkjAqovCk7oMkVD2V+f/VVSVQKWw3jFynk8RTrlkDvJ7J8zWAKkAuzaqOunuR9XOfwjjsBzorEyhhtH2Vc0MuTbMDmWqX28Ii4dlcSnwK19pz2HwD8xS7OsHQoJa5l1eDmvBYVdur2aUnLh62SL/5Qu253uCfhdZBSv15oBoALVu2lMSTi+iT4rh8aBWXD61C4+5Dhce7UalBb0pXb4NSlfmbwvEtcx16rarpL2KXoWe6weptsOxvx7bL827beL4YTu8B6ohSwU/QrP90vPxLSuJJCCHyitRVGgYnrgoQYYpnu+4823UpzX7d0VBbXcoqGVVHXQoPRUojlwO6UG4Zc3/aVKG1lswTT2CuevrS+aE8FH7zNEs/bk4p9zbM9OqT6dhoYwL/6qVZsBAFSWIS7D9uvj2k1UDtag8SUQ8SUnWCwc3Gst8b95j3JwonhQLGPJu1bQPdDwLdAahTp07OBSWyTJcYw4V9S7mwbyluXkXMSaj6fSgZ3Aql0nou5f2rx7hxdodD+3ek4umhb96DXQfg1j3Ht82zbFU8KYHidoxzMpXGgxYDPqNas0GAecEae0jiSQghckFONhd3VCI6DuhDOaAPtTymQkk1VXFLMuqEQebT5Kp/gCjAL5MxuZx4AnOz0e8S9zDBoxMBSq8Mx23RnXdZPzCZaidE7knWpTSUX7DC/JhGA7WqWCejHg8GD/eU7VZvdU28Im/o0ipryQQAVeJ2YDIAFSpkcSfCaZLiIjj372LO/bsYd+9AKtbrQaUGfQiq3AyFUulwtZOPF5Qs5ngcAf4w7Cn4+FvHt82z7EkolbRznBMZdAnsWvIaRzd+SUCZWvgXr2bXdpJ4EkKIXJad5uI5xYCRM4bbnDHc5lcOujqcwkcHbAAGZDKmFeAPROZCPKnEmpL4KnEX73t2zXDMhlzs7wS5n6wVQmRMpzOvZHj0DPyw0vyYSgU1KqUkov7a6doYhWu9PiRr223fB4s33GHhb+avVZl1Jhculxh7nzP//MiZf37E0y+I8nW6EnJkjUP7cHSa3UOffg/TvsvatnmWvYmnvMBkIvreZaLv2V/9brvphxBCiGxzZcWTyKNsHZupgYxzP041J2EncaaM58ls0OVefychRN5nMMDJC/DznzBmmrlPmCicalaBTi0c22bNNmj2DLR7Ho6eT6m2zQsX6oR94qNuc+afHzEZHWsnUbWCY6+j08GI9+Gdz6HAtX8Mx3xhMjN5JfGUBZJ4EkKIXCCJJ5HGesDWMXWP3AgkrTBTHPMT/033uVP6W1wzRuRyRClSrxAphBAib3ltsH3j9HpYvAZq94ReL8N/R82Pe3t7W8bodLbOwkV+58iUzMho6DoKvl/htHBcy4TtBuOSeBJCCJEZSTyJNCKAPTbGdMVlk+I/T9hGsiltZmx9Lk+zA1nVTggh8oMifvBsz8zHJCbBvKVQrat59cyTF6yfT514koqngq+anVPtLl8zV8VtLeirZdqabheUK1E4hSSehBAiF6ROPMmBlLBYa+N5f6BlLsSRjhvGSBYlHUjzuKun2UlzcSGEyJtG9AdPj/Sfi4kz9+Wp2BFe+hBCrqc/ztPT03I/OTnZCVGKvMSeiqd/j0DTgXC2MCymayvxJBVPQggh7CUVT8LCnh6cNq4eO9NnCVswmlJ+X+NMSfyju5TrcUjFkxBC5G1qNbwyKO3j98Jh0mwo187cl+f2vcz34+WV0uNJptoVfLaaiy/929z76154roTjepJ4EkIIkR1S8STSdQE4Z2OMi/o8AZw33GVl8jHL19t050m22ZhKCCFEYdOnA5RNdVJ87Za50XyFDuYl7yOj7dtP6sSTVDwVbIFFzNMzMzJlHgx6C5IK06+B9HgSQgiRU6TiSVixNd2uClA9NwJJ3/T4TZb7G5Jdv5qdTLUTQoi85/Uh5v+fC4GhE6ByZ5izCOITHNuPh0fKXD1JPBVsGU2zS06G596B9+cUwJXrbJEeT0IIIbJDKp5EhmwlnsClVU9HDNfZ+CDh5IrG4iBT7YQQIi9rVBvc3aDfGKjZHX76w7zsfVZIj6fCI73EU3gkdHwBflmd29HkEbYST25AkdwIJOe5aK0cIYQoXGRVO5GhPUA4EJDJmJ7AjNwJJz3TEzZRUVWUEGOY64J4QCqehBAib7l8DRo8lTP7Sp14SkxMzJmdijzp0cTThVDo9iJcuOKKaPIIW4knME+3i3B2IDlPKp6EEEIIVzIAf9sY0wwomguxZGCn7iIfxNkK0nkkWSuEEHlXWGTO7cvd3d1yXyqeCrbUjcV3HTSvXFeok05gu8cT5Ns+T5J4EkKIXPCwSkOmDIl02ZpupwKezI1AMrY0+ZBrAxBCCFHgpe7xJBVPBdvDiqdFa6DjMPM0u0Lvjh1j8mmfJ0k8CSFELpDpQSJTGwBb/TB65UYgeZ/8LQkhRMGVuuIpIcHBzuQi31AooEo5eG8ODHkbkrPYE6zA0QH3bIyRiichhBBCZEk0sMvGmC6Ah40xBZRUCgohROGQOvGUlJTkwkiEMxULgOGTYOo8V0eSB9nq8ySJJyGEEEJk2Robz3thTj4VclLxJIQQBVfqxJNMtSu47obBUte1jszbbPV5ksSTEEKIjMjJsrDJnqWDc2jVICGEECIv0mq1lvsy1U4USlLxJIQQIqsk8SRsCgUO2hjTHdDaGFMApZ5qp1TKoYsQQhRUqSue4uPjXRiJEC5iK/EkzcWFEEIIkS0rbTzvB3TIjUCEEEKI3Je64kkST6JQkoonIYQQWSUVT8IuthJPUCin2xmNRst9+VsSQoiCS6baiULPVuLJj3y52IwknoQQQoi84gJwwsaYXoA6F2IRQgghclnqxFNcXJwLIxHCRWw1F4d8WfUkiSchhMgFUqUh7Gar6qko8ERuBJI3yd+SEEIUXKkTT7GxsS6MRAgXsVXxBPmyz5MknoQQQoi8RKbbpZG6ubgQQoiCS6PRWO5LjydRKNmTeJKKJyGEEEJky0ngvI0xfSi0n+BS8SSEEAVX6sSTVDyJQikOiLExRhJPQggh0vPwZFkqN4RdbFU9BQHNcyOQvEH+boQQonBQq1OaGEriSRRatvo8SeJJCCGEENlmz3S7550dhBBCCJG7UieepLm4KLRsTbeTHk9CCCHSI9ODhEMOAVdsjBlOoal6MhqNlvvytySEEAVX6sRTTIyt+UZCFFC2Ek+VcyWKHCWJJyGEyEUyZUjYbZUdY74HtDZHCSGEEPlC6sRTdHS0CyMRwoVu2Hi+BeCfC3HkIEk8CSFELpAqDeGwpXaMqQFMdHYgricJWyGEKByUypTTU1nVThRah208rwa65UYgOUcST0IIIURedADYYse4d4HHnBxLHiJJXCGEKLhUKhUgFxxEIbceMNgY0zsX4shBkngSQggh8qqXgEQbYzTAAgr0J7qcgAghROHwMPEkRKEWDuyyMaYL4J4LseSQAnyYKoQQeY+cQAuHXAA+tGNcU+BlJ8eSR0jFkxBCFFwP3+PleEkUen/aeN4baJ8LceQQSTwJIUQukJNlkWUzgaN2jJsGlHNuKK6SelU7IYQQBdfDHk+SeBKF3mo7xvRyehQ5RhJPQgghRF6mB17A9lx/b+Bb54cjhBBCOMvDC3VywUEUeqHAERtjepJvMjr5JEwhhBCiEDsEfGnHuK7AICfH4mJSPSiEEAXXw4onSTwJge3pdiUwt1vIByTxJIQQuUB6Fohs+wC4bMe42UCgk2PJZfJ3I4QQhYsknoTAduIJ8s3qdpJ4EkIIIfKDeGCkHeMCgS+cHIsLScWTEEIUfJJ4EgI4DoTYGNMnNwLJPkk8CSFELpLKDZEtW4Ef7Rj3LNDZybHkIvm7EUKIgk+r1VouLuj1ehdHI0Qe8aeN56sANXMhjmySxJMQQgiRn7wF3LFj3HeAl5NjEUIIIXKIt7e35b5UPAnxwJ92jOnt5BhygCSehBBCiPwkHHjNjnHlgalOjsUFZKqdEEIUTD4+Ppb7UvEkxAN7gDAbY3rnQhzZJIknIYTIBdJcXOSo5cAaO8a9BjR2ciy5QK58CyFEwSeJJyHSYQDW2hjTCCiTC7FkgySehBBCiPzoJSDaxhgl8D2gcX44QgghRHZ4eaXMD5fEkxCp/GnHmJ7ODiJ7JPEkhBBC5Ec3gHfsGFcbeNvJseQimWonhBAFU+oeTzqdzoWRCJHHbMK8unFmeuVGIFkniSchhMhFMtVO5Khvgd12jJsEVHdyLE4kfzdCCFHweXp6Wu5L4kmIVBIwJ58y0xbwy4VYskgST0IIIUR+ZQJGAEk2xrkBC4ACUCwkFU9CCFEwScWTEJn408bzGuDJXIgjiyTxJIQQuUgqN0SOO4t9q9e1BEY5ORYnkebiQghR8KXu8SSJJyEesQ5zo/HM9M6FOLJIEk9CCCFEfvcpcNLOcaWdHIsQQgiRBR4eHpb7SUm2SnmFKGTCsN1eoSugzYVYskAST0IIkYuk4kk4hQ54AbBVGOQLzHN+OM6kVMqhixBCFESpK56Sk5NdGIkQedSfNp73AdrlQhxZIEdvQgiRiyTxJJxmHzDHjnE9gP5OjiWHyd+NEEIUfKkrniTxJEQ6Vtsxprezg8gaSTwJIYQQBcUkINSOcXOBIk6OxUmkubgQQhRMqRNPiYmJLoxEiDwqBDhuY0wv8uRiMpJ4EkKIXCSVG8Kp4oAX7RhXAvjcybHkIPm7EUKIgk8ST0LY4U8bzwcBTXIhDgdJ4kkIIXKBVGmIXLMBWGzHuKFAeyfHIoQQQthJmosLYYc/7RjT28kxZIEknoQQIhdJ5YbIFa8D9+wYNx/wsDkqT5EkrhBCFExubm6W+1LxJEQGjgBXbYzplRuBOEYST0II4WSpV+GSxJPIFWGYk0+2VAI+dG4oOcFoTFmuTxJPQghRMLm7u1vuJyQkuDASIfI4W03GqwPBuRGI/STxJIQQTqZWq10dgiiMfgXW2zFuLNDAybFkU+qEbf369fnuu+/YsmULu3fvZsqUKXh6erowOiGEEDkhdeJJKp6EyMSfdozp7eQYHCSJJyGEcDKpeBIu8yIQa2OMCvgeyCf5UX9/f0aOHEn79u1p0aIFkyZNIjY2lujoaH755RervzchhBD5R+qpdvHx8S6MRIg8bhcQYWNM71yIwwFydCaEEE4mJ8LCZa4CE+wYVxd407mhZMfVq5k3M1AoFPj4+PDss89y/fp1ypQpk0uRCSGEyCmpE08y1U6ITOiBdTbGNAVK5kIsdpKzISGEcLLUU+2k4knkuq+B/+wYNxmo4txQsuqXX35hxYoVXLx4kf3797NixQqmT5/OjBkzuHr1KjqdzjK2ZMmSXLp0ie7du7swYiGEEI7SarWW+5J4EsIGW32eAHo4PQqHmOQmN7nJTW7Ou/n7+5seCg8Pd3k8ciuEt1qYSMZk87/tmFDmgXizcJswYYLJYDBY/taMRqPps88+c3lccpOb3OQmN/tuhw4dsryHv/DCCy6PR25yy9M3b0wkkPlx3d95IM4HN6l4EkIIJ5OKJ+Fyp4DpdoxrA3zr3FCcZdq0abRt29ZylVyhUDBu3Dh2794tDf6FECIfSF3xFBcX58JIhMgHYoEtNsa0B/ydH4o9JPEkhBBOJs3FRZ7wMXDGjnEjgFnODcVZdu3aRZkyZQgJCbE81qJFC27evEnFihVdGJkQQghbNBqN5b40FxfCDn/aeF4LTMyFOOwgiSchhHAyaS4u8oRk4AU7x44BpjkxFicKDw+nUqVK/Pnnn5ZEb7FixTh37hz9+vVzcXRCCCEykjrxFBtra0lWIQRrAaONMWOA4FyIxYZCczZUpGR1PHyKuToMIUQhJFPtRJ7xL+Zm4/Z4lzxzlSwr+vTpw9ixYzEazUdkGo2G5cuXM3fuXKe+bmBgIPPmzeP777+nWbNmTn0tIYQoSCTxJISD7gJ7bIzRkGcq2V3eaCo3bj5Fy5uenXHZVKvNSJNCqXJ5PHKTm9wKz61cuXKWZpl37951eTxyK+Q3H0xcwXaj8Yf/jc0DMWfj1qhRI1NsbKwptYMHD5q0Wm2Ov5a3t7cpMjLSqsH5jh07TEql0uU/B7nJTW5yy+u3mzdvWt4/H3vsMZfHIze55YtbP+w7nuvu2jgLTcVTTFgoMfdDaNZ/On3e2UHJqi1cHZIQopBQKBSW+1LxJFwuBugDRNs5/nPgReeF42wHDhygVKlSnD9/3vJYgwYNuH37NjVq1MjR15o5cyZ+fn6WrxUKBU888QQrVqzI0dcRQoiCKHWFeExMjAsjESIf+R3Ybce4LzH3fHKRQpN4Agg5shaAgNI16fb6GtoOnY+nfykXRyWEKOhkRS2R5xwBngTsXTRoHjDEeeE4W3R0NMHBwfz222+W5G+RIkU4evQo5cuXz7HXqVevnuW+Xq+33O/Tpw8bNmzg22+/ZdiwYdL3TQgh0qFSqSz3ZVU7IRzwKrZ7PVUB3siFWDJQqI58Qo6utfq6csOn6P/+Xup0fA2lSpPBVkIIkT2yqp3Ik/YAPYFEO8f/ADztvHByw6BBgxg9ejQGgwEwL929f//+DJPDAQEB7NmzB51Oh8FgID4+nlu3bnHo0CEGDBiQZnxQUJDlfseOHQkNDQXMlU+dO3dm1KhRLFy4kKSkJEaPHu2E71AIIfKv1Imn6Gh7y3KFEBwFFtgxbhJQ0rmhZESBec5dodHn3Z0ULfNYmscj71xk74p3uHFmuwuiEkIUZDVq1OD06dMA3L59m5IlXfSOL0R6umJejtee8ms9MAjI5zPHWrRowa5duyxJ4aioKDZt2sTVq1e5fv06oaGhFC9enE8//dRq6tyjwsLCmDt3Lp9++imJiYnEx8fj4eEBgI+PDwEBAezfv58SJUqk2dZgMFChQgWuX7/unG9SCCHymdjYWLy8vDCZTFIZKoSjAoELgL+NcYtwSRV7oUs81e3yJg17TMjw+StH/+K/lROJDb+Wi1EJIQqyWrVqcfLkSQBu3bpFqVIyxVfkMX2B5YDK1kDAADwL/ObUiJxu7NixzJw506oHW0ZMJhMGgwGVSpXueJPJhNFotFytT05Oxs3NzfJ8586d6datGyVLlqR3796WCqsVK1bw9NP5vIxMCCFyyMPkvSSehMiiV4E5doxrDux1ciyPKHR/0VeOrM30+Qp1u9Hvvb3U6/oWKo17LkUlhCjIpLm4yPNWAc9huz8AmJNTizAnn/KxL774ggkTJlim3WXEYDAwePBgNBoNWq2WBg0a8Ntvv5GcnGwZo1AorKaILF261GofGzdu5LXXXqN///7069fP8njPnj3x9fXNoe9ICCHyN0k2CZFN84BTdoybg7kEKRcVuoongL4TdxNQyvZKNtH3r/Df7xO5emJDLkQlhCio6taty5EjRwC4ceMGZcqUcXFEQmTgBezrEQDmJNUIzL2f8jF3d3cGDRpE8+bNKVGiBIGBgQQEBKBSqdi7dy+TJk2y9GpKTa1W88EHHzB48GDKlCmDSqVCp9OxZs0a+vfvn+lrRkVFWRJOsbGxdO3ald277VmSRgghCq7k5GQ0Go1VBakQwkHtgS12jBtOrh7DFcrEU/0nx1O/29t2j792ajN7V0wg+t5lJ0YlhCio6tevz6FDhwBJPIl8wN4y7YdeBL5zUiwF1KBBg1i8eLGlGtJkMjFmzBjmzp3r4siEEMJ19Ho9KpUKvV6PRiMLPwmRZSsxt1HIzF2gGhDl/HCgEE61Awg5ssah8WVrdeSpibtp2HMSaq2nk6ISQhRUqUvHjUZ75jIJ4UJzgXccGP8t8LKTYimgfv31V4YMGWKZrqdQKJg9ezaDBw92cWRCCOE6D5PxcqwkRDa9ie1Vi4sD7+dCLA8UysRTxK2zRN6+4NA2Ko0bdTu/Qb/3/6NktVZOikwIURBJubjIdz4F3nVg/FfA684JpaBavHgxFStWtPSYUigUTJo0ycVRCSGE66SuAhVCZMMVYIYd414Fqjs3lIcKZeIJHK96esi7SGlMxswbkQohRGqpK57kYErkG58Abzkw/ktgvJNiKaBu3rzJ4cOHLV8fPHjQhdEIIUTeYGvRByGEHT4BrtkYowFmOT8UKMyJp6NZSzzdvXKI2xf/zeFohBAFmSSeRL71OTDGgfGfAhOdFEsB5eHhYbm/efNmF0YihBCuJVPthMhB8cA4O8Z1Bno6ORYKceIp/PpJorLQLPz4Zmn8KYRwTOqpdpJ4EvnOHOAlB8ZPBT5wUiwFUGxsrOX+w5XuhBCisPH0TOmjKxVPQuSQZcAuO8Z9Cbg5N5RCm3gCuHJkrUPjo+5eIvTYX06KRghRUEnFk8j35gEjAHsvQk/GnIASNsXFxVnuBwQEuDASIYRwndSJd71e78JIhChgXgNs5XIrAWOdG0ahTjw52ufpxNavMZmk9FMI4RipeBIFwvfAMOxPPk0EPnNeOAVFVFTKOsb16tVzYSRCCOE6Xl5elvtS8SREDjoGzLdj3ESgtPPCKNSJp/tXjxITdtXu8f5BwU6MRghRUEnFkygwfgaGYPvK2UPjMJdviwzt2bPHcr9Xr17cuXOHCxcucPXqVW7fvs3Nmzc5evQo33zzDfXr17fa9rnnnmPVqlVs2rSJqVOnUqpUqdwOXwghcoSPj4/lvk6nc2EkQhRA7wERNsZ4Ye7V6SQKoFCfBTXu8yF1Orxi9/gdP7/Exf3LnBiREKKg6dy5Mxs2bADg4sWLVK1a1cURCZFNA4DFgNrO8V9jXrK3UB9xpM/d3Z179+7h7e1t13i9Xk9iYiIeHh5W1ZRgbsi7du1aBg8ebNU7Sggh8roWLVqwe/duAEJDQ6lQoYJrAxKioHkZ+MqOcS2BPTZHOaxQVzwBhDjY56nloC8ILFfXOcEIIQokmWonCpxlwEDA3ovSLwPfYr7cJawkJiZSuXJljh49atf7g1qtxtvbO03SCczVlb169SI8PJyvvvrKqtpSCCHystTJd6l4EsIJvgVO2DFuDk7JEhX6I5J7oYeIjbgBgMloJPzGKc7u+YV/lozh6okNacarNe50GPkLHj7FcjtUIUQ+JVPtRIG0EugHJNs5fiTmPlGF/sgjrbt371KvXj3UajVNmzalS5cuNG3alOrVq9O4cWPefPNNdu3aRVRUlGWZcYPBwL1791iyZAlz584lJCTE8v6i0Wh4+eWXiYmJ4ZNPPiEwMNCV354QQtiUuseTNBcXwgkMwBg7xtUHhuf8yxf6qXYAZWq2w6BL5v7Vo+iSUkrTNe4+9Bq3Cf+gamm2uXVhD3/P6YvJKG+MQojM9ejRgzVrzIsZnDt3jurVq7s4IiFyUDfMSSh7l+FdBAzF/j5RIg2tVktyctqMX79+/fjmm28oVsz64pjJZCIpKYlbt25x5swZ9uzZw99//83Ro0fT3f+gQYN47bXXqF69Oj4+PphMJmJjY7l69Sp//PEHH374oSUBJoQQOWHw4MEsWrQIgOPHj/P444+7OCIhCqgVmC8cZuYeUA2IzLmXlcSTDX7Fq9Br/Ga0Hr5pnju1YwF7V7zjgqiEEPlJnz59WLVqFQBnz56lRo0aLo5IiBzWCfgT8LBz/G/As+S55JN/UDB6XQKx4dcgH1cnjh8/ng8++ABPT89MxxmNRiIjI7ly5QqXL1+mZMmSNGrUCK1Wm+l2ISEhVKpUyaGY+vTpw8CBA6lcuTJ3794lNDSUCxcucOLECY4cOcL9+/fT3e7JJ5/kpZdeolmzZvj6+qJSqTh//jzt2rXj5s2bDsUghMi7Ro0axbfffgvAwYMHadSokYsjEqKAKg+cwfYx22zg9Zx7WXvbghZaUXcvsv2nUXQatQTFI70SarUZwf1rx7nw368uik4IkR8oFCmNbWSqnSiQNgHdgbVA5rkOs2cAzYP/56HCYaNRT593tqNUaYi8fYHI2+eIuHXuwf/PEht2FZMp71f6fPbZZ8ycOZMJEyYwcOBAKlWqhLu7u9V7EZinAQcEBBAQEJBmxbyH9Ho9CoXCqqdUxYoV+e677xg1apTNWNzd3Tlx4gRVqlTJdNzDqqzY2FhiYmLw9vbG398fjUaTZmxwcDCnT5/G39/f5usLIfIHD4+Us+D0KjqFEDkkFPgM+MDGuJeB+cDpnHlZqXiyU90ub9Kwx4Q0jxt0Saz7sjv3Qg+7ICohRH7Qr18/VqxYAcCpU6d47LHHXByREE7yBLAOsG+BNvgD8wp5eaiPbOnqbej80jKUqrTX5vTJCUTeSZWQunWOiNvniLl/BZMxj5VvPcLT05MuXbrQoUMHGjRoQKVKlShSpEiaJuUmk4mIiAjWrVvH1KlTuXDhAgDly5fnyy+/pE+fPpZx//77L+3atcv0JPHChQs2k072MhqNVj3zHk2kCSHyrwkTJvDxxx8DsGPHDtq2beviiIQowDyAs0A5G+O2AB1z5iWl4slORzd+QWDZx6lQt5vV4yqNGx1G/Myfn7UnIfqui6ITQuRl6a0+JUSBtBPoAqwHfOwY3wdYhbnXQJIT43LAjbM72LfqPZr1n57mObXWg8CydQgsW8fqcb0ukag7F1NVSJ0n4tZZou+F5JlekPHx8axatcoy7fehGjVqMGzYMNq3b8+lS5eYMmUKx48fT7N9aGgoffv2tSSSFAoFLVq04OLFi1SrVo3ExMQ023zxxReWpJPJZOLvv/9my5YtlC5dmkqVKlG6dGlKlCiBv78/np6eaDSaNBWikZGR7Nq1i7lz57J161YSExNxc3OT6lEhCpjUU4OTkvLIB4IQBVUC8Baw3Ma4DkBvzO0UskkST/Yymdj5y0v4ldhIkZLWjYG9ipSi/fAf+HtOH4yGPHTZVgiRJ6S+Qi8NeUWBtwdzz6cNgJ8d47tjPqDpA6TNXbjEqR3zKVKqBtVbDLFrvFrjTtEyj1G0jHU1o0GfTNTdS6yf29dlF6fcUJOUyXzGM2fOMG7cOLv3V7t2bTZu3EirVq1QKBSULVuWkJAQ6tWrx+3bty3jxo0bx+uvv275+ttvv+Wll16yuf/KlStTpUoVLl68yKVLl9I8r1abD13lvVSIgsXd3d1yXxJPQuSCFcAOoI2NcV9gPqbL5jGaJJ4coEuKZfN3z9Jr/BbcPK2PpoOqNKNpv2n8u8z+gzchROGQuuJJrtKLQuE/zFfJNgFF7BjfBVgD9MJ8FS4P+HfZePxKVKFkleZZ3odKrSWgVA2XXpTqqq1JDVUQnyRsxpQD3RUSExN54oknmDBhAlOnTkWhUBAUFMTNmzct728KhcKqcuny5ct2JZ0ALl26lG7CCbA0FweIi4vL5ncihMhLUlc8pVdBWRB5enoSGBgo04aF60wDGgEZTM4wYeJ+ufvEvxkPH2fvpSTx5KDoe5fZ/tNIOr/4W5pm4zVbD+P+1aOc37vERdEJIfKi1BVPknjK27Qevhj0yRh0heOg16kOAu2BzUBRO8Z3BP4CegB5IKdgNOjYuuB5eo3fgk9RW00QMpecEJNDUTnuuP4mf/iOoKm6AkNiFxFlypnM3rRp00hKSmLGjBmWRFN6J0+xsbHUrl07R15Tr9djMplQKBRW1RFCiPzPzc3Ncr+gVzwpFAqGDh1Kz5490Wq1kngSrnWKDNsjmDCRTDJrRq7hx19+xHQt6+cxknjKguuntnBo3XQa9pyY5rkWA2YQcesc964cdEFkQoi8SFa1yz+UKi29396GQqki8s4Fom5fIOruRSIf/D8+6rbtnbjIELfGXDTc4199iKtDSXEEaIe5OWUxO8a3xdwf6kkg1olx2SkxNozN3/6PHm+tR+Nmb8d0a7qkOJf2ebpsvE+kMZ6ebrU5oH6LvtHfc9JwK0f2/fnnn3Py5Em+/vprypYti0KhsLzHxcXFERoayogRI4iPj8+R14uPjycmJgZfX1+0Wi1BQUFW0/uEEPlX6lXtEhLySOmrkwwdOpRnnnlGVuYUeYMKKEv6mSETEA/P3H0GFsAPXX7I8ssobQ9JxZ4rloXE0Y1fEHJkTZrHzc3Gf8LDt4QLohJC5EUy1S7/SIy9z6bv/oebZxHK1mzPY+1epMXAmXR7fTWDpp1iyMwr9Bq/hSeem0fdLm9SoV4PipSsjkrtZnvnTnbfGMd2v9d43q2Jq0OxdhxzQumOneNbYa6SyiMfo+E3T7PjZ/umiaXHldVODx013ACgqqo4+/zfYpBbwxzb98aNG6lSpQpubm5otVrc3Nxwc3MjICCAevXqcfBgzl6Ii4lJ+Xk2atQoR/cthHCd1FWMBTnx5OXlRc+ePSXpJPIOAxAGuKdz8wCKgn9xf3qW64lnB880m48aNcqul3Gs4mkaYN9+C4Wdi17Br0RVAkrVsHrcy78kHV74kb9m95Jm40IImWqXz0TeOse2H4bTafRvKJXWk961Hj4UK1+PYuXrWT1uMhqJCQu1qo56+P/caip92nAbrULNjz6Dqa0uxfi41RjIIw2YT2FuXrkNKGnH+KbAYeBpzM3KXSz02F8cXDuNhj0mOLytLjHaCRE55oj+Om00VQHwVGhZ4vMcTdUVeDPuD3QYXBydY1L3dlq9ejXXrl1j79697Nmzhw0bNnDhwgUXRlcwKVDkSH8wITKj1Wot9wtyj6eiRYtafa9C5An3MFemp80rmXmCFi2BswO52uwqFYtWZNKkSTzzzDPodDq+++47my/hWMXTQOB5h7bIs15xb80b7m2poyqNgqzNq9UnxbFl/rMkxUemea5E5SbpLsUshCh8ZKpd/nP99Fb++z3tdOqMKJRKfItVpGytjtRu/xItn/mC7m+s5X/Tz/DsjMtUrN/LidGahRrDiTOZ+2KM9WjHOt9R+Ck8bGyVi84CTwDX7RxfCtgOjHFaRA45uuFzLh1c6fB2PoEVaNB9Amo3LydEZZ8j+rQ/9Fc9nmCH32uUUtqz9GDesWjRIqsm5uXKlWPAgAHMmTOH8+fPk5SUxOHDh1m+fDkvvPBCjr2uUqlk0KBBTJ8+nRo1atjeoACprirBi+4tXR2GKOAKS8VTRv3whHC5q5k8pzBfhFBUUtApohPnL59n2LBhVlNkbXEs8QTwNVDT4a3ynD+Sj/O+ZxeOFXmHOwEfs9RnKCPcmlNJGejQfqLvhbD9xxGY0lnWt0aroQQ3fzanQhZC5FMPl/8GSTzlJ6d3LuD0rqzPZX/IzdMPlcr5VzdNmDijT5nP1kVbk//83qSq0p7mSrnkAubkU6id4zXALGApkLUWSzlq1+Ix3As96tA2KrWWel3fpP/7+6jS+GlwwQnHYf21dB9vrqnEYf/xtFZXyeWIsm7q1Kk0a9aMPXv2YEzn2Eur1VKvXj369+/P/Pnzee+997L1ep07d+bChQvodDqWLFnCO++8w6lTpwgJCWHw4MHZ2nd+EWaMY573AF52b+3qUEQBlrq5eE71hRNCOCAWCLc9bJNyE2/xVspmsfY15XS8ubgnMBvz6jP52A1jJK/G/c4inyEUU/owwK0+A9zqAxBqCGer7hxbdefZlnye26bMy+Svn97GgTVTadz7/TTPNR/wGRG3znI35IBTvg8hRN6X+spWeidKIu/au+JdfItVpEyNttnaT3JS7nTKPmW4RUNNygps1dUl2O//Fk/H/Mhm3dlcicGmy5iTT9uBinZuMwCoDTyFuXLKRQy6BDZ/N5jeb2/B0y/IoW29/EvS5rl51Gw9nL2/T8zVRUjOGu6QYErGQ5E2AVpC6ctWv1d4O34NXyRsy7WYsmPfvn20bNkSb29vnn32WZo3b07t2rWpUKECvr6+lvdchULB2LFjmTJlisOvoVarWblyJT169EhTnaBQKKhQoQK//PILNZr0Zt9VX5LiI0mOjyQpPurB/yMfPBZlvp+Q8rwu0fV9vxwRbjJPb/zKuz8qFMxJ3OniiMRDQUFBtG7dmtDQUPbt2+fqcLJFo9FY7hfkiich8rTrgB/mhuOZmM1svK95c2vyLX74wb6LtFlb1a4D5qqn01naOs9YnHSAvtrH6eP2uNXj5VUBDFM1Y5h7MwBO6W9ZElE7dRfTXYr4+ObZBJarQ6X6va0eV6m1dBjxE39+2j5Pr4YkhHAeaS6ef5mMerYtHEbPtzbiH1Qty/vRJ8XZHpQDThvSfs74Kz1Z7zuaN+P+YHbijlyJw6ZQzMmnbYC9xTY1gf3AcGCFk+KyQ3zULTbPH0K319eg1rjb3uARxSs2pNe4jVzYv5wDf35EfFTOrDCXGQNGTuhv0VhTPt3n1QoVn3v1oam6AsNilxBryh9LmcfGxjJv3jzmzZtneaxcuXIsXbqUZs3Mx3C+vr4O77dq1ars37/fqvmvyWTi9u3b3L17lzp16limy7z8bAc2vPILwc3+Z/f+jQY9yQkPElKpElUpj0Vybs8ikhOiHI49PUOHDmXYsGFUqFCBokWLolQquXv3LvPnz2fq1Kk2t9djJMqYgJ/Sg9ne/VCh5MvE7TkSW0E2ffp0SpcuzaxZszh8+HCO7tvT05MNGzbQsmVLS2I0KSmJU6dOsWLFCr755huio13fX84Rqfsepe7lVlg09M7dxRIOxhasoojJkycTGxvLzJkznfYajRo1YsaMGbRp0ybL+1i7di1ffPEF27fn0ffQZOA2UNr20I+DPgYHFlJ2fKrdQ0OzvGWeMip2KfeMmV95qqUuyWsebVjtO5KwgE/Y5/cW0zx70F4TjDsp2fldi18j/MapNNt7+gXR/oWfUKqzP9WidAno0DzbuxFC5CJpLp6/JSdEs3HeMyTGhmV5H7rE3Kl4Si/xBKBSKJnl/RTfew9Cm8VrTjnuGubk0xkHtvEBlgNfQqqP31x378ohdi95PcPnkxNiuHvlUKb7qNr4afp/sI+6Xd5ElYUElqOOZDDdLrX+bvXY7/cW1VV5ZEnBLLh69SqzZ8+2fO1oL5Unn3ySU6dOWZJOJpOJQ4cOERgYSKlSpahbty6BgYGW93JfX1/+WTKGc3uX2P0aSpUad++i+BWvTPEK9SlTsx2VG/alRquh1O38Bk36fIh3QBmH4s7Izp07+eGHH2jZsiVlypTBw8MDNzc3ypYty5QpUzh58qRd+wkzpSQCvvDuy1se7XMkvoLqwoULvPPOOzz77LMcPHiQTZs2UapUqRzb/+nTp2nVqpXV77ebmxv169dn+vTpREZGcvDgQQICAnLsNZ2tsCee8jq9Xs+8efPo1asXLVu2pFevXixYsMCqkn/y5Mk0atTI6jZ0qHXSIDk5mRkzZtChQwdatWrF2LFjuXPH3mVvXWv9+vU0b14ITsRvA/YU5mqA34FK9u0264mnZ8lqvVSecs8Uy6jYZXaPVymUNNaU513PTmzxe4WIop+y1fdVJnh0opLei83zh5AYF5FmuxKVGtG8/yfZjrdNY/jo1WzvRgiRi6S5eP4Xc/8KWxY8h0GfnKXtdblU8XRKn3n1zHD3Zmz1e4ViijzQMAngJuYV7P5wcLvXgV1A2ZwOyH4XD6zg2KY5aR6/dnorKz9uwZoZndj6/VBiwjNO+GjcvGjYYwL93ttLxXo9nRkuhw32dXWvoQ5iv99bPKWt69R4nGnz5s2W+3q93u7t3nnnHdatW2eZ8mMwGHjllVdo2LAh4eEpjS/Cw8NJSkpVFWYyOZx8skWlyf7CAHXr1qV1a+u+TCaTyepzqFatWrz6qu0DyzCj9XvYDK/evOPRMUsVZQVRcHAw27ZtQ6fTYTKZqFIlpZRToVDQsWNHrl69yujRo7P9Wu3bt6d8+ZTqxRs3bhAVFWX176pQKGjQoAHnz5/PN8mn1FPtpMdT3vPLL7+wcuVKxo0bx/Lly3nttddYvHgxy5ZZn0c3a9aM9evXW26zZs2yev6LL75gx44dfPzxx3z//fckJCTwxhtvYDDk/RVWAwMDC8eKhCbgEmBP8XMA5guCdsh64qkE8GSWt85T/kg+xuLErJUbuis0tNNW42OvHnTT1CLm/hW2//ACRmPaP57qLZ+jesvnshVr2ybQrC50aZWt3QgbPHxLUKlBXwJK1cyRSjVRuKWeaic9nvKv2xf3svu3sVnatvWzcylRqXEOR5TWFWM48abMk2MtNZU54D+Ox1V21FHnhmigLzAecOS4sylwBJceixxcM4WrJzYCkBQfxa7Fr7Lx66eJi7gBQMiRNfz+UVMOrp2WafLRp2g52r/wI93eWEvRMrWdEmt6K9tlGI/Snd99h/OZZy9U2ThUdJU33njDcv/ixYt2bbN06VKmTZtmuVAQHx9Ps2bN+Oabb9Idn7qPVKlSpcBkYs9vbxB+9k/KFFWizuaPTa01J55GjBjBgQMH+Oabb6yqZ+3h7Z2SYNbr9bRv3x53d3fc3d1Zv3695bkhQ4bY3FfqiicA+gczPfxvoqKiMBqN3Lx5kzlz5li9ZmEwZswYIiIiOHPmDG3btrVaTATM1R0PE0IqlYq5c+dmKxFUqlQp/v77b8vX//77L2XKlMHf35+KFSvy+eefc/78ectrFi1alLCwMOLi4liwYIHDv0O5KXXiyd5mxSL3nDhxgieeeIKWLVtSqlQp2rdvT5MmTThzxrpsWavVEhgYaLn5+aWsnBobG8vq1asZM2YMTZo0ITg4mI8++ohLly6xf/9+AG7evEmjRo3YvHkzI0aMoGXLlgwZMoTQ0FBOnTrFkCFDaN26Na+++ioREWmLPRYsWECnTp1o06YN06ZNQ6fTpfv9mEwmOnbsyLZtKb0NBw0aRKdOnSxfHz9+nKZNm1oSoY0aNWLHjh1WcW7bto0XX3yRli1bMmjQII4fP271OmvXrqV79+60bNmScePGERWVM1OonU4PXMS+47Kq9u0ye+8+w7K1dZ7yatwKbhgis7WPc4a7ANw4u4ODq9NvZNms/yfZOvlo+2BTqXpyroToO5QKbkXfif/w/JfX6ff+PjqM+JkG3SdYElIqtZvtHQmBTLUrSC789xvHNs22PfARxSs0oMeb62k3fCE+RdPvs5MTTJg4k8F0u9TKqwLY4/8GfbWP2xyba2YA7QFHKu6LAn8Bn+CSKmyTycj2n0ZxetcPrPy4Jef3/ppmjEGXyNENn7PiwyZc2J/5ZcGSVZrT++1ttBz0JR4+Obsa4Qn9TfQmx64oj/PswGbflymu8MnRWJytQ4cOlvtr1qzJdKxWq+XYsWMMGDDAkky6efMmFStW5MCBjC9KXruWUsl248YNjEYjBr2elXOG8cEAf74ZVYSvRhThmVaeWfoeKleuRFRUFPPnz6dhw4aMHj2au3fvUq5cOdsbP5B67Pnz59m2bRvJyckkJyczZswYy3PBwcE292VV8VTRD77qAO7mPzqFQkHJkiV59dVXuXPnjlU1Tl7WsWNHdu3axY4dO2jSpInN8dOnTyc0NJSrV69y5coVkpOTmTVrFv7+/ulO6dy3bx9ubm50797d8phKpWLLli306dPH7iTQpEmTOHPmDDdu3CAkJMSq4mLv3r2W+6Ghobz11lsEBwczbdo0q314enrywgsvcO/evTz77yOJp7zt8ccf58CBA4SGmpelPX/+PMeOHaNFixZW4w4dOkSnTp146qmnmDp1qlW16JkzZ9Dr9TRt2tTyWLFixahcuXKahM38+fMZNmwYixYtQq1WM2nSJObOncubb77JggULuHHjBt99953VNgcOHCAkJIR58+YxdepUduzYwYIFC9L9fhQKBfXq1ePQIfO0+OjoaEJCQtDr9Vy+fNnyvdSoUQNPz4zfx+fNm8fgwYNZsmQJ5cqVY9KkSZZK25MnTzJlyhT69evHkiVLaNCggd2NuPOEBMwLwuTQqUv2Ek/dMFc+FQCRpgSGx6Y9YHTEOUPK0fLxLXO5dHBVmjEqtZb2I37C06+kw/svVwoqPZhW0Kg2dG+T1UiFPf5dNp7bF/eiVKrwL1GFCnW7U6/rm7QbtoC+E//huS+vPUhI/ZKSkCpdy+6ElAtW1BYuIlPtCpYDa6Zw5ehfWdq2Uv3e9HtvL417f4DWwzlTVE7r7VvIwkvhxkrfF3jfoysK8sgb0k6gPrDHwe3extyoPOdaqNhNlxjDv8vGER95M9Nx8VG32PnzaFbP6Jxp/yeFUkn1FkPo/8EBard/GaUqZ5pZJaLjrMHxPhpttdXY5PdyjsSQWypVSmk40ahRowyng33wwQeEh4dTp04dy2N79+6lbNmy3L17N9PXeOONN9JMbUpNoVDgplHQrrY7L3b2shlznfJqBrXypE55czJn0XfT08RdtGhRy0mSPd577z3L/ZAQ6w6wFy5csEwX9PHxsWrIW65cuTT9iKwqnqa3BuWD71dvBGPKz8HT05Off/7Z7hhd5bfffmPjxo20atWKJ554gn///ZcFCxakqVh6aPLkybzzzjuUK1eOsmXLUr58eatESXJyMvv27bOssNiyZUvLyfXff/9t9btSr149Vq1ahcFgICoqisOHD1v9DqZWv359PvroI6pXr06pUqWskk4GgyHDRsqTJk3izTff5Ny5cyQmJloeDwgI4Pz587Rr187+H1YuSf2zlx5Pec9zzz1Hp06d6N+/P02bNmXw4MEMHDiQzp07W8Y0b96cKVOm8M033zBmzBhOnz7N6NGjSU42V2KHhYWh0WjSvLcFBAQQFmbdR3Pw4ME0a9aMihUrMnDgQM6ePcvw4cN5/PHHCQ4OpmfPnhw8aL06rEaj4f3336dy5cq0bNmSUaNGsWzZsgxnGzRo0MDynnrkyBGqVq1Kw4YNLY8dPnyY+vXrZ/pzGTx4MC1btqR8+fKMHDmSW7ducf26ucJ46dKlNG3alOeff57y5cszcOBAq6RbvhCFeaW7HJC9xJMaGJwzgeQFG3Vn+C5hd5a2TTbpuWIMt3rsnyVjCLuetmmjp28JOoxwvNl4m0cWO5CqJ+cyGnRsWfB8hv05UhJS3VISUhN28dyX1+ifKiFVueFT6Sakvp0Mw/uB1oUNckXukKl2BYzJxI6fX+T+1WNZ2lylcaNOx9fo/8EBarQejkKZs6U6p+yoeErtQ68nWeYzFE/yyLTim0AbzA3EHdEKOAp0zOF4cti9KwdZM7MzO34eTVxkxj25tB4+NOn7EU9N2kO5xzpnOM4Rhx2Ybpfalwl5dPWdDKRuVNuuXTsiIyO5desWK1euZOXKlRw/fpy4uDgmT56Ml5c5KWQymfj+++9p3ry5Xe/T69atY/jw4dy7dw+9Xk9SUhKxsbHcv3+f27dvW/WAql9JizaDP3Ml8MEAX1550oe2td15tZsv80YVoULZkpa4QkJCLImLwMBARo0alWY/rVu3ZsSIEZZpXOPHj7dUMplMJsaNG5dmm+XLUyrwxo4dS3JyMkajkdDQUG7cuEFERATDhpmnN4QZH/Tc0SrhiQdXQU0m6PcnlPzaKvmUl02dOpWEhAQGDhxolSxUKpW88MILxMTEWK3016dPH65evcoHH3yQ7v5MJhObN2/Gw8ODpk2bsnfvXk6ePMmePdbZ89u3039f9vX1pV69ehw7dgyDwYBOpyMpKYn4+HhiY2PZv39/mqSmyWTi/PnzdO7cOcP9grmXTvXq1fHw8GD8+PGW32utVsuWLVv477//+Oabb5g5cybjxo2zWsXRFVInnmJi7OlsLHLT5s2bWb9+PVOnTmXx4sVMnjyZJUuWsG7dOsuYTp060bJlS6pUqULr1q2ZM2cOV69eZffuzM+vTSZTmt/z1H3SHr6vPfrYo1Ptqlatirt7ymIdtWvXJj4+PsPm5Q0aNODy5ctERkZy+PBhGjRoQIMGDTh8+DB6vZ7jx4/bTDyljikwMBDAUuUVEhJC7drW0+cf/TpfuAOE2xxlU/Yn+hag6XYAb8X/yWXDfYe3u2i4hwHrAxV9cjyb5z9LYmzaf6niFRvSYsAMh16j7SNVwPVqQu8O6Y8VOSMx9j6bv/2fQ42BlUoVfqkSUm2HzrckpGq0SlnZ4e9d8P0UCNkCbw0DH9sXREU+JRVPBY8+OZ5N3/6PuMiMD/qvndqc6XuHh08gLQZ8xlMT/6FsrZzLlpw2ZN5gPD393eqx2/91yiqL5Fgc2aIHxgIDAEdmXBQDNgAfkRNHOM5jMnFx/3JWfNSEI+s/R69LzHCoX/HKdBr9K11e+R3/kranRGXGkT5PD+3QXeDnpH3Zet3c1qZNG6sTcoVCQVBQEH379qVv377Url3baupEQkICL7/8MiNGjHDodX788UeKFy+ORqPB3d0dHx8fihUrRsmSJXF3d7ecFCkUCsoXTz/z9Hx7L8oUVVt9TqhVKfe3779Eo9a9+OeffyyPPTq1pV27duzYsYP58+dz//59kpKS+OSTTyz7XLduXZo+LGDu7fSwB5ZCoUCj0VjF4e/vz/fff8+pU6doO2MYLOgMh5/D0sAqLAH23AQjPCyaNJlMaVaxcrVy5cqxcOFCIiMjmThxotWJ6Y0bN7h3757la3d3dyZOnEhcXBz37t1j1apVlC1rvYrBqVOneOKJJyhfvjzFixenU6dONpOV5cqV49NPP2Xv3r0ZHgcolUrUajVarRYPDw+8vLysLlxFRETw0ksvoVQqCQ4OZuvWrXb/DGbMmEHr1q0t1U8KhYImTZowevRo3nzzTT777DPu37+fboIyt6ROPMlUu7xn9uzZlqqnKlWq8OSTT/LMM8/w008/ZbhNYGAgJUuWtExNLlq0KDqdjujoaKtxERERaXqfpf59ePi+9Ohj9l7MzWh108qVK+Pn58fhw4ctiaf69etz+PBhTp8+TVJSEnXr1s103+nF+fBvvEAd898CjTF71RLZPyyrCTi/X2quiTUlMTRmCUaTY1UJyRjwSGdt59iwq2zLoNl4cPPB1Ghlf+bu0cQTwIevyJQtZwu/cYqdP7+U7f0olSpMqX6v1m6Hi6FQqjjMGAdXt8HUMVC8aLZfSuQxqfs4SMVTwREfdYvN3/0PfbL16jtxkbfY8PXTbPxmICs+bMy5fxdjyuTf3T+oGp1fWkrXV1YSUKpmtuM6ZedUu0fVU5eljsoFc9UysxxoBNi34ruZEngP2EyebwegT4rj0Lpp/D6lGZcPr850bJkaben77i6a9f8EN0//LL3eEUPGK+ylJ9mk50UHVv7NK8LDwylZsiTDhw/n4sWLGa6WFB8fzyeffIK3tzfz5s3L8TjOnj1rud+quhtKJRTxUlChuIpGVTR8/D8/mgWnVEPHJhqtTlRiEoz8diiAZz/cQbPmKcmmR6tpWrVqZdXsXKvVWvWr6tevX4YxVq1ald9//53ExESMRiPJycmEhYVZpsYoFApq1qxJm5f7Qe+qUOLBVTKTCT7+L2VHDyqeFAoFJ/47zNy5c3nssccc+XFlS1BQEIsWLSImJgaj0Wh1Cw0NZdiwYVZNjpOTk9mzZw9VqlShePHivP7661ZVNp6enpbqhUcdOnSIXbt2cfXqVe7ft+9itV6v55133qF58+YolUpmzZrFzz//TPv27YmMjMRgMKDX6zEYDBiNRqvVB/V6PcuWLSMgICBbv6d79uyhcuXKnDhxIt0TYpVKxaeffkqtWrWy/BrZkfpY6dHEhHC9pKSkNH3JlEplpsmVyMhI7ty5Y/lbqlGjBmq1mn37Ui5m3L9/n0uXLmU43dQRFy5csJpaevLkSTw9PSlevHi64x/2edq5cyeXLl2ibt26VKlSBYPBwKpVqwgODrZUxWZFpUqVOHnS+gDm0a/zDRN4xHhQNhvLCefM9cACVvW0S3+RWYk7HNqmrroMh/zHU09VJs1zN8/t5MCfk9Pdrln/aZSobHuuZ4XSUD6d84E6wfBUp7SPi5x15dg6Dq2bnu39JCekfJAajTB7Ucpz/r4w8UUI3Qpfvw8V0/4qiTzMzasInV78lU4vLqH5gBk83ul1qjTqT1CV5nj6pFzFKVBXPwT3rx5lR6rE9IV9y1g5tQXXT5uvRMdH3eafJWP449O23Di3K9N9la7Rhj7v7qTVoFl4+GY9Y3LFGEaCjZXt0rMocT9/6U5l+XWd5izQBPjJwe3aYZ561yZnw3GG2LCrbFs4jHVf9iDs+okMxylVamq1GUH/yQep+cQLKJSqDMem56j+hkPjpydstupfmd/88MMPVK1aFbVaTd++ffntt9+YP38+I0eOpHr16nh5efHuu+867YJA6ikozaq78d2LAXz2XBEm9vNjZCcfivul/PvdiTTwxg+RzN8US0yCkfBYI9NXmo8Zxvf2RaM2j03SGdh1KtaqXcOGDRvSvHZiYiK7d++mRo0aliRSRvr374+HhwcqlQo3NzcCAwPx8fFh9erV6X9mGU2w4DgsPp3y2K8pFVVexf155ZVXOHHiBJGRkXz22WeZ/6Cywd3dnaNHj3Lz5k0GDx6Mt7c3CoXC6pZaUlISM2bMwM3NjZYtW1pOUmfPno2vry8ff/wxCQkJlvEmk4m7d+9y8+ZNkpKSWL9+fY5UdL3xxhs8//zzbNu2jSJFiqBWq9FoNKjValQqFUqlEqVSiUajQaPRMHDgwGy/JpgTkXXq1KF27drMmjWL5cuXs3r1akvSTaFQsHr16kybKTvLw+ouk8kkF+nyoJYtW/Ljjz+ye/dubt68yfbt2/n1119p06YNYE7kz5o1i+PHj3Pz5k0OHTrE2LFj8ff3t4zx9vamV69ezJo1i/3793Pu3Dnee+89KleuTOPG2a9k0el0TJ06lcuXL7Nnzx6+++47+vfvn2kj/wYNGrBx40aqVKlief+oV68eGzZsoEGDBtmKZ8CAAezdu5dffvmF0NBQli9fbrUgQH4TFxPHN6Zv8CVrPUoVONKnPArSfZ0ooCTmzucFhDsaDvuPp4Y6yKHtdCYD78X/xYyELRgf+dG2ef5bqjTqn2abhOi7/PFp+0wbkw7tCz98nP5zpy5CnV7mRIZwIoWCdsMWUql+ryzvYsPX/bl+OmXZTi9PuL7dnHR6lMEAyzfAp9/DsbNpnxd5j3/JYLq/8RfuXtbTlXo2cqdHI/NB3LELYbw14y9iI24Q9+D28H589B1M6VRHiryvVpuRxIZfJ/T435mOK/dYZxr3+RD/oMzXntUlxXJs0xxObP0Gg87xD9fD/uOpp7b/qtRtYzQ1Iz4mwhRve7ArPQ98A3g4sI0BmAx8TI6tzOJMCoWSas3+R8OeE22ubBdx6yz//T6RG2d32L3/i0Xep7LKvhXzKoZP5ooxzPZAkS5/f3/u3buXYcNqMJ9kH7yYzPdb49I9jvN2hy+GFkGhUGAymZi9LoZT1/TERd7m1I75nN39E1qVwapaJy4uDm9v7xz5HmrVqsWrr75KcMmKtIktAyfuwfJzEJ1OMuvT1jC0drql+Pv27XNKU90tW7bQvn17q8ce9qpK/fWlS5eYPXu23Y3PH1Zr5dvqBAe9++67VivhxcfHExwcbGmSnBsSEhJwd3fHZDLZveJfflS+fHm+/fbbDCvq8qq4uDi+/fZbduzYQUREBIGBgXTu3JkXXngBjUZDYmIi48aN49y5c8TExBAYGEiDBg148cUXCQpKOZ9OSkpizpw5bNy4kcTERBo1asTbb79tGXPz5k169erF4sWLLX3qDh06xIsvvsi2bdvw8TGvsrp27Vq++OILtm839yCcPHkysbGxVK1alRUrVqDT6ejYsSPjx4+3asr/qIsXL/LMM88wePBgy2qfv/76K19++SVffvklLVu2tIxt1KgRM2bMoE2bNunGGRMTQ7t27fj2228tSas1a9bw3XffERUVRePGjalfvz4LFy60xJ2f3L9/n/j4eDz6etCd7hgfthmKBvwy3RTIqcQTmJuML7F7T/lCI3V5/vV7A7XCsSuKADt1FxgSs4irxpSmZyqNBz3e/JvAsmlLCe9eOcxfX3bHoE9K8xzAL5/Csz0zfr1n3oSlmZ/viByg1nrSfexf6f4b2mPNzM7cDbFegeGzt2Dc8My32/APfLIAdma8srPII4pXbMSTr61CrU25WtirsTvdG5q/PnUtmVlr0+9dYDToiY+6nZKMirzB7Yv/cfVE2qvZIv9SKNXUaPU89Z98G3fvgEzHxkbc4ODqKVw8+Lt5aoudFnkPYbB7I9sDH+gTvYA/k4/bHpgXPAasAKo7uN1GzMcqjrdxdAmNuw/1ur5FrTYjUdlYjCT0+Hr2rXqf6HuXbe53hc8w+rnVsyuGXxMP8r/YvL9CWV5Wo0YNFi1aRI0aNTAYDBhQY1S4kaw3cSfSwB//xXMj3Hzwfu3UFk7v/J6aTwy39H1rXFXDiI7mE63wGANvL4qy2r8uKZa3uql4rFppy2N79+6lefPmOfp9lFcGcCXgQ9sD/bUwqCZ0rQT1S4A25Rh68uTJxMfHU6RIEUsj6507d7JsWdamc1asWJFLly5ZqprOnTtnaXwsHHfhwgWrRskXL16katXML5LkpKSkJLRaLUaj0aq3VUGTXxNPovBSq9X4+flx9+5dihYtSpUqVZjNbF7ndfOAXE88bQUKYKPrjzy78Z5nl3SfCzfGEaDMeN5npDGel+KW81tSytK33gFl6f32Vty90zbyOb/3V3YtTn+puqvb4MECJ+k6FwK1epirZIRzeRUpTe/xW/DwTX++cGZ+n9qcyFvnrB4rWxIub4JMLoha7DsGn3wPq7c6dA4qclmZWh3oNGoJSpX5H7V3Yw+6NTSXaJy8mszsdfY3zQw5spat3z/vjDCFi2k9/KjbZaxdiYV7oUf4b+Uk7lz6L9NxD73r0YlpXj3sGrs86TADYn60a2ye4Q18BwxycLsbwEAgawvYuoRv8co07fsR5WqnfyzykEGfzKkd8zmyfia6xIxXhJrg0YmP7fzdAOgQ9RVbdedsDxT2USho/vRn1Gxt3afi2KY5HFwzxdILskjJ6jzW/iVadBjA9OfMxxsJyUZe+z4yzS6/GVkEjdqcfNn97wHat21pc3qdo7wVbsQUnen4hvsGQyX/TIfcvXuX4OBgIiMjHdr1oUOHLCtOnT17lho1ajgen7AydOhQFi5caEnmrV27lp49M7nynYN0Oh1qtRqDwZBplWB+J4knkZ+ULl2aoKAgkpKSCAkJoWLFiubKREw0ONiAIw2P2J14yrk6xvZAhRzbW54xJX4DR/TWzTjvGmN4NXYFQeETGRKziGhj+tMg/JWe/OrzPEu8n8NPYT7pjA2/xtaFwzEa9GnGV2s2iJpPvJDm8crlMk86AQRXhGe62flNiWyJi7jBlgXPYdA7flDXbthCSgU/YfXYtVuwcrN92zd5HP6YC6fXwfN9QJO9xQWEk1w/tcUqiZx61oGjCUOTgwsdiPwjOSGK/X98YFdj6WLl69Fj7F+0f+FHfAIr2Nz3KQdWtiuqyIdLasYC/wNGARkvCJdWaWA7MB7LKlx5XfTdS2z69n+s/6ofEbcyTgCp1FrqdHiFpycfILj5YBSK9A/x/ko+xZT4Dbwau4IB0T/SLmouj0VMo2tU+k2Lv/bqj5aCexKY60wm/l02jtO7fgBAn5zA9h9HcmD1h1bv9xG3zvLP4teYM+ZxdHrzVUUPrZLGVdN+8D8sDjEYTfx8tDIdX/qdco91ztHVZ2JNSSSb0h672nJm4kqbvQ2LFy/OhQsX8PW1vrodFBTE9u3bCQ0NJSIigps3b3Lq1Cn27NnD3bt3LUknk8nEsGEFrOGsi/z4448sXLjQ8nWPHj3S/bdxhkdXBBNCuI5CoSA4OJiSJUumuyrgmdNnONL8iPmYyt59klMVT2DuoWBHFW5+85iqJIf8x5OMgZkJW/k8YRuxppQpcRWURVnsM4QWmkoZ7uOqIZxnYxaxS29etvaxti/StF/apk1Gg46/5/Tl9sV/LY+90B8WfGQ7zouhUL2bVD3llmrNBtF68NwsbXvl2N/s/+N9ou+FANC4DuzLQqX59dvwxU+wYAXE5vHWLIXRY+1G0/SpqTzVzIMu9czJ5+NXkpn7t/0VT5cP/cG2H9ImpEXBU6JyE5o+9THFymc+DcqgT+b0zgUcWf85yQlR6Y6poizGhYD37X7t3tELWJ1fpto9qi7mqXdVbIx71DrgOSA8pwNyHvM0zaHU7/Z2ml5yj7p/7Tj//T6B2xftb2T6vfcghrs3S/P4pLh1fJyw0eF4ReYadH+XK8f+JuzasUzHLV26lAEDBgCQkGRg/KIoEh9c+yodoOSDAX4oFAoSk028+n1Ki4fI2xc4sfVrLu5fnmErB0fcDJhKSaUdl7UfuGWMokHkZ7Tu35W33noLpVJJXFwcsbGxxMTEUKJECVq3bm01Va569ZQ5tKGhoZQrV87m66xZs4ZevbLef1OktWPHDp54IuVCqclkIikpibCwMK5evcqxY8f46aefrFYnyy6DwYBSqSQ5ORk3NzfbG+RTUvEk8jqtVkvNmjWtKg9jYmI4f/48ISEhbNmyhe+++878RACwBahve7+OJZ7uApn1owwFKjq0x3yjq6Ymh/TXuGtKv3xdhZJ3PDoy2bNrhj2hjCYjnyVs5f34v9BhoM1z31KlcTrNxmPu8een7YmLMK8+s/gz+J+dVfFDJ8BPf9g3VmRfk6emUrvd6Cxta9AlcXL7txzd8AW6pFj2/ArN7Wu7kcbte1CuPeh0WdteOE+jXu8z5YN36Pwg8XTsSjJfOZB4unjgd3b8NMpZ4Ym8RqGgcsOnaNTrfbyLlM50aGJsOIf//owz//yIyWhdiaBEwX7/t4gzJRNlSiDKmEiUKYFYUxJveLRFq7CuYLlkuEfNiGkk43hFQ57gCywEMl41Pn1XgaeBnDt3yhVuXkWo3+0darR83jKlNyOXD69m/x8fEBt+LdNxYK5+O1fkPYo+0kYgwZRMrYhphEijcZfw9vYmIiLCchJwPyyST5acI05blXee8qV8MfPj52/qmPFn2uPUhOi7nNr5PWf++ZGkuKxnWk/4v8tj6nSWWE5HsklPm6g57NWHZDpu1KhRzJs3z5J8WrhwIS+88AJarZbExMR0r7Q/ZDAYWLJkCc8995z934Sw28yZMxk7dmym/wZGo5H79+/z7rvv8sMPP2Tr9YxGozmBmpiIh4cjK0jkL5J4EnmZUqmkTp06ls8bk8lEdHQ0Fy5c4P79+7z44ouEhoZab1QVuGB7344lnhYCtipZO2Du91RINVKXZ4nPEKqqMu7/c1h/jf/F/MwFZRQ9xv5FYLm6acbcCz3Kui+7YdAlcmMnlLKznVDIdajWFfT59Nwhv1EoVXQa/Rtla7a3PTgD8dF3OLB6Ko/7/caKWVnL2q7dDj1fsj1OuMZv6w4xsJv5UsDRkGS+Xu9A4mn/Cnb8/KKzQhN5lErjQe12o3m88xg0bpmvThV55yL7//jA7ib0n3r2Yrxn2qaMb8et5rOELVmKN894BfgcyLxlljUd5ql3s5wRkHMVKVmdpk99TOkabTIdp9clcmLL1xzbPBt9UlymY4e7NeN7n7TNs/5KPkX36G+zE67IhkGDBrF48eIMkwAmk4m5f8dwIjTjA0B9cjzn9/7Gye3zLBXXjtju9xptNPY1mx4Vu5T5iXvsGjtv3jxefNH8OWcymXjnnXfYsmULhw6Ze6TqdDqCgoIoU6YMNWrUoEyZMly/fp21a9cSHy/l3s7Ut29fvvjiC4KCgtBqtZn+/vXs2ZN169Zl+bUeJp5yclXGvEgSTyIvq127tqXi0GQyERoayv375lVZMkw82cmxHk/2rFo3NEtxFBgH9KHUi/g00w/b+uqyHPYfzyhVIzbPH0JCTNoldoqVr0uLgZ9TrYL9SSeAimXMvX9E7jAZDWz/4QUi71xM93mDLolzexaRFB+Z4T48fUvwxLNz0dfdwu6L9q9ClZqsaJi33bqwK8vbBpSuiX9QcA5GA03UFSimKLgHdQWBQZfA0Y1fsHxyI87u+QWjMeM51P4lqtDpxSU8+dqfFC1T2+a+pyZs5I4xOs3jkzw6U0Lhk624Xe4roCVwxYFtNMCXwErsao6Zl0TcOsv6r55i07f/I+rupQzHqTXu1Ov6Jv3f30eVxk9n2vvnh6T/+FeXdnW8btpa9NE+niNxC8f9+uuvTJw4McP+N/8cusSRi5lf1FBrPan5xHD6v7+f9i/8RPGKjh1zhBkzT1qm9p/uit1jR48ezalTpwBzX5FPPvmEAwdSlvE9dOgQ4eHhHD9+nGXLlvH555+zbNkySTrlglWrVlGhQgXc3d3RarW0atWKiRMnsn37diIiIiy/jwqFgj/++IPJkyejVCqpUaNGpkvYP0qtVluSWgbpGSJErlOr1dSqVcsq6XTlyhVL0iknOFbxpMA8na5sJmMSgJKY+0EVcr20dfje+xkClRmf4P2VfIp3i52n0Zjf0i2XV1x8l/kj5zv0uqE3oVoXSJZpV7nGr3gVeo7bhJtnylnL3SuH2LX4VSJvncPNK4AG3d+lesvnUCozXyK2cYkVPFX5IwLcb9r12gmJULyF9HjKy7788ktef/11AI5cTuKbDfYfvAOYjEYuHVrJ4b9nEJ3JyaW9xri3obG6vCyTno8ElKpJk75TbFa2mIxGzu/7jUNrpxEfdTvDccPcmrLQ539pHv8hcS/DY3/Nbriu5w/8BDja9uUy5ql3h2wNzHuUai212oykXpe30HpknkC8G3KQvb9P4N6V9L/ROqrSHPYfj+qRBuXXDBHUiJhKHDm7YpqwX8WKFVm0aBF16tTBaDSSmJjIvHnz+PDDD3H3DqTmE8Op2Xp4uqsnp+fO5f2c2PI1ocf/trmYxXfeAxnp3sKu/S5OPMCzsb/YNRbA19eXc+fOERQUZPV4TlTSFFSefkF4+ZfiXuhhl8UQEBDArVu3rJJMJpMJhUKByWQiKiqK5cuX8/LLL6PPZDqGv78/ERHm/mQREREEBAQ4PXZXkYonkddUqlSJIkWKWDX4v3XrFjdvWp+LZrfiybHEE8AUYJKNMS9iXuZYEKTw5Qef/9FVWzPDMXeNMXxYIYzk4RPSPKcw6Rlb/ymqF3Fs7efRH8K3Sx0OV2RD6Rpt6fzSMowGHYfWTefktnmYHqlSKFKqBs36TadUcKtM96VVxtGl/Bw6lfsaN1X6qyY+tGIDPP1GtsMXTjRnzhxefdW8yt1/p+6xcGfmyceMGI0GLu5fzpG/ZxATlrU3fYCvvZ7mJY9WPBk1j/W601nej8h9ZWp1oGnfKfgHVct0nC4pjuOb53Bi6zfok9NmpR/2gGqgTtu4t2HkZxzS2+4HlC+MBT4FhxZlS3qw3TdOicjpPHyK0bDHRKo1+x8KZeaF7Rf2L+fAnx8RH5V2FcQvvPryhkfbNI/PiN/C+PjMV2EUrqXSeFCt6UAeazcav+KV7dom6t5lTm6dx/n/fsOgS/+4ww013go33BUa3FDjrlDjjoZ3PDvS3826SaXeZKBKxEeEGu3vKaVUKtm6dStt2rQBIDY2ltGjR7N48WK791GQ+RarRFCVpgRVbkZQlWb4FqtIYmwYS96tmabPX2568sknWblyJe7u7pmOe1gddefOHTp06GCpcgMoV66c5WT2/v37FCuWWVPh/E0STyIvKVmyJKVLp/QUNZlM3L9/P93kUu4nnioD6c8qSrEfaJKleAqsl91bM8OrFx6KjMtOV5eFTUOeJllrfYTsrbnPpIbtKepx3e7Xu3IDKncCo6zEnqsqN3yK+9eOE3Un8w5r5R/vRpO+H+FrY1n0ALdr9KvyAQ2Lr85wZsQzb8pUu7xu7ty5vPLKKwD8sXody46VxiewfJb3ZzToOP/fbxzd8Dmx4fa/Lzy0xfcV2muDCTWEUyviY6leyGcUSjXVWwyhfre38fDJ/MA1LuImB9ZM5eKB5fDIFJ0W6krs9k+btd6ju0zLqC9zNGaXagYsI/Nq7fQsA0YA6a8pkucVLVuHZv2mEVQl7Sp1qemS4ji2aTYntn6NQZdoedxb4cZZ/0mUVvlbjdebDNSN/JRThrTJKpG3KBRKytXpSu32LxFUuald2yTGhnF61w+c2bWQhJh7dm1TUxXEqSIT0zw+O2EHr8etdChmgKeffpoKFSowc+ZMjIX0QFahUFKkdE1KVmlOicpNCarSFE/fEumO3fDNAK6fcn1/vnXr1vHkk08CEB0djYeHR4bT7UwmEx999BEXL160VFacPm2+EHb79m1KliyZa3HnNkk8OcfkyZOJjY1l5syZTnuNRo0aMWPGDEtyPCvWrl3LF198wfbt23MusGyoWrUqfn4pM3ZiYmK4cOFCuu+9uZ94AtgBPGFjzGPAKRtjCpmaqiCW+DxHXXWZDMfc8NXy48C2hJa2LpEu53OUt+t3Q6tKzGDLFEfPwPBJcFgKGfI0ldqNWm1fpF7XsTabB1f1+5eB1SZQzudEmudOnIe2z0FYpJMCFdn21Vdf8fLLLwOwYsUKXnj5HXqM/QsPXwcauKXDoE/m3L+LObrhi3QrFjJytchHlFWZl2KflbCdN+JWZSsO4Roadx/qdh7LY21HodJkvvT0/atH2bfqfW5dsO4/+JvP8wx0a5Bm/DPRP7E0OR/ON8tIUeAX4EkHtzsP9AeO53hEuaZi/V407vMhPgGZZ95iwq6y/48PCDmyxvJYP21dVvgOTzP2H90lnoiajakgLmNcQBWv2JDa7V+mwuPdbVbCgbkh/cV9yzixbZ7Ni2kAa3xH0kNr3WMuzpREufD3CTdJLwBblGotxcrXe1DN1JQSlZqg9fC1a9uLB1aw46fcWYSkp7Y2jdXluWmM4oYxihvGSG4ao7hjjMGAEU9PT9RqNdHR5j6Cn3zyCePHjwewuUKhSpVSDV6hQoUsn9zmdRklnr7bXylX4xjVOG0vv8zMnz+fBQsWWD0WEBDAxo0bLV+bTCYWLFjAH3/8QUxMDLVq1WL8+PFUrpxSeZmcnMzs2bPZuHEjSUlJNGrUiLfffpsSJdJPrNorNxJP9+/fx9fX16H+ZY/Ka4knT09PatSoYfX3aTKZiI2N5eLFi1Y911yTeBoC2GoN8jnwVhYiKuC0qJni2Y23PNqhVKT/wa9XKljbpjbrW9XElOrgoGmJ5QyrOTrDypfEJPjoG5jxg6xql594+gXRsOckqjV9JtNxCoy0LLmY3pU/xldr3ejt6BloNxQipLdanpR6xZ5ly5YxcOBAipatQ7cxa9LtxdKs+CLalfuJDVdf5dDd3jb3r9clcvafnzi2eTYJ0XczHeuJlrjAzy1fG01GmkV9wX59wTzAKwy8i5ajca/3qdTA9soSV47+xf4/JxN9z3zAWVZZhHNFJqWpxr1miCA4YgoJFKBmgQrgbWAq4Mhs10TgVeB7ZwSVfZXLQY1KsG5HxmNUGndqt3+ZxzuNQePmlen+bl3Yw3+/TyTsuvlCxwbfl+isrZFm3PMxi/k5aV92Qhcu4FusIrXavkhws0GotZ52bRN6fD0ntn7N7Yt7MxzTUl2Zf/xfT/P4+3F/MSXBvhU3CxONuw/FKzY0VzRVaUqx8vVRazKfqpYRXVIcS96pnu606pymRc1a35F0euQ9wWAyctsYzQ1j1IOkVKTl/h11PLeMUWgrBvDbmt+pVKlSpkkogPDwcIoWta9PWX6TnxNPW7du5euvv7Y8plKpKFKkiOXrn3/+mR9//JH333+fcuXK8cMPP3DkyBF+//13vLzMnz2ffPIJ//zzD++//z7+/v7MmjWLqKgoFi1aZJV8dFRuJJ5yQl5LPIE5+RQcHJzm528wGDh58iQ6nflYMHdXtXvod2yXnj+LYz0VColk9Lwdv5r20V9xzRCR7hi10USfbccZ9+NWAiNSVij5787TbLmW/hWN3Yegbh+YPl+STvlNfNRtdi16hdWfdeTO5QMZjjOh5J9bQ5i09wCbrr6E3qixPFe3BmxeCP72XRwTuSz1AVbF0iZmvQvLPzrOS7UHo1YkWZ5zV8XwQs0RDHvsdSr4HuXFx4bzfqPW1A3MfC6lWuPOY+1eZMCHh2jcZ3KmTWWrqayrrJQKJQu8n0GdxY8D4XqxYVfZ9sMLrJnZhbshBzMdW6FuN/q99y9NnpqKm6c/14wRfJawNc24sqoijPfs4KyQXcMEfAK0A+xbu8HMHViAuWIq85xNrnDTQsfm8OW7cG49XNwIK2dDsUx68Rp0iRzd8DkrPmrKxf0rMt1/yaot6P32NloO+hJ370Bejl1OoiltAnKGVy8CFPYlLkTeEX0vhL3L3+a3SXU4uHaazYsVAOXrdKX7G+voOW4zler3RpHOIim79ZfSXQ3xVY/WeKBJ83hhVLp6G5o+9TG9397KszMu0fWV36nbZSwlqzTPctIJQOPmRbnaXXIw0owlo6dv9Pfs1YVYPa5SKCmt8qexpjy93erwskdrpnn14Cefwaz3GMlRr3HsvzucoCZ/cqnmDK7M3ETUmRsZvk5AQADvvvuus78d4SCVSkVgYKDlljrpZDKZ+O233xg6dCjt2rWjSpUqTJ48mcTEREtVVGxsLKtXr2bMmDE0adKE4OBgPvroIy5dusT+/fsBuHnzJo0aNWLz5s2MGDGCli1bMmTIEEJDQzl16hRDhgyhdevWvPrqq5aG9KktWLCATp060aZNG6ZNm2ZJmjzKZDLRsWNHtm3bZnls0KBBdOrUyfL18ePHadq0qWUFzUaNGrFjxw6rOLdt28aLL75Iy5YtGTRoEMePW5dIr127lu7du9OyZUvGjRtHVFTeqxKIj4/nyJEjXL16laSkJEs/NpVKRfnyWW8N8qisnWnEY+59kJniQLcs7b1Q2KG7QJ3I6SxNyngqQ9Wr93h/3t80PRZi6c2x7PyHnLibtjH1+3PhXEiah0U+ci/0MGu/6Mr2n0YRF5nxtKkEgy8rLk7hg327OXa/k6VtS4NasHEBVPb2obyy4K4Gkl90bwMfvgqr5sIz3VIST41rGxgzBNo1hSbldvNCrVEoMFLe5yjvNWpLkyDraW9lfU7xcp1nmdSwHbWLbsr0NdVaT+p0eJUBHx2mYc9JuHn6pxnzaOIJoI66NOM8CliSoRB49ILx3ZADrJnZmW0/vEBMeMbNwZUqDbXbjab/5IPUajuKmUk7070Q8rZHB8oqi6Szh3xuF1AP2Ozgds8CB4CM1wpxmrIlYdQAWP01hO2FTQvh9SFQrYL5ea0WhvW1vZ/4yJvs+PlF1szszN0rGa+EpVAqqd5iCE9PPoBn2wF8mrgtzZhiSh+mefbI4nckXC0pLoKjGz5n6Xt1+WfJGCJvn7e5TfEK9Wk3fCFPTz5IrTYjUT9SPZdeEruY0oeh7vb1lyrowm+Ye5AUKVnD5grHjmoxcCY93lxPm+e/o2HPSQS3GELp6m3wLV4ZlTrzqdiOiiOZbtHfckLvSAbfzEvhRpX7nlT49Dx+rf+AYl9Bsa/o5F6TYsWKsXPnTsvYh4uyZOZhpUZ2pj4J+127do2uXbvSq1cvJkyYwPXrKX1Gb9y4QVhYGE2bpvy9a7Va6tevb0nGnDlzBr1ebzWmWLFiVK5cOU3CZv78+QwbNoxFixahVquZNGkSc+fO5c0332TBggXcuHGD776zXs3swIEDhISEMG/ePKZOncqOHTvSTA98SKFQUK9ePQ4dMp+LR0dHExISgl6v5/JlcxL90KFD1KhRA0/PjC+yzJs3j8GDB7NkyRLKlSvHpEmTLCs4njx5kilTptCvXz+WLFlCgwYN+OGHH2z+nF3l7t27nDhxgtu3U1ZFzux7d1TWL3Hb8zMbmuW9FwqRpgSeifmJwTE/E2VMfwURjyQ9w1ftZeSKPXgmJKNQqllweiH3E6x7NZQNSndzkd+YTFw68DsrPmqC7uJM1IqMe3rdTajCV8d/4+v9ywjf2QG+6kPjL97hovs05vn1y8WgRXrOXIaGj0GfDuDrnfoA03p2c4Pia3mzXm/ebtCV4p4ZZ4/L+x7jtcef4Z0GnQkLybw8V+PmTd3ObzDgo6PU7/a2VZ+I4HQSTwDve3ahqrLgriJTkLRsAAumwKVN4JNOBc7lQ3/w+0dN2f/nRyQnZFye7O5VhGb9ptF10nZmFE1bNu2h0PKpV6+cDD3vuAt0AT4AHOldXANz8mmIM4JKoVZD60bwyZtwfDVc3QbfToae7cArg2PAUQPSJiMzcjfkIGtmdmLHzy8RF3k7w3FaD1+a9P2I869P4ooqLu1rerSkibqCfS8q8iSDPolz/y7m96nN2fjNQG6e/8fmNj5Fy9Gs/3SemXqchj3fw9PPfBC6JvkEZ/V30ox/06MdKqmqJSHmHv+tnMjyDxtx7t/FGB9Z+Tg73Dz9KFGpMVUa9aNu5zdoNehLur66kqc/2M/Q2Td55uOT9Bj7N22e/5aGPSYS3PxZc2KqWCWUaseTNhGmeDpFfc0lg30N6G0xGo3cv3+fLl26WJoalyxZknv37rFmzRr++ecftm/fTqlSpSzbfPDBB8TGxnL27FkSExM5ePAgrVu3xtvbm23btpGUlERycjLbtm2zqtpQq9U0bNgwR+IuTGrVqsWHH37I3LlzmTBhAmFhYQwfPpzIyEgAwsLCAHO1WmoBAQGW58LCwtBoNPj6+mY45qHBgwfTrFkzKlasyMCBAzl79izDhw/n8ccfJzg4mJ49e3LwoHWVt0aj4f3336dy5cq0bNmSUaNGsWzZsgwXKWjQoIEl8XTkyBGqVq1Kw4YNLY8dPnyY+vXrZ/pzGTx4MC1btqR8+fKMHDmSW7duWRJyS5cupWnTpjz//POUL1+egQMHWiXd8qqkpJTZGEo7egLaK+uT4fYCZ4HqmYzpBpQA0n4GiVSWJB1kt+4yi3yG0EqT/rK3jU5dpfK1+/zYpylnKwXxzYlfeLvBk7ipzAmrMpJ4KlD0SXEsmTudEqUXE9xhMpXq97Y8pzQYKX8rnJqXblPj8m0qX7uP2mB9cthFXYtKnr5cjo/O5cjFQ5euQrdR0Ks9LPpFgY//gydMadvqBRfZk+axjFT2O8hTxfuxcntTqPgORSukrYB8SOvhQ/0nx1OrzUhObP2GU9u/S7fiCcBdoWG+zzO0i5orDYPzoPKlYEhveK6XuafPQ68OhmnfpR1v0CVyfPNsLvz3K/W7vU1wiyEZXmH3K16ZhNc/5fRXK6l533qFw2fcGvBNwj/s1l/Kwe8mjzACHwG7gV8xH6/YwxNzn8vWmHs/pX/dyGElAqFLS+j2BHRqAX5p279lqmIZ83Ybd9u5gcnExf3LuHJsHXU7vc5j7V/KcLqPZ6mq/DnIi9cX7Ujz3Dzvp2kUORODQxk84QwqFfRqZ15c5krGs5jSZzJx7dRmrp3aTGC5utRu/xIV6/VCqcr4VMHN05+6nV+ndvuXuHTgd05s/ZoZ4VtY6PM/q3GVVIH009ZlWXLGVXaFSVzEDf5ZMobjm+fSoPs7dvXnyy4v/5J4+ZekROX0lx2Pi7xFbNhVYsKv4mG6hidX+W/fNWLCrhIbcR2jPu3qt7dN0XSM+prdfm9QSuWXzl4dl5iYyLJly3jmGXPf08DAQHr0SKms/P3335k0aRLJycl88MEHllYGCoWCBg0aWFVMPdS2bVtCQkK4ceMGCQkJVKxYEbVaTVRUFIMHD2bdunU5EntB16JFC8v9KlWqUKdOHXr37s1ff/3F//6X8jf/aP8uUzrHval5K9zwRIOP0vrzp0qVKpb7D5NZjz726FS7qlWr4u6esp/atWsTHx/PnTt30l0psUGDBnz++edERkZy+PBhGjRoQFBQEIcPH6ZPnz4cP36cgQMHZhp/6pge9u0KDw+nQoUKhISEpFkBr3bt2uzdm3G/vNymUCgoU6YM/v7+KBQKlEqlVa+n2NjYTLZ2TPZSWD/aeF6NuTRd2BRqDKdN1GwmxK1FZ0r/CkhAdDxv/LKNfhuPcCuyFr+cnWU5h5WKp4JHr4cbodfYtnA4p2cOotGmHbz02y6+/GwlExZsove24wRfuYvakPZgX2FSsrFfYzyy3jJA5JDVW+HPLak/hLN/cva/HrBqyn+sGtabEVV7E6jMvMGvm6c/DXtMYMBHh6lbpHaG49poqjLMLe9fiSksvD3hud6w/We4shU+etU66QQw9nnzuIwkxNxjz9K3+GNaa66dTjsNxkKhYFXftuk+Ncu7LwrsLKXJj7Zhnnq3w8HthgP7gGpZe1mlEpo8bp6Se2AF3P4HfpoO/bs4nnR66JX/ZVwRlRF9UhwH137M71OaWa1o96hTVUpxsFa5NI/XU5flZfeME+DC+UoEwqTRcGULrJwD08dmb3/3rx5l+48jWT65ISe2zUOXlPmJh0qtpVqzQTw1aQ93n3+d24q02dgC1zMuB0Tdvci2H17gj+ltuHbK0bm/OethUqpKo/6UbjyWIo1nmSumJh9g2OxbGVZMhRX1o0vcfMKNaSsis2rQoEF8+umnVqtpPdSsWTO2bt3KP//8Y0lwmEwmm8mNhyfXVatWRa02J1P9/PxYs2YNZ86cYfDgwTkWf2Hh4eFBlSpVuHbNPLX/YTP4RyuXIiIiCCpRlJqVoVrlouh0OqKjo/FWuFFNVZzqqhKEhYdToWgpvBUp00If/jtBSjLr0ccyqmR6VEbN7CtXroyfnx+HDx+2JJ7q16/P4cOHOX36NElJSdStWzfTfacX58PfR1u/l3nBY489RokSJXBzc0Or1aJWqy3fh9Fo5OrVqzn2WtlLPC0CbFWJynQ7uxkxMT1hE82iPudcOqXKAEoTdP73DO8u2MS1Ex3ZfO0lwNz/QRQcxRU+DNQ2YKH3IEKLfMiemO6M3HOTemev45lo3ypTZY+15c+55ka0wrVMqZbQunE3Z6sCGpf9h2lPPMmYx/tRwSfzq8nuXgFUMmZ+RjvDqzdBCulS7yoKhbn/18+fpCQi2jTOeHxRf3OywZaIW2fZ+PXTbPi6P+E3z6Q7JrR0UXbXS7uqTgN1OZ53S/8qeYFxC+gAfOzgdrWBQ0Dmi5JaqVUVFn0Kd3bDf0vh/ZfM03JzQvc2EHsIEo7Cte1w9A/Y8gMs/QK+es+c5Hp1MDzTzdygvH5NKFcKPD3MTeq3fj+Uv2b1JOz6yXT3v6xLfRK1aStgpnh2o6RS3jdyW/N6sGQGXN0KU15LqX4f+CQ0yvgag91iw6+xb+UkfptYh/1/fpjptMyHgup0ZHeHZmker68uSwdNcPaDKoDCrp9g4zcDWftFt0xXDnSl1Impul3G0up/KYmpJl8eYdagViSoc+4CxTvvvIO7u7vNahOTyUStWrUIDg5mw4YNlt46ERERDBgwgFatWnH69OkMEwAKhYLq1avzyy+/SDNzByUnJ3PlyhVLwql06dIULVqUfftSLoa6aXQcO3qYLu3r4OkOndrWQKNWc/XAWaqrSuCrcOfWvTucvHSW5o83orwyIFsXui5cuEBiYkqbkpMnT+Lp6Unx4ulX+z/s87Rz504uXbpE3bp1qVKlCgaDgVWrVhEcHGxZjS8rKlWqxMmT1p+nj37taun1RzOZTCQkJHD69GmSk9NWO2ZV9taduwWsB7pnMqYm0ATzVUFhl0P6a9SP/IyZXr0Z7ZH+VcRytyOY9N0GVl4exOlRpyhTIm1pqcg/PNHSSlOZjtrqdNAE87i6dLb36XbDl7j737N4zhT+92ooyQVoVfT8JvWVlg3/GFm/HGa9m3NTZBUKeKzodmoFbOd4WCfWXH6Hq7GPpxnnG5uIZ1LmvwhFlJ7M8e7H0zF5t/lhQVSlvLm66dme5ml1jnhzKHy1BGLtWEn7+ult3Di7k+Dmg2nQ/V08fKz7ev3R/nEanrqKe7L18qjTvXrye/JRYkwZ953L9wzAJOAfYDEQmPlwC2/MU/WeAMZiXoAlE6cuwPe/m5OGXVtnNdjMubuZ318ceY9JSISwSLgfsYe7EW05ETOYC6qJGJQpP4hIX09Wt63DgI3WSW5fpQdfePXlmZifcuYbEBnycIdB3eHlZ6BeJo3uZ4yDNjnUiyw5IYrjm+dwcts8Kjd8itrtXyKgdK0Mx+9qWIUnd51K83kz3qMDW3TnciaoAujOpf9Y92V3ytRsT8MeEwksl/ZzPP3t9nFq5wK8A8rhU7Qs3kXL4RNQDu+iZbO1Wp4jbtWozrxB/ry6ZAeadCrxbemgCWa37hJJpHz26PV6li1bhslk4pdffsHNzVwNYzKZUCgUmEwmZs6cyZkz5ospXbt2Bcwn0qlPlmvVqoVaraZDhw54eHhw9uxZbt26xerVq2nd2vwmrFAo6Nq1K9OnT8/yz6CgmzVrFq1atSIoKIiIiAgWLlxIXFwc3bubEwEKhYJnnnmGH3/8kaqVy9Koflm+mPETnp7uDBrQGRLc8I8uzvDe/+O9Lz+mrH9xAvyK8NaXk6ldpQYdmrRGpVBRXOmd5Rh1Oh1Tp05l2LBh3Lp1i++++47+/ftn2qeoQYMGfPnll1SrVg1vb/Nr16tXjw0bNjBo0KAsxwIwYMAAhg8fzi+//MITTzzBvn378tQ0OzD/zB4mn0wmE3fu3OHGjRtOqdbKXuIJzNPtMks8gbnqSRJPDoknmZfilvO37jQLvQdRXJm2SkGrN/DM+iOcvDwarykhQM6VwoncM8a9DZ969cRNkfPLDXf8xsDG1j8xe9YcXnv9DzJYUVQ4WeoPPJPJxMpNsGE3vDcaxj4Hmhz6p1co4PHATdQpuokj97ux5vI73IhLOTMJCrOv51d/t3r0TKrNmuQTOROYSJefDzzdBZ7vY65cyKrAInDmL4hPTGkhZjI9cp/UjxuAn0kyrOJAzOsci30RA+aTk2gfD/5q/RhPbTlq9RollD5M8urK27F/ZD3Q/GIj5ql3S4EWNsamNgpoh7nFgI1jnp0HzLc6wTB+OAzoam4o7koe7qmTVUY68Qvxuj9Zd+Uttl0ficFkfqPa1qQazY9epuydSKvtB7o1YGHiXkksOEnlcjB6oHn1wiJ2tNN5ohH0aAtrM1+LIkNuWnMlnKe7+f8ebuDpocNTsZTkf5cSV7YNHlVfQVk07RTdBHctuxpWocse6+rKjtrq1FOV4YjhepptRIrrp7dy/cw2KtbtQYPuE/APqprhWH1yPDsXvUL0vctpn1Qo8PApZklI+RQtR41aZalctRwKj3LcTyyL3phziakzlYNY0K8FLy7fjdLBk9Z3PDsy1Kc1Te5NI9QYbvXc8uXLWb58OfXr1ycpKYlTp07RsGFDoqKiuHDhQpp9pVehodfr2bBhg9VjTzzxBFOnTmXixIlAzjZRzg98i1Ui+n5Iur1H03P37l0mTZpEZGQkRYoU4bHHHuOHH36w6p00asQQPN2TmDb9UyIiY2jSqBabfv8On8iKEG/+XfvyrY9Qq1Q8/fZIEpISad+4JT/NnmPpK1TsQeKphJc/lXyD0KDmvkfGK/Wm1qhRI8qWLcvIkSPR6XR07NiRkSNHZrpNgwYNMBgMNGjQwPJYvXr12LFjh83G4rbUrl2bSZMm8d133zF//nwaN27MsGHDWLhwYbb2m5OuXr1K5cqVUSgUKBQKgoKCKFGiBPfu3cvRaXYACh5dYslRGuAGkNliSLFABSAskzEiQ8UVPvzg8z+6adO/uvR3+6pUGfMh9drvJT6HmpyK3PW4qjSTPDvTzy0bZ58Z+KNjLSIbPcfI8dfQ622PFznv119/tTTKnD9/PqNGjbI8V6MyfP0etLVzJtM/B80nAY9VtT2N0mhScOhuT9aEvMPt+Gq0OniRIWv32/U6u3WXaBU1y76ghN1UKvMUp+d6Q+/25soUV7ufUJY/Lk9i/x3zaphqvYEPv/6L4uHWfV2O62/QIPIz9IWlibQamAaMc3A7A+Ype1MAO99zK5Q29+sa/pT57zuvuR1XheUXP+JEWGcAKl+9xzsL0/akmRG/hfHxq3M7vAJLqYQureDlQfBkFqrjLoTC4jWpE0cPEknu6TyW6jkPd/Nr2+NaTC02XXuZA3f6WpKTAH7R8UyftSZN9csXCdt4M64QJLBziEKpomqTAdR/8m28A8qkeX7v7xM4tT2dFSZSedgv8NXBEFwx5XGjSUFMcjHuJ5YjLLEsYQnmZFRYYjnCErOemGpx+BLPr3a84uCsWzQ1bkx0eLvsGDNmDLNmzQLgv//+o1mztNNEc1r58uX59ttvLY2oc5Obpz+efkGoHlTCxUffIT7yVvb3q4VSxSHAj5SJcglaCPODuJz5UEvWKjgen3YFXpF9xYoVo1y5cml6YT1M9D7spXX//n1efPFFQkOz9u+Q/cQTwJfA6zbGfALI1NlsGe3eis+9+uCRqjImtJwn+4ZOJfx+CBNmQUSU6+IT2VdLVZKJnp0ZoK2HUpEzV15W94jkqV/eI50ejSKX/Pbbb5Y+Bd9++y2jR49OM2bgk/D52+YP7oz8ewSeGGJuPK9Wmw8g61Y33x5/8P9iAWm3M5qU7L/zFLq5T9Nql3WPjnh3DSdU19h07ygvubeiqNKLzclneSbmJ8JMOdcstLBTKOD1IfDWsMz/jV3pUlRDVlyYwqXoxtQ9c42Xl5qXVY93VzM59m9mRf6NzmZjxwKoO+ZV7NL528rUQczVT2ft3ySwiPnk8JVBEODv4OvlgpNhbVl+4WNuxQfz7Op9tD5sXu3wfhENz4bOYUPyaRdHWDAU8TNXNr30DFQq6+po7BOeWIpt10ew68bzJBjM/b6GrN5Hqwe/I+dL6Bh7fiF/607LyqlZoFRrqdHyeep2GWuZIn374l7WzeqRYcVKxTLmHoDDn8raggXZSUx13HOGpzcdcej13iixmVmnMl7gwBleeuklvv76awAOHDhA48aZNFXMIa5IPGk9/PD0D0KteSQJZDIReec8+uSsVS5oNVCymPmzC8Bg0mCM90AZ7o06PmfLePUqBUeTJPHkLAqFwtKnS5NqKoZer+fMmTMkJSXlkcRTHeCYjTFxQEXgXrZfrVCrrirB0qKjedxYlHgV1Iv6jPOJ9pUfivwjWFWCiR6dGOTWEFU2E1BPJnzP+jhbf6DCmZYtW8bTTz8NwLx583jppZfSHefjBZNfgdcGp512ExYJdfvAdRu9XUsVT0lCPUxIVS1vvnptensUin9TuhjPG9CS6MahTBjaG6MR1vqO4rT+NhPi18rS6E6gVkOXluapdT3aQDr9HF3OZIKDd3ux8uL7PLvgMveLeHOn7xYGjPzc1aG5VnlgGeaelY5IAN4GvsKhoy0vT3ihn3kqbjkHen51fgGu3zGfBAT6m/tIBRZJuRX1Nz/+8OusnIzqjWp23hjGllNvMH7uHv6pX4VzNSYwfsq/ju9MWKlX01zdNKgb+XZV2gS9D//cHMzWay+ive5D3y3H2NSyGldvd+Cbn7JfWVHYqd28eKzNKGq0Hs5fs3qkO8WubRPzcUTPdvZXrmWFrcRU83XRdN1p39Tba0H+9FW/xsGjEc4LOB0jRoxg/vz5AJZVzZwtNxNPWg9fPP2CUGszXu5Un5xA5O3z2PqQUihVqFRalGoNGo0WHx8Nbm5a9CYNBpMWVZICv2jbvUSzygQcMVzDmA9WissvFAolHj7FSIwLx2hI+XcrW7YsxYsXt1RAGQwGjhw5ku3EU86kIo8De4HMqhO9gPE4XrIurJw13KHR3Sl8VuI5jkae4nySJJ0KonOGOwyJXcRH8RuY4NmJZ90aoVaobG+YjtDE9FdIFLkndelqZku/xsTBm5/Cj6vg6/ehdcOU555923bSCeDmXfNt/a6Ux7w8oXY1aFduBR3bHqdWdHV8L9YmKPE2JZWf8jCkF2J+5Y4pxtFvT9hJr4d1O8y3wCLmBsFD+0DdGq6OLIVCAY1KrKZu4Aa2lRzJ3nv9GVjia1eH5XqhQCtgBjDGge08gDlAT8z9Lu1sbRMXD7N/ga9/Na9A9/YLUKtK5tt8/zts2uNAbJj7ywX42UhUFbFOVvl662lfdj6NS/zOujLvEKs4S9x2STpllVYD/TqbK1Oa1XV1NNnnoY6hU7l5tCuzgEN3e7Ku4iuU9t5B2N+SdMoJ+qQ4jm78gmObZ2MyplSgurvB/3qYE051cmnxQKXChJ/bXfzc7lLZ72Ca542NFSTNHILb2oZpnjMoFfzZsRaxbl5023WS9e2q4vFv7iadAMsqeECaaUb5mcbdBy+/INRutldkU2s98PQrQVJcBEq1BqVKi+rB/5VqjSXZpHjkPESP+bjGLVlPQEyC0xJODykALzc3YlKtWieyzs2rCF7+pVCqNGjcvYm6e8ny3LVr14iLi6NixYooFApUKhV+fn7cv38/W6+ZczVwU4C/bYx5CZgJyHlwtugw8MYdWXGqMLhovMew2CVMid9gbrzo1hSNnQmow/prfBK/mRCDNFdztYcNEyHzxNNDJy/AE8/C4J4wcxwsXGmdSHJUXDz8dxT+OxrONPYCe1Eqob7RG9N/KX18JOmUe+5HwJxF5lvdGvB8b/NJw8NydUe9OtVcFQfm5NHD42er+2TwuCK9cUnAXFB8xUexcnURAB3mtgK7gB8AOxo8W3QATmA+DvrN/s30eli02tynp9sT5gRUy3QuyIdHwrtfOBDPAzod3LlvvtlLo3lYORVOUf/xBBZRcC7E8dcWZioVJCXD1VtQp5r5QkFepddDXIJ5IYP4BEhIMv8/PjHlsfhE8wqJ8Yl64hNWkZC4iuPJnuy2r72gsFPqpBOY3x+e7gI1KrkooHSZSBz1C4Zw8NyTknyK9wvnn+4/ctc/lMjEYnzZozyxen+u3Mj9CHWpVt1JfayWX2ncvPD0L4nGzbGV4Tz9gvD0c2ypZY3eQJHoeDwTnZNw0qsUGJVgUIFBaUSvMKBPkGr87FJrPfEqUhpNqqSkxt0HD9/iJETftTwWHh5OUFAQnp7mDyV1BiugqDTu+BarRMRN21Ptcy7xtB74D2iayRhPzCXnY3PsVYUoFEKMYYyKXcrH8Rt526Mjw92bZroKnsFkZETMbxw2SEVcXmBvxdOjFq8xr0gUa2N59qwwGuFgSKztgcLpjp6B18/A+JnQvQ0M7WuekmfvKmdrt8NXS5wVnSSd0lgFHAEW4diqd/7Ar0AvYDTgwMV9kymlWq55PXMCqme7lOcnzjYnM3ODTge375lvD6LLnRcuoBISYeUm883DHbq2gv5doPsT4G27WMGm9busk0Wpk0RWiSOrpNEj4x+MzfoCJU74EBNWHv4O+fuaP0f6djR/juTElM3j5+DYOfOxSExcyi311+k9Z17wyISaRfzp60E3bS3WJZ/kucuLCJ/98Hfi7oObaxSUxJNa64mXf0k07lmYP51FOrWKCF9PEtx0eCTpcEvWozLmzOdBFAlcSJL+PDlJqdLg5V8SN6/0G1Z6+ZVElxiLPjnl/Tp1RaBKpUKp0lK+zpMUr1sM/6Bq+JcMxrdoBXRJsfzyVsX0dmslZ7t+vQ9ssjFmNOZSdam4FcJhV40RvBy3nGkJmxjv0Z4R7s3xUKRtFDMncacknfKQ1Ev0mhycmx4lRUiFRrIOVm0234KKweAe5ql4NW1MsZr8Ve7EJ1IJAVpjbh/wEeBIv64BQEtgGLaPmdLx7xHo9bL592LcMPMiA/OXO74fkfckJKa8B3i4m1e069/Z3BMuK0moDf/Ak6NsjxMFR2S0+aLVw9UMu7Q0J6G6t8laTzeAgW/CmUu2x2VEj5H+0Qt52q0+vyTtz1PN5Q2pVt5ROrMhlpOYp8mVROvh65LX16lV6NQqYrzcARNanQH3JB0eycm4JRlQZvGf+p5BLozmGIUCT5/iePiVQJFZ32CFAp/A8kTduYhSpUGlcUepTjm48fAOxNMvhsa93ycyMWtNSnM28bQZ2EPmVwDdMa9u91qOvrIQhcoNYyRj4lYyPX4zb3m2Z7R7SzwfJKCuGsJ5L26diyMUqaWueDLI8oLCDrfvwcwfzLdGtc1VUM88ab6andrqrXBYFhJzDSPwKbABWAw8lvlwK6WBjZibjo/H3ITcQacvwtAJ5imS0mu14ElIhD82m2/ubqmSUG3NC1HYY+aPzo1R5G3xCSmJTI0G2jUxJ6F6t4fiRe3bx4Z/spd0eigBHT8n7cv+jnJY6oqn/JR4Umnc8fILQuvpn/svbjKgJBmNKhmtWodaoUOlTEatSEalNH+teJhcTHCDeDeIdzffN9nuo6XHQJRJ+jjlBDdPfzz9S6FS25coUqndCChdy/K1VptSBajPgWq2nE08gbnqaauNMSMxH6y5YC6vEAXJbVM0b8X9wafxm3nTox0ve7Tm5bgVxJHs6tBEKtmpeBLiwAnzbewn5hOG5/tAx+bm1Yo+/MbV0QmOAQ2Bj4E3AEfOXV4BOgLPAgey9vLyllLwJSbBn1vMN3c36NwyJQnlm0Erl6NnYOve3I1T5F06HWzcbb6N/tA8ZbdvR/OtfCYrZ375c+7F6Ar5baqdSuOGp18Qbp7+mDsy5jCTEYNBh1Gvw2BIxqhPxmjQYdQnP3g8GZMppWWERg3enuZkuLeX+f3JikeS+VY02px0epCIMsW5Q6IWRTrfQ5gxPk9VxeVHaq3Hgz5OjvX6epQq1fGMTp/xv4nCzi4iOZ942gbsBJ7IZIwbMAF4OcdfXYhC6Z4plnfi1zAtYRPRcpUgz0ld8SSJJ5FViUmw9G/zrUwQtGoAR6TaKW9IAt4C1gI/A+Ud2DYY+BeYijl5leU+OqIwSEwyVzqu3gpu2pQkVM921kkoqXYSGTEaYfch823sJ1C/JvTpCH07WE/tPn3R8ZUy85vUiaf8sKqdWuuJyWjEoE9GpX40y5M9UXcvoUt0rL+DTg8R0eYbgFplXiDBxwt8PM3TPS0/VYUJPBPBM5FwTRSh1xV4K9zwVbjjo3DHU6FBgYIwU1yOfl+FiUKpxsu/JO7eAWQ3MenrqUClNO/DZDKhy2DChkpv4Jm1+7AnR53ziSeAD4AdNsa8gLnq6apTIhCiUJKkU96UuuJJptqJnHD9Nvz2l6ujEGnsBOoAc4DnHNhODUwGugGDgfM5HpkogJKSYc02881NC51amBuT16sBy9a7OjqRXxw+bb69NxuqV4I+Hcy3BStcHZnz5bepdklxESTFmVeSUKo0aNy90bh5o3H3zlYiyqBPcjjplB69wdyb9GF/UqXSXBH1sCrKywOUCgiPAiMmok2JlnMXFUq8FVriTflv1sbNmzfp1asXixcvJjg4OEf2OWrUKKpVq8abb75px2gFHj7F8PQrgUKZ/co9L3cFRbxS/h6SdCkXzRNi7nPj8kUib51Df+MCn18O4vFk+5b5dU7iaSfmyqd2mYzRAhMBaXoohCjgpOJJiEIkGngeWAN8BwQ6sG0jzCvmjQe+zvHIRAGWlGxe4XLtdldHIvKzs5dh+nzzrTDIS4mnhl3/y9XXu3LqRcv9h8mszMyfP58FCxZYPRYQEMDGjRstX5tMJhYsWMAff/xBTEwMtWrVYvz48VSuXBkw9yTUqJP5eNpsNm7cSFJSEo0aNeLtt9+mRIkSmfZ2OnXqFF999RVnz55FoVBQs2ZNXn31VUui52Hy51GzZ8+mefPmlq8PHTrErFmzuHz5MoGBgQwZMoSnnnrK5vefV2k9fPEqUjrHKuDcNQoCfZSWcxed3sClkGvokuKIi7jBui9HEhoaSimlH3/7juZxdRDRdjaqdN5f2Ad2jBkKVHBaBEIIkSdIc3EhCqFVQG3A0co0T8xNxzcAmfReEUIIkT3JySnVNa5OPLmSPYkngEqVKrF+/XrLbenSpVbP//LLL/z666+MGzeOn376iaJFi/LKK68QF2eePmcywUdTvmDHjh18/PHHfP/99yQkJPDGG29kenwcFxfHa6+9RlBQED/++CMLFizAy8uL1157Db3een76119/bRVjo0aNLM/duHGD119/nbp167J48WKGDh3KzJkz2bZtm70/qjxDpXHHt3hlfItVytFpl77uest5i16v5+SJ4yTE3EefnGDp71VTFcRev7E8ri7t0L6d9xe2G/Mqd5nRAJOcFoEQQuQJqQ9mjEY7O/AJIfK/20B3zIuqONq2ojNwAng6p4MSQggBeaviyVV0SXEY9El2jVWpVAQGBlpuRYoUsTxnMpn47bffGDp0KO3ataNKlSpMnjyZxMRES1VUbGwsq1evZsyYMTRp0oTg4GA++ugjLl26xP79+zN83dDQUKKjoxk1ahQVKlSgcuXKjBgxgvDwcG7fvm011s/PzypGjUZjeW7VqlUEBQXx5ptvUrFiRXr37k3Pnj1ZvHixZczkyZN56623+PHHH+ncuTNt27ZlwYIF6PV6Zs+eTfv27enWrRtr1qxJN85hw4bRokULnn76aQ4dOpTpz3PFihX07duXFi1a0LlzZ95++22r500mE3PmzKF9+/Z07tyZ+fPNpYgKhRI3ryLERkfz7thRNKxZitqVAxjUtyOnTx1LiefKJUYM6UvDWqWpVdGfXp2bsnun9SpwLRtWYe4X03jrtWHUqVKU0S+OtLz26dOn0yQEG6rLsdvvDcqpAjL93tLj3L8we6qengMqOzUKIYRwKVnVTohCbgHwOODoKmMBwDJgCeCfwzEJIUQhV5gTT+E3ThFz/wrxUbfs3ubatWt07dqVXr16MWHCBK5fv2557saNG4SFhdG0aVPLY1qtlvr163P8+HEAzpw5g16vtxpTrFgxKleubBmTnvLly+Pv78+aNWvQ6XQkJiayevVqKlWqRFBQkNXYN//f3v0H13zveRx/nnMiIiE/JA25JEGjISStVcJgXFlX78WS0d1q6Rqltnf3VkwuM+0abot0W3daktjZRlOJlKkt9k5ZrTVT1yzalaUto1cZpX5c4oojkTQ/TiLn7B/fzVcO+XFO5JwEr8dMZk7OeX++33eOE77e38/n/Vm6lClTprBw4UL273cvspw8eZLU1FS358aMGcOpU6fcZk4dO3aM0tJSNm7cSGZmJh988AGZmZmEhoZSWFjIrFmzePvtt+8peuXk5PDiiy+ydetWUlJSWLp0KeXl5c3+TKdOneK9997jlVdeYefOneTm5jJixAi3mD179tCjRw8KCwvJyMjgww8/pLi4GJfLSVXZVRa8OJMrl86SnZPDtu1/IHFIInOfncJN+3UAqqp+YtLkX7J1x3+xZ/9RJvx8Ci/PS+fKn92bbOf/23s8MWQ4X/z3/7Jy5UrAWK0xZMgQYmNjzeJdMIEU9JpDhDW4xT+r1vj2N+x/gLaaGwagWU8i8lDTUjsR4RwwAaO/ZX0bsXebgzH7aXJHJyUi8uhqWmx41ApPzoZ6HNXl1Nf+5FH8sGHDWLVqFRs2bGD58uXY7XYWLlxoFlbsdjtg9H1qqnfv3uZrdrudbt26ERoa2mJMc0JCQsjLy2Pv3r2MHz+eiRMncuTIEXJycggIMFpWBwcHk5mZydq1a8nOzmbUqFEsX76czz//3DyO3W4nMjLynnM3NDS4FYhCQ0NZtmwZAwYMYMaMGcTHx+NwOHjppZeIi4tj/vz5dOvW7Z5i2XPPPUdaWhoDBw7k9ddfJyQkpNmZUQDXrl0jKCiI8ePHExMTQ2JiIs8//7xbzODBg1m0aBFxcXFMmzaNoUOHmjPDjh07xg8//MA777zDkMTB9H0snN/8+mV6hgSzc8u/cvPqKfpHh5I+cyaDBsYTHz+QZf+8mrj4QXyx7z/dzjN2/CT+4Z9+S3S/QcTFxZnPBwYG0qdPH1JSUhjQpx9R1hAC76NFuG+aizf1BvCrNmL+HvgX4KzPsxER8Ts1FxcRABowrnf2AluBJC/G9sdoYZALvA4e9vIUEZEWqMeT58aNG2c+TkhIICUlhfT0dD777DPmzp1rvtb0mhc8u+51uVzmuIyMDI4fPw5A37592b59O7W1taxZs4Ynn3ySrKwsnE4nW7duZcmSJRQVFREUFER4eDhz5swxj5mUlERlZSVbtmxh6tSpXv2sgwYNcvs89O7d22yQDsaSw7CwMG7evOk2Ljk52XwcEBBAUlISP/74Y7PnSE1NJSYmhvT0dMaOHcvYsWOZNGkSQUFBZkxCQoLbmKioKMrKjH5cp0+fpqamhsmT3e9IORwOrly5gvN2Hbcqb5Gfn8/hw4cpLS2loaEBh8PBxfOnqaupICCwh5H3kyMBY9nl6dOXSExMJDAw0PwzsVgs9IwKpzToqmdvYAt8X3g6CuzB6HHQEhvwO4wClIjIQ0YznkTEzbfASIwiVKaXYzOAX2BcM7XePkJERFrRdKnd3QUTaV2PHj1ISEjg8uXLAOZMIrvdTlTUne1cy8rKzNciIyOpr6+noqLCbdZTWVkZKSkpAKxYsQKHw+g51Tibad++fZSUlFBQUGAWhLKyskhLS+PgwYNMmTKl2RyHDx/Orl27zO8jIyPvmVl18+ZNbDYb4eHh5nON521ksViafc6Tvq0tfa5CQkLYsmULX3/9NcXFxWzcuJH8/HyKioro1atXi3k0ntPpdBIVFUVeXt49x24cn5OTw5EjR1iyZAmxsbF0796d1157jZqfyqkoPW8c53YdVlcN1beucbu+hrq6Ok6ePInFYiE6Opp+MT/DGmAztiTsFdjmz9sa/5R2Pen19AIwxNeJiIj4n3o8icg9aoHfAn8NXPZy7FCMdgYrMW7eiYiI1zTjqf3q6uq4cOGCWVTq168fkZGRFBcXmzH19fV88803ZlFp6NChBAQEuMXcuHGDc+fOmTHR0dHExsYSGxtLTEwMALW1tVgsFrciTuP3rRV/zpw547a0Ljk5+Z4m5sXFxSQlJd1T5GmP7777znx8+/Ztvv/+ewYMGNBifEBAAKmpqWRkZLBt2zauXr3K0aNHPTrXkCFDsNvt2Gw28/1q/Gosoh0/fpzp06czadIkEhISiIyMpKTk3p5et+tqqb51jbrqW+ZzVpeF8BsurNeq7wRa76846/sZTwDfALuAma3ENM56mtNKjIjIA6jpP5Ta1U5E3PwRSAY24N3M727AamAqMA+1KxAR8VLTwpNmPLUuOzubCRMm0LdvX8rKyti0aRNVVVVMn24sa7JYLLzwwgsUFhaaBZDNmzcTFBTEM888A0DPnj2ZOXMm2dnZhIWFERYWRnZ2No8//jijR49u8dypqank5uaydu1aZs+ejdPppKioCJvNxtNPPw0YjbgDAgJITEzEYrFw6NAhPvnkExYvXmweZ9asWWzfvp3169eTnp7OyZMn2bVrF2+99VaHvEc7duwgNjaWgQMH8vHHH1NZWcmMGTOajT106BBXrlxhxIgRhIaG8uWXX+JyuYiPj/foXKNHjyY5OZlly5axePFi4uPjKS0t5auvvmLixIkkJSURGxvLgQMHmDBhAhaLhby8PI9vgNuw8GfnLcJuW/kZxgyqehqYWZHP1bLLOHGZX7ednq3m8E/hCeBNWi88AcwG3gL+5PNsRET8RkvtRKRVtzCKR7uBPCCy9XA3YzCW7mVi7J4nIiIeeZSbi3vr+vXrrFixgvLyciIiIhg+fDgFBQXmrCSAefPm4XA4WLt2LZWVlQwbNowNGzYQEhJixmRmZmKz2Vi+fDm1tbWMGjWKN954A5ut5em7AwYMYN26deTn57NgwQKsVitPPPEEubm5bsv6CgoKKCkpwWazERcXx8qVK936O/Xr14/s7GzWr1/Pjh07eOyxx1i2bBlpaWkd8h69+uqrfPTRR5w5c4b+/fvz7rvvui3ha6pXr14cOHCA/Px8HA4HcXFxZGVlufWSao3FYiE7O5v333+fNWvWmEsaR4wYYTZ4z8zMZM2aNSxcuJDw8HDmzZtHVVWVR8evo4E6VwPdnXeKsy7gTMNfuNjg+U6Ibjn//zH8YyfwbBsxO4Dn/JCLiIifHD582GzKuHTpUtatW9fJGYlIlxUDFAC/bMfYPwCLgJttBYqISGBgoNlPqKKigrCwMJ+fMz4+nry8PLeCiUhXFRwcTFJSErW1tZw7d45p06Zx8eLFdh3Lv6XdVR7E/B3GlHMRkYeEltqJiMdKMHYD/keguo3Yu80CTgCTOjopEZGHj5baibSuurraXK1htVrddjf0ln8LTyeB7R7EvenjPERE/EhL7UTEa3nAU0BxG3F36w98AbyD0QdKRERa1NjzRoUnkeZVVlaajxt7erWH/xezrgLauuE/C+NiS0TkIaBd7USkXc4C4zB2r7vdRmxTVuA14CtgsA/yEhF5yKjwJNK8pjv+9enTp93H8X/h6RTw7x7EvenjPERE/ERL7USk3RqALIwm4qe9HPs0RuPxBR2dlIjIw0XNxUXuiIiIYNiwYYwcOZKePXuazxcXezsN+47O+Q1bjXEh1ZqZwEg/5CIi4mNaaici9+1r4K+AXC/HhQCbMDZviejopERExBsul0uz36VL69u3L4MGDaJHjx7m/2FcLhdOp5NNmza1+7idU3g6A3zsQZwnzchFRLq4pnfRNONJRNqtBliCsePdNS/H/i1G4/GJHZ2UiMiDz19L7ex2u1tTc5GuJiYmxq3gVF9fz/Xr1zl//jwlJSXtPm7nzSlcTdv9CqYBo/2Qi4iIDzW9mNFdLhG5b/uAFGCPl+NigT8CbwEBbcSKiDxC/FV4qqqqYvfu3ZSXl/vlfCLeqquro7a2lpqaGk6cOMGJEyc4e/Ysn376KdXV3m63e0fnXXb8AGwF5rcRtwpjW2ERkQeUltqJSIcrBf4G+A3wLhDk4TgrsByYDMwBzvkkOxGRB4LL5cJisfi1uXhhYSEAM2bMIDAwUI3NpUu5cOECYKzSKCkpoa6ujt27d5uf2/ayAJ13+30QxrK7tspf4zB2ZhEReQB9++23PPXUUwDMnz+foqKizk1IRB4uw4BtQLKX434CXgX0V5KIPKIaGhqwWq3U19cTGBjo13MHBwcTFRWlwpN0GdHR0Rw8eBCAsrIyxowZw40bN+5rplOjzp1ofR7YDLzcRtwq4Bc+z0ZExCc040lEfOpPGK0J1gIZXozriXEd9ivg10B5RycmItK1NbZA6IziT3V1NZcuXfL7eUVaEhERQVCQMYXa6XR26Oez8/eNzALq24iZDEzwQy4iIj6g5uIi4nO1GI3HpwJ/8XLsbIzG47rWEpFHjHpvitzRp08f83FNTU2HHrvzW0teBAqAV9qIWw1M8n06IiIdTc3FRcRv9mI0Ht+Mdz0y44ADGI3HVwOanCkijxCr1UpaWpr5fdNrt7t7QDW9oXh3bHOvtxbf9LWmj1uagXX3sdqb0/3k3FIe/j5nS++jJ897cr7mYlqLbc852nrd18e7e8zIkSPNxw9f4QmMi5yXgNaW1f4ceBb4D38kJCLScbTUTkT86jrGzsCLgd8D3T0cZwN+hzHTfC5wwRfJiYh0HY3XaFarlf3793dyNiJdR0f3POsahafLwAZgURtxbwNfALd8npGISIepqqqioqICAIfD0cnZiMgjwQXkYlw3FQBDvRg7HDgEZAI7Oz41EZGuorKyssVZLSKPshs3bnTo8Tp3VzsREREREREREXloqbwrIiIiIiIiIiI+ocKTiIiIiIiIiIj4hApPIiIiIiIiIiLiEyo8iYiIiIiIiIiIT6jwJCIiIiIiIiIiPqHCk4iIiIiIiIiI+IQKTyIiIiIiIiIi4hMqPImIiIiIiIiIiE+o8CQiIiIiIiIiIj7xf/P3b7KtCTqsAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1500x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create new figure\n",
+    "fig = plt.figure(figsize=(15, 12), facecolor='black')\n",
+    "\n",
+    "# Add the map and set the extent\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "ax.set_extent([-108., -91., 33., 45.])\n",
+    "ax.patch.set_fill(False)\n",
+    "\n",
+    "# Add state boundaries to plot\n",
+    "ax.add_feature(cfeature.STATES, edgecolor='white', linewidth=2)\n",
+    "\n",
+    "# Contour the MSLP\n",
+    "c = ax.contour(lon_2d, lat_2d, mslp, colors='lime', linewidths=6)\n",
+    "ax.clabel(c, fontsize=12, inline=1, inline_spacing=4, fmt='%i')\n",
+    "\n",
+    "wslice = slice(1, None, 4)\n",
+    "# Plot 850-hPa wind vectors\n",
+    "vectors850 = ax.quiver(lon_2d[wslice, wslice], lat_2d[wslice, wslice],\n",
+    "                       u_wind850[wslice, wslice], v_wind850[wslice, wslice],\n",
+    "                       headlength=4, headwidth=3, angles='xy',\n",
+    "                       scale_units='xy', scale=12, color='gold',\n",
+    "                       label='850mb wind')\n",
+    "\n",
+    "# Plot 500-hPa wind vectors\n",
+    "vectors500 = ax.quiver(lon_2d[wslice, wslice], lat_2d[wslice, wslice],\n",
+    "                       u_wind500[wslice, wslice], v_wind500[wslice, wslice],\n",
+    "                       headlength=4, headwidth=3, angles='xy',\n",
+    "                       scale_units='xy', scale=12, color='cornflowerblue',\n",
+    "                       label='500mb wind')\n",
+    "\n",
+    "# Plot 500-850 shear\n",
+    "shear = ax.quiver(lon_2d[wslice, wslice], lat_2d[wslice, wslice],\n",
+    "                  u_wind500[wslice, wslice] - u_wind850[wslice, wslice],\n",
+    "                  v_wind500[wslice, wslice] - v_wind850[wslice, wslice],\n",
+    "                  headlength=4, headwidth=3, angles='xy', scale_units='xy',\n",
+    "                  scale=12, color='deeppink', label='500-850mb shear')\n",
+    "\n",
+    "# Add a legend\n",
+    "ax.legend(('850mb wind', '500mb wind', '500-850mb shear'), loc=4)\n",
+    "\n",
+    "# Manually set colors for legend\n",
+    "legend = ax.get_legend()\n",
+    "legend.legendHandles[0].set_color('gold')\n",
+    "legend.legendHandles[1].set_color('cornflowerblue')\n",
+    "legend.legendHandles[2].set_color('deeppink')\n",
+    "\n",
+    "# Add a title to the plot\n",
+    "plt.title('MSLP, 850mb Wind, 500mb Wind, and 500-850mb Vertical Wind Shear \\n'\n",
+    "          f' for {time:%d %B %Y %H:%MZ}', color='white', size=14);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sources/notebooks/synoptic/index.md b/_preview/13/_sources/notebooks/synoptic/index.md
new file mode 100644
index 0000000..bfbd904
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/index.md
@@ -0,0 +1,3 @@
+# Synoptic Analysis
+
+This section covers how to perform a variety of analyses traditionally used in synoptic meteorolgy, ranging from basic plots on isobaric surfaces to Q-vectors and frontogenesis.
\ No newline at end of file
diff --git a/_preview/13/_sources/notebooks/synoptic/xarray_500hPa_map.ipynb b/_preview/13/_sources/notebooks/synoptic/xarray_500hPa_map.ipynb
new file mode 100644
index 0000000..657fc6e
--- /dev/null
+++ b/_preview/13/_sources/notebooks/synoptic/xarray_500hPa_map.ipynb
@@ -0,0 +1,227 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Using Xarray for Data read and selection\n",
+    "========================================\n",
+    "\n",
+    "Use Xarray module to read in model data from nomads server.\n",
+    "\n",
+    "This example uses the xarray module to access data from the nomads server for\n",
+    "archive NAM analysis data via OPeNDAP. Xarray makes it easier to select times\n",
+    "and levels, although you still have to know the coordinate variable name. A\n",
+    "simple 500 hPa plot is created after selecting with xarray."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Import all of our needed modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "import matplotlib.pyplot as plt\n",
+    "import metpy.calc as mpcalc\n",
+    "from metpy.units import units\n",
+    "import numpy as np\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################################"
+   },
+   "source": [
+    "Accessing data using Xarray\n",
+    "---------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify our date/time of product desired\n",
+    "dt = datetime(2016, 4, 16, 18)\n",
+    "\n",
+    "# Construct our OPeNDAP access URL\n",
+    "base_url = 'https://www.ncei.noaa.gov/thredds/dodsC/model-namanl-old/'\n",
+    "data = xr.open_dataset(f'{base_url}{dt:%Y%m}/{dt:%Y%m%d}/'\n",
+    "                       f'namanl_218_{dt:%Y%m%d}_{dt:%H}00_000.grb').metpy.parse_cf()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################################"
+   },
+   "source": [
+    "NAM data is in a projected coordinate and you get back the projection\n",
+    "X and Y values in km"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a 2-d meshgrid of our x, y coordinates\n",
+    "# manually converted to meters (km * 1000)\n",
+    "#x, y = np.meshgrid(data['x'].values * 1000, data['y'].values * 1000)\n",
+    "x = data.Geopotential_height_isobaric.metpy.x.metpy.convert_units('meter').values\n",
+    "y = data.Geopotential_height_isobaric.metpy.y.metpy.convert_units('meter').values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################################"
+   },
+   "source": [
+    "Getting the valid times in a more useable format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the valid times from the file\n",
+    "vtimes = data.Geopotential_height_isobaric.metpy.time.data.astype('datetime64[ms]').astype('O')\n",
+    "print(vtimes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################################"
+   },
+   "source": [
+    "Xarray has some nice functionality to choose the time and level that\n",
+    "you specifically want to use. In this example the time variable is 'time' and\n",
+    "the level variable is 'isobaric1'. Unfortunately, these can be different with\n",
+    "each file you use, so you'll always need to check what they are by listing\n",
+    "the coordinate variable names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(data.Geopotential_height.coords)\n",
+    "hght_500 = data.Geopotential_height_isobaric.metpy.sel(time1=vtimes[0], vertical=500*units.hPa)\n",
+    "uwnd_500 = data['u-component_of_wind_isobaric'].metpy.sel(time1=vtimes[0], vertical=500*units.hPa)\n",
+    "vwnd_500 = data['v-component_of_wind_isobaric'].metpy.sel(time1=vtimes[0], vertical=500*units.hPa)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_marker": "########################################"
+   },
+   "source": [
+    "Now make the 500-hPa map\n",
+    "------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Must set data projection, NAM is LCC projection\n",
+    "datacrs = data.Geopotential_height_isobaric.metpy.cartopy_crs\n",
+    "\n",
+    "# A different LCC projection for the plot.\n",
+    "plotcrs = ccrs.LambertConformal(central_latitude=45., central_longitude=-100.,\n",
+    "                                standard_parallels=[30, 60])\n",
+    "\n",
+    "fig = plt.figure(figsize=(17., 11.))\n",
+    "ax = plt.axes(projection=plotcrs)\n",
+    "ax.coastlines('50m', edgecolor='black')\n",
+    "ax.add_feature(cfeature.STATES, linewidth=0.5)\n",
+    "ax.set_extent([-130, -67, 20, 50], ccrs.PlateCarree())\n",
+    "\n",
+    "clev500 = np.arange(5100, 6000, 60)\n",
+    "cs = ax.contour(x, y, mpcalc.smooth_n_point(hght_500, 9, 5), clev500,\n",
+    "                colors='k', linewidths=2.5, linestyles='solid', transform=datacrs)\n",
+    "ax.clabel(cs, fontsize=12, colors='k', inline=1, inline_spacing=8,\n",
+    "          fmt='%i', rightside_up=True, use_clabeltext=True)\n",
+    "\n",
+    "# Here we put boxes around the clabels with a black boarder white facecolor\n",
+    "# `labelTexts` necessary as ~cartopy.mpl.contour.GeoContourSet.clabel\n",
+    "# does not return list of texts as of 0.18\n",
+    "for t in cs.labelTexts:\n",
+    "    t.set_bbox({'fc': 'w'})\n",
+    "\n",
+    "# Transform Vectors before plotting, then plot wind barbs.\n",
+    "wind_slice = slice(None, None, 16)\n",
+    "ax.barbs(x[wind_slice], y[wind_slice],\n",
+    "         uwnd_500.data[wind_slice, wind_slice], vwnd_500.data[wind_slice, wind_slice],\n",
+    "         length=7, transform=datacrs)\n",
+    "\n",
+    "# Add some titles to make the plot readable by someone else\n",
+    "plt.title('500-hPa Geopotential Heights (m)', loc='left')\n",
+    "plt.title(f'VALID: {vtimes[0]}', loc='right');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_preview/13/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/_preview/13/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
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diff --git a/_preview/13/_static/basic.css b/_preview/13/_static/basic.css
new file mode 100644
index 0000000..2af6139
--- /dev/null
+++ b/_preview/13/_static/basic.css
@@ -0,0 +1,925 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 270px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 360px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+a:visited {
+    color: #551A8B;
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/_preview/13/_static/check-solid.svg b/_preview/13/_static/check-solid.svg
new file mode 100644
index 0000000..92fad4b
--- /dev/null
+++ b/_preview/13/_static/check-solid.svg
@@ -0,0 +1,4 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>
diff --git a/_preview/13/_static/clipboard.min.js b/_preview/13/_static/clipboard.min.js
new file mode 100644
index 0000000..54b3c46
--- /dev/null
+++ b/_preview/13/_static/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.8
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},i="";return"string"==typeof t?(e=t,n="rtl"===document.documentElement.getAttribute("dir"),(o=document.createElement("textarea")).style.fontSize="12pt",o.style.border="0",o.style.padding="0",o.style.margin="0",o.style.position="absolute",o.style[n?"right":"left"]="-9999px",n=window.pageYOffset||document.documentElement.scrollTop,o.style.top="".concat(n,"px"),o.setAttribute("readonly",""),o.value=e,o=o,r.container.appendChild(o),i=c()(o),a("copy"),o.remove()):(i=c()(t),a("copy")),i};function r(t){return(r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==r(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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e=t.delegateTarget||t.currentTarget,t=s({action:this.action(e),container:this.container,target:this.target(e),text:this.text(e)});this.emit(t?"success":"error",{action:this.action,text:t,trigger:e,clearSelection:function(){e&&e.focus(),document.activeElement.blur(),window.getSelection().removeAllRanges()}})}},{key:"defaultAction",value:function(t){return v("action",t)}},{key:"defaultTarget",value:function(t){t=v("target",t);if(t)return document.querySelector(t)}},{key:"defaultText",value:function(t){return v("text",t)}},{key:"destroy",value:function(){this.listener.destroy()}}])&&p(t.prototype,e),n&&p(t,n),r}()},828:function(t){var e;"undefined"==typeof Element||Element.prototype.matches||((e=Element.prototype).matches=e.matchesSelector||e.mozMatchesSelector||e.msMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector),t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}},438:function(t,e,n){var u=n(828);function i(t,e,n,o,r){var i=function(e,n,t,o){return function(t){t.delegateTarget=u(t.target,n),t.delegateTarget&&o.call(e,t)}}.apply(this,arguments);return t.addEventListener(n,i,r),{destroy:function(){t.removeEventListener(n,i,r)}}}t.exports=function(t,e,n,o,r){return"function"==typeof t.addEventListener?i.apply(null,arguments):"function"==typeof n?i.bind(null,document).apply(null,arguments):("string"==typeof t&&(t=document.querySelectorAll(t)),Array.prototype.map.call(t,function(t){return i(t,e,n,o,r)}))}},879:function(t,n){n.node=function(t){return void 0!==t&&t instanceof HTMLElement&&1===t.nodeType},n.nodeList=function(t){var e=Object.prototype.toString.call(t);return void 0!==t&&("[object NodeList]"===e||"[object HTMLCollection]"===e)&&"length"in t&&(0===t.length||n.node(t[0]))},n.string=function(t){return"string"==typeof t||t instanceof String},n.fn=function(t){return"[object Function]"===Object.prototype.toString.call(t)}},370:function(t,e,n){var f=n(879),l=n(438);t.exports=function(t,e,n){if(!t&&!e&&!n)throw new Error("Missing required arguments");if(!f.string(e))throw new TypeError("Second argument must be a String");if(!f.fn(n))throw new TypeError("Third argument must be a Function");if(f.node(t))return c=e,a=n,(u=t).addEventListener(c,a),{destroy:function(){u.removeEventListener(c,a)}};if(f.nodeList(t))return o=t,r=e,i=n,Array.prototype.forEach.call(o,function(t){t.addEventListener(r,i)}),{destroy:function(){Array.prototype.forEach.call(o,function(t){t.removeEventListener(r,i)})}};if(f.string(t))return t=t,e=e,n=n,l(document.body,t,e,n);throw new TypeError("First argument must be a String, HTMLElement, HTMLCollection, or NodeList");var o,r,i,u,c,a}},817:function(t){t.exports=function(t){var e,n="SELECT"===t.nodeName?(t.focus(),t.value):"INPUT"===t.nodeName||"TEXTAREA"===t.nodeName?((e=t.hasAttribute("readonly"))||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),e||t.removeAttribute("readonly"),t.value):(t.hasAttribute("contenteditable")&&t.focus(),n=window.getSelection(),(e=document.createRange()).selectNodeContents(t),n.removeAllRanges(),n.addRange(e),n.toString());return n}},279:function(t){function e(){}e.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,u=o.length;i<u;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
\ No newline at end of file
diff --git a/_preview/13/_static/copy-button.svg b/_preview/13/_static/copy-button.svg
new file mode 100644
index 0000000..9c074da
--- /dev/null
+++ b/_preview/13/_static/copy-button.svg
@@ -0,0 +1,5 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>
diff --git a/_preview/13/_static/copybutton.css b/_preview/13/_static/copybutton.css
new file mode 100644
index 0000000..f1916ec
--- /dev/null
+++ b/_preview/13/_static/copybutton.css
@@ -0,0 +1,94 @@
+/* Copy buttons */
+button.copybtn {
+    position: absolute;
+    display: flex;
+    top: .3em;
+    right: .3em;
+    width: 1.7em;
+    height: 1.7em;
+	opacity: 0;
+    transition: opacity 0.3s, border .3s, background-color .3s;
+    user-select: none;
+    padding: 0;
+    border: none;
+    outline: none;
+    border-radius: 0.4em;
+    /* The colors that GitHub uses */
+    border: #1b1f2426 1px solid;
+    background-color: #f6f8fa;
+    color: #57606a;
+}
+
+button.copybtn.success {
+    border-color: #22863a;
+    color: #22863a;
+}
+
+button.copybtn svg {
+    stroke: currentColor;
+    width: 1.5em;
+    height: 1.5em;
+    padding: 0.1em;
+}
+
+div.highlight  {
+    position: relative;
+}
+
+/* Show the copybutton */
+.highlight:hover button.copybtn, button.copybtn.success {
+	opacity: 1;
+}
+
+.highlight button.copybtn:hover {
+    background-color: rgb(235, 235, 235);
+}
+
+.highlight button.copybtn:active {
+    background-color: rgb(187, 187, 187);
+}
+
+/**
+ * A minimal CSS-only tooltip copied from:
+ *   https://codepen.io/mildrenben/pen/rVBrpK
+ *
+ * To use, write HTML like the following:
+ *
+ * <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
+ */
+ .o-tooltip--left {
+  position: relative;
+ }
+
+ .o-tooltip--left:after {
+    opacity: 0;
+    visibility: hidden;
+    position: absolute;
+    content: attr(data-tooltip);
+    padding: .2em;
+    font-size: .8em;
+    left: -.2em;
+    background: grey;
+    color: white;
+    white-space: nowrap;
+    z-index: 2;
+    border-radius: 2px;
+    transform: translateX(-102%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+}
+
+.o-tooltip--left:hover:after {
+    display: block;
+    opacity: 1;
+    visibility: visible;
+    transform: translateX(-100%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+    transition-delay: .5s;
+}
+
+/* By default the copy button shouldn't show up when printing a page */
+@media print {
+    button.copybtn {
+        display: none;
+    }
+}
diff --git a/_preview/13/_static/copybutton.js b/_preview/13/_static/copybutton.js
new file mode 100644
index 0000000..2ea7ff3
--- /dev/null
+++ b/_preview/13/_static/copybutton.js
@@ -0,0 +1,248 @@
+// Localization support
+const messages = {
+  'en': {
+    'copy': 'Copy',
+    'copy_to_clipboard': 'Copy to clipboard',
+    'copy_success': 'Copied!',
+    'copy_failure': 'Failed to copy',
+  },
+  'es' : {
+    'copy': 'Copiar',
+    'copy_to_clipboard': 'Copiar al portapapeles',
+    'copy_success': '¡Copiado!',
+    'copy_failure': 'Error al copiar',
+  },
+  'de' : {
+    'copy': 'Kopieren',
+    'copy_to_clipboard': 'In die Zwischenablage kopieren',
+    'copy_success': 'Kopiert!',
+    'copy_failure': 'Fehler beim Kopieren',
+  },
+  'fr' : {
+    'copy': 'Copier',
+    'copy_to_clipboard': 'Copier dans le presse-papier',
+    'copy_success': 'Copié !',
+    'copy_failure': 'Échec de la copie',
+  },
+  'ru': {
+    'copy': 'Скопировать',
+    'copy_to_clipboard': 'Скопировать в буфер',
+    'copy_success': 'Скопировано!',
+    'copy_failure': 'Не удалось скопировать',
+  },
+  'zh-CN': {
+    'copy': '复制',
+    'copy_to_clipboard': '复制到剪贴板',
+    'copy_success': '复制成功!',
+    'copy_failure': '复制失败',
+  },
+  'it' : {
+    'copy': 'Copiare',
+    'copy_to_clipboard': 'Copiato negli appunti',
+    'copy_success': 'Copiato!',
+    'copy_failure': 'Errore durante la copia',
+  }
+}
+
+let locale = 'en'
+if( document.documentElement.lang !== undefined
+    && messages[document.documentElement.lang] !== undefined ) {
+  locale = document.documentElement.lang
+}
+
+let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
+if (doc_url_root == '#') {
+    doc_url_root = '';
+}
+
+/**
+ * SVG files for our copy buttons
+ */
+let iconCheck = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_success']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>`
+
+// If the user specified their own SVG use that, otherwise use the default
+let iconCopy = ``;
+if (!iconCopy) {
+  iconCopy = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_to_clipboard']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>`
+}
+
+/**
+ * Set up copy/paste for code blocks
+ */
+
+const runWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+
+const codeCellId = index => `codecell${index}`
+
+// Clears selected text since ClipboardJS will select the text when copying
+const clearSelection = () => {
+  if (window.getSelection) {
+    window.getSelection().removeAllRanges()
+  } else if (document.selection) {
+    document.selection.empty()
+  }
+}
+
+// Changes tooltip text for a moment, then changes it back
+// We want the timeout of our `success` class to be a bit shorter than the
+// tooltip and icon change, so that we can hide the icon before changing back.
+var timeoutIcon = 2000;
+var timeoutSuccessClass = 1500;
+
+const temporarilyChangeTooltip = (el, oldText, newText) => {
+  el.setAttribute('data-tooltip', newText)
+  el.classList.add('success')
+  // Remove success a little bit sooner than we change the tooltip
+  // So that we can use CSS to hide the copybutton first
+  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
+}
+
+// Changes the copy button icon for two seconds, then changes it back
+const temporarilyChangeIcon = (el) => {
+  el.innerHTML = iconCheck;
+  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
+}
+
+const addCopyButtonToCodeCells = () => {
+  // If ClipboardJS hasn't loaded, wait a bit and try again. This
+  // happens because we load ClipboardJS asynchronously.
+  if (window.ClipboardJS === undefined) {
+    setTimeout(addCopyButtonToCodeCells, 250)
+    return
+  }
+
+  // Add copybuttons to all of our code cells
+  const COPYBUTTON_SELECTOR = 'div.highlight pre';
+  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
+  codeCells.forEach((codeCell, index) => {
+    const id = codeCellId(index)
+    codeCell.setAttribute('id', id)
+
+    const clipboardButton = id =>
+    `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
+      ${iconCopy}
+    </button>`
+    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
+  })
+
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
+
+
+var copyTargetText = (trigger) => {
+  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
+
+  // get filtered text
+  let exclude = '.linenos';
+
+  let text = filterText(target, exclude);
+  return formatCopyText(text, '', false, true, true, true, '', '')
+}
+
+  // Initialize with a callback so we can modify the text before copy
+  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
+
+  // Update UI with error/success messages
+  clipboard.on('success', event => {
+    clearSelection()
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
+    temporarilyChangeIcon(event.trigger)
+  })
+
+  clipboard.on('error', event => {
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
+  })
+}
+
+runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/_preview/13/_static/copybutton_funcs.js b/_preview/13/_static/copybutton_funcs.js
new file mode 100644
index 0000000..dbe1aaa
--- /dev/null
+++ b/_preview/13/_static/copybutton_funcs.js
@@ -0,0 +1,73 @@
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+export function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
diff --git a/_preview/13/_static/custom.css b/_preview/13/_static/custom.css
new file mode 100644
index 0000000..0c9948b
--- /dev/null
+++ b/_preview/13/_static/custom.css
@@ -0,0 +1,6 @@
+.bd-main .bd-content .bd-article-container {
+    max-width: 100%; /* default is 60em */
+  }
+  .bd-page-width {
+    max-width: 100%; /* default is 88rem */
+  }
diff --git a/_preview/13/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/_preview/13/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
new file mode 100644
index 0000000..eb19f69
--- /dev/null
+++ b/_preview/13/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
@@ -0,0 +1 @@
+.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) !important}.sd-bg-text-secondary{color:var(--sd-color-secondary-text) !important}button.sd-bg-secondary:focus,button.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}a.sd-bg-secondary:focus,a.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}.sd-bg-success{background-color:var(--sd-color-success) !important}.sd-bg-text-success{color:var(--sd-color-success-text) !important}button.sd-bg-success:focus,button.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) 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0;position:relative}.sd-tab-set>input{opacity:0;position:absolute}.sd-tab-set>input:checked+label{border-color:var(--sd-color-tabs-underline-active);color:var(--sd-color-tabs-label-active)}.sd-tab-set>input:checked+label+.sd-tab-content{display:block}.sd-tab-set>input:not(:checked)+label:hover{color:var(--sd-color-tabs-label-hover);border-color:var(--sd-color-tabs-underline-hover)}.sd-tab-set>input:focus+label{outline-style:auto}.sd-tab-set>input:not(.focus-visible)+label{outline:none;-webkit-tap-highlight-color:transparent}.sd-tab-set>label{border-bottom:.125rem solid transparent;margin-bottom:0;color:var(--sd-color-tabs-label-inactive);border-color:var(--sd-color-tabs-underline-inactive);cursor:pointer;font-size:var(--sd-fontsize-tabs-label);font-weight:700;padding:1em 1.25em .5em;transition:color 250ms;width:auto;z-index:1}html .sd-tab-set>label:hover{color:var(--sd-color-tabs-label-active)}.sd-col>.sd-tab-set{width:100%}.sd-tab-content{box-shadow:0 -0.0625rem 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diff --git a/_preview/13/_static/design-tabs.js b/_preview/13/_static/design-tabs.js
new file mode 100644
index 0000000..36b38cf
--- /dev/null
+++ b/_preview/13/_static/design-tabs.js
@@ -0,0 +1,27 @@
+var sd_labels_by_text = {};
+
+function ready() {
+  const li = document.getElementsByClassName("sd-tab-label");
+  for (const label of li) {
+    syncId = label.getAttribute("data-sync-id");
+    if (syncId) {
+      label.onclick = onLabelClick;
+      if (!sd_labels_by_text[syncId]) {
+        sd_labels_by_text[syncId] = [];
+      }
+      sd_labels_by_text[syncId].push(label);
+    }
+  }
+}
+
+function onLabelClick() {
+  // Activate other inputs with the same sync id.
+  syncId = this.getAttribute("data-sync-id");
+  for (label of sd_labels_by_text[syncId]) {
+    if (label === this) continue;
+    label.previousElementSibling.checked = true;
+  }
+  window.localStorage.setItem("sphinx-design-last-tab", syncId);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/_preview/13/_static/doctools.js b/_preview/13/_static/doctools.js
new file mode 100644
index 0000000..4d67807
--- /dev/null
+++ b/_preview/13/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/_preview/13/_static/documentation_options.js b/_preview/13/_static/documentation_options.js
new file mode 100644
index 0000000..dab586c
--- /dev/null
+++ b/_preview/13/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
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diff --git a/_preview/13/_static/language_data.js b/_preview/13/_static/language_data.js
new file mode 100644
index 0000000..367b8ed
--- /dev/null
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+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, if available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/_preview/13/_static/locales/ar/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/ar/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..34d404c
--- /dev/null
+++ b/_preview/13/_static/locales/ar/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ar\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "طباعة إلى PDF"
+
+msgid "Theme by the"
+msgstr "موضوع بواسطة"
+
+msgid "Download source file"
+msgstr "تنزيل ملف المصدر"
+
+msgid "open issue"
+msgstr "قضية مفتوحة"
+
+msgid "Contents"
+msgstr "محتويات"
+
+msgid "previous page"
+msgstr "الصفحة السابقة"
+
+msgid "Download notebook file"
+msgstr "تنزيل ملف دفتر الملاحظات"
+
+msgid "Copyright"
+msgstr "حقوق النشر"
+
+msgid "Download this page"
+msgstr "قم بتنزيل هذه الصفحة"
+
+msgid "Source repository"
+msgstr "مستودع المصدر"
+
+msgid "By"
+msgstr "بواسطة"
+
+msgid "repository"
+msgstr "مخزن"
+
+msgid "Last updated on"
+msgstr "آخر تحديث في"
+
+msgid "Toggle navigation"
+msgstr "تبديل التنقل"
+
+msgid "Sphinx Book Theme"
+msgstr "موضوع كتاب أبو الهول"
+
+msgid "suggest edit"
+msgstr "أقترح تحرير"
+
+msgid "Open an issue"
+msgstr "افتح قضية"
+
+msgid "Launch"
+msgstr "إطلاق"
+
+msgid "Fullscreen mode"
+msgstr "وضع ملء الشاشة"
+
+msgid "Edit this page"
+msgstr "قم بتحرير هذه الصفحة"
+
+msgid "By the"
+msgstr "بواسطة"
+
+msgid "next page"
+msgstr "الصفحة التالية"
diff --git a/_preview/13/_static/locales/bg/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/bg/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..7420c19
--- /dev/null
+++ b/_preview/13/_static/locales/bg/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: bg\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Печат в PDF"
+
+msgid "Theme by the"
+msgstr "Тема от"
+
+msgid "Download source file"
+msgstr "Изтеглете изходния файл"
+
+msgid "open issue"
+msgstr "отворен брой"
+
+msgid "Contents"
+msgstr "Съдържание"
+
+msgid "previous page"
+msgstr "предишна страница"
+
+msgid "Download notebook file"
+msgstr "Изтеглете файла на бележника"
+
+msgid "Copyright"
+msgstr "Авторско право"
+
+msgid "Download this page"
+msgstr "Изтеглете тази страница"
+
+msgid "Source repository"
+msgstr "Хранилище на източника"
+
+msgid "By"
+msgstr "От"
+
+msgid "repository"
+msgstr "хранилище"
+
+msgid "Last updated on"
+msgstr "Последна актуализация на"
+
+msgid "Toggle navigation"
+msgstr "Превключване на навигацията"
+
+msgid "Sphinx Book Theme"
+msgstr "Тема на книгата Sphinx"
+
+msgid "suggest edit"
+msgstr "предложи редактиране"
+
+msgid "Open an issue"
+msgstr "Отворете проблем"
+
+msgid "Launch"
+msgstr "Стартиране"
+
+msgid "Fullscreen mode"
+msgstr "Режим на цял екран"
+
+msgid "Edit this page"
+msgstr "Редактирайте тази страница"
+
+msgid "By the"
+msgstr "По"
+
+msgid "next page"
+msgstr "Следваща страница"
diff --git a/_preview/13/_static/locales/bn/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/bn/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..63a07c3
--- /dev/null
+++ b/_preview/13/_static/locales/bn/LC_MESSAGES/booktheme.po
@@ -0,0 +1,63 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: bn\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "পিডিএফ প্রিন্ট করুন"
+
+msgid "Theme by the"
+msgstr "থিম দ্বারা"
+
+msgid "Download source file"
+msgstr "উত্স ফাইল ডাউনলোড করুন"
+
+msgid "open issue"
+msgstr "খোলা সমস্যা"
+
+msgid "previous page"
+msgstr "আগের পৃষ্ঠা"
+
+msgid "Download notebook file"
+msgstr "নোটবুক ফাইল ডাউনলোড করুন"
+
+msgid "Copyright"
+msgstr "কপিরাইট"
+
+msgid "Download this page"
+msgstr "এই পৃষ্ঠাটি ডাউনলোড করুন"
+
+msgid "Source repository"
+msgstr "উত্স সংগ্রহস্থল"
+
+msgid "By"
+msgstr "দ্বারা"
+
+msgid "Last updated on"
+msgstr "সর্বশেষ আপডেট"
+
+msgid "Toggle navigation"
+msgstr "নেভিগেশন টগল করুন"
+
+msgid "Sphinx Book Theme"
+msgstr "স্পিনিক্স বুক থিম"
+
+msgid "Open an issue"
+msgstr "একটি সমস্যা খুলুন"
+
+msgid "Launch"
+msgstr "শুরু করা"
+
+msgid "Edit this page"
+msgstr "এই পৃষ্ঠাটি সম্পাদনা করুন"
+
+msgid "By the"
+msgstr "দ্বারা"
+
+msgid "next page"
+msgstr "পরবর্তী পৃষ্ঠা"
diff --git a/_preview/13/_static/locales/ca/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/ca/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..8fb358b
--- /dev/null
+++ b/_preview/13/_static/locales/ca/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ca\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Imprimeix a PDF"
+
+msgid "Theme by the"
+msgstr "Tema del"
+
+msgid "Download source file"
+msgstr "Baixeu el fitxer font"
+
+msgid "open issue"
+msgstr "número obert"
+
+msgid "previous page"
+msgstr "Pàgina anterior"
+
+msgid "Download notebook file"
+msgstr "Descarregar fitxer de quadern"
+
+msgid "Copyright"
+msgstr "Copyright"
+
+msgid "Download this page"
+msgstr "Descarregueu aquesta pàgina"
+
+msgid "Source repository"
+msgstr "Dipòsit de fonts"
+
+msgid "By"
+msgstr "Per"
+
+msgid "Last updated on"
+msgstr "Darrera actualització el"
+
+msgid "Toggle navigation"
+msgstr "Commuta la navegació"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema del llibre Esfinx"
+
+msgid "suggest edit"
+msgstr "suggerir edició"
+
+msgid "Open an issue"
+msgstr "Obriu un número"
+
+msgid "Launch"
+msgstr "Llançament"
+
+msgid "Edit this page"
+msgstr "Editeu aquesta pàgina"
+
+msgid "By the"
+msgstr "Per la"
+
+msgid "next page"
+msgstr "pàgina següent"
diff --git a/_preview/13/_static/locales/cs/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/cs/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..c6ef469
--- /dev/null
+++ b/_preview/13/_static/locales/cs/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: cs\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Tisk do PDF"
+
+msgid "Theme by the"
+msgstr "Téma od"
+
+msgid "Download source file"
+msgstr "Stáhněte si zdrojový soubor"
+
+msgid "open issue"
+msgstr "otevřené číslo"
+
+msgid "Contents"
+msgstr "Obsah"
+
+msgid "previous page"
+msgstr "předchozí stránka"
+
+msgid "Download notebook file"
+msgstr "Stáhnout soubor poznámkového bloku"
+
+msgid "Copyright"
+msgstr "autorská práva"
+
+msgid "Download this page"
+msgstr "Stáhněte si tuto stránku"
+
+msgid "Source repository"
+msgstr "Zdrojové úložiště"
+
+msgid "By"
+msgstr "Podle"
+
+msgid "repository"
+msgstr "úložiště"
+
+msgid "Last updated on"
+msgstr "Naposledy aktualizováno"
+
+msgid "Toggle navigation"
+msgstr "Přepnout navigaci"
+
+msgid "Sphinx Book Theme"
+msgstr "Téma knihy Sfinga"
+
+msgid "suggest edit"
+msgstr "navrhnout úpravy"
+
+msgid "Open an issue"
+msgstr "Otevřete problém"
+
+msgid "Launch"
+msgstr "Zahájení"
+
+msgid "Fullscreen mode"
+msgstr "Režim celé obrazovky"
+
+msgid "Edit this page"
+msgstr "Upravit tuto stránku"
+
+msgid "By the"
+msgstr "Podle"
+
+msgid "next page"
+msgstr "další strana"
diff --git a/_preview/13/_static/locales/da/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/da/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..306a38e
--- /dev/null
+++ b/_preview/13/_static/locales/da/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: da\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Udskriv til PDF"
+
+msgid "Theme by the"
+msgstr "Tema af"
+
+msgid "Download source file"
+msgstr "Download kildefil"
+
+msgid "open issue"
+msgstr "åbent nummer"
+
+msgid "Contents"
+msgstr "Indhold"
+
+msgid "previous page"
+msgstr "forrige side"
+
+msgid "Download notebook file"
+msgstr "Download notesbog-fil"
+
+msgid "Copyright"
+msgstr "ophavsret"
+
+msgid "Download this page"
+msgstr "Download denne side"
+
+msgid "Source repository"
+msgstr "Kildelager"
+
+msgid "By"
+msgstr "Ved"
+
+msgid "repository"
+msgstr "lager"
+
+msgid "Last updated on"
+msgstr "Sidst opdateret den"
+
+msgid "Toggle navigation"
+msgstr "Skift navigation"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx bogtema"
+
+msgid "suggest edit"
+msgstr "foreslå redigering"
+
+msgid "Open an issue"
+msgstr "Åbn et problem"
+
+msgid "Launch"
+msgstr "Start"
+
+msgid "Fullscreen mode"
+msgstr "Fuldskærmstilstand"
+
+msgid "Edit this page"
+msgstr "Rediger denne side"
+
+msgid "By the"
+msgstr "Ved"
+
+msgid "next page"
+msgstr "Næste side"
diff --git a/_preview/13/_static/locales/de/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/de/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..4925360
--- /dev/null
+++ b/_preview/13/_static/locales/de/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: de\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "In PDF drucken"
+
+msgid "Theme by the"
+msgstr "Thema von der"
+
+msgid "Download source file"
+msgstr "Quelldatei herunterladen"
+
+msgid "open issue"
+msgstr "offenes Thema"
+
+msgid "Contents"
+msgstr "Inhalt"
+
+msgid "previous page"
+msgstr "vorherige Seite"
+
+msgid "Download notebook file"
+msgstr "Notebook-Datei herunterladen"
+
+msgid "Copyright"
+msgstr "Urheberrechte ©"
+
+msgid "Download this page"
+msgstr "Laden Sie diese Seite herunter"
+
+msgid "Source repository"
+msgstr "Quell-Repository"
+
+msgid "By"
+msgstr "Durch"
+
+msgid "repository"
+msgstr "Repository"
+
+msgid "Last updated on"
+msgstr "Zuletzt aktualisiert am"
+
+msgid "Toggle navigation"
+msgstr "Navigation umschalten"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx-Buch-Thema"
+
+msgid "suggest edit"
+msgstr "vorschlagen zu bearbeiten"
+
+msgid "Open an issue"
+msgstr "Öffnen Sie ein Problem"
+
+msgid "Launch"
+msgstr "Starten"
+
+msgid "Fullscreen mode"
+msgstr "Vollbildmodus"
+
+msgid "Edit this page"
+msgstr "Bearbeite diese Seite"
+
+msgid "By the"
+msgstr "Bis zum"
+
+msgid "next page"
+msgstr "Nächste Seite"
diff --git a/_preview/13/_static/locales/el/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/el/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..3e01acb
--- /dev/null
+++ b/_preview/13/_static/locales/el/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: el\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Εκτύπωση σε PDF"
+
+msgid "Theme by the"
+msgstr "Θέμα από το"
+
+msgid "Download source file"
+msgstr "Λήψη αρχείου προέλευσης"
+
+msgid "open issue"
+msgstr "ανοιχτό ζήτημα"
+
+msgid "Contents"
+msgstr "Περιεχόμενα"
+
+msgid "previous page"
+msgstr "προηγούμενη σελίδα"
+
+msgid "Download notebook file"
+msgstr "Λήψη αρχείου σημειωματάριου"
+
+msgid "Copyright"
+msgstr "Πνευματική ιδιοκτησία"
+
+msgid "Download this page"
+msgstr "Λήψη αυτής της σελίδας"
+
+msgid "Source repository"
+msgstr "Αποθήκη πηγής"
+
+msgid "By"
+msgstr "Με"
+
+msgid "repository"
+msgstr "αποθήκη"
+
+msgid "Last updated on"
+msgstr "Τελευταία ενημέρωση στις"
+
+msgid "Toggle navigation"
+msgstr "Εναλλαγή πλοήγησης"
+
+msgid "Sphinx Book Theme"
+msgstr "Θέμα βιβλίου Sphinx"
+
+msgid "suggest edit"
+msgstr "προτείνω επεξεργασία"
+
+msgid "Open an issue"
+msgstr "Ανοίξτε ένα ζήτημα"
+
+msgid "Launch"
+msgstr "Εκτόξευση"
+
+msgid "Fullscreen mode"
+msgstr "ΛΕΙΤΟΥΡΓΙΑ ΠΛΗΡΟΥΣ ΟΘΟΝΗΣ"
+
+msgid "Edit this page"
+msgstr "Επεξεργαστείτε αυτήν τη σελίδα"
+
+msgid "By the"
+msgstr "Από το"
+
+msgid "next page"
+msgstr "επόμενη σελίδα"
diff --git a/_preview/13/_static/locales/eo/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/eo/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..f7ed226
--- /dev/null
+++ b/_preview/13/_static/locales/eo/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: eo\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Presi al PDF"
+
+msgid "Theme by the"
+msgstr "Temo de la"
+
+msgid "Download source file"
+msgstr "Elŝutu fontodosieron"
+
+msgid "open issue"
+msgstr "malferma numero"
+
+msgid "Contents"
+msgstr "Enhavo"
+
+msgid "previous page"
+msgstr "antaŭa paĝo"
+
+msgid "Download notebook file"
+msgstr "Elŝutu kajeran dosieron"
+
+msgid "Copyright"
+msgstr "Kopirajto"
+
+msgid "Download this page"
+msgstr "Elŝutu ĉi tiun paĝon"
+
+msgid "Source repository"
+msgstr "Fonto-deponejo"
+
+msgid "By"
+msgstr "De"
+
+msgid "repository"
+msgstr "deponejo"
+
+msgid "Last updated on"
+msgstr "Laste ĝisdatigita la"
+
+msgid "Toggle navigation"
+msgstr "Ŝalti navigadon"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfinksa Libro-Temo"
+
+msgid "suggest edit"
+msgstr "sugesti redaktadon"
+
+msgid "Open an issue"
+msgstr "Malfermu numeron"
+
+msgid "Launch"
+msgstr "Lanĉo"
+
+msgid "Fullscreen mode"
+msgstr "Plenekrana reĝimo"
+
+msgid "Edit this page"
+msgstr "Redaktu ĉi tiun paĝon"
+
+msgid "By the"
+msgstr "Per la"
+
+msgid "next page"
+msgstr "sekva paĝo"
diff --git a/_preview/13/_static/locales/es/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/es/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..5e0029e
--- /dev/null
+++ b/_preview/13/_static/locales/es/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: es\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Imprimir en PDF"
+
+msgid "Theme by the"
+msgstr "Tema por el"
+
+msgid "Download source file"
+msgstr "Descargar archivo fuente"
+
+msgid "open issue"
+msgstr "Tema abierto"
+
+msgid "Contents"
+msgstr "Contenido"
+
+msgid "previous page"
+msgstr "pagina anterior"
+
+msgid "Download notebook file"
+msgstr "Descargar archivo de cuaderno"
+
+msgid "Copyright"
+msgstr "Derechos de autor"
+
+msgid "Download this page"
+msgstr "Descarga esta pagina"
+
+msgid "Source repository"
+msgstr "Repositorio de origen"
+
+msgid "By"
+msgstr "Por"
+
+msgid "repository"
+msgstr "repositorio"
+
+msgid "Last updated on"
+msgstr "Ultima actualización en"
+
+msgid "Toggle navigation"
+msgstr "Navegación de palanca"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema del libro de la esfinge"
+
+msgid "suggest edit"
+msgstr "sugerir editar"
+
+msgid "Open an issue"
+msgstr "Abrir un problema"
+
+msgid "Launch"
+msgstr "Lanzamiento"
+
+msgid "Fullscreen mode"
+msgstr "Modo de pantalla completa"
+
+msgid "Edit this page"
+msgstr "Edita esta página"
+
+msgid "By the"
+msgstr "Por el"
+
+msgid "next page"
+msgstr "siguiente página"
diff --git a/_preview/13/_static/locales/et/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/et/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..8680982
--- /dev/null
+++ b/_preview/13/_static/locales/et/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: et\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Prindi PDF-i"
+
+msgid "Theme by the"
+msgstr "Teema"
+
+msgid "Download source file"
+msgstr "Laadige alla lähtefail"
+
+msgid "open issue"
+msgstr "avatud küsimus"
+
+msgid "Contents"
+msgstr "Sisu"
+
+msgid "previous page"
+msgstr "eelmine leht"
+
+msgid "Download notebook file"
+msgstr "Laadige sülearvuti fail alla"
+
+msgid "Copyright"
+msgstr "Autoriõigus"
+
+msgid "Download this page"
+msgstr "Laadige see leht alla"
+
+msgid "Source repository"
+msgstr "Allikahoidla"
+
+msgid "By"
+msgstr "Kõrval"
+
+msgid "repository"
+msgstr "hoidla"
+
+msgid "Last updated on"
+msgstr "Viimati uuendatud"
+
+msgid "Toggle navigation"
+msgstr "Lülita navigeerimine sisse"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfinksiraamatu teema"
+
+msgid "suggest edit"
+msgstr "soovita muuta"
+
+msgid "Open an issue"
+msgstr "Avage probleem"
+
+msgid "Launch"
+msgstr "Käivitage"
+
+msgid "Fullscreen mode"
+msgstr "Täisekraanirežiim"
+
+msgid "Edit this page"
+msgstr "Muutke seda lehte"
+
+msgid "By the"
+msgstr "Autor"
+
+msgid "next page"
+msgstr "järgmine leht"
diff --git a/_preview/13/_static/locales/fi/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/fi/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..34dac21
--- /dev/null
+++ b/_preview/13/_static/locales/fi/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: fi\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Tulosta PDF-tiedostoon"
+
+msgid "Theme by the"
+msgstr "Teeman tekijä"
+
+msgid "Download source file"
+msgstr "Lataa lähdetiedosto"
+
+msgid "open issue"
+msgstr "avoin ongelma"
+
+msgid "Contents"
+msgstr "Sisällys"
+
+msgid "previous page"
+msgstr "Edellinen sivu"
+
+msgid "Download notebook file"
+msgstr "Lataa muistikirjatiedosto"
+
+msgid "Copyright"
+msgstr "Tekijänoikeus"
+
+msgid "Download this page"
+msgstr "Lataa tämä sivu"
+
+msgid "Source repository"
+msgstr "Lähteen arkisto"
+
+msgid "By"
+msgstr "Tekijä"
+
+msgid "repository"
+msgstr "arkisto"
+
+msgid "Last updated on"
+msgstr "Viimeksi päivitetty"
+
+msgid "Toggle navigation"
+msgstr "Vaihda navigointia"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx-kirjan teema"
+
+msgid "suggest edit"
+msgstr "ehdottaa muokkausta"
+
+msgid "Open an issue"
+msgstr "Avaa ongelma"
+
+msgid "Launch"
+msgstr "Tuoda markkinoille"
+
+msgid "Fullscreen mode"
+msgstr "Koko näytön tila"
+
+msgid "Edit this page"
+msgstr "Muokkaa tätä sivua"
+
+msgid "By the"
+msgstr "Mukaan"
+
+msgid "next page"
+msgstr "seuraava sivu"
diff --git a/_preview/13/_static/locales/fr/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/fr/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..8991a1b
--- /dev/null
+++ b/_preview/13/_static/locales/fr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: fr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Imprimer au format PDF"
+
+msgid "Theme by the"
+msgstr "Thème par le"
+
+msgid "Download source file"
+msgstr "Télécharger le fichier source"
+
+msgid "open issue"
+msgstr "signaler un problème"
+
+msgid "Contents"
+msgstr "Contenu"
+
+msgid "previous page"
+msgstr "page précédente"
+
+msgid "Download notebook file"
+msgstr "Télécharger le fichier notebook"
+
+msgid "Copyright"
+msgstr "droits d'auteur"
+
+msgid "Download this page"
+msgstr "Téléchargez cette page"
+
+msgid "Source repository"
+msgstr "Dépôt source"
+
+msgid "By"
+msgstr "Par"
+
+msgid "repository"
+msgstr "dépôt"
+
+msgid "Last updated on"
+msgstr "Dernière mise à jour le"
+
+msgid "Toggle navigation"
+msgstr "Basculer la navigation"
+
+msgid "Sphinx Book Theme"
+msgstr "Thème du livre Sphinx"
+
+msgid "suggest edit"
+msgstr "suggestion de modification"
+
+msgid "Open an issue"
+msgstr "Ouvrez un problème"
+
+msgid "Launch"
+msgstr "lancement"
+
+msgid "Fullscreen mode"
+msgstr "Mode plein écran"
+
+msgid "Edit this page"
+msgstr "Modifier cette page"
+
+msgid "By the"
+msgstr "Par le"
+
+msgid "next page"
+msgstr "page suivante"
diff --git a/_preview/13/_static/locales/hr/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/hr/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..42c4233
--- /dev/null
+++ b/_preview/13/_static/locales/hr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: hr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Ispis u PDF"
+
+msgid "Theme by the"
+msgstr "Tema autora"
+
+msgid "Download source file"
+msgstr "Preuzmi izvornu datoteku"
+
+msgid "open issue"
+msgstr "otvoreno izdanje"
+
+msgid "Contents"
+msgstr "Sadržaj"
+
+msgid "previous page"
+msgstr "Prethodna stranica"
+
+msgid "Download notebook file"
+msgstr "Preuzmi datoteku bilježnice"
+
+msgid "Copyright"
+msgstr "Autorska prava"
+
+msgid "Download this page"
+msgstr "Preuzmite ovu stranicu"
+
+msgid "Source repository"
+msgstr "Izvorno spremište"
+
+msgid "By"
+msgstr "Po"
+
+msgid "repository"
+msgstr "spremište"
+
+msgid "Last updated on"
+msgstr "Posljednje ažuriranje:"
+
+msgid "Toggle navigation"
+msgstr "Uključi / isključi navigaciju"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema knjige Sphinx"
+
+msgid "suggest edit"
+msgstr "predloži uređivanje"
+
+msgid "Open an issue"
+msgstr "Otvorite izdanje"
+
+msgid "Launch"
+msgstr "Pokrenite"
+
+msgid "Fullscreen mode"
+msgstr "Način preko cijelog zaslona"
+
+msgid "Edit this page"
+msgstr "Uredite ovu stranicu"
+
+msgid "By the"
+msgstr "Od strane"
+
+msgid "next page"
+msgstr "sljedeća stranica"
diff --git a/_preview/13/_static/locales/id/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/id/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..b8d8d89
--- /dev/null
+++ b/_preview/13/_static/locales/id/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: id\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Cetak ke PDF"
+
+msgid "Theme by the"
+msgstr "Tema oleh"
+
+msgid "Download source file"
+msgstr "Unduh file sumber"
+
+msgid "open issue"
+msgstr "masalah terbuka"
+
+msgid "Contents"
+msgstr "Isi"
+
+msgid "previous page"
+msgstr "halaman sebelumnya"
+
+msgid "Download notebook file"
+msgstr "Unduh file notebook"
+
+msgid "Copyright"
+msgstr "hak cipta"
+
+msgid "Download this page"
+msgstr "Unduh halaman ini"
+
+msgid "Source repository"
+msgstr "Repositori sumber"
+
+msgid "By"
+msgstr "Oleh"
+
+msgid "repository"
+msgstr "gudang"
+
+msgid "Last updated on"
+msgstr "Terakhir diperbarui saat"
+
+msgid "Toggle navigation"
+msgstr "Alihkan navigasi"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema Buku Sphinx"
+
+msgid "suggest edit"
+msgstr "menyarankan edit"
+
+msgid "Open an issue"
+msgstr "Buka masalah"
+
+msgid "Launch"
+msgstr "Meluncurkan"
+
+msgid "Fullscreen mode"
+msgstr "Mode layar penuh"
+
+msgid "Edit this page"
+msgstr "Edit halaman ini"
+
+msgid "By the"
+msgstr "Oleh"
+
+msgid "next page"
+msgstr "halaman selanjutnya"
diff --git a/_preview/13/_static/locales/it/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/it/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..36fca59
--- /dev/null
+++ b/_preview/13/_static/locales/it/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: it\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Stampa in PDF"
+
+msgid "Theme by the"
+msgstr "Tema di"
+
+msgid "Download source file"
+msgstr "Scarica il file sorgente"
+
+msgid "open issue"
+msgstr "questione aperta"
+
+msgid "Contents"
+msgstr "Contenuti"
+
+msgid "previous page"
+msgstr "pagina precedente"
+
+msgid "Download notebook file"
+msgstr "Scarica il file del taccuino"
+
+msgid "Copyright"
+msgstr "Diritto d'autore"
+
+msgid "Download this page"
+msgstr "Scarica questa pagina"
+
+msgid "Source repository"
+msgstr "Repository di origine"
+
+msgid "By"
+msgstr "Di"
+
+msgid "repository"
+msgstr "repository"
+
+msgid "Last updated on"
+msgstr "Ultimo aggiornamento il"
+
+msgid "Toggle navigation"
+msgstr "Attiva / disattiva la navigazione"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema del libro della Sfinge"
+
+msgid "suggest edit"
+msgstr "suggerisci modifica"
+
+msgid "Open an issue"
+msgstr "Apri un problema"
+
+msgid "Launch"
+msgstr "Lanciare"
+
+msgid "Fullscreen mode"
+msgstr "Modalità schermo intero"
+
+msgid "Edit this page"
+msgstr "Modifica questa pagina"
+
+msgid "By the"
+msgstr "Dal"
+
+msgid "next page"
+msgstr "pagina successiva"
diff --git a/_preview/13/_static/locales/iw/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/iw/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..dede9cb
--- /dev/null
+++ b/_preview/13/_static/locales/iw/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: iw\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "הדפס לקובץ PDF"
+
+msgid "Theme by the"
+msgstr "נושא מאת"
+
+msgid "Download source file"
+msgstr "הורד את קובץ המקור"
+
+msgid "open issue"
+msgstr "בעיה פתוחה"
+
+msgid "Contents"
+msgstr "תוכן"
+
+msgid "previous page"
+msgstr "עמוד קודם"
+
+msgid "Download notebook file"
+msgstr "הורד קובץ מחברת"
+
+msgid "Copyright"
+msgstr "זכויות יוצרים"
+
+msgid "Download this page"
+msgstr "הורד דף זה"
+
+msgid "Source repository"
+msgstr "מאגר המקורות"
+
+msgid "By"
+msgstr "על ידי"
+
+msgid "repository"
+msgstr "מאגר"
+
+msgid "Last updated on"
+msgstr "עודכן לאחרונה ב"
+
+msgid "Toggle navigation"
+msgstr "החלף ניווט"
+
+msgid "Sphinx Book Theme"
+msgstr "נושא ספר ספינקס"
+
+msgid "suggest edit"
+msgstr "מציע לערוך"
+
+msgid "Open an issue"
+msgstr "פתח גיליון"
+
+msgid "Launch"
+msgstr "לְהַשִׁיק"
+
+msgid "Fullscreen mode"
+msgstr "מצב מסך מלא"
+
+msgid "Edit this page"
+msgstr "ערוך דף זה"
+
+msgid "By the"
+msgstr "דרך"
+
+msgid "next page"
+msgstr "עמוד הבא"
diff --git a/_preview/13/_static/locales/ja/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/ja/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..2615f0d
--- /dev/null
+++ b/_preview/13/_static/locales/ja/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ja\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "PDFに印刷"
+
+msgid "Theme by the"
+msgstr "のテーマ"
+
+msgid "Download source file"
+msgstr "ソースファイルをダウンロード"
+
+msgid "open issue"
+msgstr "未解決の問題"
+
+msgid "Contents"
+msgstr "目次"
+
+msgid "previous page"
+msgstr "前のページ"
+
+msgid "Download notebook file"
+msgstr "ノートブックファイルをダウンロード"
+
+msgid "Copyright"
+msgstr "Copyright"
+
+msgid "Download this page"
+msgstr "このページをダウンロード"
+
+msgid "Source repository"
+msgstr "ソースリポジトリ"
+
+msgid "By"
+msgstr "著者"
+
+msgid "repository"
+msgstr "リポジトリ"
+
+msgid "Last updated on"
+msgstr "最終更新日"
+
+msgid "Toggle navigation"
+msgstr "ナビゲーションを切り替え"
+
+msgid "Sphinx Book Theme"
+msgstr "スフィンクスの本のテーマ"
+
+msgid "suggest edit"
+msgstr "編集を提案する"
+
+msgid "Open an issue"
+msgstr "問題を報告"
+
+msgid "Launch"
+msgstr "起動"
+
+msgid "Fullscreen mode"
+msgstr "全画面モード"
+
+msgid "Edit this page"
+msgstr "このページを編集"
+
+msgid "By the"
+msgstr "によって"
+
+msgid "next page"
+msgstr "次のページ"
diff --git a/_preview/13/_static/locales/ko/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/ko/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..c9e13a4
--- /dev/null
+++ b/_preview/13/_static/locales/ko/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ko\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "PDF로 인쇄"
+
+msgid "Theme by the"
+msgstr "테마별"
+
+msgid "Download source file"
+msgstr "소스 파일 다운로드"
+
+msgid "open issue"
+msgstr "열린 문제"
+
+msgid "Contents"
+msgstr "내용"
+
+msgid "previous page"
+msgstr "이전 페이지"
+
+msgid "Download notebook file"
+msgstr "노트북 파일 다운로드"
+
+msgid "Copyright"
+msgstr "저작권"
+
+msgid "Download this page"
+msgstr "이 페이지 다운로드"
+
+msgid "Source repository"
+msgstr "소스 저장소"
+
+msgid "By"
+msgstr "으로"
+
+msgid "repository"
+msgstr "저장소"
+
+msgid "Last updated on"
+msgstr "마지막 업데이트"
+
+msgid "Toggle navigation"
+msgstr "탐색 전환"
+
+msgid "Sphinx Book Theme"
+msgstr "스핑크스 도서 테마"
+
+msgid "suggest edit"
+msgstr "편집 제안"
+
+msgid "Open an issue"
+msgstr "이슈 열기"
+
+msgid "Launch"
+msgstr "시작하다"
+
+msgid "Fullscreen mode"
+msgstr "전체 화면으로보기"
+
+msgid "Edit this page"
+msgstr "이 페이지 편집"
+
+msgid "By the"
+msgstr "에 의해"
+
+msgid "next page"
+msgstr "다음 페이지"
diff --git a/_preview/13/_static/locales/lt/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/lt/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..35eabd9
--- /dev/null
+++ b/_preview/13/_static/locales/lt/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: lt\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Spausdinti į PDF"
+
+msgid "Theme by the"
+msgstr "Tema"
+
+msgid "Download source file"
+msgstr "Atsisiųsti šaltinio failą"
+
+msgid "open issue"
+msgstr "atviras klausimas"
+
+msgid "Contents"
+msgstr "Turinys"
+
+msgid "previous page"
+msgstr "Ankstesnis puslapis"
+
+msgid "Download notebook file"
+msgstr "Atsisiųsti nešiojamojo kompiuterio failą"
+
+msgid "Copyright"
+msgstr "Autorių teisės"
+
+msgid "Download this page"
+msgstr "Atsisiųskite šį puslapį"
+
+msgid "Source repository"
+msgstr "Šaltinio saugykla"
+
+msgid "By"
+msgstr "Iki"
+
+msgid "repository"
+msgstr "saugykla"
+
+msgid "Last updated on"
+msgstr "Paskutinį kartą atnaujinta"
+
+msgid "Toggle navigation"
+msgstr "Perjungti naršymą"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfinkso knygos tema"
+
+msgid "suggest edit"
+msgstr "pasiūlyti redaguoti"
+
+msgid "Open an issue"
+msgstr "Atidarykite problemą"
+
+msgid "Launch"
+msgstr "Paleiskite"
+
+msgid "Fullscreen mode"
+msgstr "Pilno ekrano režimas"
+
+msgid "Edit this page"
+msgstr "Redaguoti šį puslapį"
+
+msgid "By the"
+msgstr "Prie"
+
+msgid "next page"
+msgstr "Kitas puslapis"
diff --git a/_preview/13/_static/locales/lv/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/lv/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..ee1bd08
--- /dev/null
+++ b/_preview/13/_static/locales/lv/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: lv\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Drukāt PDF formātā"
+
+msgid "Theme by the"
+msgstr "Autora tēma"
+
+msgid "Download source file"
+msgstr "Lejupielādēt avota failu"
+
+msgid "open issue"
+msgstr "atklāts jautājums"
+
+msgid "Contents"
+msgstr "Saturs"
+
+msgid "previous page"
+msgstr "iepriekšējā lapa"
+
+msgid "Download notebook file"
+msgstr "Lejupielādēt piezīmju grāmatiņu"
+
+msgid "Copyright"
+msgstr "Autortiesības"
+
+msgid "Download this page"
+msgstr "Lejupielādējiet šo lapu"
+
+msgid "Source repository"
+msgstr "Avota krātuve"
+
+msgid "By"
+msgstr "Autors"
+
+msgid "repository"
+msgstr "krātuve"
+
+msgid "Last updated on"
+msgstr "Pēdējoreiz atjaunināts"
+
+msgid "Toggle navigation"
+msgstr "Pārslēgt navigāciju"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfinksa grāmatas tēma"
+
+msgid "suggest edit"
+msgstr "ieteikt rediģēt"
+
+msgid "Open an issue"
+msgstr "Atveriet problēmu"
+
+msgid "Launch"
+msgstr "Uzsākt"
+
+msgid "Fullscreen mode"
+msgstr "Pilnekrāna režīms"
+
+msgid "Edit this page"
+msgstr "Rediģēt šo lapu"
+
+msgid "By the"
+msgstr "Ar"
+
+msgid "next page"
+msgstr "nākamā lapaspuse"
diff --git a/_preview/13/_static/locales/ml/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/ml/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..d471277
--- /dev/null
+++ b/_preview/13/_static/locales/ml/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ml\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "PDF- ലേക്ക് പ്രിന്റുചെയ്യുക"
+
+msgid "Theme by the"
+msgstr "പ്രമേയം"
+
+msgid "Download source file"
+msgstr "ഉറവിട ഫയൽ ഡൗൺലോഡുചെയ്യുക"
+
+msgid "open issue"
+msgstr "തുറന്ന പ്രശ്നം"
+
+msgid "previous page"
+msgstr "മുൻപത്തെ താൾ"
+
+msgid "Download notebook file"
+msgstr "നോട്ട്ബുക്ക് ഫയൽ ഡൺലോഡ് ചെയ്യുക"
+
+msgid "Copyright"
+msgstr "പകർപ്പവകാശം"
+
+msgid "Download this page"
+msgstr "ഈ പേജ് ഡൗൺലോഡുചെയ്യുക"
+
+msgid "Source repository"
+msgstr "ഉറവിട ശേഖരം"
+
+msgid "By"
+msgstr "എഴുതിയത്"
+
+msgid "Last updated on"
+msgstr "അവസാനം അപ്‌ഡേറ്റുചെയ്‌തത്"
+
+msgid "Toggle navigation"
+msgstr "നാവിഗേഷൻ ടോഗിൾ ചെയ്യുക"
+
+msgid "Sphinx Book Theme"
+msgstr "സ്ഫിങ്ക്സ് പുസ്തക തീം"
+
+msgid "suggest edit"
+msgstr "എഡിറ്റുചെയ്യാൻ നിർദ്ദേശിക്കുക"
+
+msgid "Open an issue"
+msgstr "ഒരു പ്രശ്നം തുറക്കുക"
+
+msgid "Launch"
+msgstr "സമാരംഭിക്കുക"
+
+msgid "Edit this page"
+msgstr "ഈ പേജ് എഡിറ്റുചെയ്യുക"
+
+msgid "By the"
+msgstr "എഴുതിയത്"
+
+msgid "next page"
+msgstr "അടുത്ത പേജ്"
diff --git a/_preview/13/_static/locales/mr/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/mr/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..f3694ac
--- /dev/null
+++ b/_preview/13/_static/locales/mr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: mr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "पीडीएफवर मुद्रित करा"
+
+msgid "Theme by the"
+msgstr "द्वारा थीम"
+
+msgid "Download source file"
+msgstr "स्त्रोत फाइल डाउनलोड करा"
+
+msgid "open issue"
+msgstr "खुला मुद्दा"
+
+msgid "previous page"
+msgstr "मागील पान"
+
+msgid "Download notebook file"
+msgstr "नोटबुक फाईल डाउनलोड करा"
+
+msgid "Copyright"
+msgstr "कॉपीराइट"
+
+msgid "Download this page"
+msgstr "हे पृष्ठ डाउनलोड करा"
+
+msgid "Source repository"
+msgstr "स्त्रोत भांडार"
+
+msgid "By"
+msgstr "द्वारा"
+
+msgid "Last updated on"
+msgstr "अखेरचे अद्यतनित"
+
+msgid "Toggle navigation"
+msgstr "नेव्हिगेशन टॉगल करा"
+
+msgid "Sphinx Book Theme"
+msgstr "स्फिंक्स बुक थीम"
+
+msgid "suggest edit"
+msgstr "संपादन सुचवा"
+
+msgid "Open an issue"
+msgstr "एक मुद्दा उघडा"
+
+msgid "Launch"
+msgstr "लाँच करा"
+
+msgid "Edit this page"
+msgstr "हे पृष्ठ संपादित करा"
+
+msgid "By the"
+msgstr "द्वारा"
+
+msgid "next page"
+msgstr "पुढील पृष्ठ"
diff --git a/_preview/13/_static/locales/ms/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/ms/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..65b7c60
--- /dev/null
+++ b/_preview/13/_static/locales/ms/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ms\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Cetak ke PDF"
+
+msgid "Theme by the"
+msgstr "Tema oleh"
+
+msgid "Download source file"
+msgstr "Muat turun fail sumber"
+
+msgid "open issue"
+msgstr "isu terbuka"
+
+msgid "previous page"
+msgstr "halaman sebelumnya"
+
+msgid "Download notebook file"
+msgstr "Muat turun fail buku nota"
+
+msgid "Copyright"
+msgstr "hak cipta"
+
+msgid "Download this page"
+msgstr "Muat turun halaman ini"
+
+msgid "Source repository"
+msgstr "Repositori sumber"
+
+msgid "By"
+msgstr "Oleh"
+
+msgid "Last updated on"
+msgstr "Terakhir dikemas kini pada"
+
+msgid "Toggle navigation"
+msgstr "Togol navigasi"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema Buku Sphinx"
+
+msgid "suggest edit"
+msgstr "cadangkan edit"
+
+msgid "Open an issue"
+msgstr "Buka masalah"
+
+msgid "Launch"
+msgstr "Lancarkan"
+
+msgid "Edit this page"
+msgstr "Edit halaman ini"
+
+msgid "By the"
+msgstr "Oleh"
+
+msgid "next page"
+msgstr "muka surat seterusnya"
diff --git a/_preview/13/_static/locales/nl/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/nl/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..71bd1cd
--- /dev/null
+++ b/_preview/13/_static/locales/nl/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: nl\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Afdrukken naar pdf"
+
+msgid "Theme by the"
+msgstr "Thema door de"
+
+msgid "Download source file"
+msgstr "Download het bronbestand"
+
+msgid "open issue"
+msgstr "open probleem"
+
+msgid "Contents"
+msgstr "Inhoud"
+
+msgid "previous page"
+msgstr "vorige pagina"
+
+msgid "Download notebook file"
+msgstr "Download notebookbestand"
+
+msgid "Copyright"
+msgstr "auteursrechten"
+
+msgid "Download this page"
+msgstr "Download deze pagina"
+
+msgid "Source repository"
+msgstr "Bronopslagplaats"
+
+msgid "By"
+msgstr "Door"
+
+msgid "repository"
+msgstr "repository"
+
+msgid "Last updated on"
+msgstr "Laatst geupdate op"
+
+msgid "Toggle navigation"
+msgstr "Schakel navigatie"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx-boekthema"
+
+msgid "suggest edit"
+msgstr "suggereren bewerken"
+
+msgid "Open an issue"
+msgstr "Open een probleem"
+
+msgid "Launch"
+msgstr "Lancering"
+
+msgid "Fullscreen mode"
+msgstr "Volledig scherm"
+
+msgid "Edit this page"
+msgstr "bewerk deze pagina"
+
+msgid "By the"
+msgstr "Door de"
+
+msgid "next page"
+msgstr "volgende bladzijde"
diff --git a/_preview/13/_static/locales/no/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/no/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..b21346a
--- /dev/null
+++ b/_preview/13/_static/locales/no/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: no\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Skriv ut til PDF"
+
+msgid "Theme by the"
+msgstr "Tema av"
+
+msgid "Download source file"
+msgstr "Last ned kildefilen"
+
+msgid "open issue"
+msgstr "åpent nummer"
+
+msgid "Contents"
+msgstr "Innhold"
+
+msgid "previous page"
+msgstr "forrige side"
+
+msgid "Download notebook file"
+msgstr "Last ned notatbokfilen"
+
+msgid "Copyright"
+msgstr "opphavsrett"
+
+msgid "Download this page"
+msgstr "Last ned denne siden"
+
+msgid "Source repository"
+msgstr "Kildedepot"
+
+msgid "By"
+msgstr "Av"
+
+msgid "repository"
+msgstr "oppbevaringssted"
+
+msgid "Last updated on"
+msgstr "Sist oppdatert den"
+
+msgid "Toggle navigation"
+msgstr "Bytt navigasjon"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx boktema"
+
+msgid "suggest edit"
+msgstr "foreslå redigering"
+
+msgid "Open an issue"
+msgstr "Åpne et problem"
+
+msgid "Launch"
+msgstr "Start"
+
+msgid "Fullscreen mode"
+msgstr "Fullskjerm-modus"
+
+msgid "Edit this page"
+msgstr "Rediger denne siden"
+
+msgid "By the"
+msgstr "Ved"
+
+msgid "next page"
+msgstr "neste side"
diff --git a/_preview/13/_static/locales/pl/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/pl/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..1b7233f
--- /dev/null
+++ b/_preview/13/_static/locales/pl/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: pl\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Drukuj do PDF"
+
+msgid "Theme by the"
+msgstr "Motyw autorstwa"
+
+msgid "Download source file"
+msgstr "Pobierz plik źródłowy"
+
+msgid "open issue"
+msgstr "otwarty problem"
+
+msgid "Contents"
+msgstr "Zawartość"
+
+msgid "previous page"
+msgstr "Poprzednia strona"
+
+msgid "Download notebook file"
+msgstr "Pobierz plik notatnika"
+
+msgid "Copyright"
+msgstr "prawa autorskie"
+
+msgid "Download this page"
+msgstr "Pobierz tę stronę"
+
+msgid "Source repository"
+msgstr "Repozytorium źródłowe"
+
+msgid "By"
+msgstr "Przez"
+
+msgid "repository"
+msgstr "magazyn"
+
+msgid "Last updated on"
+msgstr "Ostatnia aktualizacja"
+
+msgid "Toggle navigation"
+msgstr "Przełącz nawigację"
+
+msgid "Sphinx Book Theme"
+msgstr "Motyw książki Sphinx"
+
+msgid "suggest edit"
+msgstr "zaproponuj edycję"
+
+msgid "Open an issue"
+msgstr "Otwórz problem"
+
+msgid "Launch"
+msgstr "Uruchomić"
+
+msgid "Fullscreen mode"
+msgstr "Pełny ekran"
+
+msgid "Edit this page"
+msgstr "Edytuj tę strone"
+
+msgid "By the"
+msgstr "Przez"
+
+msgid "next page"
+msgstr "Następna strona"
diff --git a/_preview/13/_static/locales/pt/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/pt/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..1b27314
--- /dev/null
+++ b/_preview/13/_static/locales/pt/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: pt\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Imprimir em PDF"
+
+msgid "Theme by the"
+msgstr "Tema por"
+
+msgid "Download source file"
+msgstr "Baixar arquivo fonte"
+
+msgid "open issue"
+msgstr "questão aberta"
+
+msgid "Contents"
+msgstr "Conteúdo"
+
+msgid "previous page"
+msgstr "página anterior"
+
+msgid "Download notebook file"
+msgstr "Baixar arquivo de notebook"
+
+msgid "Copyright"
+msgstr "direito autoral"
+
+msgid "Download this page"
+msgstr "Baixe esta página"
+
+msgid "Source repository"
+msgstr "Repositório fonte"
+
+msgid "By"
+msgstr "De"
+
+msgid "repository"
+msgstr "repositório"
+
+msgid "Last updated on"
+msgstr "Última atualização em"
+
+msgid "Toggle navigation"
+msgstr "Alternar de navegação"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema do livro Sphinx"
+
+msgid "suggest edit"
+msgstr "sugerir edição"
+
+msgid "Open an issue"
+msgstr "Abra um problema"
+
+msgid "Launch"
+msgstr "Lançamento"
+
+msgid "Fullscreen mode"
+msgstr "Modo tela cheia"
+
+msgid "Edit this page"
+msgstr "Edite essa página"
+
+msgid "By the"
+msgstr "Pelo"
+
+msgid "next page"
+msgstr "próxima página"
diff --git a/_preview/13/_static/locales/ro/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/ro/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..1783ad2
--- /dev/null
+++ b/_preview/13/_static/locales/ro/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ro\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Imprimați în PDF"
+
+msgid "Theme by the"
+msgstr "Tema de"
+
+msgid "Download source file"
+msgstr "Descărcați fișierul sursă"
+
+msgid "open issue"
+msgstr "problema deschisă"
+
+msgid "Contents"
+msgstr "Cuprins"
+
+msgid "previous page"
+msgstr "pagina anterioară"
+
+msgid "Download notebook file"
+msgstr "Descărcați fișierul notebook"
+
+msgid "Copyright"
+msgstr "Drepturi de autor"
+
+msgid "Download this page"
+msgstr "Descarcă această pagină"
+
+msgid "Source repository"
+msgstr "Depozit sursă"
+
+msgid "By"
+msgstr "De"
+
+msgid "repository"
+msgstr "repertoriu"
+
+msgid "Last updated on"
+msgstr "Ultima actualizare la"
+
+msgid "Toggle navigation"
+msgstr "Comutare navigare"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema Sphinx Book"
+
+msgid "suggest edit"
+msgstr "sugerează editare"
+
+msgid "Open an issue"
+msgstr "Deschideți o problemă"
+
+msgid "Launch"
+msgstr "Lansa"
+
+msgid "Fullscreen mode"
+msgstr "Modul ecran întreg"
+
+msgid "Edit this page"
+msgstr "Editați această pagină"
+
+msgid "By the"
+msgstr "Langa"
+
+msgid "next page"
+msgstr "pagina următoare"
diff --git a/_preview/13/_static/locales/ru/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/ru/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..b1176b7
--- /dev/null
+++ b/_preview/13/_static/locales/ru/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ru\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Распечатать в PDF"
+
+msgid "Theme by the"
+msgstr "Тема от"
+
+msgid "Download source file"
+msgstr "Скачать исходный файл"
+
+msgid "open issue"
+msgstr "открытый вопрос"
+
+msgid "Contents"
+msgstr "Содержание"
+
+msgid "previous page"
+msgstr "Предыдущая страница"
+
+msgid "Download notebook file"
+msgstr "Скачать файл записной книжки"
+
+msgid "Copyright"
+msgstr "авторское право"
+
+msgid "Download this page"
+msgstr "Загрузите эту страницу"
+
+msgid "Source repository"
+msgstr "Исходный репозиторий"
+
+msgid "By"
+msgstr "По"
+
+msgid "repository"
+msgstr "хранилище"
+
+msgid "Last updated on"
+msgstr "Последнее обновление"
+
+msgid "Toggle navigation"
+msgstr "Переключить навигацию"
+
+msgid "Sphinx Book Theme"
+msgstr "Тема книги Сфинкс"
+
+msgid "suggest edit"
+msgstr "предложить редактировать"
+
+msgid "Open an issue"
+msgstr "Открыть вопрос"
+
+msgid "Launch"
+msgstr "Запуск"
+
+msgid "Fullscreen mode"
+msgstr "Полноэкранный режим"
+
+msgid "Edit this page"
+msgstr "Редактировать эту страницу"
+
+msgid "By the"
+msgstr "Посредством"
+
+msgid "next page"
+msgstr "Следующая страница"
diff --git a/_preview/13/_static/locales/sk/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/sk/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..6501288
--- /dev/null
+++ b/_preview/13/_static/locales/sk/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: sk\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Tlač do PDF"
+
+msgid "Theme by the"
+msgstr "Téma od"
+
+msgid "Download source file"
+msgstr "Stiahnite si zdrojový súbor"
+
+msgid "open issue"
+msgstr "otvorené vydanie"
+
+msgid "Contents"
+msgstr "Obsah"
+
+msgid "previous page"
+msgstr "predchádzajúca strana"
+
+msgid "Download notebook file"
+msgstr "Stiahnite si zošit"
+
+msgid "Copyright"
+msgstr "Autorské práva"
+
+msgid "Download this page"
+msgstr "Stiahnite si túto stránku"
+
+msgid "Source repository"
+msgstr "Zdrojové úložisko"
+
+msgid "By"
+msgstr "Autor:"
+
+msgid "repository"
+msgstr "Úložisko"
+
+msgid "Last updated on"
+msgstr "Posledná aktualizácia dňa"
+
+msgid "Toggle navigation"
+msgstr "Prepnúť navigáciu"
+
+msgid "Sphinx Book Theme"
+msgstr "Téma knihy Sfinga"
+
+msgid "suggest edit"
+msgstr "navrhnúť úpravu"
+
+msgid "Open an issue"
+msgstr "Otvorte problém"
+
+msgid "Launch"
+msgstr "Spustiť"
+
+msgid "Fullscreen mode"
+msgstr "Režim celej obrazovky"
+
+msgid "Edit this page"
+msgstr "Upraviť túto stránku"
+
+msgid "By the"
+msgstr "Podľa"
+
+msgid "next page"
+msgstr "ďalšia strana"
diff --git a/_preview/13/_static/locales/sl/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/sl/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..3c7e3a8
--- /dev/null
+++ b/_preview/13/_static/locales/sl/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: sl\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Natisni v PDF"
+
+msgid "Theme by the"
+msgstr "Tema avtorja"
+
+msgid "Download source file"
+msgstr "Prenesite izvorno datoteko"
+
+msgid "open issue"
+msgstr "odprto vprašanje"
+
+msgid "Contents"
+msgstr "Vsebina"
+
+msgid "previous page"
+msgstr "Prejšnja stran"
+
+msgid "Download notebook file"
+msgstr "Prenesite datoteko zvezka"
+
+msgid "Copyright"
+msgstr "avtorske pravice"
+
+msgid "Download this page"
+msgstr "Prenesite to stran"
+
+msgid "Source repository"
+msgstr "Izvorno skladišče"
+
+msgid "By"
+msgstr "Avtor"
+
+msgid "repository"
+msgstr "odlagališče"
+
+msgid "Last updated on"
+msgstr "Nazadnje posodobljeno dne"
+
+msgid "Toggle navigation"
+msgstr "Preklopi navigacijo"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema knjige Sphinx"
+
+msgid "suggest edit"
+msgstr "predlagajte urejanje"
+
+msgid "Open an issue"
+msgstr "Odprite številko"
+
+msgid "Launch"
+msgstr "Kosilo"
+
+msgid "Fullscreen mode"
+msgstr "Celozaslonski način"
+
+msgid "Edit this page"
+msgstr "Uredite to stran"
+
+msgid "By the"
+msgstr "Avtor"
+
+msgid "next page"
+msgstr "Naslednja stran"
diff --git a/_preview/13/_static/locales/sr/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/sr/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..773b8ad
--- /dev/null
+++ b/_preview/13/_static/locales/sr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: sr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Испис у ПДФ"
+
+msgid "Theme by the"
+msgstr "Тхеме би"
+
+msgid "Download source file"
+msgstr "Преузми изворну датотеку"
+
+msgid "open issue"
+msgstr "отворено издање"
+
+msgid "Contents"
+msgstr "Садржај"
+
+msgid "previous page"
+msgstr "Претходна страница"
+
+msgid "Download notebook file"
+msgstr "Преузмите датотеку бележнице"
+
+msgid "Copyright"
+msgstr "Ауторско право"
+
+msgid "Download this page"
+msgstr "Преузмите ову страницу"
+
+msgid "Source repository"
+msgstr "Изворно спремиште"
+
+msgid "By"
+msgstr "Од стране"
+
+msgid "repository"
+msgstr "спремиште"
+
+msgid "Last updated on"
+msgstr "Последње ажурирање"
+
+msgid "Toggle navigation"
+msgstr "Укључи / искључи навигацију"
+
+msgid "Sphinx Book Theme"
+msgstr "Тема књиге Спхинк"
+
+msgid "suggest edit"
+msgstr "предложи уређивање"
+
+msgid "Open an issue"
+msgstr "Отворите издање"
+
+msgid "Launch"
+msgstr "Лансирање"
+
+msgid "Fullscreen mode"
+msgstr "Режим целог екрана"
+
+msgid "Edit this page"
+msgstr "Уредите ову страницу"
+
+msgid "By the"
+msgstr "Од"
+
+msgid "next page"
+msgstr "Следећа страна"
diff --git a/_preview/13/_static/locales/sv/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/sv/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..bcac54c
--- /dev/null
+++ b/_preview/13/_static/locales/sv/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: sv\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Skriv ut till PDF"
+
+msgid "Theme by the"
+msgstr "Tema av"
+
+msgid "Download source file"
+msgstr "Ladda ner källfil"
+
+msgid "open issue"
+msgstr "öppna problemrapport"
+
+msgid "Contents"
+msgstr "Innehåll"
+
+msgid "previous page"
+msgstr "föregående sida"
+
+msgid "Download notebook file"
+msgstr "Ladda ner notebook-fil"
+
+msgid "Copyright"
+msgstr "Upphovsrätt"
+
+msgid "Download this page"
+msgstr "Ladda ner den här sidan"
+
+msgid "Source repository"
+msgstr "Källkodsrepositorium"
+
+msgid "By"
+msgstr "Av"
+
+msgid "repository"
+msgstr "repositorium"
+
+msgid "Last updated on"
+msgstr "Senast uppdaterad den"
+
+msgid "Toggle navigation"
+msgstr "Växla navigering"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx Boktema"
+
+msgid "suggest edit"
+msgstr "föreslå ändring"
+
+msgid "Open an issue"
+msgstr "Öppna en problemrapport"
+
+msgid "Launch"
+msgstr "Öppna"
+
+msgid "Fullscreen mode"
+msgstr "Fullskärmsläge"
+
+msgid "Edit this page"
+msgstr "Redigera den här sidan"
+
+msgid "By the"
+msgstr "Av den"
+
+msgid "next page"
+msgstr "nästa sida"
diff --git a/_preview/13/_static/locales/ta/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/ta/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..b48bdfa
--- /dev/null
+++ b/_preview/13/_static/locales/ta/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ta\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "PDF இல் அச்சிடுக"
+
+msgid "Theme by the"
+msgstr "வழங்கிய தீம்"
+
+msgid "Download source file"
+msgstr "மூல கோப்பைப் பதிவிறக்குக"
+
+msgid "open issue"
+msgstr "திறந்த பிரச்சினை"
+
+msgid "previous page"
+msgstr "முந்தைய பக்கம்"
+
+msgid "Download notebook file"
+msgstr "நோட்புக் கோப்பைப் பதிவிறக்கவும்"
+
+msgid "Copyright"
+msgstr "பதிப்புரிமை"
+
+msgid "Download this page"
+msgstr "இந்தப் பக்கத்தைப் பதிவிறக்கவும்"
+
+msgid "Source repository"
+msgstr "மூல களஞ்சியம்"
+
+msgid "By"
+msgstr "வழங்கியவர்"
+
+msgid "Last updated on"
+msgstr "கடைசியாக புதுப்பிக்கப்பட்டது"
+
+msgid "Toggle navigation"
+msgstr "வழிசெலுத்தலை நிலைமாற்று"
+
+msgid "Sphinx Book Theme"
+msgstr "ஸ்பிங்க்ஸ் புத்தக தீம்"
+
+msgid "suggest edit"
+msgstr "திருத்த பரிந்துரைக்கவும்"
+
+msgid "Open an issue"
+msgstr "சிக்கலைத் திறக்கவும்"
+
+msgid "Launch"
+msgstr "தொடங்க"
+
+msgid "Edit this page"
+msgstr "இந்தப் பக்கத்தைத் திருத்தவும்"
+
+msgid "By the"
+msgstr "மூலம்"
+
+msgid "next page"
+msgstr "அடுத்த பக்கம்"
diff --git a/_preview/13/_static/locales/te/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/te/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..952278f
--- /dev/null
+++ b/_preview/13/_static/locales/te/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: te\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "PDF కి ముద్రించండి"
+
+msgid "Theme by the"
+msgstr "ద్వారా థీమ్"
+
+msgid "Download source file"
+msgstr "మూల ఫైల్‌ను డౌన్‌లోడ్ చేయండి"
+
+msgid "open issue"
+msgstr "ఓపెన్ ఇష్యూ"
+
+msgid "previous page"
+msgstr "ముందు పేజి"
+
+msgid "Download notebook file"
+msgstr "నోట్బుక్ ఫైల్ను డౌన్లోడ్ చేయండి"
+
+msgid "Copyright"
+msgstr "కాపీరైట్"
+
+msgid "Download this page"
+msgstr "ఈ పేజీని డౌన్‌లోడ్ చేయండి"
+
+msgid "Source repository"
+msgstr "మూల రిపోజిటరీ"
+
+msgid "By"
+msgstr "ద్వారా"
+
+msgid "Last updated on"
+msgstr "చివరిగా నవీకరించబడింది"
+
+msgid "Toggle navigation"
+msgstr "నావిగేషన్‌ను టోగుల్ చేయండి"
+
+msgid "Sphinx Book Theme"
+msgstr "సింహిక పుస్తక థీమ్"
+
+msgid "suggest edit"
+msgstr "సవరించమని సూచించండి"
+
+msgid "Open an issue"
+msgstr "సమస్యను తెరవండి"
+
+msgid "Launch"
+msgstr "ప్రారంభించండి"
+
+msgid "Edit this page"
+msgstr "ఈ పేజీని సవరించండి"
+
+msgid "By the"
+msgstr "ద్వారా"
+
+msgid "next page"
+msgstr "తరువాతి పేజీ"
diff --git a/_preview/13/_static/locales/tg/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/tg/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..c33dc42
--- /dev/null
+++ b/_preview/13/_static/locales/tg/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: tg\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Чоп ба PDF"
+
+msgid "Theme by the"
+msgstr "Мавзӯъи аз"
+
+msgid "Download source file"
+msgstr "Файли манбаъро зеркашӣ кунед"
+
+msgid "open issue"
+msgstr "барориши кушод"
+
+msgid "Contents"
+msgstr "Мундариҷа"
+
+msgid "previous page"
+msgstr "саҳифаи қаблӣ"
+
+msgid "Download notebook file"
+msgstr "Файли дафтарро зеркашӣ кунед"
+
+msgid "Copyright"
+msgstr "Ҳуқуқи муаллиф"
+
+msgid "Download this page"
+msgstr "Ин саҳифаро зеркашӣ кунед"
+
+msgid "Source repository"
+msgstr "Анбори манбаъ"
+
+msgid "By"
+msgstr "Бо"
+
+msgid "repository"
+msgstr "анбор"
+
+msgid "Last updated on"
+msgstr "Last навсозӣ дар"
+
+msgid "Toggle navigation"
+msgstr "Гузаришро иваз кунед"
+
+msgid "Sphinx Book Theme"
+msgstr "Сфинкс Мавзӯи китоб"
+
+msgid "suggest edit"
+msgstr "пешниҳод вироиш"
+
+msgid "Open an issue"
+msgstr "Масъаларо кушоед"
+
+msgid "Launch"
+msgstr "Оғоз"
+
+msgid "Fullscreen mode"
+msgstr "Ҳолати экрани пурра"
+
+msgid "Edit this page"
+msgstr "Ин саҳифаро таҳрир кунед"
+
+msgid "By the"
+msgstr "Бо"
+
+msgid "next page"
+msgstr "саҳифаи оянда"
diff --git a/_preview/13/_static/locales/th/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/th/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..9d24294
--- /dev/null
+++ b/_preview/13/_static/locales/th/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: th\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "พิมพ์เป็น PDF"
+
+msgid "Theme by the"
+msgstr "ธีมโดย"
+
+msgid "Download source file"
+msgstr "ดาวน์โหลดไฟล์ต้นฉบับ"
+
+msgid "open issue"
+msgstr "เปิดปัญหา"
+
+msgid "Contents"
+msgstr "สารบัญ"
+
+msgid "previous page"
+msgstr "หน้าที่แล้ว"
+
+msgid "Download notebook file"
+msgstr "ดาวน์โหลดไฟล์สมุดบันทึก"
+
+msgid "Copyright"
+msgstr "ลิขสิทธิ์"
+
+msgid "Download this page"
+msgstr "ดาวน์โหลดหน้านี้"
+
+msgid "Source repository"
+msgstr "ที่เก็บซอร์ส"
+
+msgid "By"
+msgstr "โดย"
+
+msgid "repository"
+msgstr "ที่เก็บ"
+
+msgid "Last updated on"
+msgstr "ปรับปรุงล่าสุดเมื่อ"
+
+msgid "Toggle navigation"
+msgstr "ไม่ต้องสลับช่องทาง"
+
+msgid "Sphinx Book Theme"
+msgstr "ธีมหนังสือสฟิงซ์"
+
+msgid "suggest edit"
+msgstr "แนะนำแก้ไข"
+
+msgid "Open an issue"
+msgstr "เปิดปัญหา"
+
+msgid "Launch"
+msgstr "เปิด"
+
+msgid "Fullscreen mode"
+msgstr "โหมดเต็มหน้าจอ"
+
+msgid "Edit this page"
+msgstr "แก้ไขหน้านี้"
+
+msgid "By the"
+msgstr "โดย"
+
+msgid "next page"
+msgstr "หน้าต่อไป"
diff --git a/_preview/13/_static/locales/tl/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/tl/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..20e0d07
--- /dev/null
+++ b/_preview/13/_static/locales/tl/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: tl\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "I-print sa PDF"
+
+msgid "Theme by the"
+msgstr "Tema ng"
+
+msgid "Download source file"
+msgstr "Mag-download ng file ng pinagmulan"
+
+msgid "open issue"
+msgstr "bukas na isyu"
+
+msgid "previous page"
+msgstr "Nakaraang pahina"
+
+msgid "Download notebook file"
+msgstr "Mag-download ng file ng notebook"
+
+msgid "Copyright"
+msgstr "Copyright"
+
+msgid "Download this page"
+msgstr "I-download ang pahinang ito"
+
+msgid "Source repository"
+msgstr "Pinagmulan ng imbakan"
+
+msgid "By"
+msgstr "Ni"
+
+msgid "Last updated on"
+msgstr "Huling na-update noong"
+
+msgid "Toggle navigation"
+msgstr "I-toggle ang pag-navigate"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema ng Sphinx Book"
+
+msgid "suggest edit"
+msgstr "iminumungkahi i-edit"
+
+msgid "Open an issue"
+msgstr "Magbukas ng isyu"
+
+msgid "Launch"
+msgstr "Ilunsad"
+
+msgid "Edit this page"
+msgstr "I-edit ang pahinang ito"
+
+msgid "By the"
+msgstr "Sa pamamagitan ng"
+
+msgid "next page"
+msgstr "Susunod na pahina"
diff --git a/_preview/13/_static/locales/tr/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/tr/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..a77eb02
--- /dev/null
+++ b/_preview/13/_static/locales/tr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: tr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "PDF olarak yazdır"
+
+msgid "Theme by the"
+msgstr "Tarafından tema"
+
+msgid "Download source file"
+msgstr "Kaynak dosyayı indirin"
+
+msgid "open issue"
+msgstr "Açık konu"
+
+msgid "Contents"
+msgstr "İçindekiler"
+
+msgid "previous page"
+msgstr "önceki sayfa"
+
+msgid "Download notebook file"
+msgstr "Defter dosyasını indirin"
+
+msgid "Copyright"
+msgstr "Telif hakkı"
+
+msgid "Download this page"
+msgstr "Bu sayfayı indirin"
+
+msgid "Source repository"
+msgstr "Kaynak kod deposu"
+
+msgid "By"
+msgstr "Tarafından"
+
+msgid "repository"
+msgstr "depo"
+
+msgid "Last updated on"
+msgstr "Son güncelleme tarihi"
+
+msgid "Toggle navigation"
+msgstr "Gezinmeyi değiştir"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfenks Kitap Teması"
+
+msgid "suggest edit"
+msgstr "düzenleme öner"
+
+msgid "Open an issue"
+msgstr "Bir sorunu açın"
+
+msgid "Launch"
+msgstr "Başlatmak"
+
+msgid "Fullscreen mode"
+msgstr "Tam ekran modu"
+
+msgid "Edit this page"
+msgstr "Bu sayfayı düzenle"
+
+msgid "By the"
+msgstr "Tarafından"
+
+msgid "next page"
+msgstr "sonraki Sayfa"
diff --git a/_preview/13/_static/locales/uk/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/uk/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..993dd07
--- /dev/null
+++ b/_preview/13/_static/locales/uk/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: uk\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Друк у форматі PDF"
+
+msgid "Theme by the"
+msgstr "Тема від"
+
+msgid "Download source file"
+msgstr "Завантажити вихідний файл"
+
+msgid "open issue"
+msgstr "відкритий випуск"
+
+msgid "Contents"
+msgstr "Зміст"
+
+msgid "previous page"
+msgstr "Попередня сторінка"
+
+msgid "Download notebook file"
+msgstr "Завантажте файл блокнота"
+
+msgid "Copyright"
+msgstr "Авторське право"
+
+msgid "Download this page"
+msgstr "Завантажте цю сторінку"
+
+msgid "Source repository"
+msgstr "Джерело сховища"
+
+msgid "By"
+msgstr "Автор"
+
+msgid "repository"
+msgstr "сховище"
+
+msgid "Last updated on"
+msgstr "Останнє оновлення:"
+
+msgid "Toggle navigation"
+msgstr "Переключити навігацію"
+
+msgid "Sphinx Book Theme"
+msgstr "Тема книги \"Сфінкс\""
+
+msgid "suggest edit"
+msgstr "запропонувати редагувати"
+
+msgid "Open an issue"
+msgstr "Відкрийте випуск"
+
+msgid "Launch"
+msgstr "Запуск"
+
+msgid "Fullscreen mode"
+msgstr "Повноекранний режим"
+
+msgid "Edit this page"
+msgstr "Редагувати цю сторінку"
+
+msgid "By the"
+msgstr "По"
+
+msgid "next page"
+msgstr "Наступна сторінка"
diff --git a/_preview/13/_static/locales/ur/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/ur/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..2f77426
--- /dev/null
+++ b/_preview/13/_static/locales/ur/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ur\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "پی ڈی ایف پرنٹ کریں"
+
+msgid "Theme by the"
+msgstr "کے ذریعہ تھیم"
+
+msgid "Download source file"
+msgstr "سورس فائل ڈاؤن لوڈ کریں"
+
+msgid "open issue"
+msgstr "کھلا مسئلہ"
+
+msgid "previous page"
+msgstr "سابقہ ​​صفحہ"
+
+msgid "Download notebook file"
+msgstr "نوٹ بک فائل ڈاؤن لوڈ کریں"
+
+msgid "Copyright"
+msgstr "کاپی رائٹ"
+
+msgid "Download this page"
+msgstr "اس صفحے کو ڈاؤن لوڈ کریں"
+
+msgid "Source repository"
+msgstr "ماخذ ذخیرہ"
+
+msgid "By"
+msgstr "بذریعہ"
+
+msgid "Last updated on"
+msgstr "آخری بار تازہ کاری ہوئی"
+
+msgid "Toggle navigation"
+msgstr "نیویگیشن ٹوگل کریں"
+
+msgid "Sphinx Book Theme"
+msgstr "سپنکس بک تھیم"
+
+msgid "suggest edit"
+msgstr "ترمیم کی تجویز کریں"
+
+msgid "Open an issue"
+msgstr "ایک مسئلہ کھولیں"
+
+msgid "Launch"
+msgstr "لانچ کریں"
+
+msgid "Edit this page"
+msgstr "اس صفحے میں ترمیم کریں"
+
+msgid "By the"
+msgstr "کی طرف"
+
+msgid "next page"
+msgstr "اگلا صفحہ"
diff --git a/_preview/13/_static/locales/vi/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/vi/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..33159f3
--- /dev/null
+++ b/_preview/13/_static/locales/vi/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: vi\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "In sang PDF"
+
+msgid "Theme by the"
+msgstr "Chủ đề của"
+
+msgid "Download source file"
+msgstr "Tải xuống tệp nguồn"
+
+msgid "open issue"
+msgstr "vấn đề mở"
+
+msgid "Contents"
+msgstr "Nội dung"
+
+msgid "previous page"
+msgstr "trang trước"
+
+msgid "Download notebook file"
+msgstr "Tải xuống tệp sổ tay"
+
+msgid "Copyright"
+msgstr "Bản quyền"
+
+msgid "Download this page"
+msgstr "Tải xuống trang này"
+
+msgid "Source repository"
+msgstr "Kho nguồn"
+
+msgid "By"
+msgstr "Bởi"
+
+msgid "repository"
+msgstr "kho"
+
+msgid "Last updated on"
+msgstr "Cập nhật lần cuối vào"
+
+msgid "Toggle navigation"
+msgstr "Chuyển đổi điều hướng thành"
+
+msgid "Sphinx Book Theme"
+msgstr "Chủ đề sách nhân sư"
+
+msgid "suggest edit"
+msgstr "đề nghị chỉnh sửa"
+
+msgid "Open an issue"
+msgstr "Mở một vấn đề"
+
+msgid "Launch"
+msgstr "Phóng"
+
+msgid "Fullscreen mode"
+msgstr "Chế độ toàn màn hình"
+
+msgid "Edit this page"
+msgstr "chỉnh sửa trang này"
+
+msgid "By the"
+msgstr "Bằng"
+
+msgid "next page"
+msgstr "Trang tiếp theo"
diff --git a/_preview/13/_static/locales/zh_CN/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/zh_CN/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..2e519ef
--- /dev/null
+++ b/_preview/13/_static/locales/zh_CN/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: zh_CN\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "列印成 PDF"
+
+msgid "Theme by the"
+msgstr "主题作者："
+
+msgid "Download source file"
+msgstr "下载源文件"
+
+msgid "open issue"
+msgstr "创建议题"
+
+msgid "Contents"
+msgstr "目录"
+
+msgid "previous page"
+msgstr "上一页"
+
+msgid "Download notebook file"
+msgstr "下载笔记本文件"
+
+msgid "Copyright"
+msgstr "版权"
+
+msgid "Download this page"
+msgstr "下载此页面"
+
+msgid "Source repository"
+msgstr "源码库"
+
+msgid "By"
+msgstr "作者："
+
+msgid "repository"
+msgstr "仓库"
+
+msgid "Last updated on"
+msgstr "上次更新时间："
+
+msgid "Toggle navigation"
+msgstr "显示或隐藏导航栏"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx Book 主题"
+
+msgid "suggest edit"
+msgstr "提出修改建议"
+
+msgid "Open an issue"
+msgstr "创建议题"
+
+msgid "Launch"
+msgstr "启动"
+
+msgid "Fullscreen mode"
+msgstr "全屏模式"
+
+msgid "Edit this page"
+msgstr "编辑此页面"
+
+msgid "By the"
+msgstr "作者："
+
+msgid "next page"
+msgstr "下一页"
diff --git a/_preview/13/_static/locales/zh_TW/LC_MESSAGES/booktheme.po b/_preview/13/_static/locales/zh_TW/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000..beecb07
--- /dev/null
+++ b/_preview/13/_static/locales/zh_TW/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: zh_TW\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "列印成 PDF"
+
+msgid "Theme by the"
+msgstr "佈景主題作者："
+
+msgid "Download source file"
+msgstr "下載原始檔"
+
+msgid "open issue"
+msgstr "公開的問題"
+
+msgid "Contents"
+msgstr "目錄"
+
+msgid "previous page"
+msgstr "上一頁"
+
+msgid "Download notebook file"
+msgstr "下載 Notebook 檔案"
+
+msgid "Copyright"
+msgstr "Copyright"
+
+msgid "Download this page"
+msgstr "下載此頁面"
+
+msgid "Source repository"
+msgstr "來源儲存庫"
+
+msgid "By"
+msgstr "作者："
+
+msgid "repository"
+msgstr "儲存庫"
+
+msgid "Last updated on"
+msgstr "最後更新時間："
+
+msgid "Toggle navigation"
+msgstr "顯示或隱藏導覽列"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx Book 佈景主題"
+
+msgid "suggest edit"
+msgstr "提出修改建議"
+
+msgid "Open an issue"
+msgstr "開啟議題"
+
+msgid "Launch"
+msgstr "啟動"
+
+msgid "Fullscreen mode"
+msgstr "全螢幕模式"
+
+msgid "Edit this page"
+msgstr "編輯此頁面"
+
+msgid "By the"
+msgstr "作者："
+
+msgid "next page"
+msgstr "下一頁"
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new file mode 100644
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diff --git a/_preview/13/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css b/_preview/13/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css
new file mode 100644
index 0000000..3356631
--- /dev/null
+++ b/_preview/13/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css
@@ -0,0 +1,2342 @@
+/* Variables */
+:root {
+  --mystnb-source-bg-color: #f7f7f7;
+  --mystnb-stdout-bg-color: #fcfcfc;
+  --mystnb-stderr-bg-color: #fdd;
+  --mystnb-traceback-bg-color: #fcfcfc;
+  --mystnb-source-border-color: #ccc;
+  --mystnb-source-margin-color: green;
+  --mystnb-stdout-border-color: #f7f7f7;
+  --mystnb-stderr-border-color: #f7f7f7;
+  --mystnb-traceback-border-color: #ffd6d6;
+  --mystnb-hide-prompt-opacity: 70%;
+  --mystnb-source-border-radius: .4em;
+  --mystnb-source-border-width: 1px;
+}
+
+/* Whole cell */
+div.container.cell {
+  padding-left: 0;
+  margin-bottom: 1em;
+}
+
+/* Removing all background formatting so we can control at the div level */
+.cell_input div.highlight,
+.cell_output pre,
+.cell_input pre,
+.cell_output .output {
+  border: none;
+  box-shadow: none;
+}
+
+.cell_output .output pre,
+.cell_input pre {
+  margin: 0px;
+}
+
+/* Input cells */
+div.cell div.cell_input,
+div.cell details.above-input>summary {
+  padding-left: 0em;
+  padding-right: 0em;
+  border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid;
+  background-color: var(--mystnb-source-bg-color);
+  border-left-color: var(--mystnb-source-margin-color);
+  border-left-width: medium;
+  border-radius: var(--mystnb-source-border-radius);
+}
+
+div.cell_input>div,
+div.cell_output div.output>div.highlight {
+  margin: 0em !important;
+  border: none !important;
+}
+
+/* All cell outputs */
+.cell_output {
+  padding-left: 1em;
+  padding-right: 0em;
+  margin-top: 1em;
+}
+
+/* Text outputs from cells */
+.cell_output .output.text_plain,
+.cell_output .output.traceback,
+.cell_output .output.stream,
+.cell_output .output.stderr {
+  margin-top: 1em;
+  margin-bottom: 0em;
+  box-shadow: none;
+}
+
+.cell_output .output.text_plain,
+.cell_output .output.stream {
+  background: var(--mystnb-stdout-bg-color);
+  border: 1px solid var(--mystnb-stdout-border-color);
+}
+
+.cell_output .output.stderr {
+  background: var(--mystnb-stderr-bg-color);
+  border: 1px solid var(--mystnb-stderr-border-color);
+}
+
+.cell_output .output.traceback {
+  background: var(--mystnb-traceback-bg-color);
+  border: 1px solid var(--mystnb-traceback-border-color);
+}
+
+/* Collapsible cell content */
+div.cell details.above-input div.cell_input {
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+  border-top: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed;
+}
+
+div.cell div.cell_input.above-output-prompt {
+  border-bottom-left-radius: 0;
+  border-bottom-right-radius: 0;
+}
+
+div.cell details.above-input>summary {
+  border-bottom-left-radius: 0;
+  border-bottom-right-radius: 0;
+  border-bottom: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed;
+  padding-left: 1em;
+  margin-bottom: 0;
+}
+
+div.cell details.above-output>summary {
+  background-color: var(--mystnb-source-bg-color);
+  padding-left: 1em;
+  padding-right: 0em;
+  border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid;
+  border-radius: var(--mystnb-source-border-radius);
+  border-left-color: var(--mystnb-source-margin-color);
+  border-left-width: medium;
+}
+
+div.cell details.below-input>summary {
+  background-color: var(--mystnb-source-bg-color);
+  padding-left: 1em;
+  padding-right: 0em;
+  border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid;
+  border-top: none;
+  border-bottom-left-radius: var(--mystnb-source-border-radius);
+  border-bottom-right-radius: var(--mystnb-source-border-radius);
+  border-left-color: var(--mystnb-source-margin-color);
+  border-left-width: medium;
+}
+
+div.cell details.hide>summary>span {
+  opacity: var(--mystnb-hide-prompt-opacity);
+}
+
+div.cell details.hide[open]>summary>span.collapsed {
+  display: none;
+}
+
+div.cell details.hide:not([open])>summary>span.expanded {
+  display: none;
+}
+
+@keyframes collapsed-fade-in {
+  0% {
+    opacity: 0;
+  }
+
+  100% {
+    opacity: 1;
+  }
+}
+div.cell details.hide[open]>summary~* {
+  -moz-animation: collapsed-fade-in 0.3s ease-in-out;
+  -webkit-animation: collapsed-fade-in 0.3s ease-in-out;
+  animation: collapsed-fade-in 0.3s ease-in-out;
+}
+
+/* Math align to the left */
+.cell_output .MathJax_Display {
+  text-align: left !important;
+}
+
+/* Pandas tables. Pulled from the Jupyter / nbsphinx CSS */
+div.cell_output table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 1em;
+  table-layout: fixed;
+}
+
+div.cell_output thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+
+div.cell_output tr,
+div.cell_output th,
+div.cell_output td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+
+div.cell_output th {
+  font-weight: bold;
+}
+
+div.cell_output tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+
+div.cell_output tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+
+/** source code line numbers **/
+span.linenos {
+  opacity: 0.5;
+}
+
+/* Inline text from `paste` operation */
+
+span.pasted-text {
+  font-weight: bold;
+}
+
+span.pasted-inline img {
+  max-height: 2em;
+}
+
+tbody span.pasted-inline img {
+  max-height: none;
+}
+
+/* Font colors for translated ANSI escape sequences
+Color values are copied from Jupyter Notebook
+https://github.com/jupyter/notebook/blob/52581f8eda9b319eb0390ac77fe5903c38f81e3e/notebook/static/notebook/less/ansicolors.less#L14-L21
+Background colors from
+https://nbsphinx.readthedocs.io/en/latest/code-cells.html#ANSI-Colors
+*/
+div.highlight .-Color-Bold {
+  font-weight: bold;
+}
+
+div.highlight .-Color[class*=-Black] {
+  color: #3E424D
+}
+
+div.highlight .-Color[class*=-Red] {
+  color: #E75C58
+}
+
+div.highlight .-Color[class*=-Green] {
+  color: #00A250
+}
+
+div.highlight .-Color[class*=-Yellow] {
+  color: #DDB62B
+}
+
+div.highlight .-Color[class*=-Blue] {
+  color: #208FFB
+}
+
+div.highlight .-Color[class*=-Magenta] {
+  color: #D160C4
+}
+
+div.highlight .-Color[class*=-Cyan] {
+  color: #60C6C8
+}
+
+div.highlight .-Color[class*=-White] {
+  color: #C5C1B4
+}
+
+div.highlight .-Color[class*=-BGBlack] {
+  background-color: #3E424D
+}
+
+div.highlight .-Color[class*=-BGRed] {
+  background-color: #E75C58
+}
+
+div.highlight .-Color[class*=-BGGreen] {
+  background-color: #00A250
+}
+
+div.highlight .-Color[class*=-BGYellow] {
+  background-color: #DDB62B
+}
+
+div.highlight .-Color[class*=-BGBlue] {
+  background-color: #208FFB
+}
+
+div.highlight .-Color[class*=-BGMagenta] {
+  background-color: #D160C4
+}
+
+div.highlight .-Color[class*=-BGCyan] {
+  background-color: #60C6C8
+}
+
+div.highlight .-Color[class*=-BGWhite] {
+  background-color: #C5C1B4
+}
+
+/* Font colors for 8-bit ANSI */
+
+div.highlight .-Color[class*=-C0] {
+  color: #000000
+}
+
+div.highlight .-Color[class*=-BGC0] {
+  background-color: #000000
+}
+
+div.highlight .-Color[class*=-C1] {
+  color: #800000
+}
+
+div.highlight .-Color[class*=-BGC1] {
+  background-color: #800000
+}
+
+div.highlight .-Color[class*=-C2] {
+  color: #008000
+}
+
+div.highlight .-Color[class*=-BGC2] {
+  background-color: #008000
+}
+
+div.highlight .-Color[class*=-C3] {
+  color: #808000
+}
+
+div.highlight .-Color[class*=-BGC3] {
+  background-color: #808000
+}
+
+div.highlight .-Color[class*=-C4] {
+  color: #000080
+}
+
+div.highlight .-Color[class*=-BGC4] {
+  background-color: #000080
+}
+
+div.highlight .-Color[class*=-C5] {
+  color: #800080
+}
+
+div.highlight .-Color[class*=-BGC5] {
+  background-color: #800080
+}
+
+div.highlight .-Color[class*=-C6] {
+  color: #008080
+}
+
+div.highlight .-Color[class*=-BGC6] {
+  background-color: #008080
+}
+
+div.highlight .-Color[class*=-C7] {
+  color: #C0C0C0
+}
+
+div.highlight .-Color[class*=-BGC7] {
+  background-color: #C0C0C0
+}
+
+div.highlight .-Color[class*=-C8] {
+  color: #808080
+}
+
+div.highlight .-Color[class*=-BGC8] {
+  background-color: #808080
+}
+
+div.highlight .-Color[class*=-C9] {
+  color: #FF0000
+}
+
+div.highlight .-Color[class*=-BGC9] {
+  background-color: #FF0000
+}
+
+div.highlight .-Color[class*=-C10] {
+  color: #00FF00
+}
+
+div.highlight .-Color[class*=-BGC10] {
+  background-color: #00FF00
+}
+
+div.highlight .-Color[class*=-C11] {
+  color: #FFFF00
+}
+
+div.highlight .-Color[class*=-BGC11] {
+  background-color: #FFFF00
+}
+
+div.highlight .-Color[class*=-C12] {
+  color: #0000FF
+}
+
+div.highlight .-Color[class*=-BGC12] {
+  background-color: #0000FF
+}
+
+div.highlight .-Color[class*=-C13] {
+  color: #FF00FF
+}
+
+div.highlight .-Color[class*=-BGC13] {
+  background-color: #FF00FF
+}
+
+div.highlight .-Color[class*=-C14] {
+  color: #00FFFF
+}
+
+div.highlight .-Color[class*=-BGC14] {
+  background-color: #00FFFF
+}
+
+div.highlight .-Color[class*=-C15] {
+  color: #FFFFFF
+}
+
+div.highlight .-Color[class*=-BGC15] {
+  background-color: #FFFFFF
+}
+
+div.highlight .-Color[class*=-C16] {
+  color: #000000
+}
+
+div.highlight .-Color[class*=-BGC16] {
+  background-color: #000000
+}
+
+div.highlight .-Color[class*=-C17] {
+  color: #00005F
+}
+
+div.highlight .-Color[class*=-BGC17] {
+  background-color: #00005F
+}
+
+div.highlight .-Color[class*=-C18] {
+  color: #000087
+}
+
+div.highlight .-Color[class*=-BGC18] {
+  background-color: #000087
+}
+
+div.highlight .-Color[class*=-C19] {
+  color: #0000AF
+}
+
+div.highlight .-Color[class*=-BGC19] {
+  background-color: #0000AF
+}
+
+div.highlight .-Color[class*=-C20] {
+  color: #0000D7
+}
+
+div.highlight .-Color[class*=-BGC20] {
+  background-color: #0000D7
+}
+
+div.highlight .-Color[class*=-C21] {
+  color: #0000FF
+}
+
+div.highlight .-Color[class*=-BGC21] {
+  background-color: #0000FF
+}
+
+div.highlight .-Color[class*=-C22] {
+  color: #005F00
+}
+
+div.highlight .-Color[class*=-BGC22] {
+  background-color: #005F00
+}
+
+div.highlight .-Color[class*=-C23] {
+  color: #005F5F
+}
+
+div.highlight .-Color[class*=-BGC23] {
+  background-color: #005F5F
+}
+
+div.highlight .-Color[class*=-C24] {
+  color: #005F87
+}
+
+div.highlight .-Color[class*=-BGC24] {
+  background-color: #005F87
+}
+
+div.highlight .-Color[class*=-C25] {
+  color: #005FAF
+}
+
+div.highlight .-Color[class*=-BGC25] {
+  background-color: #005FAF
+}
+
+div.highlight .-Color[class*=-C26] {
+  color: #005FD7
+}
+
+div.highlight .-Color[class*=-BGC26] {
+  background-color: #005FD7
+}
+
+div.highlight .-Color[class*=-C27] {
+  color: #005FFF
+}
+
+div.highlight .-Color[class*=-BGC27] {
+  background-color: #005FFF
+}
+
+div.highlight .-Color[class*=-C28] {
+  color: #008700
+}
+
+div.highlight .-Color[class*=-BGC28] {
+  background-color: #008700
+}
+
+div.highlight .-Color[class*=-C29] {
+  color: #00875F
+}
+
+div.highlight .-Color[class*=-BGC29] {
+  background-color: #00875F
+}
+
+div.highlight .-Color[class*=-C30] {
+  color: #008787
+}
+
+div.highlight .-Color[class*=-BGC30] {
+  background-color: #008787
+}
+
+div.highlight .-Color[class*=-C31] {
+  color: #0087AF
+}
+
+div.highlight .-Color[class*=-BGC31] {
+  background-color: #0087AF
+}
+
+div.highlight .-Color[class*=-C32] {
+  color: #0087D7
+}
+
+div.highlight .-Color[class*=-BGC32] {
+  background-color: #0087D7
+}
+
+div.highlight .-Color[class*=-C33] {
+  color: #0087FF
+}
+
+div.highlight .-Color[class*=-BGC33] {
+  background-color: #0087FF
+}
+
+div.highlight .-Color[class*=-C34] {
+  color: #00AF00
+}
+
+div.highlight .-Color[class*=-BGC34] {
+  background-color: #00AF00
+}
+
+div.highlight .-Color[class*=-C35] {
+  color: #00AF5F
+}
+
+div.highlight .-Color[class*=-BGC35] {
+  background-color: #00AF5F
+}
+
+div.highlight .-Color[class*=-C36] {
+  color: #00AF87
+}
+
+div.highlight .-Color[class*=-BGC36] {
+  background-color: #00AF87
+}
+
+div.highlight .-Color[class*=-C37] {
+  color: #00AFAF
+}
+
+div.highlight .-Color[class*=-BGC37] {
+  background-color: #00AFAF
+}
+
+div.highlight .-Color[class*=-C38] {
+  color: #00AFD7
+}
+
+div.highlight .-Color[class*=-BGC38] {
+  background-color: #00AFD7
+}
+
+div.highlight .-Color[class*=-C39] {
+  color: #00AFFF
+}
+
+div.highlight .-Color[class*=-BGC39] {
+  background-color: #00AFFF
+}
+
+div.highlight .-Color[class*=-C40] {
+  color: #00D700
+}
+
+div.highlight .-Color[class*=-BGC40] {
+  background-color: #00D700
+}
+
+div.highlight .-Color[class*=-C41] {
+  color: #00D75F
+}
+
+div.highlight .-Color[class*=-BGC41] {
+  background-color: #00D75F
+}
+
+div.highlight .-Color[class*=-C42] {
+  color: #00D787
+}
+
+div.highlight .-Color[class*=-BGC42] {
+  background-color: #00D787
+}
+
+div.highlight .-Color[class*=-C43] {
+  color: #00D7AF
+}
+
+div.highlight .-Color[class*=-BGC43] {
+  background-color: #00D7AF
+}
+
+div.highlight .-Color[class*=-C44] {
+  color: #00D7D7
+}
+
+div.highlight .-Color[class*=-BGC44] {
+  background-color: #00D7D7
+}
+
+div.highlight .-Color[class*=-C45] {
+  color: #00D7FF
+}
+
+div.highlight .-Color[class*=-BGC45] {
+  background-color: #00D7FF
+}
+
+div.highlight .-Color[class*=-C46] {
+  color: #00FF00
+}
+
+div.highlight .-Color[class*=-BGC46] {
+  background-color: #00FF00
+}
+
+div.highlight .-Color[class*=-C47] {
+  color: #00FF5F
+}
+
+div.highlight .-Color[class*=-BGC47] {
+  background-color: #00FF5F
+}
+
+div.highlight .-Color[class*=-C48] {
+  color: #00FF87
+}
+
+div.highlight .-Color[class*=-BGC48] {
+  background-color: #00FF87
+}
+
+div.highlight .-Color[class*=-C49] {
+  color: #00FFAF
+}
+
+div.highlight .-Color[class*=-BGC49] {
+  background-color: #00FFAF
+}
+
+div.highlight .-Color[class*=-C50] {
+  color: #00FFD7
+}
+
+div.highlight .-Color[class*=-BGC50] {
+  background-color: #00FFD7
+}
+
+div.highlight .-Color[class*=-C51] {
+  color: #00FFFF
+}
+
+div.highlight .-Color[class*=-BGC51] {
+  background-color: #00FFFF
+}
+
+div.highlight .-Color[class*=-C52] {
+  color: #5F0000
+}
+
+div.highlight .-Color[class*=-BGC52] {
+  background-color: #5F0000
+}
+
+div.highlight .-Color[class*=-C53] {
+  color: #5F005F
+}
+
+div.highlight .-Color[class*=-BGC53] {
+  background-color: #5F005F
+}
+
+div.highlight .-Color[class*=-C54] {
+  color: #5F0087
+}
+
+div.highlight .-Color[class*=-BGC54] {
+  background-color: #5F0087
+}
+
+div.highlight .-Color[class*=-C55] {
+  color: #5F00AF
+}
+
+div.highlight .-Color[class*=-BGC55] {
+  background-color: #5F00AF
+}
+
+div.highlight .-Color[class*=-C56] {
+  color: #5F00D7
+}
+
+div.highlight .-Color[class*=-BGC56] {
+  background-color: #5F00D7
+}
+
+div.highlight .-Color[class*=-C57] {
+  color: #5F00FF
+}
+
+div.highlight .-Color[class*=-BGC57] {
+  background-color: #5F00FF
+}
+
+div.highlight .-Color[class*=-C58] {
+  color: #5F5F00
+}
+
+div.highlight .-Color[class*=-BGC58] {
+  background-color: #5F5F00
+}
+
+div.highlight .-Color[class*=-C59] {
+  color: #5F5F5F
+}
+
+div.highlight .-Color[class*=-BGC59] {
+  background-color: #5F5F5F
+}
+
+div.highlight .-Color[class*=-C60] {
+  color: #5F5F87
+}
+
+div.highlight .-Color[class*=-BGC60] {
+  background-color: #5F5F87
+}
+
+div.highlight .-Color[class*=-C61] {
+  color: #5F5FAF
+}
+
+div.highlight .-Color[class*=-BGC61] {
+  background-color: #5F5FAF
+}
+
+div.highlight .-Color[class*=-C62] {
+  color: #5F5FD7
+}
+
+div.highlight .-Color[class*=-BGC62] {
+  background-color: #5F5FD7
+}
+
+div.highlight .-Color[class*=-C63] {
+  color: #5F5FFF
+}
+
+div.highlight .-Color[class*=-BGC63] {
+  background-color: #5F5FFF
+}
+
+div.highlight .-Color[class*=-C64] {
+  color: #5F8700
+}
+
+div.highlight .-Color[class*=-BGC64] {
+  background-color: #5F8700
+}
+
+div.highlight .-Color[class*=-C65] {
+  color: #5F875F
+}
+
+div.highlight .-Color[class*=-BGC65] {
+  background-color: #5F875F
+}
+
+div.highlight .-Color[class*=-C66] {
+  color: #5F8787
+}
+
+div.highlight .-Color[class*=-BGC66] {
+  background-color: #5F8787
+}
+
+div.highlight .-Color[class*=-C67] {
+  color: #5F87AF
+}
+
+div.highlight .-Color[class*=-BGC67] {
+  background-color: #5F87AF
+}
+
+div.highlight .-Color[class*=-C68] {
+  color: #5F87D7
+}
+
+div.highlight .-Color[class*=-BGC68] {
+  background-color: #5F87D7
+}
+
+div.highlight .-Color[class*=-C69] {
+  color: #5F87FF
+}
+
+div.highlight .-Color[class*=-BGC69] {
+  background-color: #5F87FF
+}
+
+div.highlight .-Color[class*=-C70] {
+  color: #5FAF00
+}
+
+div.highlight .-Color[class*=-BGC70] {
+  background-color: #5FAF00
+}
+
+div.highlight .-Color[class*=-C71] {
+  color: #5FAF5F
+}
+
+div.highlight .-Color[class*=-BGC71] {
+  background-color: #5FAF5F
+}
+
+div.highlight .-Color[class*=-C72] {
+  color: #5FAF87
+}
+
+div.highlight .-Color[class*=-BGC72] {
+  background-color: #5FAF87
+}
+
+div.highlight .-Color[class*=-C73] {
+  color: #5FAFAF
+}
+
+div.highlight .-Color[class*=-BGC73] {
+  background-color: #5FAFAF
+}
+
+div.highlight .-Color[class*=-C74] {
+  color: #5FAFD7
+}
+
+div.highlight .-Color[class*=-BGC74] {
+  background-color: #5FAFD7
+}
+
+div.highlight .-Color[class*=-C75] {
+  color: #5FAFFF
+}
+
+div.highlight .-Color[class*=-BGC75] {
+  background-color: #5FAFFF
+}
+
+div.highlight .-Color[class*=-C76] {
+  color: #5FD700
+}
+
+div.highlight .-Color[class*=-BGC76] {
+  background-color: #5FD700
+}
+
+div.highlight .-Color[class*=-C77] {
+  color: #5FD75F
+}
+
+div.highlight .-Color[class*=-BGC77] {
+  background-color: #5FD75F
+}
+
+div.highlight .-Color[class*=-C78] {
+  color: #5FD787
+}
+
+div.highlight .-Color[class*=-BGC78] {
+  background-color: #5FD787
+}
+
+div.highlight .-Color[class*=-C79] {
+  color: #5FD7AF
+}
+
+div.highlight .-Color[class*=-BGC79] {
+  background-color: #5FD7AF
+}
+
+div.highlight .-Color[class*=-C80] {
+  color: #5FD7D7
+}
+
+div.highlight .-Color[class*=-BGC80] {
+  background-color: #5FD7D7
+}
+
+div.highlight .-Color[class*=-C81] {
+  color: #5FD7FF
+}
+
+div.highlight .-Color[class*=-BGC81] {
+  background-color: #5FD7FF
+}
+
+div.highlight .-Color[class*=-C82] {
+  color: #5FFF00
+}
+
+div.highlight .-Color[class*=-BGC82] {
+  background-color: #5FFF00
+}
+
+div.highlight .-Color[class*=-C83] {
+  color: #5FFF5F
+}
+
+div.highlight .-Color[class*=-BGC83] {
+  background-color: #5FFF5F
+}
+
+div.highlight .-Color[class*=-C84] {
+  color: #5FFF87
+}
+
+div.highlight .-Color[class*=-BGC84] {
+  background-color: #5FFF87
+}
+
+div.highlight .-Color[class*=-C85] {
+  color: #5FFFAF
+}
+
+div.highlight .-Color[class*=-BGC85] {
+  background-color: #5FFFAF
+}
+
+div.highlight .-Color[class*=-C86] {
+  color: #5FFFD7
+}
+
+div.highlight .-Color[class*=-BGC86] {
+  background-color: #5FFFD7
+}
+
+div.highlight .-Color[class*=-C87] {
+  color: #5FFFFF
+}
+
+div.highlight .-Color[class*=-BGC87] {
+  background-color: #5FFFFF
+}
+
+div.highlight .-Color[class*=-C88] {
+  color: #870000
+}
+
+div.highlight .-Color[class*=-BGC88] {
+  background-color: #870000
+}
+
+div.highlight .-Color[class*=-C89] {
+  color: #87005F
+}
+
+div.highlight .-Color[class*=-BGC89] {
+  background-color: #87005F
+}
+
+div.highlight .-Color[class*=-C90] {
+  color: #870087
+}
+
+div.highlight .-Color[class*=-BGC90] {
+  background-color: #870087
+}
+
+div.highlight .-Color[class*=-C91] {
+  color: #8700AF
+}
+
+div.highlight .-Color[class*=-BGC91] {
+  background-color: #8700AF
+}
+
+div.highlight .-Color[class*=-C92] {
+  color: #8700D7
+}
+
+div.highlight .-Color[class*=-BGC92] {
+  background-color: #8700D7
+}
+
+div.highlight .-Color[class*=-C93] {
+  color: #8700FF
+}
+
+div.highlight .-Color[class*=-BGC93] {
+  background-color: #8700FF
+}
+
+div.highlight .-Color[class*=-C94] {
+  color: #875F00
+}
+
+div.highlight .-Color[class*=-BGC94] {
+  background-color: #875F00
+}
+
+div.highlight .-Color[class*=-C95] {
+  color: #875F5F
+}
+
+div.highlight .-Color[class*=-BGC95] {
+  background-color: #875F5F
+}
+
+div.highlight .-Color[class*=-C96] {
+  color: #875F87
+}
+
+div.highlight .-Color[class*=-BGC96] {
+  background-color: #875F87
+}
+
+div.highlight .-Color[class*=-C97] {
+  color: #875FAF
+}
+
+div.highlight .-Color[class*=-BGC97] {
+  background-color: #875FAF
+}
+
+div.highlight .-Color[class*=-C98] {
+  color: #875FD7
+}
+
+div.highlight .-Color[class*=-BGC98] {
+  background-color: #875FD7
+}
+
+div.highlight .-Color[class*=-C99] {
+  color: #875FFF
+}
+
+div.highlight .-Color[class*=-BGC99] {
+  background-color: #875FFF
+}
+
+div.highlight .-Color[class*=-C100] {
+  color: #878700
+}
+
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  background-color: #FFAF87
+}
+
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+  color: #FFAFAF
+}
+
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+  background-color: #FFAFAF
+}
+
+div.highlight .-Color[class*=-C218] {
+  color: #FFAFD7
+}
+
+div.highlight .-Color[class*=-BGC218] {
+  background-color: #FFAFD7
+}
+
+div.highlight .-Color[class*=-C219] {
+  color: #FFAFFF
+}
+
+div.highlight .-Color[class*=-BGC219] {
+  background-color: #FFAFFF
+}
+
+div.highlight .-Color[class*=-C220] {
+  color: #FFD700
+}
+
+div.highlight .-Color[class*=-BGC220] {
+  background-color: #FFD700
+}
+
+div.highlight .-Color[class*=-C221] {
+  color: #FFD75F
+}
+
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+  background-color: #FFD75F
+}
+
+div.highlight .-Color[class*=-C222] {
+  color: #FFD787
+}
+
+div.highlight .-Color[class*=-BGC222] {
+  background-color: #FFD787
+}
+
+div.highlight .-Color[class*=-C223] {
+  color: #FFD7AF
+}
+
+div.highlight .-Color[class*=-BGC223] {
+  background-color: #FFD7AF
+}
+
+div.highlight .-Color[class*=-C224] {
+  color: #FFD7D7
+}
+
+div.highlight .-Color[class*=-BGC224] {
+  background-color: #FFD7D7
+}
+
+div.highlight .-Color[class*=-C225] {
+  color: #FFD7FF
+}
+
+div.highlight .-Color[class*=-BGC225] {
+  background-color: #FFD7FF
+}
+
+div.highlight .-Color[class*=-C226] {
+  color: #FFFF00
+}
+
+div.highlight .-Color[class*=-BGC226] {
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+}
+
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+}
+
+div.highlight .-Color[class*=-BGC227] {
+  background-color: #FFFF5F
+}
+
+div.highlight .-Color[class*=-C228] {
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+}
+
+div.highlight .-Color[class*=-BGC228] {
+  background-color: #FFFF87
+}
+
+div.highlight .-Color[class*=-C229] {
+  color: #FFFFAF
+}
+
+div.highlight .-Color[class*=-BGC229] {
+  background-color: #FFFFAF
+}
+
+div.highlight .-Color[class*=-C230] {
+  color: #FFFFD7
+}
+
+div.highlight .-Color[class*=-BGC230] {
+  background-color: #FFFFD7
+}
+
+div.highlight .-Color[class*=-C231] {
+  color: #FFFFFF
+}
+
+div.highlight .-Color[class*=-BGC231] {
+  background-color: #FFFFFF
+}
+
+div.highlight .-Color[class*=-C232] {
+  color: #080808
+}
+
+div.highlight .-Color[class*=-BGC232] {
+  background-color: #080808
+}
+
+div.highlight .-Color[class*=-C233] {
+  color: #121212
+}
+
+div.highlight .-Color[class*=-BGC233] {
+  background-color: #121212
+}
+
+div.highlight .-Color[class*=-C234] {
+  color: #1C1C1C
+}
+
+div.highlight .-Color[class*=-BGC234] {
+  background-color: #1C1C1C
+}
+
+div.highlight .-Color[class*=-C235] {
+  color: #262626
+}
+
+div.highlight .-Color[class*=-BGC235] {
+  background-color: #262626
+}
+
+div.highlight .-Color[class*=-C236] {
+  color: #303030
+}
+
+div.highlight .-Color[class*=-BGC236] {
+  background-color: #303030
+}
+
+div.highlight .-Color[class*=-C237] {
+  color: #3A3A3A
+}
+
+div.highlight .-Color[class*=-BGC237] {
+  background-color: #3A3A3A
+}
+
+div.highlight .-Color[class*=-C238] {
+  color: #444444
+}
+
+div.highlight .-Color[class*=-BGC238] {
+  background-color: #444444
+}
+
+div.highlight .-Color[class*=-C239] {
+  color: #4E4E4E
+}
+
+div.highlight .-Color[class*=-BGC239] {
+  background-color: #4E4E4E
+}
+
+div.highlight .-Color[class*=-C240] {
+  color: #585858
+}
+
+div.highlight .-Color[class*=-BGC240] {
+  background-color: #585858
+}
+
+div.highlight .-Color[class*=-C241] {
+  color: #626262
+}
+
+div.highlight .-Color[class*=-BGC241] {
+  background-color: #626262
+}
+
+div.highlight .-Color[class*=-C242] {
+  color: #6C6C6C
+}
+
+div.highlight .-Color[class*=-BGC242] {
+  background-color: #6C6C6C
+}
+
+div.highlight .-Color[class*=-C243] {
+  color: #767676
+}
+
+div.highlight .-Color[class*=-BGC243] {
+  background-color: #767676
+}
+
+div.highlight .-Color[class*=-C244] {
+  color: #808080
+}
+
+div.highlight .-Color[class*=-BGC244] {
+  background-color: #808080
+}
+
+div.highlight .-Color[class*=-C245] {
+  color: #8A8A8A
+}
+
+div.highlight .-Color[class*=-BGC245] {
+  background-color: #8A8A8A
+}
+
+div.highlight .-Color[class*=-C246] {
+  color: #949494
+}
+
+div.highlight .-Color[class*=-BGC246] {
+  background-color: #949494
+}
+
+div.highlight .-Color[class*=-C247] {
+  color: #9E9E9E
+}
+
+div.highlight .-Color[class*=-BGC247] {
+  background-color: #9E9E9E
+}
+
+div.highlight .-Color[class*=-C248] {
+  color: #A8A8A8
+}
+
+div.highlight .-Color[class*=-BGC248] {
+  background-color: #A8A8A8
+}
+
+div.highlight .-Color[class*=-C249] {
+  color: #B2B2B2
+}
+
+div.highlight .-Color[class*=-BGC249] {
+  background-color: #B2B2B2
+}
+
+div.highlight .-Color[class*=-C250] {
+  color: #BCBCBC
+}
+
+div.highlight .-Color[class*=-BGC250] {
+  background-color: #BCBCBC
+}
+
+div.highlight .-Color[class*=-C251] {
+  color: #C6C6C6
+}
+
+div.highlight .-Color[class*=-BGC251] {
+  background-color: #C6C6C6
+}
+
+div.highlight .-Color[class*=-C252] {
+  color: #D0D0D0
+}
+
+div.highlight .-Color[class*=-BGC252] {
+  background-color: #D0D0D0
+}
+
+div.highlight .-Color[class*=-C253] {
+  color: #DADADA
+}
+
+div.highlight .-Color[class*=-BGC253] {
+  background-color: #DADADA
+}
+
+div.highlight .-Color[class*=-C254] {
+  color: #E4E4E4
+}
+
+div.highlight .-Color[class*=-BGC254] {
+  background-color: #E4E4E4
+}
+
+div.highlight .-Color[class*=-C255] {
+  color: #EEEEEE
+}
+
+div.highlight .-Color[class*=-BGC255] {
+  background-color: #EEEEEE
+}
diff --git a/_preview/13/_static/play-solid.svg b/_preview/13/_static/play-solid.svg
new file mode 100644
index 0000000..bcd81f7
--- /dev/null
+++ b/_preview/13/_static/play-solid.svg
@@ -0,0 +1 @@
+<svg aria-hidden="true" focusable="false" data-prefix="fas" data-icon="play" class="svg-inline--fa fa-play fa-w-14" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path fill="currentColor" d="M424.4 214.7L72.4 6.6C43.8-10.3 0 6.1 0 47.9V464c0 37.5 40.7 60.1 72.4 41.3l352-208c31.4-18.5 31.5-64.1 0-82.6z"></path></svg>
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diff --git a/_preview/13/_static/plus.png b/_preview/13/_static/plus.png
new file mode 100644
index 0000000000000000000000000000000000000000..7107cec93a979b9a5f64843235a16651d563ce2d
GIT binary patch
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literal 0
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diff --git a/_preview/13/_static/pygments.css b/_preview/13/_static/pygments.css
new file mode 100644
index 0000000..997797f
--- /dev/null
+++ b/_preview/13/_static/pygments.css
@@ -0,0 +1,152 @@
+html[data-theme="light"] .highlight pre { line-height: 125%; }
+html[data-theme="light"] .highlight td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight .hll { background-color: #7971292e }
+html[data-theme="light"] .highlight { background: #fefefe; color: #545454 }
+html[data-theme="light"] .highlight .c { color: #797129 } /* Comment */
+html[data-theme="light"] .highlight .err { color: #d91e18 } /* Error */
+html[data-theme="light"] .highlight .k { color: #7928a1 } /* Keyword */
+html[data-theme="light"] .highlight .l { color: #797129 } /* Literal */
+html[data-theme="light"] .highlight .n { color: #545454 } /* Name */
+html[data-theme="light"] .highlight .o { color: #008000 } /* Operator */
+html[data-theme="light"] .highlight .p { color: #545454 } /* Punctuation */
+html[data-theme="light"] .highlight .ch { color: #797129 } /* Comment.Hashbang */
+html[data-theme="light"] .highlight .cm { color: #797129 } /* Comment.Multiline */
+html[data-theme="light"] .highlight .cp { color: #797129 } /* Comment.Preproc */
+html[data-theme="light"] .highlight .cpf { color: #797129 } /* Comment.PreprocFile */
+html[data-theme="light"] .highlight .c1 { color: #797129 } /* Comment.Single */
+html[data-theme="light"] .highlight .cs { color: #797129 } /* Comment.Special */
+html[data-theme="light"] .highlight .gd { color: #007faa } /* Generic.Deleted */
+html[data-theme="light"] .highlight .ge { font-style: italic } /* Generic.Emph */
+html[data-theme="light"] .highlight .gh { color: #007faa } /* Generic.Heading */
+html[data-theme="light"] .highlight .gs { font-weight: bold } /* Generic.Strong */
+html[data-theme="light"] .highlight .gu { color: #007faa } /* Generic.Subheading */
+html[data-theme="light"] .highlight .kc { color: #7928a1 } /* Keyword.Constant */
+html[data-theme="light"] .highlight .kd { color: #7928a1 } /* Keyword.Declaration */
+html[data-theme="light"] .highlight .kn { color: #7928a1 } /* Keyword.Namespace */
+html[data-theme="light"] .highlight .kp { color: #7928a1 } /* Keyword.Pseudo */
+html[data-theme="light"] .highlight .kr { color: #7928a1 } /* Keyword.Reserved */
+html[data-theme="light"] .highlight .kt { color: #797129 } /* Keyword.Type */
+html[data-theme="light"] .highlight .ld { color: #797129 } /* Literal.Date */
+html[data-theme="light"] .highlight .m { color: #797129 } /* Literal.Number */
+html[data-theme="light"] .highlight .s { color: #008000 } /* Literal.String */
+html[data-theme="light"] .highlight .na { color: #797129 } /* Name.Attribute */
+html[data-theme="light"] .highlight .nb { color: #797129 } /* Name.Builtin */
+html[data-theme="light"] .highlight .nc { color: #007faa } /* Name.Class */
+html[data-theme="light"] .highlight .no { color: #007faa } /* Name.Constant */
+html[data-theme="light"] .highlight .nd { color: #797129 } /* Name.Decorator */
+html[data-theme="light"] .highlight .ni { color: #008000 } /* Name.Entity */
+html[data-theme="light"] .highlight .ne { color: #7928a1 } /* Name.Exception */
+html[data-theme="light"] .highlight .nf { color: #007faa } /* Name.Function */
+html[data-theme="light"] .highlight .nl { color: #797129 } /* Name.Label */
+html[data-theme="light"] .highlight .nn { color: #545454 } /* Name.Namespace */
+html[data-theme="light"] .highlight .nx { color: #545454 } /* Name.Other */
+html[data-theme="light"] .highlight .py { color: #007faa } /* Name.Property */
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+html[data-theme="light"] .highlight .mo { color: #797129 } /* Literal.Number.Oct */
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+html[data-theme="light"] .highlight .sb { color: #008000 } /* Literal.String.Backtick */
+html[data-theme="light"] .highlight .sc { color: #008000 } /* Literal.String.Char */
+html[data-theme="light"] .highlight .dl { color: #008000 } /* Literal.String.Delimiter */
+html[data-theme="light"] .highlight .sd { color: #008000 } /* Literal.String.Doc */
+html[data-theme="light"] .highlight .s2 { color: #008000 } /* Literal.String.Double */
+html[data-theme="light"] .highlight .se { color: #008000 } /* Literal.String.Escape */
+html[data-theme="light"] .highlight .sh { color: #008000 } /* Literal.String.Heredoc */
+html[data-theme="light"] .highlight .si { color: #008000 } /* Literal.String.Interpol */
+html[data-theme="light"] .highlight .sx { color: #008000 } /* Literal.String.Other */
+html[data-theme="light"] .highlight .sr { color: #d91e18 } /* Literal.String.Regex */
+html[data-theme="light"] .highlight .s1 { color: #008000 } /* Literal.String.Single */
+html[data-theme="light"] .highlight .ss { color: #007faa } /* Literal.String.Symbol */
+html[data-theme="light"] .highlight .bp { color: #797129 } /* Name.Builtin.Pseudo */
+html[data-theme="light"] .highlight .fm { color: #007faa } /* Name.Function.Magic */
+html[data-theme="light"] .highlight .vc { color: #d91e18 } /* Name.Variable.Class */
+html[data-theme="light"] .highlight .vg { color: #d91e18 } /* Name.Variable.Global */
+html[data-theme="light"] .highlight .vi { color: #d91e18 } /* Name.Variable.Instance */
+html[data-theme="light"] .highlight .vm { color: #797129 } /* Name.Variable.Magic */
+html[data-theme="light"] .highlight .il { color: #797129 } /* Literal.Number.Integer.Long */
+html[data-theme="dark"] .highlight pre { line-height: 125%; }
+html[data-theme="dark"] .highlight td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="dark"] .highlight span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="dark"] .highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+html[data-theme="dark"] .highlight span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+html[data-theme="dark"] .highlight .hll { background-color: #ffd9002e }
+html[data-theme="dark"] .highlight { background: #2b2b2b; color: #f8f8f2 }
+html[data-theme="dark"] .highlight .c { color: #ffd900 } /* Comment */
+html[data-theme="dark"] .highlight .err { color: #ffa07a } /* Error */
+html[data-theme="dark"] .highlight .k { color: #dcc6e0 } /* Keyword */
+html[data-theme="dark"] .highlight .l { color: #ffd900 } /* Literal */
+html[data-theme="dark"] .highlight .n { color: #f8f8f2 } /* Name */
+html[data-theme="dark"] .highlight .o { color: #abe338 } /* Operator */
+html[data-theme="dark"] .highlight .p { color: #f8f8f2 } /* Punctuation */
+html[data-theme="dark"] .highlight .ch { color: #ffd900 } /* Comment.Hashbang */
+html[data-theme="dark"] .highlight .cm { color: #ffd900 } /* Comment.Multiline */
+html[data-theme="dark"] .highlight .cp { color: #ffd900 } /* Comment.Preproc */
+html[data-theme="dark"] .highlight .cpf { color: #ffd900 } /* Comment.PreprocFile */
+html[data-theme="dark"] .highlight .c1 { color: #ffd900 } /* Comment.Single */
+html[data-theme="dark"] .highlight .cs { color: #ffd900 } /* Comment.Special */
+html[data-theme="dark"] .highlight .gd { color: #00e0e0 } /* Generic.Deleted */
+html[data-theme="dark"] .highlight .ge { font-style: italic } /* Generic.Emph */
+html[data-theme="dark"] .highlight .gh { color: #00e0e0 } /* Generic.Heading */
+html[data-theme="dark"] .highlight .gs { font-weight: bold } /* Generic.Strong */
+html[data-theme="dark"] .highlight .gu { color: #00e0e0 } /* Generic.Subheading */
+html[data-theme="dark"] .highlight .kc { color: #dcc6e0 } /* Keyword.Constant */
+html[data-theme="dark"] .highlight .kd { color: #dcc6e0 } /* Keyword.Declaration */
+html[data-theme="dark"] .highlight .kn { color: #dcc6e0 } /* Keyword.Namespace */
+html[data-theme="dark"] .highlight .kp { color: #dcc6e0 } /* Keyword.Pseudo */
+html[data-theme="dark"] .highlight .kr { color: #dcc6e0 } /* Keyword.Reserved */
+html[data-theme="dark"] .highlight .kt { color: #ffd900 } /* Keyword.Type */
+html[data-theme="dark"] .highlight .ld { color: #ffd900 } /* Literal.Date */
+html[data-theme="dark"] .highlight .m { color: #ffd900 } /* Literal.Number */
+html[data-theme="dark"] .highlight .s { color: #abe338 } /* Literal.String */
+html[data-theme="dark"] .highlight .na { color: #ffd900 } /* Name.Attribute */
+html[data-theme="dark"] .highlight .nb { color: #ffd900 } /* Name.Builtin */
+html[data-theme="dark"] .highlight .nc { color: #00e0e0 } /* Name.Class */
+html[data-theme="dark"] .highlight .no { color: #00e0e0 } /* Name.Constant */
+html[data-theme="dark"] .highlight .nd { color: #ffd900 } /* Name.Decorator */
+html[data-theme="dark"] .highlight .ni { color: #abe338 } /* Name.Entity */
+html[data-theme="dark"] .highlight .ne { color: #dcc6e0 } /* Name.Exception */
+html[data-theme="dark"] .highlight .nf { color: #00e0e0 } /* Name.Function */
+html[data-theme="dark"] .highlight .nl { color: #ffd900 } /* Name.Label */
+html[data-theme="dark"] .highlight .nn { color: #f8f8f2 } /* Name.Namespace */
+html[data-theme="dark"] .highlight .nx { color: #f8f8f2 } /* Name.Other */
+html[data-theme="dark"] .highlight .py { color: #00e0e0 } /* Name.Property */
+html[data-theme="dark"] .highlight .nt { color: #00e0e0 } /* Name.Tag */
+html[data-theme="dark"] .highlight .nv { color: #ffa07a } /* Name.Variable */
+html[data-theme="dark"] .highlight .ow { color: #dcc6e0 } /* Operator.Word */
+html[data-theme="dark"] .highlight .pm { color: #f8f8f2 } /* Punctuation.Marker */
+html[data-theme="dark"] .highlight .w { color: #f8f8f2 } /* Text.Whitespace */
+html[data-theme="dark"] .highlight .mb { color: #ffd900 } /* Literal.Number.Bin */
+html[data-theme="dark"] .highlight .mf { color: #ffd900 } /* Literal.Number.Float */
+html[data-theme="dark"] .highlight .mh { color: #ffd900 } /* Literal.Number.Hex */
+html[data-theme="dark"] .highlight .mi { color: #ffd900 } /* Literal.Number.Integer */
+html[data-theme="dark"] .highlight .mo { color: #ffd900 } /* Literal.Number.Oct */
+html[data-theme="dark"] .highlight .sa { color: #abe338 } /* Literal.String.Affix */
+html[data-theme="dark"] .highlight .sb { color: #abe338 } /* Literal.String.Backtick */
+html[data-theme="dark"] .highlight .sc { color: #abe338 } /* Literal.String.Char */
+html[data-theme="dark"] .highlight .dl { color: #abe338 } /* Literal.String.Delimiter */
+html[data-theme="dark"] .highlight .sd { color: #abe338 } /* Literal.String.Doc */
+html[data-theme="dark"] .highlight .s2 { color: #abe338 } /* Literal.String.Double */
+html[data-theme="dark"] .highlight .se { color: #abe338 } /* Literal.String.Escape */
+html[data-theme="dark"] .highlight .sh { color: #abe338 } /* Literal.String.Heredoc */
+html[data-theme="dark"] .highlight .si { color: #abe338 } /* Literal.String.Interpol */
+html[data-theme="dark"] .highlight .sx { color: #abe338 } /* Literal.String.Other */
+html[data-theme="dark"] .highlight .sr { color: #ffa07a } /* Literal.String.Regex */
+html[data-theme="dark"] .highlight .s1 { color: #abe338 } /* Literal.String.Single */
+html[data-theme="dark"] .highlight .ss { color: #00e0e0 } /* Literal.String.Symbol */
+html[data-theme="dark"] .highlight .bp { color: #ffd900 } /* Name.Builtin.Pseudo */
+html[data-theme="dark"] .highlight .fm { color: #00e0e0 } /* Name.Function.Magic */
+html[data-theme="dark"] .highlight .vc { color: #ffa07a } /* Name.Variable.Class */
+html[data-theme="dark"] .highlight .vg { color: #ffa07a } /* Name.Variable.Global */
+html[data-theme="dark"] .highlight .vi { color: #ffa07a } /* Name.Variable.Instance */
+html[data-theme="dark"] .highlight .vm { color: #ffd900 } /* Name.Variable.Magic */
+html[data-theme="dark"] .highlight .il { color: #ffd900 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/_preview/13/_static/sbt-webpack-macros.html b/_preview/13/_static/sbt-webpack-macros.html
new file mode 100644
index 0000000..6cbf559
--- /dev/null
+++ b/_preview/13/_static/sbt-webpack-macros.html
@@ -0,0 +1,11 @@
+<!--
+  All these macros are auto-generated and must **NOT** be edited by hand.
+  See the webpack.config.js file, to learn more about how this is generated.
+-->
+{% macro head_pre_bootstrap() %}
+  <link href="{{ pathto('_static/styles/sphinx-book-theme.css', 1) }}" rel="stylesheet">
+{% endmacro %}
+
+{% macro body_post() %}
+  <script src="{{ pathto('_static/scripts/sphinx-book-theme.js', 1) }}"></script>
+{% endmacro %}
diff --git a/_preview/13/_static/scripts/bootstrap.js b/_preview/13/_static/scripts/bootstrap.js
new file mode 100644
index 0000000..c8178de
--- /dev/null
+++ b/_preview/13/_static/scripts/bootstrap.js
@@ -0,0 +1,3 @@
+/*! For license information please see bootstrap.js.LICENSE.txt */
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this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const Fe=".bs.carousel",He=".data-api",Be="ArrowLeft",We="ArrowRight",ze="next",Re="prev",qe="left",Ve="right",Ye=`slide${Fe}`,Ke=`slid${Fe}`,Qe=`keydown${Fe}`,Xe=`mouseenter${Fe}`,Ue=`mouseleave${Fe}`,Ge=`dragstart${Fe}`,Je=`load${Fe}${He}`,Ze=`click${Fe}${He}`,ti="carousel",ei="active",ii=".active",ni=".carousel-item",si=ii+ni,oi={[Be]:Ve,[We]:qe},ri={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},ai={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class li extends 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i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?ze:Re;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&fe.on(this._element,Qe,(t=>this._keydown(t))),"hover"===this._config.pause&&(fe.on(this._element,Xe,(()=>this.pause())),fe.on(this._element,Ue,(()=>this._maybeEnableCycle()))),this._config.touch&&je.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of we.find(".carousel-item img",this._element))fe.on(t,Ge,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(qe)),rightCallback:()=>this._slide(this._directionToOrder(Ve)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new je(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=oi[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=we.findOne(ii,this._indicatorsElement);e.classList.remove(ei),e.removeAttribute("aria-current");const i=we.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(ei),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===ze,s=e||Gt(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>fe.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(Ye).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(ei),i.classList.remove(ei,c,l),this._isSliding=!1,r(Ke)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return we.findOne(si,this._element)}_getItems(){return we.find(ni,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===qe?Re:ze:t===qe?ze:Re}_orderToDirection(t){return Kt()?t===Re?qe:Ve:t===Re?Ve:qe}static jQueryInterface(t){return this.each((function(){const e=li.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}fe.on(document,Ze,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=we.getElementFromSelector(this);if(!e||!e.classList.contains(ti))return;t.preventDefault();const i=li.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===_e.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),fe.on(window,Je,(()=>{const t=we.find('[data-bs-ride="carousel"]');for(const e of t)li.getOrCreateInstance(e)})),Qt(li);const ci=".bs.collapse",hi=`show${ci}`,di=`shown${ci}`,ui=`hide${ci}`,fi=`hidden${ci}`,pi=`click${ci}.data-api`,mi="show",gi="collapse",_i="collapsing",bi=`:scope .${gi} .${gi}`,vi='[data-bs-toggle="collapse"]',yi={parent:null,toggle:!0},wi={parent:"(null|element)",toggle:"boolean"};class Ei extends ve{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=we.find(vi);for(const t of i){const e=we.getSelectorFromElement(t),i=we.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return yi}static get DefaultType(){return wi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>Ei.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(fe.trigger(this._element,hi).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(gi),this._element.classList.add(_i),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(_i),this._element.classList.add(gi,mi),this._element.style[e]="",fe.trigger(this._element,di)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(fe.trigger(this._element,ui).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(_i),this._element.classList.remove(gi,mi);for(const t of this._triggerArray){const e=we.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(_i),this._element.classList.add(gi),fe.trigger(this._element,fi)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(mi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(vi);for(const e of t){const t=we.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=we.find(bi,this._config.parent);return we.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=Ei.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}fe.on(document,pi,vi,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of we.getMultipleElementsFromSelector(this))Ei.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(Ei);const Ai="dropdown",Ti=".bs.dropdown",Ci=".data-api",Oi="ArrowUp",xi="ArrowDown",ki=`hide${Ti}`,Li=`hidden${Ti}`,Si=`show${Ti}`,Di=`shown${Ti}`,$i=`click${Ti}${Ci}`,Ii=`keydown${Ti}${Ci}`,Ni=`keyup${Ti}${Ci}`,Pi="show",Mi='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',ji=`${Mi}.${Pi}`,Fi=".dropdown-menu",Hi=Kt()?"top-end":"top-start",Bi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",zi=Kt()?"bottom-start":"bottom-end",Ri=Kt()?"left-start":"right-start",qi=Kt()?"right-start":"left-start",Vi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Yi={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Ki extends ve{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=we.next(this._element,Fi)[0]||we.prev(this._element,Fi)[0]||we.findOne(Fi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return Vi}static get DefaultType(){return Yi}static get NAME(){return Ai}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Wt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!fe.trigger(this._element,Si,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Pi),this._element.classList.add(Pi),fe.trigger(this._element,Di,t)}}hide(){if(Wt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!fe.trigger(this._element,ki,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Pi),this._element.classList.remove(Pi),this._element.setAttribute("aria-expanded","false"),_e.removeDataAttribute(this._menu,"popper"),fe.trigger(this._element,Li,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Ft(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ai.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Ft(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Pi)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Ri;if(t.classList.contains("dropstart"))return qi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Bi:Hi:e?zi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(_e.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...Xt(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=we.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Bt(t)));i.length&&Gt(i,e,t===xi,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Ki.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=we.find(ji);for(const i of e){const e=Ki.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Oi,xi].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Mi)?this:we.prev(this,Mi)[0]||we.next(this,Mi)[0]||we.findOne(Mi,t.delegateTarget.parentNode),o=Ki.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}fe.on(document,Ii,Mi,Ki.dataApiKeydownHandler),fe.on(document,Ii,Fi,Ki.dataApiKeydownHandler),fe.on(document,$i,Ki.clearMenus),fe.on(document,Ni,Ki.clearMenus),fe.on(document,$i,Mi,(function(t){t.preventDefault(),Ki.getOrCreateInstance(this).toggle()})),Qt(Ki);const Qi="backdrop",Xi="show",Ui=`mousedown.bs.${Qi}`,Gi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Ji={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Zi extends be{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Gi}static get DefaultType(){return Ji}static get NAME(){return Qi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Xi),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Xi),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(fe.off(this._element,Ui),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),fe.on(t,Ui,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Ut(t,this._getElement(),this._config.isAnimated)}}const tn=".bs.focustrap",en=`focusin${tn}`,nn=`keydown.tab${tn}`,sn="backward",on={autofocus:!0,trapElement:null},rn={autofocus:"boolean",trapElement:"element"};class an extends be{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return on}static get DefaultType(){return rn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),fe.off(document,tn),fe.on(document,en,(t=>this._handleFocusin(t))),fe.on(document,nn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,fe.off(document,tn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=we.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===sn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?sn:"forward")}}const ln=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",cn=".sticky-top",hn="padding-right",dn="margin-right";class un{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,hn,(e=>e+t)),this._setElementAttributes(ln,hn,(e=>e+t)),this._setElementAttributes(cn,dn,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,hn),this._resetElementAttributes(ln,hn),this._resetElementAttributes(cn,dn)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&_e.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=_e.getDataAttribute(t,e);null!==i?(_e.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Ft(t))e(t);else for(const i of we.find(t,this._element))e(i)}}const fn=".bs.modal",pn=`hide${fn}`,mn=`hidePrevented${fn}`,gn=`hidden${fn}`,_n=`show${fn}`,bn=`shown${fn}`,vn=`resize${fn}`,yn=`click.dismiss${fn}`,wn=`mousedown.dismiss${fn}`,En=`keydown.dismiss${fn}`,An=`click${fn}.data-api`,Tn="modal-open",Cn="show",On="modal-static",xn={backdrop:!0,focus:!0,keyboard:!0},kn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class Ln extends ve{constructor(t,e){super(t,e),this._dialog=we.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new un,this._addEventListeners()}static get Default(){return xn}static get DefaultType(){return kn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||fe.trigger(this._element,_n,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(Tn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(fe.trigger(this._element,pn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(Cn),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){fe.off(window,fn),fe.off(this._dialog,fn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Zi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new an({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=we.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(Cn),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,fe.trigger(this._element,bn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){fe.on(this._element,En,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),fe.on(window,vn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),fe.on(this._element,wn,(t=>{fe.one(this._element,yn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Tn),this._resetAdjustments(),this._scrollBar.reset(),fe.trigger(this._element,gn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(fe.trigger(this._element,mn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(On)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(On),this._queueCallback((()=>{this._element.classList.remove(On),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Ln.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}fe.on(document,An,'[data-bs-toggle="modal"]',(function(t){const e=we.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),fe.one(e,_n,(t=>{t.defaultPrevented||fe.one(e,gn,(()=>{Bt(this)&&this.focus()}))}));const i=we.findOne(".modal.show");i&&Ln.getInstance(i).hide(),Ln.getOrCreateInstance(e).toggle(this)})),Ee(Ln),Qt(Ln);const Sn=".bs.offcanvas",Dn=".data-api",$n=`load${Sn}${Dn}`,In="show",Nn="showing",Pn="hiding",Mn=".offcanvas.show",jn=`show${Sn}`,Fn=`shown${Sn}`,Hn=`hide${Sn}`,Bn=`hidePrevented${Sn}`,Wn=`hidden${Sn}`,zn=`resize${Sn}`,Rn=`click${Sn}${Dn}`,qn=`keydown.dismiss${Sn}`,Vn={backdrop:!0,keyboard:!0,scroll:!1},Yn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Kn extends ve{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return Vn}static get DefaultType(){return Yn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||fe.trigger(this._element,jn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new un).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Nn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(In),this._element.classList.remove(Nn),fe.trigger(this._element,Fn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(fe.trigger(this._element,Hn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(Pn),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(In,Pn),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new un).reset(),fe.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Zi({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():fe.trigger(this._element,Bn)}:null})}_initializeFocusTrap(){return new an({trapElement:this._element})}_addEventListeners(){fe.on(this._element,qn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():fe.trigger(this._element,Bn))}))}static jQueryInterface(t){return this.each((function(){const e=Kn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}fe.on(document,Rn,'[data-bs-toggle="offcanvas"]',(function(t){const e=we.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this))return;fe.one(e,Wn,(()=>{Bt(this)&&this.focus()}));const i=we.findOne(Mn);i&&i!==e&&Kn.getInstance(i).hide(),Kn.getOrCreateInstance(e).toggle(this)})),fe.on(window,$n,(()=>{for(const t of we.find(Mn))Kn.getOrCreateInstance(t).show()})),fe.on(window,zn,(()=>{for(const t of we.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Kn.getOrCreateInstance(t).hide()})),Ee(Kn),Qt(Kn);const Qn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],dd:[],div:[],dl:[],dt:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Xn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Un=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Gn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Xn.has(i)||Boolean(Un.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Jn={allowList:Qn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Zn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},ts={entry:"(string|element|function|null)",selector:"(string|element)"};class es extends be{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Jn}static get DefaultType(){return Zn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},ts)}_setContent(t,e,i){const n=we.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Ft(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Gn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return Xt(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const is=new Set(["sanitize","allowList","sanitizeFn"]),ns="fade",ss="show",os=".tooltip-inner",rs=".modal",as="hide.bs.modal",ls="hover",cs="focus",hs={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},ds={allowList:Qn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},us={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class fs extends ve{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return ds}static get DefaultType(){return us}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),fe.off(this._element.closest(rs),as,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=fe.trigger(this._element,this.constructor.eventName("show")),e=(zt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),fe.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ss),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._queueCallback((()=>{fe.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!fe.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ss),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._activeTrigger.click=!1,this._activeTrigger[cs]=!1,this._activeTrigger[ls]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),fe.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ns,ss),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ns),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new es({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{[os]:this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ns)}_isShown(){return this.tip&&this.tip.classList.contains(ss)}_createPopper(t){const e=Xt(this._config.placement,[this,t,this._element]),i=hs[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return Xt(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...Xt(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)fe.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ls?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ls?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");fe.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?cs:ls]=!0,e._enter()})),fe.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?cs:ls]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},fe.on(this._element.closest(rs),as,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=_e.getDataAttributes(this._element);for(const t of Object.keys(e))is.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=fs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(fs);const ps=".popover-header",ms=".popover-body",gs={...fs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},_s={...fs.DefaultType,content:"(null|string|element|function)"};class bs extends fs{static get Default(){return gs}static get DefaultType(){return _s}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{[ps]:this._getTitle(),[ms]:this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=bs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(bs);const vs=".bs.scrollspy",ys=`activate${vs}`,ws=`click${vs}`,Es=`load${vs}.data-api`,As="active",Ts="[href]",Cs=".nav-link",Os=`${Cs}, .nav-item > ${Cs}, .list-group-item`,xs={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},ks={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ls extends ve{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return xs}static get DefaultType(){return ks}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(fe.off(this._config.target,ws),fe.on(this._config.target,ws,Ts,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=we.find(Ts,this._config.target);for(const e of t){if(!e.hash||Wt(e))continue;const t=we.findOne(decodeURI(e.hash),this._element);Bt(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(As),this._activateParents(t),fe.trigger(this._element,ys,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))we.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(As);else for(const e of we.parents(t,".nav, .list-group"))for(const t of we.prev(e,Os))t.classList.add(As)}_clearActiveClass(t){t.classList.remove(As);const e=we.find(`${Ts}.${As}`,t);for(const t of e)t.classList.remove(As)}static jQueryInterface(t){return this.each((function(){const e=Ls.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(window,Es,(()=>{for(const t of we.find('[data-bs-spy="scroll"]'))Ls.getOrCreateInstance(t)})),Qt(Ls);const Ss=".bs.tab",Ds=`hide${Ss}`,$s=`hidden${Ss}`,Is=`show${Ss}`,Ns=`shown${Ss}`,Ps=`click${Ss}`,Ms=`keydown${Ss}`,js=`load${Ss}`,Fs="ArrowLeft",Hs="ArrowRight",Bs="ArrowUp",Ws="ArrowDown",zs="Home",Rs="End",qs="active",Vs="fade",Ys="show",Ks=".dropdown-toggle",Qs=`:not(${Ks})`,Xs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Us=`.nav-link${Qs}, .list-group-item${Qs}, [role="tab"]${Qs}, ${Xs}`,Gs=`.${qs}[data-bs-toggle="tab"], .${qs}[data-bs-toggle="pill"], .${qs}[data-bs-toggle="list"]`;class Js extends ve{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),fe.on(this._element,Ms,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?fe.trigger(e,Ds,{relatedTarget:t}):null;fe.trigger(t,Is,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(qs),this._activate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),fe.trigger(t,Ns,{relatedTarget:e})):t.classList.add(Ys)}),t,t.classList.contains(Vs)))}_deactivate(t,e){t&&(t.classList.remove(qs),t.blur(),this._deactivate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),fe.trigger(t,$s,{relatedTarget:e})):t.classList.remove(Ys)}),t,t.classList.contains(Vs)))}_keydown(t){if(![Fs,Hs,Bs,Ws,zs,Rs].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!Wt(t)));let i;if([zs,Rs].includes(t.key))i=e[t.key===zs?0:e.length-1];else{const n=[Hs,Ws].includes(t.key);i=Gt(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Js.getOrCreateInstance(i).show())}_getChildren(){return we.find(Us,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const 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"${t}"`);e[t]()}}))}}fe.on(document,Ps,Xs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this)||Js.getOrCreateInstance(this).show()})),fe.on(window,js,(()=>{for(const t of we.find(Gs))Js.getOrCreateInstance(t)})),Qt(Js);const Zs=".bs.toast",to=`mouseover${Zs}`,eo=`mouseout${Zs}`,io=`focusin${Zs}`,no=`focusout${Zs}`,so=`hide${Zs}`,oo=`hidden${Zs}`,ro=`show${Zs}`,ao=`shown${Zs}`,lo="hide",co="show",ho="showing",uo={animation:"boolean",autohide:"boolean",delay:"number"},fo={animation:!0,autohide:!0,delay:5e3};class po extends ve{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return fo}static get DefaultType(){return uo}static get NAME(){return"toast"}show(){fe.trigger(this._element,ro).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(lo),qt(this._element),this._element.classList.add(co,ho),this._queueCallback((()=>{this._element.classList.remove(ho),fe.trigger(this._element,ao),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(fe.trigger(this._element,so).defaultPrevented||(this._element.classList.add(ho),this._queueCallback((()=>{this._element.classList.add(lo),this._element.classList.remove(ho,co),fe.trigger(this._element,oo)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(co),super.dispose()}isShown(){return this._element.classList.contains(co)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){fe.on(this._element,to,(t=>this._onInteraction(t,!0))),fe.on(this._element,eo,(t=>this._onInteraction(t,!1))),fe.on(this._element,io,(t=>this._onInteraction(t,!0))),fe.on(this._element,no,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=po.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}function mo(t){"loading"!=document.readyState?t():document.addEventListener("DOMContentLoaded",t)}Ee(po),Qt(po),mo((function(){[].slice.call(document.querySelectorAll('[data-bs-toggle="tooltip"]')).map((function(t){return new fs(t,{delay:{show:500,hide:100}})}))})),mo((function(){document.getElementById("pst-back-to-top").addEventListener("click",(function(){document.body.scrollTop=0,document.documentElement.scrollTop=0}))})),mo((function(){var t=document.getElementById("pst-back-to-top"),e=document.getElementsByClassName("bd-header")[0].getBoundingClientRect();window.addEventListener("scroll",(function(){this.oldScroll>this.scrollY&&this.scrollY>e.bottom?t.style.display="block":t.style.display="none",this.oldScroll=this.scrollY}))})),window.bootstrap=i})();
+//# sourceMappingURL=bootstrap.js.map
\ No newline at end of file
diff --git a/_preview/13/_static/scripts/bootstrap.js.LICENSE.txt b/_preview/13/_static/scripts/bootstrap.js.LICENSE.txt
new file mode 100644
index 0000000..28755c2
--- /dev/null
+++ b/_preview/13/_static/scripts/bootstrap.js.LICENSE.txt
@@ -0,0 +1,5 @@
+/*!
+  * Bootstrap v5.3.3 (https://getbootstrap.com/)
+  * Copyright 2011-2024 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
diff --git a/_preview/13/_static/scripts/bootstrap.js.map b/_preview/13/_static/scripts/bootstrap.js.map
new file mode 100644
index 0000000..e9e8158
--- /dev/null
+++ b/_preview/13/_static/scripts/bootstrap.js.map
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(element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        });\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref) {\n        var name = _ref.name,\n            _ref$options = _ref.options,\n            options = _ref$options === void 0 ? {} : _ref$options,\n            effect = _ref.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n  * Bootstrap v5.3.3 (https://getbootstrap.com/)\n  * Copyright 2011-2024 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n  */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map();\nconst Data = {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map());\n    }\n    const instanceMap = elementMap.get(element);\n\n    // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n      return;\n    }\n    instanceMap.set(key, instance);\n  },\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null;\n    }\n    return null;\n  },\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return;\n    }\n    const instanceMap = elementMap.get(element);\n    instanceMap.delete(key);\n\n    // free up element references if there are no instances left for an element\n    if (instanceMap.size === 0) {\n      elementMap.delete(element);\n    }\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend';\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n  if (selector && window.CSS && window.CSS.escape) {\n    // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n    selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`);\n  }\n  return selector;\n};\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`;\n  }\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID);\n  } while (document.getElementById(prefix));\n  return prefix;\n};\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0;\n  }\n\n  // Get transition-duration of the element\n  let {\n    transitionDuration,\n    transitionDelay\n  } = window.getComputedStyle(element);\n  const floatTransitionDuration = Number.parseFloat(transitionDuration);\n  const floatTransitionDelay = Number.parseFloat(transitionDelay);\n\n  // Return 0 if element or transition duration is not found\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0;\n  }\n\n  // If multiple durations are defined, take the first\n  transitionDuration = transitionDuration.split(',')[0];\n  transitionDelay = transitionDelay.split(',')[0];\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END));\n};\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false;\n  }\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0];\n  }\n  return typeof object.nodeType !== 'undefined';\n};\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object;\n  }\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(parseSelector(object));\n  }\n  return null;\n};\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false;\n  }\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible';\n  // Handle `details` element as its content may falsie appear visible when it is closed\n  const closedDetails = element.closest('details:not([open])');\n  if (!closedDetails) {\n    return elementIsVisible;\n  }\n  if (closedDetails !== element) {\n    const summary = element.closest('summary');\n    if (summary && summary.parentNode !== closedDetails) {\n      return false;\n    }\n    if (summary === null) {\n      return false;\n    }\n  }\n  return elementIsVisible;\n};\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true;\n  }\n  if (element.classList.contains('disabled')) {\n    return true;\n  }\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled;\n  }\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null;\n  }\n\n  // Can find the shadow root otherwise it'll return the document\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode();\n    return root instanceof ShadowRoot ? root : null;\n  }\n  if (element instanceof ShadowRoot) {\n    return element;\n  }\n\n  // when we don't find a shadow root\n  if (!element.parentNode) {\n    return null;\n  }\n  return findShadowRoot(element.parentNode);\n};\nconst noop = () => {};\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n  element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery;\n  }\n  return null;\n};\nconst DOMContentLoadedCallbacks = [];\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback();\n        }\n      });\n    }\n    DOMContentLoadedCallbacks.push(callback);\n  } else {\n    callback();\n  }\n};\nconst isRTL = () => document.documentElement.dir === 'rtl';\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery();\n    /* istanbul ignore if */\n    if ($) {\n      const name = plugin.NAME;\n      const JQUERY_NO_CONFLICT = $.fn[name];\n      $.fn[name] = plugin.jQueryInterface;\n      $.fn[name].Constructor = plugin;\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT;\n        return plugin.jQueryInterface;\n      };\n    }\n  });\n};\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n  return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue;\n};\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback);\n    return;\n  }\n  const durationPadding = 5;\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n  let called = false;\n  const handler = ({\n    target\n  }) => {\n    if (target !== transitionElement) {\n      return;\n    }\n    called = true;\n    transitionElement.removeEventListener(TRANSITION_END, handler);\n    execute(callback);\n  };\n  transitionElement.addEventListener(TRANSITION_END, handler);\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement);\n    }\n  }, emulatedDuration);\n};\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length;\n  let index = list.indexOf(activeElement);\n\n  // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n  }\n  index += shouldGetNext ? 1 : -1;\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength;\n  }\n  return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\nlet uidEvent = 1;\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element);\n  element.uidEvent = uid;\n  eventRegistry[uid] = eventRegistry[uid] || {};\n  return eventRegistry[uid];\n}\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, {\n      delegateTarget: element\n    });\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn);\n    }\n    return fn.apply(element, [event]);\n  };\n}\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector);\n    for (let {\n      target\n    } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue;\n        }\n        hydrateObj(event, {\n          delegateTarget: target\n        });\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn);\n        }\n        return fn.apply(target, [event]);\n      }\n    }\n  };\n}\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string';\n  // TODO: tooltip passes `false` instead of selector, so we need to check\n  const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n  let typeEvent = getTypeEvent(originalTypeEvent);\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent;\n  }\n  return [isDelegated, callable, typeEvent];\n}\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return;\n  }\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n\n  // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n          return fn.call(this, event);\n        }\n      };\n    };\n    callable = wrapFunction(callable);\n  }\n  const events = getElementEvents(element);\n  const handlers = events[typeEvent] || (events[typeEvent] = {});\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff;\n    return;\n  }\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n  const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n  fn.delegationSelector = isDelegated ? handler : null;\n  fn.callable = callable;\n  fn.oneOff = oneOff;\n  fn.uidEvent = uid;\n  handlers[uid] = fn;\n  element.addEventListener(typeEvent, fn, isDelegated);\n}\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector);\n  if (!fn) {\n    return;\n  }\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n  delete events[typeEvent][fn.uidEvent];\n}\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {};\n  for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n    }\n  }\n}\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '');\n  return customEvents[event] || event;\n}\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false);\n  },\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true);\n  },\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return;\n    }\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n    const inNamespace = typeEvent !== originalTypeEvent;\n    const events = getElementEvents(element);\n    const storeElementEvent = events[typeEvent] || {};\n    const isNamespace = originalTypeEvent.startsWith('.');\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return;\n      }\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n      return;\n    }\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n      }\n    }\n    for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '');\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n      }\n    }\n  },\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null;\n    }\n    const $ = getjQuery();\n    const typeEvent = getTypeEvent(event);\n    const inNamespace = event !== typeEvent;\n    let jQueryEvent = null;\n    let bubbles = true;\n    let nativeDispatch = true;\n    let defaultPrevented = false;\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args);\n      $(element).trigger(jQueryEvent);\n      bubbles = !jQueryEvent.isPropagationStopped();\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n      defaultPrevented = jQueryEvent.isDefaultPrevented();\n    }\n    const evt = hydrateObj(new Event(event, {\n      bubbles,\n      cancelable: true\n    }), args);\n    if (defaultPrevented) {\n      evt.preventDefault();\n    }\n    if (nativeDispatch) {\n      element.dispatchEvent(evt);\n    }\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault();\n    }\n    return evt;\n  }\n};\nfunction hydrateObj(obj, meta = {}) {\n  for (const [key, value] of Object.entries(meta)) {\n    try {\n      obj[key] = value;\n    } catch (_unused) {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n        get() {\n          return value;\n        }\n      });\n    }\n  }\n  return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true;\n  }\n  if (value === 'false') {\n    return false;\n  }\n  if (value === Number(value).toString()) {\n    return Number(value);\n  }\n  if (value === '' || value === 'null') {\n    return null;\n  }\n  if (typeof value !== 'string') {\n    return value;\n  }\n  try {\n    return JSON.parse(decodeURIComponent(value));\n  } catch (_unused) {\n    return value;\n  }\n}\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n  },\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n  },\n  getDataAttributes(element) {\n    if (!element) {\n      return {};\n    }\n    const attributes = {};\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '');\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n      attributes[pureKey] = normalizeData(element.dataset[key]);\n    }\n    return attributes;\n  },\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {};\n  }\n  static get DefaultType() {\n    return {};\n  }\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!');\n  }\n  _getConfig(config) {\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n  _configAfterMerge(config) {\n    return config;\n  }\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n    return {\n      ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    };\n  }\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const [property, expectedTypes] of Object.entries(configTypes)) {\n      const value = config[property];\n      const valueType = isElement(value) ? 'element' : toType(value);\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n      }\n    }\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.3';\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super();\n    element = getElement(element);\n    if (!element) {\n      return;\n    }\n    this._element = element;\n    this._config = this._getConfig(config);\n    Data.set(this._element, this.constructor.DATA_KEY, this);\n  }\n\n  // Public\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY);\n    EventHandler.off(this._element, this.constructor.EVENT_KEY);\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null;\n    }\n  }\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated);\n  }\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n\n  // Static\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY);\n  }\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n  }\n  static get VERSION() {\n    return VERSION;\n  }\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`;\n  }\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`;\n  }\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target');\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href');\n\n    // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n    if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n      return null;\n    }\n\n    // Just in case some CMS puts out a full URL with the anchor appended\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n    }\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n  }\n  return selector ? selector.split(',').map(sel => parseSelector(sel)).join(',') : null;\n};\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n  },\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector);\n  },\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector));\n  },\n  parents(element, selector) {\n    const parents = [];\n    let ancestor = element.parentNode.closest(selector);\n    while (ancestor) {\n      parents.push(ancestor);\n      ancestor = ancestor.parentNode.closest(selector);\n    }\n    return parents;\n  },\n  prev(element, selector) {\n    let previous = element.previousElementSibling;\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous];\n      }\n      previous = previous.previousElementSibling;\n    }\n    return [];\n  },\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling;\n    while (next) {\n      if (next.matches(selector)) {\n        return [next];\n      }\n      next = next.nextElementSibling;\n    }\n    return [];\n  },\n  focusableChildren(element) {\n    const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n  },\n  getSelectorFromElement(element) {\n    const selector = getSelector(element);\n    if (selector) {\n      return SelectorEngine.findOne(selector) ? selector : null;\n    }\n    return null;\n  },\n  getElementFromSelector(element) {\n    const selector = getSelector(element);\n    return selector ? SelectorEngine.findOne(selector) : null;\n  },\n  getMultipleElementsFromSelector(element) {\n    const selector = getSelector(element);\n    return selector ? SelectorEngine.find(selector) : [];\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n  const name = component.NAME;\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault();\n    }\n    if (isDisabled(this)) {\n      return;\n    }\n    const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`);\n    const instance = component.getOrCreateInstance(target);\n\n    // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n    instance[method]();\n  });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$f;\n  }\n\n  // Public\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n    if (closeEvent.defaultPrevented) {\n      return;\n    }\n    this._element.classList.remove(CLASS_NAME_SHOW$8);\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n  }\n\n  // Private\n  _destroyElement() {\n    this._element.remove();\n    EventHandler.trigger(this._element, EVENT_CLOSED);\n    this.dispose();\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](this);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close');\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$e;\n  }\n\n  // Public\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this);\n      if (config === 'toggle') {\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n  event.preventDefault();\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n  const data = Button.getOrCreateInstance(button);\n  data.toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n};\nconst DefaultType$c = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n};\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super();\n    this._element = element;\n    if (!element || !Swipe.isSupported()) {\n      return;\n    }\n    this._config = this._getConfig(config);\n    this._deltaX = 0;\n    this._supportPointerEvents = Boolean(window.PointerEvent);\n    this._initEvents();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$c;\n  }\n  static get DefaultType() {\n    return DefaultType$c;\n  }\n  static get NAME() {\n    return NAME$d;\n  }\n\n  // Public\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY$9);\n  }\n\n  // Private\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX;\n      return;\n    }\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX;\n    }\n  }\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX;\n    }\n    this._handleSwipe();\n    execute(this._config.endCallback);\n  }\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n  }\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX);\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return;\n    }\n    const direction = absDeltaX / this._deltaX;\n    this._deltaX = 0;\n    if (!direction) {\n      return;\n    }\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n  }\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n    }\n  }\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n  }\n\n  // Static\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n};\nconst DefaultType$b = {\n  interval: '(number|boolean)',\n  // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._interval = null;\n    this._activeElement = null;\n    this._isSliding = false;\n    this.touchTimeout = null;\n    this._swipeHelper = null;\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n    this._addEventListeners();\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$b;\n  }\n  static get DefaultType() {\n    return DefaultType$b;\n  }\n  static get NAME() {\n    return NAME$c;\n  }\n\n  // Public\n  next() {\n    this._slide(ORDER_NEXT);\n  }\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next();\n    }\n  }\n  prev() {\n    this._slide(ORDER_PREV);\n  }\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element);\n    }\n    this._clearInterval();\n  }\n  cycle() {\n    this._clearInterval();\n    this._updateInterval();\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n  }\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return;\n    }\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n      return;\n    }\n    this.cycle();\n  }\n  to(index) {\n    const items = this._getItems();\n    if (index > items.length - 1 || index < 0) {\n      return;\n    }\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n      return;\n    }\n    const activeIndex = this._getItemIndex(this._getActive());\n    if (activeIndex === index) {\n      return;\n    }\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n    this._slide(order, items[index]);\n  }\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose();\n    }\n    super.dispose();\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval;\n    return config;\n  }\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n    }\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n      EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n    }\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners();\n    }\n  }\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n    }\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return;\n      }\n\n      // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n      this.pause();\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout);\n      }\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n    };\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    };\n    this._swipeHelper = new Swipe(this._element, swipeConfig);\n  }\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return;\n    }\n    const direction = KEY_TO_DIRECTION[event.key];\n    if (direction) {\n      event.preventDefault();\n      this._slide(this._directionToOrder(direction));\n    }\n  }\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element);\n  }\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return;\n    }\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n    activeIndicator.removeAttribute('aria-current');\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n      newActiveIndicator.setAttribute('aria-current', 'true');\n    }\n  }\n  _updateInterval() {\n    const element = this._activeElement || this._getActive();\n    if (!element) {\n      return;\n    }\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n    this._config.interval = elementInterval || this._config.defaultInterval;\n  }\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return;\n    }\n    const activeElement = this._getActive();\n    const isNext = order === ORDER_NEXT;\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n    if (nextElement === activeElement) {\n      return;\n    }\n    const nextElementIndex = this._getItemIndex(nextElement);\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      });\n    };\n    const slideEvent = triggerEvent(EVENT_SLIDE);\n    if (slideEvent.defaultPrevented) {\n      return;\n    }\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // TODO: change tests that use empty divs to avoid this check\n      return;\n    }\n    const isCycling = Boolean(this._interval);\n    this.pause();\n    this._isSliding = true;\n    this._setActiveIndicatorElement(nextElementIndex);\n    this._activeElement = nextElement;\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n    nextElement.classList.add(orderClassName);\n    reflow(nextElement);\n    activeElement.classList.add(directionalClassName);\n    nextElement.classList.add(directionalClassName);\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName);\n      nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n      activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n      this._isSliding = false;\n      triggerEvent(EVENT_SLID);\n    };\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n    if (isCycling) {\n      this.cycle();\n    }\n  }\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE);\n  }\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n  }\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element);\n  }\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval);\n      this._interval = null;\n    }\n  }\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n    }\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n  }\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n    }\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config);\n      if (typeof config === 'number') {\n        data.to(config);\n        return;\n      }\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return;\n  }\n  event.preventDefault();\n  const carousel = Carousel.getOrCreateInstance(target);\n  const slideIndex = this.getAttribute('data-bs-slide-to');\n  if (slideIndex) {\n    carousel.to(slideIndex);\n    carousel._maybeEnableCycle();\n    return;\n  }\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next();\n    carousel._maybeEnableCycle();\n    return;\n  }\n  carousel.prev();\n  carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel);\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n  parent: null,\n  toggle: true\n};\nconst DefaultType$a = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isTransitioning = false;\n    this._triggerArray = [];\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n    for (const elem of toggleList) {\n      const selector = SelectorEngine.getSelectorFromElement(elem);\n      const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem);\n      }\n    }\n    this._initializeChildren();\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n    }\n    if (this._config.toggle) {\n      this.toggle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$a;\n  }\n  static get DefaultType() {\n    return DefaultType$a;\n  }\n  static get NAME() {\n    return NAME$b;\n  }\n\n  // Public\n  toggle() {\n    if (this._isShown()) {\n      this.hide();\n    } else {\n      this.show();\n    }\n  }\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return;\n    }\n    let activeChildren = [];\n\n    // find active children\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n        toggle: false\n      }));\n    }\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return;\n    }\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide();\n    }\n    const dimension = this._getDimension();\n    this._element.classList.remove(CLASS_NAME_COLLAPSE);\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n    this._element.style[dimension] = 0;\n    this._addAriaAndCollapsedClass(this._triggerArray, true);\n    this._isTransitioning = true;\n    const complete = () => {\n      this._isTransitioning = false;\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n      this._element.style[dimension] = '';\n      EventHandler.trigger(this._element, EVENT_SHOWN$6);\n    };\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n    const scrollSize = `scroll${capitalizedDimension}`;\n    this._queueCallback(complete, this._element, true);\n    this._element.style[dimension] = `${this._element[scrollSize]}px`;\n  }\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return;\n    }\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n    const dimension = this._getDimension();\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n    for (const trigger of this._triggerArray) {\n      const element = SelectorEngine.getElementFromSelector(trigger);\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false);\n      }\n    }\n    this._isTransitioning = true;\n    const complete = () => {\n      this._isTransitioning = false;\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n      this._element.classList.add(CLASS_NAME_COLLAPSE);\n      EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n    };\n    this._element.style[dimension] = '';\n    this._queueCallback(complete, this._element, true);\n  }\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW$7);\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle); // Coerce string values\n    config.parent = getElement(config.parent);\n    return config;\n  }\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n  }\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return;\n    }\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n    for (const element of children) {\n      const selected = SelectorEngine.getElementFromSelector(element);\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected));\n      }\n    }\n  }\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent);\n    // remove children if greater depth\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n  }\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return;\n    }\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n      element.setAttribute('aria-expanded', isOpen);\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    const _config = {};\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false;\n    }\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config);\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n    event.preventDefault();\n  }\n  for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n    Collapse.getOrCreateInstance(element, {\n      toggle: false\n    }).toggle();\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n};\nconst DefaultType$9 = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n};\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._popper = null;\n    this._parent = this._element.parentNode; // dropdown wrapper\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n    this._inNavbar = this._detectNavbar();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$9;\n  }\n  static get DefaultType() {\n    return DefaultType$9;\n  }\n  static get NAME() {\n    return NAME$a;\n  }\n\n  // Public\n  toggle() {\n    return this._isShown() ? this.hide() : this.show();\n  }\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return;\n    }\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._createPopper();\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n    this._element.focus();\n    this._element.setAttribute('aria-expanded', true);\n    this._menu.classList.add(CLASS_NAME_SHOW$6);\n    this._element.classList.add(CLASS_NAME_SHOW$6);\n    EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n  }\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return;\n    }\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    this._completeHide(relatedTarget);\n  }\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy();\n    }\n    super.dispose();\n  }\n  update() {\n    this._inNavbar = this._detectNavbar();\n    if (this._popper) {\n      this._popper.update();\n    }\n  }\n\n  // Private\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n    if (this._popper) {\n      this._popper.destroy();\n    }\n    this._menu.classList.remove(CLASS_NAME_SHOW$6);\n    this._element.classList.remove(CLASS_NAME_SHOW$6);\n    this._element.setAttribute('aria-expanded', 'false');\n    Manipulator.removeDataAttribute(this._menu, 'popper');\n    EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n  }\n  _getConfig(config) {\n    config = super._getConfig(config);\n    if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n    }\n    return config;\n  }\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n    }\n    let referenceElement = this._element;\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent;\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference);\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference;\n    }\n    const popperConfig = this._getPopperConfig();\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n  }\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n  }\n  _getPlacement() {\n    const parentDropdown = this._parent;\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER;\n    }\n\n    // We need to trim the value because custom properties can also include spaces\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n    }\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n  }\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null;\n  }\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n    return offset;\n  }\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    };\n\n    // Disable Popper if we have a static display or Dropdown is in Navbar\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // TODO: v6 remove\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }];\n    }\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    };\n  }\n  _selectMenuItem({\n    key,\n    target\n  }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n    if (!items.length) {\n      return;\n    }\n\n    // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n      return;\n    }\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle);\n      if (!context || context._config.autoClose === false) {\n        continue;\n      }\n      const composedPath = event.composedPath();\n      const isMenuTarget = composedPath.includes(context._menu);\n      if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n        continue;\n      }\n\n      // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n      if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue;\n      }\n      const relatedTarget = {\n        relatedTarget: context._element\n      };\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event;\n      }\n      context._completeHide(relatedTarget);\n    }\n  }\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n    const isInput = /input|textarea/i.test(event.target.tagName);\n    const isEscapeEvent = event.key === ESCAPE_KEY$2;\n    const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return;\n    }\n    if (isInput && !isEscapeEvent) {\n      return;\n    }\n    event.preventDefault();\n\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n    const instance = Dropdown.getOrCreateInstance(getToggleButton);\n    if (isUpOrDownEvent) {\n      event.stopPropagation();\n      instance.show();\n      instance._selectMenuItem(event);\n      return;\n    }\n    if (instance._isShown()) {\n      // else is escape and we check if it is shown\n      event.stopPropagation();\n      instance.hide();\n      getToggleButton.focus();\n    }\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n  event.preventDefault();\n  Dropdown.getOrCreateInstance(this).toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true,\n  // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n};\nconst DefaultType$8 = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n};\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isAppended = false;\n    this._element = null;\n  }\n\n  // Getters\n  static get Default() {\n    return Default$8;\n  }\n  static get DefaultType() {\n    return DefaultType$8;\n  }\n  static get NAME() {\n    return NAME$9;\n  }\n\n  // Public\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n    this._append();\n    const element = this._getElement();\n    if (this._config.isAnimated) {\n      reflow(element);\n    }\n    element.classList.add(CLASS_NAME_SHOW$5);\n    this._emulateAnimation(() => {\n      execute(callback);\n    });\n  }\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n    this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n    this._emulateAnimation(() => {\n      this.dispose();\n      execute(callback);\n    });\n  }\n  dispose() {\n    if (!this._isAppended) {\n      return;\n    }\n    EventHandler.off(this._element, EVENT_MOUSEDOWN);\n    this._element.remove();\n    this._isAppended = false;\n  }\n\n  // Private\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div');\n      backdrop.className = this._config.className;\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE$4);\n      }\n      this._element = backdrop;\n    }\n    return this._element;\n  }\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement);\n    return config;\n  }\n  _append() {\n    if (this._isAppended) {\n      return;\n    }\n    const element = this._getElement();\n    this._config.rootElement.append(element);\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback);\n    });\n    this._isAppended = true;\n  }\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n};\nconst DefaultType$7 = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n};\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isActive = false;\n    this._lastTabNavDirection = null;\n  }\n\n  // Getters\n  static get Default() {\n    return Default$7;\n  }\n  static get DefaultType() {\n    return DefaultType$7;\n  }\n  static get NAME() {\n    return NAME$8;\n  }\n\n  // Public\n  activate() {\n    if (this._isActive) {\n      return;\n    }\n    if (this._config.autofocus) {\n      this._config.trapElement.focus();\n    }\n    EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n    EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n    this._isActive = true;\n  }\n  deactivate() {\n    if (!this._isActive) {\n      return;\n    }\n    this._isActive = false;\n    EventHandler.off(document, EVENT_KEY$5);\n  }\n\n  // Private\n  _handleFocusin(event) {\n    const {\n      trapElement\n    } = this._config;\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return;\n    }\n    const elements = SelectorEngine.focusableChildren(trapElement);\n    if (elements.length === 0) {\n      trapElement.focus();\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus();\n    } else {\n      elements[0].focus();\n    }\n  }\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return;\n    }\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body;\n  }\n\n  // Public\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth;\n    return Math.abs(window.innerWidth - documentWidth);\n  }\n  hide() {\n    const width = this.getWidth();\n    this._disableOverFlow();\n    // give padding to element to balance the hidden scrollbar width\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n    // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n  }\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow');\n    this._resetElementAttributes(this._element, PROPERTY_PADDING);\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n  }\n  isOverflowing() {\n    return this.getWidth() > 0;\n  }\n\n  // Private\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow');\n    this._element.style.overflow = 'hidden';\n  }\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth();\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return;\n      }\n      this._saveInitialAttribute(element, styleProperty);\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n    };\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty);\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue);\n    }\n  }\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty);\n      // We only want to remove the property if the value is `null`; the value can also be zero\n      if (value === null) {\n        element.style.removeProperty(styleProperty);\n        return;\n      }\n      Manipulator.removeDataAttribute(element, styleProperty);\n      element.style.setProperty(styleProperty, value);\n    };\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector);\n      return;\n    }\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel);\n    }\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n};\nconst DefaultType$6 = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._isShown = false;\n    this._isTransitioning = false;\n    this._scrollBar = new ScrollBarHelper();\n    this._addEventListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$6;\n  }\n  static get DefaultType() {\n    return DefaultType$6;\n  }\n  static get NAME() {\n    return NAME$7;\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n      relatedTarget\n    });\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = true;\n    this._isTransitioning = true;\n    this._scrollBar.hide();\n    document.body.classList.add(CLASS_NAME_OPEN);\n    this._adjustDialog();\n    this._backdrop.show(() => this._showElement(relatedTarget));\n  }\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = false;\n    this._isTransitioning = true;\n    this._focustrap.deactivate();\n    this._element.classList.remove(CLASS_NAME_SHOW$4);\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n  }\n  dispose() {\n    EventHandler.off(window, EVENT_KEY$4);\n    EventHandler.off(this._dialog, EVENT_KEY$4);\n    this._backdrop.dispose();\n    this._focustrap.deactivate();\n    super.dispose();\n  }\n  handleUpdate() {\n    this._adjustDialog();\n  }\n\n  // Private\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop),\n      // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    });\n  }\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element);\n    }\n    this._element.style.display = 'block';\n    this._element.removeAttribute('aria-hidden');\n    this._element.setAttribute('aria-modal', true);\n    this._element.setAttribute('role', 'dialog');\n    this._element.scrollTop = 0;\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n    if (modalBody) {\n      modalBody.scrollTop = 0;\n    }\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_SHOW$4);\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate();\n      }\n      this._isTransitioning = false;\n      EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n        relatedTarget\n      });\n    };\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n  }\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n      if (event.key !== ESCAPE_KEY$1) {\n        return;\n      }\n      if (this._config.keyboard) {\n        this.hide();\n        return;\n      }\n      this._triggerBackdropTransition();\n    });\n    EventHandler.on(window, EVENT_RESIZE$1, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog();\n      }\n    });\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return;\n        }\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition();\n          return;\n        }\n        if (this._config.backdrop) {\n          this.hide();\n        }\n      });\n    });\n  }\n  _hideModal() {\n    this._element.style.display = 'none';\n    this._element.setAttribute('aria-hidden', true);\n    this._element.removeAttribute('aria-modal');\n    this._element.removeAttribute('role');\n    this._isTransitioning = false;\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN);\n      this._resetAdjustments();\n      this._scrollBar.reset();\n      EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n    });\n  }\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE$3);\n  }\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const initialOverflowY = this._element.style.overflowY;\n    // return if the following background transition hasn't yet completed\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return;\n    }\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden';\n    }\n    this._element.classList.add(CLASS_NAME_STATIC);\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC);\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY;\n      }, this._dialog);\n    }, this._dialog);\n    this._element.focus();\n  }\n\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const scrollbarWidth = this._scrollBar.getWidth();\n    const isBodyOverflowing = scrollbarWidth > 0;\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n  }\n  _resetAdjustments() {\n    this._element.style.paddingLeft = '';\n    this._element.style.paddingRight = '';\n  }\n\n  // Static\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](relatedTarget);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return;\n    }\n    EventHandler.one(target, EVENT_HIDDEN$4, () => {\n      if (isVisible(this)) {\n        this.focus();\n      }\n    });\n  });\n\n  // avoid conflict when clicking modal toggler while another one is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide();\n  }\n  const data = Modal.getOrCreateInstance(target);\n  data.toggle(this);\n});\nenableDismissTrigger(Modal);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n};\nconst DefaultType$5 = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isShown = false;\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._addEventListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$5;\n  }\n  static get DefaultType() {\n    return DefaultType$5;\n  }\n  static get NAME() {\n    return NAME$6;\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n  show(relatedTarget) {\n    if (this._isShown) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n      relatedTarget\n    });\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = true;\n    this._backdrop.show();\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide();\n    }\n    this._element.setAttribute('aria-modal', true);\n    this._element.setAttribute('role', 'dialog');\n    this._element.classList.add(CLASS_NAME_SHOWING$1);\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate();\n      }\n      this._element.classList.add(CLASS_NAME_SHOW$3);\n      this._element.classList.remove(CLASS_NAME_SHOWING$1);\n      EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n        relatedTarget\n      });\n    };\n    this._queueCallback(completeCallBack, this._element, true);\n  }\n  hide() {\n    if (!this._isShown) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    this._focustrap.deactivate();\n    this._element.blur();\n    this._isShown = false;\n    this._element.classList.add(CLASS_NAME_HIDING);\n    this._backdrop.hide();\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n      this._element.removeAttribute('aria-modal');\n      this._element.removeAttribute('role');\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset();\n      }\n      EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n    };\n    this._queueCallback(completeCallback, this._element, true);\n  }\n  dispose() {\n    this._backdrop.dispose();\n    this._focustrap.deactivate();\n    super.dispose();\n  }\n\n  // Private\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n      this.hide();\n    };\n\n    // 'static' option will be translated to true, and booleans will keep their value\n    const isVisible = Boolean(this._config.backdrop);\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    });\n  }\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return;\n      }\n      if (this._config.keyboard) {\n        this.hide();\n        return;\n      }\n      EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n    });\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](this);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  if (isDisabled(this)) {\n    return;\n  }\n  EventHandler.one(target, EVENT_HIDDEN$3, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus();\n    }\n  });\n\n  // avoid conflict when clicking a toggler of an offcanvas, while another is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide();\n  }\n  const data = Offcanvas.getOrCreateInstance(target);\n  data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show();\n  }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide();\n    }\n  }\n});\nenableDismissTrigger(Offcanvas);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\nconst DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  dd: [],\n  div: [],\n  dl: [],\n  dt: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n};\n// js-docs-end allow-list\n\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i;\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase();\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue));\n    }\n    return true;\n  }\n\n  // Check if a regular expression validates the attribute.\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml;\n  }\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml);\n  }\n  const domParser = new window.DOMParser();\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase();\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove();\n      continue;\n    }\n    const attributeList = [].concat(...element.attributes);\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName);\n      }\n    }\n  }\n  return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n  allowList: DefaultAllowlist,\n  content: {},\n  // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n};\nconst DefaultType$4 = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n};\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n};\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n  }\n\n  // Getters\n  static get Default() {\n    return Default$4;\n  }\n  static get DefaultType() {\n    return DefaultType$4;\n  }\n  static get NAME() {\n    return NAME$5;\n  }\n\n  // Public\n  getContent() {\n    return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n  }\n  hasContent() {\n    return this.getContent().length > 0;\n  }\n  changeContent(content) {\n    this._checkContent(content);\n    this._config.content = {\n      ...this._config.content,\n      ...content\n    };\n    return this;\n  }\n  toHtml() {\n    const templateWrapper = document.createElement('div');\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector);\n    }\n    const template = templateWrapper.children[0];\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '));\n    }\n    return template;\n  }\n\n  // Private\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config);\n    this._checkContent(config.content);\n  }\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({\n        selector,\n        entry: content\n      }, DefaultContentType);\n    }\n  }\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template);\n    if (!templateElement) {\n      return;\n    }\n    content = this._resolvePossibleFunction(content);\n    if (!content) {\n      templateElement.remove();\n      return;\n    }\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement);\n      return;\n    }\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content);\n      return;\n    }\n    templateElement.textContent = content;\n  }\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n  }\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this]);\n  }\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = '';\n      templateElement.append(element);\n      return;\n    }\n    templateElement.textContent = element.textContent;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 6],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' + '<div class=\"tooltip-arrow\"></div>' + '<div class=\"tooltip-inner\"></div>' + '</div>',\n  title: '',\n  trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n};\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n    }\n    super(element, config);\n\n    // Private\n    this._isEnabled = true;\n    this._timeout = 0;\n    this._isHovered = null;\n    this._activeTrigger = {};\n    this._popper = null;\n    this._templateFactory = null;\n    this._newContent = null;\n\n    // Protected\n    this.tip = null;\n    this._setListeners();\n    if (!this._config.selector) {\n      this._fixTitle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$3;\n  }\n  static get DefaultType() {\n    return DefaultType$3;\n  }\n  static get NAME() {\n    return NAME$4;\n  }\n\n  // Public\n  enable() {\n    this._isEnabled = true;\n  }\n  disable() {\n    this._isEnabled = false;\n  }\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled;\n  }\n  toggle() {\n    if (!this._isEnabled) {\n      return;\n    }\n    this._activeTrigger.click = !this._activeTrigger.click;\n    if (this._isShown()) {\n      this._leave();\n      return;\n    }\n    this._enter();\n  }\n  dispose() {\n    clearTimeout(this._timeout);\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n    }\n    this._disposePopper();\n    super.dispose();\n  }\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements');\n    }\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n    const shadowRoot = findShadowRoot(this._element);\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return;\n    }\n\n    // TODO: v6 remove this or make it optional\n    this._disposePopper();\n    const tip = this._getTipElement();\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n    const {\n      container\n    } = this._config;\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip);\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n    }\n    this._popper = this._createPopper(tip);\n    tip.classList.add(CLASS_NAME_SHOW$2);\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n      if (this._isHovered === false) {\n        this._leave();\n      }\n      this._isHovered = false;\n    };\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n  hide() {\n    if (!this._isShown()) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const tip = this._getTipElement();\n    tip.classList.remove(CLASS_NAME_SHOW$2);\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n    this._activeTrigger[TRIGGER_CLICK] = false;\n    this._activeTrigger[TRIGGER_FOCUS] = false;\n    this._activeTrigger[TRIGGER_HOVER] = false;\n    this._isHovered = null; // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return;\n      }\n      if (!this._isHovered) {\n        this._disposePopper();\n      }\n      this._element.removeAttribute('aria-describedby');\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n    };\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n  update() {\n    if (this._popper) {\n      this._popper.update();\n    }\n  }\n\n  // Protected\n  _isWithContent() {\n    return Boolean(this._getTitle());\n  }\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n    }\n    return this.tip;\n  }\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml();\n\n    // TODO: remove this check in v6\n    if (!tip) {\n      return null;\n    }\n    tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2);\n    // TODO: v6 the following can be achieved with CSS only\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n    const tipId = getUID(this.constructor.NAME).toString();\n    tip.setAttribute('id', tipId);\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE$2);\n    }\n    return tip;\n  }\n  setContent(content) {\n    this._newContent = content;\n    if (this._isShown()) {\n      this._disposePopper();\n      this.show();\n    }\n  }\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content);\n    } else {\n      this._templateFactory = new TemplateFactory({\n        ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      });\n    }\n    return this._templateFactory;\n  }\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    };\n  }\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n  }\n\n  // Private\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n  }\n  _isAnimated() {\n    return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n  }\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n  }\n  _createPopper(tip) {\n    const placement = execute(this._config.placement, [this, tip, this._element]);\n    const attachment = AttachmentMap[placement.toUpperCase()];\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n  }\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n    return offset;\n  }\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this._element]);\n  }\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [{\n        name: 'flip',\n        options: {\n          fallbackPlacements: this._config.fallbackPlacements\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }, {\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'arrow',\n        options: {\n          element: `.${this.constructor.NAME}-arrow`\n        }\n      }, {\n        name: 'preSetPlacement',\n        enabled: true,\n        phase: 'beforeMain',\n        fn: data => {\n          // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n          // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n          this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n        }\n      }]\n    };\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    };\n  }\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ');\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context.toggle();\n        });\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n        const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n          context._enter();\n        });\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n          context._leave();\n        });\n      }\n    }\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide();\n      }\n    };\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n  }\n  _fixTitle() {\n    const title = this._element.getAttribute('title');\n    if (!title) {\n      return;\n    }\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title);\n    }\n    this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n    this._element.removeAttribute('title');\n  }\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true;\n      return;\n    }\n    this._isHovered = true;\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show();\n      }\n    }, this._config.delay.show);\n  }\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return;\n    }\n    this._isHovered = false;\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide();\n      }\n    }, this._config.delay.hide);\n  }\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout);\n    this._timeout = setTimeout(handler, timeout);\n  }\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true);\n  }\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element);\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute];\n      }\n    }\n    config = {\n      ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    };\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container);\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      };\n    }\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString();\n    }\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString();\n    }\n    return config;\n  }\n  _getDelegateConfig() {\n    const config = {};\n    for (const [key, value] of Object.entries(this._config)) {\n      if (this.constructor.Default[key] !== value) {\n        config[key] = value;\n      }\n    }\n    config.selector = false;\n    config.trigger = 'manual';\n\n    // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n    return config;\n  }\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy();\n      this._popper = null;\n    }\n    if (this.tip) {\n      this.tip.remove();\n      this.tip = null;\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = {\n  ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' + '<div class=\"popover-arrow\"></div>' + '<h3 class=\"popover-header\"></h3>' + '<div class=\"popover-body\"></div>' + '</div>',\n  trigger: 'click'\n};\nconst DefaultType$2 = {\n  ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n};\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default$2;\n  }\n  static get DefaultType() {\n    return DefaultType$2;\n  }\n  static get NAME() {\n    return NAME$3;\n  }\n\n  // Overrides\n  _isWithContent() {\n    return this._getTitle() || this._getContent();\n  }\n\n  // Private\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    };\n  }\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content);\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n  offset: null,\n  // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n  offset: '(number|null)',\n  // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n};\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n\n    // this._element is the observablesContainer and config.target the menu links wrapper\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n    this._activeTarget = null;\n    this._observer = null;\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    };\n    this.refresh(); // initialize\n  }\n\n  // Getters\n  static get Default() {\n    return Default$1;\n  }\n  static get DefaultType() {\n    return DefaultType$1;\n  }\n  static get NAME() {\n    return NAME$2;\n  }\n\n  // Public\n  refresh() {\n    this._initializeTargetsAndObservables();\n    this._maybeEnableSmoothScroll();\n    if (this._observer) {\n      this._observer.disconnect();\n    } else {\n      this._observer = this._getNewObserver();\n    }\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section);\n    }\n  }\n  dispose() {\n    this._observer.disconnect();\n    super.dispose();\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body;\n\n    // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n    }\n    return config;\n  }\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return;\n    }\n\n    // unregister any previous listeners\n    EventHandler.off(this._config.target, EVENT_CLICK);\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash);\n      if (observableSection) {\n        event.preventDefault();\n        const root = this._rootElement || window;\n        const height = observableSection.offsetTop - this._element.offsetTop;\n        if (root.scrollTo) {\n          root.scrollTo({\n            top: height,\n            behavior: 'smooth'\n          });\n          return;\n        }\n\n        // Chrome 60 doesn't support `scrollTo`\n        root.scrollTop = height;\n      }\n    });\n  }\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    };\n    return new IntersectionObserver(entries => this._observerCallback(entries), options);\n  }\n\n  // The logic of selection\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n      this._process(targetElement(entry));\n    };\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n    this._previousScrollData.parentScrollTop = parentScrollTop;\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null;\n        this._clearActiveClass(targetElement(entry));\n        continue;\n      }\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop;\n      // if we are scrolling down, pick the bigger offsetTop\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry);\n        // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n        if (!parentScrollTop) {\n          return;\n        }\n        continue;\n      }\n\n      // if we are scrolling up, pick the smallest offsetTop\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry);\n      }\n    }\n  }\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue;\n      }\n      const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element);\n\n      // ensure that the observableSection exists & is visible\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(decodeURI(anchor.hash), anchor);\n        this._observableSections.set(anchor.hash, observableSection);\n      }\n    }\n  }\n  _process(target) {\n    if (this._activeTarget === target) {\n      return;\n    }\n    this._clearActiveClass(this._config.target);\n    this._activeTarget = target;\n    target.classList.add(CLASS_NAME_ACTIVE$1);\n    this._activateParents(target);\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n      relatedTarget: target\n    });\n  }\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n      return;\n    }\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE$1);\n      }\n    }\n  }\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE$1);\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE$1}`, parent);\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE$1);\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API$1, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy);\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$1 = 'tab';\nconst DATA_KEY$1 = 'bs.tab';\nconst EVENT_KEY$1 = `.${DATA_KEY$1}`;\nconst EVENT_HIDE$1 = `hide${EVENT_KEY$1}`;\nconst EVENT_HIDDEN$1 = `hidden${EVENT_KEY$1}`;\nconst EVENT_SHOW$1 = `show${EVENT_KEY$1}`;\nconst EVENT_SHOWN$1 = `shown${EVENT_KEY$1}`;\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY$1}`;\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY$1}`;\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY$1}`;\nconst ARROW_LEFT_KEY = 'ArrowLeft';\nconst ARROW_RIGHT_KEY = 'ArrowRight';\nconst ARROW_UP_KEY = 'ArrowUp';\nconst ARROW_DOWN_KEY = 'ArrowDown';\nconst HOME_KEY = 'Home';\nconst END_KEY = 'End';\nconst CLASS_NAME_ACTIVE = 'active';\nconst CLASS_NAME_FADE$1 = 'fade';\nconst CLASS_NAME_SHOW$1 = 'show';\nconst CLASS_DROPDOWN = 'dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle';\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu';\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = `:not(${SELECTOR_DROPDOWN_TOGGLE})`;\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]';\nconst SELECTOR_OUTER = '.nav-item, .list-group-item';\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]'; // TODO: could only be `tab` in v6\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`;\n\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element);\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL);\n    if (!this._parent) {\n      return;\n      // TODO: should throw exception in v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    }\n\n    // Set up initial aria attributes\n    this._setInitialAttributes(this._parent, this._getChildren());\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event));\n  }\n\n  // Getters\n  static get NAME() {\n    return NAME$1;\n  }\n\n  // Public\n  show() {\n    // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element;\n    if (this._elemIsActive(innerElem)) {\n      return;\n    }\n\n    // Search for active tab on same parent to deactivate it\n    const active = this._getActiveElem();\n    const hideEvent = active ? EventHandler.trigger(active, EVENT_HIDE$1, {\n      relatedTarget: innerElem\n    }) : null;\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW$1, {\n      relatedTarget: active\n    });\n    if (showEvent.defaultPrevented || hideEvent && hideEvent.defaultPrevented) {\n      return;\n    }\n    this._deactivate(active, innerElem);\n    this._activate(innerElem, active);\n  }\n\n  // Private\n  _activate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n    element.classList.add(CLASS_NAME_ACTIVE);\n    this._activate(SelectorEngine.getElementFromSelector(element)); // Search and activate/show the proper section\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW$1);\n        return;\n      }\n      element.removeAttribute('tabindex');\n      element.setAttribute('aria-selected', true);\n      this._toggleDropDown(element, true);\n      EventHandler.trigger(element, EVENT_SHOWN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n    element.classList.remove(CLASS_NAME_ACTIVE);\n    element.blur();\n    this._deactivate(SelectorEngine.getElementFromSelector(element)); // Search and deactivate the shown section too\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW$1);\n        return;\n      }\n      element.setAttribute('aria-selected', false);\n      element.setAttribute('tabindex', '-1');\n      this._toggleDropDown(element, false);\n      EventHandler.trigger(element, EVENT_HIDDEN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n  _keydown(event) {\n    if (![ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY, HOME_KEY, END_KEY].includes(event.key)) {\n      return;\n    }\n    event.stopPropagation(); // stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n    event.preventDefault();\n    const children = this._getChildren().filter(element => !isDisabled(element));\n    let nextActiveElement;\n    if ([HOME_KEY, END_KEY].includes(event.key)) {\n      nextActiveElement = children[event.key === HOME_KEY ? 0 : children.length - 1];\n    } else {\n      const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key);\n      nextActiveElement = getNextActiveElement(children, event.target, isNext, true);\n    }\n    if (nextActiveElement) {\n      nextActiveElement.focus({\n        preventScroll: true\n      });\n      Tab.getOrCreateInstance(nextActiveElement).show();\n    }\n  }\n  _getChildren() {\n    // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent);\n  }\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null;\n  }\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist');\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child);\n    }\n  }\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child);\n    const isActive = this._elemIsActive(child);\n    const outerElem = this._getOuterElement(child);\n    child.setAttribute('aria-selected', isActive);\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation');\n    }\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1');\n    }\n    this._setAttributeIfNotExists(child, 'role', 'tab');\n\n    // set attributes to the related panel too\n    this._setInitialAttributesOnTargetPanel(child);\n  }\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = SelectorEngine.getElementFromSelector(child);\n    if (!target) {\n      return;\n    }\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel');\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `${child.id}`);\n    }\n  }\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element);\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return;\n    }\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem);\n      if (element) {\n        element.classList.toggle(className, open);\n      }\n    };\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE);\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW$1);\n    outerElem.setAttribute('aria-expanded', open);\n  }\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value);\n    }\n  }\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE);\n  }\n\n  // Try to get the inner element (usually the .nav-link)\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem);\n  }\n\n  // Try to get the outer element (usually the .nav-item)\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  if (isDisabled(this)) {\n    return;\n  }\n  Tab.getOrCreateInstance(this).show();\n});\n\n/**\n * Initialize on focus\n */\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME = 'toast';\nconst DATA_KEY = 'bs.toast';\nconst EVENT_KEY = `.${DATA_KEY}`;\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`;\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`;\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`;\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`;\nconst EVENT_HIDE = `hide${EVENT_KEY}`;\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`;\nconst EVENT_SHOW = `show${EVENT_KEY}`;\nconst EVENT_SHOWN = `shown${EVENT_KEY}`;\nconst CLASS_NAME_FADE = 'fade';\nconst CLASS_NAME_HIDE = 'hide'; // @deprecated - kept here only for backwards compatibility\nconst CLASS_NAME_SHOW = 'show';\nconst CLASS_NAME_SHOWING = 'showing';\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n};\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n};\n\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._timeout = null;\n    this._hasMouseInteraction = false;\n    this._hasKeyboardInteraction = false;\n    this._setListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default;\n  }\n  static get DefaultType() {\n    return DefaultType;\n  }\n  static get NAME() {\n    return NAME;\n  }\n\n  // Public\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW);\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._clearTimeout();\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE);\n    }\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING);\n      EventHandler.trigger(this._element, EVENT_SHOWN);\n      this._maybeScheduleHide();\n    };\n    this._element.classList.remove(CLASS_NAME_HIDE); // @deprecated\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING);\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n  hide() {\n    if (!this.isShown()) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE); // @deprecated\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW);\n      EventHandler.trigger(this._element, EVENT_HIDDEN);\n    };\n    this._element.classList.add(CLASS_NAME_SHOWING);\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n  dispose() {\n    this._clearTimeout();\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW);\n    }\n    super.dispose();\n  }\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW);\n  }\n\n  // Private\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return;\n    }\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return;\n    }\n    this._timeout = setTimeout(() => {\n      this.hide();\n    }, this._config.delay);\n  }\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout':\n        {\n          this._hasMouseInteraction = isInteracting;\n          break;\n        }\n      case 'focusin':\n      case 'focusout':\n        {\n          this._hasKeyboardInteraction = isInteracting;\n          break;\n        }\n    }\n    if (isInteracting) {\n      this._clearTimeout();\n      return;\n    }\n    const nextElement = event.relatedTarget;\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return;\n    }\n    this._maybeScheduleHide();\n  }\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false));\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false));\n  }\n  _clearTimeout() {\n    clearTimeout(this._timeout);\n    this._timeout = null;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config);\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config](this);\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Toast);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast);\n\nexport { Alert, Button, Carousel, Collapse, Dropdown, Modal, Offcanvas, Popover, ScrollSpy, Tab, Toast, Tooltip };\n//# sourceMappingURL=bootstrap.esm.js.map\n","/* define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if 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index 0000000..920b186
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define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if (document.readyState != \"loading\") callback();\n  else document.addEventListener(\"DOMContentLoaded\", callback);\n}\n","/**\n * Compare [semver](https://semver.org/) version strings to find greater, equal or lesser.\n * This library supports the full semver specification, including comparing versions with different number of digits like `1.0.0`, `1.0`, `1`, and pre-release versions like `1.0.0-alpha`.\n * @param v1 - First version to compare\n * @param v2 - Second version to compare\n * @returns Numeric value compatible with the [Array.sort(fn) interface](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/sort#Parameters).\n */\nexport const compareVersions = (v1, v2) => {\n    // validate input and split into segments\n    const n1 = validateAndParse(v1);\n    const n2 = validateAndParse(v2);\n    // pop off the patch\n    const p1 = n1.pop();\n    const p2 = n2.pop();\n    // validate numbers\n    const r = compareSegments(n1, n2);\n    if (r !== 0)\n        return r;\n    // validate pre-release\n    if (p1 && p2) {\n        return compareSegments(p1.split('.'), p2.split('.'));\n    }\n    else if (p1 || p2) {\n        return p1 ? -1 : 1;\n    }\n    return 0;\n};\n/**\n * Validate [semver](https://semver.org/) version strings.\n *\n * @param version Version number to validate\n * @returns `true` if the version number is a valid semver version number, `false` otherwise.\n *\n * @example\n * ```\n * validate('1.0.0-rc.1'); // return true\n * validate('1.0-rc.1'); // return false\n * validate('foo'); // return false\n * ```\n */\nexport const validate = (version) => typeof version === 'string' && /^[v\\d]/.test(version) && semver.test(version);\n/**\n * Compare [semver](https://semver.org/) version strings using the specified operator.\n *\n * @param v1 First version to compare\n * @param v2 Second version to compare\n * @param operator Allowed arithmetic operator to use\n * @returns `true` if the comparison between the firstVersion and the secondVersion satisfies the operator, `false` otherwise.\n *\n * @example\n * ```\n * compare('10.1.8', '10.0.4', '>'); // return true\n * compare('10.0.1', '10.0.1', '='); // return true\n * compare('10.1.1', '10.2.2', '<'); // return true\n * compare('10.1.1', '10.2.2', '<='); // return true\n * compare('10.1.1', '10.2.2', '>='); // return false\n * ```\n */\nexport const compare = (v1, v2, operator) => {\n    // validate input operator\n    assertValidOperator(operator);\n    // since result of compareVersions can only be -1 or 0 or 1\n    // a simple map can be used to replace switch\n    const res = compareVersions(v1, v2);\n    return operatorResMap[operator].includes(res);\n};\n/**\n * Match [npm semver](https://docs.npmjs.com/cli/v6/using-npm/semver) version range.\n *\n * @param version Version number to match\n * @param range Range pattern for version\n * @returns `true` if the version number is within the range, `false` otherwise.\n *\n * @example\n * ```\n * satisfies('1.1.0', '^1.0.0'); // return true\n * satisfies('1.1.0', '~1.0.0'); // return false\n * ```\n */\nexport const satisfies = (version, range) => {\n    // if no range operator then \"=\"\n    const m = range.match(/^([<>=~^]+)/);\n    const op = m ? m[1] : '=';\n    // if gt/lt/eq then operator compare\n    if (op !== '^' && op !== '~')\n        return compare(version, range, op);\n    // else range of either \"~\" or \"^\" is assumed\n    const [v1, v2, v3, , vp] = validateAndParse(version);\n    const [r1, r2, r3, , rp] = validateAndParse(range);\n    const v = [v1, v2, v3];\n    const r = [r1, r2 !== null && r2 !== void 0 ? r2 : 'x', r3 !== null && r3 !== void 0 ? r3 : 'x'];\n    // validate pre-release\n    if (rp) {\n        if (!vp)\n            return false;\n        if (compareSegments(v, r) !== 0)\n            return false;\n        if (compareSegments(vp.split('.'), rp.split('.')) === -1)\n            return false;\n    }\n    // first non-zero number\n    const nonZero = r.findIndex((v) => v !== '0') + 1;\n    // pointer to where segments can be >=\n    const i = op === '~' ? 2 : nonZero > 1 ? nonZero : 1;\n    // before pointer must be equal\n    if (compareSegments(v.slice(0, i), r.slice(0, i)) !== 0)\n        return false;\n    // after pointer must be >=\n    if (compareSegments(v.slice(i), r.slice(i)) === -1)\n        return false;\n    return true;\n};\nconst semver = /^[v^~<>=]*?(\\d+)(?:\\.([x*]|\\d+)(?:\\.([x*]|\\d+)(?:\\.([x*]|\\d+))?(?:-([\\da-z\\-]+(?:\\.[\\da-z\\-]+)*))?(?:\\+[\\da-z\\-]+(?:\\.[\\da-z\\-]+)*)?)?)?$/i;\nconst validateAndParse = (version) => {\n    if (typeof version !== 'string') {\n        throw new TypeError('Invalid argument expected string');\n    }\n    const match = version.match(semver);\n    if (!match) {\n        throw new Error(`Invalid argument not valid semver ('${version}' received)`);\n    }\n    match.shift();\n    return match;\n};\nconst isWildcard = (s) => s === '*' || s === 'x' || s === 'X';\nconst tryParse = (v) => {\n    const n = parseInt(v, 10);\n    return isNaN(n) ? v : n;\n};\nconst forceType = (a, b) => typeof a !== typeof b ? [String(a), String(b)] : [a, b];\nconst compareStrings = (a, b) => {\n    if (isWildcard(a) || isWildcard(b))\n        return 0;\n    const [ap, bp] = forceType(tryParse(a), tryParse(b));\n    if (ap > bp)\n        return 1;\n    if (ap < bp)\n        return -1;\n    return 0;\n};\nconst compareSegments = (a, b) => {\n    for (let i = 0; i < Math.max(a.length, b.length); i++) {\n        const r = compareStrings(a[i] || '0', b[i] || '0');\n        if (r !== 0)\n            return r;\n    }\n    return 0;\n};\nconst operatorResMap = {\n    '>': [1],\n    '>=': [0, 1],\n    '=': [0],\n    '<=': [-1, 0],\n    '<': [-1],\n};\nconst allowedOperators = Object.keys(operatorResMap);\nconst assertValidOperator = (op) => {\n    if (typeof op !== 'string') {\n        throw new TypeError(`Invalid operator type, expected string but got ${typeof op}`);\n    }\n    if (allowedOperators.indexOf(op) === -1) {\n        throw new Error(`Invalid operator, expected one of ${allowedOperators.join('|')}`);\n    }\n};\n//# sourceMappingURL=index.js.map","// Define the custom behavior of the page\nimport { documentReady } from \"./mixin\";\nimport { compare, validate } from \"compare-versions\";\n\nimport \"../styles/pydata-sphinx-theme.scss\";\n\n/*******************************************************************************\n * Theme interaction\n */\n\nvar prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\");\n\n/**\n * set the the body theme to the one specified by the user browser\n *\n * @param {event} e\n */\nfunction autoTheme(e) {\n  document.documentElement.dataset.theme = prefersDark.matches\n    ? \"dark\"\n    : \"light\";\n}\n\n/**\n * Set the theme using the specified mode.\n * It can be one of [\"auto\", \"dark\", \"light\"]\n *\n * @param {str} mode\n */\nfunction setTheme(mode) {\n  if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n    console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n    mode = \"auto\";\n  }\n\n  // get the theme\n  var colorScheme = prefersDark.matches ? \"dark\" : \"light\";\n  document.documentElement.dataset.mode = mode;\n  var theme = mode == \"auto\" ? colorScheme : mode;\n  document.documentElement.dataset.theme = theme;\n  // TODO: remove this line after Bootstrap upgrade\n  // v5.3 has a colors mode: https://getbootstrap.com/docs/5.3/customize/color-modes/\n  document.querySelectorAll(\".dropdown-menu\").forEach((el) => {\n    if (theme === \"dark\") {\n      el.classList.add(\"dropdown-menu-dark\");\n    } else {\n      el.classList.remove(\"dropdown-menu-dark\");\n    }\n  });\n\n  // save mode and theme\n  localStorage.setItem(\"mode\", mode);\n  localStorage.setItem(\"theme\", theme);\n  console.log(`[PST]: Changed to ${mode} mode using the ${theme} theme.`);\n\n  // add a listener if set on auto\n  prefersDark.onchange = mode == \"auto\" ? autoTheme : \"\";\n}\n\n/**\n * Change the theme option order so that clicking on the btn is always a change\n * from \"auto\"\n */\nfunction cycleMode() {\n  const defaultMode = document.documentElement.dataset.defaultMode || \"auto\";\n  const currentMode = localStorage.getItem(\"mode\") || defaultMode;\n\n  var loopArray = (arr, current) => {\n    var nextPosition = arr.indexOf(current) + 1;\n    if (nextPosition === arr.length) {\n      nextPosition = 0;\n    }\n    return arr[nextPosition];\n  };\n\n  // make sure the next theme after auto is always a change\n  var modeList = prefersDark.matches\n    ? [\"auto\", \"light\", \"dark\"]\n    : [\"auto\", \"dark\", \"light\"];\n  var newMode = loopArray(modeList, currentMode);\n  setTheme(newMode);\n}\n\n/**\n * add the theme listener on the btns of the navbar\n */\nfunction addModeListener() {\n  // the theme was set a first time using the initial mini-script\n  // running setMode will ensure the use of the dark mode if auto is selected\n  setTheme(document.documentElement.dataset.mode);\n\n  // Attach event handlers for toggling themes colors\n  document.querySelectorAll(\".theme-switch-button\").forEach((el) => {\n    el.addEventListener(\"click\", cycleMode);\n  });\n}\n\n/*******************************************************************************\n * TOC interactivity\n */\n\n/**\n * TOC sidebar - add \"active\" class to parent list\n *\n * Bootstrap's scrollspy adds the active class to the <a> link,\n * but for the automatic collapsing we need this on the parent list item.\n *\n * The event is triggered on \"window\" (and not the nav item as documented),\n * see https://github.com/twbs/bootstrap/issues/20086\n */\nfunction addTOCInteractivity() {\n  window.addEventListener(\"activate.bs.scrollspy\", function () {\n    const navLinks = document.querySelectorAll(\".bd-toc-nav a\");\n\n    navLinks.forEach((navLink) => {\n      navLink.parentElement.classList.remove(\"active\");\n    });\n\n    const activeNavLinks = document.querySelectorAll(\".bd-toc-nav a.active\");\n    activeNavLinks.forEach((navLink) => {\n      navLink.parentElement.classList.add(\"active\");\n    });\n  });\n}\n\n/*******************************************************************************\n * Scroll\n */\n\n/**\n * Navigation sidebar scrolling to active page\n */\nfunction scrollToActive() {\n  // If the docs nav doesn't exist, do nothing (e.g., on search page)\n  if (!document.querySelector(\".bd-docs-nav\")) {\n    return;\n  }\n\n  var sidebar = document.querySelector(\"div.bd-sidebar\");\n\n  // Remember the sidebar scroll position between page loads\n  // Inspired on source of revealjs.com\n  let storedScrollTop = parseInt(\n    sessionStorage.getItem(\"sidebar-scroll-top\"),\n    10,\n  );\n\n  if (!isNaN(storedScrollTop)) {\n    // If we've got a saved scroll position, just use that\n    sidebar.scrollTop = storedScrollTop;\n    console.log(\"[PST]: Scrolled sidebar using stored browser position...\");\n  } else {\n    // Otherwise, calculate a position to scroll to based on the lowest `active` link\n    var sidebarNav = document.querySelector(\".bd-docs-nav\");\n    var active_pages = sidebarNav.querySelectorAll(\".active\");\n    if (active_pages.length > 0) {\n      // Use the last active page as the offset since it's the page we're on\n      var latest_active = active_pages[active_pages.length - 1];\n      var offset =\n        latest_active.getBoundingClientRect().y -\n        sidebar.getBoundingClientRect().y;\n      // Only scroll the navbar if the active link is lower than 50% of the page\n      if (latest_active.getBoundingClientRect().y > window.innerHeight * 0.5) {\n        let buffer = 0.25; // Buffer so we have some space above the scrolled item\n        sidebar.scrollTop = offset - sidebar.clientHeight * buffer;\n        console.log(\"[PST]: Scrolled sidebar using last active link...\");\n      }\n    }\n  }\n\n  // Store the sidebar scroll position\n  window.addEventListener(\"beforeunload\", () => {\n    sessionStorage.setItem(\"sidebar-scroll-top\", sidebar.scrollTop);\n  });\n}\n\n/*******************************************************************************\n * Search\n */\n\n/**\n * Find any search forms on the page and return their input element\n */\nvar findSearchInput = () => {\n  let forms = document.querySelectorAll(\"form.bd-search\");\n  if (!forms.length) {\n    // no search form found\n    return;\n  } else {\n    var form;\n    if (forms.length == 1) {\n      // there is exactly one search form (persistent or hidden)\n      form = forms[0];\n    } else {\n      // must be at least one persistent form, use the first persistent one\n      form = document.querySelector(\n        \"div:not(.search-button__search-container) > form.bd-search\",\n      );\n    }\n    return form.querySelector(\"input\");\n  }\n};\n\n/**\n * Activate the search field on the page.\n * - If there is a search field already visible it will be activated.\n * - If not, then a search field will pop up.\n */\nvar toggleSearchField = () => {\n  // Find the search input to highlight\n  let input = findSearchInput();\n\n  // if the input field is the hidden one (the one associated with the\n  // search button) then toggle the button state (to show/hide the field)\n  let searchPopupWrapper = document.querySelector(\".search-button__wrapper\");\n  let hiddenInput = searchPopupWrapper.querySelector(\"input\");\n  if (input === hiddenInput) {\n    searchPopupWrapper.classList.toggle(\"show\");\n  }\n  // when toggling off the search field, remove its focus\n  if (document.activeElement === input) {\n    input.blur();\n  } else {\n    input.focus();\n    input.select();\n    input.scrollIntoView({ block: \"center\" });\n  }\n};\n\n/**\n * Add an event listener for toggleSearchField() for Ctrl/Cmd + K\n */\nvar addEventListenerForSearchKeyboard = () => {\n  window.addEventListener(\n    \"keydown\",\n    (event) => {\n      let input = findSearchInput();\n      // toggle on Ctrl+k or ⌘+k\n      if (\n        // Ignore if shift or alt are pressed\n        !event.shiftKey &&\n        !event.altKey &&\n        // On Mac use ⌘, all other OS use Ctrl\n        (useCommandKey\n          ? event.metaKey && !event.ctrlKey\n          : !event.metaKey && event.ctrlKey) &&\n        // Case-insensitive so the shortcut still works with caps lock\n        /^k$/i.test(event.key)\n      ) {\n        event.preventDefault();\n        toggleSearchField();\n      }\n      // also allow Escape key to hide (but not show) the dynamic search field\n      else if (document.activeElement === input && /Escape/i.test(event.key)) {\n        toggleSearchField();\n      }\n    },\n    true,\n  );\n};\n\n/**\n * If the user is on a Mac, use command (⌘) instead of control (ctrl) key\n *\n * Note: `navigator.platform` is deprecated; however MDN still recommends using\n * it for the one specific use case of detecting whether a keyboard shortcut\n * should use control or command:\n * https://developer.mozilla.org/en-US/docs/Web/API/Navigator/platform#examples\n */\nvar useCommandKey =\n  navigator.platform.indexOf(\"Mac\") === 0 || navigator.platform === \"iPhone\";\n\n/**\n * Change the search hint to `meta key` if we are a Mac\n */\n\nvar changeSearchShortcutKey = () => {\n  let shortcuts = document.querySelectorAll(\".search-button__kbd-shortcut\");\n  if (useCommandKey) {\n    shortcuts.forEach(\n      (f) => (f.querySelector(\"kbd.kbd-shortcut__modifier\").innerText = \"⌘\"),\n    );\n  }\n};\n\n/**\n * Activate callbacks for search button popup\n */\nvar setupSearchButtons = () => {\n  changeSearchShortcutKey();\n  addEventListenerForSearchKeyboard();\n\n  // Add the search button trigger event callback\n  document.querySelectorAll(\".search-button__button\").forEach((btn) => {\n    btn.onclick = toggleSearchField;\n  });\n\n  // Add the search button overlay event callback\n  let overlay = document.querySelector(\".search-button__overlay\");\n  if (overlay) {\n    overlay.onclick = toggleSearchField;\n  }\n};\n\n/*******************************************************************************\n * Version Switcher\n * Note that this depends on two variables existing that are defined in\n * and `html-page-context` hook:\n *\n * - DOCUMENTATION_OPTIONS.pagename\n * - DOCUMENTATION_OPTIONS.theme_switcher_url\n */\n\n/**\n * path component of URL\n */\nvar getCurrentUrlPath = () => {\n  if (DOCUMENTATION_OPTIONS.BUILDER == \"dirhtml\") {\n    return DOCUMENTATION_OPTIONS.pagename == \"index\"\n      ? `/`\n      : `${DOCUMENTATION_OPTIONS.pagename}/`;\n  }\n  return `${DOCUMENTATION_OPTIONS.pagename}.html`;\n};\n\n/**\n * Allow user to dismiss the warning banner about the docs version being dev / old.\n * We store the dismissal date and version, to give us flexibility about making the\n * dismissal last for longer than one browser session, if we decide to do that.\n *\n * @param {event} event the event that trigger the check\n */\nasync function DismissBannerAndStorePref(event) {\n  const banner = document.querySelector(\"#bd-header-version-warning\");\n  banner.remove();\n  const version = DOCUMENTATION_OPTIONS.VERSION;\n  const now = new Date();\n  const banner_pref = JSON.parse(\n    localStorage.getItem(\"pst_banner_pref\") || \"{}\",\n  );\n  console.debug(\n    `[PST] Dismissing the version warning banner on ${version} starting ${now}.`,\n  );\n  banner_pref[version] = now;\n  localStorage.setItem(\"pst_banner_pref\", JSON.stringify(banner_pref));\n}\n\n/**\n * Check if corresponding page path exists in other version of docs\n * and, if so, go there instead of the homepage of the other docs version\n *\n * @param {event} event the event that trigger the check\n */\nasync function checkPageExistsAndRedirect(event) {\n  // ensure we don't follow the initial link\n  event.preventDefault();\n  const currentFilePath = getCurrentUrlPath();\n  let tryUrl = event.currentTarget.getAttribute(\"href\");\n  let otherDocsHomepage = tryUrl.replace(currentFilePath, \"\");\n  try {\n    let head = await fetch(tryUrl, { method: \"HEAD\" });\n    if (head.ok) {\n      location.href = tryUrl; // the page exists, go there\n    } else {\n      location.href = otherDocsHomepage;\n    }\n  } catch (err) {\n    // something went wrong, probably CORS restriction, fallback to other docs homepage\n    location.href = otherDocsHomepage;\n  }\n}\n\n/**\n * Load and parse the version switcher JSON file from an absolute or relative URL.\n *\n * @param {string} url The URL to load version switcher entries from.\n */\nasync function fetchVersionSwitcherJSON(url) {\n  // first check if it's a valid URL\n  try {\n    var result = new URL(url);\n  } catch (err) {\n    if (err instanceof TypeError) {\n      if (!window.location.origin) {\n        // window.location.origin is null for local static sites\n        // (ie. window.location.protocol == 'file:')\n        //\n        // TODO: Fix this to return the static version switcher by working out\n        // how to get the correct path to the switcher JSON file on local static builds\n        return null;\n      }\n      // assume we got a relative path, and fix accordingly. But first, we need to\n      // use `fetch()` to follow redirects so we get the correct final base URL\n      const origin = await fetch(window.location.origin, { method: \"HEAD\" });\n      result = new URL(url, origin.url);\n    } else {\n      // something unexpected happened\n      throw err;\n    }\n  }\n  // load and return the JSON\n  const response = await fetch(result);\n  const data = await response.json();\n  return data;\n}\n\n// Populate the version switcher from the JSON data\nfunction populateVersionSwitcher(data, versionSwitcherBtns) {\n  const currentFilePath = getCurrentUrlPath();\n  versionSwitcherBtns.forEach((btn) => {\n    // Set empty strings by default so that these attributes exist and can be used in CSS selectors\n    btn.dataset[\"activeVersionName\"] = \"\";\n    btn.dataset[\"activeVersion\"] = \"\";\n  });\n  // in case there are multiple entries with the same version string, this helps us\n  // decide which entry's `name` to put on the button itself. Without this, it would\n  // always be the *last* version-matching entry; now it will be either the\n  // version-matching entry that is also marked as `\"preferred\": true`, or if that\n  // doesn't exist: the *first* version-matching entry.\n  data = data.map((entry) => {\n    // does this entry match the version that we're currently building/viewing?\n    entry.match =\n      entry.version == DOCUMENTATION_OPTIONS.theme_switcher_version_match;\n    entry.preferred = entry.preferred || false;\n    // if no custom name specified (e.g., \"latest\"), use version string\n    if (!(\"name\" in entry)) {\n      entry.name = entry.version;\n    }\n    return entry;\n  });\n  const hasMatchingPreferredEntry = data\n    .map((entry) => entry.preferred && entry.match)\n    .some(Boolean);\n  var foundMatch = false;\n  // create links to the corresponding page in the other docs versions\n  data.forEach((entry) => {\n    // create the node\n    const anchor = document.createElement(\"a\");\n    anchor.setAttribute(\n      \"class\",\n      \"dropdown-item list-group-item list-group-item-action py-1\",\n    );\n    anchor.setAttribute(\"href\", `${entry.url}${currentFilePath}`);\n    anchor.setAttribute(\"role\", \"option\");\n    const span = document.createElement(\"span\");\n    span.textContent = `${entry.name}`;\n    anchor.appendChild(span);\n    // Add dataset values for the version and name in case people want\n    // to apply CSS styling based on this information.\n    anchor.dataset[\"versionName\"] = entry.name;\n    anchor.dataset[\"version\"] = entry.version;\n    // replace dropdown button text with the preferred display name of the\n    // currently-viewed version, rather than using sphinx's {{ version }} variable.\n    // also highlight the dropdown entry for the currently-viewed version's entry\n    let matchesAndIsPreferred = hasMatchingPreferredEntry && entry.preferred;\n    let matchesAndIsFirst =\n      !hasMatchingPreferredEntry && !foundMatch && entry.match;\n    if (matchesAndIsPreferred || matchesAndIsFirst) {\n      anchor.classList.add(\"active\");\n      versionSwitcherBtns.forEach((btn) => {\n        btn.innerText = entry.name;\n        btn.dataset[\"activeVersionName\"] = entry.name;\n        btn.dataset[\"activeVersion\"] = entry.version;\n      });\n      foundMatch = true;\n    }\n    // There may be multiple version-switcher elements, e.g. one\n    // in a slide-over panel displayed on smaller screens.\n    document.querySelectorAll(\".version-switcher__menu\").forEach((menu) => {\n      // we need to clone the node for each menu, but onclick attributes are not\n      // preserved by `.cloneNode()` so we add onclick here after cloning.\n      let node = anchor.cloneNode(true);\n      node.onclick = checkPageExistsAndRedirect;\n      // on click, AJAX calls will check if the linked page exists before\n      // trying to redirect, and if not, will redirect to the homepage\n      // for that version of the docs.\n      menu.append(node);\n    });\n  });\n}\n\n/*******************************************************************************\n * Warning banner when viewing non-stable version of the docs.\n */\n\n/**\n * Show a warning banner when viewing a non-stable version of the docs.\n *\n * adapted 2023-06 from https://mne.tools/versionwarning.js, which was\n * originally adapted 2020-05 from https://scikit-learn.org/versionwarning.js\n *\n * @param {Array} data The version data used to populate the switcher menu.\n */\nfunction showVersionWarningBanner(data) {\n  var version = DOCUMENTATION_OPTIONS.VERSION;\n  // figure out what latest stable version is\n  var preferredEntries = data.filter((entry) => entry.preferred);\n  if (preferredEntries.length !== 1) {\n    const howMany = preferredEntries.length == 0 ? \"No\" : \"Multiple\";\n    console.log(\n      `[PST] ${howMany} versions marked \"preferred\" found in versions JSON, ignoring.`,\n    );\n    return;\n  }\n  const preferredVersion = preferredEntries[0].version;\n  const preferredURL = preferredEntries[0].url;\n  // if already on preferred version, nothing to do\n  const versionsAreComparable = validate(version) && validate(preferredVersion);\n  if (versionsAreComparable && compare(version, preferredVersion, \"=\")) {\n    console.log(\n      \"This is the prefered version of the docs, not showing the warning banner.\",\n    );\n    return;\n  }\n  // check if banner has been dismissed recently\n  const dismiss_date_str = JSON.parse(\n    localStorage.getItem(\"pst_banner_pref\") || \"{}\",\n  )[version];\n  if (dismiss_date_str != null) {\n    const dismiss_date = new Date(dismiss_date_str);\n    const now = new Date();\n    const milliseconds_in_a_day = 24 * 60 * 60 * 1000;\n    const days_passed = (now - dismiss_date) / milliseconds_in_a_day;\n    const timeout_in_days = 14;\n    if (days_passed < timeout_in_days) {\n      console.info(\n        `[PST] Suppressing version warning banner; was dismissed ${Math.floor(days_passed)} day(s) ago`,\n      );\n      return;\n    }\n  }\n\n  // now construct the warning banner\n  const banner = document.querySelector(\"#bd-header-version-warning\");\n  const middle = document.createElement(\"div\");\n  const inner = document.createElement(\"div\");\n  const bold = document.createElement(\"strong\");\n  const button = document.createElement(\"a\");\n  const close_btn = document.createElement(\"a\");\n  // these classes exist since pydata-sphinx-theme v0.10.0\n  // the init class is used for animation\n  middle.classList = \"bd-header-announcement__content  ms-auto me-auto\";\n  inner.classList = \"sidebar-message\";\n  button.classList =\n    \"btn text-wrap font-weight-bold ms-3 my-1 align-baseline pst-button-link-to-stable-version\";\n  button.href = `${preferredURL}${getCurrentUrlPath()}`;\n  button.innerText = \"Switch to stable version\";\n  button.onclick = checkPageExistsAndRedirect;\n  close_btn.classList = \"ms-3 my-1 align-baseline\";\n  const close_x = document.createElement(\"i\");\n  close_btn.append(close_x);\n  close_x.classList = \"fa-solid fa-xmark\";\n  close_btn.onclick = DismissBannerAndStorePref;\n  // add the version-dependent text\n  inner.innerText = \"This is documentation for \";\n  const isDev =\n    version.includes(\"dev\") ||\n    version.includes(\"rc\") ||\n    version.includes(\"pre\");\n  const newerThanPreferred =\n    versionsAreComparable && compare(version, preferredVersion, \">\");\n  if (isDev || newerThanPreferred) {\n    bold.innerText = \"an unstable development version\";\n  } else if (versionsAreComparable && compare(version, preferredVersion, \"<\")) {\n    bold.innerText = `an old version (${version})`;\n  } else if (!version) {\n    bold.innerText = \"an unknown version\"; // e.g., an empty string\n  } else {\n    bold.innerText = `version ${version}`;\n  }\n  banner.appendChild(middle);\n  banner.append(close_btn);\n  middle.appendChild(inner);\n  inner.appendChild(bold);\n  inner.appendChild(document.createTextNode(\".\"));\n  inner.appendChild(button);\n  banner.classList.remove(\"d-none\");\n}\n\n/*******************************************************************************\n * MutationObserver to move the ReadTheDocs button\n */\n\n/**\n * intercept the RTD flyout and place it in the rtd-footer-container if existing\n * if not it stays where on top of the page\n */\nfunction initRTDObserver() {\n  const mutatedCallback = (mutationList, observer) => {\n    mutationList.forEach((mutation) => {\n      // Check whether the mutation is for RTD, which will have a specific structure\n      if (mutation.addedNodes.length === 0) {\n        return;\n      }\n      if (mutation.addedNodes[0].data === undefined) {\n        return;\n      }\n      if (mutation.addedNodes[0].data.search(\"Inserted RTD Footer\") != -1) {\n        mutation.addedNodes.forEach((node) => {\n          document.getElementById(\"rtd-footer-container\").append(node);\n        });\n      }\n    });\n  };\n\n  const observer = new MutationObserver(mutatedCallback);\n  const config = { childList: true };\n  observer.observe(document.body, config);\n}\n\nasync function fetchAndUseVersions() {\n  // fetch the JSON version data (only once), then use it to populate the version\n  // switcher and maybe show the version warning bar\n  var versionSwitcherBtns = document.querySelectorAll(\n    \".version-switcher__button\",\n  );\n  const hasSwitcherMenu = versionSwitcherBtns.length > 0;\n  const hasVersionsJSON = DOCUMENTATION_OPTIONS.hasOwnProperty(\n    \"theme_switcher_json_url\",\n  );\n  const wantsWarningBanner = DOCUMENTATION_OPTIONS.show_version_warning_banner;\n\n  if (hasVersionsJSON && (hasSwitcherMenu || wantsWarningBanner)) {\n    const data = await fetchVersionSwitcherJSON(\n      DOCUMENTATION_OPTIONS.theme_switcher_json_url,\n    );\n    // TODO: remove the `if(data)` once the `return null` is fixed within fetchVersionSwitcherJSON.\n    // We don't really want the switcher and warning bar to silently not work.\n    if (data) {\n      populateVersionSwitcher(data, versionSwitcherBtns);\n      if (wantsWarningBanner) {\n        showVersionWarningBanner(data);\n      }\n    }\n  }\n}\n\n/*******************************************************************************\n * Add keyboard functionality to mobile sidebars.\n *\n * Wire up the hamburger-style buttons using the click event which (on buttons)\n * handles both mouse clicks and the space and enter keys.\n */\nfunction setupMobileSidebarKeyboardHandlers() {\n  // These are hidden checkboxes at the top of the page whose :checked property\n  // allows the mobile sidebars to be hidden or revealed via CSS.\n  const primaryToggle = document.getElementById(\"pst-primary-sidebar-checkbox\");\n  const secondaryToggle = document.getElementById(\n    \"pst-secondary-sidebar-checkbox\",\n  );\n  const primarySidebar = document.querySelector(\".bd-sidebar-primary\");\n  const secondarySidebar = document.querySelector(\".bd-sidebar-secondary\");\n\n  // Toggle buttons -\n  //\n  // These are the hamburger-style buttons in the header nav bar. When the user\n  // clicks, the button transmits the click to the hidden checkboxes used by the\n  // CSS to control whether the sidebar is open or closed.\n  const primaryClickTransmitter = document.querySelector(\".primary-toggle\");\n  const secondaryClickTransmitter = document.querySelector(\".secondary-toggle\");\n  [\n    [primaryClickTransmitter, primaryToggle, primarySidebar],\n    [secondaryClickTransmitter, secondaryToggle, secondarySidebar],\n  ].forEach(([clickTransmitter, toggle, sidebar]) => {\n    if (!clickTransmitter) {\n      return;\n    }\n    clickTransmitter.addEventListener(\"click\", (event) => {\n      event.preventDefault();\n      event.stopPropagation();\n      toggle.checked = !toggle.checked;\n\n      // If we are opening the sidebar, move focus to the first focusable item\n      // in the sidebar\n      if (toggle.checked) {\n        // Note: this selector is not exhaustive, and we may need to update it\n        // in the future\n        const tabStop = sidebar.querySelector(\"a, button\");\n        // use setTimeout because you cannot move focus synchronously during a\n        // click in the handler for the click event\n        setTimeout(() => tabStop.focus(), 100);\n      }\n    });\n  });\n\n  // Escape key -\n  //\n  // When sidebar is open, user should be able to press escape key to close the\n  // sidebar.\n  [\n    [primarySidebar, primaryToggle, primaryClickTransmitter],\n    [secondarySidebar, secondaryToggle, secondaryClickTransmitter],\n  ].forEach(([sidebar, toggle, transmitter]) => {\n    if (!sidebar) {\n      return;\n    }\n    sidebar.addEventListener(\"keydown\", (event) => {\n      if (event.key === \"Escape\") {\n        event.preventDefault();\n        event.stopPropagation();\n        toggle.checked = false;\n        transmitter.focus();\n      }\n    });\n  });\n\n  // When the <label> overlay is clicked to close the sidebar, return focus to\n  // the opener button in the nav bar.\n  [\n    [primaryToggle, primaryClickTransmitter],\n    [secondaryToggle, secondaryClickTransmitter],\n  ].forEach(([toggle, transmitter]) => {\n    toggle.addEventListener(\"change\", (event) => {\n      if (!event.currentTarget.checked) {\n        transmitter.focus();\n      }\n    });\n  });\n}\n\n/**\n * When the page loads, or the window resizes, or descendant nodes are added or\n * removed from the main element, check all code blocks and Jupyter notebook\n * outputs, and for each one that has scrollable overflow, set tabIndex = 0.\n */\nfunction addTabStopsToScrollableElements() {\n  const updateTabStops = () => {\n    document\n      .querySelectorAll(\n        \"pre, \" + // code blocks\n          \".nboutput > .output_area, \" + // NBSphinx notebook output\n          \".cell_output > .output, \" + // Myst-NB\n          \".jp-RenderedHTMLCommon\", // ipywidgets\n      )\n      .forEach((el) => {\n        el.tabIndex =\n          el.scrollWidth > el.clientWidth || el.scrollHeight > el.clientHeight\n            ? 0\n            : -1;\n      });\n  };\n  const debouncedUpdateTabStops = debounce(updateTabStops, 300);\n\n  // On window resize\n  window.addEventListener(\"resize\", debouncedUpdateTabStops);\n\n  // The following MutationObserver is for ipywidgets, which take some time to\n  // finish loading and rendering on the page (so even after the \"load\" event is\n  // fired, they still have not finished rendering). Would be nice to replace\n  // the MutationObserver if there is a way to hook into the ipywidgets code to\n  // know when it is done.\n  const mainObserver = new MutationObserver(debouncedUpdateTabStops);\n\n  // On descendant nodes added/removed from main element\n  mainObserver.observe(document.getElementById(\"main-content\"), {\n    subtree: true,\n    childList: true,\n  });\n\n  // On page load (when this function gets called)\n  updateTabStops();\n}\nfunction debounce(callback, wait) {\n  let timeoutId = null;\n  return (...args) => {\n    clearTimeout(timeoutId);\n    timeoutId = setTimeout(() => {\n      callback(...args);\n    }, wait);\n  };\n}\n\n/*******************************************************************************\n * Announcement banner - fetch and load remote HTML\n */\nasync function setupAnnouncementBanner() {\n  const banner = document.querySelector(\".bd-header-announcement\");\n  const { pstAnnouncementUrl } = banner.dataset;\n\n  if (!pstAnnouncementUrl) {\n    return;\n  }\n\n  try {\n    const response = await fetch(pstAnnouncementUrl);\n    if (!response.ok) {\n      throw new Error(\n        `[PST]: HTTP response status not ok: ${response.status} ${response.statusText}`,\n      );\n    }\n    const data = await response.text();\n    if (data.length === 0) {\n      console.log(`[PST]: Empty announcement at: ${pstAnnouncementUrl}`);\n      return;\n    }\n    banner.innerHTML = `<div class=\"bd-header-announcement__content\">${data}</div>`;\n    banner.classList.remove(\"d-none\");\n  } catch (_error) {\n    console.log(`[PST]: Failed to load announcement at: ${pstAnnouncementUrl}`);\n    console.error(_error);\n  }\n}\n\n/*******************************************************************************\n * Reveal (and animate) the banners (version warning, announcement) together\n */\nasync function fetchRevealBannersTogether() {\n  // Wait until finished fetching and loading banners\n  await Promise.allSettled([fetchAndUseVersions(), 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\ No newline at end of file
diff --git a/_preview/13/_static/scripts/sphinx-book-theme.js b/_preview/13/_static/scripts/sphinx-book-theme.js
new file mode 100644
index 0000000..481ba0c
--- /dev/null
+++ b/_preview/13/_static/scripts/sphinx-book-theme.js
@@ -0,0 +1,2 @@
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+//# sourceMappingURL=sphinx-book-theme.js.map
\ No newline at end of file
diff --git a/_preview/13/_static/scripts/sphinx-book-theme.js.map b/_preview/13/_static/scripts/sphinx-book-theme.js.map
new file mode 100644
index 0000000..2cc145a
--- /dev/null
+++ b/_preview/13/_static/scripts/sphinx-book-theme.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"scripts/sphinx-book-theme.js","mappings":"mBASA,IAAIA,EAAsBC,IACG,WAAvBC,SAASC,WACXF,IACSC,SAASE,iBAClBF,SAASE,iBAAiB,mBAAoBH,GAE9CC,SAASG,YAAY,sBAAsB,WACd,YAAvBH,SAASC,YAA0BF,GACzC,GACF,EAkKFK,OAAOC,aAtCY,KACjB,IAAIC,EAAQN,SAASO,cAAc,cACrBD,EAAME,mBAGPC,UAAUC,SAAS,wBAC9BJ,EAAMK,mBACJ,WACA,iDAIJC,WAAW,EA2BbR,OAAOS,iBAzJgB,KACrB,IAAIC,EACDd,SAASe,mBAAoD,OAA/Bf,SAASe,mBACvCf,SAASgB,yBAC6B,OAArChB,SAASgB,wBACb,IAAIC,EAASjB,SAASkB,gBACjBJ,GAQHK,QAAQC,IAAI,8BACRpB,SAASqB,eACXrB,SAASqB,iBACArB,SAASsB,sBAClBtB,SAASsB,yBAXXH,QAAQC,IAAI,+BACRH,EAAOM,kBACTN,EAAOM,oBACEN,EAAOO,yBAChBP,EAAOO,0BASX,EA0IF1B,GAzHkB,KAChB,IAAI2B,EAAgB,GACpB,IAwCIC,EAAc,IAAIC,sBAxCA,CAACC,EAASC,KAE9BD,EAAQE,SAASC,IACf,GAAIA,EAAMC,eAERP,EAAcQ,KAAKF,EAAMG,aAGzB,IAAK,IAAIC,EAAK,EAAGA,EAAKV,EAAcW,OAAQD,IAC1C,GAAIV,EAAcU,KAAQJ,EAAMG,OAAQ,CACtCT,EAAcY,OAAOF,EAAI,GACzB,KACF,CAEJ,IAIEV,EAAcW,OAAS,EACzBpC,SAASO,cAAc,4BAA4BE,UAAU6B,IAAI,QAEjEtC,SACGO,cAAc,4BACdE,UAAU8B,OAAO,OACtB,GAYY,CAEZC,WAAY,qBAaVC,EAAiB,GATG,CACtB,aACA,WACA,SACA,iBACA,aACA,UACA,UAGcX,SAASK,IAEvBM,EAAeR,KAEX,IAAIE,IACJ,QAAQA,IACR,IAAIA,EAAGO,QAAQ,IAAK,OACpB,QAAQP,EAAGO,QAAQ,IAAK,OAE3B,IAEH1C,SAAS2C,iBAAiBF,EAAeG,KAAK,OAAOd,SAASK,IAC5DT,EAAYmB,QAAQV,EAAG,IAIJ,IAAIR,sBA1CO,CAACC,EAASC,KAEpCD,EAAQ,GAAGkB,mBAAmBC,EAAI,EACpC/C,SAASgD,KAAKvC,UAAU6B,IAAI,YAE5BtC,SAASgD,KAAKvC,UAAU8B,OAAO,WACjC,IAqCaM,QAAQ7C,SAASO,cAAc,4BAA4B,IAmD5ET,GA1BA,WACE,IAAImD,EAAkB,CACpB,0BACA,aACA,qBACA,sBACA,wBACA,qBACA,qBACA,cACAL,KAAK,KACP5C,SAAS2C,iBAAiBM,GAAiBnB,SAASK,IAClDA,EAAG1B,UAAU6B,IAAI,UAAU,GAE/B,G","sources":["webpack://sphinx_book_theme/./src/sphinx_book_theme/assets/scripts/index.js"],"sourcesContent":["// Import CSS variables\n// ref: https://css-tricks.com/getting-javascript-to-talk-to-css-and-sass/\nimport \"../styles/index.scss\";\n\n/**\n * A helper function to load scripts when the DOM is loaded.\n * This waits for everything to be on the page first before running, since\n * some functionality doesn't behave properly until everything is ready.\n */\nvar sbRunWhenDOMLoaded = (cb) => {\n  if (document.readyState != \"loading\") {\n    cb();\n  } else if (document.addEventListener) {\n    document.addEventListener(\"DOMContentLoaded\", cb);\n  } else {\n    document.attachEvent(\"onreadystatechange\", function () {\n      if (document.readyState == \"complete\") cb();\n    });\n  }\n};\n\n/**\n * Toggle full-screen with button\n *\n * There are some browser-specific hacks in here:\n * - Safari requires a `webkit` prefix, so this uses conditionals to check for that\n *   ref: https://developer.mozilla.org/en-US/docs/Web/API/Fullscreen_API\n */\nvar toggleFullScreen = () => {\n  var isInFullScreen =\n    (document.fullscreenElement && document.fullscreenElement !== null) ||\n    (document.webkitFullscreenElement &&\n      document.webkitFullscreenElement !== null);\n  let docElm = document.documentElement;\n  if (!isInFullScreen) {\n    console.log(\"[SBT]: Entering full screen\");\n    if (docElm.requestFullscreen) {\n      docElm.requestFullscreen();\n    } else if (docElm.webkitRequestFullscreen) {\n      docElm.webkitRequestFullscreen();\n    }\n  } else {\n    console.log(\"[SBT]: Exiting full screen\");\n    if (document.exitFullscreen) {\n      document.exitFullscreen();\n    } else if (document.webkitExitFullscreen) {\n      document.webkitExitFullscreen();\n    }\n  }\n};\n\n/**\n * Manage scrolling behavior. This is primarily two things:\n *\n * 1. Hide the Table of Contents any time sidebar content is on the screen.\n *\n * This will be triggered any time a sidebar item enters or exits the screen.\n * It adds/removes items from an array if they have entered the screen, and\n * removes them when they exit the screen. It hides the TOC if anything is\n * on-screen.\n *\n * ref: https://developer.mozilla.org/en-US/docs/Web/API/Intersection_Observer_API\n *\n * 2. Add a `scrolled` class to <body> to trigger CSS changes.\n */\nvar initTocHide = () => {\n  var onScreenItems = [];\n  let hideTocCallback = (entries, observer) => {\n    // Check whether any sidebar item is displayed\n    entries.forEach((entry) => {\n      if (entry.isIntersecting) {\n        // If an element just came on screen, add it our list\n        onScreenItems.push(entry.target);\n      } else {\n        // Otherwise, if it's in our list then remove it\n        for (let ii = 0; ii < onScreenItems.length; ii++) {\n          if (onScreenItems[ii] === entry.target) {\n            onScreenItems.splice(ii, 1);\n            break;\n          }\n        }\n      }\n    });\n\n    // Hide the TOC if any margin content is displayed on the screen\n    if (onScreenItems.length > 0) {\n      document.querySelector(\"div.bd-sidebar-secondary\").classList.add(\"hide\");\n    } else {\n      document\n        .querySelector(\"div.bd-sidebar-secondary\")\n        .classList.remove(\"hide\");\n    }\n  };\n  let manageScrolledClassOnBody = (entries, observer) => {\n    // The pixel is at the top, so if we're < 0 that it means we've scrolled\n    if (entries[0].boundingClientRect.y < 0) {\n      document.body.classList.add(\"scrolled\");\n    } else {\n      document.body.classList.remove(\"scrolled\");\n    }\n  };\n\n  // Set up the intersection observer to watch all margin content\n  let options = {\n    // Trigger callback when the top of a margin item is 1/3 up the screen\n    rootMargin: \"0px 0px -33% 0px\",\n  };\n  let tocObserver = new IntersectionObserver(hideTocCallback, options);\n  // TODO: deprecate popout after v0.5.0\n  const selectorClasses = [\n    \"marginnote\",\n    \"sidenote\",\n    \"margin\",\n    \"margin-caption\",\n    \"full-width\",\n    \"sidebar\",\n    \"popout\",\n  ];\n  let marginSelector = [];\n  selectorClasses.forEach((ii) => {\n    // Use three permutations of each class name because `tag_` and `_` used to be supported\n    marginSelector.push(\n      ...[\n        `.${ii}`,\n        `.tag_${ii}`,\n        `.${ii.replace(\"-\", \"_\")}`,\n        `.tag_${ii.replace(\"-\", \"_\")}`,\n      ],\n    );\n  });\n  document.querySelectorAll(marginSelector.join(\", \")).forEach((ii) => {\n    tocObserver.observe(ii);\n  });\n\n  // Set up the observer to check if we've scrolled from top of page\n  let scrollObserver = new IntersectionObserver(manageScrolledClassOnBody);\n  scrollObserver.observe(document.querySelector(\".sbt-scroll-pixel-helper\"));\n};\n\n/**\n * Activate Thebe with a custom button click.\n */\nvar initThebeSBT = () => {\n  var title = document.querySelector(\"section h1\");\n  var sibling = title.nextElementSibling;\n  // If the next element after the title isn't a thebe button, add one now.\n  // That way it is initiatlized when thebe is first-clicked and isn't re-added after.\n  if (!sibling.classList.contains(\"thebe-launch-button\")) {\n    title.insertAdjacentHTML(\n      \"afterend\",\n      \"<button class='thebe-launch-button'></button>\",\n    );\n  }\n  // This function is provided by sphinx-thebe\n  initThebe();\n};\n\n/**\n * Add no print class to certain DOM elements\n */\n\nfunction addNoPrint() {\n  var noPrintSelector = [\n    \".bd-header-announcement\",\n    \".bd-header\",\n    \".bd-header-article\",\n    \".bd-sidebar-primary\",\n    \".bd-sidebar-secondary\",\n    \".bd-footer-article\",\n    \".bd-footer-content\",\n    \".bd-footer\",\n  ].join(\",\");\n  document.querySelectorAll(noPrintSelector).forEach((ii) => {\n    ii.classList.add(\"noprint\");\n  });\n}\n\n/**\n * Set up callback functions for UI click actions\n */\nwindow.initThebeSBT = initThebeSBT;\nwindow.toggleFullScreen = toggleFullScreen;\n\n/**\n * Set up functions to load when the DOM is ready\n */\nsbRunWhenDOMLoaded(initTocHide);\nsbRunWhenDOMLoaded(addNoPrint);\n"],"names":["sbRunWhenDOMLoaded","cb","document","readyState","addEventListener","attachEvent","window","initThebeSBT","title","querySelector","nextElementSibling","classList","contains","insertAdjacentHTML","initThebe","toggleFullScreen","isInFullScreen","fullscreenElement","webkitFullscreenElement","docElm","documentElement","console","log","exitFullscreen","webkitExitFullscreen","requestFullscreen","webkitRequestFullscreen","onScreenItems","tocObserver","IntersectionObserver","entries","observer","forEach","entry","isIntersecting","push","target","ii","length","splice","add","remove","rootMargin","marginSelector","replace","querySelectorAll","join","observe","boundingClientRect","y","body","noPrintSelector"],"sourceRoot":""}
\ No newline at end of file
diff --git a/_preview/13/_static/searchtools.js b/_preview/13/_static/searchtools.js
new file mode 100644
index 0000000..92da3f8
--- /dev/null
+++ b/_preview/13/_static/searchtools.js
@@ -0,0 +1,619 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms, anchor)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      "Search finished, found ${resultCount} page(s) matching the search query."
+    ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString, anchor) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    for (const removalQuery of [".headerlinks", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent) return docContent.textContent;
+
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  _parseQuery: (query) => {
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          normalResults.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/_preview/13/_static/sphinx-pythia-theme.min.css b/_preview/13/_static/sphinx-pythia-theme.min.css
new file mode 100644
index 0000000..0609200
--- /dev/null
+++ b/_preview/13/_static/sphinx-pythia-theme.min.css
@@ -0,0 +1,2 @@
+@import url(sphinx-book-theme.css);:root{--spt-font-body: 'Poppins';--spt-font-headings: 'Poppins';--spt-font-footnote: 'Cormorant';--spt-color-gray-100: 248, 249, 250;--spt-color-gray-200: 233, 236, 239;--spt-color-gray-300: 222, 226, 230;--spt-color-gray-400: 206, 212, 218;--spt-color-gray-500: 173, 181, 189;--spt-color-gray-600: 108, 117, 125;--spt-color-gray-700: 73, 80, 87;--spt-color-gray-800: 52, 58, 64;--spt-color-gray-900: 33, 37, 41;--spt-color-light: var(--spt-color-gray-100);--spt-color-dark: var(--spt-color-gray-800);--spt-color-green: 0, 121, 124;--spt-color-green-lighter: 20, 141, 144;--spt-color-green-lightest: 130, 231, 234;--spt-color-blue: 26, 101, 143;--spt-color-blue-lighter: 46, 121, 163;--spt-color-blue-lightest: 156, 191, 213;--spt-color-deep-blue: 1, 33, 105;--spt-color-green-highlight: 168, 199, 0;--pst-color-primary: var(--spt-color-blue) !important;--pst-color-primary-hover: var(--spt-color-blue-lighter) !important;--pst-color-secondary: var(--spt-color-blue-lightest) !important;--pst-color-link: #1a648f !important;--pst-color-link-hover: #9cbfd5 !important;--pst-color-headerlink: var(--spt-color-gray-300) !important;--pst-color-headerlink-hover: var(--spt-color-gray-100)!important;--pst-color-text-muted: #6c757d !important;--tabs-size-label: 0.8rem !important;--tabs-color-label-active: rgba(var(--pst-color-primary), 1) !important;--tabs-color-label-inactive: rgba(var(--spt-color-blue-lightest), 1) !important
+}body{font-family:var(--spt-font-body),sans-serif}html{scroll-behavior:smooth;scroll-padding-top:128px}.admonition.alert-info,div.admonition.alert-info{border-color:#0c5460 !important}.admonition.alert-info .admonition-title::before,div.admonition.alert-info .admonition-title::before{color:#0c5460 !important}.admonition.alert-success,div.admonition.alert-success{border-color:#155724 !important}.admonition.alert-success .admonition-title::before,div.admonition.alert-success .admonition-title::before{color:#155724 !important}.admonition.alert-warning,div.admonition.alert-warning{border-color:#856404 !important}.admonition.alert-warning .admonition-title::before,div.admonition.alert-warning .admonition-title::before{color:#856404 !important}.admonition.alert-danger,div.admonition.alert-danger{border-color:#721c24 !important}.admonition.alert-danger .admonition-title::before,div.admonition.alert-danger .admonition-title::before{color:#721c24 !important}.bg-primary{background-color:rgba(var(--pst-color-primary), 1) !important;color:white !important}.btn-primary{background-color:rgba(var(--pst-color-primary), 1) !important;border-color:rgba(var(--pst-color-primary), 1) !important;color:white !important}.btn-primary:hover{background-color:rgba(var(--pst-color-primary-hover), 1) !important;color:white !important}.btn-outline-primary{border-color:rgba(var(--pst-color-primary), 1) !important;color:rgba(var(--pst-color-primary), 1) !important}.btn-outline-primary:hover,.show>.btn-outline-primary.dropdown-toggle{background-color:rgba(var(--pst-color-primary), 1) !important;color:white !important}.btn-link{color:rgba(var(--pst-color-primary), 1) !important}.display-1{font-size:5rem !important}.display-2{font-size:3rem !important}.display-3{font-size:2rem !important}.display-4{font-size:1.6667rem !important}.display-5{font-size:1.3333rem !important}.display-6{font-size:1rem !important}.form-control:focus{outline:none !important;box-shadow:none !important}.navbar-brand{padding:.75rem 0 !important}.row{margin-left:0 !important;margin-right:0 !important}a.badge{text-decoration:none !important}a.navbar-brand img{vertical-align:middle !important}body>div.container-fluid,body>div.container-xl{padding-right:0;padding-left:0}i.fab,i.far,i.fas{font-size:70%}a.headerlink i.fab,a.headerlink i.far,a.headerlink i.fas{position:absolute;top:50%;transform:translateY(-50%)}a.headerlink{position:relative;text-decoration:none;font-size:unset !important;padding:0 0.7rem !important}a.headerlink:hover{color:rgba(var(--spt-color-gray-500), 1);background-color:transparent}div.highlight pre{border:1px solid rgba(var(--spt-color-gray-300), 1) !important;border-radius:4px !important;background-color:rgba(var(--spt-color-gray-200), 1) !important}.bd-sidebar .nav li>a{padding-top:0.1rem;padding-bottom:0.1rem}.bd-toc nav>.nav{border-left:1px solid #eee;border-bottom:1px solid #eee;border-radius:0 0 0 6px;padding-bottom:0.5em}.prev-next-area{margin:2rem auto 0 auto;border-top:1px solid rgba(var(--spt-color-gray-300), 1);max-width:calc(0.5 * (var(--bs-breakpoint-md) + var(--bs-breakpoint-lg)))}.prev-next-area i{font-size:1.1rem;color:rgba(var(--spt-color-gray-600), 1)}.prev-next-area a p.prev-next-title{font-size:1rem;font-weight:600;color:rgba(var(--spt-color-gray-600), 1)}#banner{margin-top:var(--pst-header-height) !important}#site-navigation{top:var(--pst-header-height) !important;height:calc(100vh - var(--pst-header-height)) !important;padding-top:0}#main-content{min-height:calc(100vh - var(--pst-header-height))}.topbar{top:var(--pst-header-height) !important;padding:0 !important}.topbar-main button:focus{outline:none !important}.cell .cell_input,.cell .cell_output{padding:0 !important}.cell .cell_input{border:1px solid rgba(var(--spt-color-gray-300), 1) !important;border-radius:4px !important;border-left-color:rgba(var(--pst-color-primary), 1) !important;border-left-width:medium !important;background-color:rgba(var(--spt-color-gray-200), 1) !important}.cell .cell_input pre{border:none !important;background-color:rgba(var(--spt-color-gray-200), 1) !important}.cell .cell_output{border:none !important}.cell .cell_output pre{border:1px solid rgba(var(--spt-color-gray-300), 1) !important;border-radius:4px !important;background-color:rgba(var(--spt-color-gray-200), 1) !important}.tabbed-content{box-shadow:0 -0.0625rem var(--tabs-color-overline) !important;padding-bottom:0 !important}.tabbed-set>label{padding:0 1rem !important;margin:0 !important}.thebe-launch-button{height:2.2em !important;font-size:.8em !important;border:1px solid rgba(var(--spt-color-gray-600), 1) !important;border-radius:4px !important;color:rgba(var(--spt-color-gray-600), 1)}#shim{padding:0;margin-top:var(--pst-header-height)}#navbar-main{z-index:5000;padding-left:15px;padding-right:15px}#navbar-main .container-xl{padding:0}.bd-header{background:#343a40 !important}#navbar-icon-links i:before{color:var(--spt-color-light) !important}main.standalone-main{padding:0 !important;background-color:white}main.standalone-main div.sectionwrapper-1,main.standalone-main div.sectionwrapper-2{color:rgba(var(--spt-color-dark), 1)}main.standalone-main div.section-1,main.standalone-main div.section-2{position:relative;padding-top:1rem;padding-bottom:1rem;max-width:calc(0.5 * (var(--bs-breakpoint-md) + var(--bs-breakpoint-lg)))}main.standalone-main div.section-1 h6,main.standalone-main div.section-1 .h6,main.standalone-main div.section-1 h5,main.standalone-main div.section-1 .h5,main.standalone-main div.section-1 h4,main.standalone-main div.section-1 .h4,main.standalone-main div.section-1 h3,main.standalone-main div.section-1 .h3,main.standalone-main div.section-1 h2,main.standalone-main div.section-1 .h2,main.standalone-main div.section-1 h1,main.standalone-main div.section-1 .h1,main.standalone-main div.section-2 h6,main.standalone-main div.section-2 .h6,main.standalone-main div.section-2 h5,main.standalone-main div.section-2 .h5,main.standalone-main div.section-2 h4,main.standalone-main div.section-2 .h4,main.standalone-main div.section-2 h3,main.standalone-main div.section-2 .h3,main.standalone-main div.section-2 h2,main.standalone-main div.section-2 .h2,main.standalone-main div.section-2 h1,main.standalone-main div.section-2 .h1{margin-top:0;margin-bottom:0.5rem}main.standalone-main div.section-1 h3,main.standalone-main div.section-1 .h3,main.standalone-main div.section-1 h4,main.standalone-main div.section-1 .h4,main.standalone-main div.section-1 h5,main.standalone-main div.section-1 .h5,main.standalone-main div.section-1 h6,main.standalone-main div.section-1 .h6,main.standalone-main div.section-2 h3,main.standalone-main div.section-2 .h3,main.standalone-main div.section-2 h4,main.standalone-main div.section-2 .h4,main.standalone-main div.section-2 h5,main.standalone-main div.section-2 .h5,main.standalone-main div.section-2 h6,main.standalone-main div.section-2 .h6{padding:1rem 0}main.standalone-main div.section-title-wrapper{display:flex;justify-content:flex-start}main.standalone-main div.section-title{padding:0 4rem 0.1rem 0;margin:0 0 1.6rem 0;border-bottom:2px solid rgba(var(--spt-color-deep-blue), 1)}main.banner-main{padding:0 !important}main.banner-main #main-content{padding:0 !important}main.banner-main div.sectionwrapper-1{position:relative;background-size:cover;background-position:center center;color:rgba(var(--spt-color-light), 1);background-color:rgba(var(--spt-color-gray-700), 1)}main.banner-main div.sectionwrapper-1 div.section-banner-caption{position:absolute;right:0;bottom:0;font-size:0.8rem;opacity:0.6;padding-right:0.5rem}main.banner-main div.sectionwrapper-2{color:rgba(var(--spt-color-dark), 1);background-color:rgba(var(--spt-color-light), 1)}main.banner-main div.sectionwrapper-2:nth-child(even){background-color:rgba(var(--spt-color-gray-300), 1)}main.banner-main div.section-1,main.banner-main div.section-2{position:relative;padding-top:7rem;padding-bottom:7rem;max-width:calc(0.5 * (var(--bs-breakpoint-md) + var(--bs-breakpoint-lg)))}main.banner-main div.section-1 h3,main.banner-main div.section-1 h4,main.banner-main div.section-1 h5,main.banner-main div.section-1 h6,main.banner-main div.section-2 h3,main.banner-main div.section-2 h4,main.banner-main div.section-2 h5,main.banner-main div.section-2 h6{padding:2rem 0 1rem 0;text-align:center}main.banner-main div.section-title-wrapper{display:flex;justify-content:center}main.banner-main div.section-title{padding:0 4rem 1rem 4rem;margin-bottom:3rem;border-bottom:2px solid rgba(var(--spt-color-deep-blue), 1)}main.banner-main p{font-size:1.25rem;font-weight:300}main.banner-main div.section.dark-banner h1,main.banner-main div.section.dark-banner h2,main.banner-main div.section.dark-banner h3,main.banner-main div.section.dark-banner h4,main.banner-main div.section.dark-banner h5,main.banner-main div.section.dark-banner h6{color:var(--spt-color-light) !important}main.banner-main div.section.dark-banner p{color:var(--spt-color-light) !important}.bd-main .bd-content .bd-article-container{max-width:100%}.bd-page-width{max-width:85%}.bd-footer{color:rgba(var(--spt-color-light), 1);font-size:0.8rem;font-family:var(--spt-font-footnote);padding:0}.bd-footer h1,.bd-footer h2,.bd-footer h3,.bd-footer h4,.bd-footer h5,.bd-footer h6,.bd-footer p,.bd-footer a{color:rgba(var(--spt-color-light), 1);font-family:var(--spt-font-footnote)}.bd-footer h1,.bd-footer h2,.bd-footer h3,.bd-footer h4,.bd-footer h5,.bd-footer h6{font-size:1rem;margin-bottom:5px}.bd-footer p{font-size:0.8rem}.bd-footer .footer-logos{background-color:rgba(var(--spt-color-gray-200), 1)}.bd-footer .footer-info,.bd-footer .footer-menu{color:rgba(var(--spt-color-gray-400), 1);background-color:rgba(var(--spt-color-dark), 1);padding-top:0.4rem;padding-bottom:0.4rem}.bd-footer .footer-info li,.bd-footer .footer-menu li{line-height:1rem;margin-bottom:5px}.bd-footer .footer-extra{background-color:rgba(var(--spt-color-gray-700), 1);padding-top:1rem;padding-bottom:1rem}.bd-footer .footer-extra img{height:60px}.iframe,.iframe-4x3,.iframe-16x9{position:relative;height:0;width:75%;margin-bottom:1rem;margin-left:auto;margin-right:auto}.iframe iframe,.iframe-4x3 iframe,.iframe-16x9 iframe{border:0;position:absolute;top:0;left:0;bottom:0;right:0;width:100%;height:100%}.iframe-16x9{padding-bottom:calc(0.75 * 56.25%)}.iframe-4x3{padding-bottom:calc(0.75 * 75%)}.sd-btn-primary,.sd-btn-outline-primary:hover,.sd-btn-outline-primary:focus{color:#0a7d91 !important}@media (prefers-color-scheme: dark){p{color:black}}html[data-theme="dark"] p{color:white}html[data-theme="dark"] .section-title{color:white}html[data-theme="dark"] h1.display-1{color:white}html[data-theme="dark"] h2.display-2{color:white}html[data-theme="dark"] h3.display-3{color:white}html[data-theme="dark"] main.banner-main div.sectionwrapper-2:nth-child(even){background-color:#495057}html[data-theme="dark"] main.banner-main div.sectionwrapper-2{background-color:#14181e}html[data-theme="dark"] .container-fluid{background-color:#14181e}html[data-theme="dark"] .sd-btn-primary,.sd-btn-outline-primary:hover,.sd-btn-outline-primary:focus{color:#0a7d91 !important}html[data-theme="dark"] .btn{--bs-btn-color: white}html[data-theme="dark"] .search-button{color:white}html[data-theme="dark"] .nav-link{color:white}html[data-theme="dark"] .highlight .c{color:#797129}html[data-theme="dark"] .highlight .c1{color:#797129}html[data-theme="dark"] .highlight .cp{color:#797129}html[data-theme="dark"] .highlight .k{color:#7928a1}html[data-theme="dark"] .highlight .kc{color:#7928a1}html[data-theme="dark"] .highlight .kn{color:#7928a1}html[data-theme="dark"] .highlight .l{color:#797129}html[data-theme="dark"] .highlight .m{color:#797129}html[data-theme="dark"] .highlight .mf{color:#797129}html[data-theme="dark"] .highlight .mi{color:#797129}html[data-theme="dark"] .highlight .n{color:#545454}html[data-theme="dark"] .highlight .nb{color:#797129}html[data-theme=dark] .highlight .nc{color:#0078a1 !important}html[data-theme="dark"] .highlight .nd{color:#797129}html[data-theme=dark] .highlight .nf{color:#0078a1 !important}html[data-theme="dark"] .highlight .nn{color:#545454}html[data-theme="dark"] .highlight .nt{color:#007faa}html[data-theme="dark"] .highlight .o{color:#008000}html[data-theme="dark"] .highlight .ow{color:#7928a1}html[data-theme="dark"] .highlight .p{color:#545454}html[data-theme="dark"] .highlight .s{color:#008000}html[data-theme="dark"] .highlight .sa{color:#008000}html[data-theme="dark"] .highlight .sd{color:#008000}html[data-theme="dark"] .highlight .se{color:#008000}html[data-theme="dark"] .highlight .si{color:#008000}html[data-theme="dark"] .highlight .s1{color:#008000}html[data-theme="dark"] .highlight .s2{color:#008000}html[data-theme="dark"] .highlight pre{color:black}html[data-theme="dark"] summary{color:black}html[data-theme="dark"] svg.tb-icon.toggle-chevron-right{color:white}html[data-theme="dark"] span.toggle-details__summary-text{color:white}html[data-theme="dark"] .output.text_html table thead tr{background-color:white}html[data-theme="dark"] .output.text_html table tbody tr:nth-child(2n){background-color:white}html[data-theme="dark"] .output.text_html table.dataframe thead tr{background-color:#29313d}html[data-theme="dark"] .output.text_html table.dataframe tbody tr:nth-child(2n){background-color:#222832}
diff --git a/_preview/13/_static/sphinx-pythia-theme.min.js b/_preview/13/_static/sphinx-pythia-theme.min.js
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/_preview/13/_static/sphinx-pythia-theme.min.js
@@ -0,0 +1 @@
+
diff --git a/_preview/13/_static/sphinx-thebe.css b/_preview/13/_static/sphinx-thebe.css
new file mode 100644
index 0000000..1da2793
--- /dev/null
+++ b/_preview/13/_static/sphinx-thebe.css
@@ -0,0 +1,129 @@
+/* Thebelab Buttons */
+.thebelab-button {
+  z-index: 999;
+  display: inline-block;
+  padding: 0.35em 1.2em;
+  margin: 0px 1px;
+  border-radius: 0.12em;
+  box-sizing: border-box;
+  text-decoration: none;
+  font-family: "Roboto", sans-serif;
+  font-weight: 300;
+  text-align: center;
+  transition: all 0.2s;
+  background-color: #dddddd;
+  border: 0.05em solid white;
+  color: #000000;
+}
+
+.thebelab-button:hover {
+  border: 0.05em solid black;
+  background-color: #fcfcfc;
+}
+
+.thebe-launch-button {
+  height: 2.2em;
+  font-size: 0.8em;
+  border: 1px black solid;
+}
+
+/* Thebelab Cell */
+.thebelab-cell pre {
+  background: none;
+}
+
+.thebelab-cell .thebelab-input {
+  padding-left: 1em;
+  margin-bottom: 0.5em;
+  margin-top: 0.5em;
+}
+
+.thebelab-cell .jp-OutputArea {
+  margin-top: 0.5em;
+  margin-left: 1em;
+}
+
+button.thebelab-button.thebelab-run-button {
+  margin-left: 1.5em;
+  margin-bottom: 0.5em;
+}
+
+/* Loading button */
+button.thebe-launch-button div.spinner {
+  float: left;
+  margin-right: 1em;
+}
+
+/* Remove the spinner when thebelab is ready */
+.thebe-launch-button.thebe-status-ready .spinner {
+  display: none;
+}
+
+.thebe-launch-button span.status {
+  font-family: monospace;
+  font-weight: bold;
+}
+
+.thebe-launch-button.thebe-status-ready span.status {
+  color: green;
+}
+
+.spinner {
+  height: 2em;
+  text-align: center;
+  font-size: 0.7em;
+}
+
+.spinner > div {
+  background-color: #f37726;
+  height: 100%;
+  width: 6px;
+  display: inline-block;
+
+  -webkit-animation: sk-stretchdelay 1.2s infinite ease-in-out;
+  animation: sk-stretchdelay 1.2s infinite ease-in-out;
+}
+
+.spinner .rect2 {
+  -webkit-animation-delay: -1.1s;
+  animation-delay: -1.1s;
+}
+
+.spinner .rect3 {
+  -webkit-animation-delay: -1s;
+  animation-delay: -1s;
+}
+
+.spinner .rect4 {
+  -webkit-animation-delay: -0.9s;
+  animation-delay: -0.9s;
+}
+
+.spinner .rect5 {
+  -webkit-animation-delay: -0.8s;
+  animation-delay: -0.8s;
+}
+
+@-webkit-keyframes sk-stretchdelay {
+  0%,
+  40%,
+  100% {
+    -webkit-transform: scaleY(0.4);
+  }
+  20% {
+    -webkit-transform: scaleY(1);
+  }
+}
+
+@keyframes sk-stretchdelay {
+  0%,
+  40%,
+  100% {
+    transform: scaleY(0.4);
+    -webkit-transform: scaleY(0.4);
+  }
+  20% {
+    transform: scaleY(1);
+    -webkit-transform: scaleY(1);
+  }
+}
diff --git a/_preview/13/_static/sphinx-thebe.js b/_preview/13/_static/sphinx-thebe.js
new file mode 100644
index 0000000..d92b1d3
--- /dev/null
+++ b/_preview/13/_static/sphinx-thebe.js
@@ -0,0 +1,127 @@
+/**
+ * Add attributes to Thebe blocks to initialize thebe properly
+ */
+var configureThebe = () => {
+  // Load thebe config in case we want to update it as some point
+  console.log("[sphinx-thebe]: Loading thebe config...");
+  thebe_config = document.querySelector("script[type=\"text/x-thebe-config\"]");
+
+  // If we already detect a Thebe cell, don't re-run
+  if (document.querySelectorAll("div.thebe-cell").length > 0) {
+    return;
+  }
+
+  // Update thebe buttons with loading message
+  document.querySelectorAll(".thebe-launch-button").forEach((button) => {
+    button.innerHTML = `
+        <div class="spinner">
+            <div class="rect1"></div>
+            <div class="rect2"></div>
+            <div class="rect3"></div>
+            <div class="rect4"></div>
+        </div>
+        <span class="loading-text"></span>`;
+  });
+
+  // Set thebe event hooks
+  var thebeStatus;
+  thebelab.on("status", function (evt, data) {
+    console.log("Status changed:", data.status, data.message);
+
+    const button = document.querySelector(".thebe-launch-button ");
+    button.classList.replace(
+	    `thebe-status-${thebeStatus}`, 
+	    `thebe-status-${data.status}`
+    )
+    button.querySelector(".loading-text")
+      .innerHTML = (
+        `<span class='launch_msg'>Launching from mybinder.org: </span>
+	 <span class='status'>${data.status}</span>`
+      );
+
+    // Now update our thebe status
+    thebeStatus = data.status;
+
+    // Find any cells with an initialization tag and ask thebe to run them when ready
+    if (data.status === "ready") {
+      var thebeInitCells = document.querySelectorAll(
+        ".thebe-init, .tag_thebe-init"
+      );
+      thebeInitCells.forEach((cell) => {
+        console.log("Initializing Thebe with cell: " + cell.id);
+        cell.querySelector(".thebelab-run-button").click();
+      });
+    }
+  });
+};
+
+/**
+ * Update the page DOM to use Thebe elements
+ */
+var modifyDOMForThebe = () => {
+  // Find all code cells, replace with Thebe interactive code cells
+  const codeCells = document.querySelectorAll(thebe_selector);
+  codeCells.forEach((codeCell, index) => {
+    const codeCellId = (index) => `codecell${index}`;
+    codeCell.id = codeCellId(index);
+    codeCellText = codeCell.querySelector(thebe_selector_input);
+    codeCellOutput = codeCell.querySelector(thebe_selector_output);
+
+    // Clean up the language to make it work w/ CodeMirror and add it to the cell
+    dataLanguage = detectLanguage(kernelName);
+
+    // Re-arrange the cell and add metadata
+    if (codeCellText) {
+      codeCellText.setAttribute("data-language", dataLanguage);
+      codeCellText.setAttribute("data-executable", "true");
+
+      // If we had an output, insert it just after the `pre` cell
+      if (codeCellOutput) {
+        codeCellOutput.setAttribute("data-output", "");
+        codeCellText.insertAdjacentElement('afterend', codeCellOutput);
+      }
+    }
+
+    // Remove sphinx-copybutton blocks, which are common in Sphinx
+    codeCell.querySelectorAll("button.copybtn").forEach((el) => {
+      el.remove();
+    });
+  });
+};
+
+var initThebe = () => {
+  // Load thebe dynamically if it's not already loaded
+  if (typeof thebelab === "undefined") {
+    console.log("[sphinx-thebe]: Loading thebe from CDN...");
+    document.querySelector(".thebe-launch-button ").innerText = "Loading thebe from CDN...";
+
+    const script = document.createElement("script");
+    script.src = `${THEBE_JS_URL}`;
+    document.head.appendChild(script);
+
+    // Runs once the script has finished loading
+    script.addEventListener("load", () => {
+      console.log("[sphinx-thebe]: Finished loading thebe from CDN...");
+      configureThebe();
+      modifyDOMForThebe();
+      thebelab.bootstrap();
+    });
+  } else {
+    console.log(
+      "[sphinx-thebe]: thebe already loaded, not loading from CDN..."
+    );
+    configureThebe();
+    modifyDOMForThebe();
+    thebelab.bootstrap();
+  }
+};
+
+// Helper function to munge the language name
+var detectLanguage = (language) => {
+  if (language.indexOf("python") > -1) {
+    language = "python";
+  } else if (language === "ir") {
+    language = "r";
+  }
+  return language;
+};
diff --git a/_preview/13/_static/sphinx_highlight.js b/_preview/13/_static/sphinx_highlight.js
new file mode 100644
index 0000000..8a96c69
--- /dev/null
+++ b/_preview/13/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/_preview/13/_static/styles/bootstrap.css b/_preview/13/_static/styles/bootstrap.css
new file mode 100644
index 0000000..5ac709f
--- /dev/null
+++ b/_preview/13/_static/styles/bootstrap.css
@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap  v5.3.3 (https://getbootstrap.com/)
+ * Copyright 2011-2024 The Bootstrap Authors
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+ */:root,[data-bs-theme=light]{--bs-blue:#0d6efd;--bs-indigo:#6610f2;--bs-purple:#6f42c1;--bs-pink:#d63384;--bs-red:#dc3545;--bs-orange:#fd7e14;--bs-yellow:#ffc107;--bs-green:#198754;--bs-teal:#20c997;--bs-cyan:#0dcaf0;--bs-black:#000;--bs-white:#fff;--bs-gray:#6c757d;--bs-gray-dark:#343a40;--bs-gray-100:#f8f9fa;--bs-gray-200:#e9ecef;--bs-gray-300:#dee2e6;--bs-gray-400:#ced4da;--bs-gray-500:#adb5bd;--bs-gray-600:#6c757d;--bs-gray-700:#495057;--bs-gray-800:#343a40;--bs-gray-900:#212529;--bs-primary:#0d6efd;--bs-secondary:#6c757d;--bs-success:#198754;--bs-info:#0dcaf0;--bs-warning:#ffc107;--bs-danger:#dc3545;--bs-light:#f8f9fa;--bs-dark:#212529;--bs-primary-rgb:13,110,253;--bs-secondary-rgb:108,117,125;--bs-success-rgb:25,135,84;--bs-info-rgb:13,202,240;--bs-warning-rgb:255,193,7;--bs-danger-rgb:220,53,69;--bs-light-rgb:248,249,250;--bs-dark-rgb:33,37,41;--bs-primary-text-emphasis:#052c65;--bs-secondary-text-emphasis:#2b2f32;--bs-success-text-emphasis:#0a3622;--bs-info-text-emphasis:#055160;--bs-warning-text-emphasis:#664d03;--bs-danger-text-emphasis:#58151c;--bs-light-text-emphasis:#495057;--bs-dark-text-emphasis:#495057;--bs-primary-bg-subtle:#cfe2ff;--bs-secondary-bg-subtle:#e2e3e5;--bs-success-bg-subtle:#d1e7dd;--bs-info-bg-subtle:#cff4fc;--bs-warning-bg-subtle:#fff3cd;--bs-danger-bg-subtle:#f8d7da;--bs-light-bg-subtle:#fcfcfd;--bs-dark-bg-subtle:#ced4da;--bs-primary-border-subtle:#9ec5fe;--bs-secondary-border-subtle:#c4c8cb;--bs-success-border-subtle:#a3cfbb;--bs-info-border-subtle:#9eeaf9;--bs-warning-border-subtle:#ffe69c;--bs-danger-border-subtle:#f1aeb5;--bs-light-border-subtle:#e9ecef;--bs-dark-border-subtle:#adb5bd;--bs-white-rgb:255,255,255;--bs-black-rgb:0,0,0;--bs-font-sans-serif:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue","Noto Sans","Liberation Sans",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";--bs-font-monospace:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;--bs-gradient:linear-gradient(180deg,hsla(0,0%,100%,.15),hsla(0,0%,100%,0));--bs-body-font-family:var(--bs-font-sans-serif);--bs-body-font-size:1rem;--bs-body-font-weight:400;--bs-body-line-height:1.5;--bs-body-color:#212529;--bs-body-color-rgb:33,37,41;--bs-body-bg:#fff;--bs-body-bg-rgb:255,255,255;--bs-emphasis-color:#000;--bs-emphasis-color-rgb:0,0,0;--bs-secondary-color:rgba(33,37,41,.75);--bs-secondary-color-rgb:33,37,41;--bs-secondary-bg:#e9ecef;--bs-secondary-bg-rgb:233,236,239;--bs-tertiary-color:rgba(33,37,41,.5);--bs-tertiary-color-rgb:33,37,41;--bs-tertiary-bg:#f8f9fa;--bs-tertiary-bg-rgb:248,249,250;--bs-heading-color:inherit;--bs-link-color:#0d6efd;--bs-link-color-rgb:13,110,253;--bs-link-decoration:underline;--bs-link-hover-color:#0a58ca;--bs-link-hover-color-rgb:10,88,202;--bs-code-color:#d63384;--bs-highlight-color:#212529;--bs-highlight-bg:#fff3cd;--bs-border-width:1px;--bs-border-style:solid;--bs-border-color:#dee2e6;--bs-border-color-translucent:rgba(0,0,0,.175);--bs-border-radius:0.375rem;--bs-border-radius-sm:0.25rem;--bs-border-radius-lg:0.5rem;--bs-border-radius-xl:1rem;--bs-border-radius-xxl:2rem;--bs-border-radius-2xl:var(--bs-border-radius-xxl);--bs-border-radius-pill:50rem;--bs-box-shadow:0 0.5rem 1rem rgba(0,0,0,.15);--bs-box-shadow-sm:0 0.125rem 0.25rem rgba(0,0,0,.075);--bs-box-shadow-lg:0 1rem 3rem rgba(0,0,0,.175);--bs-box-shadow-inset:inset 0 1px 2px 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.offcanvas{background-color:transparent!important;border:0!important;flex-grow:1;height:auto!important;position:static;transform:none!important;transition:none;visibility:visible!important;width:auto!important;z-index:auto}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;flex-grow:0;overflow-y:visible;padding:0}}@media(min-width:960px){.navbar-expand-lg{flex-wrap:nowrap;justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-left:var(--bs-navbar-nav-link-padding-x);padding-right:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex!important;flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{background-color:transparent!important;border:0!important;flex-grow:1;height:auto!important;position:static;transform:none!important;transition:none;visibility:visible!important;width:auto!important;z-index:auto}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;flex-grow:0;overflow-y:visible;padding:0}}@media(min-width:1200px){.navbar-expand-xl{flex-wrap:nowrap;justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-left:var(--bs-navbar-nav-link-padding-x);padding-right:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex!important;flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl 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.offcanvas{background-color:transparent!important;border:0!important;flex-grow:1;height:auto!important;position:static;transform:none!important;transition:none;visibility:visible!important;width:auto!important;z-index:auto}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;flex-grow:0;overflow-y:visible;padding:0}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color:hsla(0,0%,100%,.55);--bs-navbar-hover-color:hsla(0,0%,100%,.75);--bs-navbar-disabled-color:hsla(0,0%,100%,.25);--bs-navbar-active-color:#fff;--bs-navbar-brand-color:#fff;--bs-navbar-brand-hover-color:#fff;--bs-navbar-toggler-border-color:hsla(0,0%,100%,.1)}.navbar-dark,.navbar[data-bs-theme=dark],[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3E%3Cpath stroke='rgba(255, 255, 255, 0.55)' stroke-linecap='round' stroke-miterlimit='10' 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var(--bs-accordion-border-color);color:var(--bs-accordion-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type>.accordion-header .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-left-radius:var(--bs-accordion-border-radius);border-bottom-right-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type>.accordion-header 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16 16' fill='%236ea8fe'%3E%3Cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3E%3C/svg%3E");--bs-accordion-btn-active-icon:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3E%3Cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3E%3C/svg%3E")}.breadcrumb{--bs-breadcrumb-padding-x:0;--bs-breadcrumb-padding-y:0;--bs-breadcrumb-margin-bottom:1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color:var(--bs-secondary-color);--bs-breadcrumb-item-padding-x:0.5rem;--bs-breadcrumb-item-active-color:var(--bs-secondary-color);background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius);display:flex;flex-wrap:wrap;font-size:var(--bs-breadcrumb-font-size);list-style:none;margin-bottom:var(--bs-breadcrumb-margin-bottom);padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item:before{color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider,"/");float:left;padding-right:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x:0.75rem;--bs-pagination-padding-y:0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color:var(--bs-link-color);--bs-pagination-bg:var(--bs-body-bg);--bs-pagination-border-width:var(--bs-border-width);--bs-pagination-border-color:var(--bs-border-color);--bs-pagination-border-radius:var(--bs-border-radius);--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:var(--bs-tertiary-bg);--bs-pagination-hover-border-color:var(--bs-border-color);--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:var(--bs-secondary-bg);--bs-pagination-focus-box-shadow:0 0 0 0.1875rem var(--pst-color-accent);--bs-pagination-active-color:#fff;--bs-pagination-active-bg:#0d6efd;--bs-pagination-active-border-color:#0d6efd;--bs-pagination-disabled-color:var(--bs-secondary-color);--bs-pagination-disabled-bg:var(--bs-secondary-bg);--bs-pagination-disabled-border-color:var(--bs-border-color);display:flex;list-style:none;padding-left:0}.page-link{background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);color:var(--bs-pagination-color);display:block;font-size:var(--bs-pagination-font-size);padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);position:relative;text-decoration:none;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion:reduce){.page-link{transition:none}}.page-link:hover{background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color);color:var(--bs-pagination-hover-color);z-index:2}.page-link:focus{background-color:var(--bs-pagination-focus-bg);box-shadow:var(--bs-pagination-focus-box-shadow);color:var(--bs-pagination-focus-color);outline:0;z-index:3}.active>.page-link,.page-link.active{background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color);color:var(--bs-pagination-active-color);z-index:3}.disabled>.page-link,.page-link.disabled{background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color);color:var(--bs-pagination-disabled-color);pointer-events:none}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width)*-1)}.page-item:first-child .page-link{border-bottom-left-radius:var(--bs-pagination-border-radius);border-top-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-bottom-right-radius:var(--bs-pagination-border-radius);border-top-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x:1.5rem;--bs-pagination-padding-y:0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius:var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x:0.5rem;--bs-pagination-padding-y:0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius:var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x:0.65em;--bs-badge-padding-y:0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight:700;--bs-badge-color:#fff;--bs-badge-border-radius:var(--bs-border-radius);border-radius:var(--bs-badge-border-radius);color:var(--bs-badge-color);display:inline-block;font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);text-align:center;vertical-align:baseline;white-space:nowrap}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg:transparent;--bs-alert-padding-x:1rem;--bs-alert-padding-y:1rem;--bs-alert-margin-bottom:1rem;--bs-alert-color:inherit;--bs-alert-border-color:transparent;--bs-alert-border:var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius:var(--bs-border-radius);--bs-alert-link-color:inherit;background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius);color:var(--bs-alert-color);margin-bottom:var(--bs-alert-margin-bottom);padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);position:relative}.alert-heading{color:inherit}.alert-link{color:var(--bs-alert-link-color);font-weight:700}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{padding:1.25rem 1rem;position:absolute;right:0;top:0;z-index:2}.alert-primary{--bs-alert-color:var(--bs-primary-text-emphasis);--bs-alert-bg:var(--bs-primary-bg-subtle);--bs-alert-border-color:var(--bs-primary-border-subtle);--bs-alert-link-color:var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color:var(--bs-secondary-text-emphasis);--bs-alert-bg:var(--bs-secondary-bg-subtle);--bs-alert-border-color:var(--bs-secondary-border-subtle);--bs-alert-link-color:var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color:var(--bs-success-text-emphasis);--bs-alert-bg:var(--bs-success-bg-subtle);--bs-alert-border-color:var(--bs-success-border-subtle);--bs-alert-link-color:var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color:var(--bs-info-text-emphasis);--bs-alert-bg:var(--bs-info-bg-subtle);--bs-alert-border-color:var(--bs-info-border-subtle);--bs-alert-link-color:var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color:var(--bs-warning-text-emphasis);--bs-alert-bg:var(--bs-warning-bg-subtle);--bs-alert-border-color:var(--bs-warning-border-subtle);--bs-alert-link-color:var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color:var(--bs-danger-text-emphasis);--bs-alert-bg:var(--bs-danger-bg-subtle);--bs-alert-border-color:var(--bs-danger-border-subtle);--bs-alert-link-color:var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color:var(--bs-light-text-emphasis);--bs-alert-bg:var(--bs-light-bg-subtle);--bs-alert-border-color:var(--bs-light-border-subtle);--bs-alert-link-color:var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color:var(--bs-dark-text-emphasis);--bs-alert-bg:var(--bs-dark-bg-subtle);--bs-alert-border-color:var(--bs-dark-border-subtle);--bs-alert-link-color:var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height:1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg:var(--bs-secondary-bg);--bs-progress-border-radius:var(--bs-border-radius);--bs-progress-box-shadow:var(--bs-box-shadow-inset);--bs-progress-bar-color:#fff;--bs-progress-bar-bg:#0d6efd;--bs-progress-bar-transition:width 0.6s ease;background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius);display:flex;font-size:var(--bs-progress-font-size);height:var(--bs-progress-height);overflow:hidden}.progress-bar{background-color:var(--bs-progress-bar-bg);color:var(--bs-progress-bar-color);display:flex;flex-direction:column;justify-content:center;overflow:hidden;text-align:center;transition:var(--bs-progress-bar-transition);white-space:nowrap}@media(prefers-reduced-motion:reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg,hsla(0,0%,100%,.15) 25%,transparent 0,transparent 50%,hsla(0,0%,100%,.15) 0,hsla(0,0%,100%,.15) 75%,transparent 0,transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:progress-bar-stripes 1s linear infinite}@media(prefers-reduced-motion:reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color:var(--bs-body-color);--bs-list-group-bg:var(--bs-body-bg);--bs-list-group-border-color:var(--bs-border-color);--bs-list-group-border-width:var(--bs-border-width);--bs-list-group-border-radius:var(--bs-border-radius);--bs-list-group-item-padding-x:1rem;--bs-list-group-item-padding-y:0.5rem;--bs-list-group-action-color:var(--bs-secondary-color);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-tertiary-bg);--bs-list-group-action-active-color:var(--bs-body-color);--bs-list-group-action-active-bg:var(--bs-secondary-bg);--bs-list-group-disabled-color:var(--bs-secondary-color);--bs-list-group-disabled-bg:var(--bs-body-bg);--bs-list-group-active-color:#fff;--bs-list-group-active-bg:#0d6efd;--bs-list-group-active-border-color:#0d6efd;border-radius:var(--bs-list-group-border-radius);display:flex;flex-direction:column;margin-bottom:0;padding-left:0}.list-group-numbered{counter-reset:section;list-style-type:none}.list-group-numbered>.list-group-item:before{content:counters(section,".") ". ";counter-increment:section}.list-group-item-action{color:var(--bs-list-group-action-color);text-align:inherit;width:100%}.list-group-item-action:focus,.list-group-item-action:hover{background-color:var(--bs-list-group-action-hover-bg);color:var(--bs-list-group-action-hover-color);text-decoration:none;z-index:1}.list-group-item-action:active{background-color:var(--bs-list-group-action-active-bg);color:var(--bs-list-group-action-active-color)}.list-group-item{background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color);color:var(--bs-list-group-color);display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);position:relative;text-decoration:none}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-left-radius:inherit;border-bottom-right-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{background-color:var(--bs-list-group-disabled-bg);color:var(--bs-list-group-disabled-color);pointer-events:none}.list-group-item.active{background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color);color:var(--bs-list-group-active-color);z-index:2}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{border-top-width:var(--bs-list-group-border-width);margin-top:calc(var(--bs-list-group-border-width)*-1)}.list-group-horizontal{flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}@media(min-width:540px){.list-group-horizontal-sm{flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media(min-width:720px){.list-group-horizontal-md{flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-md>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media(min-width:960px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media(min-width:1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-primary{--bs-list-group-color:var(--bs-primary-text-emphasis);--bs-list-group-bg:var(--bs-primary-bg-subtle);--bs-list-group-border-color:var(--bs-primary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-primary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-primary-border-subtle);--bs-list-group-active-color:var(--bs-primary-bg-subtle);--bs-list-group-active-bg:var(--bs-primary-text-emphasis);--bs-list-group-active-border-color:var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color:var(--bs-secondary-text-emphasis);--bs-list-group-bg:var(--bs-secondary-bg-subtle);--bs-list-group-border-color:var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-secondary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-secondary-border-subtle);--bs-list-group-active-color:var(--bs-secondary-bg-subtle);--bs-list-group-active-bg:var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color:var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color:var(--bs-success-text-emphasis);--bs-list-group-bg:var(--bs-success-bg-subtle);--bs-list-group-border-color:var(--bs-success-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-success-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-success-border-subtle);--bs-list-group-active-color:var(--bs-success-bg-subtle);--bs-list-group-active-bg:var(--bs-success-text-emphasis);--bs-list-group-active-border-color:var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color:var(--bs-info-text-emphasis);--bs-list-group-bg:var(--bs-info-bg-subtle);--bs-list-group-border-color:var(--bs-info-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-info-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-info-border-subtle);--bs-list-group-active-color:var(--bs-info-bg-subtle);--bs-list-group-active-bg:var(--bs-info-text-emphasis);--bs-list-group-active-border-color:var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color:var(--bs-warning-text-emphasis);--bs-list-group-bg:var(--bs-warning-bg-subtle);--bs-list-group-border-color:var(--bs-warning-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-warning-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-warning-border-subtle);--bs-list-group-active-color:var(--bs-warning-bg-subtle);--bs-list-group-active-bg:var(--bs-warning-text-emphasis);--bs-list-group-active-border-color:var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color:var(--bs-danger-text-emphasis);--bs-list-group-bg:var(--bs-danger-bg-subtle);--bs-list-group-border-color:var(--bs-danger-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-danger-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-danger-border-subtle);--bs-list-group-active-color:var(--bs-danger-bg-subtle);--bs-list-group-active-bg:var(--bs-danger-text-emphasis);--bs-list-group-active-border-color:var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color:var(--bs-light-text-emphasis);--bs-list-group-bg:var(--bs-light-bg-subtle);--bs-list-group-border-color:var(--bs-light-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-light-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-light-border-subtle);--bs-list-group-active-color:var(--bs-light-bg-subtle);--bs-list-group-active-bg:var(--bs-light-text-emphasis);--bs-list-group-active-border-color:var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color:var(--bs-dark-text-emphasis);--bs-list-group-bg:var(--bs-dark-bg-subtle);--bs-list-group-border-color:var(--bs-dark-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-dark-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-dark-border-subtle);--bs-list-group-active-color:var(--bs-dark-bg-subtle);--bs-list-group-active-bg:var(--bs-dark-text-emphasis);--bs-list-group-active-border-color:var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color:#000;--bs-btn-close-bg:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3E%3Cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3E%3C/svg%3E");--bs-btn-close-opacity:0.5;--bs-btn-close-hover-opacity:0.75;--bs-btn-close-focus-shadow:0 0 0 0.1875rem var(--pst-color-accent);--bs-btn-close-focus-opacity:1;--bs-btn-close-disabled-opacity:0.25;--bs-btn-close-white-filter:invert(1) grayscale(100%) brightness(200%);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;box-sizing:content-box;height:1em;opacity:var(--bs-btn-close-opacity);padding:.25em;width:1em}.btn-close,.btn-close:hover{color:var(--bs-btn-close-color)}.btn-close:hover{opacity:var(--bs-btn-close-hover-opacity);text-decoration:none}.btn-close:focus{box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity);outline:0}.btn-close.disabled,.btn-close:disabled{opacity:var(--bs-btn-close-disabled-opacity);pointer-events:none;user-select:none}.btn-close-white,[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex:1090;--bs-toast-padding-x:0.75rem;--bs-toast-padding-y:0.5rem;--bs-toast-spacing:1.5rem;--bs-toast-max-width:350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg:rgba(var(--bs-body-bg-rgb),0.85);--bs-toast-border-width:var(--bs-border-width);--bs-toast-border-color:var(--bs-border-color-translucent);--bs-toast-border-radius:var(--bs-border-radius);--bs-toast-box-shadow:var(--bs-box-shadow);--bs-toast-header-color:var(--bs-secondary-color);--bs-toast-header-bg:rgba(var(--bs-body-bg-rgb),0.85);--bs-toast-header-border-color:var(--bs-border-color-translucent);background-clip:padding-box;background-color:var(--bs-toast-bg);border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);border-radius:var(--bs-toast-border-radius);box-shadow:var(--bs-toast-box-shadow);color:var(--bs-toast-color);font-size:var(--bs-toast-font-size);max-width:100%;pointer-events:auto;width:var(--bs-toast-max-width)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex:1090;max-width:100%;pointer-events:none;position:absolute;width:max-content;z-index:var(--bs-toast-zindex)}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{align-items:center;background-clip:padding-box;background-color:var(--bs-toast-header-bg);border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));color:var(--bs-toast-header-color);display:flex;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x)}.toast-header .btn-close{margin-left:var(--bs-toast-padding-x);margin-right:calc(var(--bs-toast-padding-x)*-.5)}.toast-body{word-wrap:break-word;padding:var(--bs-toast-padding-x)}.modal{--bs-modal-zindex:1055;--bs-modal-width:500px;--bs-modal-padding:1rem;--bs-modal-margin:0.5rem;--bs-modal-color: ;--bs-modal-bg:var(--bs-body-bg);--bs-modal-border-color:var(--bs-border-color-translucent);--bs-modal-border-width:var(--bs-border-width);--bs-modal-border-radius:var(--bs-border-radius-lg);--bs-modal-box-shadow:var(--bs-box-shadow-sm);--bs-modal-inner-border-radius:calc(var(--bs-border-radius-lg) - var(--bs-border-width));--bs-modal-header-padding-x:1rem;--bs-modal-header-padding-y:1rem;--bs-modal-header-padding:1rem 1rem;--bs-modal-header-border-color:var(--bs-border-color);--bs-modal-header-border-width:var(--bs-border-width);--bs-modal-title-line-height:1.5;--bs-modal-footer-gap:0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color:var(--bs-border-color);--bs-modal-footer-border-width:var(--bs-border-width);display:none;height:100%;left:0;outline:0;overflow-x:hidden;overflow-y:auto;position:fixed;top:0;width:100%;z-index:var(--bs-modal-zindex)}.modal-dialog{margin:var(--bs-modal-margin);pointer-events:none;position:relative;width:auto}.modal.fade .modal-dialog{transform:translateY(-50px);transition:transform .3s ease-out}@media(prefers-reduced-motion:reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{align-items:center;display:flex;min-height:calc(100% - 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0)}.dropdown-item:hover,.dropdown-item:focus{color:var(--bs-dropdown-link-hover-color);background-color:var(--bs-dropdown-link-hover-bg)}.dropdown-item.active,.dropdown-item:active{color:var(--bs-dropdown-link-active-color);text-decoration:none;background-color:var(--bs-dropdown-link-active-bg)}.dropdown-item.disabled,.dropdown-item:disabled{color:var(--bs-dropdown-link-disabled-color);pointer-events:none;background-color:rgba(0,0,0,0)}.dropdown-menu.show{display:block}.dropdown-header{display:block;padding:var(--bs-dropdown-header-padding-y) var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:0.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: var(--pst-color-surface);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: var(--pst-color-surface);--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;flex-wrap:wrap;justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width)*-1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n+3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;align-items:flex-start;justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width)*-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: 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1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25rem;--bs-navbar-toggler-padding-x: 0.75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%2833, 37, 41, 0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\");--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: 0.1875rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;flex-wrap:wrap;align-items:center;justify-content:space-between;padding:var(--bs-navbar-padding-y) 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.offcanvas-body{display:flex;flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: rgba(255, 255, 255, 0.55);--bs-navbar-hover-color: rgba(255, 255, 255, 0.75);--bs-navbar-disabled-color: rgba(255, 255, 255, 0.25);--bs-navbar-active-color: #fff;--bs-navbar-brand-color: #fff;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(255, 255, 255, 0.1);--bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255, 255, 255, 0.55%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255, 255, 255, 0.55%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 540px){.card-group{display:flex;flex-flow:row wrap}.card-group>.card{flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='%23212529' stroke-linecap='round' stroke-linejoin='round'%3e%3cpath d='M2 5L8 11L14 5'/%3e%3c/svg%3e\");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='%23052c65' stroke-linecap='round' stroke-linejoin='round'%3e%3cpath d='M2 5L8 11L14 5'/%3e%3c/svg%3e\");--bs-accordion-btn-focus-box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:\"\";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type>.accordion-header .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type>.accordion-header .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type>.accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush>.accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush>.accordion-item:first-child{border-top:0}.accordion-flush>.accordion-item:last-child{border-bottom:0}.accordion-flush>.accordion-item>.accordion-header .accordion-button,.accordion-flush>.accordion-item>.accordion-header .accordion-button.collapsed{border-radius:0}.accordion-flush>.accordion-item>.accordion-collapse{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\");--bs-accordion-btn-active-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, \"/\") /* rtl: var(--bs-breadcrumb-divider, \"/\") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width)*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;flex-direction:column;justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: var(--bs-body-bg);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: 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var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 540px){.list-group-horizontal-sm{flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 720px){.list-group-horizontal-md{flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 960px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e\");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.1875rem var(--pst-color-accent);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width: var(--bs-border-width);--bs-toast-border-color: var(--bs-border-color-translucent);--bs-toast-border-radius: var(--bs-border-radius);--bs-toast-box-shadow: var(--bs-box-shadow);--bs-toast-header-color: var(--bs-secondary-color);--bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color: var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: var(--bs-box-shadow-sm);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;flex-shrink:0;align-items:center;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;flex-shrink:0;flex-wrap:wrap;align-items:center;justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 540px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: var(--bs-box-shadow)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 960px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen 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!important}.p-lg-5{padding:3rem !important}.px-lg-0{padding-right:0 !important;padding-left:0 !important}.px-lg-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-lg-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-lg-3{padding-right:1rem !important;padding-left:1rem !important}.px-lg-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-lg-5{padding-right:3rem !important;padding-left:3rem !important}.py-lg-0{padding-top:0 !important;padding-bottom:0 !important}.py-lg-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-lg-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-lg-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-lg-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-lg-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-lg-0{padding-top:0 !important}.pt-lg-1{padding-top:.25rem !important}.pt-lg-2{padding-top:.5rem !important}.pt-lg-3{padding-top:1rem !important}.pt-lg-4{padding-top:1.5rem !important}.pt-lg-5{padding-top:3rem !important}.pe-lg-0{padding-right:0 !important}.pe-lg-1{padding-right:.25rem !important}.pe-lg-2{padding-right:.5rem !important}.pe-lg-3{padding-right:1rem !important}.pe-lg-4{padding-right:1.5rem !important}.pe-lg-5{padding-right:3rem !important}.pb-lg-0{padding-bottom:0 !important}.pb-lg-1{padding-bottom:.25rem !important}.pb-lg-2{padding-bottom:.5rem !important}.pb-lg-3{padding-bottom:1rem !important}.pb-lg-4{padding-bottom:1.5rem !important}.pb-lg-5{padding-bottom:3rem !important}.ps-lg-0{padding-left:0 !important}.ps-lg-1{padding-left:.25rem !important}.ps-lg-2{padding-left:.5rem !important}.ps-lg-3{padding-left:1rem !important}.ps-lg-4{padding-left:1.5rem !important}.ps-lg-5{padding-left:3rem !important}.gap-lg-0{gap:0 !important}.gap-lg-1{gap:.25rem !important}.gap-lg-2{gap:.5rem !important}.gap-lg-3{gap:1rem !important}.gap-lg-4{gap:1.5rem !important}.gap-lg-5{gap:3rem !important}.row-gap-lg-0{row-gap:0 !important}.row-gap-lg-1{row-gap:.25rem !important}.row-gap-lg-2{row-gap:.5rem !important}.row-gap-lg-3{row-gap:1rem !important}.row-gap-lg-4{row-gap:1.5rem !important}.row-gap-lg-5{row-gap:3rem !important}.column-gap-lg-0{column-gap:0 !important}.column-gap-lg-1{column-gap:.25rem !important}.column-gap-lg-2{column-gap:.5rem !important}.column-gap-lg-3{column-gap:1rem !important}.column-gap-lg-4{column-gap:1.5rem !important}.column-gap-lg-5{column-gap:3rem !important}.text-lg-start{text-align:left !important}.text-lg-end{text-align:right !important}.text-lg-center{text-align:center !important}}@media(min-width: 1200px){.float-xl-start{float:left !important}.float-xl-end{float:right !important}.float-xl-none{float:none !important}.object-fit-xl-contain{object-fit:contain !important}.object-fit-xl-cover{object-fit:cover !important}.object-fit-xl-fill{object-fit:fill !important}.object-fit-xl-scale{object-fit:scale-down !important}.object-fit-xl-none{object-fit:none !important}.d-xl-inline{display:inline !important}.d-xl-inline-block{display:inline-block !important}.d-xl-block{display:block !important}.d-xl-grid{display:grid !important}.d-xl-inline-grid{display:inline-grid !important}.d-xl-table{display:table !important}.d-xl-table-row{display:table-row !important}.d-xl-table-cell{display:table-cell !important}.d-xl-flex{display:flex !important}.d-xl-inline-flex{display:inline-flex !important}.d-xl-none{display:none !important}.flex-xl-fill{flex:1 1 auto !important}.flex-xl-row{flex-direction:row !important}.flex-xl-column{flex-direction:column !important}.flex-xl-row-reverse{flex-direction:row-reverse !important}.flex-xl-column-reverse{flex-direction:column-reverse !important}.flex-xl-grow-0{flex-grow:0 !important}.flex-xl-grow-1{flex-grow:1 !important}.flex-xl-shrink-0{flex-shrink:0 !important}.flex-xl-shrink-1{flex-shrink:1 !important}.flex-xl-wrap{flex-wrap:wrap !important}.flex-xl-nowrap{flex-wrap:nowrap !important}.flex-xl-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-xl-start{justify-content:flex-start !important}.justify-content-xl-end{justify-content:flex-end !important}.justify-content-xl-center{justify-content:center !important}.justify-content-xl-between{justify-content:space-between !important}.justify-content-xl-around{justify-content:space-around !important}.justify-content-xl-evenly{justify-content:space-evenly !important}.align-items-xl-start{align-items:flex-start !important}.align-items-xl-end{align-items:flex-end !important}.align-items-xl-center{align-items:center !important}.align-items-xl-baseline{align-items:baseline !important}.align-items-xl-stretch{align-items:stretch !important}.align-content-xl-start{align-content:flex-start !important}.align-content-xl-end{align-content:flex-end !important}.align-content-xl-center{align-content:center !important}.align-content-xl-between{align-content:space-between !important}.align-content-xl-around{align-content:space-around !important}.align-content-xl-stretch{align-content:stretch !important}.align-self-xl-auto{align-self:auto !important}.align-self-xl-start{align-self:flex-start !important}.align-self-xl-end{align-self:flex-end !important}.align-self-xl-center{align-self:center !important}.align-self-xl-baseline{align-self:baseline !important}.align-self-xl-stretch{align-self:stretch !important}.order-xl-first{order:-1 !important}.order-xl-0{order:0 !important}.order-xl-1{order:1 !important}.order-xl-2{order:2 !important}.order-xl-3{order:3 !important}.order-xl-4{order:4 !important}.order-xl-5{order:5 !important}.order-xl-last{order:6 !important}.m-xl-0{margin:0 !important}.m-xl-1{margin:.25rem !important}.m-xl-2{margin:.5rem !important}.m-xl-3{margin:1rem !important}.m-xl-4{margin:1.5rem !important}.m-xl-5{margin:3rem !important}.m-xl-auto{margin:auto !important}.mx-xl-0{margin-right:0 !important;margin-left:0 !important}.mx-xl-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-xl-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-xl-3{margin-right:1rem !important;margin-left:1rem !important}.mx-xl-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-xl-5{margin-right:3rem !important;margin-left:3rem !important}.mx-xl-auto{margin-right:auto !important;margin-left:auto !important}.my-xl-0{margin-top:0 !important;margin-bottom:0 !important}.my-xl-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-xl-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-xl-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-xl-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-xl-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-xl-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-xl-0{margin-top:0 !important}.mt-xl-1{margin-top:.25rem !important}.mt-xl-2{margin-top:.5rem !important}.mt-xl-3{margin-top:1rem !important}.mt-xl-4{margin-top:1.5rem !important}.mt-xl-5{margin-top:3rem !important}.mt-xl-auto{margin-top:auto !important}.me-xl-0{margin-right:0 !important}.me-xl-1{margin-right:.25rem !important}.me-xl-2{margin-right:.5rem !important}.me-xl-3{margin-right:1rem !important}.me-xl-4{margin-right:1.5rem !important}.me-xl-5{margin-right:3rem !important}.me-xl-auto{margin-right:auto !important}.mb-xl-0{margin-bottom:0 !important}.mb-xl-1{margin-bottom:.25rem !important}.mb-xl-2{margin-bottom:.5rem !important}.mb-xl-3{margin-bottom:1rem !important}.mb-xl-4{margin-bottom:1.5rem !important}.mb-xl-5{margin-bottom:3rem !important}.mb-xl-auto{margin-bottom:auto !important}.ms-xl-0{margin-left:0 !important}.ms-xl-1{margin-left:.25rem !important}.ms-xl-2{margin-left:.5rem !important}.ms-xl-3{margin-left:1rem !important}.ms-xl-4{margin-left:1.5rem !important}.ms-xl-5{margin-left:3rem !important}.ms-xl-auto{margin-left:auto !important}.p-xl-0{padding:0 !important}.p-xl-1{padding:.25rem !important}.p-xl-2{padding:.5rem !important}.p-xl-3{padding:1rem !important}.p-xl-4{padding:1.5rem !important}.p-xl-5{padding:3rem !important}.px-xl-0{padding-right:0 !important;padding-left:0 !important}.px-xl-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-xl-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-xl-3{padding-right:1rem !important;padding-left:1rem !important}.px-xl-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-xl-5{padding-right:3rem !important;padding-left:3rem !important}.py-xl-0{padding-top:0 !important;padding-bottom:0 !important}.py-xl-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-xl-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-xl-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-xl-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-xl-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-xl-0{padding-top:0 !important}.pt-xl-1{padding-top:.25rem !important}.pt-xl-2{padding-top:.5rem !important}.pt-xl-3{padding-top:1rem !important}.pt-xl-4{padding-top:1.5rem !important}.pt-xl-5{padding-top:3rem !important}.pe-xl-0{padding-right:0 !important}.pe-xl-1{padding-right:.25rem !important}.pe-xl-2{padding-right:.5rem !important}.pe-xl-3{padding-right:1rem !important}.pe-xl-4{padding-right:1.5rem !important}.pe-xl-5{padding-right:3rem !important}.pb-xl-0{padding-bottom:0 !important}.pb-xl-1{padding-bottom:.25rem !important}.pb-xl-2{padding-bottom:.5rem !important}.pb-xl-3{padding-bottom:1rem !important}.pb-xl-4{padding-bottom:1.5rem !important}.pb-xl-5{padding-bottom:3rem !important}.ps-xl-0{padding-left:0 !important}.ps-xl-1{padding-left:.25rem !important}.ps-xl-2{padding-left:.5rem !important}.ps-xl-3{padding-left:1rem !important}.ps-xl-4{padding-left:1.5rem !important}.ps-xl-5{padding-left:3rem !important}.gap-xl-0{gap:0 !important}.gap-xl-1{gap:.25rem !important}.gap-xl-2{gap:.5rem !important}.gap-xl-3{gap:1rem !important}.gap-xl-4{gap:1.5rem !important}.gap-xl-5{gap:3rem !important}.row-gap-xl-0{row-gap:0 !important}.row-gap-xl-1{row-gap:.25rem !important}.row-gap-xl-2{row-gap:.5rem !important}.row-gap-xl-3{row-gap:1rem !important}.row-gap-xl-4{row-gap:1.5rem !important}.row-gap-xl-5{row-gap:3rem !important}.column-gap-xl-0{column-gap:0 !important}.column-gap-xl-1{column-gap:.25rem !important}.column-gap-xl-2{column-gap:.5rem !important}.column-gap-xl-3{column-gap:1rem !important}.column-gap-xl-4{column-gap:1.5rem !important}.column-gap-xl-5{column-gap:3rem !important}.text-xl-start{text-align:left !important}.text-xl-end{text-align:right !important}.text-xl-center{text-align:center !important}}@media(min-width: 1200px){.fs-1{font-size:2.5rem !important}.fs-2{font-size:2rem !important}.fs-3{font-size:1.75rem !important}.fs-4{font-size:1.5rem !important}}@media print{.d-print-inline{display:inline !important}.d-print-inline-block{display:inline-block !important}.d-print-block{display:block !important}.d-print-grid{display:grid !important}.d-print-inline-grid{display:inline-grid !important}.d-print-table{display:table !important}.d-print-table-row{display:table-row !important}.d-print-table-cell{display:table-cell !important}.d-print-flex{display:flex !important}.d-print-inline-flex{display:inline-flex !important}.d-print-none{display:none !important}}","@mixin bsBanner($file) {\n  /*!\n   * Bootstrap #{$file} v5.3.3 (https://getbootstrap.com/)\n   * Copyright 2011-2024 The Bootstrap Authors\n   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n   */\n}\n",":root,\n[data-bs-theme=\"light\"] {\n  // Note: Custom variable values only support SassScript inside `#{}`.\n\n  // Colors\n  //\n  // Generate palettes for full colors, grays, and theme colors.\n\n  @each $color, $value in $colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $grays {\n    --#{$prefix}gray-#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-rgb {\n    --#{$prefix}#{$color}-rgb: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-text {\n    --#{$prefix}#{$color}-text-emphasis: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-bg-subtle {\n    --#{$prefix}#{$color}-bg-subtle: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-border-subtle {\n    --#{$prefix}#{$color}-border-subtle: #{$value};\n  }\n\n  --#{$prefix}white-rgb: #{to-rgb($white)};\n  --#{$prefix}black-rgb: #{to-rgb($black)};\n\n  // Fonts\n\n  // Note: Use `inspect` for lists so that quoted items keep the quotes.\n  // See https://github.com/sass/sass/issues/2383#issuecomment-336349172\n  --#{$prefix}font-sans-serif: #{inspect($font-family-sans-serif)};\n  --#{$prefix}font-monospace: #{inspect($font-family-monospace)};\n  --#{$prefix}gradient: #{$gradient};\n\n  // Root and body\n  // scss-docs-start root-body-variables\n  @if $font-size-root != null {\n    --#{$prefix}root-font-size: #{$font-size-root};\n  }\n  --#{$prefix}body-font-family: #{inspect($font-family-base)};\n  @include rfs($font-size-base, --#{$prefix}body-font-size);\n  --#{$prefix}body-font-weight: #{$font-weight-base};\n  --#{$prefix}body-line-height: #{$line-height-base};\n  @if $body-text-align != null {\n    --#{$prefix}body-text-align: #{$body-text-align};\n  }\n\n  --#{$prefix}body-color: #{$body-color};\n  --#{$prefix}body-color-rgb: #{to-rgb($body-color)};\n  --#{$prefix}body-bg: #{$body-bg};\n  --#{$prefix}body-bg-rgb: #{to-rgb($body-bg)};\n\n  --#{$prefix}emphasis-color: #{$body-emphasis-color};\n  --#{$prefix}emphasis-color-rgb: #{to-rgb($body-emphasis-color)};\n\n  --#{$prefix}secondary-color: #{$body-secondary-color};\n  --#{$prefix}secondary-color-rgb: #{to-rgb($body-secondary-color)};\n  --#{$prefix}secondary-bg: #{$body-secondary-bg};\n  --#{$prefix}secondary-bg-rgb: #{to-rgb($body-secondary-bg)};\n\n  --#{$prefix}tertiary-color: #{$body-tertiary-color};\n  --#{$prefix}tertiary-color-rgb: #{to-rgb($body-tertiary-color)};\n  --#{$prefix}tertiary-bg: #{$body-tertiary-bg};\n  --#{$prefix}tertiary-bg-rgb: #{to-rgb($body-tertiary-bg)};\n  // scss-docs-end root-body-variables\n\n  --#{$prefix}heading-color: #{$headings-color};\n\n  --#{$prefix}link-color: #{$link-color};\n  --#{$prefix}link-color-rgb: #{to-rgb($link-color)};\n  --#{$prefix}link-decoration: #{$link-decoration};\n\n  --#{$prefix}link-hover-color: #{$link-hover-color};\n  --#{$prefix}link-hover-color-rgb: #{to-rgb($link-hover-color)};\n\n  @if $link-hover-decoration != null {\n    --#{$prefix}link-hover-decoration: #{$link-hover-decoration};\n  }\n\n  --#{$prefix}code-color: #{$code-color};\n  --#{$prefix}highlight-color: #{$mark-color};\n  --#{$prefix}highlight-bg: #{$mark-bg};\n\n  // scss-docs-start root-border-var\n  --#{$prefix}border-width: #{$border-width};\n  --#{$prefix}border-style: #{$border-style};\n  --#{$prefix}border-color: #{$border-color};\n  --#{$prefix}border-color-translucent: #{$border-color-translucent};\n\n  --#{$prefix}border-radius: #{$border-radius};\n  --#{$prefix}border-radius-sm: #{$border-radius-sm};\n  --#{$prefix}border-radius-lg: #{$border-radius-lg};\n  --#{$prefix}border-radius-xl: #{$border-radius-xl};\n  --#{$prefix}border-radius-xxl: #{$border-radius-xxl};\n  --#{$prefix}border-radius-2xl: var(--#{$prefix}border-radius-xxl); // Deprecated in v5.3.0 for consistency\n  --#{$prefix}border-radius-pill: #{$border-radius-pill};\n  // scss-docs-end root-border-var\n\n  --#{$prefix}box-shadow: #{$box-shadow};\n  --#{$prefix}box-shadow-sm: #{$box-shadow-sm};\n  --#{$prefix}box-shadow-lg: #{$box-shadow-lg};\n  --#{$prefix}box-shadow-inset: #{$box-shadow-inset};\n\n  // Focus styles\n  // scss-docs-start root-focus-variables\n  --#{$prefix}focus-ring-width: #{$focus-ring-width};\n  --#{$prefix}focus-ring-opacity: #{$focus-ring-opacity};\n  --#{$prefix}focus-ring-color: #{$focus-ring-color};\n  // scss-docs-end root-focus-variables\n\n  // scss-docs-start root-form-validation-variables\n  --#{$prefix}form-valid-color: #{$form-valid-color};\n  --#{$prefix}form-valid-border-color: #{$form-valid-border-color};\n  --#{$prefix}form-invalid-color: #{$form-invalid-color};\n  --#{$prefix}form-invalid-border-color: #{$form-invalid-border-color};\n  // scss-docs-end root-form-validation-variables\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark, true) {\n    color-scheme: dark;\n\n    // scss-docs-start root-dark-mode-vars\n    --#{$prefix}body-color: #{$body-color-dark};\n    --#{$prefix}body-color-rgb: #{to-rgb($body-color-dark)};\n    --#{$prefix}body-bg: #{$body-bg-dark};\n    --#{$prefix}body-bg-rgb: #{to-rgb($body-bg-dark)};\n\n    --#{$prefix}emphasis-color: #{$body-emphasis-color-dark};\n    --#{$prefix}emphasis-color-rgb: #{to-rgb($body-emphasis-color-dark)};\n\n    --#{$prefix}secondary-color: #{$body-secondary-color-dark};\n    --#{$prefix}secondary-color-rgb: #{to-rgb($body-secondary-color-dark)};\n    --#{$prefix}secondary-bg: #{$body-secondary-bg-dark};\n    --#{$prefix}secondary-bg-rgb: #{to-rgb($body-secondary-bg-dark)};\n\n    --#{$prefix}tertiary-color: #{$body-tertiary-color-dark};\n    --#{$prefix}tertiary-color-rgb: #{to-rgb($body-tertiary-color-dark)};\n    --#{$prefix}tertiary-bg: #{$body-tertiary-bg-dark};\n    --#{$prefix}tertiary-bg-rgb: #{to-rgb($body-tertiary-bg-dark)};\n\n    @each $color, $value in $theme-colors-text-dark {\n      --#{$prefix}#{$color}-text-emphasis: #{$value};\n    }\n\n    @each $color, $value in $theme-colors-bg-subtle-dark {\n      --#{$prefix}#{$color}-bg-subtle: #{$value};\n    }\n\n    @each $color, $value in $theme-colors-border-subtle-dark {\n      --#{$prefix}#{$color}-border-subtle: #{$value};\n    }\n\n    --#{$prefix}heading-color: #{$headings-color-dark};\n\n    --#{$prefix}link-color: #{$link-color-dark};\n    --#{$prefix}link-hover-color: #{$link-hover-color-dark};\n    --#{$prefix}link-color-rgb: #{to-rgb($link-color-dark)};\n    --#{$prefix}link-hover-color-rgb: #{to-rgb($link-hover-color-dark)};\n\n    --#{$prefix}code-color: #{$code-color-dark};\n    --#{$prefix}highlight-color: #{$mark-color-dark};\n    --#{$prefix}highlight-bg: #{$mark-bg-dark};\n\n    --#{$prefix}border-color: #{$border-color-dark};\n    --#{$prefix}border-color-translucent: #{$border-color-translucent-dark};\n\n    --#{$prefix}form-valid-color: #{$form-valid-color-dark};\n    --#{$prefix}form-valid-border-color: #{$form-valid-border-color-dark};\n    --#{$prefix}form-invalid-color: #{$form-invalid-color-dark};\n    --#{$prefix}form-invalid-border-color: #{$form-invalid-border-color-dark};\n    // scss-docs-end root-dark-mode-vars\n  }\n}\n","// stylelint-disable scss/dimension-no-non-numeric-values\n\n// SCSS RFS mixin\n//\n// Automated responsive values for font sizes, paddings, margins and much more\n//\n// Licensed under MIT (https://github.com/twbs/rfs/blob/main/LICENSE)\n\n// Configuration\n\n// Base value\n$rfs-base-value: 1.25rem !default;\n$rfs-unit: rem !default;\n\n@if $rfs-unit != rem and $rfs-unit != px {\n  @error \"`#{$rfs-unit}` is not a valid unit for $rfs-unit. Use `px` or `rem`.\";\n}\n\n// Breakpoint at where values start decreasing if screen width is smaller\n$rfs-breakpoint: 1200px !default;\n$rfs-breakpoint-unit: px !default;\n\n@if $rfs-breakpoint-unit != px and $rfs-breakpoint-unit != em and $rfs-breakpoint-unit != rem {\n  @error \"`#{$rfs-breakpoint-unit}` is not a valid unit for $rfs-breakpoint-unit. Use `px`, `em` or `rem`.\";\n}\n\n// Resize values based on screen height and width\n$rfs-two-dimensional: false !default;\n\n// Factor of decrease\n$rfs-factor: 10 !default;\n\n@if type-of($rfs-factor) != number or $rfs-factor <= 1 {\n  @error \"`#{$rfs-factor}` is not a valid  $rfs-factor, it must be greater than 1.\";\n}\n\n// Mode. Possibilities: \"min-media-query\", \"max-media-query\"\n$rfs-mode: min-media-query !default;\n\n// Generate enable or disable classes. Possibilities: false, \"enable\" or \"disable\"\n$rfs-class: false !default;\n\n// 1 rem = $rfs-rem-value px\n$rfs-rem-value: 16 !default;\n\n// Safari iframe resize bug: https://github.com/twbs/rfs/issues/14\n$rfs-safari-iframe-resize-bug-fix: false !default;\n\n// Disable RFS by setting $enable-rfs to false\n$enable-rfs: true !default;\n\n// Cache $rfs-base-value unit\n$rfs-base-value-unit: unit($rfs-base-value);\n\n@function divide($dividend, $divisor, $precision: 10) {\n  $sign: if($dividend > 0 and $divisor > 0 or $dividend < 0 and $divisor < 0, 1, -1);\n  $dividend: abs($dividend);\n  $divisor: abs($divisor);\n  @if $dividend == 0 {\n    @return 0;\n  }\n  @if $divisor == 0 {\n    @error \"Cannot divide by 0\";\n  }\n  $remainder: $dividend;\n  $result: 0;\n  $factor: 10;\n  @while ($remainder > 0 and $precision >= 0) {\n    $quotient: 0;\n    @while ($remainder >= $divisor) {\n      $remainder: $remainder - $divisor;\n      $quotient: $quotient + 1;\n    }\n    $result: $result * 10 + $quotient;\n    $factor: $factor * .1;\n    $remainder: $remainder * 10;\n    $precision: $precision - 1;\n    @if ($precision < 0 and $remainder >= $divisor * 5) {\n      $result: $result + 1;\n    }\n  }\n  $result: $result * $factor * $sign;\n  $dividend-unit: unit($dividend);\n  $divisor-unit: unit($divisor);\n  $unit-map: (\n    \"px\": 1px,\n    \"rem\": 1rem,\n    \"em\": 1em,\n    \"%\": 1%\n  );\n  @if ($dividend-unit != $divisor-unit and map-has-key($unit-map, $dividend-unit)) {\n    $result: $result * map-get($unit-map, $dividend-unit);\n  }\n  @return $result;\n}\n\n// Remove px-unit from $rfs-base-value for calculations\n@if $rfs-base-value-unit == px {\n  $rfs-base-value: divide($rfs-base-value, $rfs-base-value * 0 + 1);\n}\n@else if $rfs-base-value-unit == rem {\n  $rfs-base-value: divide($rfs-base-value, divide($rfs-base-value * 0 + 1, $rfs-rem-value));\n}\n\n// Cache $rfs-breakpoint unit to prevent multiple calls\n$rfs-breakpoint-unit-cache: unit($rfs-breakpoint);\n\n// Remove unit from $rfs-breakpoint for calculations\n@if $rfs-breakpoint-unit-cache == px {\n  $rfs-breakpoint: divide($rfs-breakpoint, $rfs-breakpoint * 0 + 1);\n}\n@else if $rfs-breakpoint-unit-cache == rem or $rfs-breakpoint-unit-cache == \"em\" {\n  $rfs-breakpoint: divide($rfs-breakpoint, divide($rfs-breakpoint * 0 + 1, $rfs-rem-value));\n}\n\n// Calculate the media query value\n$rfs-mq-value: if($rfs-breakpoint-unit == px, #{$rfs-breakpoint}px, #{divide($rfs-breakpoint, $rfs-rem-value)}#{$rfs-breakpoint-unit});\n$rfs-mq-property-width: if($rfs-mode == max-media-query, max-width, min-width);\n$rfs-mq-property-height: if($rfs-mode == max-media-query, max-height, min-height);\n\n// Internal mixin used to determine which media query needs to be used\n@mixin _rfs-media-query {\n  @if $rfs-two-dimensional {\n    @if $rfs-mode == max-media-query {\n      @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}), (#{$rfs-mq-property-height}: #{$rfs-mq-value}) {\n        @content;\n      }\n    }\n    @else {\n      @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}) and (#{$rfs-mq-property-height}: #{$rfs-mq-value}) {\n        @content;\n      }\n    }\n  }\n  @else {\n    @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}) {\n      @content;\n    }\n  }\n}\n\n// Internal mixin that adds disable classes to the selector if needed.\n@mixin _rfs-rule {\n  @if $rfs-class == disable and $rfs-mode == max-media-query {\n    // Adding an extra class increases specificity, which prevents the media query to override the property\n    &,\n    .disable-rfs &,\n    &.disable-rfs {\n      @content;\n    }\n  }\n  @else if $rfs-class == enable and $rfs-mode == min-media-query {\n    .enable-rfs &,\n    &.enable-rfs {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Internal mixin that adds enable classes to the selector if needed.\n@mixin _rfs-media-query-rule {\n\n  @if $rfs-class == enable {\n    @if $rfs-mode == min-media-query {\n      @content;\n    }\n\n    @include _rfs-media-query () {\n      .enable-rfs &,\n      &.enable-rfs {\n        @content;\n      }\n    }\n  }\n  @else {\n    @if $rfs-class == disable and $rfs-mode == min-media-query {\n      .disable-rfs &,\n      &.disable-rfs {\n        @content;\n      }\n    }\n    @include _rfs-media-query () {\n      @content;\n    }\n  }\n}\n\n// Helper function to get the formatted non-responsive value\n@function rfs-value($values) {\n  // Convert to list\n  $values: if(type-of($values) != list, ($values,), $values);\n\n  $val: \"\";\n\n  // Loop over each value and calculate value\n  @each $value in $values {\n    @if $value == 0 {\n      $val: $val + \" 0\";\n    }\n    @else {\n      // Cache $value unit\n      $unit: if(type-of($value) == \"number\", unit($value), false);\n\n      @if $unit == px {\n        // Convert to rem if needed\n        $val: $val + \" \" + if($rfs-unit == rem, #{divide($value, $value * 0 + $rfs-rem-value)}rem, $value);\n      }\n      @else if $unit == rem {\n        // Convert to px if needed\n        $val: $val + \" \" + if($rfs-unit == px, #{divide($value, $value * 0 + 1) * $rfs-rem-value}px, $value);\n      } @else {\n        // If $value isn't a number (like inherit) or $value has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n        $val: $val + \" \" + $value;\n      }\n    }\n  }\n\n  // Remove first space\n  @return unquote(str-slice($val, 2));\n}\n\n// Helper function to get the responsive value calculated by RFS\n@function rfs-fluid-value($values) {\n  // Convert to list\n  $values: if(type-of($values) != list, ($values,), $values);\n\n  $val: \"\";\n\n  // Loop over each value and calculate value\n  @each $value in $values {\n    @if $value == 0 {\n      $val: $val + \" 0\";\n    } @else {\n      // Cache $value unit\n      $unit: if(type-of($value) == \"number\", unit($value), false);\n\n      // If $value isn't a number (like inherit) or $value has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n      @if not $unit or $unit != px and $unit != rem {\n        $val: $val + \" \" + $value;\n      } @else {\n        // Remove unit from $value for calculations\n        $value: divide($value, $value * 0 + if($unit == px, 1, divide(1, $rfs-rem-value)));\n\n        // Only add the media query if the value is greater than the minimum value\n        @if abs($value) <= $rfs-base-value or not $enable-rfs {\n          $val: $val + \" \" + if($rfs-unit == rem, #{divide($value, $rfs-rem-value)}rem, #{$value}px);\n        }\n        @else {\n          // Calculate the minimum value\n          $value-min: $rfs-base-value + divide(abs($value) - $rfs-base-value, $rfs-factor);\n\n          // Calculate difference between $value and the minimum value\n          $value-diff: abs($value) - $value-min;\n\n          // Base value formatting\n          $min-width: if($rfs-unit == rem, #{divide($value-min, $rfs-rem-value)}rem, #{$value-min}px);\n\n          // Use negative value if needed\n          $min-width: if($value < 0, -$min-width, $min-width);\n\n          // Use `vmin` if two-dimensional is enabled\n          $variable-unit: if($rfs-two-dimensional, vmin, vw);\n\n          // Calculate the variable width between 0 and $rfs-breakpoint\n          $variable-width: #{divide($value-diff * 100, $rfs-breakpoint)}#{$variable-unit};\n\n          // Return the calculated value\n          $val: $val + \" calc(\" + $min-width + if($value < 0, \" - \", \" + \") + $variable-width + \")\";\n        }\n      }\n    }\n  }\n\n  // Remove first space\n  @return unquote(str-slice($val, 2));\n}\n\n// RFS mixin\n@mixin rfs($values, $property: font-size) {\n  @if $values != null {\n    $val: rfs-value($values);\n    $fluid-val: rfs-fluid-value($values);\n\n    // Do not print the media query if responsive & non-responsive values are the same\n    @if $val == $fluid-val {\n      #{$property}: $val;\n    }\n    @else {\n      @include _rfs-rule () {\n        #{$property}: if($rfs-mode == max-media-query, $val, $fluid-val);\n\n        // Include safari iframe resize fix if needed\n        min-width: if($rfs-safari-iframe-resize-bug-fix, (0 * 1vw), null);\n      }\n\n      @include _rfs-media-query-rule () {\n        #{$property}: if($rfs-mode == max-media-query, $fluid-val, $val);\n      }\n    }\n  }\n}\n\n// Shorthand helper mixins\n@mixin font-size($value) {\n  @include rfs($value);\n}\n\n@mixin padding($value) {\n  @include rfs($value, padding);\n}\n\n@mixin padding-top($value) {\n  @include rfs($value, padding-top);\n}\n\n@mixin padding-right($value) {\n  @include rfs($value, padding-right);\n}\n\n@mixin padding-bottom($value) {\n  @include rfs($value, padding-bottom);\n}\n\n@mixin padding-left($value) {\n  @include rfs($value, padding-left);\n}\n\n@mixin margin($value) {\n  @include rfs($value, margin);\n}\n\n@mixin margin-top($value) {\n  @include rfs($value, margin-top);\n}\n\n@mixin margin-right($value) {\n  @include rfs($value, margin-right);\n}\n\n@mixin margin-bottom($value) {\n  @include rfs($value, margin-bottom);\n}\n\n@mixin margin-left($value) {\n  @include rfs($value, margin-left);\n}\n","// scss-docs-start color-mode-mixin\n@mixin color-mode($mode: light, $root: false) {\n  @if $color-mode-type == \"media-query\" {\n    @if $root == true {\n      @media (prefers-color-scheme: $mode) {\n        :root {\n          @content;\n        }\n      }\n    } @else {\n      @media (prefers-color-scheme: $mode) {\n        @content;\n      }\n    }\n  } @else {\n    [data-bs-theme=\"#{$mode}\"] {\n      @content;\n    }\n  }\n}\n// scss-docs-end color-mode-mixin\n","// stylelint-disable declaration-no-important, selector-no-qualifying-type, property-no-vendor-prefix\n\n\n// Reboot\n//\n// Normalization of HTML elements, manually forked from Normalize.css to remove\n// styles targeting irrelevant browsers while applying new styles.\n//\n// Normalize is licensed MIT. https://github.com/necolas/normalize.css\n\n\n// Document\n//\n// Change from `box-sizing: content-box` so that `width` is not affected by `padding` or `border`.\n\n*,\n*::before,\n*::after {\n  box-sizing: border-box;\n}\n\n\n// Root\n//\n// Ability to the value of the root font sizes, affecting the value of `rem`.\n// null by default, thus nothing is generated.\n\n:root {\n  @if $font-size-root != null {\n    @include font-size(var(--#{$prefix}root-font-size));\n  }\n\n  @if $enable-smooth-scroll {\n    @media (prefers-reduced-motion: no-preference) {\n      scroll-behavior: smooth;\n    }\n  }\n}\n\n\n// Body\n//\n// 1. Remove the margin in all browsers.\n// 2. As a best practice, apply a default `background-color`.\n// 3. Prevent adjustments of font size after orientation changes in iOS.\n// 4. Change the default tap highlight to be completely transparent in iOS.\n\n// scss-docs-start reboot-body-rules\nbody {\n  margin: 0; // 1\n  font-family: var(--#{$prefix}body-font-family);\n  @include font-size(var(--#{$prefix}body-font-size));\n  font-weight: var(--#{$prefix}body-font-weight);\n  line-height: var(--#{$prefix}body-line-height);\n  color: var(--#{$prefix}body-color);\n  text-align: var(--#{$prefix}body-text-align);\n  background-color: var(--#{$prefix}body-bg); // 2\n  -webkit-text-size-adjust: 100%; // 3\n  -webkit-tap-highlight-color: rgba($black, 0); // 4\n}\n// scss-docs-end reboot-body-rules\n\n\n// Content grouping\n//\n// 1. Reset Firefox's gray color\n\nhr {\n  margin: $hr-margin-y 0;\n  color: $hr-color; // 1\n  border: 0;\n  border-top: $hr-border-width solid $hr-border-color;\n  opacity: $hr-opacity;\n}\n\n\n// Typography\n//\n// 1. Remove top margins from headings\n//    By default, `<h1>`-`<h6>` all receive top and bottom margins. We nuke the top\n//    margin for easier control within type scales as it avoids margin collapsing.\n\n%heading {\n  margin-top: 0; // 1\n  margin-bottom: $headings-margin-bottom;\n  font-family: $headings-font-family;\n  font-style: $headings-font-style;\n  font-weight: $headings-font-weight;\n  line-height: $headings-line-height;\n  color: var(--#{$prefix}heading-color);\n}\n\nh1 {\n  @extend %heading;\n  @include font-size($h1-font-size);\n}\n\nh2 {\n  @extend %heading;\n  @include font-size($h2-font-size);\n}\n\nh3 {\n  @extend %heading;\n  @include font-size($h3-font-size);\n}\n\nh4 {\n  @extend %heading;\n  @include font-size($h4-font-size);\n}\n\nh5 {\n  @extend %heading;\n  @include font-size($h5-font-size);\n}\n\nh6 {\n  @extend %heading;\n  @include font-size($h6-font-size);\n}\n\n\n// Reset margins on paragraphs\n//\n// Similarly, the top margin on `<p>`s get reset. However, we also reset the\n// bottom margin to use `rem` units instead of `em`.\n\np {\n  margin-top: 0;\n  margin-bottom: $paragraph-margin-bottom;\n}\n\n\n// Abbreviations\n//\n// 1. Add the correct text decoration in Chrome, Edge, Opera, and Safari.\n// 2. Add explicit cursor to indicate changed behavior.\n// 3. Prevent the text-decoration to be skipped.\n\nabbr[title] {\n  text-decoration: underline dotted; // 1\n  cursor: help; // 2\n  text-decoration-skip-ink: none; // 3\n}\n\n\n// Address\n\naddress {\n  margin-bottom: 1rem;\n  font-style: normal;\n  line-height: inherit;\n}\n\n\n// Lists\n\nol,\nul {\n  padding-left: 2rem;\n}\n\nol,\nul,\ndl {\n  margin-top: 0;\n  margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n  margin-bottom: 0;\n}\n\ndt {\n  font-weight: $dt-font-weight;\n}\n\n// 1. Undo browser default\n\ndd {\n  margin-bottom: .5rem;\n  margin-left: 0; // 1\n}\n\n\n// Blockquote\n\nblockquote {\n  margin: 0 0 1rem;\n}\n\n\n// Strong\n//\n// Add the correct font weight in Chrome, Edge, and Safari\n\nb,\nstrong {\n  font-weight: $font-weight-bolder;\n}\n\n\n// Small\n//\n// Add the correct font size in all browsers\n\nsmall {\n  @include font-size($small-font-size);\n}\n\n\n// Mark\n\nmark {\n  padding: $mark-padding;\n  color: var(--#{$prefix}highlight-color);\n  background-color: var(--#{$prefix}highlight-bg);\n}\n\n\n// Sub and Sup\n//\n// Prevent `sub` and `sup` elements from affecting the line height in\n// all browsers.\n\nsub,\nsup {\n  position: relative;\n  @include font-size($sub-sup-font-size);\n  line-height: 0;\n  vertical-align: baseline;\n}\n\nsub { bottom: -.25em; }\nsup { top: -.5em; }\n\n\n// Links\n\na {\n  color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, 1));\n  text-decoration: $link-decoration;\n\n  &:hover {\n    --#{$prefix}link-color-rgb: var(--#{$prefix}link-hover-color-rgb);\n    text-decoration: $link-hover-decoration;\n  }\n}\n\n// And undo these styles for placeholder links/named anchors (without href).\n// It would be more straightforward to just use a[href] in previous block, but that\n// causes specificity issues in many other styles that are too complex to fix.\n// See https://github.com/twbs/bootstrap/issues/19402\n\na:not([href]):not([class]) {\n  &,\n  &:hover {\n    color: inherit;\n    text-decoration: none;\n  }\n}\n\n\n// Code\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: $font-family-code;\n  @include font-size(1em); // Correct the odd `em` font sizing in all browsers.\n}\n\n// 1. Remove browser default top margin\n// 2. Reset browser default of `1em` to use `rem`s\n// 3. Don't allow content to break outside\n\npre {\n  display: block;\n  margin-top: 0; // 1\n  margin-bottom: 1rem; // 2\n  overflow: auto; // 3\n  @include font-size($code-font-size);\n  color: $pre-color;\n\n  // Account for some code outputs that place code tags in pre tags\n  code {\n    @include font-size(inherit);\n    color: inherit;\n    word-break: normal;\n  }\n}\n\ncode {\n  @include font-size($code-font-size);\n  color: var(--#{$prefix}code-color);\n  word-wrap: break-word;\n\n  // Streamline the style when inside anchors to avoid broken underline and more\n  a > & {\n    color: inherit;\n  }\n}\n\nkbd {\n  padding: $kbd-padding-y $kbd-padding-x;\n  @include font-size($kbd-font-size);\n  color: $kbd-color;\n  background-color: $kbd-bg;\n  @include border-radius($border-radius-sm);\n\n  kbd {\n    padding: 0;\n    @include font-size(1em);\n    font-weight: $nested-kbd-font-weight;\n  }\n}\n\n\n// Figures\n//\n// Apply a consistent margin strategy (matches our type styles).\n\nfigure {\n  margin: 0 0 1rem;\n}\n\n\n// Images and content\n\nimg,\nsvg {\n  vertical-align: middle;\n}\n\n\n// Tables\n//\n// Prevent double borders\n\ntable {\n  caption-side: bottom;\n  border-collapse: collapse;\n}\n\ncaption {\n  padding-top: $table-cell-padding-y;\n  padding-bottom: $table-cell-padding-y;\n  color: $table-caption-color;\n  text-align: left;\n}\n\n// 1. Removes font-weight bold by inheriting\n// 2. Matches default `<td>` alignment by inheriting `text-align`.\n// 3. Fix alignment for Safari\n\nth {\n  font-weight: $table-th-font-weight; // 1\n  text-align: inherit; // 2\n  text-align: -webkit-match-parent; // 3\n}\n\nthead,\ntbody,\ntfoot,\ntr,\ntd,\nth {\n  border-color: inherit;\n  border-style: solid;\n  border-width: 0;\n}\n\n\n// Forms\n//\n// 1. Allow labels to use `margin` for spacing.\n\nlabel {\n  display: inline-block; // 1\n}\n\n// Remove the default `border-radius` that macOS Chrome adds.\n// See https://github.com/twbs/bootstrap/issues/24093\n\nbutton {\n  // stylelint-disable-next-line property-disallowed-list\n  border-radius: 0;\n}\n\n// Explicitly remove focus outline in Chromium when it shouldn't be\n// visible (e.g. as result of mouse click or touch tap). It already\n// should be doing this automatically, but seems to currently be\n// confused and applies its very visible two-tone outline anyway.\n\nbutton:focus:not(:focus-visible) {\n  outline: 0;\n}\n\n// 1. Remove the margin in Firefox and Safari\n\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n  margin: 0; // 1\n  font-family: inherit;\n  @include font-size(inherit);\n  line-height: inherit;\n}\n\n// Remove the inheritance of text transform in Firefox\nbutton,\nselect {\n  text-transform: none;\n}\n// Set the cursor for non-`<button>` buttons\n//\n// Details at https://github.com/twbs/bootstrap/pull/30562\n[role=\"button\"] {\n  cursor: pointer;\n}\n\nselect {\n  // Remove the inheritance of word-wrap in Safari.\n  // See https://github.com/twbs/bootstrap/issues/24990\n  word-wrap: normal;\n\n  // Undo the opacity change from Chrome\n  &:disabled {\n    opacity: 1;\n  }\n}\n\n// Remove the dropdown arrow only from text type inputs built with datalists in Chrome.\n// See https://stackoverflow.com/a/54997118\n\n[list]:not([type=\"date\"]):not([type=\"datetime-local\"]):not([type=\"month\"]):not([type=\"week\"]):not([type=\"time\"])::-webkit-calendar-picker-indicator {\n  display: none !important;\n}\n\n// 1. Prevent a WebKit bug where (2) destroys native `audio` and `video`\n//    controls in Android 4.\n// 2. Correct the inability to style clickable types in iOS and Safari.\n// 3. Opinionated: add \"hand\" cursor to non-disabled button elements.\n\nbutton,\n[type=\"button\"], // 1\n[type=\"reset\"],\n[type=\"submit\"] {\n  -webkit-appearance: button; // 2\n\n  @if $enable-button-pointers {\n    &:not(:disabled) {\n      cursor: pointer; // 3\n    }\n  }\n}\n\n// Remove inner border and padding from Firefox, but don't restore the outline like Normalize.\n\n::-moz-focus-inner {\n  padding: 0;\n  border-style: none;\n}\n\n// 1. Textareas should really only resize vertically so they don't break their (horizontal) containers.\n\ntextarea {\n  resize: vertical; // 1\n}\n\n// 1. Browsers set a default `min-width: min-content;` on fieldsets,\n//    unlike e.g. `<div>`s, which have `min-width: 0;` by default.\n//    So we reset that to ensure fieldsets behave more like a standard block element.\n//    See https://github.com/twbs/bootstrap/issues/12359\n//    and https://html.spec.whatwg.org/multipage/#the-fieldset-and-legend-elements\n// 2. Reset the default outline behavior of fieldsets so they don't affect page layout.\n\nfieldset {\n  min-width: 0; // 1\n  padding: 0; // 2\n  margin: 0; // 2\n  border: 0; // 2\n}\n\n// 1. By using `float: left`, the legend will behave like a block element.\n//    This way the border of a fieldset wraps around the legend if present.\n// 2. Fix wrapping bug.\n//    See https://github.com/twbs/bootstrap/issues/29712\n\nlegend {\n  float: left; // 1\n  width: 100%;\n  padding: 0;\n  margin-bottom: $legend-margin-bottom;\n  @include font-size($legend-font-size);\n  font-weight: $legend-font-weight;\n  line-height: inherit;\n\n  + * {\n    clear: left; // 2\n  }\n}\n\n// Fix height of inputs with a type of datetime-local, date, month, week, or time\n// See https://github.com/twbs/bootstrap/issues/18842\n\n::-webkit-datetime-edit-fields-wrapper,\n::-webkit-datetime-edit-text,\n::-webkit-datetime-edit-minute,\n::-webkit-datetime-edit-hour-field,\n::-webkit-datetime-edit-day-field,\n::-webkit-datetime-edit-month-field,\n::-webkit-datetime-edit-year-field {\n  padding: 0;\n}\n\n::-webkit-inner-spin-button {\n  height: auto;\n}\n\n// 1. This overrides the extra rounded corners on search inputs in iOS so that our\n//    `.form-control` class can properly style them. Note that this cannot simply\n//    be added to `.form-control` as it's not specific enough. For details, see\n//    https://github.com/twbs/bootstrap/issues/11586.\n// 2. Correct the outline style in Safari.\n\n[type=\"search\"] {\n  -webkit-appearance: textfield; // 1\n  outline-offset: -2px; // 2\n}\n\n// 1. A few input types should stay LTR\n// See https://rtlstyling.com/posts/rtl-styling#form-inputs\n// 2. RTL only output\n// See https://rtlcss.com/learn/usage-guide/control-directives/#raw\n\n/* rtl:raw:\n[type=\"tel\"],\n[type=\"url\"],\n[type=\"email\"],\n[type=\"number\"] {\n  direction: ltr;\n}\n*/\n\n// Remove the inner padding in Chrome and Safari on macOS.\n\n::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n// Remove padding around color pickers in webkit browsers\n\n::-webkit-color-swatch-wrapper {\n  padding: 0;\n}\n\n\n// 1. Inherit font family and line height for file input buttons\n// 2. Correct the inability to style clickable types in iOS and Safari.\n\n::file-selector-button {\n  font: inherit; // 1\n  -webkit-appearance: button; // 2\n}\n\n// Correct element displays\n\noutput {\n  display: inline-block;\n}\n\n// Remove border from iframe\n\niframe {\n  border: 0;\n}\n\n// Summary\n//\n// 1. Add the correct display in all browsers\n\nsummary {\n  display: list-item; // 1\n  cursor: pointer;\n}\n\n\n// Progress\n//\n// Add the correct vertical alignment in Chrome, Firefox, and Opera.\n\nprogress {\n  vertical-align: baseline;\n}\n\n\n// Hidden attribute\n//\n// Always hide an element with the `hidden` HTML attribute.\n\n[hidden] {\n  display: none !important;\n}\n","// Variables\n//\n// Variables should follow the `$component-state-property-size` formula for\n// consistent naming. Ex: $nav-link-disabled-color and $modal-content-box-shadow-xs.\n\n// Color system\n\n// scss-docs-start gray-color-variables\n$white:    #fff !default;\n$gray-100: #f8f9fa !default;\n$gray-200: #e9ecef !default;\n$gray-300: #dee2e6 !default;\n$gray-400: #ced4da !default;\n$gray-500: #adb5bd !default;\n$gray-600: #6c757d !default;\n$gray-700: #495057 !default;\n$gray-800: #343a40 !default;\n$gray-900: #212529 !default;\n$black:    #000 !default;\n// scss-docs-end gray-color-variables\n\n// fusv-disable\n// scss-docs-start gray-colors-map\n$grays: (\n  \"100\": $gray-100,\n  \"200\": $gray-200,\n  \"300\": $gray-300,\n  \"400\": $gray-400,\n  \"500\": $gray-500,\n  \"600\": $gray-600,\n  \"700\": $gray-700,\n  \"800\": $gray-800,\n  \"900\": $gray-900\n) !default;\n// scss-docs-end gray-colors-map\n// fusv-enable\n\n// scss-docs-start color-variables\n$blue:    #0d6efd !default;\n$indigo:  #6610f2 !default;\n$purple:  #6f42c1 !default;\n$pink:    #d63384 !default;\n$red:     #dc3545 !default;\n$orange:  #fd7e14 !default;\n$yellow:  #ffc107 !default;\n$green:   #198754 !default;\n$teal:    #20c997 !default;\n$cyan:    #0dcaf0 !default;\n// scss-docs-end color-variables\n\n// scss-docs-start colors-map\n$colors: (\n  \"blue\":       $blue,\n  \"indigo\":     $indigo,\n  \"purple\":     $purple,\n  \"pink\":       $pink,\n  \"red\":        $red,\n  \"orange\":     $orange,\n  \"yellow\":     $yellow,\n  \"green\":      $green,\n  \"teal\":       $teal,\n  \"cyan\":       $cyan,\n  \"black\":      $black,\n  \"white\":      $white,\n  \"gray\":       $gray-600,\n  \"gray-dark\":  $gray-800\n) !default;\n// scss-docs-end colors-map\n\n// The contrast ratio to reach against white, to determine if color changes from \"light\" to \"dark\". Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n$min-contrast-ratio:   4.5 !default;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$color-contrast-dark:      $black !default;\n$color-contrast-light:     $white !default;\n\n// fusv-disable\n$blue-100: tint-color($blue, 80%) !default;\n$blue-200: tint-color($blue, 60%) !default;\n$blue-300: tint-color($blue, 40%) !default;\n$blue-400: tint-color($blue, 20%) !default;\n$blue-500: $blue !default;\n$blue-600: shade-color($blue, 20%) !default;\n$blue-700: shade-color($blue, 40%) !default;\n$blue-800: shade-color($blue, 60%) !default;\n$blue-900: shade-color($blue, 80%) !default;\n\n$indigo-100: tint-color($indigo, 80%) !default;\n$indigo-200: tint-color($indigo, 60%) !default;\n$indigo-300: tint-color($indigo, 40%) !default;\n$indigo-400: tint-color($indigo, 20%) !default;\n$indigo-500: $indigo !default;\n$indigo-600: shade-color($indigo, 20%) !default;\n$indigo-700: shade-color($indigo, 40%) !default;\n$indigo-800: shade-color($indigo, 60%) !default;\n$indigo-900: shade-color($indigo, 80%) !default;\n\n$purple-100: tint-color($purple, 80%) !default;\n$purple-200: tint-color($purple, 60%) !default;\n$purple-300: tint-color($purple, 40%) !default;\n$purple-400: tint-color($purple, 20%) !default;\n$purple-500: $purple !default;\n$purple-600: shade-color($purple, 20%) !default;\n$purple-700: shade-color($purple, 40%) !default;\n$purple-800: shade-color($purple, 60%) !default;\n$purple-900: shade-color($purple, 80%) !default;\n\n$pink-100: tint-color($pink, 80%) !default;\n$pink-200: tint-color($pink, 60%) !default;\n$pink-300: tint-color($pink, 40%) !default;\n$pink-400: tint-color($pink, 20%) !default;\n$pink-500: $pink !default;\n$pink-600: shade-color($pink, 20%) !default;\n$pink-700: shade-color($pink, 40%) !default;\n$pink-800: shade-color($pink, 60%) !default;\n$pink-900: shade-color($pink, 80%) !default;\n\n$red-100: tint-color($red, 80%) !default;\n$red-200: tint-color($red, 60%) !default;\n$red-300: tint-color($red, 40%) !default;\n$red-400: tint-color($red, 20%) !default;\n$red-500: $red !default;\n$red-600: shade-color($red, 20%) !default;\n$red-700: shade-color($red, 40%) !default;\n$red-800: shade-color($red, 60%) !default;\n$red-900: shade-color($red, 80%) !default;\n\n$orange-100: tint-color($orange, 80%) !default;\n$orange-200: tint-color($orange, 60%) !default;\n$orange-300: tint-color($orange, 40%) !default;\n$orange-400: tint-color($orange, 20%) !default;\n$orange-500: $orange !default;\n$orange-600: shade-color($orange, 20%) !default;\n$orange-700: shade-color($orange, 40%) !default;\n$orange-800: shade-color($orange, 60%) !default;\n$orange-900: shade-color($orange, 80%) !default;\n\n$yellow-100: tint-color($yellow, 80%) !default;\n$yellow-200: tint-color($yellow, 60%) !default;\n$yellow-300: tint-color($yellow, 40%) !default;\n$yellow-400: tint-color($yellow, 20%) !default;\n$yellow-500: $yellow !default;\n$yellow-600: shade-color($yellow, 20%) !default;\n$yellow-700: shade-color($yellow, 40%) !default;\n$yellow-800: shade-color($yellow, 60%) !default;\n$yellow-900: shade-color($yellow, 80%) !default;\n\n$green-100: tint-color($green, 80%) !default;\n$green-200: tint-color($green, 60%) !default;\n$green-300: tint-color($green, 40%) !default;\n$green-400: tint-color($green, 20%) !default;\n$green-500: $green !default;\n$green-600: shade-color($green, 20%) !default;\n$green-700: shade-color($green, 40%) !default;\n$green-800: shade-color($green, 60%) !default;\n$green-900: shade-color($green, 80%) !default;\n\n$teal-100: tint-color($teal, 80%) !default;\n$teal-200: tint-color($teal, 60%) !default;\n$teal-300: tint-color($teal, 40%) !default;\n$teal-400: tint-color($teal, 20%) !default;\n$teal-500: $teal !default;\n$teal-600: shade-color($teal, 20%) !default;\n$teal-700: shade-color($teal, 40%) !default;\n$teal-800: shade-color($teal, 60%) !default;\n$teal-900: shade-color($teal, 80%) !default;\n\n$cyan-100: tint-color($cyan, 80%) !default;\n$cyan-200: tint-color($cyan, 60%) !default;\n$cyan-300: tint-color($cyan, 40%) !default;\n$cyan-400: tint-color($cyan, 20%) !default;\n$cyan-500: $cyan !default;\n$cyan-600: shade-color($cyan, 20%) !default;\n$cyan-700: shade-color($cyan, 40%) !default;\n$cyan-800: shade-color($cyan, 60%) !default;\n$cyan-900: shade-color($cyan, 80%) !default;\n\n$blues: (\n  \"blue-100\": $blue-100,\n  \"blue-200\": $blue-200,\n  \"blue-300\": $blue-300,\n  \"blue-400\": $blue-400,\n  \"blue-500\": $blue-500,\n  \"blue-600\": $blue-600,\n  \"blue-700\": $blue-700,\n  \"blue-800\": $blue-800,\n  \"blue-900\": $blue-900\n) !default;\n\n$indigos: (\n  \"indigo-100\": $indigo-100,\n  \"indigo-200\": $indigo-200,\n  \"indigo-300\": $indigo-300,\n  \"indigo-400\": $indigo-400,\n  \"indigo-500\": $indigo-500,\n  \"indigo-600\": $indigo-600,\n  \"indigo-700\": $indigo-700,\n  \"indigo-800\": $indigo-800,\n  \"indigo-900\": $indigo-900\n) !default;\n\n$purples: (\n  \"purple-100\": $purple-100,\n  \"purple-200\": $purple-200,\n  \"purple-300\": $purple-300,\n  \"purple-400\": $purple-400,\n  \"purple-500\": $purple-500,\n  \"purple-600\": $purple-600,\n  \"purple-700\": $purple-700,\n  \"purple-800\": $purple-800,\n  \"purple-900\": $purple-900\n) !default;\n\n$pinks: (\n  \"pink-100\": $pink-100,\n  \"pink-200\": $pink-200,\n  \"pink-300\": $pink-300,\n  \"pink-400\": $pink-400,\n  \"pink-500\": $pink-500,\n  \"pink-600\": $pink-600,\n  \"pink-700\": $pink-700,\n  \"pink-800\": $pink-800,\n  \"pink-900\": $pink-900\n) !default;\n\n$reds: (\n  \"red-100\": $red-100,\n  \"red-200\": $red-200,\n  \"red-300\": $red-300,\n  \"red-400\": $red-400,\n  \"red-500\": $red-500,\n  \"red-600\": $red-600,\n  \"red-700\": $red-700,\n  \"red-800\": $red-800,\n  \"red-900\": $red-900\n) !default;\n\n$oranges: (\n  \"orange-100\": $orange-100,\n  \"orange-200\": $orange-200,\n  \"orange-300\": $orange-300,\n  \"orange-400\": $orange-400,\n  \"orange-500\": $orange-500,\n  \"orange-600\": $orange-600,\n  \"orange-700\": $orange-700,\n  \"orange-800\": $orange-800,\n  \"orange-900\": $orange-900\n) !default;\n\n$yellows: (\n  \"yellow-100\": $yellow-100,\n  \"yellow-200\": $yellow-200,\n  \"yellow-300\": $yellow-300,\n  \"yellow-400\": $yellow-400,\n  \"yellow-500\": $yellow-500,\n  \"yellow-600\": $yellow-600,\n  \"yellow-700\": $yellow-700,\n  \"yellow-800\": $yellow-800,\n  \"yellow-900\": $yellow-900\n) !default;\n\n$greens: (\n  \"green-100\": $green-100,\n  \"green-200\": $green-200,\n  \"green-300\": $green-300,\n  \"green-400\": $green-400,\n  \"green-500\": $green-500,\n  \"green-600\": $green-600,\n  \"green-700\": $green-700,\n  \"green-800\": $green-800,\n  \"green-900\": $green-900\n) !default;\n\n$teals: (\n  \"teal-100\": $teal-100,\n  \"teal-200\": $teal-200,\n  \"teal-300\": $teal-300,\n  \"teal-400\": $teal-400,\n  \"teal-500\": $teal-500,\n  \"teal-600\": $teal-600,\n  \"teal-700\": $teal-700,\n  \"teal-800\": $teal-800,\n  \"teal-900\": $teal-900\n) !default;\n\n$cyans: (\n  \"cyan-100\": $cyan-100,\n  \"cyan-200\": $cyan-200,\n  \"cyan-300\": $cyan-300,\n  \"cyan-400\": $cyan-400,\n  \"cyan-500\": $cyan-500,\n  \"cyan-600\": $cyan-600,\n  \"cyan-700\": $cyan-700,\n  \"cyan-800\": $cyan-800,\n  \"cyan-900\": $cyan-900\n) !default;\n// fusv-enable\n\n// scss-docs-start theme-color-variables\n$primary:       $blue !default;\n$secondary:     $gray-600 !default;\n$success:       $green !default;\n$info:          $cyan !default;\n$warning:       $yellow !default;\n$danger:        $red !default;\n$light:         $gray-100 !default;\n$dark:          $gray-900 !default;\n// scss-docs-end theme-color-variables\n\n// scss-docs-start theme-colors-map\n$theme-colors: (\n  \"primary\":    $primary,\n  \"secondary\":  $secondary,\n  \"success\":    $success,\n  \"info\":       $info,\n  \"warning\":    $warning,\n  \"danger\":     $danger,\n  \"light\":      $light,\n  \"dark\":       $dark\n) !default;\n// scss-docs-end theme-colors-map\n\n// scss-docs-start theme-text-variables\n$primary-text-emphasis:   shade-color($primary, 60%) !default;\n$secondary-text-emphasis: shade-color($secondary, 60%) !default;\n$success-text-emphasis:   shade-color($success, 60%) !default;\n$info-text-emphasis:      shade-color($info, 60%) !default;\n$warning-text-emphasis:   shade-color($warning, 60%) !default;\n$danger-text-emphasis:    shade-color($danger, 60%) !default;\n$light-text-emphasis:     $gray-700 !default;\n$dark-text-emphasis:      $gray-700 !default;\n// scss-docs-end theme-text-variables\n\n// scss-docs-start theme-bg-subtle-variables\n$primary-bg-subtle:       tint-color($primary, 80%) !default;\n$secondary-bg-subtle:     tint-color($secondary, 80%) !default;\n$success-bg-subtle:       tint-color($success, 80%) !default;\n$info-bg-subtle:          tint-color($info, 80%) !default;\n$warning-bg-subtle:       tint-color($warning, 80%) !default;\n$danger-bg-subtle:        tint-color($danger, 80%) !default;\n$light-bg-subtle:         mix($gray-100, $white) !default;\n$dark-bg-subtle:          $gray-400 !default;\n// scss-docs-end theme-bg-subtle-variables\n\n// scss-docs-start theme-border-subtle-variables\n$primary-border-subtle:   tint-color($primary, 60%) !default;\n$secondary-border-subtle: tint-color($secondary, 60%) !default;\n$success-border-subtle:   tint-color($success, 60%) !default;\n$info-border-subtle:      tint-color($info, 60%) !default;\n$warning-border-subtle:   tint-color($warning, 60%) !default;\n$danger-border-subtle:    tint-color($danger, 60%) !default;\n$light-border-subtle:     $gray-200 !default;\n$dark-border-subtle:      $gray-500 !default;\n// scss-docs-end theme-border-subtle-variables\n\n// Characters which are escaped by the escape-svg function\n$escaped-characters: (\n  (\"<\", \"%3c\"),\n  (\">\", \"%3e\"),\n  (\"#\", \"%23\"),\n  (\"(\", \"%28\"),\n  (\")\", \"%29\"),\n) !default;\n\n// Options\n//\n// Quickly modify global styling by enabling or disabling optional features.\n\n$enable-caret:                true !default;\n$enable-rounded:              true !default;\n$enable-shadows:              false !default;\n$enable-gradients:            false !default;\n$enable-transitions:          true !default;\n$enable-reduced-motion:       true !default;\n$enable-smooth-scroll:        true !default;\n$enable-grid-classes:         true !default;\n$enable-container-classes:    true !default;\n$enable-cssgrid:              false !default;\n$enable-button-pointers:      true !default;\n$enable-rfs:                  true !default;\n$enable-validation-icons:     true !default;\n$enable-negative-margins:     false !default;\n$enable-deprecation-messages: true !default;\n$enable-important-utilities:  true !default;\n\n$enable-dark-mode:            true !default;\n$color-mode-type:             data !default; // `data` or `media-query`\n\n// Prefix for :root CSS variables\n\n$variable-prefix:             bs- !default; // Deprecated in v5.2.0 for the shorter `$prefix`\n$prefix:                      $variable-prefix !default;\n\n// Gradient\n//\n// The gradient which is added to components if `$enable-gradients` is `true`\n// This gradient is also added to elements with `.bg-gradient`\n// scss-docs-start variable-gradient\n$gradient: linear-gradient(180deg, rgba($white, .15), rgba($white, 0)) !default;\n// scss-docs-end variable-gradient\n\n// Spacing\n//\n// Control the default styling of most Bootstrap elements by modifying these\n// variables. Mostly focused on spacing.\n// You can add more entries to the $spacers map, should you need more variation.\n\n// scss-docs-start spacer-variables-maps\n$spacer: 1rem !default;\n$spacers: (\n  0: 0,\n  1: $spacer * .25,\n  2: $spacer * .5,\n  3: $spacer,\n  4: $spacer * 1.5,\n  5: $spacer * 3,\n) !default;\n// scss-docs-end spacer-variables-maps\n\n// Position\n//\n// Define the edge positioning anchors of the position utilities.\n\n// scss-docs-start position-map\n$position-values: (\n  0: 0,\n  50: 50%,\n  100: 100%\n) !default;\n// scss-docs-end position-map\n\n// Body\n//\n// Settings for the `<body>` element.\n\n$body-text-align:           null !default;\n$body-color:                $gray-900 !default;\n$body-bg:                   $white !default;\n\n$body-secondary-color:      rgba($body-color, .75) !default;\n$body-secondary-bg:         $gray-200 !default;\n\n$body-tertiary-color:       rgba($body-color, .5) !default;\n$body-tertiary-bg:          $gray-100 !default;\n\n$body-emphasis-color:       $black !default;\n\n// Links\n//\n// Style anchor elements.\n\n$link-color:                              $primary !default;\n$link-decoration:                         underline !default;\n$link-shade-percentage:                   20% !default;\n$link-hover-color:                        shift-color($link-color, $link-shade-percentage) !default;\n$link-hover-decoration:                   null !default;\n\n$stretched-link-pseudo-element:           after !default;\n$stretched-link-z-index:                  1 !default;\n\n// Icon links\n// scss-docs-start icon-link-variables\n$icon-link-gap:               .375rem !default;\n$icon-link-underline-offset:  .25em !default;\n$icon-link-icon-size:         1em !default;\n$icon-link-icon-transition:   .2s ease-in-out transform !default;\n$icon-link-icon-transform:    translate3d(.25em, 0, 0) !default;\n// scss-docs-end icon-link-variables\n\n// Paragraphs\n//\n// Style p element.\n\n$paragraph-margin-bottom:   1rem !default;\n\n\n// Grid breakpoints\n//\n// Define the minimum dimensions at which your layout will change,\n// adapting to different screen sizes, for use in media queries.\n\n// scss-docs-start grid-breakpoints\n$grid-breakpoints: (\n  xs: 0,\n  sm: 576px,\n  md: 768px,\n  lg: 992px,\n  xl: 1200px,\n  xxl: 1400px\n) !default;\n// scss-docs-end grid-breakpoints\n\n@include _assert-ascending($grid-breakpoints, \"$grid-breakpoints\");\n@include _assert-starts-at-zero($grid-breakpoints, \"$grid-breakpoints\");\n\n\n// Grid containers\n//\n// Define the maximum width of `.container` for different screen sizes.\n\n// scss-docs-start container-max-widths\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1140px,\n  xxl: 1320px\n) !default;\n// scss-docs-end container-max-widths\n\n@include _assert-ascending($container-max-widths, \"$container-max-widths\");\n\n\n// Grid columns\n//\n// Set the number of columns and specify the width of the gutters.\n\n$grid-columns:                12 !default;\n$grid-gutter-width:           1.5rem !default;\n$grid-row-columns:            6 !default;\n\n// Container padding\n\n$container-padding-x: $grid-gutter-width !default;\n\n\n// Components\n//\n// Define common padding and border radius sizes and more.\n\n// scss-docs-start border-variables\n$border-width:                1px !default;\n$border-widths: (\n  1: 1px,\n  2: 2px,\n  3: 3px,\n  4: 4px,\n  5: 5px\n) !default;\n$border-style:                solid !default;\n$border-color:                $gray-300 !default;\n$border-color-translucent:    rgba($black, .175) !default;\n// scss-docs-end border-variables\n\n// scss-docs-start border-radius-variables\n$border-radius:               .375rem !default;\n$border-radius-sm:            .25rem !default;\n$border-radius-lg:            .5rem !default;\n$border-radius-xl:            1rem !default;\n$border-radius-xxl:           2rem !default;\n$border-radius-pill:          50rem !default;\n// scss-docs-end border-radius-variables\n// fusv-disable\n$border-radius-2xl:           $border-radius-xxl !default; // Deprecated in v5.3.0\n// fusv-enable\n\n// scss-docs-start box-shadow-variables\n$box-shadow:                  0 .5rem 1rem rgba($black, .15) !default;\n$box-shadow-sm:               0 .125rem .25rem rgba($black, .075) !default;\n$box-shadow-lg:               0 1rem 3rem rgba($black, .175) !default;\n$box-shadow-inset:            inset 0 1px 2px rgba($black, .075) !default;\n// scss-docs-end box-shadow-variables\n\n$component-active-color:      $white !default;\n$component-active-bg:         $primary !default;\n\n// scss-docs-start focus-ring-variables\n$focus-ring-width:      .25rem !default;\n$focus-ring-opacity:    .25 !default;\n$focus-ring-color:      rgba($primary, $focus-ring-opacity) !default;\n$focus-ring-blur:       0 !default;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color !default;\n// scss-docs-end focus-ring-variables\n\n// scss-docs-start caret-variables\n$caret-width:                 .3em !default;\n$caret-vertical-align:        $caret-width * .85 !default;\n$caret-spacing:               $caret-width * .85 !default;\n// scss-docs-end caret-variables\n\n$transition-base:             all .2s ease-in-out !default;\n$transition-fade:             opacity .15s linear !default;\n// scss-docs-start collapse-transition\n$transition-collapse:         height .35s ease !default;\n$transition-collapse-width:   width .35s ease !default;\n// scss-docs-end collapse-transition\n\n// stylelint-disable function-disallowed-list\n// scss-docs-start aspect-ratios\n$aspect-ratios: (\n  \"1x1\": 100%,\n  \"4x3\": calc(3 / 4 * 100%),\n  \"16x9\": calc(9 / 16 * 100%),\n  \"21x9\": calc(9 / 21 * 100%)\n) !default;\n// scss-docs-end aspect-ratios\n// stylelint-enable function-disallowed-list\n\n// Typography\n//\n// Font, line-height, and color for body text, headings, and more.\n\n// scss-docs-start font-variables\n// stylelint-disable value-keyword-case\n$font-family-sans-serif:      system-ui, -apple-system, \"Segoe UI\", Roboto, \"Helvetica Neue\", \"Noto Sans\", \"Liberation Sans\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\" !default;\n$font-family-monospace:       SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace !default;\n// stylelint-enable value-keyword-case\n$font-family-base:            var(--#{$prefix}font-sans-serif) !default;\n$font-family-code:            var(--#{$prefix}font-monospace) !default;\n\n// $font-size-root affects the value of `rem`, which is used for as well font sizes, paddings, and margins\n// $font-size-base affects the font size of the body text\n$font-size-root:              null !default;\n$font-size-base:              1rem !default; // Assumes the browser default, typically `16px`\n$font-size-sm:                $font-size-base * .875 !default;\n$font-size-lg:                $font-size-base * 1.25 !default;\n\n$font-weight-lighter:         lighter !default;\n$font-weight-light:           300 !default;\n$font-weight-normal:          400 !default;\n$font-weight-medium:          500 !default;\n$font-weight-semibold:        600 !default;\n$font-weight-bold:            700 !default;\n$font-weight-bolder:          bolder !default;\n\n$font-weight-base:            $font-weight-normal !default;\n\n$line-height-base:            1.5 !default;\n$line-height-sm:              1.25 !default;\n$line-height-lg:              2 !default;\n\n$h1-font-size:                $font-size-base * 2.5 !default;\n$h2-font-size:                $font-size-base * 2 !default;\n$h3-font-size:                $font-size-base * 1.75 !default;\n$h4-font-size:                $font-size-base * 1.5 !default;\n$h5-font-size:                $font-size-base * 1.25 !default;\n$h6-font-size:                $font-size-base !default;\n// scss-docs-end font-variables\n\n// scss-docs-start font-sizes\n$font-sizes: (\n  1: $h1-font-size,\n  2: $h2-font-size,\n  3: $h3-font-size,\n  4: $h4-font-size,\n  5: $h5-font-size,\n  6: $h6-font-size\n) !default;\n// scss-docs-end font-sizes\n\n// scss-docs-start headings-variables\n$headings-margin-bottom:      $spacer * .5 !default;\n$headings-font-family:        null !default;\n$headings-font-style:         null !default;\n$headings-font-weight:        500 !default;\n$headings-line-height:        1.2 !default;\n$headings-color:              inherit !default;\n// scss-docs-end headings-variables\n\n// scss-docs-start display-headings\n$display-font-sizes: (\n  1: 5rem,\n  2: 4.5rem,\n  3: 4rem,\n  4: 3.5rem,\n  5: 3rem,\n  6: 2.5rem\n) !default;\n\n$display-font-family: null !default;\n$display-font-style:  null !default;\n$display-font-weight: 300 !default;\n$display-line-height: $headings-line-height !default;\n// scss-docs-end display-headings\n\n// scss-docs-start type-variables\n$lead-font-size:              $font-size-base * 1.25 !default;\n$lead-font-weight:            300 !default;\n\n$small-font-size:             .875em !default;\n\n$sub-sup-font-size:           .75em !default;\n\n// fusv-disable\n$text-muted:                  var(--#{$prefix}secondary-color) !default; // Deprecated in 5.3.0\n// fusv-enable\n\n$initialism-font-size:        $small-font-size !default;\n\n$blockquote-margin-y:         $spacer !default;\n$blockquote-font-size:        $font-size-base * 1.25 !default;\n$blockquote-footer-color:     $gray-600 !default;\n$blockquote-footer-font-size: $small-font-size !default;\n\n$hr-margin-y:                 $spacer !default;\n$hr-color:                    inherit !default;\n\n// fusv-disable\n$hr-bg-color:                 null !default; // Deprecated in v5.2.0\n$hr-height:                   null !default; // Deprecated in v5.2.0\n// fusv-enable\n\n$hr-border-color:             null !default; // Allows for inherited colors\n$hr-border-width:             var(--#{$prefix}border-width) !default;\n$hr-opacity:                  .25 !default;\n\n// scss-docs-start vr-variables\n$vr-border-width:             var(--#{$prefix}border-width) !default;\n// scss-docs-end vr-variables\n\n$legend-margin-bottom:        .5rem !default;\n$legend-font-size:            1.5rem !default;\n$legend-font-weight:          null !default;\n\n$dt-font-weight:              $font-weight-bold !default;\n\n$list-inline-padding:         .5rem !default;\n\n$mark-padding:                .1875em !default;\n$mark-color:                  $body-color !default;\n$mark-bg:                     $yellow-100 !default;\n// scss-docs-end type-variables\n\n\n// Tables\n//\n// Customizes the `.table` component with basic values, each used across all table variations.\n\n// scss-docs-start table-variables\n$table-cell-padding-y:        .5rem !default;\n$table-cell-padding-x:        .5rem !default;\n$table-cell-padding-y-sm:     .25rem !default;\n$table-cell-padding-x-sm:     .25rem !default;\n\n$table-cell-vertical-align:   top !default;\n\n$table-color:                 var(--#{$prefix}emphasis-color) !default;\n$table-bg:                    var(--#{$prefix}body-bg) !default;\n$table-accent-bg:             transparent !default;\n\n$table-th-font-weight:        null !default;\n\n$table-striped-color:         $table-color !default;\n$table-striped-bg-factor:     .05 !default;\n$table-striped-bg:            rgba(var(--#{$prefix}emphasis-color-rgb), $table-striped-bg-factor) !default;\n\n$table-active-color:          $table-color !default;\n$table-active-bg-factor:      .1 !default;\n$table-active-bg:             rgba(var(--#{$prefix}emphasis-color-rgb), $table-active-bg-factor) !default;\n\n$table-hover-color:           $table-color !default;\n$table-hover-bg-factor:       .075 !default;\n$table-hover-bg:              rgba(var(--#{$prefix}emphasis-color-rgb), $table-hover-bg-factor) !default;\n\n$table-border-factor:         .2 !default;\n$table-border-width:          var(--#{$prefix}border-width) !default;\n$table-border-color:          var(--#{$prefix}border-color) !default;\n\n$table-striped-order:         odd !default;\n$table-striped-columns-order: even !default;\n\n$table-group-separator-color: currentcolor !default;\n\n$table-caption-color:         var(--#{$prefix}secondary-color) !default;\n\n$table-bg-scale:              -80% !default;\n// scss-docs-end table-variables\n\n// scss-docs-start table-loop\n$table-variants: (\n  \"primary\":    shift-color($primary, $table-bg-scale),\n  \"secondary\":  shift-color($secondary, $table-bg-scale),\n  \"success\":    shift-color($success, $table-bg-scale),\n  \"info\":       shift-color($info, $table-bg-scale),\n  \"warning\":    shift-color($warning, $table-bg-scale),\n  \"danger\":     shift-color($danger, $table-bg-scale),\n  \"light\":      $light,\n  \"dark\":       $dark,\n) !default;\n// scss-docs-end table-loop\n\n\n// Buttons + Forms\n//\n// Shared variables that are reassigned to `$input-` and `$btn-` specific variables.\n\n// scss-docs-start input-btn-variables\n$input-btn-padding-y:         .375rem !default;\n$input-btn-padding-x:         .75rem !default;\n$input-btn-font-family:       null !default;\n$input-btn-font-size:         $font-size-base !default;\n$input-btn-line-height:       $line-height-base !default;\n\n$input-btn-focus-width:         $focus-ring-width !default;\n$input-btn-focus-color-opacity: $focus-ring-opacity !default;\n$input-btn-focus-color:         $focus-ring-color !default;\n$input-btn-focus-blur:          $focus-ring-blur !default;\n$input-btn-focus-box-shadow:    $focus-ring-box-shadow !default;\n\n$input-btn-padding-y-sm:      .25rem !default;\n$input-btn-padding-x-sm:      .5rem !default;\n$input-btn-font-size-sm:      $font-size-sm !default;\n\n$input-btn-padding-y-lg:      .5rem !default;\n$input-btn-padding-x-lg:      1rem !default;\n$input-btn-font-size-lg:      $font-size-lg !default;\n\n$input-btn-border-width:      var(--#{$prefix}border-width) !default;\n// scss-docs-end input-btn-variables\n\n\n// Buttons\n//\n// For each of Bootstrap's buttons, define text, background, and border color.\n\n// scss-docs-start btn-variables\n$btn-color:                   var(--#{$prefix}body-color) !default;\n$btn-padding-y:               $input-btn-padding-y !default;\n$btn-padding-x:               $input-btn-padding-x !default;\n$btn-font-family:             $input-btn-font-family !default;\n$btn-font-size:               $input-btn-font-size !default;\n$btn-line-height:             $input-btn-line-height !default;\n$btn-white-space:             null !default; // Set to `nowrap` to prevent text wrapping\n\n$btn-padding-y-sm:            $input-btn-padding-y-sm !default;\n$btn-padding-x-sm:            $input-btn-padding-x-sm !default;\n$btn-font-size-sm:            $input-btn-font-size-sm !default;\n\n$btn-padding-y-lg:            $input-btn-padding-y-lg !default;\n$btn-padding-x-lg:            $input-btn-padding-x-lg !default;\n$btn-font-size-lg:            $input-btn-font-size-lg !default;\n\n$btn-border-width:            $input-btn-border-width !default;\n\n$btn-font-weight:             $font-weight-normal !default;\n$btn-box-shadow:              inset 0 1px 0 rgba($white, .15), 0 1px 1px rgba($black, .075) !default;\n$btn-focus-width:             $input-btn-focus-width !default;\n$btn-focus-box-shadow:        $input-btn-focus-box-shadow !default;\n$btn-disabled-opacity:        .65 !default;\n$btn-active-box-shadow:       inset 0 3px 5px rgba($black, .125) !default;\n\n$btn-link-color:              var(--#{$prefix}link-color) !default;\n$btn-link-hover-color:        var(--#{$prefix}link-hover-color) !default;\n$btn-link-disabled-color:     $gray-600 !default;\n$btn-link-focus-shadow-rgb:   to-rgb(mix(color-contrast($link-color), $link-color, 15%)) !default;\n\n// Allows for customizing button radius independently from global border radius\n$btn-border-radius:           var(--#{$prefix}border-radius) !default;\n$btn-border-radius-sm:        var(--#{$prefix}border-radius-sm) !default;\n$btn-border-radius-lg:        var(--#{$prefix}border-radius-lg) !default;\n\n$btn-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$btn-hover-bg-shade-amount:       15% !default;\n$btn-hover-bg-tint-amount:        15% !default;\n$btn-hover-border-shade-amount:   20% !default;\n$btn-hover-border-tint-amount:    10% !default;\n$btn-active-bg-shade-amount:      20% !default;\n$btn-active-bg-tint-amount:       20% !default;\n$btn-active-border-shade-amount:  25% !default;\n$btn-active-border-tint-amount:   10% !default;\n// scss-docs-end btn-variables\n\n\n// Forms\n\n// scss-docs-start form-text-variables\n$form-text-margin-top:                  .25rem !default;\n$form-text-font-size:                   $small-font-size !default;\n$form-text-font-style:                  null !default;\n$form-text-font-weight:                 null !default;\n$form-text-color:                       var(--#{$prefix}secondary-color) !default;\n// scss-docs-end form-text-variables\n\n// scss-docs-start form-label-variables\n$form-label-margin-bottom:              .5rem !default;\n$form-label-font-size:                  null !default;\n$form-label-font-style:                 null !default;\n$form-label-font-weight:                null !default;\n$form-label-color:                      null !default;\n// scss-docs-end form-label-variables\n\n// scss-docs-start form-input-variables\n$input-padding-y:                       $input-btn-padding-y !default;\n$input-padding-x:                       $input-btn-padding-x !default;\n$input-font-family:                     $input-btn-font-family !default;\n$input-font-size:                       $input-btn-font-size !default;\n$input-font-weight:                     $font-weight-base !default;\n$input-line-height:                     $input-btn-line-height !default;\n\n$input-padding-y-sm:                    $input-btn-padding-y-sm !default;\n$input-padding-x-sm:                    $input-btn-padding-x-sm !default;\n$input-font-size-sm:                    $input-btn-font-size-sm !default;\n\n$input-padding-y-lg:                    $input-btn-padding-y-lg !default;\n$input-padding-x-lg:                    $input-btn-padding-x-lg !default;\n$input-font-size-lg:                    $input-btn-font-size-lg !default;\n\n$input-bg:                              var(--#{$prefix}body-bg) !default;\n$input-disabled-color:                  null !default;\n$input-disabled-bg:                     var(--#{$prefix}secondary-bg) !default;\n$input-disabled-border-color:           null !default;\n\n$input-color:                           var(--#{$prefix}body-color) !default;\n$input-border-color:                    var(--#{$prefix}border-color) !default;\n$input-border-width:                    $input-btn-border-width !default;\n$input-box-shadow:                      var(--#{$prefix}box-shadow-inset) !default;\n\n$input-border-radius:                   var(--#{$prefix}border-radius) !default;\n$input-border-radius-sm:                var(--#{$prefix}border-radius-sm) !default;\n$input-border-radius-lg:                var(--#{$prefix}border-radius-lg) !default;\n\n$input-focus-bg:                        $input-bg !default;\n$input-focus-border-color:              tint-color($component-active-bg, 50%) !default;\n$input-focus-color:                     $input-color !default;\n$input-focus-width:                     $input-btn-focus-width !default;\n$input-focus-box-shadow:                $input-btn-focus-box-shadow !default;\n\n$input-placeholder-color:               var(--#{$prefix}secondary-color) !default;\n$input-plaintext-color:                 var(--#{$prefix}body-color) !default;\n\n$input-height-border:                   calc(#{$input-border-width} * 2) !default; // stylelint-disable-line function-disallowed-list\n\n$input-height-inner:                    add($input-line-height * 1em, $input-padding-y * 2) !default;\n$input-height-inner-half:               add($input-line-height * .5em, $input-padding-y) !default;\n$input-height-inner-quarter:            add($input-line-height * .25em, $input-padding-y * .5) !default;\n\n$input-height:                          add($input-line-height * 1em, add($input-padding-y * 2, $input-height-border, false)) !default;\n$input-height-sm:                       add($input-line-height * 1em, add($input-padding-y-sm * 2, $input-height-border, false)) !default;\n$input-height-lg:                       add($input-line-height * 1em, add($input-padding-y-lg * 2, $input-height-border, false)) !default;\n\n$input-transition:                      border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$form-color-width:                      3rem !default;\n// scss-docs-end form-input-variables\n\n// scss-docs-start form-check-variables\n$form-check-input-width:                  1em !default;\n$form-check-min-height:                   $font-size-base * $line-height-base !default;\n$form-check-padding-start:                $form-check-input-width + .5em !default;\n$form-check-margin-bottom:                .125rem !default;\n$form-check-label-color:                  null !default;\n$form-check-label-cursor:                 null !default;\n$form-check-transition:                   null !default;\n\n$form-check-input-active-filter:          brightness(90%) !default;\n\n$form-check-input-bg:                     $input-bg !default;\n$form-check-input-border:                 var(--#{$prefix}border-width) solid var(--#{$prefix}border-color) !default;\n$form-check-input-border-radius:          .25em !default;\n$form-check-radio-border-radius:          50% !default;\n$form-check-input-focus-border:           $input-focus-border-color !default;\n$form-check-input-focus-box-shadow:       $focus-ring-box-shadow !default;\n\n$form-check-input-checked-color:          $component-active-color !default;\n$form-check-input-checked-bg-color:       $component-active-bg !default;\n$form-check-input-checked-border-color:   $form-check-input-checked-bg-color !default;\n$form-check-input-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-checked-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/></svg>\") !default;\n$form-check-radio-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='2' fill='#{$form-check-input-checked-color}'/></svg>\") !default;\n\n$form-check-input-indeterminate-color:          $component-active-color !default;\n$form-check-input-indeterminate-bg-color:       $component-active-bg !default;\n$form-check-input-indeterminate-border-color:   $form-check-input-indeterminate-bg-color !default;\n$form-check-input-indeterminate-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-indeterminate-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/></svg>\") !default;\n\n$form-check-input-disabled-opacity:        .5 !default;\n$form-check-label-disabled-opacity:        $form-check-input-disabled-opacity !default;\n$form-check-btn-check-disabled-opacity:    $btn-disabled-opacity !default;\n\n$form-check-inline-margin-end:    1rem !default;\n// scss-docs-end form-check-variables\n\n// scss-docs-start form-switch-variables\n$form-switch-color:               rgba($black, .25) !default;\n$form-switch-width:               2em !default;\n$form-switch-padding-start:       $form-switch-width + .5em !default;\n$form-switch-bg-image:            url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-color}'/></svg>\") !default;\n$form-switch-border-radius:       $form-switch-width !default;\n$form-switch-transition:          background-position .15s ease-in-out !default;\n\n$form-switch-focus-color:         $input-focus-border-color !default;\n$form-switch-focus-bg-image:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-focus-color}'/></svg>\") !default;\n\n$form-switch-checked-color:       $component-active-color !default;\n$form-switch-checked-bg-image:    url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-checked-color}'/></svg>\") !default;\n$form-switch-checked-bg-position: right center !default;\n// scss-docs-end form-switch-variables\n\n// scss-docs-start input-group-variables\n$input-group-addon-padding-y:           $input-padding-y !default;\n$input-group-addon-padding-x:           $input-padding-x !default;\n$input-group-addon-font-weight:         $input-font-weight !default;\n$input-group-addon-color:               $input-color !default;\n$input-group-addon-bg:                  var(--#{$prefix}tertiary-bg) !default;\n$input-group-addon-border-color:        $input-border-color !default;\n// scss-docs-end input-group-variables\n\n// scss-docs-start form-select-variables\n$form-select-padding-y:             $input-padding-y !default;\n$form-select-padding-x:             $input-padding-x !default;\n$form-select-font-family:           $input-font-family !default;\n$form-select-font-size:             $input-font-size !default;\n$form-select-indicator-padding:     $form-select-padding-x * 3 !default; // Extra padding for background-image\n$form-select-font-weight:           $input-font-weight !default;\n$form-select-line-height:           $input-line-height !default;\n$form-select-color:                 $input-color !default;\n$form-select-bg:                    $input-bg !default;\n$form-select-disabled-color:        null !default;\n$form-select-disabled-bg:           $input-disabled-bg !default;\n$form-select-disabled-border-color: $input-disabled-border-color !default;\n$form-select-bg-position:           right $form-select-padding-x center !default;\n$form-select-bg-size:               16px 12px !default; // In pixels because image dimensions\n$form-select-indicator-color:       $gray-800 !default;\n$form-select-indicator:             url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'><path fill='none' stroke='#{$form-select-indicator-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/></svg>\") !default;\n\n$form-select-feedback-icon-padding-end: $form-select-padding-x * 2.5 + $form-select-indicator-padding !default;\n$form-select-feedback-icon-position:    center right $form-select-indicator-padding !default;\n$form-select-feedback-icon-size:        $input-height-inner-half $input-height-inner-half !default;\n\n$form-select-border-width:        $input-border-width !default;\n$form-select-border-color:        $input-border-color !default;\n$form-select-border-radius:       $input-border-radius !default;\n$form-select-box-shadow:          var(--#{$prefix}box-shadow-inset) !default;\n\n$form-select-focus-border-color:  $input-focus-border-color !default;\n$form-select-focus-width:         $input-focus-width !default;\n$form-select-focus-box-shadow:    0 0 0 $form-select-focus-width $input-btn-focus-color !default;\n\n$form-select-padding-y-sm:        $input-padding-y-sm !default;\n$form-select-padding-x-sm:        $input-padding-x-sm !default;\n$form-select-font-size-sm:        $input-font-size-sm !default;\n$form-select-border-radius-sm:    $input-border-radius-sm !default;\n\n$form-select-padding-y-lg:        $input-padding-y-lg !default;\n$form-select-padding-x-lg:        $input-padding-x-lg !default;\n$form-select-font-size-lg:        $input-font-size-lg !default;\n$form-select-border-radius-lg:    $input-border-radius-lg !default;\n\n$form-select-transition:          $input-transition !default;\n// scss-docs-end form-select-variables\n\n// scss-docs-start form-range-variables\n$form-range-track-width:          100% !default;\n$form-range-track-height:         .5rem !default;\n$form-range-track-cursor:         pointer !default;\n$form-range-track-bg:             var(--#{$prefix}secondary-bg) !default;\n$form-range-track-border-radius:  1rem !default;\n$form-range-track-box-shadow:     var(--#{$prefix}box-shadow-inset) !default;\n\n$form-range-thumb-width:                   1rem !default;\n$form-range-thumb-height:                  $form-range-thumb-width !default;\n$form-range-thumb-bg:                      $component-active-bg !default;\n$form-range-thumb-border:                  0 !default;\n$form-range-thumb-border-radius:           1rem !default;\n$form-range-thumb-box-shadow:              0 .1rem .25rem rgba($black, .1) !default;\n$form-range-thumb-focus-box-shadow:        0 0 0 1px $body-bg, $input-focus-box-shadow !default;\n$form-range-thumb-focus-box-shadow-width:  $input-focus-width !default; // For focus box shadow issue in Edge\n$form-range-thumb-active-bg:               tint-color($component-active-bg, 70%) !default;\n$form-range-thumb-disabled-bg:             var(--#{$prefix}secondary-color) !default;\n$form-range-thumb-transition:              background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n// scss-docs-end form-range-variables\n\n// scss-docs-start form-file-variables\n$form-file-button-color:          $input-color !default;\n$form-file-button-bg:             var(--#{$prefix}tertiary-bg) !default;\n$form-file-button-hover-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end form-file-variables\n\n// scss-docs-start form-floating-variables\n$form-floating-height:                  add(3.5rem, $input-height-border) !default;\n$form-floating-line-height:             1.25 !default;\n$form-floating-padding-x:               $input-padding-x !default;\n$form-floating-padding-y:               1rem !default;\n$form-floating-input-padding-t:         1.625rem !default;\n$form-floating-input-padding-b:         .625rem !default;\n$form-floating-label-height:            1.5em !default;\n$form-floating-label-opacity:           .65 !default;\n$form-floating-label-transform:         scale(.85) translateY(-.5rem) translateX(.15rem) !default;\n$form-floating-label-disabled-color:    $gray-600 !default;\n$form-floating-transition:              opacity .1s ease-in-out, transform .1s ease-in-out !default;\n// scss-docs-end form-floating-variables\n\n// Form validation\n\n// scss-docs-start form-feedback-variables\n$form-feedback-margin-top:          $form-text-margin-top !default;\n$form-feedback-font-size:           $form-text-font-size !default;\n$form-feedback-font-style:          $form-text-font-style !default;\n$form-feedback-valid-color:         $success !default;\n$form-feedback-invalid-color:       $danger !default;\n\n$form-feedback-icon-valid-color:    $form-feedback-valid-color !default;\n$form-feedback-icon-valid:          url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'><path fill='#{$form-feedback-icon-valid-color}' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/></svg>\") !default;\n$form-feedback-icon-invalid-color:  $form-feedback-invalid-color !default;\n$form-feedback-icon-invalid:        url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='#{$form-feedback-icon-invalid-color}'><circle cx='6' cy='6' r='4.5'/><path stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/><circle cx='6' cy='8.2' r='.6' fill='#{$form-feedback-icon-invalid-color}' stroke='none'/></svg>\") !default;\n// scss-docs-end form-feedback-variables\n\n// scss-docs-start form-validation-colors\n$form-valid-color:                  $form-feedback-valid-color !default;\n$form-valid-border-color:           $form-feedback-valid-color !default;\n$form-invalid-color:                $form-feedback-invalid-color !default;\n$form-invalid-border-color:         $form-feedback-invalid-color !default;\n// scss-docs-end form-validation-colors\n\n// scss-docs-start form-validation-states\n$form-validation-states: (\n  \"valid\": (\n    \"color\": var(--#{$prefix}form-valid-color),\n    \"icon\": $form-feedback-icon-valid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}success),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}success-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-valid-border-color),\n  ),\n  \"invalid\": (\n    \"color\": var(--#{$prefix}form-invalid-color),\n    \"icon\": $form-feedback-icon-invalid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}danger),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}danger-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-invalid-border-color),\n  )\n) !default;\n// scss-docs-end form-validation-states\n\n// Z-index master list\n//\n// Warning: Avoid customizing these values. They're used for a bird's eye view\n// of components dependent on the z-axis and are designed to all work together.\n\n// scss-docs-start zindex-stack\n$zindex-dropdown:                   1000 !default;\n$zindex-sticky:                     1020 !default;\n$zindex-fixed:                      1030 !default;\n$zindex-offcanvas-backdrop:         1040 !default;\n$zindex-offcanvas:                  1045 !default;\n$zindex-modal-backdrop:             1050 !default;\n$zindex-modal:                      1055 !default;\n$zindex-popover:                    1070 !default;\n$zindex-tooltip:                    1080 !default;\n$zindex-toast:                      1090 !default;\n// scss-docs-end zindex-stack\n\n// scss-docs-start zindex-levels-map\n$zindex-levels: (\n  n1: -1,\n  0: 0,\n  1: 1,\n  2: 2,\n  3: 3\n) !default;\n// scss-docs-end zindex-levels-map\n\n\n// Navs\n\n// scss-docs-start nav-variables\n$nav-link-padding-y:                .5rem !default;\n$nav-link-padding-x:                1rem !default;\n$nav-link-font-size:                null !default;\n$nav-link-font-weight:              null !default;\n$nav-link-color:                    var(--#{$prefix}link-color) !default;\n$nav-link-hover-color:              var(--#{$prefix}link-hover-color) !default;\n$nav-link-transition:               color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out !default;\n$nav-link-disabled-color:           var(--#{$prefix}secondary-color) !default;\n$nav-link-focus-box-shadow:         $focus-ring-box-shadow !default;\n\n$nav-tabs-border-color:             var(--#{$prefix}border-color) !default;\n$nav-tabs-border-width:             var(--#{$prefix}border-width) !default;\n$nav-tabs-border-radius:            var(--#{$prefix}border-radius) !default;\n$nav-tabs-link-hover-border-color:  var(--#{$prefix}secondary-bg) var(--#{$prefix}secondary-bg) $nav-tabs-border-color !default;\n$nav-tabs-link-active-color:        var(--#{$prefix}emphasis-color) !default;\n$nav-tabs-link-active-bg:           var(--#{$prefix}body-bg) !default;\n$nav-tabs-link-active-border-color: var(--#{$prefix}border-color) var(--#{$prefix}border-color) $nav-tabs-link-active-bg !default;\n\n$nav-pills-border-radius:           var(--#{$prefix}border-radius) !default;\n$nav-pills-link-active-color:       $component-active-color !default;\n$nav-pills-link-active-bg:          $component-active-bg !default;\n\n$nav-underline-gap:                 1rem !default;\n$nav-underline-border-width:        .125rem !default;\n$nav-underline-link-active-color:   var(--#{$prefix}emphasis-color) !default;\n// scss-docs-end nav-variables\n\n\n// Navbar\n\n// scss-docs-start navbar-variables\n$navbar-padding-y:                  $spacer * .5 !default;\n$navbar-padding-x:                  null !default;\n\n$navbar-nav-link-padding-x:         .5rem !default;\n\n$navbar-brand-font-size:            $font-size-lg !default;\n// Compute the navbar-brand padding-y so the navbar-brand will have the same height as navbar-text and nav-link\n$nav-link-height:                   $font-size-base * $line-height-base + $nav-link-padding-y * 2 !default;\n$navbar-brand-height:               $navbar-brand-font-size * $line-height-base !default;\n$navbar-brand-padding-y:            ($nav-link-height - $navbar-brand-height) * .5 !default;\n$navbar-brand-margin-end:           1rem !default;\n\n$navbar-toggler-padding-y:          .25rem !default;\n$navbar-toggler-padding-x:          .75rem !default;\n$navbar-toggler-font-size:          $font-size-lg !default;\n$navbar-toggler-border-radius:      $btn-border-radius !default;\n$navbar-toggler-focus-width:        $btn-focus-width !default;\n$navbar-toggler-transition:         box-shadow .15s ease-in-out !default;\n\n$navbar-light-color:                rgba(var(--#{$prefix}emphasis-color-rgb), .65) !default;\n$navbar-light-hover-color:          rgba(var(--#{$prefix}emphasis-color-rgb), .8) !default;\n$navbar-light-active-color:         rgba(var(--#{$prefix}emphasis-color-rgb), 1) !default;\n$navbar-light-disabled-color:       rgba(var(--#{$prefix}emphasis-color-rgb), .3) !default;\n$navbar-light-icon-color:           rgba($body-color, .75) !default;\n$navbar-light-toggler-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-light-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-light-toggler-border-color: rgba(var(--#{$prefix}emphasis-color-rgb), .15) !default;\n$navbar-light-brand-color:          $navbar-light-active-color !default;\n$navbar-light-brand-hover-color:    $navbar-light-active-color !default;\n// scss-docs-end navbar-variables\n\n// scss-docs-start navbar-dark-variables\n$navbar-dark-color:                 rgba($white, .55) !default;\n$navbar-dark-hover-color:           rgba($white, .75) !default;\n$navbar-dark-active-color:          $white !default;\n$navbar-dark-disabled-color:        rgba($white, .25) !default;\n$navbar-dark-icon-color:            $navbar-dark-color !default;\n$navbar-dark-toggler-icon-bg:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-dark-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-dark-toggler-border-color:  rgba($white, .1) !default;\n$navbar-dark-brand-color:           $navbar-dark-active-color !default;\n$navbar-dark-brand-hover-color:     $navbar-dark-active-color !default;\n// scss-docs-end navbar-dark-variables\n\n\n// Dropdowns\n//\n// Dropdown menu container and contents.\n\n// scss-docs-start dropdown-variables\n$dropdown-min-width:                10rem !default;\n$dropdown-padding-x:                0 !default;\n$dropdown-padding-y:                .5rem !default;\n$dropdown-spacer:                   .125rem !default;\n$dropdown-font-size:                $font-size-base !default;\n$dropdown-color:                    var(--#{$prefix}body-color) !default;\n$dropdown-bg:                       var(--#{$prefix}body-bg) !default;\n$dropdown-border-color:             var(--#{$prefix}border-color-translucent) !default;\n$dropdown-border-radius:            var(--#{$prefix}border-radius) !default;\n$dropdown-border-width:             var(--#{$prefix}border-width) !default;\n$dropdown-inner-border-radius:      calc(#{$dropdown-border-radius} - #{$dropdown-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$dropdown-divider-bg:               $dropdown-border-color !default;\n$dropdown-divider-margin-y:         $spacer * .5 !default;\n$dropdown-box-shadow:               var(--#{$prefix}box-shadow) !default;\n\n$dropdown-link-color:               var(--#{$prefix}body-color) !default;\n$dropdown-link-hover-color:         $dropdown-link-color !default;\n$dropdown-link-hover-bg:            var(--#{$prefix}tertiary-bg) !default;\n\n$dropdown-link-active-color:        $component-active-color !default;\n$dropdown-link-active-bg:           $component-active-bg !default;\n\n$dropdown-link-disabled-color:      var(--#{$prefix}tertiary-color) !default;\n\n$dropdown-item-padding-y:           $spacer * .25 !default;\n$dropdown-item-padding-x:           $spacer !default;\n\n$dropdown-header-color:             $gray-600 !default;\n$dropdown-header-padding-x:         $dropdown-item-padding-x !default;\n$dropdown-header-padding-y:         $dropdown-padding-y !default;\n// fusv-disable\n$dropdown-header-padding:           $dropdown-header-padding-y $dropdown-header-padding-x !default; // Deprecated in v5.2.0\n// fusv-enable\n// scss-docs-end dropdown-variables\n\n// scss-docs-start dropdown-dark-variables\n$dropdown-dark-color:               $gray-300 !default;\n$dropdown-dark-bg:                  $gray-800 !default;\n$dropdown-dark-border-color:        $dropdown-border-color !default;\n$dropdown-dark-divider-bg:          $dropdown-divider-bg !default;\n$dropdown-dark-box-shadow:          null !default;\n$dropdown-dark-link-color:          $dropdown-dark-color !default;\n$dropdown-dark-link-hover-color:    $white !default;\n$dropdown-dark-link-hover-bg:       rgba($white, .15) !default;\n$dropdown-dark-link-active-color:   $dropdown-link-active-color !default;\n$dropdown-dark-link-active-bg:      $dropdown-link-active-bg !default;\n$dropdown-dark-link-disabled-color: $gray-500 !default;\n$dropdown-dark-header-color:        $gray-500 !default;\n// scss-docs-end dropdown-dark-variables\n\n\n// Pagination\n\n// scss-docs-start pagination-variables\n$pagination-padding-y:              .375rem !default;\n$pagination-padding-x:              .75rem !default;\n$pagination-padding-y-sm:           .25rem !default;\n$pagination-padding-x-sm:           .5rem !default;\n$pagination-padding-y-lg:           .75rem !default;\n$pagination-padding-x-lg:           1.5rem !default;\n\n$pagination-font-size:              $font-size-base !default;\n\n$pagination-color:                  var(--#{$prefix}link-color) !default;\n$pagination-bg:                     var(--#{$prefix}body-bg) !default;\n$pagination-border-radius:          var(--#{$prefix}border-radius) !default;\n$pagination-border-width:           var(--#{$prefix}border-width) !default;\n$pagination-margin-start:           calc(#{$pagination-border-width} * -1) !default; // stylelint-disable-line function-disallowed-list\n$pagination-border-color:           var(--#{$prefix}border-color) !default;\n\n$pagination-focus-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-focus-bg:               var(--#{$prefix}secondary-bg) !default;\n$pagination-focus-box-shadow:       $focus-ring-box-shadow !default;\n$pagination-focus-outline:          0 !default;\n\n$pagination-hover-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$pagination-hover-border-color:     var(--#{$prefix}border-color) !default; // Todo in v6: remove this?\n\n$pagination-active-color:           $component-active-color !default;\n$pagination-active-bg:              $component-active-bg !default;\n$pagination-active-border-color:    $component-active-bg !default;\n\n$pagination-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$pagination-disabled-bg:            var(--#{$prefix}secondary-bg) !default;\n$pagination-disabled-border-color:  var(--#{$prefix}border-color) !default;\n\n$pagination-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$pagination-border-radius-sm:       var(--#{$prefix}border-radius-sm) !default;\n$pagination-border-radius-lg:       var(--#{$prefix}border-radius-lg) !default;\n// scss-docs-end pagination-variables\n\n\n// Placeholders\n\n// scss-docs-start placeholders\n$placeholder-opacity-max:           .5 !default;\n$placeholder-opacity-min:           .2 !default;\n// scss-docs-end placeholders\n\n// Cards\n\n// scss-docs-start card-variables\n$card-spacer-y:                     $spacer !default;\n$card-spacer-x:                     $spacer !default;\n$card-title-spacer-y:               $spacer * .5 !default;\n$card-title-color:                  null !default;\n$card-subtitle-color:               null !default;\n$card-border-width:                 var(--#{$prefix}border-width) !default;\n$card-border-color:                 var(--#{$prefix}border-color-translucent) !default;\n$card-border-radius:                var(--#{$prefix}border-radius) !default;\n$card-box-shadow:                   null !default;\n$card-inner-border-radius:          subtract($card-border-radius, $card-border-width) !default;\n$card-cap-padding-y:                $card-spacer-y * .5 !default;\n$card-cap-padding-x:                $card-spacer-x !default;\n$card-cap-bg:                       rgba(var(--#{$prefix}body-color-rgb), .03) !default;\n$card-cap-color:                    null !default;\n$card-height:                       null !default;\n$card-color:                        null !default;\n$card-bg:                           var(--#{$prefix}body-bg) !default;\n$card-img-overlay-padding:          $spacer !default;\n$card-group-margin:                 $grid-gutter-width * .5 !default;\n// scss-docs-end card-variables\n\n// Accordion\n\n// scss-docs-start accordion-variables\n$accordion-padding-y:                     1rem !default;\n$accordion-padding-x:                     1.25rem !default;\n$accordion-color:                         var(--#{$prefix}body-color) !default;\n$accordion-bg:                            var(--#{$prefix}body-bg) !default;\n$accordion-border-width:                  var(--#{$prefix}border-width) !default;\n$accordion-border-color:                  var(--#{$prefix}border-color) !default;\n$accordion-border-radius:                 var(--#{$prefix}border-radius) !default;\n$accordion-inner-border-radius:           subtract($accordion-border-radius, $accordion-border-width) !default;\n\n$accordion-body-padding-y:                $accordion-padding-y !default;\n$accordion-body-padding-x:                $accordion-padding-x !default;\n\n$accordion-button-padding-y:              $accordion-padding-y !default;\n$accordion-button-padding-x:              $accordion-padding-x !default;\n$accordion-button-color:                  var(--#{$prefix}body-color) !default;\n$accordion-button-bg:                     var(--#{$prefix}accordion-bg) !default;\n$accordion-transition:                    $btn-transition, border-radius .15s ease !default;\n$accordion-button-active-bg:              var(--#{$prefix}primary-bg-subtle) !default;\n$accordion-button-active-color:           var(--#{$prefix}primary-text-emphasis) !default;\n\n// fusv-disable\n$accordion-button-focus-border-color:     $input-focus-border-color !default; // Deprecated in v5.3.3\n// fusv-enable\n$accordion-button-focus-box-shadow:       $btn-focus-box-shadow !default;\n\n$accordion-icon-width:                    1.25rem !default;\n$accordion-icon-color:                    $body-color !default;\n$accordion-icon-active-color:             $primary-text-emphasis !default;\n$accordion-icon-transition:               transform .2s ease-in-out !default;\n$accordion-icon-transform:                rotate(-180deg) !default;\n\n$accordion-button-icon:         url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='#{$accordion-icon-color}' stroke-linecap='round' stroke-linejoin='round'><path d='M2 5L8 11L14 5'/></svg>\") !default;\n$accordion-button-active-icon:  url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='#{$accordion-icon-active-color}' stroke-linecap='round' stroke-linejoin='round'><path d='M2 5L8 11L14 5'/></svg>\") !default;\n// scss-docs-end accordion-variables\n\n// Tooltips\n\n// scss-docs-start tooltip-variables\n$tooltip-font-size:                 $font-size-sm !default;\n$tooltip-max-width:                 200px !default;\n$tooltip-color:                     var(--#{$prefix}body-bg) !default;\n$tooltip-bg:                        var(--#{$prefix}emphasis-color) !default;\n$tooltip-border-radius:             var(--#{$prefix}border-radius) !default;\n$tooltip-opacity:                   .9 !default;\n$tooltip-padding-y:                 $spacer * .25 !default;\n$tooltip-padding-x:                 $spacer * .5 !default;\n$tooltip-margin:                    null !default; // TODO: remove this in v6\n\n$tooltip-arrow-width:               .8rem !default;\n$tooltip-arrow-height:              .4rem !default;\n// fusv-disable\n$tooltip-arrow-color:               null !default; // Deprecated in Bootstrap 5.2.0 for CSS variables\n// fusv-enable\n// scss-docs-end tooltip-variables\n\n// Form tooltips must come after regular tooltips\n// scss-docs-start tooltip-feedback-variables\n$form-feedback-tooltip-padding-y:     $tooltip-padding-y !default;\n$form-feedback-tooltip-padding-x:     $tooltip-padding-x !default;\n$form-feedback-tooltip-font-size:     $tooltip-font-size !default;\n$form-feedback-tooltip-line-height:   null !default;\n$form-feedback-tooltip-opacity:       $tooltip-opacity !default;\n$form-feedback-tooltip-border-radius: $tooltip-border-radius !default;\n// scss-docs-end tooltip-feedback-variables\n\n\n// Popovers\n\n// scss-docs-start popover-variables\n$popover-font-size:                 $font-size-sm !default;\n$popover-bg:                        var(--#{$prefix}body-bg) !default;\n$popover-max-width:                 276px !default;\n$popover-border-width:              var(--#{$prefix}border-width) !default;\n$popover-border-color:              var(--#{$prefix}border-color-translucent) !default;\n$popover-border-radius:             var(--#{$prefix}border-radius-lg) !default;\n$popover-inner-border-radius:       calc(#{$popover-border-radius} - #{$popover-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$popover-box-shadow:                var(--#{$prefix}box-shadow) !default;\n\n$popover-header-font-size:          $font-size-base !default;\n$popover-header-bg:                 var(--#{$prefix}secondary-bg) !default;\n$popover-header-color:              $headings-color !default;\n$popover-header-padding-y:          .5rem !default;\n$popover-header-padding-x:          $spacer !default;\n\n$popover-body-color:                var(--#{$prefix}body-color) !default;\n$popover-body-padding-y:            $spacer !default;\n$popover-body-padding-x:            $spacer !default;\n\n$popover-arrow-width:               1rem !default;\n$popover-arrow-height:              .5rem !default;\n// scss-docs-end popover-variables\n\n// fusv-disable\n// Deprecated in Bootstrap 5.2.0 for CSS variables\n$popover-arrow-color:               $popover-bg !default;\n$popover-arrow-outer-color:         var(--#{$prefix}border-color-translucent) !default;\n// fusv-enable\n\n\n// Toasts\n\n// scss-docs-start toast-variables\n$toast-max-width:                   350px !default;\n$toast-padding-x:                   .75rem !default;\n$toast-padding-y:                   .5rem !default;\n$toast-font-size:                   .875rem !default;\n$toast-color:                       null !default;\n$toast-background-color:            rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-border-width:                var(--#{$prefix}border-width) !default;\n$toast-border-color:                var(--#{$prefix}border-color-translucent) !default;\n$toast-border-radius:               var(--#{$prefix}border-radius) !default;\n$toast-box-shadow:                  var(--#{$prefix}box-shadow) !default;\n$toast-spacing:                     $container-padding-x !default;\n\n$toast-header-color:                var(--#{$prefix}secondary-color) !default;\n$toast-header-background-color:     rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-header-border-color:         $toast-border-color !default;\n// scss-docs-end toast-variables\n\n\n// Badges\n\n// scss-docs-start badge-variables\n$badge-font-size:                   .75em !default;\n$badge-font-weight:                 $font-weight-bold !default;\n$badge-color:                       $white !default;\n$badge-padding-y:                   .35em !default;\n$badge-padding-x:                   .65em !default;\n$badge-border-radius:               var(--#{$prefix}border-radius) !default;\n// scss-docs-end badge-variables\n\n\n// Modals\n\n// scss-docs-start modal-variables\n$modal-inner-padding:               $spacer !default;\n\n$modal-footer-margin-between:       .5rem !default;\n\n$modal-dialog-margin:               .5rem !default;\n$modal-dialog-margin-y-sm-up:       1.75rem !default;\n\n$modal-title-line-height:           $line-height-base !default;\n\n$modal-content-color:               null !default;\n$modal-content-bg:                  var(--#{$prefix}body-bg) !default;\n$modal-content-border-color:        var(--#{$prefix}border-color-translucent) !default;\n$modal-content-border-width:        var(--#{$prefix}border-width) !default;\n$modal-content-border-radius:       var(--#{$prefix}border-radius-lg) !default;\n$modal-content-inner-border-radius: subtract($modal-content-border-radius, $modal-content-border-width) !default;\n$modal-content-box-shadow-xs:       var(--#{$prefix}box-shadow-sm) !default;\n$modal-content-box-shadow-sm-up:    var(--#{$prefix}box-shadow) !default;\n\n$modal-backdrop-bg:                 $black !default;\n$modal-backdrop-opacity:            .5 !default;\n\n$modal-header-border-color:         var(--#{$prefix}border-color) !default;\n$modal-header-border-width:         $modal-content-border-width !default;\n$modal-header-padding-y:            $modal-inner-padding !default;\n$modal-header-padding-x:            $modal-inner-padding !default;\n$modal-header-padding:              $modal-header-padding-y $modal-header-padding-x !default; // Keep this for backwards compatibility\n\n$modal-footer-bg:                   null !default;\n$modal-footer-border-color:         $modal-header-border-color !default;\n$modal-footer-border-width:         $modal-header-border-width !default;\n\n$modal-sm:                          300px !default;\n$modal-md:                          500px !default;\n$modal-lg:                          800px !default;\n$modal-xl:                          1140px !default;\n\n$modal-fade-transform:              translate(0, -50px) !default;\n$modal-show-transform:              none !default;\n$modal-transition:                  transform .3s ease-out !default;\n$modal-scale-transform:             scale(1.02) !default;\n// scss-docs-end modal-variables\n\n\n// Alerts\n//\n// Define alert colors, border radius, and padding.\n\n// scss-docs-start alert-variables\n$alert-padding-y:               $spacer !default;\n$alert-padding-x:               $spacer !default;\n$alert-margin-bottom:           1rem !default;\n$alert-border-radius:           var(--#{$prefix}border-radius) !default;\n$alert-link-font-weight:        $font-weight-bold !default;\n$alert-border-width:            var(--#{$prefix}border-width) !default;\n$alert-dismissible-padding-r:   $alert-padding-x * 3 !default; // 3x covers width of x plus default padding on either side\n// scss-docs-end alert-variables\n\n// fusv-disable\n$alert-bg-scale:                -80% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-border-scale:            -70% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-color-scale:             40% !default; // Deprecated in v5.2.0, to be removed in v6\n// fusv-enable\n\n// Progress bars\n\n// scss-docs-start progress-variables\n$progress-height:                   1rem !default;\n$progress-font-size:                $font-size-base * .75 !default;\n$progress-bg:                       var(--#{$prefix}secondary-bg) !default;\n$progress-border-radius:            var(--#{$prefix}border-radius) !default;\n$progress-box-shadow:               var(--#{$prefix}box-shadow-inset) !default;\n$progress-bar-color:                $white !default;\n$progress-bar-bg:                   $primary !default;\n$progress-bar-animation-timing:     1s linear infinite !default;\n$progress-bar-transition:           width .6s ease !default;\n// scss-docs-end progress-variables\n\n\n// List group\n\n// scss-docs-start list-group-variables\n$list-group-color:                  var(--#{$prefix}body-color) !default;\n$list-group-bg:                     var(--#{$prefix}body-bg) !default;\n$list-group-border-color:           var(--#{$prefix}border-color) !default;\n$list-group-border-width:           var(--#{$prefix}border-width) !default;\n$list-group-border-radius:          var(--#{$prefix}border-radius) !default;\n\n$list-group-item-padding-y:         $spacer * .5 !default;\n$list-group-item-padding-x:         $spacer !default;\n// fusv-disable\n$list-group-item-bg-scale:          -80% !default; // Deprecated in v5.3.0\n$list-group-item-color-scale:       40% !default; // Deprecated in v5.3.0\n// fusv-enable\n\n$list-group-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$list-group-active-color:           $component-active-color !default;\n$list-group-active-bg:              $component-active-bg !default;\n$list-group-active-border-color:    $list-group-active-bg !default;\n\n$list-group-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$list-group-disabled-bg:            $list-group-bg !default;\n\n$list-group-action-color:           var(--#{$prefix}secondary-color) !default;\n$list-group-action-hover-color:     var(--#{$prefix}emphasis-color) !default;\n\n$list-group-action-active-color:    var(--#{$prefix}body-color) !default;\n$list-group-action-active-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end list-group-variables\n\n\n// Image thumbnails\n\n// scss-docs-start thumbnail-variables\n$thumbnail-padding:                 .25rem !default;\n$thumbnail-bg:                      var(--#{$prefix}body-bg) !default;\n$thumbnail-border-width:            var(--#{$prefix}border-width) !default;\n$thumbnail-border-color:            var(--#{$prefix}border-color) !default;\n$thumbnail-border-radius:           var(--#{$prefix}border-radius) !default;\n$thumbnail-box-shadow:              var(--#{$prefix}box-shadow-sm) !default;\n// scss-docs-end thumbnail-variables\n\n\n// Figures\n\n// scss-docs-start figure-variables\n$figure-caption-font-size:          $small-font-size !default;\n$figure-caption-color:              var(--#{$prefix}secondary-color) !default;\n// scss-docs-end figure-variables\n\n\n// Breadcrumbs\n\n// scss-docs-start breadcrumb-variables\n$breadcrumb-font-size:              null !default;\n$breadcrumb-padding-y:              0 !default;\n$breadcrumb-padding-x:              0 !default;\n$breadcrumb-item-padding-x:         .5rem !default;\n$breadcrumb-margin-bottom:          1rem !default;\n$breadcrumb-bg:                     null !default;\n$breadcrumb-divider-color:          var(--#{$prefix}secondary-color) !default;\n$breadcrumb-active-color:           var(--#{$prefix}secondary-color) !default;\n$breadcrumb-divider:                quote(\"/\") !default;\n$breadcrumb-divider-flipped:        $breadcrumb-divider !default;\n$breadcrumb-border-radius:          null !default;\n// scss-docs-end breadcrumb-variables\n\n// Carousel\n\n// scss-docs-start carousel-variables\n$carousel-control-color:             $white !default;\n$carousel-control-width:             15% !default;\n$carousel-control-opacity:           .5 !default;\n$carousel-control-hover-opacity:     .9 !default;\n$carousel-control-transition:        opacity .15s ease !default;\n\n$carousel-indicator-width:           30px !default;\n$carousel-indicator-height:          3px !default;\n$carousel-indicator-hit-area-height: 10px !default;\n$carousel-indicator-spacer:          3px !default;\n$carousel-indicator-opacity:         .5 !default;\n$carousel-indicator-active-bg:       $white !default;\n$carousel-indicator-active-opacity:  1 !default;\n$carousel-indicator-transition:      opacity .6s ease !default;\n\n$carousel-caption-width:             70% !default;\n$carousel-caption-color:             $white !default;\n$carousel-caption-padding-y:         1.25rem !default;\n$carousel-caption-spacer:            1.25rem !default;\n\n$carousel-control-icon-width:        2rem !default;\n\n$carousel-control-prev-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/></svg>\") !default;\n$carousel-control-next-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n\n$carousel-transition-duration:       .6s !default;\n$carousel-transition:                transform $carousel-transition-duration ease-in-out !default; // Define transform transition first if using multiple transitions (e.g., `transform 2s ease, opacity .5s ease-out`)\n// scss-docs-end carousel-variables\n\n// scss-docs-start carousel-dark-variables\n$carousel-dark-indicator-active-bg:  $black !default;\n$carousel-dark-caption-color:        $black !default;\n$carousel-dark-control-icon-filter:  invert(1) grayscale(100) !default;\n// scss-docs-end carousel-dark-variables\n\n\n// Spinners\n\n// scss-docs-start spinner-variables\n$spinner-width:           2rem !default;\n$spinner-height:          $spinner-width !default;\n$spinner-vertical-align:  -.125em !default;\n$spinner-border-width:    .25em !default;\n$spinner-animation-speed: .75s !default;\n\n$spinner-width-sm:        1rem !default;\n$spinner-height-sm:       $spinner-width-sm !default;\n$spinner-border-width-sm: .2em !default;\n// scss-docs-end spinner-variables\n\n\n// Close\n\n// scss-docs-start close-variables\n$btn-close-width:            1em !default;\n$btn-close-height:           $btn-close-width !default;\n$btn-close-padding-x:        .25em !default;\n$btn-close-padding-y:        $btn-close-padding-x !default;\n$btn-close-color:            $black !default;\n$btn-close-bg:               url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$btn-close-color}'><path d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/></svg>\") !default;\n$btn-close-focus-shadow:     $focus-ring-box-shadow !default;\n$btn-close-opacity:          .5 !default;\n$btn-close-hover-opacity:    .75 !default;\n$btn-close-focus-opacity:    1 !default;\n$btn-close-disabled-opacity: .25 !default;\n$btn-close-white-filter:     invert(1) grayscale(100%) brightness(200%) !default;\n// scss-docs-end close-variables\n\n\n// Offcanvas\n\n// scss-docs-start offcanvas-variables\n$offcanvas-padding-y:               $modal-inner-padding !default;\n$offcanvas-padding-x:               $modal-inner-padding !default;\n$offcanvas-horizontal-width:        400px !default;\n$offcanvas-vertical-height:         30vh !default;\n$offcanvas-transition-duration:     .3s !default;\n$offcanvas-border-color:            $modal-content-border-color !default;\n$offcanvas-border-width:            $modal-content-border-width !default;\n$offcanvas-title-line-height:       $modal-title-line-height !default;\n$offcanvas-bg-color:                var(--#{$prefix}body-bg) !default;\n$offcanvas-color:                   var(--#{$prefix}body-color) !default;\n$offcanvas-box-shadow:              $modal-content-box-shadow-xs !default;\n$offcanvas-backdrop-bg:             $modal-backdrop-bg !default;\n$offcanvas-backdrop-opacity:        $modal-backdrop-opacity !default;\n// scss-docs-end offcanvas-variables\n\n// Code\n\n$code-font-size:                    $small-font-size !default;\n$code-color:                        $pink !default;\n\n$kbd-padding-y:                     .1875rem !default;\n$kbd-padding-x:                     .375rem !default;\n$kbd-font-size:                     $code-font-size !default;\n$kbd-color:                         var(--#{$prefix}body-bg) !default;\n$kbd-bg:                            var(--#{$prefix}body-color) !default;\n$nested-kbd-font-weight:            null !default; // Deprecated in v5.2.0, removing in v6\n\n$pre-color:                         null !default;\n\n@import \"variables-dark\"; // TODO: can be removed safely in v6, only here to avoid breaking changes in v5.3\n","// stylelint-disable property-disallowed-list\n// Single side border-radius\n\n// Helper function to replace negative values with 0\n@function valid-radius($radius) {\n  $return: ();\n  @each $value in $radius {\n    @if type-of($value) == number {\n      $return: append($return, max($value, 0));\n    } @else {\n      $return: append($return, $value);\n    }\n  }\n  @return $return;\n}\n\n// scss-docs-start border-radius-mixins\n@mixin border-radius($radius: $border-radius, $fallback-border-radius: false) {\n  @if $enable-rounded {\n    border-radius: valid-radius($radius);\n  }\n  @else if $fallback-border-radius != false {\n    border-radius: $fallback-border-radius;\n  }\n}\n\n@mixin border-top-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n    border-top-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-right-radius: valid-radius($radius);\n    border-bottom-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-right-radius: valid-radius($radius);\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-top-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-top-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n// scss-docs-end border-radius-mixins\n","//\n// Headings\n//\n.h1 {\n  @extend h1;\n}\n\n.h2 {\n  @extend h2;\n}\n\n.h3 {\n  @extend h3;\n}\n\n.h4 {\n  @extend h4;\n}\n\n.h5 {\n  @extend h5;\n}\n\n.h6 {\n  @extend h6;\n}\n\n\n.lead {\n  @include font-size($lead-font-size);\n  font-weight: $lead-font-weight;\n}\n\n// Type display classes\n@each $display, $font-size in $display-font-sizes {\n  .display-#{$display} {\n    @include font-size($font-size);\n    font-family: $display-font-family;\n    font-style: $display-font-style;\n    font-weight: $display-font-weight;\n    line-height: $display-line-height;\n  }\n}\n\n//\n// Emphasis\n//\n.small {\n  @extend small;\n}\n\n.mark {\n  @extend mark;\n}\n\n//\n// Lists\n//\n\n.list-unstyled {\n  @include list-unstyled();\n}\n\n// Inline turns list items into inline-block\n.list-inline {\n  @include list-unstyled();\n}\n.list-inline-item {\n  display: inline-block;\n\n  &:not(:last-child) {\n    margin-right: $list-inline-padding;\n  }\n}\n\n\n//\n// Misc\n//\n\n// Builds on `abbr`\n.initialism {\n  @include font-size($initialism-font-size);\n  text-transform: uppercase;\n}\n\n// Blockquotes\n.blockquote {\n  margin-bottom: $blockquote-margin-y;\n  @include font-size($blockquote-font-size);\n\n  > :last-child {\n    margin-bottom: 0;\n  }\n}\n\n.blockquote-footer {\n  margin-top: -$blockquote-margin-y;\n  margin-bottom: $blockquote-margin-y;\n  @include font-size($blockquote-footer-font-size);\n  color: $blockquote-footer-color;\n\n  &::before {\n    content: \"\\2014\\00A0\"; // em dash, nbsp\n  }\n}\n","// Lists\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n@mixin list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n","// Override bootstrap variables\n$spacer: 1rem;\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1400px,\n);\n$grid-breakpoints: (\n  xs: 0,\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1200px,\n);\n$dropdown-link-hover-bg: var(--pst-color-surface);\n\n// --pst-color-surface can also be assigned to the dark variant because it is\n// scoped to different values depending on light/dark theme\n$dropdown-dark-link-hover-bg: var(--pst-color-surface);\n$dropdown-link-active-bg: var(--pst-color-surface);\n$dropdown-dark-link-active-bg: var(--pst-color-surface);\n$focus-ring-width: 0.1875rem; // 3px at 100% zoom (0.1875 * 16px base font = 3px)\n$focus-ring-opacity: 1;\n$focus-ring-color: var(--pst-color-accent);\n$focus-ring-blur: 0;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color;\n\n// outline creates the same style of focus ring, it just uses CSS outline instead of box shadow\n$focus-ring-outline: $focus-ring-color solid $focus-ring-width;\n$btn-focus-box-shadow: $focus-ring-box-shadow;\n","// Responsive images (ensure images don't scale beyond their parents)\n//\n// This is purposefully opt-in via an explicit class rather than being the default for all `<img>`s.\n// We previously tried the \"images are responsive by default\" approach in Bootstrap v2,\n// and abandoned it in Bootstrap v3 because it breaks lots of third-party widgets (including Google Maps)\n// which weren't expecting the images within themselves to be involuntarily resized.\n// See also https://github.com/twbs/bootstrap/issues/18178\n.img-fluid {\n  @include img-fluid();\n}\n\n\n// Image thumbnails\n.img-thumbnail {\n  padding: $thumbnail-padding;\n  background-color: $thumbnail-bg;\n  border: $thumbnail-border-width solid $thumbnail-border-color;\n  @include border-radius($thumbnail-border-radius);\n  @include box-shadow($thumbnail-box-shadow);\n\n  // Keep them at most 100% wide\n  @include img-fluid();\n}\n\n//\n// Figures\n//\n\n.figure {\n  // Ensures the caption's text aligns with the image.\n  display: inline-block;\n}\n\n.figure-img {\n  margin-bottom: $spacer * .5;\n  line-height: 1;\n}\n\n.figure-caption {\n  @include font-size($figure-caption-font-size);\n  color: $figure-caption-color;\n}\n","// Image Mixins\n// - Responsive image\n// - Retina image\n\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n\n@mixin img-fluid {\n  // Part 1: Set a maximum relative to the parent\n  max-width: 100%;\n  // Part 2: Override the height to auto, otherwise images will be stretched\n  // when setting a width and height attribute on the img element.\n  height: auto;\n}\n","// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n@if $enable-container-classes {\n  // Single container class with breakpoint max-widths\n  .container,\n  // 100% wide container at all breakpoints\n  .container-fluid {\n    @include make-container();\n  }\n\n  // Responsive containers that are 100% wide until a breakpoint\n  @each $breakpoint, $container-max-width in $container-max-widths {\n    .container-#{$breakpoint} {\n      @extend .container-fluid;\n    }\n\n    @include media-breakpoint-up($breakpoint, $grid-breakpoints) {\n      %responsive-container-#{$breakpoint} {\n        max-width: $container-max-width;\n      }\n\n      // Extend each breakpoint which is smaller or equal to the current breakpoint\n      $extend-breakpoint: true;\n\n      @each $name, $width in $grid-breakpoints {\n        @if ($extend-breakpoint) {\n          .container#{breakpoint-infix($name, $grid-breakpoints)} {\n            @extend %responsive-container-#{$breakpoint};\n          }\n\n          // Once the current breakpoint is reached, stop extending\n          @if ($breakpoint == $name) {\n            $extend-breakpoint: false;\n          }\n        }\n      }\n    }\n  }\n}\n","// Container mixins\n\n@mixin make-container($gutter: $container-padding-x) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  width: 100%;\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-right: auto;\n  margin-left: auto;\n}\n","// Breakpoint viewport sizes and media queries.\n//\n// Breakpoints are defined as a map of (name: minimum width), order from small to large:\n//\n//    (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px)\n//\n// The map defined in the `$grid-breakpoints` global variable is used as the `$breakpoints` argument by default.\n\n// Name of the next breakpoint, or null for the last breakpoint.\n//\n//    >> breakpoint-next(sm)\n//    md\n//    >> breakpoint-next(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    md\n//    >> breakpoint-next(sm, $breakpoint-names: (xs sm md lg xl xxl))\n//    md\n@function breakpoint-next($name, $breakpoints: $grid-breakpoints, $breakpoint-names: map-keys($breakpoints)) {\n  $n: index($breakpoint-names, $name);\n  @if not $n {\n    @error \"breakpoint `#{$name}` not found in `#{$breakpoints}`\";\n  }\n  @return if($n < length($breakpoint-names), nth($breakpoint-names, $n + 1), null);\n}\n\n// Minimum breakpoint width. Null for the smallest (first) breakpoint.\n//\n//    >> breakpoint-min(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    576px\n@function breakpoint-min($name, $breakpoints: $grid-breakpoints) {\n  $min: map-get($breakpoints, $name);\n  @return if($min != 0, $min, null);\n}\n\n// Maximum breakpoint width.\n// The maximum value is reduced by 0.02px to work around the limitations of\n// `min-` and `max-` prefixes and viewports with fractional widths.\n// See https://www.w3.org/TR/mediaqueries-4/#mq-min-max\n// Uses 0.02px rather than 0.01px to work around a current rounding bug in Safari.\n// See https://bugs.webkit.org/show_bug.cgi?id=178261\n//\n//    >> breakpoint-max(md, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    767.98px\n@function breakpoint-max($name, $breakpoints: $grid-breakpoints) {\n  $max: map-get($breakpoints, $name);\n  @return if($max and $max > 0, $max - .02, null);\n}\n\n// Returns a blank string if smallest breakpoint, otherwise returns the name with a dash in front.\n// Useful for making responsive utilities.\n//\n//    >> breakpoint-infix(xs, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"\"  (Returns a blank string)\n//    >> breakpoint-infix(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"-sm\"\n@function breakpoint-infix($name, $breakpoints: $grid-breakpoints) {\n  @return if(breakpoint-min($name, $breakpoints) == null, \"\", \"-#{$name}\");\n}\n\n// Media of at least the minimum breakpoint width. No query for the smallest breakpoint.\n// Makes the @content apply to the given breakpoint and wider.\n@mixin media-breakpoint-up($name, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($name, $breakpoints);\n  @if $min {\n    @media (min-width: $min) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media of at most the maximum breakpoint width. No query for the largest breakpoint.\n// Makes the @content apply to the given breakpoint and narrower.\n@mixin media-breakpoint-down($name, $breakpoints: $grid-breakpoints) {\n  $max: breakpoint-max($name, $breakpoints);\n  @if $max {\n    @media (max-width: $max) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media that spans multiple breakpoint widths.\n// Makes the @content apply between the min and max breakpoints\n@mixin media-breakpoint-between($lower, $upper, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($lower, $breakpoints);\n  $max: breakpoint-max($upper, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($lower, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($upper, $breakpoints) {\n      @content;\n    }\n  }\n}\n\n// Media between the breakpoint's minimum and maximum widths.\n// No minimum for the smallest breakpoint, and no maximum for the largest one.\n// Makes the @content apply only to the given breakpoint, not viewports any wider or narrower.\n@mixin media-breakpoint-only($name, $breakpoints: $grid-breakpoints) {\n  $min:  breakpoint-min($name, $breakpoints);\n  $next: breakpoint-next($name, $breakpoints);\n  $max:  breakpoint-max($next, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($name, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($next, $breakpoints) {\n      @content;\n    }\n  }\n}\n","// Row\n//\n// Rows contain your columns.\n\n:root {\n  @each $name, $value in $grid-breakpoints {\n    --#{$prefix}breakpoint-#{$name}: #{$value};\n  }\n}\n\n@if $enable-grid-classes {\n  .row {\n    @include make-row();\n\n    > * {\n      @include make-col-ready();\n    }\n  }\n}\n\n@if $enable-cssgrid {\n  .grid {\n    display: grid;\n    grid-template-rows: repeat(var(--#{$prefix}rows, 1), 1fr);\n    grid-template-columns: repeat(var(--#{$prefix}columns, #{$grid-columns}), 1fr);\n    gap: var(--#{$prefix}gap, #{$grid-gutter-width});\n\n    @include make-cssgrid();\n  }\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n@if $enable-grid-classes {\n  @include make-grid-columns();\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n@mixin make-row($gutter: $grid-gutter-width) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  display: flex;\n  flex-wrap: wrap;\n  // TODO: Revisit calc order after https://github.com/react-bootstrap/react-bootstrap/issues/6039 is fixed\n  margin-top: calc(-1 * var(--#{$prefix}gutter-y)); // stylelint-disable-line function-disallowed-list\n  margin-right: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n}\n\n@mixin make-col-ready() {\n  // Add box sizing if only the grid is loaded\n  box-sizing: if(variable-exists(include-column-box-sizing) and $include-column-box-sizing, border-box, null);\n  // Prevent columns from becoming too narrow when at smaller grid tiers by\n  // always setting `width: 100%;`. This works because we set the width\n  // later on to override this initial width.\n  flex-shrink: 0;\n  width: 100%;\n  max-width: 100%; // Prevent `.col-auto`, `.col` (& responsive variants) from breaking out the grid\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-top: var(--#{$prefix}gutter-y);\n}\n\n@mixin make-col($size: false, $columns: $grid-columns) {\n  @if $size {\n    flex: 0 0 auto;\n    width: percentage(divide($size, $columns));\n\n  } @else {\n    flex: 1 1 0;\n    max-width: 100%;\n  }\n}\n\n@mixin make-col-auto() {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n@mixin make-col-offset($size, $columns: $grid-columns) {\n  $num: divide($size, $columns);\n  margin-left: if($num == 0, 0, percentage($num));\n}\n\n// Row columns\n//\n// Specify on a parent element(e.g., .row) to force immediate children into NN\n// number of columns. Supports wrapping to new lines, but does not do a Masonry\n// style grid.\n@mixin row-cols($count) {\n  > * {\n    flex: 0 0 auto;\n    width: percentage(divide(1, $count));\n  }\n}\n\n// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `$grid-columns`.\n\n@mixin make-grid-columns($columns: $grid-columns, $gutter: $grid-gutter-width, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      // Provide basic `.col-{bp}` classes for equal-width flexbox columns\n      .col#{$infix} {\n        flex: 1 0 0%; // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      }\n\n      .row-cols#{$infix}-auto > * {\n        @include make-col-auto();\n      }\n\n      @if $grid-row-columns > 0 {\n        @for $i from 1 through $grid-row-columns {\n          .row-cols#{$infix}-#{$i} {\n            @include row-cols($i);\n          }\n        }\n      }\n\n      .col#{$infix}-auto {\n        @include make-col-auto();\n      }\n\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .col#{$infix}-#{$i} {\n            @include make-col($i, $columns);\n          }\n        }\n\n        // `$columns - 1` because offsetting by the width of an entire row isn't possible\n        @for $i from 0 through ($columns - 1) {\n          @if not ($infix == \"\" and $i == 0) { // Avoid emitting useless .offset-0\n            .offset#{$infix}-#{$i} {\n              @include make-col-offset($i, $columns);\n            }\n          }\n        }\n      }\n\n      // Gutters\n      //\n      // Make use of `.g-*`, `.gx-*` or `.gy-*` utilities to change spacing between the columns.\n      @each $key, $value in $gutters {\n        .g#{$infix}-#{$key},\n        .gx#{$infix}-#{$key} {\n          --#{$prefix}gutter-x: #{$value};\n        }\n\n        .g#{$infix}-#{$key},\n        .gy#{$infix}-#{$key} {\n          --#{$prefix}gutter-y: #{$value};\n        }\n      }\n    }\n  }\n}\n\n@mixin make-cssgrid($columns: $grid-columns, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .g-col#{$infix}-#{$i} {\n            grid-column: auto / span $i;\n          }\n        }\n\n        // Start with `1` because `0` is an invalid value.\n        // Ends with `$columns - 1` because offsetting by the width of an entire row isn't possible.\n        @for $i from 1 through ($columns - 1) {\n          .g-start#{$infix}-#{$i} {\n            grid-column-start: $i;\n          }\n        }\n      }\n    }\n  }\n}\n","//\n// Basic Bootstrap table\n//\n\n.table {\n  // Reset needed for nesting tables\n  --#{$prefix}table-color-type: initial;\n  --#{$prefix}table-bg-type: initial;\n  --#{$prefix}table-color-state: initial;\n  --#{$prefix}table-bg-state: initial;\n  // End of reset\n  --#{$prefix}table-color: #{$table-color};\n  --#{$prefix}table-bg: #{$table-bg};\n  --#{$prefix}table-border-color: #{$table-border-color};\n  --#{$prefix}table-accent-bg: #{$table-accent-bg};\n  --#{$prefix}table-striped-color: #{$table-striped-color};\n  --#{$prefix}table-striped-bg: #{$table-striped-bg};\n  --#{$prefix}table-active-color: #{$table-active-color};\n  --#{$prefix}table-active-bg: #{$table-active-bg};\n  --#{$prefix}table-hover-color: #{$table-hover-color};\n  --#{$prefix}table-hover-bg: #{$table-hover-bg};\n\n  width: 100%;\n  margin-bottom: $spacer;\n  vertical-align: $table-cell-vertical-align;\n  border-color: var(--#{$prefix}table-border-color);\n\n  // Target th & td\n  // We need the child combinator to prevent styles leaking to nested tables which doesn't have a `.table` class.\n  // We use the universal selectors here to simplify the selector (else we would need 6 different selectors).\n  // Another advantage is that this generates less code and makes the selector less specific making it easier to override.\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    padding: $table-cell-padding-y $table-cell-padding-x;\n    // Following the precept of cascades: https://codepen.io/miriamsuzanne/full/vYNgodb\n    color: var(--#{$prefix}table-color-state, var(--#{$prefix}table-color-type, var(--#{$prefix}table-color)));\n    background-color: var(--#{$prefix}table-bg);\n    border-bottom-width: $table-border-width;\n    box-shadow: inset 0 0 0 9999px var(--#{$prefix}table-bg-state, var(--#{$prefix}table-bg-type, var(--#{$prefix}table-accent-bg)));\n  }\n\n  > tbody {\n    vertical-align: inherit;\n  }\n\n  > thead {\n    vertical-align: bottom;\n  }\n}\n\n.table-group-divider {\n  border-top: calc(#{$table-border-width} * 2) solid $table-group-separator-color; // stylelint-disable-line function-disallowed-list\n}\n\n//\n// Change placement of captions with a class\n//\n\n.caption-top {\n  caption-side: top;\n}\n\n\n//\n// Condensed table w/ half padding\n//\n\n.table-sm {\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    padding: $table-cell-padding-y-sm $table-cell-padding-x-sm;\n  }\n}\n\n\n// Border versions\n//\n// Add or remove borders all around the table and between all the columns.\n//\n// When borders are added on all sides of the cells, the corners can render odd when\n// these borders do not have the same color or if they are semi-transparent.\n// Therefore we add top and border bottoms to the `tr`s and left and right borders\n// to the `td`s or `th`s\n\n.table-bordered {\n  > :not(caption) > * {\n    border-width: $table-border-width 0;\n\n    // stylelint-disable-next-line selector-max-universal\n    > * {\n      border-width: 0 $table-border-width;\n    }\n  }\n}\n\n.table-borderless {\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    border-bottom-width: 0;\n  }\n\n  > :not(:first-child) {\n    border-top-width: 0;\n  }\n}\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n// For rows\n.table-striped {\n  > tbody > tr:nth-of-type(#{$table-striped-order}) > * {\n    --#{$prefix}table-color-type: var(--#{$prefix}table-striped-color);\n    --#{$prefix}table-bg-type: var(--#{$prefix}table-striped-bg);\n  }\n}\n\n// For columns\n.table-striped-columns {\n  > :not(caption) > tr > :nth-child(#{$table-striped-columns-order}) {\n    --#{$prefix}table-color-type: var(--#{$prefix}table-striped-color);\n    --#{$prefix}table-bg-type: var(--#{$prefix}table-striped-bg);\n  }\n}\n\n// Active table\n//\n// The `.table-active` class can be added to highlight rows or cells\n\n.table-active {\n  --#{$prefix}table-color-state: var(--#{$prefix}table-active-color);\n  --#{$prefix}table-bg-state: var(--#{$prefix}table-active-bg);\n}\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n  > tbody > tr:hover > * {\n    --#{$prefix}table-color-state: var(--#{$prefix}table-hover-color);\n    --#{$prefix}table-bg-state: var(--#{$prefix}table-hover-bg);\n  }\n}\n\n\n// Table variants\n//\n// Table variants set the table cell backgrounds, border colors\n// and the colors of the striped, hovered & active tables\n\n@each $color, $value in $table-variants {\n  @include table-variant($color, $value);\n}\n\n// Responsive tables\n//\n// Generate series of `.table-responsive-*` classes for configuring the screen\n// size of where your table will overflow.\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n  @include media-breakpoint-down($breakpoint) {\n    .table-responsive#{$infix} {\n      overflow-x: auto;\n      -webkit-overflow-scrolling: touch;\n    }\n  }\n}\n","// scss-docs-start table-variant\n@mixin table-variant($state, $background) {\n  .table-#{$state} {\n    $color: color-contrast(opaque($body-bg, $background));\n    $hover-bg: mix($color, $background, percentage($table-hover-bg-factor));\n    $striped-bg: mix($color, $background, percentage($table-striped-bg-factor));\n    $active-bg: mix($color, $background, percentage($table-active-bg-factor));\n    $table-border-color: mix($color, $background, percentage($table-border-factor));\n\n    --#{$prefix}table-color: #{$color};\n    --#{$prefix}table-bg: #{$background};\n    --#{$prefix}table-border-color: #{$table-border-color};\n    --#{$prefix}table-striped-bg: #{$striped-bg};\n    --#{$prefix}table-striped-color: #{color-contrast($striped-bg)};\n    --#{$prefix}table-active-bg: #{$active-bg};\n    --#{$prefix}table-active-color: #{color-contrast($active-bg)};\n    --#{$prefix}table-hover-bg: #{$hover-bg};\n    --#{$prefix}table-hover-color: #{color-contrast($hover-bg)};\n\n    color: var(--#{$prefix}table-color);\n    border-color: var(--#{$prefix}table-border-color);\n  }\n}\n// scss-docs-end table-variant\n","//\n// Labels\n//\n\n.form-label {\n  margin-bottom: $form-label-margin-bottom;\n  @include font-size($form-label-font-size);\n  font-style: $form-label-font-style;\n  font-weight: $form-label-font-weight;\n  color: $form-label-color;\n}\n\n// For use with horizontal and inline forms, when you need the label (or legend)\n// text to align with the form controls.\n.col-form-label {\n  padding-top: add($input-padding-y, $input-border-width);\n  padding-bottom: add($input-padding-y, $input-border-width);\n  margin-bottom: 0; // Override the `<legend>` default\n  @include font-size(inherit); // Override the `<legend>` default\n  font-style: $form-label-font-style;\n  font-weight: $form-label-font-weight;\n  line-height: $input-line-height;\n  color: $form-label-color;\n}\n\n.col-form-label-lg {\n  padding-top: add($input-padding-y-lg, $input-border-width);\n  padding-bottom: add($input-padding-y-lg, $input-border-width);\n  @include font-size($input-font-size-lg);\n}\n\n.col-form-label-sm {\n  padding-top: add($input-padding-y-sm, $input-border-width);\n  padding-bottom: add($input-padding-y-sm, $input-border-width);\n  @include font-size($input-font-size-sm);\n}\n","//\n// Form text\n//\n\n.form-text {\n  margin-top: $form-text-margin-top;\n  @include font-size($form-text-font-size);\n  font-style: $form-text-font-style;\n  font-weight: $form-text-font-weight;\n  color: $form-text-color;\n}\n","//\n// General form controls (plus a few specific high-level interventions)\n//\n\n.form-control {\n  display: block;\n  width: 100%;\n  padding: $input-padding-y $input-padding-x;\n  font-family: $input-font-family;\n  @include font-size($input-font-size);\n  font-weight: $input-font-weight;\n  line-height: $input-line-height;\n  color: $input-color;\n  appearance: none; // Fix appearance for date inputs in Safari\n  background-color: $input-bg;\n  background-clip: padding-box;\n  border: $input-border-width solid $input-border-color;\n\n  // Note: This has no effect on <select>s in some browsers, due to the limited stylability of `<select>`s in CSS.\n  @include border-radius($input-border-radius, 0);\n\n  @include box-shadow($input-box-shadow);\n  @include transition($input-transition);\n\n  &[type=\"file\"] {\n    overflow: hidden; // prevent pseudo element button overlap\n\n    &:not(:disabled):not([readonly]) {\n      cursor: pointer;\n    }\n  }\n\n  // Customize the `:focus` state to imitate native WebKit styles.\n  &:focus {\n    color: $input-focus-color;\n    background-color: $input-focus-bg;\n    border-color: $input-focus-border-color;\n    outline: 0;\n    @if $enable-shadows {\n      @include box-shadow($input-box-shadow, $input-focus-box-shadow);\n    } @else {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      box-shadow: $input-focus-box-shadow;\n    }\n  }\n\n  &::-webkit-date-and-time-value {\n    // On Android Chrome, form-control's \"width: 100%\" makes the input width too small\n    // Tested under Android 11 / Chrome 89, Android 12 / Chrome 100, Android 13 / Chrome 109\n    //\n    // On iOS Safari, form-control's \"appearance: none\" + \"width: 100%\" makes the input width too small\n    // Tested under iOS 16.2 / Safari 16.2\n    min-width: 85px; // Seems to be a good minimum safe width\n\n    // Add some height to date inputs on iOS\n    // https://github.com/twbs/bootstrap/issues/23307\n    // TODO: we can remove this workaround once https://bugs.webkit.org/show_bug.cgi?id=198959 is resolved\n    // Multiply line-height by 1em if it has no unit\n    height: if(unit($input-line-height) == \"\", $input-line-height * 1em, $input-line-height);\n\n    // Android Chrome type=\"date\" is taller than the other inputs\n    // because of \"margin: 1px 24px 1px 4px\" inside the shadow DOM\n    // Tested under Android 11 / Chrome 89, Android 12 / Chrome 100, Android 13 / Chrome 109\n    margin: 0;\n  }\n\n  // Prevent excessive date input height in Webkit\n  // https://github.com/twbs/bootstrap/issues/34433\n  &::-webkit-datetime-edit {\n    display: block;\n    padding: 0;\n  }\n\n  // Placeholder\n  &::placeholder {\n    color: $input-placeholder-color;\n    // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526.\n    opacity: 1;\n  }\n\n  // Disabled inputs\n  //\n  // HTML5 says that controls under a fieldset > legend:first-child won't be\n  // disabled if the fieldset is disabled. Due to implementation difficulty, we\n  // don't honor that edge case; we style them as disabled anyway.\n  &:disabled {\n    color: $input-disabled-color;\n    background-color: $input-disabled-bg;\n    border-color: $input-disabled-border-color;\n    // iOS fix for unreadable disabled content; see https://github.com/twbs/bootstrap/issues/11655.\n    opacity: 1;\n  }\n\n  // File input buttons theming\n  &::file-selector-button {\n    padding: $input-padding-y $input-padding-x;\n    margin: (-$input-padding-y) (-$input-padding-x);\n    margin-inline-end: $input-padding-x;\n    color: $form-file-button-color;\n    @include gradient-bg($form-file-button-bg);\n    pointer-events: none;\n    border-color: inherit;\n    border-style: solid;\n    border-width: 0;\n    border-inline-end-width: $input-border-width;\n    border-radius: 0; // stylelint-disable-line property-disallowed-list\n    @include transition($btn-transition);\n  }\n\n  &:hover:not(:disabled):not([readonly])::file-selector-button {\n    background-color: $form-file-button-hover-bg;\n  }\n}\n\n// Readonly controls as plain text\n//\n// Apply class to a readonly input to make it appear like regular plain\n// text (without any border, background color, focus indicator)\n\n.form-control-plaintext {\n  display: block;\n  width: 100%;\n  padding: $input-padding-y 0;\n  margin-bottom: 0; // match inputs if this class comes on inputs with default margins\n  line-height: $input-line-height;\n  color: $input-plaintext-color;\n  background-color: transparent;\n  border: solid transparent;\n  border-width: $input-border-width 0;\n\n  &:focus {\n    outline: 0;\n  }\n\n  &.form-control-sm,\n  &.form-control-lg {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n\n// Form control sizing\n//\n// Build on `.form-control` with modifier classes to decrease or increase the\n// height and font-size of form controls.\n//\n// Repeated in `_input_group.scss` to avoid Sass extend issues.\n\n.form-control-sm {\n  min-height: $input-height-sm;\n  padding: $input-padding-y-sm $input-padding-x-sm;\n  @include font-size($input-font-size-sm);\n  @include border-radius($input-border-radius-sm);\n\n  &::file-selector-button {\n    padding: $input-padding-y-sm $input-padding-x-sm;\n    margin: (-$input-padding-y-sm) (-$input-padding-x-sm);\n    margin-inline-end: $input-padding-x-sm;\n  }\n}\n\n.form-control-lg {\n  min-height: $input-height-lg;\n  padding: $input-padding-y-lg $input-padding-x-lg;\n  @include font-size($input-font-size-lg);\n  @include border-radius($input-border-radius-lg);\n\n  &::file-selector-button {\n    padding: $input-padding-y-lg $input-padding-x-lg;\n    margin: (-$input-padding-y-lg) (-$input-padding-x-lg);\n    margin-inline-end: $input-padding-x-lg;\n  }\n}\n\n// Make sure textareas don't shrink too much when resized\n// https://github.com/twbs/bootstrap/pull/29124\n// stylelint-disable selector-no-qualifying-type\ntextarea {\n  &.form-control {\n    min-height: $input-height;\n  }\n\n  &.form-control-sm {\n    min-height: $input-height-sm;\n  }\n\n  &.form-control-lg {\n    min-height: $input-height-lg;\n  }\n}\n// stylelint-enable selector-no-qualifying-type\n\n.form-control-color {\n  width: $form-color-width;\n  height: $input-height;\n  padding: $input-padding-y;\n\n  &:not(:disabled):not([readonly]) {\n    cursor: pointer;\n  }\n\n  &::-moz-color-swatch {\n    border: 0 !important; // stylelint-disable-line declaration-no-important\n    @include border-radius($input-border-radius);\n  }\n\n  &::-webkit-color-swatch {\n    border: 0 !important; // stylelint-disable-line declaration-no-important\n    @include border-radius($input-border-radius);\n  }\n\n  &.form-control-sm { height: $input-height-sm; }\n  &.form-control-lg { height: $input-height-lg; }\n}\n","// stylelint-disable property-disallowed-list\n@mixin transition($transition...) {\n  @if length($transition) == 0 {\n    $transition: $transition-base;\n  }\n\n  @if length($transition) > 1 {\n    @each $value in $transition {\n      @if $value == null or $value == none {\n        @warn \"The keyword 'none' or 'null' must be used as a single argument.\";\n      }\n    }\n  }\n\n  @if $enable-transitions {\n    @if nth($transition, 1) != null {\n      transition: $transition;\n    }\n\n    @if $enable-reduced-motion and nth($transition, 1) != null and nth($transition, 1) != none {\n      @media (prefers-reduced-motion: reduce) {\n        transition: none;\n      }\n    }\n  }\n}\n","// Gradients\n\n// scss-docs-start gradient-bg-mixin\n@mixin gradient-bg($color: null) {\n  background-color: $color;\n\n  @if $enable-gradients {\n    background-image: var(--#{$prefix}gradient);\n  }\n}\n// scss-docs-end gradient-bg-mixin\n\n// scss-docs-start gradient-mixins\n// Horizontal gradient, from left to right\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-x($start-color: $gray-700, $end-color: $gray-800, $start-percent: 0%, $end-percent: 100%) {\n  background-image: linear-gradient(to right, $start-color $start-percent, $end-color $end-percent);\n}\n\n// Vertical gradient, from top to bottom\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-y($start-color: $gray-700, $end-color: $gray-800, $start-percent: null, $end-percent: null) {\n  background-image: linear-gradient(to bottom, $start-color $start-percent, $end-color $end-percent);\n}\n\n@mixin gradient-directional($start-color: $gray-700, $end-color: $gray-800, $deg: 45deg) {\n  background-image: linear-gradient($deg, $start-color, $end-color);\n}\n\n@mixin gradient-x-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient(to right, $start-color, $mid-color $color-stop, $end-color);\n}\n\n@mixin gradient-y-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient($start-color, $mid-color $color-stop, $end-color);\n}\n\n@mixin gradient-radial($inner-color: $gray-700, $outer-color: $gray-800) {\n  background-image: radial-gradient(circle, $inner-color, $outer-color);\n}\n\n@mixin gradient-striped($color: rgba($white, .15), $angle: 45deg) {\n  background-image: linear-gradient($angle, $color 25%, transparent 25%, transparent 50%, $color 50%, $color 75%, transparent 75%, transparent);\n}\n// scss-docs-end gradient-mixins\n","// Select\n//\n// Replaces the browser default select with a custom one, mostly pulled from\n// https://primer.github.io/.\n\n.form-select {\n  --#{$prefix}form-select-bg-img: #{escape-svg($form-select-indicator)};\n\n  display: block;\n  width: 100%;\n  padding: $form-select-padding-y $form-select-indicator-padding $form-select-padding-y $form-select-padding-x;\n  font-family: $form-select-font-family;\n  @include font-size($form-select-font-size);\n  font-weight: $form-select-font-weight;\n  line-height: $form-select-line-height;\n  color: $form-select-color;\n  appearance: none;\n  background-color: $form-select-bg;\n  background-image: var(--#{$prefix}form-select-bg-img), var(--#{$prefix}form-select-bg-icon, none);\n  background-repeat: no-repeat;\n  background-position: $form-select-bg-position;\n  background-size: $form-select-bg-size;\n  border: $form-select-border-width solid $form-select-border-color;\n  @include border-radius($form-select-border-radius, 0);\n  @include box-shadow($form-select-box-shadow);\n  @include transition($form-select-transition);\n\n  &:focus {\n    border-color: $form-select-focus-border-color;\n    outline: 0;\n    @if $enable-shadows {\n      @include box-shadow($form-select-box-shadow, $form-select-focus-box-shadow);\n    } @else {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      box-shadow: $form-select-focus-box-shadow;\n    }\n  }\n\n  &[multiple],\n  &[size]:not([size=\"1\"]) {\n    padding-right: $form-select-padding-x;\n    background-image: none;\n  }\n\n  &:disabled {\n    color: $form-select-disabled-color;\n    background-color: $form-select-disabled-bg;\n    border-color: $form-select-disabled-border-color;\n  }\n\n  // Remove outline from select box in FF\n  &:-moz-focusring {\n    color: transparent;\n    text-shadow: 0 0 0 $form-select-color;\n  }\n}\n\n.form-select-sm {\n  padding-top: $form-select-padding-y-sm;\n  padding-bottom: $form-select-padding-y-sm;\n  padding-left: $form-select-padding-x-sm;\n  @include font-size($form-select-font-size-sm);\n  @include border-radius($form-select-border-radius-sm);\n}\n\n.form-select-lg {\n  padding-top: $form-select-padding-y-lg;\n  padding-bottom: $form-select-padding-y-lg;\n  padding-left: $form-select-padding-x-lg;\n  @include font-size($form-select-font-size-lg);\n  @include border-radius($form-select-border-radius-lg);\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .form-select {\n      --#{$prefix}form-select-bg-img: #{escape-svg($form-select-indicator-dark)};\n    }\n  }\n}\n","//\n// Check/radio\n//\n\n.form-check {\n  display: block;\n  min-height: $form-check-min-height;\n  padding-left: $form-check-padding-start;\n  margin-bottom: $form-check-margin-bottom;\n\n  .form-check-input {\n    float: left;\n    margin-left: $form-check-padding-start * -1;\n  }\n}\n\n.form-check-reverse {\n  padding-right: $form-check-padding-start;\n  padding-left: 0;\n  text-align: right;\n\n  .form-check-input {\n    float: right;\n    margin-right: $form-check-padding-start * -1;\n    margin-left: 0;\n  }\n}\n\n.form-check-input {\n  --#{$prefix}form-check-bg: #{$form-check-input-bg};\n\n  flex-shrink: 0;\n  width: $form-check-input-width;\n  height: $form-check-input-width;\n  margin-top: ($line-height-base - $form-check-input-width) * .5; // line-height minus check height\n  vertical-align: top;\n  appearance: none;\n  background-color: var(--#{$prefix}form-check-bg);\n  background-image: var(--#{$prefix}form-check-bg-image);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: contain;\n  border: $form-check-input-border;\n  print-color-adjust: exact; // Keep themed appearance for print\n  @include transition($form-check-transition);\n\n  &[type=\"checkbox\"] {\n    @include border-radius($form-check-input-border-radius);\n  }\n\n  &[type=\"radio\"] {\n    // stylelint-disable-next-line property-disallowed-list\n    border-radius: $form-check-radio-border-radius;\n  }\n\n  &:active {\n    filter: $form-check-input-active-filter;\n  }\n\n  &:focus {\n    border-color: $form-check-input-focus-border;\n    outline: 0;\n    box-shadow: $form-check-input-focus-box-shadow;\n  }\n\n  &:checked {\n    background-color: $form-check-input-checked-bg-color;\n    border-color: $form-check-input-checked-border-color;\n\n    &[type=\"checkbox\"] {\n      @if $enable-gradients {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-checked-bg-image)};\n      }\n    }\n\n    &[type=\"radio\"] {\n      @if $enable-gradients {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-radio-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-radio-checked-bg-image)};\n      }\n    }\n  }\n\n  &[type=\"checkbox\"]:indeterminate {\n    background-color: $form-check-input-indeterminate-bg-color;\n    border-color: $form-check-input-indeterminate-border-color;\n\n    @if $enable-gradients {\n      --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-indeterminate-bg-image)}, var(--#{$prefix}gradient);\n    } @else {\n      --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-indeterminate-bg-image)};\n    }\n  }\n\n  &:disabled {\n    pointer-events: none;\n    filter: none;\n    opacity: $form-check-input-disabled-opacity;\n  }\n\n  // Use disabled attribute in addition of :disabled pseudo-class\n  // See: https://github.com/twbs/bootstrap/issues/28247\n  &[disabled],\n  &:disabled {\n    ~ .form-check-label {\n      cursor: default;\n      opacity: $form-check-label-disabled-opacity;\n    }\n  }\n}\n\n.form-check-label {\n  color: $form-check-label-color;\n  cursor: $form-check-label-cursor;\n}\n\n//\n// Switch\n//\n\n.form-switch {\n  padding-left: $form-switch-padding-start;\n\n  .form-check-input {\n    --#{$prefix}form-switch-bg: #{escape-svg($form-switch-bg-image)};\n\n    width: $form-switch-width;\n    margin-left: $form-switch-padding-start * -1;\n    background-image: var(--#{$prefix}form-switch-bg);\n    background-position: left center;\n    @include border-radius($form-switch-border-radius, 0);\n    @include transition($form-switch-transition);\n\n    &:focus {\n      --#{$prefix}form-switch-bg: #{escape-svg($form-switch-focus-bg-image)};\n    }\n\n    &:checked {\n      background-position: $form-switch-checked-bg-position;\n\n      @if $enable-gradients {\n        --#{$prefix}form-switch-bg: #{escape-svg($form-switch-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-switch-bg: #{escape-svg($form-switch-checked-bg-image)};\n      }\n    }\n  }\n\n  &.form-check-reverse {\n    padding-right: $form-switch-padding-start;\n    padding-left: 0;\n\n    .form-check-input {\n      margin-right: $form-switch-padding-start * -1;\n      margin-left: 0;\n    }\n  }\n}\n\n.form-check-inline {\n  display: inline-block;\n  margin-right: $form-check-inline-margin-end;\n}\n\n.btn-check {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n\n  &[disabled],\n  &:disabled {\n    + .btn {\n      pointer-events: none;\n      filter: none;\n      opacity: $form-check-btn-check-disabled-opacity;\n    }\n  }\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .form-switch .form-check-input:not(:checked):not(:focus) {\n      --#{$prefix}form-switch-bg: #{escape-svg($form-switch-bg-image-dark)};\n    }\n  }\n}\n","// Range\n//\n// Style range inputs the same across browsers. Vendor-specific rules for pseudo\n// elements cannot be mixed. As such, there are no shared styles for focus or\n// active states on prefixed selectors.\n\n.form-range {\n  width: 100%;\n  height: add($form-range-thumb-height, $form-range-thumb-focus-box-shadow-width * 2);\n  padding: 0; // Need to reset padding\n  appearance: none;\n  background-color: transparent;\n\n  &:focus {\n    outline: 0;\n\n    // Pseudo-elements must be split across multiple rulesets to have an effect.\n    // No box-shadow() mixin for focus accessibility.\n    &::-webkit-slider-thumb { box-shadow: $form-range-thumb-focus-box-shadow; }\n    &::-moz-range-thumb     { box-shadow: $form-range-thumb-focus-box-shadow; }\n  }\n\n  &::-moz-focus-outer {\n    border: 0;\n  }\n\n  &::-webkit-slider-thumb {\n    width: $form-range-thumb-width;\n    height: $form-range-thumb-height;\n    margin-top: ($form-range-track-height - $form-range-thumb-height) * .5; // Webkit specific\n    appearance: none;\n    @include gradient-bg($form-range-thumb-bg);\n    border: $form-range-thumb-border;\n    @include border-radius($form-range-thumb-border-radius);\n    @include box-shadow($form-range-thumb-box-shadow);\n    @include transition($form-range-thumb-transition);\n\n    &:active {\n      @include gradient-bg($form-range-thumb-active-bg);\n    }\n  }\n\n  &::-webkit-slider-runnable-track {\n    width: $form-range-track-width;\n    height: $form-range-track-height;\n    color: transparent; // Why?\n    cursor: $form-range-track-cursor;\n    background-color: $form-range-track-bg;\n    border-color: transparent;\n    @include border-radius($form-range-track-border-radius);\n    @include box-shadow($form-range-track-box-shadow);\n  }\n\n  &::-moz-range-thumb {\n    width: $form-range-thumb-width;\n    height: $form-range-thumb-height;\n    appearance: none;\n    @include gradient-bg($form-range-thumb-bg);\n    border: $form-range-thumb-border;\n    @include border-radius($form-range-thumb-border-radius);\n    @include box-shadow($form-range-thumb-box-shadow);\n    @include transition($form-range-thumb-transition);\n\n    &:active {\n      @include gradient-bg($form-range-thumb-active-bg);\n    }\n  }\n\n  &::-moz-range-track {\n    width: $form-range-track-width;\n    height: $form-range-track-height;\n    color: transparent;\n    cursor: $form-range-track-cursor;\n    background-color: $form-range-track-bg;\n    border-color: transparent; // Firefox specific?\n    @include border-radius($form-range-track-border-radius);\n    @include box-shadow($form-range-track-box-shadow);\n  }\n\n  &:disabled {\n    pointer-events: none;\n\n    &::-webkit-slider-thumb {\n      background-color: $form-range-thumb-disabled-bg;\n    }\n\n    &::-moz-range-thumb {\n      background-color: $form-range-thumb-disabled-bg;\n    }\n  }\n}\n",".form-floating {\n  position: relative;\n\n  > .form-control,\n  > .form-control-plaintext,\n  > .form-select {\n    height: $form-floating-height;\n    min-height: $form-floating-height;\n    line-height: $form-floating-line-height;\n  }\n\n  > label {\n    position: absolute;\n    top: 0;\n    left: 0;\n    z-index: 2;\n    height: 100%; // allow textareas\n    padding: $form-floating-padding-y $form-floating-padding-x;\n    overflow: hidden;\n    text-align: start;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n    pointer-events: none;\n    border: $input-border-width solid transparent; // Required for aligning label's text with the input as it affects inner box model\n    transform-origin: 0 0;\n    @include transition($form-floating-transition);\n  }\n\n  > .form-control,\n  > .form-control-plaintext {\n    padding: $form-floating-padding-y $form-floating-padding-x;\n\n    &::placeholder {\n      color: transparent;\n    }\n\n    &:focus,\n    &:not(:placeholder-shown) {\n      padding-top: $form-floating-input-padding-t;\n      padding-bottom: $form-floating-input-padding-b;\n    }\n    // Duplicated because `:-webkit-autofill` invalidates other selectors when grouped\n    &:-webkit-autofill {\n      padding-top: $form-floating-input-padding-t;\n      padding-bottom: $form-floating-input-padding-b;\n    }\n  }\n\n  > .form-select {\n    padding-top: $form-floating-input-padding-t;\n    padding-bottom: $form-floating-input-padding-b;\n  }\n\n  > .form-control:focus,\n  > .form-control:not(:placeholder-shown),\n  > .form-control-plaintext,\n  > .form-select {\n    ~ label {\n      color: rgba(var(--#{$prefix}body-color-rgb), #{$form-floating-label-opacity});\n      transform: $form-floating-label-transform;\n\n      &::after {\n        position: absolute;\n        inset: $form-floating-padding-y ($form-floating-padding-x * .5);\n        z-index: -1;\n        height: $form-floating-label-height;\n        content: \"\";\n        background-color: $input-bg;\n        @include border-radius($input-border-radius);\n      }\n    }\n  }\n  // Duplicated because `:-webkit-autofill` invalidates other selectors when grouped\n  > .form-control:-webkit-autofill {\n    ~ label {\n      color: rgba(var(--#{$prefix}body-color-rgb), #{$form-floating-label-opacity});\n      transform: $form-floating-label-transform;\n    }\n  }\n\n  > .form-control-plaintext {\n    ~ label {\n      border-width: $input-border-width 0; // Required to properly position label text - as explained above\n    }\n  }\n\n  > :disabled ~ label,\n  > .form-control:disabled ~ label { // Required for `.form-control`s because of specificity\n    color: $form-floating-label-disabled-color;\n\n    &::after {\n      background-color: $input-disabled-bg;\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.input-group {\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // For form validation feedback\n  align-items: stretch;\n  width: 100%;\n\n  > .form-control,\n  > .form-select,\n  > .form-floating {\n    position: relative; // For focus state's z-index\n    flex: 1 1 auto;\n    width: 1%;\n    min-width: 0; // https://stackoverflow.com/questions/36247140/why-dont-flex-items-shrink-past-content-size\n  }\n\n  // Bring the \"active\" form control to the top of surrounding elements\n  > .form-control:focus,\n  > .form-select:focus,\n  > .form-floating:focus-within {\n    z-index: 5;\n  }\n\n  // Ensure buttons are always above inputs for more visually pleasing borders.\n  // This isn't needed for `.input-group-text` since it shares the same border-color\n  // as our inputs.\n  .btn {\n    position: relative;\n    z-index: 2;\n\n    &:focus {\n      z-index: 5;\n    }\n  }\n}\n\n\n// Textual addons\n//\n// Serves as a catch-all element for any text or radio/checkbox input you wish\n// to prepend or append to an input.\n\n.input-group-text {\n  display: flex;\n  align-items: center;\n  padding: $input-group-addon-padding-y $input-group-addon-padding-x;\n  @include font-size($input-font-size); // Match inputs\n  font-weight: $input-group-addon-font-weight;\n  line-height: $input-line-height;\n  color: $input-group-addon-color;\n  text-align: center;\n  white-space: nowrap;\n  background-color: $input-group-addon-bg;\n  border: $input-border-width solid $input-group-addon-border-color;\n  @include border-radius($input-border-radius);\n}\n\n\n// Sizing\n//\n// Remix the default form control sizing classes into new ones for easier\n// manipulation.\n\n.input-group-lg > .form-control,\n.input-group-lg > .form-select,\n.input-group-lg > .input-group-text,\n.input-group-lg > .btn {\n  padding: $input-padding-y-lg $input-padding-x-lg;\n  @include font-size($input-font-size-lg);\n  @include border-radius($input-border-radius-lg);\n}\n\n.input-group-sm > .form-control,\n.input-group-sm > .form-select,\n.input-group-sm > .input-group-text,\n.input-group-sm > .btn {\n  padding: $input-padding-y-sm $input-padding-x-sm;\n  @include font-size($input-font-size-sm);\n  @include border-radius($input-border-radius-sm);\n}\n\n.input-group-lg > .form-select,\n.input-group-sm > .form-select {\n  padding-right: $form-select-padding-x + $form-select-indicator-padding;\n}\n\n\n// Rounded corners\n//\n// These rulesets must come after the sizing ones to properly override sm and lg\n// border-radius values when extending. They're more specific than we'd like\n// with the `.input-group >` part, but without it, we cannot override the sizing.\n\n// stylelint-disable-next-line no-duplicate-selectors\n.input-group {\n  &:not(.has-validation) {\n    > :not(:last-child):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n    > .dropdown-toggle:nth-last-child(n + 3),\n    > .form-floating:not(:last-child) > .form-control,\n    > .form-floating:not(:last-child) > .form-select {\n      @include border-end-radius(0);\n    }\n  }\n\n  &.has-validation {\n    > :nth-last-child(n + 3):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n    > .dropdown-toggle:nth-last-child(n + 4),\n    > .form-floating:nth-last-child(n + 3) > .form-control,\n    > .form-floating:nth-last-child(n + 3) > .form-select {\n      @include border-end-radius(0);\n    }\n  }\n\n  $validation-messages: \"\";\n  @each $state in map-keys($form-validation-states) {\n    $validation-messages: $validation-messages + \":not(.\" + unquote($state) + \"-tooltip)\" + \":not(.\" + unquote($state) + \"-feedback)\";\n  }\n\n  > :not(:first-child):not(.dropdown-menu)#{$validation-messages} {\n    margin-left: calc(#{$input-border-width} * -1); // stylelint-disable-line function-disallowed-list\n    @include border-start-radius(0);\n  }\n\n  > .form-floating:not(:first-child) > .form-control,\n  > .form-floating:not(:first-child) > .form-select {\n    @include border-start-radius(0);\n  }\n}\n","// This mixin uses an `if()` technique to be compatible with Dart Sass\n// See https://github.com/sass/sass/issues/1873#issuecomment-152293725 for more details\n\n// scss-docs-start form-validation-mixins\n@mixin form-validation-state-selector($state) {\n  @if ($state == \"valid\" or $state == \"invalid\") {\n    .was-validated #{if(&, \"&\", \"\")}:#{$state},\n    #{if(&, \"&\", \"\")}.is-#{$state} {\n      @content;\n    }\n  } @else {\n    #{if(&, \"&\", \"\")}.is-#{$state} {\n      @content;\n    }\n  }\n}\n\n@mixin form-validation-state(\n  $state,\n  $color,\n  $icon,\n  $tooltip-color: color-contrast($color),\n  $tooltip-bg-color: rgba($color, $form-feedback-tooltip-opacity),\n  $focus-box-shadow: 0 0 $input-btn-focus-blur $input-focus-width rgba($color, $input-btn-focus-color-opacity),\n  $border-color: $color\n) {\n  .#{$state}-feedback {\n    display: none;\n    width: 100%;\n    margin-top: $form-feedback-margin-top;\n    @include font-size($form-feedback-font-size);\n    font-style: $form-feedback-font-style;\n    color: $color;\n  }\n\n  .#{$state}-tooltip {\n    position: absolute;\n    top: 100%;\n    z-index: 5;\n    display: none;\n    max-width: 100%; // Contain to parent when possible\n    padding: $form-feedback-tooltip-padding-y $form-feedback-tooltip-padding-x;\n    margin-top: .1rem;\n    @include font-size($form-feedback-tooltip-font-size);\n    line-height: $form-feedback-tooltip-line-height;\n    color: $tooltip-color;\n    background-color: $tooltip-bg-color;\n    @include border-radius($form-feedback-tooltip-border-radius);\n  }\n\n  @include form-validation-state-selector($state) {\n    ~ .#{$state}-feedback,\n    ~ .#{$state}-tooltip {\n      display: block;\n    }\n  }\n\n  .form-control {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      @if $enable-validation-icons {\n        padding-right: $input-height-inner;\n        background-image: escape-svg($icon);\n        background-repeat: no-repeat;\n        background-position: right $input-height-inner-quarter center;\n        background-size: $input-height-inner-half $input-height-inner-half;\n      }\n\n      &:focus {\n        border-color: $border-color;\n        @if $enable-shadows {\n          @include box-shadow($input-box-shadow, $focus-box-shadow);\n        } @else {\n          // Avoid using mixin so we can pass custom focus shadow properly\n          box-shadow: $focus-box-shadow;\n        }\n      }\n    }\n  }\n\n  // stylelint-disable-next-line selector-no-qualifying-type\n  textarea.form-control {\n    @include form-validation-state-selector($state) {\n      @if $enable-validation-icons {\n        padding-right: $input-height-inner;\n        background-position: top $input-height-inner-quarter right $input-height-inner-quarter;\n      }\n    }\n  }\n\n  .form-select {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      @if $enable-validation-icons {\n        &:not([multiple]):not([size]),\n        &:not([multiple])[size=\"1\"] {\n          --#{$prefix}form-select-bg-icon: #{escape-svg($icon)};\n          padding-right: $form-select-feedback-icon-padding-end;\n          background-position: $form-select-bg-position, $form-select-feedback-icon-position;\n          background-size: $form-select-bg-size, $form-select-feedback-icon-size;\n        }\n      }\n\n      &:focus {\n        border-color: $border-color;\n        @if $enable-shadows {\n          @include box-shadow($form-select-box-shadow, $focus-box-shadow);\n        } @else {\n          // Avoid using mixin so we can pass custom focus shadow properly\n          box-shadow: $focus-box-shadow;\n        }\n      }\n    }\n  }\n\n  .form-control-color {\n    @include form-validation-state-selector($state) {\n      @if $enable-validation-icons {\n        width: add($form-color-width, $input-height-inner);\n      }\n    }\n  }\n\n  .form-check-input {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      &:checked {\n        background-color: $color;\n      }\n\n      &:focus {\n        box-shadow: $focus-box-shadow;\n      }\n\n      ~ .form-check-label {\n        color: $color;\n      }\n    }\n  }\n  .form-check-inline .form-check-input {\n    ~ .#{$state}-feedback {\n      margin-left: .5em;\n    }\n  }\n\n  .input-group {\n    > .form-control:not(:focus),\n    > .form-select:not(:focus),\n    > .form-floating:not(:focus-within) {\n      @include form-validation-state-selector($state) {\n        @if $state == \"valid\" {\n          z-index: 3;\n        } @else if $state == \"invalid\" {\n          z-index: 4;\n        }\n      }\n    }\n  }\n}\n// scss-docs-end form-validation-mixins\n","//\n// Base styles\n//\n\n.btn {\n  // scss-docs-start btn-css-vars\n  --#{$prefix}btn-padding-x: #{$btn-padding-x};\n  --#{$prefix}btn-padding-y: #{$btn-padding-y};\n  --#{$prefix}btn-font-family: #{$btn-font-family};\n  @include rfs($btn-font-size, --#{$prefix}btn-font-size);\n  --#{$prefix}btn-font-weight: #{$btn-font-weight};\n  --#{$prefix}btn-line-height: #{$btn-line-height};\n  --#{$prefix}btn-color: #{$btn-color};\n  --#{$prefix}btn-bg: transparent;\n  --#{$prefix}btn-border-width: #{$btn-border-width};\n  --#{$prefix}btn-border-color: transparent;\n  --#{$prefix}btn-border-radius: #{$btn-border-radius};\n  --#{$prefix}btn-hover-border-color: transparent;\n  --#{$prefix}btn-box-shadow: #{$btn-box-shadow};\n  --#{$prefix}btn-disabled-opacity: #{$btn-disabled-opacity};\n  --#{$prefix}btn-focus-box-shadow: 0 0 0 #{$btn-focus-width} rgba(var(--#{$prefix}btn-focus-shadow-rgb), .5);\n  // scss-docs-end btn-css-vars\n\n  display: inline-block;\n  padding: var(--#{$prefix}btn-padding-y) var(--#{$prefix}btn-padding-x);\n  font-family: var(--#{$prefix}btn-font-family);\n  @include font-size(var(--#{$prefix}btn-font-size));\n  font-weight: var(--#{$prefix}btn-font-weight);\n  line-height: var(--#{$prefix}btn-line-height);\n  color: var(--#{$prefix}btn-color);\n  text-align: center;\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: $btn-white-space;\n  vertical-align: middle;\n  cursor: if($enable-button-pointers, pointer, null);\n  user-select: none;\n  border: var(--#{$prefix}btn-border-width) solid var(--#{$prefix}btn-border-color);\n  @include border-radius(var(--#{$prefix}btn-border-radius));\n  @include gradient-bg(var(--#{$prefix}btn-bg));\n  @include box-shadow(var(--#{$prefix}btn-box-shadow));\n  @include transition($btn-transition);\n\n  &:hover {\n    color: var(--#{$prefix}btn-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    background-color: var(--#{$prefix}btn-hover-bg);\n    border-color: var(--#{$prefix}btn-hover-border-color);\n  }\n\n  .btn-check + &:hover {\n    // override for the checkbox/radio buttons\n    color: var(--#{$prefix}btn-color);\n    background-color: var(--#{$prefix}btn-bg);\n    border-color: var(--#{$prefix}btn-border-color);\n  }\n\n  &:focus-visible {\n    color: var(--#{$prefix}btn-hover-color);\n    @include gradient-bg(var(--#{$prefix}btn-hover-bg));\n    border-color: var(--#{$prefix}btn-hover-border-color);\n    outline: 0;\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-box-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  .btn-check:focus-visible + & {\n    border-color: var(--#{$prefix}btn-hover-border-color);\n    outline: 0;\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-box-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  .btn-check:checked + &,\n  :not(.btn-check) + &:active,\n  &:first-child:active,\n  &.active,\n  &.show {\n    color: var(--#{$prefix}btn-active-color);\n    background-color: var(--#{$prefix}btn-active-bg);\n    // Remove CSS gradients if they're enabled\n    background-image: if($enable-gradients, none, null);\n    border-color: var(--#{$prefix}btn-active-border-color);\n    @include box-shadow(var(--#{$prefix}btn-active-shadow));\n\n    &:focus-visible {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      @if $enable-shadows {\n        box-shadow: var(--#{$prefix}btn-active-shadow), var(--#{$prefix}btn-focus-box-shadow);\n      } @else {\n        box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n      }\n    }\n  }\n\n  .btn-check:checked:focus-visible + & {\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-active-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  &:disabled,\n  &.disabled,\n  fieldset:disabled & {\n    color: var(--#{$prefix}btn-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}btn-disabled-bg);\n    background-image: if($enable-gradients, none, null);\n    border-color: var(--#{$prefix}btn-disabled-border-color);\n    opacity: var(--#{$prefix}btn-disabled-opacity);\n    @include box-shadow(none);\n  }\n}\n\n\n//\n// Alternate buttons\n//\n\n// scss-docs-start btn-variant-loops\n@each $color, $value in $theme-colors {\n  .btn-#{$color} {\n    @if $color == \"light\" {\n      @include button-variant(\n        $value,\n        $value,\n        $hover-background: shade-color($value, $btn-hover-bg-shade-amount),\n        $hover-border: shade-color($value, $btn-hover-border-shade-amount),\n        $active-background: shade-color($value, $btn-active-bg-shade-amount),\n        $active-border: shade-color($value, $btn-active-border-shade-amount)\n      );\n    } @else if $color == \"dark\" {\n      @include button-variant(\n        $value,\n        $value,\n        $hover-background: tint-color($value, $btn-hover-bg-tint-amount),\n        $hover-border: tint-color($value, $btn-hover-border-tint-amount),\n        $active-background: tint-color($value, $btn-active-bg-tint-amount),\n        $active-border: tint-color($value, $btn-active-border-tint-amount)\n      );\n    } @else {\n      @include button-variant($value, $value);\n    }\n  }\n}\n\n@each $color, $value in $theme-colors {\n  .btn-outline-#{$color} {\n    @include button-outline-variant($value);\n  }\n}\n// scss-docs-end btn-variant-loops\n\n\n//\n// Link buttons\n//\n\n// Make a button look and behave like a link\n.btn-link {\n  --#{$prefix}btn-font-weight: #{$font-weight-normal};\n  --#{$prefix}btn-color: #{$btn-link-color};\n  --#{$prefix}btn-bg: transparent;\n  --#{$prefix}btn-border-color: transparent;\n  --#{$prefix}btn-hover-color: #{$btn-link-hover-color};\n  --#{$prefix}btn-hover-border-color: transparent;\n  --#{$prefix}btn-active-color: #{$btn-link-hover-color};\n  --#{$prefix}btn-active-border-color: transparent;\n  --#{$prefix}btn-disabled-color: #{$btn-link-disabled-color};\n  --#{$prefix}btn-disabled-border-color: transparent;\n  --#{$prefix}btn-box-shadow: 0 0 0 #000; // Can't use `none` as keyword negates all values when used with multiple shadows\n  --#{$prefix}btn-focus-shadow-rgb: #{$btn-link-focus-shadow-rgb};\n\n  text-decoration: $link-decoration;\n  @if $enable-gradients {\n    background-image: none;\n  }\n\n  &:hover,\n  &:focus-visible {\n    text-decoration: $link-hover-decoration;\n  }\n\n  &:focus-visible {\n    color: var(--#{$prefix}btn-color);\n  }\n\n  &:hover {\n    color: var(--#{$prefix}btn-hover-color);\n  }\n\n  // No need for an active state here\n}\n\n\n//\n// Button Sizes\n//\n\n.btn-lg {\n  @include button-size($btn-padding-y-lg, $btn-padding-x-lg, $btn-font-size-lg, $btn-border-radius-lg);\n}\n\n.btn-sm {\n  @include button-size($btn-padding-y-sm, $btn-padding-x-sm, $btn-font-size-sm, $btn-border-radius-sm);\n}\n","// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n// scss-docs-start btn-variant-mixin\n@mixin button-variant(\n  $background,\n  $border,\n  $color: color-contrast($background),\n  $hover-background: if($color == $color-contrast-light, shade-color($background, $btn-hover-bg-shade-amount), tint-color($background, $btn-hover-bg-tint-amount)),\n  $hover-border: if($color == $color-contrast-light, shade-color($border, $btn-hover-border-shade-amount), tint-color($border, $btn-hover-border-tint-amount)),\n  $hover-color: color-contrast($hover-background),\n  $active-background: if($color == $color-contrast-light, shade-color($background, $btn-active-bg-shade-amount), tint-color($background, $btn-active-bg-tint-amount)),\n  $active-border: if($color == $color-contrast-light, shade-color($border, $btn-active-border-shade-amount), tint-color($border, $btn-active-border-tint-amount)),\n  $active-color: color-contrast($active-background),\n  $disabled-background: $background,\n  $disabled-border: $border,\n  $disabled-color: color-contrast($disabled-background)\n) {\n  --#{$prefix}btn-color: #{$color};\n  --#{$prefix}btn-bg: #{$background};\n  --#{$prefix}btn-border-color: #{$border};\n  --#{$prefix}btn-hover-color: #{$hover-color};\n  --#{$prefix}btn-hover-bg: #{$hover-background};\n  --#{$prefix}btn-hover-border-color: #{$hover-border};\n  --#{$prefix}btn-focus-shadow-rgb: #{to-rgb(mix($color, $border, 15%))};\n  --#{$prefix}btn-active-color: #{$active-color};\n  --#{$prefix}btn-active-bg: #{$active-background};\n  --#{$prefix}btn-active-border-color: #{$active-border};\n  --#{$prefix}btn-active-shadow: #{$btn-active-box-shadow};\n  --#{$prefix}btn-disabled-color: #{$disabled-color};\n  --#{$prefix}btn-disabled-bg: #{$disabled-background};\n  --#{$prefix}btn-disabled-border-color: #{$disabled-border};\n}\n// scss-docs-end btn-variant-mixin\n\n// scss-docs-start btn-outline-variant-mixin\n@mixin button-outline-variant(\n  $color,\n  $color-hover: color-contrast($color),\n  $active-background: $color,\n  $active-border: $color,\n  $active-color: color-contrast($active-background)\n) {\n  --#{$prefix}btn-color: #{$color};\n  --#{$prefix}btn-border-color: #{$color};\n  --#{$prefix}btn-hover-color: #{$color-hover};\n  --#{$prefix}btn-hover-bg: #{$active-background};\n  --#{$prefix}btn-hover-border-color: #{$active-border};\n  --#{$prefix}btn-focus-shadow-rgb: #{to-rgb($color)};\n  --#{$prefix}btn-active-color: #{$active-color};\n  --#{$prefix}btn-active-bg: #{$active-background};\n  --#{$prefix}btn-active-border-color: #{$active-border};\n  --#{$prefix}btn-active-shadow: #{$btn-active-box-shadow};\n  --#{$prefix}btn-disabled-color: #{$color};\n  --#{$prefix}btn-disabled-bg: transparent;\n  --#{$prefix}btn-disabled-border-color: #{$color};\n  --#{$prefix}gradient: none;\n}\n// scss-docs-end btn-outline-variant-mixin\n\n// scss-docs-start btn-size-mixin\n@mixin button-size($padding-y, $padding-x, $font-size, $border-radius) {\n  --#{$prefix}btn-padding-y: #{$padding-y};\n  --#{$prefix}btn-padding-x: #{$padding-x};\n  @include rfs($font-size, --#{$prefix}btn-font-size);\n  --#{$prefix}btn-border-radius: #{$border-radius};\n}\n// scss-docs-end btn-size-mixin\n",".fade {\n  @include transition($transition-fade);\n\n  &:not(.show) {\n    opacity: 0;\n  }\n}\n\n// scss-docs-start collapse-classes\n.collapse {\n  &:not(.show) {\n    display: none;\n  }\n}\n\n.collapsing {\n  height: 0;\n  overflow: hidden;\n  @include transition($transition-collapse);\n\n  &.collapse-horizontal {\n    width: 0;\n    height: auto;\n    @include transition($transition-collapse-width);\n  }\n}\n// scss-docs-end collapse-classes\n","// The dropdown wrapper (`<div>`)\n.dropup,\n.dropend,\n.dropdown,\n.dropstart,\n.dropup-center,\n.dropdown-center {\n  position: relative;\n}\n\n.dropdown-toggle {\n  white-space: nowrap;\n\n  // Generate the caret automatically\n  @include caret();\n}\n\n// The dropdown menu\n.dropdown-menu {\n  // scss-docs-start dropdown-css-vars\n  --#{$prefix}dropdown-zindex: #{$zindex-dropdown};\n  --#{$prefix}dropdown-min-width: #{$dropdown-min-width};\n  --#{$prefix}dropdown-padding-x: #{$dropdown-padding-x};\n  --#{$prefix}dropdown-padding-y: #{$dropdown-padding-y};\n  --#{$prefix}dropdown-spacer: #{$dropdown-spacer};\n  @include rfs($dropdown-font-size, --#{$prefix}dropdown-font-size);\n  --#{$prefix}dropdown-color: #{$dropdown-color};\n  --#{$prefix}dropdown-bg: #{$dropdown-bg};\n  --#{$prefix}dropdown-border-color: #{$dropdown-border-color};\n  --#{$prefix}dropdown-border-radius: #{$dropdown-border-radius};\n  --#{$prefix}dropdown-border-width: #{$dropdown-border-width};\n  --#{$prefix}dropdown-inner-border-radius: #{$dropdown-inner-border-radius};\n  --#{$prefix}dropdown-divider-bg: #{$dropdown-divider-bg};\n  --#{$prefix}dropdown-divider-margin-y: #{$dropdown-divider-margin-y};\n  --#{$prefix}dropdown-box-shadow: #{$dropdown-box-shadow};\n  --#{$prefix}dropdown-link-color: #{$dropdown-link-color};\n  --#{$prefix}dropdown-link-hover-color: #{$dropdown-link-hover-color};\n  --#{$prefix}dropdown-link-hover-bg: #{$dropdown-link-hover-bg};\n  --#{$prefix}dropdown-link-active-color: #{$dropdown-link-active-color};\n  --#{$prefix}dropdown-link-active-bg: #{$dropdown-link-active-bg};\n  --#{$prefix}dropdown-link-disabled-color: #{$dropdown-link-disabled-color};\n  --#{$prefix}dropdown-item-padding-x: #{$dropdown-item-padding-x};\n  --#{$prefix}dropdown-item-padding-y: #{$dropdown-item-padding-y};\n  --#{$prefix}dropdown-header-color: #{$dropdown-header-color};\n  --#{$prefix}dropdown-header-padding-x: #{$dropdown-header-padding-x};\n  --#{$prefix}dropdown-header-padding-y: #{$dropdown-header-padding-y};\n  // scss-docs-end dropdown-css-vars\n\n  position: absolute;\n  z-index: var(--#{$prefix}dropdown-zindex);\n  display: none; // none by default, but block on \"open\" of the menu\n  min-width: var(--#{$prefix}dropdown-min-width);\n  padding: var(--#{$prefix}dropdown-padding-y) var(--#{$prefix}dropdown-padding-x);\n  margin: 0; // Override default margin of ul\n  @include font-size(var(--#{$prefix}dropdown-font-size));\n  color: var(--#{$prefix}dropdown-color);\n  text-align: left; // Ensures proper alignment if parent has it changed (e.g., modal footer)\n  list-style: none;\n  background-color: var(--#{$prefix}dropdown-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}dropdown-border-width) solid var(--#{$prefix}dropdown-border-color);\n  @include border-radius(var(--#{$prefix}dropdown-border-radius));\n  @include box-shadow(var(--#{$prefix}dropdown-box-shadow));\n\n  &[data-bs-popper] {\n    top: 100%;\n    left: 0;\n    margin-top: var(--#{$prefix}dropdown-spacer);\n  }\n\n  @if $dropdown-padding-y == 0 {\n    > .dropdown-item:first-child,\n    > li:first-child .dropdown-item {\n      @include border-top-radius(var(--#{$prefix}dropdown-inner-border-radius));\n    }\n    > .dropdown-item:last-child,\n    > li:last-child .dropdown-item {\n      @include border-bottom-radius(var(--#{$prefix}dropdown-inner-border-radius));\n    }\n\n  }\n}\n\n// scss-docs-start responsive-breakpoints\n// We deliberately hardcode the `bs-` prefix because we check\n// this custom property in JS to determine Popper's positioning\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .dropdown-menu#{$infix}-start {\n      --bs-position: start;\n\n      &[data-bs-popper] {\n        right: auto;\n        left: 0;\n      }\n    }\n\n    .dropdown-menu#{$infix}-end {\n      --bs-position: end;\n\n      &[data-bs-popper] {\n        right: 0;\n        left: auto;\n      }\n    }\n  }\n}\n// scss-docs-end responsive-breakpoints\n\n// Allow for dropdowns to go bottom up (aka, dropup-menu)\n// Just add .dropup after the standard .dropdown class and you're set.\n.dropup {\n  .dropdown-menu[data-bs-popper] {\n    top: auto;\n    bottom: 100%;\n    margin-top: 0;\n    margin-bottom: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(up);\n  }\n}\n\n.dropend {\n  .dropdown-menu[data-bs-popper] {\n    top: 0;\n    right: auto;\n    left: 100%;\n    margin-top: 0;\n    margin-left: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(end);\n    &::after {\n      vertical-align: 0;\n    }\n  }\n}\n\n.dropstart {\n  .dropdown-menu[data-bs-popper] {\n    top: 0;\n    right: 100%;\n    left: auto;\n    margin-top: 0;\n    margin-right: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(start);\n    &::before {\n      vertical-align: 0;\n    }\n  }\n}\n\n\n// Dividers (basically an `<hr>`) within the dropdown\n.dropdown-divider {\n  height: 0;\n  margin: var(--#{$prefix}dropdown-divider-margin-y) 0;\n  overflow: hidden;\n  border-top: 1px solid var(--#{$prefix}dropdown-divider-bg);\n  opacity: 1; // Revisit in v6 to de-dupe styles that conflict with <hr> element\n}\n\n// Links, buttons, and more within the dropdown menu\n//\n// `<button>`-specific styles are denoted with `// For <button>s`\n.dropdown-item {\n  display: block;\n  width: 100%; // For `<button>`s\n  padding: var(--#{$prefix}dropdown-item-padding-y) var(--#{$prefix}dropdown-item-padding-x);\n  clear: both;\n  font-weight: $font-weight-normal;\n  color: var(--#{$prefix}dropdown-link-color);\n  text-align: inherit; // For `<button>`s\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: nowrap; // prevent links from randomly breaking onto new lines\n  background-color: transparent; // For `<button>`s\n  border: 0; // For `<button>`s\n  @include border-radius(var(--#{$prefix}dropdown-item-border-radius, 0));\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}dropdown-link-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    @include gradient-bg(var(--#{$prefix}dropdown-link-hover-bg));\n  }\n\n  &.active,\n  &:active {\n    color: var(--#{$prefix}dropdown-link-active-color);\n    text-decoration: none;\n    @include gradient-bg(var(--#{$prefix}dropdown-link-active-bg));\n  }\n\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}dropdown-link-disabled-color);\n    pointer-events: none;\n    background-color: transparent;\n    // Remove CSS gradients if they're enabled\n    background-image: if($enable-gradients, none, null);\n  }\n}\n\n.dropdown-menu.show {\n  display: block;\n}\n\n// Dropdown section headers\n.dropdown-header {\n  display: block;\n  padding: var(--#{$prefix}dropdown-header-padding-y) var(--#{$prefix}dropdown-header-padding-x);\n  margin-bottom: 0; // for use with heading elements\n  @include font-size($font-size-sm);\n  color: var(--#{$prefix}dropdown-header-color);\n  white-space: nowrap; // as with > li > a\n}\n\n// Dropdown text\n.dropdown-item-text {\n  display: block;\n  padding: var(--#{$prefix}dropdown-item-padding-y) var(--#{$prefix}dropdown-item-padding-x);\n  color: var(--#{$prefix}dropdown-link-color);\n}\n\n// Dark dropdowns\n.dropdown-menu-dark {\n  // scss-docs-start dropdown-dark-css-vars\n  --#{$prefix}dropdown-color: #{$dropdown-dark-color};\n  --#{$prefix}dropdown-bg: #{$dropdown-dark-bg};\n  --#{$prefix}dropdown-border-color: #{$dropdown-dark-border-color};\n  --#{$prefix}dropdown-box-shadow: #{$dropdown-dark-box-shadow};\n  --#{$prefix}dropdown-link-color: #{$dropdown-dark-link-color};\n  --#{$prefix}dropdown-link-hover-color: #{$dropdown-dark-link-hover-color};\n  --#{$prefix}dropdown-divider-bg: #{$dropdown-dark-divider-bg};\n  --#{$prefix}dropdown-link-hover-bg: #{$dropdown-dark-link-hover-bg};\n  --#{$prefix}dropdown-link-active-color: #{$dropdown-dark-link-active-color};\n  --#{$prefix}dropdown-link-active-bg: #{$dropdown-dark-link-active-bg};\n  --#{$prefix}dropdown-link-disabled-color: #{$dropdown-dark-link-disabled-color};\n  --#{$prefix}dropdown-header-color: #{$dropdown-dark-header-color};\n  // scss-docs-end dropdown-dark-css-vars\n}\n","// scss-docs-start caret-mixins\n@mixin caret-down($width: $caret-width) {\n  border-top: $width solid;\n  border-right: $width solid transparent;\n  border-bottom: 0;\n  border-left: $width solid transparent;\n}\n\n@mixin caret-up($width: $caret-width) {\n  border-top: 0;\n  border-right: $width solid transparent;\n  border-bottom: $width solid;\n  border-left: $width solid transparent;\n}\n\n@mixin caret-end($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: 0;\n  border-bottom: $width solid transparent;\n  border-left: $width solid;\n}\n\n@mixin caret-start($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: $width solid;\n  border-bottom: $width solid transparent;\n}\n\n@mixin caret(\n  $direction: down,\n  $width: $caret-width,\n  $spacing: $caret-spacing,\n  $vertical-align: $caret-vertical-align\n) {\n  @if $enable-caret {\n    &::after {\n      display: inline-block;\n      margin-left: $spacing;\n      vertical-align: $vertical-align;\n      content: \"\";\n      @if $direction == down {\n        @include caret-down($width);\n      } @else if $direction == up {\n        @include caret-up($width);\n      } @else if $direction == end {\n        @include caret-end($width);\n      }\n    }\n\n    @if $direction == start {\n      &::after {\n        display: none;\n      }\n\n      &::before {\n        display: inline-block;\n        margin-right: $spacing;\n        vertical-align: $vertical-align;\n        content: \"\";\n        @include caret-start($width);\n      }\n    }\n\n    &:empty::after {\n      margin-left: 0;\n    }\n  }\n}\n// scss-docs-end caret-mixins\n","// Make the div behave like a button\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-flex;\n  vertical-align: middle; // match .btn alignment given font-size hack above\n\n  > .btn {\n    position: relative;\n    flex: 1 1 auto;\n  }\n\n  // Bring the hover, focused, and \"active\" buttons to the front to overlay\n  // the borders properly\n  > .btn-check:checked + .btn,\n  > .btn-check:focus + .btn,\n  > .btn:hover,\n  > .btn:focus,\n  > .btn:active,\n  > .btn.active {\n    z-index: 1;\n  }\n}\n\n// Optional: Group multiple button groups together for a toolbar\n.btn-toolbar {\n  display: flex;\n  flex-wrap: wrap;\n  justify-content: flex-start;\n\n  .input-group {\n    width: auto;\n  }\n}\n\n.btn-group {\n  @include border-radius($btn-border-radius);\n\n  // Prevent double borders when buttons are next to each other\n  > :not(.btn-check:first-child) + .btn,\n  > .btn-group:not(:first-child) {\n    margin-left: calc(#{$btn-border-width} * -1); // stylelint-disable-line function-disallowed-list\n  }\n\n  // Reset rounded corners\n  > .btn:not(:last-child):not(.dropdown-toggle),\n  > .btn.dropdown-toggle-split:first-child,\n  > .btn-group:not(:last-child) > .btn {\n    @include border-end-radius(0);\n  }\n\n  // The left radius should be 0 if the button is:\n  // - the \"third or more\" child\n  // - the second child and the previous element isn't `.btn-check` (making it the first child visually)\n  // - part of a btn-group which isn't the first child\n  > .btn:nth-child(n + 3),\n  > :not(.btn-check) + .btn,\n  > .btn-group:not(:first-child) > .btn {\n    @include border-start-radius(0);\n  }\n}\n\n// Sizing\n//\n// Remix the default button sizing classes into new ones for easier manipulation.\n\n.btn-group-sm > .btn { @extend .btn-sm; }\n.btn-group-lg > .btn { @extend .btn-lg; }\n\n\n//\n// Split button dropdowns\n//\n\n.dropdown-toggle-split {\n  padding-right: $btn-padding-x * .75;\n  padding-left: $btn-padding-x * .75;\n\n  &::after,\n  .dropup &::after,\n  .dropend &::after {\n    margin-left: 0;\n  }\n\n  .dropstart &::before {\n    margin-right: 0;\n  }\n}\n\n.btn-sm + .dropdown-toggle-split {\n  padding-right: $btn-padding-x-sm * .75;\n  padding-left: $btn-padding-x-sm * .75;\n}\n\n.btn-lg + .dropdown-toggle-split {\n  padding-right: $btn-padding-x-lg * .75;\n  padding-left: $btn-padding-x-lg * .75;\n}\n\n\n// The clickable button for toggling the menu\n// Set the same inset shadow as the :active state\n.btn-group.show .dropdown-toggle {\n  @include box-shadow($btn-active-box-shadow);\n\n  // Show no shadow for `.btn-link` since it has no other button styles.\n  &.btn-link {\n    @include box-shadow(none);\n  }\n}\n\n\n//\n// Vertical button groups\n//\n\n.btn-group-vertical {\n  flex-direction: column;\n  align-items: flex-start;\n  justify-content: center;\n\n  > .btn,\n  > .btn-group {\n    width: 100%;\n  }\n\n  > .btn:not(:first-child),\n  > .btn-group:not(:first-child) {\n    margin-top: calc(#{$btn-border-width} * -1); // stylelint-disable-line function-disallowed-list\n  }\n\n  // Reset rounded corners\n  > .btn:not(:last-child):not(.dropdown-toggle),\n  > .btn-group:not(:last-child) > .btn {\n    @include border-bottom-radius(0);\n  }\n\n  > .btn ~ .btn,\n  > .btn-group:not(:first-child) > .btn {\n    @include border-top-radius(0);\n  }\n}\n","// Base class\n//\n// Kickstart any navigation component with a set of style resets. Works with\n// `<nav>`s, `<ul>`s or `<ol>`s.\n\n.nav {\n  // scss-docs-start nav-css-vars\n  --#{$prefix}nav-link-padding-x: #{$nav-link-padding-x};\n  --#{$prefix}nav-link-padding-y: #{$nav-link-padding-y};\n  @include rfs($nav-link-font-size, --#{$prefix}nav-link-font-size);\n  --#{$prefix}nav-link-font-weight: #{$nav-link-font-weight};\n  --#{$prefix}nav-link-color: #{$nav-link-color};\n  --#{$prefix}nav-link-hover-color: #{$nav-link-hover-color};\n  --#{$prefix}nav-link-disabled-color: #{$nav-link-disabled-color};\n  // scss-docs-end nav-css-vars\n\n  display: flex;\n  flex-wrap: wrap;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n\n.nav-link {\n  display: block;\n  padding: var(--#{$prefix}nav-link-padding-y) var(--#{$prefix}nav-link-padding-x);\n  @include font-size(var(--#{$prefix}nav-link-font-size));\n  font-weight: var(--#{$prefix}nav-link-font-weight);\n  color: var(--#{$prefix}nav-link-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background: none;\n  border: 0;\n  @include transition($nav-link-transition);\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}nav-link-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n\n  &:focus-visible {\n    outline: 0;\n    box-shadow: $nav-link-focus-box-shadow;\n  }\n\n  // Disabled state lightens text\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}nav-link-disabled-color);\n    pointer-events: none;\n    cursor: default;\n  }\n}\n\n//\n// Tabs\n//\n\n.nav-tabs {\n  // scss-docs-start nav-tabs-css-vars\n  --#{$prefix}nav-tabs-border-width: #{$nav-tabs-border-width};\n  --#{$prefix}nav-tabs-border-color: #{$nav-tabs-border-color};\n  --#{$prefix}nav-tabs-border-radius: #{$nav-tabs-border-radius};\n  --#{$prefix}nav-tabs-link-hover-border-color: #{$nav-tabs-link-hover-border-color};\n  --#{$prefix}nav-tabs-link-active-color: #{$nav-tabs-link-active-color};\n  --#{$prefix}nav-tabs-link-active-bg: #{$nav-tabs-link-active-bg};\n  --#{$prefix}nav-tabs-link-active-border-color: #{$nav-tabs-link-active-border-color};\n  // scss-docs-end nav-tabs-css-vars\n\n  border-bottom: var(--#{$prefix}nav-tabs-border-width) solid var(--#{$prefix}nav-tabs-border-color);\n\n  .nav-link {\n    margin-bottom: calc(-1 * var(--#{$prefix}nav-tabs-border-width)); // stylelint-disable-line function-disallowed-list\n    border: var(--#{$prefix}nav-tabs-border-width) solid transparent;\n    @include border-top-radius(var(--#{$prefix}nav-tabs-border-radius));\n\n    &:hover,\n    &:focus {\n      // Prevents active .nav-link tab overlapping focus outline of previous/next .nav-link\n      isolation: isolate;\n      border-color: var(--#{$prefix}nav-tabs-link-hover-border-color);\n    }\n  }\n\n  .nav-link.active,\n  .nav-item.show .nav-link {\n    color: var(--#{$prefix}nav-tabs-link-active-color);\n    background-color: var(--#{$prefix}nav-tabs-link-active-bg);\n    border-color: var(--#{$prefix}nav-tabs-link-active-border-color);\n  }\n\n  .dropdown-menu {\n    // Make dropdown border overlap tab border\n    margin-top: calc(-1 * var(--#{$prefix}nav-tabs-border-width)); // stylelint-disable-line function-disallowed-list\n    // Remove the top rounded corners here since there is a hard edge above the menu\n    @include border-top-radius(0);\n  }\n}\n\n\n//\n// Pills\n//\n\n.nav-pills {\n  // scss-docs-start nav-pills-css-vars\n  --#{$prefix}nav-pills-border-radius: #{$nav-pills-border-radius};\n  --#{$prefix}nav-pills-link-active-color: #{$nav-pills-link-active-color};\n  --#{$prefix}nav-pills-link-active-bg: #{$nav-pills-link-active-bg};\n  // scss-docs-end nav-pills-css-vars\n\n  .nav-link {\n    @include border-radius(var(--#{$prefix}nav-pills-border-radius));\n  }\n\n  .nav-link.active,\n  .show > .nav-link {\n    color: var(--#{$prefix}nav-pills-link-active-color);\n    @include gradient-bg(var(--#{$prefix}nav-pills-link-active-bg));\n  }\n}\n\n\n//\n// Underline\n//\n\n.nav-underline {\n  // scss-docs-start nav-underline-css-vars\n  --#{$prefix}nav-underline-gap: #{$nav-underline-gap};\n  --#{$prefix}nav-underline-border-width: #{$nav-underline-border-width};\n  --#{$prefix}nav-underline-link-active-color: #{$nav-underline-link-active-color};\n  // scss-docs-end nav-underline-css-vars\n\n  gap: var(--#{$prefix}nav-underline-gap);\n\n  .nav-link {\n    padding-right: 0;\n    padding-left: 0;\n    border-bottom: var(--#{$prefix}nav-underline-border-width) solid transparent;\n\n    &:hover,\n    &:focus {\n      border-bottom-color: currentcolor;\n    }\n  }\n\n  .nav-link.active,\n  .show > .nav-link {\n    font-weight: $font-weight-bold;\n    color: var(--#{$prefix}nav-underline-link-active-color);\n    border-bottom-color: currentcolor;\n  }\n}\n\n\n//\n// Justified variants\n//\n\n.nav-fill {\n  > .nav-link,\n  .nav-item {\n    flex: 1 1 auto;\n    text-align: center;\n  }\n}\n\n.nav-justified {\n  > .nav-link,\n  .nav-item {\n    flex-basis: 0;\n    flex-grow: 1;\n    text-align: center;\n  }\n}\n\n.nav-fill,\n.nav-justified {\n  .nav-item .nav-link {\n    width: 100%; // Make sure button will grow\n  }\n}\n\n\n// Tabbable tabs\n//\n// Hide tabbable panes to start, show them when `.active`\n\n.tab-content {\n  > .tab-pane {\n    display: none;\n  }\n  > .active {\n    display: block;\n  }\n}\n","// Navbar\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n  // scss-docs-start navbar-css-vars\n  --#{$prefix}navbar-padding-x: #{if($navbar-padding-x == null, 0, $navbar-padding-x)};\n  --#{$prefix}navbar-padding-y: #{$navbar-padding-y};\n  --#{$prefix}navbar-color: #{$navbar-light-color};\n  --#{$prefix}navbar-hover-color: #{$navbar-light-hover-color};\n  --#{$prefix}navbar-disabled-color: #{$navbar-light-disabled-color};\n  --#{$prefix}navbar-active-color: #{$navbar-light-active-color};\n  --#{$prefix}navbar-brand-padding-y: #{$navbar-brand-padding-y};\n  --#{$prefix}navbar-brand-margin-end: #{$navbar-brand-margin-end};\n  --#{$prefix}navbar-brand-font-size: #{$navbar-brand-font-size};\n  --#{$prefix}navbar-brand-color: #{$navbar-light-brand-color};\n  --#{$prefix}navbar-brand-hover-color: #{$navbar-light-brand-hover-color};\n  --#{$prefix}navbar-nav-link-padding-x: #{$navbar-nav-link-padding-x};\n  --#{$prefix}navbar-toggler-padding-y: #{$navbar-toggler-padding-y};\n  --#{$prefix}navbar-toggler-padding-x: #{$navbar-toggler-padding-x};\n  --#{$prefix}navbar-toggler-font-size: #{$navbar-toggler-font-size};\n  --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-light-toggler-icon-bg)};\n  --#{$prefix}navbar-toggler-border-color: #{$navbar-light-toggler-border-color};\n  --#{$prefix}navbar-toggler-border-radius: #{$navbar-toggler-border-radius};\n  --#{$prefix}navbar-toggler-focus-width: #{$navbar-toggler-focus-width};\n  --#{$prefix}navbar-toggler-transition: #{$navbar-toggler-transition};\n  // scss-docs-end navbar-css-vars\n\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // allow us to do the line break for collapsing content\n  align-items: center;\n  justify-content: space-between; // space out brand from logo\n  padding: var(--#{$prefix}navbar-padding-y) var(--#{$prefix}navbar-padding-x);\n  @include gradient-bg();\n\n  // Because flex properties aren't inherited, we need to redeclare these first\n  // few properties so that content nested within behave properly.\n  // The `flex-wrap` property is inherited to simplify the expanded navbars\n  %container-flex-properties {\n    display: flex;\n    flex-wrap: inherit;\n    align-items: center;\n    justify-content: space-between;\n  }\n\n  > .container,\n  > .container-fluid {\n    @extend %container-flex-properties;\n  }\n\n  @each $breakpoint, $container-max-width in $container-max-widths {\n    > .container#{breakpoint-infix($breakpoint, $container-max-widths)} {\n      @extend %container-flex-properties;\n    }\n  }\n}\n\n\n// Navbar brand\n//\n// Used for brand, project, or site names.\n\n.navbar-brand {\n  padding-top: var(--#{$prefix}navbar-brand-padding-y);\n  padding-bottom: var(--#{$prefix}navbar-brand-padding-y);\n  margin-right: var(--#{$prefix}navbar-brand-margin-end);\n  @include font-size(var(--#{$prefix}navbar-brand-font-size));\n  color: var(--#{$prefix}navbar-brand-color);\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: nowrap;\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}navbar-brand-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n}\n\n\n// Navbar nav\n//\n// Custom navbar navigation (doesn't require `.nav`, but does make use of `.nav-link`).\n\n.navbar-nav {\n  // scss-docs-start navbar-nav-css-vars\n  --#{$prefix}nav-link-padding-x: 0;\n  --#{$prefix}nav-link-padding-y: #{$nav-link-padding-y};\n  @include rfs($nav-link-font-size, --#{$prefix}nav-link-font-size);\n  --#{$prefix}nav-link-font-weight: #{$nav-link-font-weight};\n  --#{$prefix}nav-link-color: var(--#{$prefix}navbar-color);\n  --#{$prefix}nav-link-hover-color: var(--#{$prefix}navbar-hover-color);\n  --#{$prefix}nav-link-disabled-color: var(--#{$prefix}navbar-disabled-color);\n  // scss-docs-end navbar-nav-css-vars\n\n  display: flex;\n  flex-direction: column; // cannot use `inherit` to get the `.navbar`s value\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n\n  .nav-link {\n    &.active,\n    &.show {\n      color: var(--#{$prefix}navbar-active-color);\n    }\n  }\n\n  .dropdown-menu {\n    position: static;\n  }\n}\n\n\n// Navbar text\n//\n//\n\n.navbar-text {\n  padding-top: $nav-link-padding-y;\n  padding-bottom: $nav-link-padding-y;\n  color: var(--#{$prefix}navbar-color);\n\n  a,\n  a:hover,\n  a:focus  {\n    color: var(--#{$prefix}navbar-active-color);\n  }\n}\n\n\n// Responsive navbar\n//\n// Custom styles for responsive collapsing and toggling of navbar contents.\n// Powered by the collapse Bootstrap JavaScript plugin.\n\n// When collapsed, prevent the toggleable navbar contents from appearing in\n// the default flexbox row orientation. Requires the use of `flex-wrap: wrap`\n// on the `.navbar` parent.\n.navbar-collapse {\n  flex-basis: 100%;\n  flex-grow: 1;\n  // For always expanded or extra full navbars, ensure content aligns itself\n  // properly vertically. Can be easily overridden with flex utilities.\n  align-items: center;\n}\n\n// Button for toggling the navbar when in its collapsed state\n.navbar-toggler {\n  padding: var(--#{$prefix}navbar-toggler-padding-y) var(--#{$prefix}navbar-toggler-padding-x);\n  @include font-size(var(--#{$prefix}navbar-toggler-font-size));\n  line-height: 1;\n  color: var(--#{$prefix}navbar-color);\n  background-color: transparent; // remove default button style\n  border: var(--#{$prefix}border-width) solid var(--#{$prefix}navbar-toggler-border-color); // remove default button style\n  @include border-radius(var(--#{$prefix}navbar-toggler-border-radius));\n  @include transition(var(--#{$prefix}navbar-toggler-transition));\n\n  &:hover {\n    text-decoration: none;\n  }\n\n  &:focus {\n    text-decoration: none;\n    outline: 0;\n    box-shadow: 0 0 0 var(--#{$prefix}navbar-toggler-focus-width);\n  }\n}\n\n// Keep as a separate element so folks can easily override it with another icon\n// or image file as needed.\n.navbar-toggler-icon {\n  display: inline-block;\n  width: 1.5em;\n  height: 1.5em;\n  vertical-align: middle;\n  background-image: var(--#{$prefix}navbar-toggler-icon-bg);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: 100%;\n}\n\n.navbar-nav-scroll {\n  max-height: var(--#{$prefix}scroll-height, 75vh);\n  overflow-y: auto;\n}\n\n// scss-docs-start navbar-expand-loop\n// Generate series of `.navbar-expand-*` responsive classes for configuring\n// where your navbar collapses.\n.navbar-expand {\n  @each $breakpoint in map-keys($grid-breakpoints) {\n    $next: breakpoint-next($breakpoint, $grid-breakpoints);\n    $infix: breakpoint-infix($next, $grid-breakpoints);\n\n    // stylelint-disable-next-line scss/selector-no-union-class-name\n    &#{$infix} {\n      @include media-breakpoint-up($next) {\n        flex-wrap: nowrap;\n        justify-content: flex-start;\n\n        .navbar-nav {\n          flex-direction: row;\n\n          .dropdown-menu {\n            position: absolute;\n          }\n\n          .nav-link {\n            padding-right: var(--#{$prefix}navbar-nav-link-padding-x);\n            padding-left: var(--#{$prefix}navbar-nav-link-padding-x);\n          }\n        }\n\n        .navbar-nav-scroll {\n          overflow: visible;\n        }\n\n        .navbar-collapse {\n          display: flex !important; // stylelint-disable-line declaration-no-important\n          flex-basis: auto;\n        }\n\n        .navbar-toggler {\n          display: none;\n        }\n\n        .offcanvas {\n          // stylelint-disable declaration-no-important\n          position: static;\n          z-index: auto;\n          flex-grow: 1;\n          width: auto !important;\n          height: auto !important;\n          visibility: visible !important;\n          background-color: transparent !important;\n          border: 0 !important;\n          transform: none !important;\n          @include box-shadow(none);\n          @include transition(none);\n          // stylelint-enable declaration-no-important\n\n          .offcanvas-header {\n            display: none;\n          }\n\n          .offcanvas-body {\n            display: flex;\n            flex-grow: 0;\n            padding: 0;\n            overflow-y: visible;\n          }\n        }\n      }\n    }\n  }\n}\n// scss-docs-end navbar-expand-loop\n\n// Navbar themes\n//\n// Styles for switching between navbars with light or dark background.\n\n.navbar-light {\n  @include deprecate(\"`.navbar-light`\", \"v5.2.0\", \"v6.0.0\", true);\n}\n\n.navbar-dark,\n.navbar[data-bs-theme=\"dark\"] {\n  // scss-docs-start navbar-dark-css-vars\n  --#{$prefix}navbar-color: #{$navbar-dark-color};\n  --#{$prefix}navbar-hover-color: #{$navbar-dark-hover-color};\n  --#{$prefix}navbar-disabled-color: #{$navbar-dark-disabled-color};\n  --#{$prefix}navbar-active-color: #{$navbar-dark-active-color};\n  --#{$prefix}navbar-brand-color: #{$navbar-dark-brand-color};\n  --#{$prefix}navbar-brand-hover-color: #{$navbar-dark-brand-hover-color};\n  --#{$prefix}navbar-toggler-border-color: #{$navbar-dark-toggler-border-color};\n  --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-dark-toggler-icon-bg)};\n  // scss-docs-end navbar-dark-css-vars\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .navbar-toggler-icon {\n      --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-dark-toggler-icon-bg)};\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.card {\n  // scss-docs-start card-css-vars\n  --#{$prefix}card-spacer-y: #{$card-spacer-y};\n  --#{$prefix}card-spacer-x: #{$card-spacer-x};\n  --#{$prefix}card-title-spacer-y: #{$card-title-spacer-y};\n  --#{$prefix}card-title-color: #{$card-title-color};\n  --#{$prefix}card-subtitle-color: #{$card-subtitle-color};\n  --#{$prefix}card-border-width: #{$card-border-width};\n  --#{$prefix}card-border-color: #{$card-border-color};\n  --#{$prefix}card-border-radius: #{$card-border-radius};\n  --#{$prefix}card-box-shadow: #{$card-box-shadow};\n  --#{$prefix}card-inner-border-radius: #{$card-inner-border-radius};\n  --#{$prefix}card-cap-padding-y: #{$card-cap-padding-y};\n  --#{$prefix}card-cap-padding-x: #{$card-cap-padding-x};\n  --#{$prefix}card-cap-bg: #{$card-cap-bg};\n  --#{$prefix}card-cap-color: #{$card-cap-color};\n  --#{$prefix}card-height: #{$card-height};\n  --#{$prefix}card-color: #{$card-color};\n  --#{$prefix}card-bg: #{$card-bg};\n  --#{$prefix}card-img-overlay-padding: #{$card-img-overlay-padding};\n  --#{$prefix}card-group-margin: #{$card-group-margin};\n  // scss-docs-end card-css-vars\n\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  min-width: 0; // See https://github.com/twbs/bootstrap/pull/22740#issuecomment-305868106\n  height: var(--#{$prefix}card-height);\n  color: var(--#{$prefix}body-color);\n  word-wrap: break-word;\n  background-color: var(--#{$prefix}card-bg);\n  background-clip: border-box;\n  border: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n  @include border-radius(var(--#{$prefix}card-border-radius));\n  @include box-shadow(var(--#{$prefix}card-box-shadow));\n\n  > hr {\n    margin-right: 0;\n    margin-left: 0;\n  }\n\n  > .list-group {\n    border-top: inherit;\n    border-bottom: inherit;\n\n    &:first-child {\n      border-top-width: 0;\n      @include border-top-radius(var(--#{$prefix}card-inner-border-radius));\n    }\n\n    &:last-child  {\n      border-bottom-width: 0;\n      @include border-bottom-radius(var(--#{$prefix}card-inner-border-radius));\n    }\n  }\n\n  // Due to specificity of the above selector (`.card > .list-group`), we must\n  // use a child selector here to prevent double borders.\n  > .card-header + .list-group,\n  > .list-group + .card-footer {\n    border-top: 0;\n  }\n}\n\n.card-body {\n  // Enable `flex-grow: 1` for decks and groups so that card blocks take up\n  // as much space as possible, ensuring footers are aligned to the bottom.\n  flex: 1 1 auto;\n  padding: var(--#{$prefix}card-spacer-y) var(--#{$prefix}card-spacer-x);\n  color: var(--#{$prefix}card-color);\n}\n\n.card-title {\n  margin-bottom: var(--#{$prefix}card-title-spacer-y);\n  color: var(--#{$prefix}card-title-color);\n}\n\n.card-subtitle {\n  margin-top: calc(-.5 * var(--#{$prefix}card-title-spacer-y)); // stylelint-disable-line function-disallowed-list\n  margin-bottom: 0;\n  color: var(--#{$prefix}card-subtitle-color);\n}\n\n.card-text:last-child {\n  margin-bottom: 0;\n}\n\n.card-link {\n  &:hover {\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n\n  + .card-link {\n    margin-left: var(--#{$prefix}card-spacer-x);\n  }\n}\n\n//\n// Optional textual caps\n//\n\n.card-header {\n  padding: var(--#{$prefix}card-cap-padding-y) var(--#{$prefix}card-cap-padding-x);\n  margin-bottom: 0; // Removes the default margin-bottom of <hN>\n  color: var(--#{$prefix}card-cap-color);\n  background-color: var(--#{$prefix}card-cap-bg);\n  border-bottom: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n\n  &:first-child {\n    @include border-radius(var(--#{$prefix}card-inner-border-radius) var(--#{$prefix}card-inner-border-radius) 0 0);\n  }\n}\n\n.card-footer {\n  padding: var(--#{$prefix}card-cap-padding-y) var(--#{$prefix}card-cap-padding-x);\n  color: var(--#{$prefix}card-cap-color);\n  background-color: var(--#{$prefix}card-cap-bg);\n  border-top: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n\n  &:last-child {\n    @include border-radius(0 0 var(--#{$prefix}card-inner-border-radius) var(--#{$prefix}card-inner-border-radius));\n  }\n}\n\n\n//\n// Header navs\n//\n\n.card-header-tabs {\n  margin-right: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  margin-bottom: calc(-1 * var(--#{$prefix}card-cap-padding-y)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  border-bottom: 0;\n\n  .nav-link.active {\n    background-color: var(--#{$prefix}card-bg);\n    border-bottom-color: var(--#{$prefix}card-bg);\n  }\n}\n\n.card-header-pills {\n  margin-right: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n}\n\n// Card image\n.card-img-overlay {\n  position: absolute;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  padding: var(--#{$prefix}card-img-overlay-padding);\n  @include border-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n.card-img,\n.card-img-top,\n.card-img-bottom {\n  width: 100%; // Required because we use flexbox and this inherently applies align-self: stretch\n}\n\n.card-img,\n.card-img-top {\n  @include border-top-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n.card-img,\n.card-img-bottom {\n  @include border-bottom-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n\n//\n// Card groups\n//\n\n.card-group {\n  // The child selector allows nested `.card` within `.card-group`\n  // to display properly.\n  > .card {\n    margin-bottom: var(--#{$prefix}card-group-margin);\n  }\n\n  @include media-breakpoint-up(sm) {\n    display: flex;\n    flex-flow: row wrap;\n    // The child selector allows nested `.card` within `.card-group`\n    // to display properly.\n    > .card {\n      // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      flex: 1 0 0%;\n      margin-bottom: 0;\n\n      + .card {\n        margin-left: 0;\n        border-left: 0;\n      }\n\n      // Handle rounded corners\n      @if $enable-rounded {\n        &:not(:last-child) {\n          @include border-end-radius(0);\n\n          .card-img-top,\n          .card-header {\n            // stylelint-disable-next-line property-disallowed-list\n            border-top-right-radius: 0;\n          }\n          .card-img-bottom,\n          .card-footer {\n            // stylelint-disable-next-line property-disallowed-list\n            border-bottom-right-radius: 0;\n          }\n        }\n\n        &:not(:first-child) {\n          @include border-start-radius(0);\n\n          .card-img-top,\n          .card-header {\n            // stylelint-disable-next-line property-disallowed-list\n            border-top-left-radius: 0;\n          }\n          .card-img-bottom,\n          .card-footer {\n            // stylelint-disable-next-line property-disallowed-list\n            border-bottom-left-radius: 0;\n          }\n        }\n      }\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.accordion {\n  // scss-docs-start accordion-css-vars\n  --#{$prefix}accordion-color: #{$accordion-color};\n  --#{$prefix}accordion-bg: #{$accordion-bg};\n  --#{$prefix}accordion-transition: #{$accordion-transition};\n  --#{$prefix}accordion-border-color: #{$accordion-border-color};\n  --#{$prefix}accordion-border-width: #{$accordion-border-width};\n  --#{$prefix}accordion-border-radius: #{$accordion-border-radius};\n  --#{$prefix}accordion-inner-border-radius: #{$accordion-inner-border-radius};\n  --#{$prefix}accordion-btn-padding-x: #{$accordion-button-padding-x};\n  --#{$prefix}accordion-btn-padding-y: #{$accordion-button-padding-y};\n  --#{$prefix}accordion-btn-color: #{$accordion-button-color};\n  --#{$prefix}accordion-btn-bg: #{$accordion-button-bg};\n  --#{$prefix}accordion-btn-icon: #{escape-svg($accordion-button-icon)};\n  --#{$prefix}accordion-btn-icon-width: #{$accordion-icon-width};\n  --#{$prefix}accordion-btn-icon-transform: #{$accordion-icon-transform};\n  --#{$prefix}accordion-btn-icon-transition: #{$accordion-icon-transition};\n  --#{$prefix}accordion-btn-active-icon: #{escape-svg($accordion-button-active-icon)};\n  --#{$prefix}accordion-btn-focus-box-shadow: #{$accordion-button-focus-box-shadow};\n  --#{$prefix}accordion-body-padding-x: #{$accordion-body-padding-x};\n  --#{$prefix}accordion-body-padding-y: #{$accordion-body-padding-y};\n  --#{$prefix}accordion-active-color: #{$accordion-button-active-color};\n  --#{$prefix}accordion-active-bg: #{$accordion-button-active-bg};\n  // scss-docs-end accordion-css-vars\n}\n\n.accordion-button {\n  position: relative;\n  display: flex;\n  align-items: center;\n  width: 100%;\n  padding: var(--#{$prefix}accordion-btn-padding-y) var(--#{$prefix}accordion-btn-padding-x);\n  @include font-size($font-size-base);\n  color: var(--#{$prefix}accordion-btn-color);\n  text-align: left; // Reset button style\n  background-color: var(--#{$prefix}accordion-btn-bg);\n  border: 0;\n  @include border-radius(0);\n  overflow-anchor: none;\n  @include transition(var(--#{$prefix}accordion-transition));\n\n  &:not(.collapsed) {\n    color: var(--#{$prefix}accordion-active-color);\n    background-color: var(--#{$prefix}accordion-active-bg);\n    box-shadow: inset 0 calc(-1 * var(--#{$prefix}accordion-border-width)) 0 var(--#{$prefix}accordion-border-color); // stylelint-disable-line function-disallowed-list\n\n    &::after {\n      background-image: var(--#{$prefix}accordion-btn-active-icon);\n      transform: var(--#{$prefix}accordion-btn-icon-transform);\n    }\n  }\n\n  // Accordion icon\n  &::after {\n    flex-shrink: 0;\n    width: var(--#{$prefix}accordion-btn-icon-width);\n    height: var(--#{$prefix}accordion-btn-icon-width);\n    margin-left: auto;\n    content: \"\";\n    background-image: var(--#{$prefix}accordion-btn-icon);\n    background-repeat: no-repeat;\n    background-size: var(--#{$prefix}accordion-btn-icon-width);\n    @include transition(var(--#{$prefix}accordion-btn-icon-transition));\n  }\n\n  &:hover {\n    z-index: 2;\n  }\n\n  &:focus {\n    z-index: 3;\n    outline: 0;\n    box-shadow: var(--#{$prefix}accordion-btn-focus-box-shadow);\n  }\n}\n\n.accordion-header {\n  margin-bottom: 0;\n}\n\n.accordion-item {\n  color: var(--#{$prefix}accordion-color);\n  background-color: var(--#{$prefix}accordion-bg);\n  border: var(--#{$prefix}accordion-border-width) solid var(--#{$prefix}accordion-border-color);\n\n  &:first-of-type {\n    @include border-top-radius(var(--#{$prefix}accordion-border-radius));\n\n    > .accordion-header .accordion-button {\n      @include border-top-radius(var(--#{$prefix}accordion-inner-border-radius));\n    }\n  }\n\n  &:not(:first-of-type) {\n    border-top: 0;\n  }\n\n  // Only set a border-radius on the last item if the accordion is collapsed\n  &:last-of-type {\n    @include border-bottom-radius(var(--#{$prefix}accordion-border-radius));\n\n    > .accordion-header .accordion-button {\n      &.collapsed {\n        @include border-bottom-radius(var(--#{$prefix}accordion-inner-border-radius));\n      }\n    }\n\n    > .accordion-collapse {\n      @include border-bottom-radius(var(--#{$prefix}accordion-border-radius));\n    }\n  }\n}\n\n.accordion-body {\n  padding: var(--#{$prefix}accordion-body-padding-y) var(--#{$prefix}accordion-body-padding-x);\n}\n\n\n// Flush accordion items\n//\n// Remove borders and border-radius to keep accordion items edge-to-edge.\n\n.accordion-flush {\n  > .accordion-item {\n    border-right: 0;\n    border-left: 0;\n    @include border-radius(0);\n\n    &:first-child { border-top: 0; }\n    &:last-child { border-bottom: 0; }\n\n    // stylelint-disable selector-max-class\n    > .accordion-header .accordion-button {\n      &,\n      &.collapsed {\n        @include border-radius(0);\n      }\n    }\n    // stylelint-enable selector-max-class\n\n    > .accordion-collapse {\n      @include border-radius(0);\n    }\n  }\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .accordion-button::after {\n      --#{$prefix}accordion-btn-icon: #{escape-svg($accordion-button-icon-dark)};\n      --#{$prefix}accordion-btn-active-icon: #{escape-svg($accordion-button-active-icon-dark)};\n    }\n  }\n}\n",".breadcrumb {\n  // scss-docs-start breadcrumb-css-vars\n  --#{$prefix}breadcrumb-padding-x: #{$breadcrumb-padding-x};\n  --#{$prefix}breadcrumb-padding-y: #{$breadcrumb-padding-y};\n  --#{$prefix}breadcrumb-margin-bottom: #{$breadcrumb-margin-bottom};\n  @include rfs($breadcrumb-font-size, --#{$prefix}breadcrumb-font-size);\n  --#{$prefix}breadcrumb-bg: #{$breadcrumb-bg};\n  --#{$prefix}breadcrumb-border-radius: #{$breadcrumb-border-radius};\n  --#{$prefix}breadcrumb-divider-color: #{$breadcrumb-divider-color};\n  --#{$prefix}breadcrumb-item-padding-x: #{$breadcrumb-item-padding-x};\n  --#{$prefix}breadcrumb-item-active-color: #{$breadcrumb-active-color};\n  // scss-docs-end breadcrumb-css-vars\n\n  display: flex;\n  flex-wrap: wrap;\n  padding: var(--#{$prefix}breadcrumb-padding-y) var(--#{$prefix}breadcrumb-padding-x);\n  margin-bottom: var(--#{$prefix}breadcrumb-margin-bottom);\n  @include font-size(var(--#{$prefix}breadcrumb-font-size));\n  list-style: none;\n  background-color: var(--#{$prefix}breadcrumb-bg);\n  @include border-radius(var(--#{$prefix}breadcrumb-border-radius));\n}\n\n.breadcrumb-item {\n  // The separator between breadcrumbs (by default, a forward-slash: \"/\")\n  + .breadcrumb-item {\n    padding-left: var(--#{$prefix}breadcrumb-item-padding-x);\n\n    &::before {\n      float: left; // Suppress inline spacings and underlining of the separator\n      padding-right: var(--#{$prefix}breadcrumb-item-padding-x);\n      color: var(--#{$prefix}breadcrumb-divider-color);\n      content: var(--#{$prefix}breadcrumb-divider, escape-svg($breadcrumb-divider)) #{\"/* rtl:\"} var(--#{$prefix}breadcrumb-divider, escape-svg($breadcrumb-divider-flipped)) #{\"*/\"};\n    }\n  }\n\n  &.active {\n    color: var(--#{$prefix}breadcrumb-item-active-color);\n  }\n}\n",".pagination {\n  // scss-docs-start pagination-css-vars\n  --#{$prefix}pagination-padding-x: #{$pagination-padding-x};\n  --#{$prefix}pagination-padding-y: #{$pagination-padding-y};\n  @include rfs($pagination-font-size, --#{$prefix}pagination-font-size);\n  --#{$prefix}pagination-color: #{$pagination-color};\n  --#{$prefix}pagination-bg: #{$pagination-bg};\n  --#{$prefix}pagination-border-width: #{$pagination-border-width};\n  --#{$prefix}pagination-border-color: #{$pagination-border-color};\n  --#{$prefix}pagination-border-radius: #{$pagination-border-radius};\n  --#{$prefix}pagination-hover-color: #{$pagination-hover-color};\n  --#{$prefix}pagination-hover-bg: #{$pagination-hover-bg};\n  --#{$prefix}pagination-hover-border-color: #{$pagination-hover-border-color};\n  --#{$prefix}pagination-focus-color: #{$pagination-focus-color};\n  --#{$prefix}pagination-focus-bg: #{$pagination-focus-bg};\n  --#{$prefix}pagination-focus-box-shadow: #{$pagination-focus-box-shadow};\n  --#{$prefix}pagination-active-color: #{$pagination-active-color};\n  --#{$prefix}pagination-active-bg: #{$pagination-active-bg};\n  --#{$prefix}pagination-active-border-color: #{$pagination-active-border-color};\n  --#{$prefix}pagination-disabled-color: #{$pagination-disabled-color};\n  --#{$prefix}pagination-disabled-bg: #{$pagination-disabled-bg};\n  --#{$prefix}pagination-disabled-border-color: #{$pagination-disabled-border-color};\n  // scss-docs-end pagination-css-vars\n\n  display: flex;\n  @include list-unstyled();\n}\n\n.page-link {\n  position: relative;\n  display: block;\n  padding: var(--#{$prefix}pagination-padding-y) var(--#{$prefix}pagination-padding-x);\n  @include font-size(var(--#{$prefix}pagination-font-size));\n  color: var(--#{$prefix}pagination-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background-color: var(--#{$prefix}pagination-bg);\n  border: var(--#{$prefix}pagination-border-width) solid var(--#{$prefix}pagination-border-color);\n  @include transition($pagination-transition);\n\n  &:hover {\n    z-index: 2;\n    color: var(--#{$prefix}pagination-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    background-color: var(--#{$prefix}pagination-hover-bg);\n    border-color: var(--#{$prefix}pagination-hover-border-color);\n  }\n\n  &:focus {\n    z-index: 3;\n    color: var(--#{$prefix}pagination-focus-color);\n    background-color: var(--#{$prefix}pagination-focus-bg);\n    outline: $pagination-focus-outline;\n    box-shadow: var(--#{$prefix}pagination-focus-box-shadow);\n  }\n\n  &.active,\n  .active > & {\n    z-index: 3;\n    color: var(--#{$prefix}pagination-active-color);\n    @include gradient-bg(var(--#{$prefix}pagination-active-bg));\n    border-color: var(--#{$prefix}pagination-active-border-color);\n  }\n\n  &.disabled,\n  .disabled > & {\n    color: var(--#{$prefix}pagination-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}pagination-disabled-bg);\n    border-color: var(--#{$prefix}pagination-disabled-border-color);\n  }\n}\n\n.page-item {\n  &:not(:first-child) .page-link {\n    margin-left: $pagination-margin-start;\n  }\n\n  @if $pagination-margin-start == calc(#{$pagination-border-width} * -1) {\n    &:first-child {\n      .page-link {\n        @include border-start-radius(var(--#{$prefix}pagination-border-radius));\n      }\n    }\n\n    &:last-child {\n      .page-link {\n        @include border-end-radius(var(--#{$prefix}pagination-border-radius));\n      }\n    }\n  } @else {\n    // Add border-radius to all pageLinks in case they have left margin\n    .page-link {\n      @include border-radius(var(--#{$prefix}pagination-border-radius));\n    }\n  }\n}\n\n\n//\n// Sizing\n//\n\n.pagination-lg {\n  @include pagination-size($pagination-padding-y-lg, $pagination-padding-x-lg, $font-size-lg, $pagination-border-radius-lg);\n}\n\n.pagination-sm {\n  @include pagination-size($pagination-padding-y-sm, $pagination-padding-x-sm, $font-size-sm, $pagination-border-radius-sm);\n}\n","// Pagination\n\n// scss-docs-start pagination-mixin\n@mixin pagination-size($padding-y, $padding-x, $font-size, $border-radius) {\n  --#{$prefix}pagination-padding-x: #{$padding-x};\n  --#{$prefix}pagination-padding-y: #{$padding-y};\n  @include rfs($font-size, --#{$prefix}pagination-font-size);\n  --#{$prefix}pagination-border-radius: #{$border-radius};\n}\n// scss-docs-end pagination-mixin\n","// Base class\n//\n// Requires one of the contextual, color modifier classes for `color` and\n// `background-color`.\n\n.badge {\n  // scss-docs-start badge-css-vars\n  --#{$prefix}badge-padding-x: #{$badge-padding-x};\n  --#{$prefix}badge-padding-y: #{$badge-padding-y};\n  @include rfs($badge-font-size, --#{$prefix}badge-font-size);\n  --#{$prefix}badge-font-weight: #{$badge-font-weight};\n  --#{$prefix}badge-color: #{$badge-color};\n  --#{$prefix}badge-border-radius: #{$badge-border-radius};\n  // scss-docs-end badge-css-vars\n\n  display: inline-block;\n  padding: var(--#{$prefix}badge-padding-y) var(--#{$prefix}badge-padding-x);\n  @include font-size(var(--#{$prefix}badge-font-size));\n  font-weight: var(--#{$prefix}badge-font-weight);\n  line-height: 1;\n  color: var(--#{$prefix}badge-color);\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  @include border-radius(var(--#{$prefix}badge-border-radius));\n  @include gradient-bg();\n\n  // Empty badges collapse automatically\n  &:empty {\n    display: none;\n  }\n}\n\n// Quick fix for badges in buttons\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n","//\n// Base styles\n//\n\n.alert {\n  // scss-docs-start alert-css-vars\n  --#{$prefix}alert-bg: transparent;\n  --#{$prefix}alert-padding-x: #{$alert-padding-x};\n  --#{$prefix}alert-padding-y: #{$alert-padding-y};\n  --#{$prefix}alert-margin-bottom: #{$alert-margin-bottom};\n  --#{$prefix}alert-color: inherit;\n  --#{$prefix}alert-border-color: transparent;\n  --#{$prefix}alert-border: #{$alert-border-width} solid var(--#{$prefix}alert-border-color);\n  --#{$prefix}alert-border-radius: #{$alert-border-radius};\n  --#{$prefix}alert-link-color: inherit;\n  // scss-docs-end alert-css-vars\n\n  position: relative;\n  padding: var(--#{$prefix}alert-padding-y) var(--#{$prefix}alert-padding-x);\n  margin-bottom: var(--#{$prefix}alert-margin-bottom);\n  color: var(--#{$prefix}alert-color);\n  background-color: var(--#{$prefix}alert-bg);\n  border: var(--#{$prefix}alert-border);\n  @include border-radius(var(--#{$prefix}alert-border-radius));\n}\n\n// Headings for larger alerts\n.alert-heading {\n  // Specified to prevent conflicts of changing $headings-color\n  color: inherit;\n}\n\n// Provide class for links that match alerts\n.alert-link {\n  font-weight: $alert-link-font-weight;\n  color: var(--#{$prefix}alert-link-color);\n}\n\n\n// Dismissible alerts\n//\n// Expand the right padding and account for the close button's positioning.\n\n.alert-dismissible {\n  padding-right: $alert-dismissible-padding-r;\n\n  // Adjust close link position\n  .btn-close {\n    position: absolute;\n    top: 0;\n    right: 0;\n    z-index: $stretched-link-z-index + 1;\n    padding: $alert-padding-y * 1.25 $alert-padding-x;\n  }\n}\n\n\n// scss-docs-start alert-modifiers\n// Generate contextual modifier classes for colorizing the alert\n@each $state in map-keys($theme-colors) {\n  .alert-#{$state} {\n    --#{$prefix}alert-color: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}alert-bg: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}alert-border-color: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}alert-link-color: var(--#{$prefix}#{$state}-text-emphasis);\n  }\n}\n// scss-docs-end alert-modifiers\n","// Disable animation if transitions are disabled\n\n// scss-docs-start progress-keyframes\n@if $enable-transitions {\n  @keyframes progress-bar-stripes {\n    0% { background-position-x: $progress-height; }\n  }\n}\n// scss-docs-end progress-keyframes\n\n.progress,\n.progress-stacked {\n  // scss-docs-start progress-css-vars\n  --#{$prefix}progress-height: #{$progress-height};\n  @include rfs($progress-font-size, --#{$prefix}progress-font-size);\n  --#{$prefix}progress-bg: #{$progress-bg};\n  --#{$prefix}progress-border-radius: #{$progress-border-radius};\n  --#{$prefix}progress-box-shadow: #{$progress-box-shadow};\n  --#{$prefix}progress-bar-color: #{$progress-bar-color};\n  --#{$prefix}progress-bar-bg: #{$progress-bar-bg};\n  --#{$prefix}progress-bar-transition: #{$progress-bar-transition};\n  // scss-docs-end progress-css-vars\n\n  display: flex;\n  height: var(--#{$prefix}progress-height);\n  overflow: hidden; // force rounded corners by cropping it\n  @include font-size(var(--#{$prefix}progress-font-size));\n  background-color: var(--#{$prefix}progress-bg);\n  @include border-radius(var(--#{$prefix}progress-border-radius));\n  @include box-shadow(var(--#{$prefix}progress-box-shadow));\n}\n\n.progress-bar {\n  display: flex;\n  flex-direction: column;\n  justify-content: center;\n  overflow: hidden;\n  color: var(--#{$prefix}progress-bar-color);\n  text-align: center;\n  white-space: nowrap;\n  background-color: var(--#{$prefix}progress-bar-bg);\n  @include transition(var(--#{$prefix}progress-bar-transition));\n}\n\n.progress-bar-striped {\n  @include gradient-striped();\n  background-size: var(--#{$prefix}progress-height) var(--#{$prefix}progress-height);\n}\n\n.progress-stacked > .progress {\n  overflow: visible;\n}\n\n.progress-stacked > .progress > .progress-bar {\n  width: 100%;\n}\n\n@if $enable-transitions {\n  .progress-bar-animated {\n    animation: $progress-bar-animation-timing progress-bar-stripes;\n\n    @if $enable-reduced-motion {\n      @media (prefers-reduced-motion: reduce) {\n        animation: none;\n      }\n    }\n  }\n}\n","// Base class\n//\n// Easily usable on <ul>, <ol>, or <div>.\n\n.list-group {\n  // scss-docs-start list-group-css-vars\n  --#{$prefix}list-group-color: #{$list-group-color};\n  --#{$prefix}list-group-bg: #{$list-group-bg};\n  --#{$prefix}list-group-border-color: #{$list-group-border-color};\n  --#{$prefix}list-group-border-width: #{$list-group-border-width};\n  --#{$prefix}list-group-border-radius: #{$list-group-border-radius};\n  --#{$prefix}list-group-item-padding-x: #{$list-group-item-padding-x};\n  --#{$prefix}list-group-item-padding-y: #{$list-group-item-padding-y};\n  --#{$prefix}list-group-action-color: #{$list-group-action-color};\n  --#{$prefix}list-group-action-hover-color: #{$list-group-action-hover-color};\n  --#{$prefix}list-group-action-hover-bg: #{$list-group-hover-bg};\n  --#{$prefix}list-group-action-active-color: #{$list-group-action-active-color};\n  --#{$prefix}list-group-action-active-bg: #{$list-group-action-active-bg};\n  --#{$prefix}list-group-disabled-color: #{$list-group-disabled-color};\n  --#{$prefix}list-group-disabled-bg: #{$list-group-disabled-bg};\n  --#{$prefix}list-group-active-color: #{$list-group-active-color};\n  --#{$prefix}list-group-active-bg: #{$list-group-active-bg};\n  --#{$prefix}list-group-active-border-color: #{$list-group-active-border-color};\n  // scss-docs-end list-group-css-vars\n\n  display: flex;\n  flex-direction: column;\n\n  // No need to set list-style: none; since .list-group-item is block level\n  padding-left: 0; // reset padding because ul and ol\n  margin-bottom: 0;\n  @include border-radius(var(--#{$prefix}list-group-border-radius));\n}\n\n.list-group-numbered {\n  list-style-type: none;\n  counter-reset: section;\n\n  > .list-group-item::before {\n    // Increments only this instance of the section counter\n    content: counters(section, \".\") \". \";\n    counter-increment: section;\n  }\n}\n\n// Interactive list items\n//\n// Use anchor or button elements instead of `li`s or `div`s to create interactive\n// list items. Includes an extra `.active` modifier class for selected items.\n\n.list-group-item-action {\n  width: 100%; // For `<button>`s (anchors become 100% by default though)\n  color: var(--#{$prefix}list-group-action-color);\n  text-align: inherit; // For `<button>`s (anchors inherit)\n\n  // Hover state\n  &:hover,\n  &:focus {\n    z-index: 1; // Place hover/focus items above their siblings for proper border styling\n    color: var(--#{$prefix}list-group-action-hover-color);\n    text-decoration: none;\n    background-color: var(--#{$prefix}list-group-action-hover-bg);\n  }\n\n  &:active {\n    color: var(--#{$prefix}list-group-action-active-color);\n    background-color: var(--#{$prefix}list-group-action-active-bg);\n  }\n}\n\n// Individual list items\n//\n// Use on `li`s or `div`s within the `.list-group` parent.\n\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: var(--#{$prefix}list-group-item-padding-y) var(--#{$prefix}list-group-item-padding-x);\n  color: var(--#{$prefix}list-group-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background-color: var(--#{$prefix}list-group-bg);\n  border: var(--#{$prefix}list-group-border-width) solid var(--#{$prefix}list-group-border-color);\n\n  &:first-child {\n    @include border-top-radius(inherit);\n  }\n\n  &:last-child {\n    @include border-bottom-radius(inherit);\n  }\n\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}list-group-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}list-group-disabled-bg);\n  }\n\n  // Include both here for `<a>`s and `<button>`s\n  &.active {\n    z-index: 2; // Place active items above their siblings for proper border styling\n    color: var(--#{$prefix}list-group-active-color);\n    background-color: var(--#{$prefix}list-group-active-bg);\n    border-color: var(--#{$prefix}list-group-active-border-color);\n  }\n\n  // stylelint-disable-next-line scss/selector-no-redundant-nesting-selector\n  & + .list-group-item {\n    border-top-width: 0;\n\n    &.active {\n      margin-top: calc(-1 * var(--#{$prefix}list-group-border-width)); // stylelint-disable-line function-disallowed-list\n      border-top-width: var(--#{$prefix}list-group-border-width);\n    }\n  }\n}\n\n// Horizontal\n//\n// Change the layout of list group items from vertical (default) to horizontal.\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .list-group-horizontal#{$infix} {\n      flex-direction: row;\n\n      > .list-group-item {\n        &:first-child:not(:last-child) {\n          @include border-bottom-start-radius(var(--#{$prefix}list-group-border-radius));\n          @include border-top-end-radius(0);\n        }\n\n        &:last-child:not(:first-child) {\n          @include border-top-end-radius(var(--#{$prefix}list-group-border-radius));\n          @include border-bottom-start-radius(0);\n        }\n\n        &.active {\n          margin-top: 0;\n        }\n\n        + .list-group-item {\n          border-top-width: var(--#{$prefix}list-group-border-width);\n          border-left-width: 0;\n\n          &.active {\n            margin-left: calc(-1 * var(--#{$prefix}list-group-border-width)); // stylelint-disable-line function-disallowed-list\n            border-left-width: var(--#{$prefix}list-group-border-width);\n          }\n        }\n      }\n    }\n  }\n}\n\n\n// Flush list items\n//\n// Remove borders and border-radius to keep list group items edge-to-edge. Most\n// useful within other components (e.g., cards).\n\n.list-group-flush {\n  @include border-radius(0);\n\n  > .list-group-item {\n    border-width: 0 0 var(--#{$prefix}list-group-border-width);\n\n    &:last-child {\n      border-bottom-width: 0;\n    }\n  }\n}\n\n\n// scss-docs-start list-group-modifiers\n// List group contextual variants\n//\n// Add modifier classes to change text and background color on individual items.\n// Organizationally, this must come after the `:hover` states.\n\n@each $state in map-keys($theme-colors) {\n  .list-group-item-#{$state} {\n    --#{$prefix}list-group-color: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}list-group-bg: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}list-group-border-color: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-action-hover-color: var(--#{$prefix}emphasis-color);\n    --#{$prefix}list-group-action-hover-bg: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-action-active-color: var(--#{$prefix}emphasis-color);\n    --#{$prefix}list-group-action-active-bg: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-active-color: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}list-group-active-bg: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}list-group-active-border-color: var(--#{$prefix}#{$state}-text-emphasis);\n  }\n}\n// scss-docs-end list-group-modifiers\n","// Transparent background and border properties included for button version.\n// iOS requires the button element instead of an anchor tag.\n// If you want the anchor version, it requires `href=\"#\"`.\n// See https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n.btn-close {\n  // scss-docs-start close-css-vars\n  --#{$prefix}btn-close-color: #{$btn-close-color};\n  --#{$prefix}btn-close-bg: #{ escape-svg($btn-close-bg) };\n  --#{$prefix}btn-close-opacity: #{$btn-close-opacity};\n  --#{$prefix}btn-close-hover-opacity: #{$btn-close-hover-opacity};\n  --#{$prefix}btn-close-focus-shadow: #{$btn-close-focus-shadow};\n  --#{$prefix}btn-close-focus-opacity: #{$btn-close-focus-opacity};\n  --#{$prefix}btn-close-disabled-opacity: #{$btn-close-disabled-opacity};\n  --#{$prefix}btn-close-white-filter: #{$btn-close-white-filter};\n  // scss-docs-end close-css-vars\n\n  box-sizing: content-box;\n  width: $btn-close-width;\n  height: $btn-close-height;\n  padding: $btn-close-padding-y $btn-close-padding-x;\n  color: var(--#{$prefix}btn-close-color);\n  background: transparent var(--#{$prefix}btn-close-bg) center / $btn-close-width auto no-repeat; // include transparent for button elements\n  border: 0; // for button elements\n  @include border-radius();\n  opacity: var(--#{$prefix}btn-close-opacity);\n\n  // Override <a>'s hover style\n  &:hover {\n    color: var(--#{$prefix}btn-close-color);\n    text-decoration: none;\n    opacity: var(--#{$prefix}btn-close-hover-opacity);\n  }\n\n  &:focus {\n    outline: 0;\n    box-shadow: var(--#{$prefix}btn-close-focus-shadow);\n    opacity: var(--#{$prefix}btn-close-focus-opacity);\n  }\n\n  &:disabled,\n  &.disabled {\n    pointer-events: none;\n    user-select: none;\n    opacity: var(--#{$prefix}btn-close-disabled-opacity);\n  }\n}\n\n@mixin btn-close-white() {\n  filter: var(--#{$prefix}btn-close-white-filter);\n}\n\n.btn-close-white {\n  @include btn-close-white();\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .btn-close {\n      @include btn-close-white();\n    }\n  }\n}\n",".toast {\n  // scss-docs-start toast-css-vars\n  --#{$prefix}toast-zindex: #{$zindex-toast};\n  --#{$prefix}toast-padding-x: #{$toast-padding-x};\n  --#{$prefix}toast-padding-y: #{$toast-padding-y};\n  --#{$prefix}toast-spacing: #{$toast-spacing};\n  --#{$prefix}toast-max-width: #{$toast-max-width};\n  @include rfs($toast-font-size, --#{$prefix}toast-font-size);\n  --#{$prefix}toast-color: #{$toast-color};\n  --#{$prefix}toast-bg: #{$toast-background-color};\n  --#{$prefix}toast-border-width: #{$toast-border-width};\n  --#{$prefix}toast-border-color: #{$toast-border-color};\n  --#{$prefix}toast-border-radius: #{$toast-border-radius};\n  --#{$prefix}toast-box-shadow: #{$toast-box-shadow};\n  --#{$prefix}toast-header-color: #{$toast-header-color};\n  --#{$prefix}toast-header-bg: #{$toast-header-background-color};\n  --#{$prefix}toast-header-border-color: #{$toast-header-border-color};\n  // scss-docs-end toast-css-vars\n\n  width: var(--#{$prefix}toast-max-width);\n  max-width: 100%;\n  @include font-size(var(--#{$prefix}toast-font-size));\n  color: var(--#{$prefix}toast-color);\n  pointer-events: auto;\n  background-color: var(--#{$prefix}toast-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}toast-border-width) solid var(--#{$prefix}toast-border-color);\n  box-shadow: var(--#{$prefix}toast-box-shadow);\n  @include border-radius(var(--#{$prefix}toast-border-radius));\n\n  &.showing {\n    opacity: 0;\n  }\n\n  &:not(.show) {\n    display: none;\n  }\n}\n\n.toast-container {\n  --#{$prefix}toast-zindex: #{$zindex-toast};\n\n  position: absolute;\n  z-index: var(--#{$prefix}toast-zindex);\n  width: max-content;\n  max-width: 100%;\n  pointer-events: none;\n\n  > :not(:last-child) {\n    margin-bottom: var(--#{$prefix}toast-spacing);\n  }\n}\n\n.toast-header {\n  display: flex;\n  align-items: center;\n  padding: var(--#{$prefix}toast-padding-y) var(--#{$prefix}toast-padding-x);\n  color: var(--#{$prefix}toast-header-color);\n  background-color: var(--#{$prefix}toast-header-bg);\n  background-clip: padding-box;\n  border-bottom: var(--#{$prefix}toast-border-width) solid var(--#{$prefix}toast-header-border-color);\n  @include border-top-radius(calc(var(--#{$prefix}toast-border-radius) - var(--#{$prefix}toast-border-width)));\n\n  .btn-close {\n    margin-right: calc(-.5 * var(--#{$prefix}toast-padding-x)); // stylelint-disable-line function-disallowed-list\n    margin-left: var(--#{$prefix}toast-padding-x);\n  }\n}\n\n.toast-body {\n  padding: var(--#{$prefix}toast-padding-x);\n  word-wrap: break-word;\n}\n","// stylelint-disable function-disallowed-list\n\n// .modal-open      - body class for killing the scroll\n// .modal           - container to scroll within\n// .modal-dialog    - positioning shell for the actual modal\n// .modal-content   - actual modal w/ bg and corners and stuff\n\n\n// Container that the modal scrolls within\n.modal {\n  // scss-docs-start modal-css-vars\n  --#{$prefix}modal-zindex: #{$zindex-modal};\n  --#{$prefix}modal-width: #{$modal-md};\n  --#{$prefix}modal-padding: #{$modal-inner-padding};\n  --#{$prefix}modal-margin: #{$modal-dialog-margin};\n  --#{$prefix}modal-color: #{$modal-content-color};\n  --#{$prefix}modal-bg: #{$modal-content-bg};\n  --#{$prefix}modal-border-color: #{$modal-content-border-color};\n  --#{$prefix}modal-border-width: #{$modal-content-border-width};\n  --#{$prefix}modal-border-radius: #{$modal-content-border-radius};\n  --#{$prefix}modal-box-shadow: #{$modal-content-box-shadow-xs};\n  --#{$prefix}modal-inner-border-radius: #{$modal-content-inner-border-radius};\n  --#{$prefix}modal-header-padding-x: #{$modal-header-padding-x};\n  --#{$prefix}modal-header-padding-y: #{$modal-header-padding-y};\n  --#{$prefix}modal-header-padding: #{$modal-header-padding}; // Todo in v6: Split this padding into x and y\n  --#{$prefix}modal-header-border-color: #{$modal-header-border-color};\n  --#{$prefix}modal-header-border-width: #{$modal-header-border-width};\n  --#{$prefix}modal-title-line-height: #{$modal-title-line-height};\n  --#{$prefix}modal-footer-gap: #{$modal-footer-margin-between};\n  --#{$prefix}modal-footer-bg: #{$modal-footer-bg};\n  --#{$prefix}modal-footer-border-color: #{$modal-footer-border-color};\n  --#{$prefix}modal-footer-border-width: #{$modal-footer-border-width};\n  // scss-docs-end modal-css-vars\n\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: var(--#{$prefix}modal-zindex);\n  display: none;\n  width: 100%;\n  height: 100%;\n  overflow-x: hidden;\n  overflow-y: auto;\n  // Prevent Chrome on Windows from adding a focus outline. For details, see\n  // https://github.com/twbs/bootstrap/pull/10951.\n  outline: 0;\n  // We deliberately don't use `-webkit-overflow-scrolling: touch;` due to a\n  // gnarly iOS Safari bug: https://bugs.webkit.org/show_bug.cgi?id=158342\n  // See also https://github.com/twbs/bootstrap/issues/17695\n}\n\n// Shell div to position the modal with bottom padding\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: var(--#{$prefix}modal-margin);\n  // allow clicks to pass through for custom click handling to close modal\n  pointer-events: none;\n\n  // When fading in the modal, animate it to slide down\n  .modal.fade & {\n    @include transition($modal-transition);\n    transform: $modal-fade-transform;\n  }\n  .modal.show & {\n    transform: $modal-show-transform;\n  }\n\n  // When trying to close, animate focus to scale\n  .modal.modal-static & {\n    transform: $modal-scale-transform;\n  }\n}\n\n.modal-dialog-scrollable {\n  height: calc(100% - var(--#{$prefix}modal-margin) * 2);\n\n  .modal-content {\n    max-height: 100%;\n    overflow: hidden;\n  }\n\n  .modal-body {\n    overflow-y: auto;\n  }\n}\n\n.modal-dialog-centered {\n  display: flex;\n  align-items: center;\n  min-height: calc(100% - var(--#{$prefix}modal-margin) * 2);\n}\n\n// Actual modal\n.modal-content {\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  width: 100%; // Ensure `.modal-content` extends the full width of the parent `.modal-dialog`\n  // counteract the pointer-events: none; in the .modal-dialog\n  color: var(--#{$prefix}modal-color);\n  pointer-events: auto;\n  background-color: var(--#{$prefix}modal-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}modal-border-width) solid var(--#{$prefix}modal-border-color);\n  @include border-radius(var(--#{$prefix}modal-border-radius));\n  @include box-shadow(var(--#{$prefix}modal-box-shadow));\n  // Remove focus outline from opened modal\n  outline: 0;\n}\n\n// Modal background\n.modal-backdrop {\n  // scss-docs-start modal-backdrop-css-vars\n  --#{$prefix}backdrop-zindex: #{$zindex-modal-backdrop};\n  --#{$prefix}backdrop-bg: #{$modal-backdrop-bg};\n  --#{$prefix}backdrop-opacity: #{$modal-backdrop-opacity};\n  // scss-docs-end modal-backdrop-css-vars\n\n  @include overlay-backdrop(var(--#{$prefix}backdrop-zindex), var(--#{$prefix}backdrop-bg), var(--#{$prefix}backdrop-opacity));\n}\n\n// Modal header\n// Top section of the modal w/ title and dismiss\n.modal-header {\n  display: flex;\n  flex-shrink: 0;\n  align-items: center;\n  padding: var(--#{$prefix}modal-header-padding);\n  border-bottom: var(--#{$prefix}modal-header-border-width) solid var(--#{$prefix}modal-header-border-color);\n  @include border-top-radius(var(--#{$prefix}modal-inner-border-radius));\n\n  .btn-close {\n    padding: calc(var(--#{$prefix}modal-header-padding-y) * .5) calc(var(--#{$prefix}modal-header-padding-x) * .5);\n    margin: calc(-.5 * var(--#{$prefix}modal-header-padding-y)) calc(-.5 * var(--#{$prefix}modal-header-padding-x)) calc(-.5 * var(--#{$prefix}modal-header-padding-y)) auto;\n  }\n}\n\n// Title text within header\n.modal-title {\n  margin-bottom: 0;\n  line-height: var(--#{$prefix}modal-title-line-height);\n}\n\n// Modal body\n// Where all modal content resides (sibling of .modal-header and .modal-footer)\n.modal-body {\n  position: relative;\n  // Enable `flex-grow: 1` so that the body take up as much space as possible\n  // when there should be a fixed height on `.modal-dialog`.\n  flex: 1 1 auto;\n  padding: var(--#{$prefix}modal-padding);\n}\n\n// Footer (for actions)\n.modal-footer {\n  display: flex;\n  flex-shrink: 0;\n  flex-wrap: wrap;\n  align-items: center; // vertically center\n  justify-content: flex-end; // Right align buttons with flex property because text-align doesn't work on flex items\n  padding: calc(var(--#{$prefix}modal-padding) - var(--#{$prefix}modal-footer-gap) * .5);\n  background-color: var(--#{$prefix}modal-footer-bg);\n  border-top: var(--#{$prefix}modal-footer-border-width) solid var(--#{$prefix}modal-footer-border-color);\n  @include border-bottom-radius(var(--#{$prefix}modal-inner-border-radius));\n\n  // Place margin between footer elements\n  // This solution is far from ideal because of the universal selector usage,\n  // but is needed to fix https://github.com/twbs/bootstrap/issues/24800\n  > * {\n    margin: calc(var(--#{$prefix}modal-footer-gap) * .5); // Todo in v6: replace with gap on parent class\n  }\n}\n\n// Scale up the modal\n@include media-breakpoint-up(sm) {\n  .modal {\n    --#{$prefix}modal-margin: #{$modal-dialog-margin-y-sm-up};\n    --#{$prefix}modal-box-shadow: #{$modal-content-box-shadow-sm-up};\n  }\n\n  // Automatically set modal's width for larger viewports\n  .modal-dialog {\n    max-width: var(--#{$prefix}modal-width);\n    margin-right: auto;\n    margin-left: auto;\n  }\n\n  .modal-sm {\n    --#{$prefix}modal-width: #{$modal-sm};\n  }\n}\n\n@include media-breakpoint-up(lg) {\n  .modal-lg,\n  .modal-xl {\n    --#{$prefix}modal-width: #{$modal-lg};\n  }\n}\n\n@include media-breakpoint-up(xl) {\n  .modal-xl {\n    --#{$prefix}modal-width: #{$modal-xl};\n  }\n}\n\n// scss-docs-start modal-fullscreen-loop\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n  $postfix: if($infix != \"\", $infix + \"-down\", \"\");\n\n  @include media-breakpoint-down($breakpoint) {\n    .modal-fullscreen#{$postfix} {\n      width: 100vw;\n      max-width: none;\n      height: 100%;\n      margin: 0;\n\n      .modal-content {\n        height: 100%;\n        border: 0;\n        @include border-radius(0);\n      }\n\n      .modal-header,\n      .modal-footer {\n        @include border-radius(0);\n      }\n\n      .modal-body {\n        overflow-y: auto;\n      }\n    }\n  }\n}\n// scss-docs-end modal-fullscreen-loop\n","// Shared between modals and offcanvases\n@mixin overlay-backdrop($zindex, $backdrop-bg, $backdrop-opacity) {\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: $zindex;\n  width: 100vw;\n  height: 100vh;\n  background-color: $backdrop-bg;\n\n  // Fade for backdrop\n  &.fade { opacity: 0; }\n  &.show { opacity: $backdrop-opacity; }\n}\n","// Base class\n.tooltip {\n  // scss-docs-start tooltip-css-vars\n  --#{$prefix}tooltip-zindex: #{$zindex-tooltip};\n  --#{$prefix}tooltip-max-width: #{$tooltip-max-width};\n  --#{$prefix}tooltip-padding-x: #{$tooltip-padding-x};\n  --#{$prefix}tooltip-padding-y: #{$tooltip-padding-y};\n  --#{$prefix}tooltip-margin: #{$tooltip-margin};\n  @include rfs($tooltip-font-size, --#{$prefix}tooltip-font-size);\n  --#{$prefix}tooltip-color: #{$tooltip-color};\n  --#{$prefix}tooltip-bg: #{$tooltip-bg};\n  --#{$prefix}tooltip-border-radius: #{$tooltip-border-radius};\n  --#{$prefix}tooltip-opacity: #{$tooltip-opacity};\n  --#{$prefix}tooltip-arrow-width: #{$tooltip-arrow-width};\n  --#{$prefix}tooltip-arrow-height: #{$tooltip-arrow-height};\n  // scss-docs-end tooltip-css-vars\n\n  z-index: var(--#{$prefix}tooltip-zindex);\n  display: block;\n  margin: var(--#{$prefix}tooltip-margin);\n  @include deprecate(\"`$tooltip-margin`\", \"v5\", \"v5.x\", true);\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  @include reset-text();\n  @include font-size(var(--#{$prefix}tooltip-font-size));\n  // Allow breaking very long words so they don't overflow the tooltip's bounds\n  word-wrap: break-word;\n  opacity: 0;\n\n  &.show { opacity: var(--#{$prefix}tooltip-opacity); }\n\n  .tooltip-arrow {\n    display: block;\n    width: var(--#{$prefix}tooltip-arrow-width);\n    height: var(--#{$prefix}tooltip-arrow-height);\n\n    &::before {\n      position: absolute;\n      content: \"\";\n      border-color: transparent;\n      border-style: solid;\n    }\n  }\n}\n\n.bs-tooltip-top .tooltip-arrow {\n  bottom: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n\n  &::before {\n    top: -1px;\n    border-width: var(--#{$prefix}tooltip-arrow-height) calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    border-top-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-end .tooltip-arrow {\n  left: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n  width: var(--#{$prefix}tooltip-arrow-height);\n  height: var(--#{$prefix}tooltip-arrow-width);\n\n  &::before {\n    right: -1px;\n    border-width: calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height) calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    border-right-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-tooltip-bottom .tooltip-arrow {\n  top: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n\n  &::before {\n    bottom: -1px;\n    border-width: 0 calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height); // stylelint-disable-line function-disallowed-list\n    border-bottom-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-start .tooltip-arrow {\n  right: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n  width: var(--#{$prefix}tooltip-arrow-height);\n  height: var(--#{$prefix}tooltip-arrow-width);\n\n  &::before {\n    left: -1px;\n    border-width: calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0 calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height); // stylelint-disable-line function-disallowed-list\n    border-left-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-tooltip-auto {\n  &[data-popper-placement^=\"top\"] {\n    @extend .bs-tooltip-top;\n  }\n  &[data-popper-placement^=\"right\"] {\n    @extend .bs-tooltip-end;\n  }\n  &[data-popper-placement^=\"bottom\"] {\n    @extend .bs-tooltip-bottom;\n  }\n  &[data-popper-placement^=\"left\"] {\n    @extend .bs-tooltip-start;\n  }\n}\n\n// Wrapper for the tooltip content\n.tooltip-inner {\n  max-width: var(--#{$prefix}tooltip-max-width);\n  padding: var(--#{$prefix}tooltip-padding-y) var(--#{$prefix}tooltip-padding-x);\n  color: var(--#{$prefix}tooltip-color);\n  text-align: center;\n  background-color: var(--#{$prefix}tooltip-bg);\n  @include border-radius(var(--#{$prefix}tooltip-border-radius));\n}\n","@mixin reset-text {\n  font-family: $font-family-base;\n  // We deliberately do NOT reset font-size or overflow-wrap / word-wrap.\n  font-style: normal;\n  font-weight: $font-weight-normal;\n  line-height: $line-height-base;\n  text-align: left; // Fallback for where `start` is not supported\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  white-space: normal;\n  word-spacing: normal;\n  line-break: auto;\n}\n",".popover {\n  // scss-docs-start popover-css-vars\n  --#{$prefix}popover-zindex: #{$zindex-popover};\n  --#{$prefix}popover-max-width: #{$popover-max-width};\n  @include rfs($popover-font-size, --#{$prefix}popover-font-size);\n  --#{$prefix}popover-bg: #{$popover-bg};\n  --#{$prefix}popover-border-width: #{$popover-border-width};\n  --#{$prefix}popover-border-color: #{$popover-border-color};\n  --#{$prefix}popover-border-radius: #{$popover-border-radius};\n  --#{$prefix}popover-inner-border-radius: #{$popover-inner-border-radius};\n  --#{$prefix}popover-box-shadow: #{$popover-box-shadow};\n  --#{$prefix}popover-header-padding-x: #{$popover-header-padding-x};\n  --#{$prefix}popover-header-padding-y: #{$popover-header-padding-y};\n  @include rfs($popover-header-font-size, --#{$prefix}popover-header-font-size);\n  --#{$prefix}popover-header-color: #{$popover-header-color};\n  --#{$prefix}popover-header-bg: #{$popover-header-bg};\n  --#{$prefix}popover-body-padding-x: #{$popover-body-padding-x};\n  --#{$prefix}popover-body-padding-y: #{$popover-body-padding-y};\n  --#{$prefix}popover-body-color: #{$popover-body-color};\n  --#{$prefix}popover-arrow-width: #{$popover-arrow-width};\n  --#{$prefix}popover-arrow-height: #{$popover-arrow-height};\n  --#{$prefix}popover-arrow-border: var(--#{$prefix}popover-border-color);\n  // scss-docs-end popover-css-vars\n\n  z-index: var(--#{$prefix}popover-zindex);\n  display: block;\n  max-width: var(--#{$prefix}popover-max-width);\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  @include reset-text();\n  @include font-size(var(--#{$prefix}popover-font-size));\n  // Allow breaking very long words so they don't overflow the popover's bounds\n  word-wrap: break-word;\n  background-color: var(--#{$prefix}popover-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-border-color);\n  @include border-radius(var(--#{$prefix}popover-border-radius));\n  @include box-shadow(var(--#{$prefix}popover-box-shadow));\n\n  .popover-arrow {\n    display: block;\n    width: var(--#{$prefix}popover-arrow-width);\n    height: var(--#{$prefix}popover-arrow-height);\n\n    &::before,\n    &::after {\n      position: absolute;\n      display: block;\n      content: \"\";\n      border-color: transparent;\n      border-style: solid;\n      border-width: 0;\n    }\n  }\n}\n\n.bs-popover-top {\n  > .popover-arrow {\n    bottom: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n\n    &::before,\n    &::after {\n      border-width: var(--#{$prefix}popover-arrow-height) calc(var(--#{$prefix}popover-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      bottom: 0;\n      border-top-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      bottom: var(--#{$prefix}popover-border-width);\n      border-top-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-popover-end {\n  > .popover-arrow {\n    left: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n    width: var(--#{$prefix}popover-arrow-height);\n    height: var(--#{$prefix}popover-arrow-width);\n\n    &::before,\n    &::after {\n      border-width: calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height) calc(var(--#{$prefix}popover-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      left: 0;\n      border-right-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      left: var(--#{$prefix}popover-border-width);\n      border-right-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-popover-bottom {\n  > .popover-arrow {\n    top: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n\n    &::before,\n    &::after {\n      border-width: 0 calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height); // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      top: 0;\n      border-bottom-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      top: var(--#{$prefix}popover-border-width);\n      border-bottom-color: var(--#{$prefix}popover-bg);\n    }\n  }\n\n  // This will remove the popover-header's border just below the arrow\n  .popover-header::before {\n    position: absolute;\n    top: 0;\n    left: 50%;\n    display: block;\n    width: var(--#{$prefix}popover-arrow-width);\n    margin-left: calc(-.5 * var(--#{$prefix}popover-arrow-width)); // stylelint-disable-line function-disallowed-list\n    content: \"\";\n    border-bottom: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-header-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-popover-start {\n  > .popover-arrow {\n    right: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n    width: var(--#{$prefix}popover-arrow-height);\n    height: var(--#{$prefix}popover-arrow-width);\n\n    &::before,\n    &::after {\n      border-width: calc(var(--#{$prefix}popover-arrow-width) * .5) 0 calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height); // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      right: 0;\n      border-left-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      right: var(--#{$prefix}popover-border-width);\n      border-left-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-popover-auto {\n  &[data-popper-placement^=\"top\"] {\n    @extend .bs-popover-top;\n  }\n  &[data-popper-placement^=\"right\"] {\n    @extend .bs-popover-end;\n  }\n  &[data-popper-placement^=\"bottom\"] {\n    @extend .bs-popover-bottom;\n  }\n  &[data-popper-placement^=\"left\"] {\n    @extend .bs-popover-start;\n  }\n}\n\n// Offset the popover to account for the popover arrow\n.popover-header {\n  padding: var(--#{$prefix}popover-header-padding-y) var(--#{$prefix}popover-header-padding-x);\n  margin-bottom: 0; // Reset the default from Reboot\n  @include font-size(var(--#{$prefix}popover-header-font-size));\n  color: var(--#{$prefix}popover-header-color);\n  background-color: var(--#{$prefix}popover-header-bg);\n  border-bottom: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-border-color);\n  @include border-top-radius(var(--#{$prefix}popover-inner-border-radius));\n\n  &:empty {\n    display: none;\n  }\n}\n\n.popover-body {\n  padding: var(--#{$prefix}popover-body-padding-y) var(--#{$prefix}popover-body-padding-x);\n  color: var(--#{$prefix}popover-body-color);\n}\n","// Notes on the classes:\n//\n// 1. .carousel.pointer-event should ideally be pan-y (to allow for users to scroll vertically)\n//    even when their scroll action started on a carousel, but for compatibility (with Firefox)\n//    we're preventing all actions instead\n// 2. The .carousel-item-start and .carousel-item-end is used to indicate where\n//    the active slide is heading.\n// 3. .active.carousel-item is the current slide.\n// 4. .active.carousel-item-start and .active.carousel-item-end is the current\n//    slide in its in-transition state. Only one of these occurs at a time.\n// 5. .carousel-item-next.carousel-item-start and .carousel-item-prev.carousel-item-end\n//    is the upcoming slide in transition.\n\n.carousel {\n  position: relative;\n}\n\n.carousel.pointer-event {\n  touch-action: pan-y;\n}\n\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n  @include clearfix();\n}\n\n.carousel-item {\n  position: relative;\n  display: none;\n  float: left;\n  width: 100%;\n  margin-right: -100%;\n  backface-visibility: hidden;\n  @include transition($carousel-transition);\n}\n\n.carousel-item.active,\n.carousel-item-next,\n.carousel-item-prev {\n  display: block;\n}\n\n.carousel-item-next:not(.carousel-item-start),\n.active.carousel-item-end {\n  transform: translateX(100%);\n}\n\n.carousel-item-prev:not(.carousel-item-end),\n.active.carousel-item-start {\n  transform: translateX(-100%);\n}\n\n\n//\n// Alternate transitions\n//\n\n.carousel-fade {\n  .carousel-item {\n    opacity: 0;\n    transition-property: opacity;\n    transform: none;\n  }\n\n  .carousel-item.active,\n  .carousel-item-next.carousel-item-start,\n  .carousel-item-prev.carousel-item-end {\n    z-index: 1;\n    opacity: 1;\n  }\n\n  .active.carousel-item-start,\n  .active.carousel-item-end {\n    z-index: 0;\n    opacity: 0;\n    @include transition(opacity 0s $carousel-transition-duration);\n  }\n}\n\n\n//\n// Left/right controls for nav\n//\n\n.carousel-control-prev,\n.carousel-control-next {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  z-index: 1;\n  // Use flex for alignment (1-3)\n  display: flex; // 1. allow flex styles\n  align-items: center; // 2. vertically center contents\n  justify-content: center; // 3. horizontally center contents\n  width: $carousel-control-width;\n  padding: 0;\n  color: $carousel-control-color;\n  text-align: center;\n  background: none;\n  border: 0;\n  opacity: $carousel-control-opacity;\n  @include transition($carousel-control-transition);\n\n  // Hover/focus state\n  &:hover,\n  &:focus {\n    color: $carousel-control-color;\n    text-decoration: none;\n    outline: 0;\n    opacity: $carousel-control-hover-opacity;\n  }\n}\n.carousel-control-prev {\n  left: 0;\n  background-image: if($enable-gradients, linear-gradient(90deg, rgba($black, .25), rgba($black, .001)), null);\n}\n.carousel-control-next {\n  right: 0;\n  background-image: if($enable-gradients, linear-gradient(270deg, rgba($black, .25), rgba($black, .001)), null);\n}\n\n// Icons for within\n.carousel-control-prev-icon,\n.carousel-control-next-icon {\n  display: inline-block;\n  width: $carousel-control-icon-width;\n  height: $carousel-control-icon-width;\n  background-repeat: no-repeat;\n  background-position: 50%;\n  background-size: 100% 100%;\n}\n\n.carousel-control-prev-icon {\n  background-image: escape-svg($carousel-control-prev-icon-bg) #{\"/*rtl:\" + escape-svg($carousel-control-next-icon-bg) + \"*/\"};\n}\n.carousel-control-next-icon {\n  background-image: escape-svg($carousel-control-next-icon-bg) #{\"/*rtl:\" + escape-svg($carousel-control-prev-icon-bg) + \"*/\"};\n}\n\n// Optional indicator pips/controls\n//\n// Add a container (such as a list) with the following class and add an item (ideally a focusable control,\n// like a button) with data-bs-target for each slide your carousel holds.\n\n.carousel-indicators {\n  position: absolute;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 2;\n  display: flex;\n  justify-content: center;\n  padding: 0;\n  // Use the .carousel-control's width as margin so we don't overlay those\n  margin-right: $carousel-control-width;\n  margin-bottom: 1rem;\n  margin-left: $carousel-control-width;\n\n  [data-bs-target] {\n    box-sizing: content-box;\n    flex: 0 1 auto;\n    width: $carousel-indicator-width;\n    height: $carousel-indicator-height;\n    padding: 0;\n    margin-right: $carousel-indicator-spacer;\n    margin-left: $carousel-indicator-spacer;\n    text-indent: -999px;\n    cursor: pointer;\n    background-color: $carousel-indicator-active-bg;\n    background-clip: padding-box;\n    border: 0;\n    // Use transparent borders to increase the hit area by 10px on top and bottom.\n    border-top: $carousel-indicator-hit-area-height solid transparent;\n    border-bottom: $carousel-indicator-hit-area-height solid transparent;\n    opacity: $carousel-indicator-opacity;\n    @include transition($carousel-indicator-transition);\n  }\n\n  .active {\n    opacity: $carousel-indicator-active-opacity;\n  }\n}\n\n\n// Optional captions\n//\n//\n\n.carousel-caption {\n  position: absolute;\n  right: (100% - $carousel-caption-width) * .5;\n  bottom: $carousel-caption-spacer;\n  left: (100% - $carousel-caption-width) * .5;\n  padding-top: $carousel-caption-padding-y;\n  padding-bottom: $carousel-caption-padding-y;\n  color: $carousel-caption-color;\n  text-align: center;\n}\n\n// Dark mode carousel\n\n@mixin carousel-dark() {\n  .carousel-control-prev-icon,\n  .carousel-control-next-icon {\n    filter: $carousel-dark-control-icon-filter;\n  }\n\n  .carousel-indicators [data-bs-target] {\n    background-color: $carousel-dark-indicator-active-bg;\n  }\n\n  .carousel-caption {\n    color: $carousel-dark-caption-color;\n  }\n}\n\n.carousel-dark {\n  @include carousel-dark();\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    @if $color-mode-type == \"media-query\" {\n      .carousel {\n        @include carousel-dark();\n      }\n    } @else {\n      .carousel,\n      &.carousel {\n        @include carousel-dark();\n      }\n    }\n  }\n}\n","// scss-docs-start clearfix\n@mixin clearfix() {\n  &::after {\n    display: block;\n    clear: both;\n    content: \"\";\n  }\n}\n// scss-docs-end clearfix\n","//\n// Rotating border\n//\n\n.spinner-grow,\n.spinner-border {\n  display: inline-block;\n  width: var(--#{$prefix}spinner-width);\n  height: var(--#{$prefix}spinner-height);\n  vertical-align: var(--#{$prefix}spinner-vertical-align);\n  // stylelint-disable-next-line property-disallowed-list\n  border-radius: 50%;\n  animation: var(--#{$prefix}spinner-animation-speed) linear infinite var(--#{$prefix}spinner-animation-name);\n}\n\n// scss-docs-start spinner-border-keyframes\n@keyframes spinner-border {\n  to { transform: rotate(360deg) #{\"/* rtl:ignore */\"}; }\n}\n// scss-docs-end spinner-border-keyframes\n\n.spinner-border {\n  // scss-docs-start spinner-border-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width};\n  --#{$prefix}spinner-height: #{$spinner-height};\n  --#{$prefix}spinner-vertical-align: #{$spinner-vertical-align};\n  --#{$prefix}spinner-border-width: #{$spinner-border-width};\n  --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed};\n  --#{$prefix}spinner-animation-name: spinner-border;\n  // scss-docs-end spinner-border-css-vars\n\n  border: var(--#{$prefix}spinner-border-width) solid currentcolor;\n  border-right-color: transparent;\n}\n\n.spinner-border-sm {\n  // scss-docs-start spinner-border-sm-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width-sm};\n  --#{$prefix}spinner-height: #{$spinner-height-sm};\n  --#{$prefix}spinner-border-width: #{$spinner-border-width-sm};\n  // scss-docs-end spinner-border-sm-css-vars\n}\n\n//\n// Growing circle\n//\n\n// scss-docs-start spinner-grow-keyframes\n@keyframes spinner-grow {\n  0% {\n    transform: scale(0);\n  }\n  50% {\n    opacity: 1;\n    transform: none;\n  }\n}\n// scss-docs-end spinner-grow-keyframes\n\n.spinner-grow {\n  // scss-docs-start spinner-grow-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width};\n  --#{$prefix}spinner-height: #{$spinner-height};\n  --#{$prefix}spinner-vertical-align: #{$spinner-vertical-align};\n  --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed};\n  --#{$prefix}spinner-animation-name: spinner-grow;\n  // scss-docs-end spinner-grow-css-vars\n\n  background-color: currentcolor;\n  opacity: 0;\n}\n\n.spinner-grow-sm {\n  --#{$prefix}spinner-width: #{$spinner-width-sm};\n  --#{$prefix}spinner-height: #{$spinner-height-sm};\n}\n\n@if $enable-reduced-motion {\n  @media (prefers-reduced-motion: reduce) {\n    .spinner-border,\n    .spinner-grow {\n      --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed * 2};\n    }\n  }\n}\n","// stylelint-disable function-disallowed-list\n\n%offcanvas-css-vars {\n  // scss-docs-start offcanvas-css-vars\n  --#{$prefix}offcanvas-zindex: #{$zindex-offcanvas};\n  --#{$prefix}offcanvas-width: #{$offcanvas-horizontal-width};\n  --#{$prefix}offcanvas-height: #{$offcanvas-vertical-height};\n  --#{$prefix}offcanvas-padding-x: #{$offcanvas-padding-x};\n  --#{$prefix}offcanvas-padding-y: #{$offcanvas-padding-y};\n  --#{$prefix}offcanvas-color: #{$offcanvas-color};\n  --#{$prefix}offcanvas-bg: #{$offcanvas-bg-color};\n  --#{$prefix}offcanvas-border-width: #{$offcanvas-border-width};\n  --#{$prefix}offcanvas-border-color: #{$offcanvas-border-color};\n  --#{$prefix}offcanvas-box-shadow: #{$offcanvas-box-shadow};\n  --#{$prefix}offcanvas-transition: #{transform $offcanvas-transition-duration ease-in-out};\n  --#{$prefix}offcanvas-title-line-height: #{$offcanvas-title-line-height};\n  // scss-docs-end offcanvas-css-vars\n}\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $next: breakpoint-next($breakpoint, $grid-breakpoints);\n  $infix: breakpoint-infix($next, $grid-breakpoints);\n\n  .offcanvas#{$infix} {\n    @extend %offcanvas-css-vars;\n  }\n}\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $next: breakpoint-next($breakpoint, $grid-breakpoints);\n  $infix: breakpoint-infix($next, $grid-breakpoints);\n\n  .offcanvas#{$infix} {\n    @include media-breakpoint-down($next) {\n      position: fixed;\n      bottom: 0;\n      z-index: var(--#{$prefix}offcanvas-zindex);\n      display: flex;\n      flex-direction: column;\n      max-width: 100%;\n      color: var(--#{$prefix}offcanvas-color);\n      visibility: hidden;\n      background-color: var(--#{$prefix}offcanvas-bg);\n      background-clip: padding-box;\n      outline: 0;\n      @include box-shadow(var(--#{$prefix}offcanvas-box-shadow));\n      @include transition(var(--#{$prefix}offcanvas-transition));\n\n      &.offcanvas-start {\n        top: 0;\n        left: 0;\n        width: var(--#{$prefix}offcanvas-width);\n        border-right: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateX(-100%);\n      }\n\n      &.offcanvas-end {\n        top: 0;\n        right: 0;\n        width: var(--#{$prefix}offcanvas-width);\n        border-left: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateX(100%);\n      }\n\n      &.offcanvas-top {\n        top: 0;\n        right: 0;\n        left: 0;\n        height: var(--#{$prefix}offcanvas-height);\n        max-height: 100%;\n        border-bottom: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateY(-100%);\n      }\n\n      &.offcanvas-bottom {\n        right: 0;\n        left: 0;\n        height: var(--#{$prefix}offcanvas-height);\n        max-height: 100%;\n        border-top: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateY(100%);\n      }\n\n      &.showing,\n      &.show:not(.hiding) {\n        transform: none;\n      }\n\n      &.showing,\n      &.hiding,\n      &.show {\n        visibility: visible;\n      }\n    }\n\n    @if not ($infix == \"\") {\n      @include media-breakpoint-up($next) {\n        --#{$prefix}offcanvas-height: auto;\n        --#{$prefix}offcanvas-border-width: 0;\n        background-color: transparent !important; // stylelint-disable-line declaration-no-important\n\n        .offcanvas-header {\n          display: none;\n        }\n\n        .offcanvas-body {\n          display: flex;\n          flex-grow: 0;\n          padding: 0;\n          overflow-y: visible;\n          // Reset `background-color` in case `.bg-*` classes are used in offcanvas\n          background-color: transparent !important; // stylelint-disable-line declaration-no-important\n        }\n      }\n    }\n  }\n}\n\n.offcanvas-backdrop {\n  @include overlay-backdrop($zindex-offcanvas-backdrop, $offcanvas-backdrop-bg, $offcanvas-backdrop-opacity);\n}\n\n.offcanvas-header {\n  display: flex;\n  align-items: center;\n  padding: var(--#{$prefix}offcanvas-padding-y) var(--#{$prefix}offcanvas-padding-x);\n\n  .btn-close {\n    padding: calc(var(--#{$prefix}offcanvas-padding-y) * .5) calc(var(--#{$prefix}offcanvas-padding-x) * .5);\n    margin: calc(-.5 * var(--#{$prefix}offcanvas-padding-y)) calc(-.5 * var(--#{$prefix}offcanvas-padding-x)) calc(-.5 * var(--#{$prefix}offcanvas-padding-y)) auto;\n  }\n}\n\n.offcanvas-title {\n  margin-bottom: 0;\n  line-height: var(--#{$prefix}offcanvas-title-line-height);\n}\n\n.offcanvas-body {\n  flex-grow: 1;\n  padding: var(--#{$prefix}offcanvas-padding-y) var(--#{$prefix}offcanvas-padding-x);\n  overflow-y: auto;\n}\n",".placeholder {\n  display: inline-block;\n  min-height: 1em;\n  vertical-align: middle;\n  cursor: wait;\n  background-color: currentcolor;\n  opacity: $placeholder-opacity-max;\n\n  &.btn::before {\n    display: inline-block;\n    content: \"\";\n  }\n}\n\n// Sizing\n.placeholder-xs {\n  min-height: .6em;\n}\n\n.placeholder-sm {\n  min-height: .8em;\n}\n\n.placeholder-lg {\n  min-height: 1.2em;\n}\n\n// Animation\n.placeholder-glow {\n  .placeholder {\n    animation: placeholder-glow 2s ease-in-out infinite;\n  }\n}\n\n@keyframes placeholder-glow {\n  50% {\n    opacity: $placeholder-opacity-min;\n  }\n}\n\n.placeholder-wave {\n  mask-image: linear-gradient(130deg, $black 55%, rgba(0, 0, 0, (1 - $placeholder-opacity-min)) 75%, $black 95%);\n  mask-size: 200% 100%;\n  animation: placeholder-wave 2s linear infinite;\n}\n\n@keyframes placeholder-wave {\n  100% {\n    mask-position: -200% 0%;\n  }\n}\n","// All-caps `RGBA()` function used because of this Sass bug: https://github.com/sass/node-sass/issues/2251\n@each $color, $value in $theme-colors {\n  .text-bg-#{$color} {\n    color: color-contrast($value) if($enable-important-utilities, !important, null);\n    background-color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}bg-opacity, 1)) if($enable-important-utilities, !important, null);\n  }\n}\n","// All-caps `RGBA()` function used because of this Sass bug: https://github.com/sass/node-sass/issues/2251\n@each $color, $value in $theme-colors {\n  .link-#{$color} {\n    color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n    text-decoration-color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n\n    @if $link-shade-percentage != 0 {\n      &:hover,\n      &:focus {\n        $hover-color: if(color-contrast($value) == $color-contrast-light, shade-color($value, $link-shade-percentage), tint-color($value, $link-shade-percentage));\n        color: RGBA(#{to-rgb($hover-color)}, var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n        text-decoration-color: RGBA(to-rgb($hover-color), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n      }\n    }\n  }\n}\n\n// One-off special link helper as a bridge until v6\n.link-body-emphasis {\n  color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n  text-decoration-color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n\n  @if $link-shade-percentage != 0 {\n    &:hover,\n    &:focus {\n      color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-opacity, .75)) if($enable-important-utilities, !important, null);\n      text-decoration-color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-underline-opacity, .75)) if($enable-important-utilities, !important, null);\n    }\n  }\n}\n",".focus-ring:focus {\n  outline: 0;\n  // By default, there is no `--bs-focus-ring-x`, `--bs-focus-ring-y`, or `--bs-focus-ring-blur`, but we provide CSS variables with fallbacks to initial `0` values\n  box-shadow: var(--#{$prefix}focus-ring-x, 0) var(--#{$prefix}focus-ring-y, 0) var(--#{$prefix}focus-ring-blur, 0) var(--#{$prefix}focus-ring-width) var(--#{$prefix}focus-ring-color);\n}\n",".icon-link {\n  display: inline-flex;\n  gap: $icon-link-gap;\n  align-items: center;\n  text-decoration-color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, .5));\n  text-underline-offset: $icon-link-underline-offset;\n  backface-visibility: hidden;\n\n  > .bi {\n    flex-shrink: 0;\n    width: $icon-link-icon-size;\n    height: $icon-link-icon-size;\n    fill: currentcolor;\n    @include transition($icon-link-icon-transition);\n  }\n}\n\n.icon-link-hover {\n  &:hover,\n  &:focus-visible {\n    > .bi {\n      transform: var(--#{$prefix}icon-link-transform, $icon-link-icon-transform);\n    }\n  }\n}\n","// Credit: Nicolas Gallagher and SUIT CSS.\n\n.ratio {\n  position: relative;\n  width: 100%;\n\n  &::before {\n    display: block;\n    padding-top: var(--#{$prefix}aspect-ratio);\n    content: \"\";\n  }\n\n  > * {\n    position: absolute;\n    top: 0;\n    left: 0;\n    width: 100%;\n    height: 100%;\n  }\n}\n\n@each $key, $ratio in $aspect-ratios {\n  .ratio-#{$key} {\n    --#{$prefix}aspect-ratio: #{$ratio};\n  }\n}\n","// Shorthand\n\n.fixed-top {\n  position: fixed;\n  top: 0;\n  right: 0;\n  left: 0;\n  z-index: $zindex-fixed;\n}\n\n.fixed-bottom {\n  position: fixed;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: $zindex-fixed;\n}\n\n// Responsive sticky top and bottom\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .sticky#{$infix}-top {\n      position: sticky;\n      top: 0;\n      z-index: $zindex-sticky;\n    }\n\n    .sticky#{$infix}-bottom {\n      position: sticky;\n      bottom: 0;\n      z-index: $zindex-sticky;\n    }\n  }\n}\n","// scss-docs-start stacks\n.hstack {\n  display: flex;\n  flex-direction: row;\n  align-items: center;\n  align-self: stretch;\n}\n\n.vstack {\n  display: flex;\n  flex: 1 1 auto;\n  flex-direction: column;\n  align-self: stretch;\n}\n// scss-docs-end stacks\n","//\n// Visually hidden\n//\n\n.visually-hidden,\n.visually-hidden-focusable:not(:focus):not(:focus-within) {\n  @include visually-hidden();\n}\n","// stylelint-disable declaration-no-important\n\n// Hide content visually while keeping it accessible to assistive technologies\n//\n// See: https://www.a11yproject.com/posts/2013-01-11-how-to-hide-content/\n// See: https://kittygiraudel.com/2016/10/13/css-hide-and-seek/\n\n@mixin visually-hidden() {\n  width: 1px !important;\n  height: 1px !important;\n  padding: 0 !important;\n  margin: -1px !important; // Fix for https://github.com/twbs/bootstrap/issues/25686\n  overflow: hidden !important;\n  clip: rect(0, 0, 0, 0) !important;\n  white-space: nowrap !important;\n  border: 0 !important;\n\n  // Fix for positioned table caption that could become anonymous cells\n  &:not(caption) {\n    position: absolute !important;\n  }\n}\n\n// Use to only display content when it's focused, or one of its child elements is focused\n// (i.e. when focus is within the element/container that the class was applied to)\n//\n// Useful for \"Skip to main content\" links; see https://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n\n@mixin visually-hidden-focusable() {\n  &:not(:focus):not(:focus-within) {\n    @include visually-hidden();\n  }\n}\n","//\n// Stretched link\n//\n\n.stretched-link {\n  &::#{$stretched-link-pseudo-element} {\n    position: absolute;\n    top: 0;\n    right: 0;\n    bottom: 0;\n    left: 0;\n    z-index: $stretched-link-z-index;\n    content: \"\";\n  }\n}\n","//\n// Text truncation\n//\n\n.text-truncate {\n  @include text-truncate();\n}\n","// Text truncate\n// Requires inline-block or block for proper styling\n\n@mixin text-truncate() {\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n",".vr {\n  display: inline-block;\n  align-self: stretch;\n  width: $vr-border-width;\n  min-height: 1em;\n  background-color: currentcolor;\n  opacity: $hr-opacity;\n}\n","// Utility generator\n// Used to generate utilities & print utilities\n@mixin generate-utility($utility, $infix: \"\", $is-rfs-media-query: false) {\n  $values: map-get($utility, values);\n\n  // If the values are a list or string, convert it into a map\n  @if type-of($values) == \"string\" or type-of(nth($values, 1)) != \"list\" {\n    $values: zip($values, $values);\n  }\n\n  @each $key, $value in $values {\n    $properties: map-get($utility, property);\n\n    // Multiple properties are possible, for example with vertical or horizontal margins or paddings\n    @if type-of($properties) == \"string\" {\n      $properties: append((), $properties);\n    }\n\n    // Use custom class if present\n    $property-class: if(map-has-key($utility, class), map-get($utility, class), nth($properties, 1));\n    $property-class: if($property-class == null, \"\", $property-class);\n\n    // Use custom CSS variable name if present, otherwise default to `class`\n    $css-variable-name: if(map-has-key($utility, css-variable-name), map-get($utility, css-variable-name), map-get($utility, class));\n\n    // State params to generate pseudo-classes\n    $state: if(map-has-key($utility, state), map-get($utility, state), ());\n\n    $infix: if($property-class == \"\" and str-slice($infix, 1, 1) == \"-\", str-slice($infix, 2), $infix);\n\n    // Don't prefix if value key is null (e.g. with shadow class)\n    $property-class-modifier: if($key, if($property-class == \"\" and $infix == \"\", \"\", \"-\") + $key, \"\");\n\n    @if map-get($utility, rfs) {\n      // Inside the media query\n      @if $is-rfs-media-query {\n        $val: rfs-value($value);\n\n        // Do not render anything if fluid and non fluid values are the same\n        $value: if($val == rfs-fluid-value($value), null, $val);\n      }\n      @else {\n        $value: rfs-fluid-value($value);\n      }\n    }\n\n    $is-css-var: map-get($utility, css-var);\n    $is-local-vars: map-get($utility, local-vars);\n    $is-rtl: map-get($utility, rtl);\n\n    @if $value != null {\n      @if $is-rtl == false {\n        /* rtl:begin:remove */\n      }\n\n      @if $is-css-var {\n        .#{$property-class + $infix + $property-class-modifier} {\n          --#{$prefix}#{$css-variable-name}: #{$value};\n        }\n\n        @each $pseudo in $state {\n          .#{$property-class + $infix + $property-class-modifier}-#{$pseudo}:#{$pseudo} {\n            --#{$prefix}#{$css-variable-name}: #{$value};\n          }\n        }\n      } @else {\n        .#{$property-class + $infix + $property-class-modifier} {\n          @each $property in $properties {\n            @if $is-local-vars {\n              @each $local-var, $variable in $is-local-vars {\n                --#{$prefix}#{$local-var}: #{$variable};\n              }\n            }\n            #{$property}: $value if($enable-important-utilities, !important, null);\n          }\n        }\n\n        @each $pseudo in $state {\n          .#{$property-class + $infix + $property-class-modifier}-#{$pseudo}:#{$pseudo} {\n            @each $property in $properties {\n              @if $is-local-vars {\n                @each $local-var, $variable in $is-local-vars {\n                  --#{$prefix}#{$local-var}: #{$variable};\n                }\n              }\n              #{$property}: $value if($enable-important-utilities, !important, null);\n            }\n          }\n        }\n      }\n\n      @if $is-rtl == false {\n        /* rtl:end:remove */\n      }\n    }\n  }\n}\n","// Loop over each breakpoint\n@each $breakpoint in map-keys($grid-breakpoints) {\n\n  // Generate media query if needed\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    // Loop over each utility property\n    @each $key, $utility in $utilities {\n      // The utility can be disabled with `false`, thus check if the utility is a map first\n      // Only proceed if responsive media queries are enabled or if it's the base media query\n      @if type-of($utility) == \"map\" and (map-get($utility, responsive) or $infix == \"\") {\n        @include generate-utility($utility, $infix);\n      }\n    }\n  }\n}\n\n// RFS rescaling\n@media (min-width: $rfs-mq-value) {\n  @each $breakpoint in map-keys($grid-breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    @if (map-get($grid-breakpoints, $breakpoint) < $rfs-breakpoint) {\n      // Loop over each utility property\n      @each $key, $utility in $utilities {\n        // The utility can be disabled with `false`, thus check if the utility is a map first\n        // Only proceed if responsive media queries are enabled or if it's the base media query\n        @if type-of($utility) == \"map\" and map-get($utility, rfs) and (map-get($utility, responsive) or $infix == \"\") {\n          @include generate-utility($utility, $infix, true);\n        }\n      }\n    }\n  }\n}\n\n\n// Print utilities\n@media print {\n  @each $key, $utility in $utilities {\n    // The utility can be disabled with `false`, thus check if the utility is a map first\n    // Then check if the utility needs print styles\n    @if type-of($utility) == \"map\" and map-get($utility, print) == true {\n      @include generate-utility($utility, \"-print\");\n    }\n  }\n}\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/_preview/13/_static/styles/pydata-sphinx-theme.css b/_preview/13/_static/styles/pydata-sphinx-theme.css
new file mode 100644
index 0000000..90ba101
--- /dev/null
+++ b/_preview/13/_static/styles/pydata-sphinx-theme.css
@@ -0,0 +1,2 @@
+html{--pst-header-height:4rem;--pst-header-article-height:calc(var(--pst-header-height)*2/3);--pst-sidebar-secondary:17rem;--pst-font-size-base:1rem;--pst-font-size-h1:2.5rem;--pst-font-size-h2:2rem;--pst-font-size-h3:1.75rem;--pst-font-size-h4:1.5rem;--pst-font-size-h5:1.25rem;--pst-font-size-h6:1.1rem;--pst-font-size-milli:0.9rem;--pst-sidebar-font-size:0.9rem;--pst-sidebar-font-size-mobile:1.1rem;--pst-sidebar-header-font-size:1.2rem;--pst-sidebar-header-font-weight:600;--pst-admonition-font-weight-heading:600;--pst-font-weight-caption:300;--pst-font-weight-heading:400;--pst-font-family-base-system:-apple-system,"BlinkMacSystemFont","Segoe UI","Helvetica Neue","Arial",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";--pst-font-family-monospace-system:"SFMono-Regular","Menlo","Consolas","Monaco","Liberation Mono","Lucida Console",monospace;--pst-font-family-base:var(--pst-font-family-base-system);--pst-font-family-heading:var(--pst-font-family-base-system);--pst-font-family-monospace:var(--pst-font-family-monospace-system);--pst-font-size-icon:1.5rem;--pst-icon-check-circle:"";--pst-icon-info-circle:"";--pst-icon-exclamation-triangle:"";--pst-icon-exclamation-circle:"";--pst-icon-times-circle:"";--pst-icon-lightbulb:"";--pst-icon-download:"";--pst-icon-angle-left:"";--pst-icon-angle-right:"";--pst-icon-external-link:"";--pst-icon-search-minus:"";--pst-icon-github:"";--pst-icon-gitlab:"";--pst-icon-share:"";--pst-icon-bell:"";--pst-icon-pencil:"";--pst-breadcrumb-divider:"";--pst-icon-admonition-default:var(--pst-icon-bell);--pst-icon-admonition-note:var(--pst-icon-info-circle);--pst-icon-admonition-attention:var(--pst-icon-exclamation-circle);--pst-icon-admonition-caution:var(--pst-icon-exclamation-triangle);--pst-icon-admonition-warning:var(--pst-icon-exclamation-triangle);--pst-icon-admonition-danger:var(--pst-icon-exclamation-triangle);--pst-icon-admonition-error:var(--pst-icon-times-circle);--pst-icon-admonition-hint:var(--pst-icon-lightbulb);--pst-icon-admonition-tip:var(--pst-icon-lightbulb);--pst-icon-admonition-important:var(--pst-icon-exclamation-circle);--pst-icon-admonition-seealso:var(--pst-icon-share);--pst-icon-admonition-todo:var(--pst-icon-pencil);--pst-icon-versionmodified-default:var(--pst-icon-exclamation-circle);--pst-icon-versionmodified-added:var(--pst-icon-exclamation-circle);--pst-icon-versionmodified-changed:var(--pst-icon-exclamation-circle);--pst-icon-versionmodified-deprecated:var(--pst-icon-exclamation-circle);font-size:var(--pst-font-size-base);scroll-padding-top:calc(var(--pst-header-height) + 1rem)}body{background-color:var(--pst-color-background);color:var(--pst-color-text-base);display:flex;flex-direction:column;font-family:var(--pst-font-family-base);font-weight:400;line-height:1.65;min-height:100vh}body::-webkit-scrollbar-track{background-color:var(--pst-color-background)}p{font-size:1em;margin-bottom:1.15rem}p.rubric{border-bottom:1px solid var(--pst-color-border)}p.centered{text-align:center}a{word-wrap:break-word;color:var(--pst-color-link);text-decoration:underline;text-decoration-thickness:max(1px,.0625rem);text-underline-offset:.1578em}a:hover{color:var(--pst-color-link-hover);text-decoration-skip-ink:none;text-decoration-thickness:max(3px,.1875rem,.12em)}a:active,a:visited{color:var(--pst-color-link)}a:visited:hover{color:var(--pst-color-link-hover)}a.headerlink{color:var(--pst-color-secondary);font-size:.8em;margin-left:.2em;opacity:.7;padding:0 4px;text-decoration:none;transition:all .2s ease-out;user-select:none}a.headerlink:hover{opacity:1}a.github:before,a.gitlab:before{color:var(--pst-color-text-muted);font:var(--fa-font-brands);margin-right:.25rem}a.github:before{content:var(--pst-icon-github)}a.gitlab:before{content:var(--pst-icon-gitlab)}h1,h2,h3,h4,h5,h6{font-family:var(--pst-font-family-heading);font-weight:var(--pst-font-weight-heading);line-height:1.15;margin:2.75rem 0 1.05rem}h1{font-size:var(--pst-font-size-h1);margin-top:0}h1,h2{color:var(--pst-heading-color)}h2{font-size:var(--pst-font-size-h2)}h3{font-size:var(--pst-font-size-h3)}h3,h4{color:var(--pst-heading-color)}h4{font-size:var(--pst-font-size-h4)}h5{font-size:var(--pst-font-size-h5)}h5,h6{color:var(--pst-color-text-base)}h6{font-size:var(--pst-font-size-h6)}.text_small,small{font-size:var(--pst-font-size-milli)}hr{border:0;border-top:1px solid var(--pst-color-border)}code,kbd,pre,samp{font-family:var(--pst-font-family-monospace)}kbd{background-color:var(--pst-color-on-background);color:var(--pst-color-text-muted)}kbd:not(.compound){border:1px solid var(--pst-color-border);box-shadow:1px 1px 1px var(--pst-color-shadow);margin:0 .1rem;padding:.1rem .4rem}code{color:var(--pst-color-inline-code)}pre{background-color:var(--pst-color-surface);border:1px solid var(--pst-color-border);border-radius:.25rem;color:var(--pst-color-text-base);line-height:1.2em;margin:1.5em 0;padding:1rem}pre .linenos{opacity:.8;padding-right:10px}#pst-back-to-top{background-color:var(--pst-color-secondary);border:none;color:var(--pst-color-secondary-text);display:none;left:50vw;position:fixed;top:90vh;transform:translate(-50%);z-index:1080}#pst-back-to-top 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table.dataframe tbody tr:hover{background-color:var(--pst-color-table-row-hover-bg)}html[data-theme=dark] .bd-content .nboutput .output_area.rendered_html:not(:has(table.dataframe)),html[data-theme=dark] .bd-content .nboutput .output_area .widget-subarea{color:var(--pst-color-on-background);background-color:var(--pst-color-text-base);border-radius:.25rem;padding:.5rem}html[data-theme=dark] .bd-content .nboutput .output_area.stderr{background-color:var(--pst-color-danger)}div.nblast.container{margin-bottom:1rem}div.cell_output .output{max-width:100%;overflow-x:auto}.bd-content div.cell_output table.dataframe{color:var(--pst-color-table);border:1px solid var(--pst-color-table-outer-border)}.bd-content div.cell_output table.dataframe th~th,.bd-content div.cell_output table.dataframe th~td,.bd-content div.cell_output table.dataframe td~th,.bd-content div.cell_output table.dataframe td~td{border-left:1px solid var(--pst-color-table-inner-border)}.bd-content div.cell_output table.dataframe thead tr{background-color:var(--pst-color-table-heading-bg);border-bottom:2px solid var(--pst-color-primary)}.bd-content div.cell_output table.dataframe tbody tr:nth-child(odd){background-color:var(--pst-color-table-row-zebra-low-bg)}.bd-content div.cell_output table.dataframe tbody tr:nth-child(even){background-color:var(--pst-color-table-row-zebra-high-bg)}.bd-content div.cell_output table.dataframe tbody tr:hover{background-color:var(--pst-color-table-row-hover-bg)}html[data-theme=dark] .bd-content div.cell_output img,html[data-theme=dark] .bd-content div.cell_output .text_html:not(:has(table.dataframe)),html[data-theme=dark] .bd-content div.cell_output .widget-subarea{color:var(--pst-color-on-background);background-color:var(--pst-color-text-base);border-radius:.25rem;padding:.5rem}.bd-content div.cell_input{display:flex;flex-direction:column;justify-content:stretch}.bd-content div.cell_input,.bd-content div.output{border-radius:.25rem}.bd-content div.output table{table-layout:auto}html[data-theme=dark] .bd-content img.leaflet-tile.leaflet-tile-loaded{border-radius:0;padding:0}.bd-search-container div#search-results>h2{font-size:var(--pst-font-size-icon);margin-top:1rem}.bd-search-container div#search-results p.search-summary{color:var(--pst-color-text-muted)}.bd-search-container ul.search{margin:0;list-style:none}.bd-search-container ul.search li{background-image:none;padding:1rem 0;margin:1rem 0;border-top:1px solid var(--pst-color-text-muted)}.bd-search-container ul.search li>a{font-size:1.2em}.bd-search-container ul.search li div.context,.bd-search-container ul.search li p.context{color:var(--pst-color-text-base);margin:.5em 0 0}.bd-search-container ul.search li div.context a::before,.bd-search-container ul.search li p.context a::before{content:\"#\";padding-right:.2em;color:var(--pst-color-text-muted)}","html {\n  /*****************************************************************************\n  * Overall Layout Variables\n  */\n\n  // Header height will impact the top offset for many sections\n  // Article header is 66% of Header\n  --pst-header-height: 4rem;\n  --pst-header-article-height: calc(var(--pst-header-height) * 2 / 3);\n  --pst-sidebar-secondary: 17rem;\n}\n\n/*******************************************************************************\n* Breakpoints that trigger UI changes\n*\n* Note that media-breakpoint-down begins at the next highest level!\n* So we should choose a media-breakpoint-down one *lower* than when we want to start\n* example: media-breakpoint-up(md) and media-breakpoint-down(sm) trigger at the same time\n* ref: https://github.com/twbs/bootstrap/issues/31214\n*/\n$breakpoint-sidebar-primary: lg; // When we collapse the primary sidebar\n$breakpoint-sidebar-secondary: xl; // When we collapse the secondary sidebar\n$breakpoint-page-width: 88rem; // taken from sphinx-basic-ng, which we are ultimately going to inherit\n\n/*******************************************************************************\n* Define the animation behaviour\n*/\n$animation-time: 200ms;\n\n/*******************************************************************************\n* UI shaping and padding\n*/\n$admonition-border-radius: 0.25rem;\n\n// In this theme, some focus rings have rounded corners while others do not.\n// This variable sets the border radius for the rounded focus rings.\n$focus-ring-radius: 0.125rem; // 2px at 100% zoom and 16px base font.\n","html {\n  /*****************************************************************************\n  * Font features used in this theme\n  */\n\n  // base font size - applied at body/html level\n  --pst-font-size-base: 1rem;\n\n  // heading font sizes based on bootstrap sizing\n  --pst-font-size-h1: 2.5rem;\n  --pst-font-size-h2: 2rem;\n  --pst-font-size-h3: 1.75rem;\n  --pst-font-size-h4: 1.5rem;\n  --pst-font-size-h5: 1.25rem;\n  --pst-font-size-h6: 1.1rem;\n\n  // smaller than heading font sizes\n  --pst-font-size-milli: 0.9rem;\n\n  // Sidebar styles\n  --pst-sidebar-font-size: 0.9rem;\n  --pst-sidebar-font-size-mobile: 1.1rem;\n  --pst-sidebar-header-font-size: 1.2rem;\n  --pst-sidebar-header-font-weight: 600;\n\n  // Admonition styles\n  --pst-admonition-font-weight-heading: 600;\n\n  // Font weights\n  --pst-font-weight-caption: 300;\n  --pst-font-weight-heading: 400;\n\n  // Font family\n  // These are adapted from https://systemfontstack.com/ */\n  --pst-font-family-base-system: -apple-system, \"BlinkMacSystemFont\", \"Segoe UI\",\n    \"Helvetica Neue\", \"Arial\", sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\",\n    \"Segoe UI Symbol\";\n  --pst-font-family-monospace-system: \"SFMono-Regular\", \"Menlo\", \"Consolas\",\n    \"Monaco\", \"Liberation Mono\", \"Lucida Console\", monospace;\n  --pst-font-family-base: var(--pst-font-family-base-system);\n  --pst-font-family-heading: var(--pst-font-family-base-system);\n  --pst-font-family-monospace: var(--pst-font-family-monospace-system);\n}\n","html {\n  /*****************************************************************************\n  * Icon\n  */\n\n  // Font size across all icons\n  --pst-font-size-icon: 1.5rem;\n\n  // Font Awesome default icons\n  --pst-icon-check-circle: \"\\f058\"; // fa-solid fa-circle-check\n  --pst-icon-info-circle: \"\\f05a\"; // fa-solid fa-circle-info\n  --pst-icon-exclamation-triangle: \"\\f071\"; // fa-solid fa-triangle-exclamation\n  --pst-icon-exclamation-circle: \"\\f06a\"; // fa-solid fa-circle-exclamation\n  --pst-icon-times-circle: \"\\f057\"; // fa-solid fa-circle-xmark\n  --pst-icon-lightbulb: \"\\f0eb\"; // fa-solid fa-lightbulb\n  --pst-icon-download: \"\\f019\"; // fa-solid fa-download\n  --pst-icon-angle-left: \"\\f104\"; // fa-solid fa-angle-left\n  --pst-icon-angle-right: \"\\f105\"; // fa-solid fa-angle-right\n  --pst-icon-external-link: \"\\f35d\"; // fa-solid fa-up-right-from-square\n  --pst-icon-search-minus: \"\\f010\"; // fa-solid fa-magnifying-glass-minus\n  --pst-icon-github: \"\\f09b\"; // fa-brands fa-github\n  --pst-icon-gitlab: \"\\f296\"; // fa-brands fa-gitlab\n  --pst-icon-share: \"\\f064\"; // fa-solid fa-share\n  --pst-icon-bell: \"\\f0f3\"; // fa-solid fa-bell\n  --pst-icon-pencil: \"\\f303\"; // fa-solid fa-pencil\n\n  // Bootstrap icons\n  --pst-breadcrumb-divider: \"\\f105\";\n}\n","html {\n  /*****************************************************************************\n  * Admonitions\n  **/\n\n  --pst-icon-admonition-default: var(--pst-icon-bell);\n  --pst-icon-admonition-note: var(--pst-icon-info-circle);\n  --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-error: var(--pst-icon-times-circle);\n  --pst-icon-admonition-hint: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-tip: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-important: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-seealso: var(--pst-icon-share);\n  --pst-icon-admonition-todo: var(--pst-icon-pencil);\n}\n","html {\n  /*****************************************************************************\n  * versionmodified\n  **/\n\n  --pst-icon-versionmodified-default: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-added: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-changed: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-deprecated: var(--pst-icon-exclamation-circle);\n}\n","html {\n  font-size: var(--pst-font-size-base);\n  scroll-padding-top: calc(var(--pst-header-height) + 1rem);\n}\n\nbody {\n  background-color: var(--pst-color-background);\n  font-family: var(--pst-font-family-base);\n  font-weight: 400;\n  line-height: 1.65;\n  color: var(--pst-color-text-base);\n  min-height: 100vh;\n  display: flex;\n  flex-direction: column;\n\n  // hack to avoid the black background on some browser including Safari\n  &::-webkit-scrollbar-track {\n    background-color: var(--pst-color-background);\n  }\n}\n\np {\n  margin-bottom: 1.15rem;\n  font-size: 1em;\n\n  /* section header in docstring pages */\n  &.rubric {\n    border-bottom: 1px solid var(--pst-color-border);\n  }\n\n  &.centered {\n    text-align: center;\n  }\n}\n\na {\n  @include link-style-default;\n\n  // The # anchor that appears on hover over headings\n  &.headerlink {\n    color: var(--pst-color-secondary);\n    opacity: 0.7;\n    font-size: 0.8em;\n    padding: 0 4px;\n    margin-left: 0.2em;\n    text-decoration: none;\n    transition: all 0.2s ease-out;\n    user-select: none;\n\n    &:hover {\n      opacity: 1;\n    }\n  }\n\n  // set up a icon next to the shorten links from github and gitlab\n  &.github,\n  &.gitlab {\n    &::before {\n      color: var(--pst-color-text-muted);\n      font: var(--fa-font-brands);\n      margin-right: 0.25rem;\n    }\n  }\n\n  &.github::before {\n    content: var(--pst-icon-github);\n  }\n\n  &.gitlab::before {\n    content: var(--pst-icon-gitlab);\n  }\n}\n\n%heading-style {\n  margin: 2.75rem 0 1.05rem;\n  font-family: var(--pst-font-family-heading);\n  font-weight: var(--pst-font-weight-heading);\n  line-height: 1.15;\n}\n\nh1 {\n  @extend %heading-style;\n\n  margin-top: 0;\n  font-size: var(--pst-font-size-h1);\n  color: var(--pst-heading-color);\n}\n\nh2 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h2);\n  color: var(--pst-heading-color);\n}\n\nh3 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h3);\n  color: var(--pst-heading-color);\n}\n\nh4 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h4);\n  color: var(--pst-heading-color);\n}\n\nh5 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h5);\n  color: var(--pst-color-text-base);\n}\n\nh6 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h6);\n  color: var(--pst-color-text-base);\n}\n\nsmall,\n.text_small {\n  font-size: var(--pst-font-size-milli);\n}\n\nhr {\n  border: 0;\n  border-top: 1px solid var(--pst-color-border);\n}\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: var(--pst-font-family-monospace);\n}\n\nkbd {\n  // use theme negative\n  background-color: var(--pst-color-on-background);\n  color: var(--pst-color-text-muted);\n\n  // Compound keyboard elements will have nested kbd in them, so this prevents double lines\n  &:not(.compound) {\n    border: 1px solid var(--pst-color-border);\n    margin: 0 0.1rem;\n    padding: 0.1rem 0.4rem;\n    box-shadow: 1px 1px 1px var(--pst-color-shadow);\n  }\n}\n\ncode {\n  color: var(--pst-color-inline-code);\n}\n\npre {\n  margin: 1.5em 0;\n  padding: 1rem;\n  background-color: var(--pst-color-surface);\n  color: var(--pst-color-text-base);\n  line-height: 1.2em;\n  border: 1px solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n\n  .linenos {\n    // minimum opacity to make the line numbers WCAG AA conformant\n    opacity: 0.8;\n    padding-right: 10px;\n  }\n}\n\n// the back to top btn\n#pst-back-to-top {\n  z-index: $zindex-tooltip;\n  position: fixed;\n  display: none;\n  top: 90vh;\n  left: 50vw;\n  transform: translate(-50%);\n  color: var(--pst-color-secondary-text);\n  background-color: var(--pst-color-secondary);\n  border: none;\n\n  .fa-arrow-up {\n    // Using margin instead of a space character prevents the space between the\n    // icon and the text from being underlined when the button is hovered.\n    margin-inline-end: 0.5em;\n  }\n\n  &:hover {\n    @include link-style-hover;\n\n    text-decoration-thickness: 1px;\n    background-color: var(--pst-violet-600);\n    color: var(--pst-color-secondary-text);\n  }\n\n  &:focus-visible {\n    box-shadow: none;\n    outline: $focus-ring-outline;\n    outline-color: var(--pst-color-secondary);\n    outline-offset: $focus-ring-width;\n  }\n}\n\n// Focus ring\n// ----------\n// Note: The Bootstrap stylesheet provides the focus ring (customized by this\n// theme via Sass variables in _bootstrap.scss) in some cases. This rule covers\n// all other cases.\n:focus-visible {\n  outline: $focus-ring-outline;\n  box-shadow: none; // override Bootstrap\n}\n","// Consistent styling for links\n// ============================\n\n@use \"sass:string\";\n\n// Define some useful variables for links styling consistency\n\n// The default thickness of the underline for links will be either:\n//  - 1px\n//  - 0.0625rem if it's thicker than 1px because the user has changed the text\n//    size in their browser\n$link-underline-thickness: string.unquote(\"max(1px, .0625rem)\") !default;\n\n// Offset of link underlines from text baseline\n// The default is 3px expressed as ems, as calculated against the default body\n// font size (on desktop).\n$link-underline-offset: 0.1578em !default;\n\n// Thickness of link underlines in hover state\n// The default for each link will be the thickest of the following:\n//  - 3px\n//  - 0.1875rem, if it's thicker than 3px because the user has changed the text\n//    size in their browser\n//  - 0.12em (relative to the link's text size)\n$link-hover-decoration-thickness: string.unquote(\n  \"max(3px, .1875rem, .12em)\"\n) !default;\n\n// Ensures links have an underline decoration by default - needed to meet\n// WCAG SC 1.4.1\n@mixin link-decoration {\n  text-decoration: underline;\n\n  @if $link-underline-thickness {\n    text-decoration-thickness: $link-underline-thickness;\n  }\n\n  @if $link-underline-offset {\n    text-underline-offset: $link-underline-offset;\n  }\n}\n\n// Ensures links have an underline decoration on hover - distinct from the\n// default behaviour\n@mixin link-decoration-hover {\n  @if $link-hover-decoration-thickness {\n    text-decoration-thickness: $link-hover-decoration-thickness;\n\n    // Disable ink skipping on underlines on hover.\n    text-decoration-skip-ink: none;\n  }\n}\n\n// Simple hover style - can be used alone or in conjunction with other mixins\n// Add the text underline and change in thickness on hover.\n// Intended for use with the `:hover` pseudo-class.\n@mixin link-style-hover {\n  @include link-decoration;\n  @include link-decoration-hover;\n\n  color: var(--pst-color-link-hover);\n}\n\n// Default link styles\n// -------------------\n// Defines: default unvisited, visited, hover, and active.\n// TODO: @trallard to improve focus styles in subsequent PR\n@mixin link-style-default {\n  // So that really long links don't spill out of their container\n  word-wrap: break-word;\n  color: var(--pst-color-link);\n\n  @include link-decoration;\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n\n    @include link-decoration-hover;\n  }\n\n  // TODO: @trallard to add active styles in subsequent PR\n  &:active {\n    color: var(--pst-color-link);\n  }\n\n  // Visited should still be hoverable\n  &:visited {\n    color: var(--pst-color-link);\n\n    &:hover {\n      color: var(--pst-color-link-hover);\n    }\n  }\n}\n\n// Text link styles\n// ----------------\n// Makes links use the muted text colour and removes the underline.\n// Use this mixin for navigation bar links.\n@mixin link-style-text {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n\n    @include link-decoration;\n    @include link-decoration-hover;\n  }\n}\n\n// Sidebar and TOC links\n// ---------------------\n// Makes links use the muted text colour and removes the underline.\n// Use this mixin for navigation the primary sidebar and table of contents.\n// Active and hover should work together rather than one overriding the other.\n@mixin link-sidebar {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n\n  &:hover {\n    text-decoration: underline;\n    background-color: transparent;\n    color: var(--pst-color-link-hover);\n\n    @include link-decoration-hover;\n  }\n\n  // TODO: @trallard to update active styles in subsequent PR\n  &:active {\n    color: var(--pst-color-link-hover);\n  }\n\n  &:focus-visible {\n    box-shadow: $focus-ring-box-shadow;\n    outline: none;\n    z-index: 10; // keep focus ring on top (prevent the link-sidebar-current notch from lying on top of the ring)\n  }\n}\n\n// Sidebar current page link styles\n// --------------------------------\n// Adds a vertical line on the left hand side of the link to indicate that\n// it's the current page. Note this is distinct from an active state.\n// Used on the primary sidebar and the TOC.\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin link-sidebar-current {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n\n  @if $link-hover-decoration-thickness {\n    $notch-shadow: inset\n      $link-hover-decoration-thickness\n      0\n      0\n      var(--pst-color-primary);\n\n    box-shadow: $notch-shadow;\n\n    &:focus-visible {\n      box-shadow: $notch-shadow, $focus-ring-box-shadow;\n      outline: none;\n    }\n  }\n}\n\n// Navigation bar current page link styles\n// ---------------------------------------\n// Adds a bottom underline, this leaves enough space for the hover state without\n// cluttering the navbar.\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin link-navbar-current {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n\n  @if $link-hover-decoration-thickness {\n    border-bottom: $link-hover-decoration-thickness\n      solid\n      var(--pst-color-primary);\n  }\n}\n\n// Navigation bar icon links hover styles\n// --------------------------------------\n// Adds a bottom box-shadow - since there is no text we cannot use text-decoration\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin icon-navbar-hover {\n  &:hover {\n    color: var(--pst-color-link-hover);\n\n    @if $link-hover-decoration-thickness {\n      box-shadow: 0\n        $link-hover-decoration-thickness\n        0\n        var(--pst-color-link-hover);\n    }\n  }\n}\n\n// Mixin for links in the header (and the More dropdown toggle).\n\n// The mixin assumes it will be applied to some element X with a markup structure\n// like: X > .nav-link, or X > .dropdown-toggle.\n\n// It also assumes X.current is how the app annotates which item in the header nav\n// corresponds to the section in the docs that the user is currently reading.\n@mixin header-link {\n  // Target the child and not the parent because we want the underline in the\n  // mobile sidebar to only span the width of the text not the entire row/line.\n  > .nav-link,\n  > .dropdown-toggle {\n    border-radius: 2px;\n    color: var(--pst-color-text-muted);\n  }\n\n  > .nav-link {\n    // Set up pseudo-element for hover and current states below.\n    position: relative;\n\n    &::before {\n      content: \"\";\n      display: block;\n      position: absolute;\n      inset: 0;\n      background-color: transparent;\n    }\n\n    // Underline on hover.\n    // - Don't use text-decoration because it will wrap across two lines if\n    //   the link text also wraps across two lines.\n    // - Use pseudo-element in order to avoid the border-radius values\n    //   rounding the edges of the underline. (And since a header link can be\n    //   both focused and hovered at the same time and we want the focus ring\n    //   but not the underline to be rounded, we cannot use a box shadow or\n    //   bottom border link element to create the underline, or else it will\n    //   be rounded and if we apply border-radius 0 then the hovered focus\n    //   ring would go from rounded to sharp. So we have to use the\n    //   pseudo-element.)\n    &:hover {\n      color: var(--pst-color-secondary);\n      text-decoration: none; // override the link-style-hover mixin\n      &::before {\n        border-bottom: 3px solid var(--pst-color-secondary);\n      }\n    }\n\n    &:focus-visible {\n      box-shadow: none; // override Bootstrap\n      outline: 3px solid var(--pst-color-accent);\n      outline-offset: 3px;\n    }\n  }\n\n  > .dropdown-toggle {\n    &:focus-visible {\n      box-shadow: $focus-ring-box-shadow;\n    }\n\n    &:hover {\n      text-decoration: none;\n      box-shadow: 0 0 0 $focus-ring-width var(--pst-color-link-hover); // purple focus ring\n      // Brighten the text on hover (muted -> base)\n      color: var(--pst-color-text-base);\n    }\n  }\n\n  &.current {\n    > .nav-link {\n      color: var(--pst-color-primary);\n\n      // Underline the current navbar item\n      &::before {\n        border-bottom: 3px solid var(--pst-color-primary);\n      }\n    }\n  }\n}\n","// Variables\n//\n// Variables should follow the `$component-state-property-size` formula for\n// consistent naming. Ex: $nav-link-disabled-color and $modal-content-box-shadow-xs.\n\n// Color system\n\n// scss-docs-start gray-color-variables\n$white:    #fff !default;\n$gray-100: #f8f9fa !default;\n$gray-200: #e9ecef !default;\n$gray-300: #dee2e6 !default;\n$gray-400: #ced4da !default;\n$gray-500: #adb5bd !default;\n$gray-600: #6c757d !default;\n$gray-700: #495057 !default;\n$gray-800: #343a40 !default;\n$gray-900: #212529 !default;\n$black:    #000 !default;\n// scss-docs-end gray-color-variables\n\n// fusv-disable\n// scss-docs-start gray-colors-map\n$grays: (\n  \"100\": $gray-100,\n  \"200\": $gray-200,\n  \"300\": $gray-300,\n  \"400\": $gray-400,\n  \"500\": $gray-500,\n  \"600\": $gray-600,\n  \"700\": $gray-700,\n  \"800\": $gray-800,\n  \"900\": $gray-900\n) !default;\n// scss-docs-end gray-colors-map\n// fusv-enable\n\n// scss-docs-start color-variables\n$blue:    #0d6efd !default;\n$indigo:  #6610f2 !default;\n$purple:  #6f42c1 !default;\n$pink:    #d63384 !default;\n$red:     #dc3545 !default;\n$orange:  #fd7e14 !default;\n$yellow:  #ffc107 !default;\n$green:   #198754 !default;\n$teal:    #20c997 !default;\n$cyan:    #0dcaf0 !default;\n// scss-docs-end color-variables\n\n// scss-docs-start colors-map\n$colors: (\n  \"blue\":       $blue,\n  \"indigo\":     $indigo,\n  \"purple\":     $purple,\n  \"pink\":       $pink,\n  \"red\":        $red,\n  \"orange\":     $orange,\n  \"yellow\":     $yellow,\n  \"green\":      $green,\n  \"teal\":       $teal,\n  \"cyan\":       $cyan,\n  \"black\":      $black,\n  \"white\":      $white,\n  \"gray\":       $gray-600,\n  \"gray-dark\":  $gray-800\n) !default;\n// scss-docs-end colors-map\n\n// The contrast ratio to reach against white, to determine if color changes from \"light\" to \"dark\". Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n$min-contrast-ratio:   4.5 !default;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$color-contrast-dark:      $black !default;\n$color-contrast-light:     $white !default;\n\n// fusv-disable\n$blue-100: tint-color($blue, 80%) !default;\n$blue-200: tint-color($blue, 60%) !default;\n$blue-300: tint-color($blue, 40%) !default;\n$blue-400: tint-color($blue, 20%) !default;\n$blue-500: $blue !default;\n$blue-600: shade-color($blue, 20%) !default;\n$blue-700: shade-color($blue, 40%) !default;\n$blue-800: shade-color($blue, 60%) !default;\n$blue-900: shade-color($blue, 80%) !default;\n\n$indigo-100: tint-color($indigo, 80%) !default;\n$indigo-200: tint-color($indigo, 60%) !default;\n$indigo-300: tint-color($indigo, 40%) !default;\n$indigo-400: tint-color($indigo, 20%) !default;\n$indigo-500: $indigo !default;\n$indigo-600: shade-color($indigo, 20%) !default;\n$indigo-700: shade-color($indigo, 40%) !default;\n$indigo-800: shade-color($indigo, 60%) !default;\n$indigo-900: shade-color($indigo, 80%) !default;\n\n$purple-100: tint-color($purple, 80%) !default;\n$purple-200: tint-color($purple, 60%) !default;\n$purple-300: tint-color($purple, 40%) !default;\n$purple-400: tint-color($purple, 20%) !default;\n$purple-500: $purple !default;\n$purple-600: shade-color($purple, 20%) !default;\n$purple-700: shade-color($purple, 40%) !default;\n$purple-800: shade-color($purple, 60%) !default;\n$purple-900: shade-color($purple, 80%) !default;\n\n$pink-100: tint-color($pink, 80%) !default;\n$pink-200: tint-color($pink, 60%) !default;\n$pink-300: tint-color($pink, 40%) !default;\n$pink-400: tint-color($pink, 20%) !default;\n$pink-500: $pink !default;\n$pink-600: shade-color($pink, 20%) !default;\n$pink-700: shade-color($pink, 40%) !default;\n$pink-800: shade-color($pink, 60%) !default;\n$pink-900: shade-color($pink, 80%) !default;\n\n$red-100: tint-color($red, 80%) !default;\n$red-200: tint-color($red, 60%) !default;\n$red-300: tint-color($red, 40%) !default;\n$red-400: tint-color($red, 20%) !default;\n$red-500: $red !default;\n$red-600: shade-color($red, 20%) !default;\n$red-700: shade-color($red, 40%) !default;\n$red-800: shade-color($red, 60%) !default;\n$red-900: shade-color($red, 80%) !default;\n\n$orange-100: tint-color($orange, 80%) !default;\n$orange-200: tint-color($orange, 60%) !default;\n$orange-300: tint-color($orange, 40%) !default;\n$orange-400: tint-color($orange, 20%) !default;\n$orange-500: $orange !default;\n$orange-600: shade-color($orange, 20%) !default;\n$orange-700: shade-color($orange, 40%) !default;\n$orange-800: shade-color($orange, 60%) !default;\n$orange-900: shade-color($orange, 80%) !default;\n\n$yellow-100: tint-color($yellow, 80%) !default;\n$yellow-200: tint-color($yellow, 60%) !default;\n$yellow-300: tint-color($yellow, 40%) !default;\n$yellow-400: tint-color($yellow, 20%) !default;\n$yellow-500: $yellow !default;\n$yellow-600: shade-color($yellow, 20%) !default;\n$yellow-700: shade-color($yellow, 40%) !default;\n$yellow-800: shade-color($yellow, 60%) !default;\n$yellow-900: shade-color($yellow, 80%) !default;\n\n$green-100: tint-color($green, 80%) !default;\n$green-200: tint-color($green, 60%) !default;\n$green-300: tint-color($green, 40%) !default;\n$green-400: tint-color($green, 20%) !default;\n$green-500: $green !default;\n$green-600: shade-color($green, 20%) !default;\n$green-700: shade-color($green, 40%) !default;\n$green-800: shade-color($green, 60%) !default;\n$green-900: shade-color($green, 80%) !default;\n\n$teal-100: tint-color($teal, 80%) !default;\n$teal-200: tint-color($teal, 60%) !default;\n$teal-300: tint-color($teal, 40%) !default;\n$teal-400: tint-color($teal, 20%) !default;\n$teal-500: $teal !default;\n$teal-600: shade-color($teal, 20%) !default;\n$teal-700: shade-color($teal, 40%) !default;\n$teal-800: shade-color($teal, 60%) !default;\n$teal-900: shade-color($teal, 80%) !default;\n\n$cyan-100: tint-color($cyan, 80%) !default;\n$cyan-200: tint-color($cyan, 60%) !default;\n$cyan-300: tint-color($cyan, 40%) !default;\n$cyan-400: tint-color($cyan, 20%) !default;\n$cyan-500: $cyan !default;\n$cyan-600: shade-color($cyan, 20%) !default;\n$cyan-700: shade-color($cyan, 40%) !default;\n$cyan-800: shade-color($cyan, 60%) !default;\n$cyan-900: shade-color($cyan, 80%) !default;\n\n$blues: (\n  \"blue-100\": $blue-100,\n  \"blue-200\": $blue-200,\n  \"blue-300\": $blue-300,\n  \"blue-400\": $blue-400,\n  \"blue-500\": $blue-500,\n  \"blue-600\": $blue-600,\n  \"blue-700\": $blue-700,\n  \"blue-800\": $blue-800,\n  \"blue-900\": $blue-900\n) !default;\n\n$indigos: (\n  \"indigo-100\": $indigo-100,\n  \"indigo-200\": $indigo-200,\n  \"indigo-300\": $indigo-300,\n  \"indigo-400\": $indigo-400,\n  \"indigo-500\": $indigo-500,\n  \"indigo-600\": $indigo-600,\n  \"indigo-700\": $indigo-700,\n  \"indigo-800\": $indigo-800,\n  \"indigo-900\": $indigo-900\n) !default;\n\n$purples: (\n  \"purple-100\": $purple-100,\n  \"purple-200\": $purple-200,\n  \"purple-300\": $purple-300,\n  \"purple-400\": $purple-400,\n  \"purple-500\": $purple-500,\n  \"purple-600\": $purple-600,\n  \"purple-700\": $purple-700,\n  \"purple-800\": $purple-800,\n  \"purple-900\": $purple-900\n) !default;\n\n$pinks: (\n  \"pink-100\": $pink-100,\n  \"pink-200\": $pink-200,\n  \"pink-300\": $pink-300,\n  \"pink-400\": $pink-400,\n  \"pink-500\": $pink-500,\n  \"pink-600\": $pink-600,\n  \"pink-700\": $pink-700,\n  \"pink-800\": $pink-800,\n  \"pink-900\": $pink-900\n) !default;\n\n$reds: (\n  \"red-100\": $red-100,\n  \"red-200\": $red-200,\n  \"red-300\": $red-300,\n  \"red-400\": $red-400,\n  \"red-500\": $red-500,\n  \"red-600\": $red-600,\n  \"red-700\": $red-700,\n  \"red-800\": $red-800,\n  \"red-900\": $red-900\n) !default;\n\n$oranges: (\n  \"orange-100\": $orange-100,\n  \"orange-200\": $orange-200,\n  \"orange-300\": $orange-300,\n  \"orange-400\": $orange-400,\n  \"orange-500\": $orange-500,\n  \"orange-600\": $orange-600,\n  \"orange-700\": $orange-700,\n  \"orange-800\": $orange-800,\n  \"orange-900\": $orange-900\n) !default;\n\n$yellows: (\n  \"yellow-100\": $yellow-100,\n  \"yellow-200\": $yellow-200,\n  \"yellow-300\": $yellow-300,\n  \"yellow-400\": $yellow-400,\n  \"yellow-500\": $yellow-500,\n  \"yellow-600\": $yellow-600,\n  \"yellow-700\": $yellow-700,\n  \"yellow-800\": $yellow-800,\n  \"yellow-900\": $yellow-900\n) !default;\n\n$greens: (\n  \"green-100\": $green-100,\n  \"green-200\": $green-200,\n  \"green-300\": $green-300,\n  \"green-400\": $green-400,\n  \"green-500\": $green-500,\n  \"green-600\": $green-600,\n  \"green-700\": $green-700,\n  \"green-800\": $green-800,\n  \"green-900\": $green-900\n) !default;\n\n$teals: (\n  \"teal-100\": $teal-100,\n  \"teal-200\": $teal-200,\n  \"teal-300\": $teal-300,\n  \"teal-400\": $teal-400,\n  \"teal-500\": $teal-500,\n  \"teal-600\": $teal-600,\n  \"teal-700\": $teal-700,\n  \"teal-800\": $teal-800,\n  \"teal-900\": $teal-900\n) !default;\n\n$cyans: (\n  \"cyan-100\": $cyan-100,\n  \"cyan-200\": $cyan-200,\n  \"cyan-300\": $cyan-300,\n  \"cyan-400\": $cyan-400,\n  \"cyan-500\": $cyan-500,\n  \"cyan-600\": $cyan-600,\n  \"cyan-700\": $cyan-700,\n  \"cyan-800\": $cyan-800,\n  \"cyan-900\": $cyan-900\n) !default;\n// fusv-enable\n\n// scss-docs-start theme-color-variables\n$primary:       $blue !default;\n$secondary:     $gray-600 !default;\n$success:       $green !default;\n$info:          $cyan !default;\n$warning:       $yellow !default;\n$danger:        $red !default;\n$light:         $gray-100 !default;\n$dark:          $gray-900 !default;\n// scss-docs-end theme-color-variables\n\n// scss-docs-start theme-colors-map\n$theme-colors: (\n  \"primary\":    $primary,\n  \"secondary\":  $secondary,\n  \"success\":    $success,\n  \"info\":       $info,\n  \"warning\":    $warning,\n  \"danger\":     $danger,\n  \"light\":      $light,\n  \"dark\":       $dark\n) !default;\n// scss-docs-end theme-colors-map\n\n// scss-docs-start theme-text-variables\n$primary-text-emphasis:   shade-color($primary, 60%) !default;\n$secondary-text-emphasis: shade-color($secondary, 60%) !default;\n$success-text-emphasis:   shade-color($success, 60%) !default;\n$info-text-emphasis:      shade-color($info, 60%) !default;\n$warning-text-emphasis:   shade-color($warning, 60%) !default;\n$danger-text-emphasis:    shade-color($danger, 60%) !default;\n$light-text-emphasis:     $gray-700 !default;\n$dark-text-emphasis:      $gray-700 !default;\n// scss-docs-end theme-text-variables\n\n// scss-docs-start theme-bg-subtle-variables\n$primary-bg-subtle:       tint-color($primary, 80%) !default;\n$secondary-bg-subtle:     tint-color($secondary, 80%) !default;\n$success-bg-subtle:       tint-color($success, 80%) !default;\n$info-bg-subtle:          tint-color($info, 80%) !default;\n$warning-bg-subtle:       tint-color($warning, 80%) !default;\n$danger-bg-subtle:        tint-color($danger, 80%) !default;\n$light-bg-subtle:         mix($gray-100, $white) !default;\n$dark-bg-subtle:          $gray-400 !default;\n// scss-docs-end theme-bg-subtle-variables\n\n// scss-docs-start theme-border-subtle-variables\n$primary-border-subtle:   tint-color($primary, 60%) !default;\n$secondary-border-subtle: tint-color($secondary, 60%) !default;\n$success-border-subtle:   tint-color($success, 60%) !default;\n$info-border-subtle:      tint-color($info, 60%) !default;\n$warning-border-subtle:   tint-color($warning, 60%) !default;\n$danger-border-subtle:    tint-color($danger, 60%) !default;\n$light-border-subtle:     $gray-200 !default;\n$dark-border-subtle:      $gray-500 !default;\n// scss-docs-end theme-border-subtle-variables\n\n// Characters which are escaped by the escape-svg function\n$escaped-characters: (\n  (\"<\", \"%3c\"),\n  (\">\", \"%3e\"),\n  (\"#\", \"%23\"),\n  (\"(\", \"%28\"),\n  (\")\", \"%29\"),\n) !default;\n\n// Options\n//\n// Quickly modify global styling by enabling or disabling optional features.\n\n$enable-caret:                true !default;\n$enable-rounded:              true !default;\n$enable-shadows:              false !default;\n$enable-gradients:            false !default;\n$enable-transitions:          true !default;\n$enable-reduced-motion:       true !default;\n$enable-smooth-scroll:        true !default;\n$enable-grid-classes:         true !default;\n$enable-container-classes:    true !default;\n$enable-cssgrid:              false !default;\n$enable-button-pointers:      true !default;\n$enable-rfs:                  true !default;\n$enable-validation-icons:     true !default;\n$enable-negative-margins:     false !default;\n$enable-deprecation-messages: true !default;\n$enable-important-utilities:  true !default;\n\n$enable-dark-mode:            true !default;\n$color-mode-type:             data !default; // `data` or `media-query`\n\n// Prefix for :root CSS variables\n\n$variable-prefix:             bs- !default; // Deprecated in v5.2.0 for the shorter `$prefix`\n$prefix:                      $variable-prefix !default;\n\n// Gradient\n//\n// The gradient which is added to components if `$enable-gradients` is `true`\n// This gradient is also added to elements with `.bg-gradient`\n// scss-docs-start variable-gradient\n$gradient: linear-gradient(180deg, rgba($white, .15), rgba($white, 0)) !default;\n// scss-docs-end variable-gradient\n\n// Spacing\n//\n// Control the default styling of most Bootstrap elements by modifying these\n// variables. Mostly focused on spacing.\n// You can add more entries to the $spacers map, should you need more variation.\n\n// scss-docs-start spacer-variables-maps\n$spacer: 1rem !default;\n$spacers: (\n  0: 0,\n  1: $spacer * .25,\n  2: $spacer * .5,\n  3: $spacer,\n  4: $spacer * 1.5,\n  5: $spacer * 3,\n) !default;\n// scss-docs-end spacer-variables-maps\n\n// Position\n//\n// Define the edge positioning anchors of the position utilities.\n\n// scss-docs-start position-map\n$position-values: (\n  0: 0,\n  50: 50%,\n  100: 100%\n) !default;\n// scss-docs-end position-map\n\n// Body\n//\n// Settings for the `<body>` element.\n\n$body-text-align:           null !default;\n$body-color:                $gray-900 !default;\n$body-bg:                   $white !default;\n\n$body-secondary-color:      rgba($body-color, .75) !default;\n$body-secondary-bg:         $gray-200 !default;\n\n$body-tertiary-color:       rgba($body-color, .5) !default;\n$body-tertiary-bg:          $gray-100 !default;\n\n$body-emphasis-color:       $black !default;\n\n// Links\n//\n// Style anchor elements.\n\n$link-color:                              $primary !default;\n$link-decoration:                         underline !default;\n$link-shade-percentage:                   20% !default;\n$link-hover-color:                        shift-color($link-color, $link-shade-percentage) !default;\n$link-hover-decoration:                   null !default;\n\n$stretched-link-pseudo-element:           after !default;\n$stretched-link-z-index:                  1 !default;\n\n// Icon links\n// scss-docs-start icon-link-variables\n$icon-link-gap:               .375rem !default;\n$icon-link-underline-offset:  .25em !default;\n$icon-link-icon-size:         1em !default;\n$icon-link-icon-transition:   .2s ease-in-out transform !default;\n$icon-link-icon-transform:    translate3d(.25em, 0, 0) !default;\n// scss-docs-end icon-link-variables\n\n// Paragraphs\n//\n// Style p element.\n\n$paragraph-margin-bottom:   1rem !default;\n\n\n// Grid breakpoints\n//\n// Define the minimum dimensions at which your layout will change,\n// adapting to different screen sizes, for use in media queries.\n\n// scss-docs-start grid-breakpoints\n$grid-breakpoints: (\n  xs: 0,\n  sm: 576px,\n  md: 768px,\n  lg: 992px,\n  xl: 1200px,\n  xxl: 1400px\n) !default;\n// scss-docs-end grid-breakpoints\n\n@include _assert-ascending($grid-breakpoints, \"$grid-breakpoints\");\n@include _assert-starts-at-zero($grid-breakpoints, \"$grid-breakpoints\");\n\n\n// Grid containers\n//\n// Define the maximum width of `.container` for different screen sizes.\n\n// scss-docs-start container-max-widths\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1140px,\n  xxl: 1320px\n) !default;\n// scss-docs-end container-max-widths\n\n@include _assert-ascending($container-max-widths, \"$container-max-widths\");\n\n\n// Grid columns\n//\n// Set the number of columns and specify the width of the gutters.\n\n$grid-columns:                12 !default;\n$grid-gutter-width:           1.5rem !default;\n$grid-row-columns:            6 !default;\n\n// Container padding\n\n$container-padding-x: $grid-gutter-width !default;\n\n\n// Components\n//\n// Define common padding and border radius sizes and more.\n\n// scss-docs-start border-variables\n$border-width:                1px !default;\n$border-widths: (\n  1: 1px,\n  2: 2px,\n  3: 3px,\n  4: 4px,\n  5: 5px\n) !default;\n$border-style:                solid !default;\n$border-color:                $gray-300 !default;\n$border-color-translucent:    rgba($black, .175) !default;\n// scss-docs-end border-variables\n\n// scss-docs-start border-radius-variables\n$border-radius:               .375rem !default;\n$border-radius-sm:            .25rem !default;\n$border-radius-lg:            .5rem !default;\n$border-radius-xl:            1rem !default;\n$border-radius-xxl:           2rem !default;\n$border-radius-pill:          50rem !default;\n// scss-docs-end border-radius-variables\n// fusv-disable\n$border-radius-2xl:           $border-radius-xxl !default; // Deprecated in v5.3.0\n// fusv-enable\n\n// scss-docs-start box-shadow-variables\n$box-shadow:                  0 .5rem 1rem rgba($black, .15) !default;\n$box-shadow-sm:               0 .125rem .25rem rgba($black, .075) !default;\n$box-shadow-lg:               0 1rem 3rem rgba($black, .175) !default;\n$box-shadow-inset:            inset 0 1px 2px rgba($black, .075) !default;\n// scss-docs-end box-shadow-variables\n\n$component-active-color:      $white !default;\n$component-active-bg:         $primary !default;\n\n// scss-docs-start focus-ring-variables\n$focus-ring-width:      .25rem !default;\n$focus-ring-opacity:    .25 !default;\n$focus-ring-color:      rgba($primary, $focus-ring-opacity) !default;\n$focus-ring-blur:       0 !default;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color !default;\n// scss-docs-end focus-ring-variables\n\n// scss-docs-start caret-variables\n$caret-width:                 .3em !default;\n$caret-vertical-align:        $caret-width * .85 !default;\n$caret-spacing:               $caret-width * .85 !default;\n// scss-docs-end caret-variables\n\n$transition-base:             all .2s ease-in-out !default;\n$transition-fade:             opacity .15s linear !default;\n// scss-docs-start collapse-transition\n$transition-collapse:         height .35s ease !default;\n$transition-collapse-width:   width .35s ease !default;\n// scss-docs-end collapse-transition\n\n// stylelint-disable function-disallowed-list\n// scss-docs-start aspect-ratios\n$aspect-ratios: (\n  \"1x1\": 100%,\n  \"4x3\": calc(3 / 4 * 100%),\n  \"16x9\": calc(9 / 16 * 100%),\n  \"21x9\": calc(9 / 21 * 100%)\n) !default;\n// scss-docs-end aspect-ratios\n// stylelint-enable function-disallowed-list\n\n// Typography\n//\n// Font, line-height, and color for body text, headings, and more.\n\n// scss-docs-start font-variables\n// stylelint-disable value-keyword-case\n$font-family-sans-serif:      system-ui, -apple-system, \"Segoe UI\", Roboto, \"Helvetica Neue\", \"Noto Sans\", \"Liberation Sans\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\" !default;\n$font-family-monospace:       SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace !default;\n// stylelint-enable value-keyword-case\n$font-family-base:            var(--#{$prefix}font-sans-serif) !default;\n$font-family-code:            var(--#{$prefix}font-monospace) !default;\n\n// $font-size-root affects the value of `rem`, which is used for as well font sizes, paddings, and margins\n// $font-size-base affects the font size of the body text\n$font-size-root:              null !default;\n$font-size-base:              1rem !default; // Assumes the browser default, typically `16px`\n$font-size-sm:                $font-size-base * .875 !default;\n$font-size-lg:                $font-size-base * 1.25 !default;\n\n$font-weight-lighter:         lighter !default;\n$font-weight-light:           300 !default;\n$font-weight-normal:          400 !default;\n$font-weight-medium:          500 !default;\n$font-weight-semibold:        600 !default;\n$font-weight-bold:            700 !default;\n$font-weight-bolder:          bolder !default;\n\n$font-weight-base:            $font-weight-normal !default;\n\n$line-height-base:            1.5 !default;\n$line-height-sm:              1.25 !default;\n$line-height-lg:              2 !default;\n\n$h1-font-size:                $font-size-base * 2.5 !default;\n$h2-font-size:                $font-size-base * 2 !default;\n$h3-font-size:                $font-size-base * 1.75 !default;\n$h4-font-size:                $font-size-base * 1.5 !default;\n$h5-font-size:                $font-size-base * 1.25 !default;\n$h6-font-size:                $font-size-base !default;\n// scss-docs-end font-variables\n\n// scss-docs-start font-sizes\n$font-sizes: (\n  1: $h1-font-size,\n  2: $h2-font-size,\n  3: $h3-font-size,\n  4: $h4-font-size,\n  5: $h5-font-size,\n  6: $h6-font-size\n) !default;\n// scss-docs-end font-sizes\n\n// scss-docs-start headings-variables\n$headings-margin-bottom:      $spacer * .5 !default;\n$headings-font-family:        null !default;\n$headings-font-style:         null !default;\n$headings-font-weight:        500 !default;\n$headings-line-height:        1.2 !default;\n$headings-color:              inherit !default;\n// scss-docs-end headings-variables\n\n// scss-docs-start display-headings\n$display-font-sizes: (\n  1: 5rem,\n  2: 4.5rem,\n  3: 4rem,\n  4: 3.5rem,\n  5: 3rem,\n  6: 2.5rem\n) !default;\n\n$display-font-family: null !default;\n$display-font-style:  null !default;\n$display-font-weight: 300 !default;\n$display-line-height: $headings-line-height !default;\n// scss-docs-end display-headings\n\n// scss-docs-start type-variables\n$lead-font-size:              $font-size-base * 1.25 !default;\n$lead-font-weight:            300 !default;\n\n$small-font-size:             .875em !default;\n\n$sub-sup-font-size:           .75em !default;\n\n// fusv-disable\n$text-muted:                  var(--#{$prefix}secondary-color) !default; // Deprecated in 5.3.0\n// fusv-enable\n\n$initialism-font-size:        $small-font-size !default;\n\n$blockquote-margin-y:         $spacer !default;\n$blockquote-font-size:        $font-size-base * 1.25 !default;\n$blockquote-footer-color:     $gray-600 !default;\n$blockquote-footer-font-size: $small-font-size !default;\n\n$hr-margin-y:                 $spacer !default;\n$hr-color:                    inherit !default;\n\n// fusv-disable\n$hr-bg-color:                 null !default; // Deprecated in v5.2.0\n$hr-height:                   null !default; // Deprecated in v5.2.0\n// fusv-enable\n\n$hr-border-color:             null !default; // Allows for inherited colors\n$hr-border-width:             var(--#{$prefix}border-width) !default;\n$hr-opacity:                  .25 !default;\n\n// scss-docs-start vr-variables\n$vr-border-width:             var(--#{$prefix}border-width) !default;\n// scss-docs-end vr-variables\n\n$legend-margin-bottom:        .5rem !default;\n$legend-font-size:            1.5rem !default;\n$legend-font-weight:          null !default;\n\n$dt-font-weight:              $font-weight-bold !default;\n\n$list-inline-padding:         .5rem !default;\n\n$mark-padding:                .1875em !default;\n$mark-color:                  $body-color !default;\n$mark-bg:                     $yellow-100 !default;\n// scss-docs-end type-variables\n\n\n// Tables\n//\n// Customizes the `.table` component with basic values, each used across all table variations.\n\n// scss-docs-start table-variables\n$table-cell-padding-y:        .5rem !default;\n$table-cell-padding-x:        .5rem !default;\n$table-cell-padding-y-sm:     .25rem !default;\n$table-cell-padding-x-sm:     .25rem !default;\n\n$table-cell-vertical-align:   top !default;\n\n$table-color:                 var(--#{$prefix}emphasis-color) !default;\n$table-bg:                    var(--#{$prefix}body-bg) !default;\n$table-accent-bg:             transparent !default;\n\n$table-th-font-weight:        null !default;\n\n$table-striped-color:         $table-color !default;\n$table-striped-bg-factor:     .05 !default;\n$table-striped-bg:            rgba(var(--#{$prefix}emphasis-color-rgb), $table-striped-bg-factor) !default;\n\n$table-active-color:          $table-color !default;\n$table-active-bg-factor:      .1 !default;\n$table-active-bg:             rgba(var(--#{$prefix}emphasis-color-rgb), $table-active-bg-factor) !default;\n\n$table-hover-color:           $table-color !default;\n$table-hover-bg-factor:       .075 !default;\n$table-hover-bg:              rgba(var(--#{$prefix}emphasis-color-rgb), $table-hover-bg-factor) !default;\n\n$table-border-factor:         .2 !default;\n$table-border-width:          var(--#{$prefix}border-width) !default;\n$table-border-color:          var(--#{$prefix}border-color) !default;\n\n$table-striped-order:         odd !default;\n$table-striped-columns-order: even !default;\n\n$table-group-separator-color: currentcolor !default;\n\n$table-caption-color:         var(--#{$prefix}secondary-color) !default;\n\n$table-bg-scale:              -80% !default;\n// scss-docs-end table-variables\n\n// scss-docs-start table-loop\n$table-variants: (\n  \"primary\":    shift-color($primary, $table-bg-scale),\n  \"secondary\":  shift-color($secondary, $table-bg-scale),\n  \"success\":    shift-color($success, $table-bg-scale),\n  \"info\":       shift-color($info, $table-bg-scale),\n  \"warning\":    shift-color($warning, $table-bg-scale),\n  \"danger\":     shift-color($danger, $table-bg-scale),\n  \"light\":      $light,\n  \"dark\":       $dark,\n) !default;\n// scss-docs-end table-loop\n\n\n// Buttons + Forms\n//\n// Shared variables that are reassigned to `$input-` and `$btn-` specific variables.\n\n// scss-docs-start input-btn-variables\n$input-btn-padding-y:         .375rem !default;\n$input-btn-padding-x:         .75rem !default;\n$input-btn-font-family:       null !default;\n$input-btn-font-size:         $font-size-base !default;\n$input-btn-line-height:       $line-height-base !default;\n\n$input-btn-focus-width:         $focus-ring-width !default;\n$input-btn-focus-color-opacity: $focus-ring-opacity !default;\n$input-btn-focus-color:         $focus-ring-color !default;\n$input-btn-focus-blur:          $focus-ring-blur !default;\n$input-btn-focus-box-shadow:    $focus-ring-box-shadow !default;\n\n$input-btn-padding-y-sm:      .25rem !default;\n$input-btn-padding-x-sm:      .5rem !default;\n$input-btn-font-size-sm:      $font-size-sm !default;\n\n$input-btn-padding-y-lg:      .5rem !default;\n$input-btn-padding-x-lg:      1rem !default;\n$input-btn-font-size-lg:      $font-size-lg !default;\n\n$input-btn-border-width:      var(--#{$prefix}border-width) !default;\n// scss-docs-end input-btn-variables\n\n\n// Buttons\n//\n// For each of Bootstrap's buttons, define text, background, and border color.\n\n// scss-docs-start btn-variables\n$btn-color:                   var(--#{$prefix}body-color) !default;\n$btn-padding-y:               $input-btn-padding-y !default;\n$btn-padding-x:               $input-btn-padding-x !default;\n$btn-font-family:             $input-btn-font-family !default;\n$btn-font-size:               $input-btn-font-size !default;\n$btn-line-height:             $input-btn-line-height !default;\n$btn-white-space:             null !default; // Set to `nowrap` to prevent text wrapping\n\n$btn-padding-y-sm:            $input-btn-padding-y-sm !default;\n$btn-padding-x-sm:            $input-btn-padding-x-sm !default;\n$btn-font-size-sm:            $input-btn-font-size-sm !default;\n\n$btn-padding-y-lg:            $input-btn-padding-y-lg !default;\n$btn-padding-x-lg:            $input-btn-padding-x-lg !default;\n$btn-font-size-lg:            $input-btn-font-size-lg !default;\n\n$btn-border-width:            $input-btn-border-width !default;\n\n$btn-font-weight:             $font-weight-normal !default;\n$btn-box-shadow:              inset 0 1px 0 rgba($white, .15), 0 1px 1px rgba($black, .075) !default;\n$btn-focus-width:             $input-btn-focus-width !default;\n$btn-focus-box-shadow:        $input-btn-focus-box-shadow !default;\n$btn-disabled-opacity:        .65 !default;\n$btn-active-box-shadow:       inset 0 3px 5px rgba($black, .125) !default;\n\n$btn-link-color:              var(--#{$prefix}link-color) !default;\n$btn-link-hover-color:        var(--#{$prefix}link-hover-color) !default;\n$btn-link-disabled-color:     $gray-600 !default;\n$btn-link-focus-shadow-rgb:   to-rgb(mix(color-contrast($link-color), $link-color, 15%)) !default;\n\n// Allows for customizing button radius independently from global border radius\n$btn-border-radius:           var(--#{$prefix}border-radius) !default;\n$btn-border-radius-sm:        var(--#{$prefix}border-radius-sm) !default;\n$btn-border-radius-lg:        var(--#{$prefix}border-radius-lg) !default;\n\n$btn-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$btn-hover-bg-shade-amount:       15% !default;\n$btn-hover-bg-tint-amount:        15% !default;\n$btn-hover-border-shade-amount:   20% !default;\n$btn-hover-border-tint-amount:    10% !default;\n$btn-active-bg-shade-amount:      20% !default;\n$btn-active-bg-tint-amount:       20% !default;\n$btn-active-border-shade-amount:  25% !default;\n$btn-active-border-tint-amount:   10% !default;\n// scss-docs-end btn-variables\n\n\n// Forms\n\n// scss-docs-start form-text-variables\n$form-text-margin-top:                  .25rem !default;\n$form-text-font-size:                   $small-font-size !default;\n$form-text-font-style:                  null !default;\n$form-text-font-weight:                 null !default;\n$form-text-color:                       var(--#{$prefix}secondary-color) !default;\n// scss-docs-end form-text-variables\n\n// scss-docs-start form-label-variables\n$form-label-margin-bottom:              .5rem !default;\n$form-label-font-size:                  null !default;\n$form-label-font-style:                 null !default;\n$form-label-font-weight:                null !default;\n$form-label-color:                      null !default;\n// scss-docs-end form-label-variables\n\n// scss-docs-start form-input-variables\n$input-padding-y:                       $input-btn-padding-y !default;\n$input-padding-x:                       $input-btn-padding-x !default;\n$input-font-family:                     $input-btn-font-family !default;\n$input-font-size:                       $input-btn-font-size !default;\n$input-font-weight:                     $font-weight-base !default;\n$input-line-height:                     $input-btn-line-height !default;\n\n$input-padding-y-sm:                    $input-btn-padding-y-sm !default;\n$input-padding-x-sm:                    $input-btn-padding-x-sm !default;\n$input-font-size-sm:                    $input-btn-font-size-sm !default;\n\n$input-padding-y-lg:                    $input-btn-padding-y-lg !default;\n$input-padding-x-lg:                    $input-btn-padding-x-lg !default;\n$input-font-size-lg:                    $input-btn-font-size-lg !default;\n\n$input-bg:                              var(--#{$prefix}body-bg) !default;\n$input-disabled-color:                  null !default;\n$input-disabled-bg:                     var(--#{$prefix}secondary-bg) !default;\n$input-disabled-border-color:           null !default;\n\n$input-color:                           var(--#{$prefix}body-color) !default;\n$input-border-color:                    var(--#{$prefix}border-color) !default;\n$input-border-width:                    $input-btn-border-width !default;\n$input-box-shadow:                      var(--#{$prefix}box-shadow-inset) !default;\n\n$input-border-radius:                   var(--#{$prefix}border-radius) !default;\n$input-border-radius-sm:                var(--#{$prefix}border-radius-sm) !default;\n$input-border-radius-lg:                var(--#{$prefix}border-radius-lg) !default;\n\n$input-focus-bg:                        $input-bg !default;\n$input-focus-border-color:              tint-color($component-active-bg, 50%) !default;\n$input-focus-color:                     $input-color !default;\n$input-focus-width:                     $input-btn-focus-width !default;\n$input-focus-box-shadow:                $input-btn-focus-box-shadow !default;\n\n$input-placeholder-color:               var(--#{$prefix}secondary-color) !default;\n$input-plaintext-color:                 var(--#{$prefix}body-color) !default;\n\n$input-height-border:                   calc(#{$input-border-width} * 2) !default; // stylelint-disable-line function-disallowed-list\n\n$input-height-inner:                    add($input-line-height * 1em, $input-padding-y * 2) !default;\n$input-height-inner-half:               add($input-line-height * .5em, $input-padding-y) !default;\n$input-height-inner-quarter:            add($input-line-height * .25em, $input-padding-y * .5) !default;\n\n$input-height:                          add($input-line-height * 1em, add($input-padding-y * 2, $input-height-border, false)) !default;\n$input-height-sm:                       add($input-line-height * 1em, add($input-padding-y-sm * 2, $input-height-border, false)) !default;\n$input-height-lg:                       add($input-line-height * 1em, add($input-padding-y-lg * 2, $input-height-border, false)) !default;\n\n$input-transition:                      border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$form-color-width:                      3rem !default;\n// scss-docs-end form-input-variables\n\n// scss-docs-start form-check-variables\n$form-check-input-width:                  1em !default;\n$form-check-min-height:                   $font-size-base * $line-height-base !default;\n$form-check-padding-start:                $form-check-input-width + .5em !default;\n$form-check-margin-bottom:                .125rem !default;\n$form-check-label-color:                  null !default;\n$form-check-label-cursor:                 null !default;\n$form-check-transition:                   null !default;\n\n$form-check-input-active-filter:          brightness(90%) !default;\n\n$form-check-input-bg:                     $input-bg !default;\n$form-check-input-border:                 var(--#{$prefix}border-width) solid var(--#{$prefix}border-color) !default;\n$form-check-input-border-radius:          .25em !default;\n$form-check-radio-border-radius:          50% !default;\n$form-check-input-focus-border:           $input-focus-border-color !default;\n$form-check-input-focus-box-shadow:       $focus-ring-box-shadow !default;\n\n$form-check-input-checked-color:          $component-active-color !default;\n$form-check-input-checked-bg-color:       $component-active-bg !default;\n$form-check-input-checked-border-color:   $form-check-input-checked-bg-color !default;\n$form-check-input-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-checked-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/></svg>\") !default;\n$form-check-radio-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='2' fill='#{$form-check-input-checked-color}'/></svg>\") !default;\n\n$form-check-input-indeterminate-color:          $component-active-color !default;\n$form-check-input-indeterminate-bg-color:       $component-active-bg !default;\n$form-check-input-indeterminate-border-color:   $form-check-input-indeterminate-bg-color !default;\n$form-check-input-indeterminate-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-indeterminate-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/></svg>\") !default;\n\n$form-check-input-disabled-opacity:        .5 !default;\n$form-check-label-disabled-opacity:        $form-check-input-disabled-opacity !default;\n$form-check-btn-check-disabled-opacity:    $btn-disabled-opacity !default;\n\n$form-check-inline-margin-end:    1rem !default;\n// scss-docs-end form-check-variables\n\n// scss-docs-start form-switch-variables\n$form-switch-color:               rgba($black, .25) !default;\n$form-switch-width:               2em !default;\n$form-switch-padding-start:       $form-switch-width + .5em !default;\n$form-switch-bg-image:            url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-color}'/></svg>\") !default;\n$form-switch-border-radius:       $form-switch-width !default;\n$form-switch-transition:          background-position .15s ease-in-out !default;\n\n$form-switch-focus-color:         $input-focus-border-color !default;\n$form-switch-focus-bg-image:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-focus-color}'/></svg>\") !default;\n\n$form-switch-checked-color:       $component-active-color !default;\n$form-switch-checked-bg-image:    url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-checked-color}'/></svg>\") !default;\n$form-switch-checked-bg-position: right center !default;\n// scss-docs-end form-switch-variables\n\n// scss-docs-start input-group-variables\n$input-group-addon-padding-y:           $input-padding-y !default;\n$input-group-addon-padding-x:           $input-padding-x !default;\n$input-group-addon-font-weight:         $input-font-weight !default;\n$input-group-addon-color:               $input-color !default;\n$input-group-addon-bg:                  var(--#{$prefix}tertiary-bg) !default;\n$input-group-addon-border-color:        $input-border-color !default;\n// scss-docs-end input-group-variables\n\n// scss-docs-start form-select-variables\n$form-select-padding-y:             $input-padding-y !default;\n$form-select-padding-x:             $input-padding-x !default;\n$form-select-font-family:           $input-font-family !default;\n$form-select-font-size:             $input-font-size !default;\n$form-select-indicator-padding:     $form-select-padding-x * 3 !default; // Extra padding for background-image\n$form-select-font-weight:           $input-font-weight !default;\n$form-select-line-height:           $input-line-height !default;\n$form-select-color:                 $input-color !default;\n$form-select-bg:                    $input-bg !default;\n$form-select-disabled-color:        null !default;\n$form-select-disabled-bg:           $input-disabled-bg !default;\n$form-select-disabled-border-color: $input-disabled-border-color !default;\n$form-select-bg-position:           right $form-select-padding-x center !default;\n$form-select-bg-size:               16px 12px !default; // In pixels because image dimensions\n$form-select-indicator-color:       $gray-800 !default;\n$form-select-indicator:             url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'><path fill='none' stroke='#{$form-select-indicator-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/></svg>\") !default;\n\n$form-select-feedback-icon-padding-end: $form-select-padding-x * 2.5 + $form-select-indicator-padding !default;\n$form-select-feedback-icon-position:    center right $form-select-indicator-padding !default;\n$form-select-feedback-icon-size:        $input-height-inner-half $input-height-inner-half !default;\n\n$form-select-border-width:        $input-border-width !default;\n$form-select-border-color:        $input-border-color !default;\n$form-select-border-radius:       $input-border-radius !default;\n$form-select-box-shadow:          var(--#{$prefix}box-shadow-inset) !default;\n\n$form-select-focus-border-color:  $input-focus-border-color !default;\n$form-select-focus-width:         $input-focus-width !default;\n$form-select-focus-box-shadow:    0 0 0 $form-select-focus-width $input-btn-focus-color !default;\n\n$form-select-padding-y-sm:        $input-padding-y-sm !default;\n$form-select-padding-x-sm:        $input-padding-x-sm !default;\n$form-select-font-size-sm:        $input-font-size-sm !default;\n$form-select-border-radius-sm:    $input-border-radius-sm !default;\n\n$form-select-padding-y-lg:        $input-padding-y-lg !default;\n$form-select-padding-x-lg:        $input-padding-x-lg !default;\n$form-select-font-size-lg:        $input-font-size-lg !default;\n$form-select-border-radius-lg:    $input-border-radius-lg !default;\n\n$form-select-transition:          $input-transition !default;\n// scss-docs-end form-select-variables\n\n// scss-docs-start form-range-variables\n$form-range-track-width:          100% !default;\n$form-range-track-height:         .5rem !default;\n$form-range-track-cursor:         pointer !default;\n$form-range-track-bg:             var(--#{$prefix}secondary-bg) !default;\n$form-range-track-border-radius:  1rem !default;\n$form-range-track-box-shadow:     var(--#{$prefix}box-shadow-inset) !default;\n\n$form-range-thumb-width:                   1rem !default;\n$form-range-thumb-height:                  $form-range-thumb-width !default;\n$form-range-thumb-bg:                      $component-active-bg !default;\n$form-range-thumb-border:                  0 !default;\n$form-range-thumb-border-radius:           1rem !default;\n$form-range-thumb-box-shadow:              0 .1rem .25rem rgba($black, .1) !default;\n$form-range-thumb-focus-box-shadow:        0 0 0 1px $body-bg, $input-focus-box-shadow !default;\n$form-range-thumb-focus-box-shadow-width:  $input-focus-width !default; // For focus box shadow issue in Edge\n$form-range-thumb-active-bg:               tint-color($component-active-bg, 70%) !default;\n$form-range-thumb-disabled-bg:             var(--#{$prefix}secondary-color) !default;\n$form-range-thumb-transition:              background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n// scss-docs-end form-range-variables\n\n// scss-docs-start form-file-variables\n$form-file-button-color:          $input-color !default;\n$form-file-button-bg:             var(--#{$prefix}tertiary-bg) !default;\n$form-file-button-hover-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end form-file-variables\n\n// scss-docs-start form-floating-variables\n$form-floating-height:                  add(3.5rem, $input-height-border) !default;\n$form-floating-line-height:             1.25 !default;\n$form-floating-padding-x:               $input-padding-x !default;\n$form-floating-padding-y:               1rem !default;\n$form-floating-input-padding-t:         1.625rem !default;\n$form-floating-input-padding-b:         .625rem !default;\n$form-floating-label-height:            1.5em !default;\n$form-floating-label-opacity:           .65 !default;\n$form-floating-label-transform:         scale(.85) translateY(-.5rem) translateX(.15rem) !default;\n$form-floating-label-disabled-color:    $gray-600 !default;\n$form-floating-transition:              opacity .1s ease-in-out, transform .1s ease-in-out !default;\n// scss-docs-end form-floating-variables\n\n// Form validation\n\n// scss-docs-start form-feedback-variables\n$form-feedback-margin-top:          $form-text-margin-top !default;\n$form-feedback-font-size:           $form-text-font-size !default;\n$form-feedback-font-style:          $form-text-font-style !default;\n$form-feedback-valid-color:         $success !default;\n$form-feedback-invalid-color:       $danger !default;\n\n$form-feedback-icon-valid-color:    $form-feedback-valid-color !default;\n$form-feedback-icon-valid:          url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'><path fill='#{$form-feedback-icon-valid-color}' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/></svg>\") !default;\n$form-feedback-icon-invalid-color:  $form-feedback-invalid-color !default;\n$form-feedback-icon-invalid:        url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='#{$form-feedback-icon-invalid-color}'><circle cx='6' cy='6' r='4.5'/><path stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/><circle cx='6' cy='8.2' r='.6' fill='#{$form-feedback-icon-invalid-color}' stroke='none'/></svg>\") !default;\n// scss-docs-end form-feedback-variables\n\n// scss-docs-start form-validation-colors\n$form-valid-color:                  $form-feedback-valid-color !default;\n$form-valid-border-color:           $form-feedback-valid-color !default;\n$form-invalid-color:                $form-feedback-invalid-color !default;\n$form-invalid-border-color:         $form-feedback-invalid-color !default;\n// scss-docs-end form-validation-colors\n\n// scss-docs-start form-validation-states\n$form-validation-states: (\n  \"valid\": (\n    \"color\": var(--#{$prefix}form-valid-color),\n    \"icon\": $form-feedback-icon-valid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}success),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}success-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-valid-border-color),\n  ),\n  \"invalid\": (\n    \"color\": var(--#{$prefix}form-invalid-color),\n    \"icon\": $form-feedback-icon-invalid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}danger),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}danger-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-invalid-border-color),\n  )\n) !default;\n// scss-docs-end form-validation-states\n\n// Z-index master list\n//\n// Warning: Avoid customizing these values. They're used for a bird's eye view\n// of components dependent on the z-axis and are designed to all work together.\n\n// scss-docs-start zindex-stack\n$zindex-dropdown:                   1000 !default;\n$zindex-sticky:                     1020 !default;\n$zindex-fixed:                      1030 !default;\n$zindex-offcanvas-backdrop:         1040 !default;\n$zindex-offcanvas:                  1045 !default;\n$zindex-modal-backdrop:             1050 !default;\n$zindex-modal:                      1055 !default;\n$zindex-popover:                    1070 !default;\n$zindex-tooltip:                    1080 !default;\n$zindex-toast:                      1090 !default;\n// scss-docs-end zindex-stack\n\n// scss-docs-start zindex-levels-map\n$zindex-levels: (\n  n1: -1,\n  0: 0,\n  1: 1,\n  2: 2,\n  3: 3\n) !default;\n// scss-docs-end zindex-levels-map\n\n\n// Navs\n\n// scss-docs-start nav-variables\n$nav-link-padding-y:                .5rem !default;\n$nav-link-padding-x:                1rem !default;\n$nav-link-font-size:                null !default;\n$nav-link-font-weight:              null !default;\n$nav-link-color:                    var(--#{$prefix}link-color) !default;\n$nav-link-hover-color:              var(--#{$prefix}link-hover-color) !default;\n$nav-link-transition:               color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out !default;\n$nav-link-disabled-color:           var(--#{$prefix}secondary-color) !default;\n$nav-link-focus-box-shadow:         $focus-ring-box-shadow !default;\n\n$nav-tabs-border-color:             var(--#{$prefix}border-color) !default;\n$nav-tabs-border-width:             var(--#{$prefix}border-width) !default;\n$nav-tabs-border-radius:            var(--#{$prefix}border-radius) !default;\n$nav-tabs-link-hover-border-color:  var(--#{$prefix}secondary-bg) var(--#{$prefix}secondary-bg) $nav-tabs-border-color !default;\n$nav-tabs-link-active-color:        var(--#{$prefix}emphasis-color) !default;\n$nav-tabs-link-active-bg:           var(--#{$prefix}body-bg) !default;\n$nav-tabs-link-active-border-color: var(--#{$prefix}border-color) var(--#{$prefix}border-color) $nav-tabs-link-active-bg !default;\n\n$nav-pills-border-radius:           var(--#{$prefix}border-radius) !default;\n$nav-pills-link-active-color:       $component-active-color !default;\n$nav-pills-link-active-bg:          $component-active-bg !default;\n\n$nav-underline-gap:                 1rem !default;\n$nav-underline-border-width:        .125rem !default;\n$nav-underline-link-active-color:   var(--#{$prefix}emphasis-color) !default;\n// scss-docs-end nav-variables\n\n\n// Navbar\n\n// scss-docs-start navbar-variables\n$navbar-padding-y:                  $spacer * .5 !default;\n$navbar-padding-x:                  null !default;\n\n$navbar-nav-link-padding-x:         .5rem !default;\n\n$navbar-brand-font-size:            $font-size-lg !default;\n// Compute the navbar-brand padding-y so the navbar-brand will have the same height as navbar-text and nav-link\n$nav-link-height:                   $font-size-base * $line-height-base + $nav-link-padding-y * 2 !default;\n$navbar-brand-height:               $navbar-brand-font-size * $line-height-base !default;\n$navbar-brand-padding-y:            ($nav-link-height - $navbar-brand-height) * .5 !default;\n$navbar-brand-margin-end:           1rem !default;\n\n$navbar-toggler-padding-y:          .25rem !default;\n$navbar-toggler-padding-x:          .75rem !default;\n$navbar-toggler-font-size:          $font-size-lg !default;\n$navbar-toggler-border-radius:      $btn-border-radius !default;\n$navbar-toggler-focus-width:        $btn-focus-width !default;\n$navbar-toggler-transition:         box-shadow .15s ease-in-out !default;\n\n$navbar-light-color:                rgba(var(--#{$prefix}emphasis-color-rgb), .65) !default;\n$navbar-light-hover-color:          rgba(var(--#{$prefix}emphasis-color-rgb), .8) !default;\n$navbar-light-active-color:         rgba(var(--#{$prefix}emphasis-color-rgb), 1) !default;\n$navbar-light-disabled-color:       rgba(var(--#{$prefix}emphasis-color-rgb), .3) !default;\n$navbar-light-icon-color:           rgba($body-color, .75) !default;\n$navbar-light-toggler-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-light-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-light-toggler-border-color: rgba(var(--#{$prefix}emphasis-color-rgb), .15) !default;\n$navbar-light-brand-color:          $navbar-light-active-color !default;\n$navbar-light-brand-hover-color:    $navbar-light-active-color !default;\n// scss-docs-end navbar-variables\n\n// scss-docs-start navbar-dark-variables\n$navbar-dark-color:                 rgba($white, .55) !default;\n$navbar-dark-hover-color:           rgba($white, .75) !default;\n$navbar-dark-active-color:          $white !default;\n$navbar-dark-disabled-color:        rgba($white, .25) !default;\n$navbar-dark-icon-color:            $navbar-dark-color !default;\n$navbar-dark-toggler-icon-bg:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-dark-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-dark-toggler-border-color:  rgba($white, .1) !default;\n$navbar-dark-brand-color:           $navbar-dark-active-color !default;\n$navbar-dark-brand-hover-color:     $navbar-dark-active-color !default;\n// scss-docs-end navbar-dark-variables\n\n\n// Dropdowns\n//\n// Dropdown menu container and contents.\n\n// scss-docs-start dropdown-variables\n$dropdown-min-width:                10rem !default;\n$dropdown-padding-x:                0 !default;\n$dropdown-padding-y:                .5rem !default;\n$dropdown-spacer:                   .125rem !default;\n$dropdown-font-size:                $font-size-base !default;\n$dropdown-color:                    var(--#{$prefix}body-color) !default;\n$dropdown-bg:                       var(--#{$prefix}body-bg) !default;\n$dropdown-border-color:             var(--#{$prefix}border-color-translucent) !default;\n$dropdown-border-radius:            var(--#{$prefix}border-radius) !default;\n$dropdown-border-width:             var(--#{$prefix}border-width) !default;\n$dropdown-inner-border-radius:      calc(#{$dropdown-border-radius} - #{$dropdown-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$dropdown-divider-bg:               $dropdown-border-color !default;\n$dropdown-divider-margin-y:         $spacer * .5 !default;\n$dropdown-box-shadow:               var(--#{$prefix}box-shadow) !default;\n\n$dropdown-link-color:               var(--#{$prefix}body-color) !default;\n$dropdown-link-hover-color:         $dropdown-link-color !default;\n$dropdown-link-hover-bg:            var(--#{$prefix}tertiary-bg) !default;\n\n$dropdown-link-active-color:        $component-active-color !default;\n$dropdown-link-active-bg:           $component-active-bg !default;\n\n$dropdown-link-disabled-color:      var(--#{$prefix}tertiary-color) !default;\n\n$dropdown-item-padding-y:           $spacer * .25 !default;\n$dropdown-item-padding-x:           $spacer !default;\n\n$dropdown-header-color:             $gray-600 !default;\n$dropdown-header-padding-x:         $dropdown-item-padding-x !default;\n$dropdown-header-padding-y:         $dropdown-padding-y !default;\n// fusv-disable\n$dropdown-header-padding:           $dropdown-header-padding-y $dropdown-header-padding-x !default; // Deprecated in v5.2.0\n// fusv-enable\n// scss-docs-end dropdown-variables\n\n// scss-docs-start dropdown-dark-variables\n$dropdown-dark-color:               $gray-300 !default;\n$dropdown-dark-bg:                  $gray-800 !default;\n$dropdown-dark-border-color:        $dropdown-border-color !default;\n$dropdown-dark-divider-bg:          $dropdown-divider-bg !default;\n$dropdown-dark-box-shadow:          null !default;\n$dropdown-dark-link-color:          $dropdown-dark-color !default;\n$dropdown-dark-link-hover-color:    $white !default;\n$dropdown-dark-link-hover-bg:       rgba($white, .15) !default;\n$dropdown-dark-link-active-color:   $dropdown-link-active-color !default;\n$dropdown-dark-link-active-bg:      $dropdown-link-active-bg !default;\n$dropdown-dark-link-disabled-color: $gray-500 !default;\n$dropdown-dark-header-color:        $gray-500 !default;\n// scss-docs-end dropdown-dark-variables\n\n\n// Pagination\n\n// scss-docs-start pagination-variables\n$pagination-padding-y:              .375rem !default;\n$pagination-padding-x:              .75rem !default;\n$pagination-padding-y-sm:           .25rem !default;\n$pagination-padding-x-sm:           .5rem !default;\n$pagination-padding-y-lg:           .75rem !default;\n$pagination-padding-x-lg:           1.5rem !default;\n\n$pagination-font-size:              $font-size-base !default;\n\n$pagination-color:                  var(--#{$prefix}link-color) !default;\n$pagination-bg:                     var(--#{$prefix}body-bg) !default;\n$pagination-border-radius:          var(--#{$prefix}border-radius) !default;\n$pagination-border-width:           var(--#{$prefix}border-width) !default;\n$pagination-margin-start:           calc(#{$pagination-border-width} * -1) !default; // stylelint-disable-line function-disallowed-list\n$pagination-border-color:           var(--#{$prefix}border-color) !default;\n\n$pagination-focus-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-focus-bg:               var(--#{$prefix}secondary-bg) !default;\n$pagination-focus-box-shadow:       $focus-ring-box-shadow !default;\n$pagination-focus-outline:          0 !default;\n\n$pagination-hover-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$pagination-hover-border-color:     var(--#{$prefix}border-color) !default; // Todo in v6: remove this?\n\n$pagination-active-color:           $component-active-color !default;\n$pagination-active-bg:              $component-active-bg !default;\n$pagination-active-border-color:    $component-active-bg !default;\n\n$pagination-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$pagination-disabled-bg:            var(--#{$prefix}secondary-bg) !default;\n$pagination-disabled-border-color:  var(--#{$prefix}border-color) !default;\n\n$pagination-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$pagination-border-radius-sm:       var(--#{$prefix}border-radius-sm) !default;\n$pagination-border-radius-lg:       var(--#{$prefix}border-radius-lg) !default;\n// scss-docs-end pagination-variables\n\n\n// Placeholders\n\n// scss-docs-start placeholders\n$placeholder-opacity-max:           .5 !default;\n$placeholder-opacity-min:           .2 !default;\n// scss-docs-end placeholders\n\n// Cards\n\n// scss-docs-start card-variables\n$card-spacer-y:                     $spacer !default;\n$card-spacer-x:                     $spacer !default;\n$card-title-spacer-y:               $spacer * .5 !default;\n$card-title-color:                  null !default;\n$card-subtitle-color:               null !default;\n$card-border-width:                 var(--#{$prefix}border-width) !default;\n$card-border-color:                 var(--#{$prefix}border-color-translucent) !default;\n$card-border-radius:                var(--#{$prefix}border-radius) !default;\n$card-box-shadow:                   null !default;\n$card-inner-border-radius:          subtract($card-border-radius, $card-border-width) !default;\n$card-cap-padding-y:                $card-spacer-y * .5 !default;\n$card-cap-padding-x:                $card-spacer-x !default;\n$card-cap-bg:                       rgba(var(--#{$prefix}body-color-rgb), .03) !default;\n$card-cap-color:                    null !default;\n$card-height:                       null !default;\n$card-color:                        null !default;\n$card-bg:                           var(--#{$prefix}body-bg) !default;\n$card-img-overlay-padding:          $spacer !default;\n$card-group-margin:                 $grid-gutter-width * .5 !default;\n// scss-docs-end card-variables\n\n// Accordion\n\n// scss-docs-start accordion-variables\n$accordion-padding-y:                     1rem !default;\n$accordion-padding-x:                     1.25rem !default;\n$accordion-color:                         var(--#{$prefix}body-color) !default;\n$accordion-bg:                            var(--#{$prefix}body-bg) !default;\n$accordion-border-width:                  var(--#{$prefix}border-width) !default;\n$accordion-border-color:                  var(--#{$prefix}border-color) !default;\n$accordion-border-radius:                 var(--#{$prefix}border-radius) !default;\n$accordion-inner-border-radius:           subtract($accordion-border-radius, $accordion-border-width) !default;\n\n$accordion-body-padding-y:                $accordion-padding-y !default;\n$accordion-body-padding-x:                $accordion-padding-x !default;\n\n$accordion-button-padding-y:              $accordion-padding-y !default;\n$accordion-button-padding-x:              $accordion-padding-x !default;\n$accordion-button-color:                  var(--#{$prefix}body-color) !default;\n$accordion-button-bg:                     var(--#{$prefix}accordion-bg) !default;\n$accordion-transition:                    $btn-transition, border-radius .15s ease !default;\n$accordion-button-active-bg:              var(--#{$prefix}primary-bg-subtle) !default;\n$accordion-button-active-color:           var(--#{$prefix}primary-text-emphasis) !default;\n\n// fusv-disable\n$accordion-button-focus-border-color:     $input-focus-border-color !default; // Deprecated in v5.3.3\n// fusv-enable\n$accordion-button-focus-box-shadow:       $btn-focus-box-shadow !default;\n\n$accordion-icon-width:                    1.25rem !default;\n$accordion-icon-color:                    $body-color !default;\n$accordion-icon-active-color:             $primary-text-emphasis !default;\n$accordion-icon-transition:               transform .2s ease-in-out !default;\n$accordion-icon-transform:                rotate(-180deg) !default;\n\n$accordion-button-icon:         url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='#{$accordion-icon-color}' stroke-linecap='round' stroke-linejoin='round'><path d='M2 5L8 11L14 5'/></svg>\") !default;\n$accordion-button-active-icon:  url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='#{$accordion-icon-active-color}' stroke-linecap='round' stroke-linejoin='round'><path d='M2 5L8 11L14 5'/></svg>\") !default;\n// scss-docs-end accordion-variables\n\n// Tooltips\n\n// scss-docs-start tooltip-variables\n$tooltip-font-size:                 $font-size-sm !default;\n$tooltip-max-width:                 200px !default;\n$tooltip-color:                     var(--#{$prefix}body-bg) !default;\n$tooltip-bg:                        var(--#{$prefix}emphasis-color) !default;\n$tooltip-border-radius:             var(--#{$prefix}border-radius) !default;\n$tooltip-opacity:                   .9 !default;\n$tooltip-padding-y:                 $spacer * .25 !default;\n$tooltip-padding-x:                 $spacer * .5 !default;\n$tooltip-margin:                    null !default; // TODO: remove this in v6\n\n$tooltip-arrow-width:               .8rem !default;\n$tooltip-arrow-height:              .4rem !default;\n// fusv-disable\n$tooltip-arrow-color:               null !default; // Deprecated in Bootstrap 5.2.0 for CSS variables\n// fusv-enable\n// scss-docs-end tooltip-variables\n\n// Form tooltips must come after regular tooltips\n// scss-docs-start tooltip-feedback-variables\n$form-feedback-tooltip-padding-y:     $tooltip-padding-y !default;\n$form-feedback-tooltip-padding-x:     $tooltip-padding-x !default;\n$form-feedback-tooltip-font-size:     $tooltip-font-size !default;\n$form-feedback-tooltip-line-height:   null !default;\n$form-feedback-tooltip-opacity:       $tooltip-opacity !default;\n$form-feedback-tooltip-border-radius: $tooltip-border-radius !default;\n// scss-docs-end tooltip-feedback-variables\n\n\n// Popovers\n\n// scss-docs-start popover-variables\n$popover-font-size:                 $font-size-sm !default;\n$popover-bg:                        var(--#{$prefix}body-bg) !default;\n$popover-max-width:                 276px !default;\n$popover-border-width:              var(--#{$prefix}border-width) !default;\n$popover-border-color:              var(--#{$prefix}border-color-translucent) !default;\n$popover-border-radius:             var(--#{$prefix}border-radius-lg) !default;\n$popover-inner-border-radius:       calc(#{$popover-border-radius} - #{$popover-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$popover-box-shadow:                var(--#{$prefix}box-shadow) !default;\n\n$popover-header-font-size:          $font-size-base !default;\n$popover-header-bg:                 var(--#{$prefix}secondary-bg) !default;\n$popover-header-color:              $headings-color !default;\n$popover-header-padding-y:          .5rem !default;\n$popover-header-padding-x:          $spacer !default;\n\n$popover-body-color:                var(--#{$prefix}body-color) !default;\n$popover-body-padding-y:            $spacer !default;\n$popover-body-padding-x:            $spacer !default;\n\n$popover-arrow-width:               1rem !default;\n$popover-arrow-height:              .5rem !default;\n// scss-docs-end popover-variables\n\n// fusv-disable\n// Deprecated in Bootstrap 5.2.0 for CSS variables\n$popover-arrow-color:               $popover-bg !default;\n$popover-arrow-outer-color:         var(--#{$prefix}border-color-translucent) !default;\n// fusv-enable\n\n\n// Toasts\n\n// scss-docs-start toast-variables\n$toast-max-width:                   350px !default;\n$toast-padding-x:                   .75rem !default;\n$toast-padding-y:                   .5rem !default;\n$toast-font-size:                   .875rem !default;\n$toast-color:                       null !default;\n$toast-background-color:            rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-border-width:                var(--#{$prefix}border-width) !default;\n$toast-border-color:                var(--#{$prefix}border-color-translucent) !default;\n$toast-border-radius:               var(--#{$prefix}border-radius) !default;\n$toast-box-shadow:                  var(--#{$prefix}box-shadow) !default;\n$toast-spacing:                     $container-padding-x !default;\n\n$toast-header-color:                var(--#{$prefix}secondary-color) !default;\n$toast-header-background-color:     rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-header-border-color:         $toast-border-color !default;\n// scss-docs-end toast-variables\n\n\n// Badges\n\n// scss-docs-start badge-variables\n$badge-font-size:                   .75em !default;\n$badge-font-weight:                 $font-weight-bold !default;\n$badge-color:                       $white !default;\n$badge-padding-y:                   .35em !default;\n$badge-padding-x:                   .65em !default;\n$badge-border-radius:               var(--#{$prefix}border-radius) !default;\n// scss-docs-end badge-variables\n\n\n// Modals\n\n// scss-docs-start modal-variables\n$modal-inner-padding:               $spacer !default;\n\n$modal-footer-margin-between:       .5rem !default;\n\n$modal-dialog-margin:               .5rem !default;\n$modal-dialog-margin-y-sm-up:       1.75rem !default;\n\n$modal-title-line-height:           $line-height-base !default;\n\n$modal-content-color:               null !default;\n$modal-content-bg:                  var(--#{$prefix}body-bg) !default;\n$modal-content-border-color:        var(--#{$prefix}border-color-translucent) !default;\n$modal-content-border-width:        var(--#{$prefix}border-width) !default;\n$modal-content-border-radius:       var(--#{$prefix}border-radius-lg) !default;\n$modal-content-inner-border-radius: subtract($modal-content-border-radius, $modal-content-border-width) !default;\n$modal-content-box-shadow-xs:       var(--#{$prefix}box-shadow-sm) !default;\n$modal-content-box-shadow-sm-up:    var(--#{$prefix}box-shadow) !default;\n\n$modal-backdrop-bg:                 $black !default;\n$modal-backdrop-opacity:            .5 !default;\n\n$modal-header-border-color:         var(--#{$prefix}border-color) !default;\n$modal-header-border-width:         $modal-content-border-width !default;\n$modal-header-padding-y:            $modal-inner-padding !default;\n$modal-header-padding-x:            $modal-inner-padding !default;\n$modal-header-padding:              $modal-header-padding-y $modal-header-padding-x !default; // Keep this for backwards compatibility\n\n$modal-footer-bg:                   null !default;\n$modal-footer-border-color:         $modal-header-border-color !default;\n$modal-footer-border-width:         $modal-header-border-width !default;\n\n$modal-sm:                          300px !default;\n$modal-md:                          500px !default;\n$modal-lg:                          800px !default;\n$modal-xl:                          1140px !default;\n\n$modal-fade-transform:              translate(0, -50px) !default;\n$modal-show-transform:              none !default;\n$modal-transition:                  transform .3s ease-out !default;\n$modal-scale-transform:             scale(1.02) !default;\n// scss-docs-end modal-variables\n\n\n// Alerts\n//\n// Define alert colors, border radius, and padding.\n\n// scss-docs-start alert-variables\n$alert-padding-y:               $spacer !default;\n$alert-padding-x:               $spacer !default;\n$alert-margin-bottom:           1rem !default;\n$alert-border-radius:           var(--#{$prefix}border-radius) !default;\n$alert-link-font-weight:        $font-weight-bold !default;\n$alert-border-width:            var(--#{$prefix}border-width) !default;\n$alert-dismissible-padding-r:   $alert-padding-x * 3 !default; // 3x covers width of x plus default padding on either side\n// scss-docs-end alert-variables\n\n// fusv-disable\n$alert-bg-scale:                -80% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-border-scale:            -70% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-color-scale:             40% !default; // Deprecated in v5.2.0, to be removed in v6\n// fusv-enable\n\n// Progress bars\n\n// scss-docs-start progress-variables\n$progress-height:                   1rem !default;\n$progress-font-size:                $font-size-base * .75 !default;\n$progress-bg:                       var(--#{$prefix}secondary-bg) !default;\n$progress-border-radius:            var(--#{$prefix}border-radius) !default;\n$progress-box-shadow:               var(--#{$prefix}box-shadow-inset) !default;\n$progress-bar-color:                $white !default;\n$progress-bar-bg:                   $primary !default;\n$progress-bar-animation-timing:     1s linear infinite !default;\n$progress-bar-transition:           width .6s ease !default;\n// scss-docs-end progress-variables\n\n\n// List group\n\n// scss-docs-start list-group-variables\n$list-group-color:                  var(--#{$prefix}body-color) !default;\n$list-group-bg:                     var(--#{$prefix}body-bg) !default;\n$list-group-border-color:           var(--#{$prefix}border-color) !default;\n$list-group-border-width:           var(--#{$prefix}border-width) !default;\n$list-group-border-radius:          var(--#{$prefix}border-radius) !default;\n\n$list-group-item-padding-y:         $spacer * .5 !default;\n$list-group-item-padding-x:         $spacer !default;\n// fusv-disable\n$list-group-item-bg-scale:          -80% !default; // Deprecated in v5.3.0\n$list-group-item-color-scale:       40% !default; // Deprecated in v5.3.0\n// fusv-enable\n\n$list-group-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$list-group-active-color:           $component-active-color !default;\n$list-group-active-bg:              $component-active-bg !default;\n$list-group-active-border-color:    $list-group-active-bg !default;\n\n$list-group-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$list-group-disabled-bg:            $list-group-bg !default;\n\n$list-group-action-color:           var(--#{$prefix}secondary-color) !default;\n$list-group-action-hover-color:     var(--#{$prefix}emphasis-color) !default;\n\n$list-group-action-active-color:    var(--#{$prefix}body-color) !default;\n$list-group-action-active-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end list-group-variables\n\n\n// Image thumbnails\n\n// scss-docs-start thumbnail-variables\n$thumbnail-padding:                 .25rem !default;\n$thumbnail-bg:                      var(--#{$prefix}body-bg) !default;\n$thumbnail-border-width:            var(--#{$prefix}border-width) !default;\n$thumbnail-border-color:            var(--#{$prefix}border-color) !default;\n$thumbnail-border-radius:           var(--#{$prefix}border-radius) !default;\n$thumbnail-box-shadow:              var(--#{$prefix}box-shadow-sm) !default;\n// scss-docs-end thumbnail-variables\n\n\n// Figures\n\n// scss-docs-start figure-variables\n$figure-caption-font-size:          $small-font-size !default;\n$figure-caption-color:              var(--#{$prefix}secondary-color) !default;\n// scss-docs-end figure-variables\n\n\n// Breadcrumbs\n\n// scss-docs-start breadcrumb-variables\n$breadcrumb-font-size:              null !default;\n$breadcrumb-padding-y:              0 !default;\n$breadcrumb-padding-x:              0 !default;\n$breadcrumb-item-padding-x:         .5rem !default;\n$breadcrumb-margin-bottom:          1rem !default;\n$breadcrumb-bg:                     null !default;\n$breadcrumb-divider-color:          var(--#{$prefix}secondary-color) !default;\n$breadcrumb-active-color:           var(--#{$prefix}secondary-color) !default;\n$breadcrumb-divider:                quote(\"/\") !default;\n$breadcrumb-divider-flipped:        $breadcrumb-divider !default;\n$breadcrumb-border-radius:          null !default;\n// scss-docs-end breadcrumb-variables\n\n// Carousel\n\n// scss-docs-start carousel-variables\n$carousel-control-color:             $white !default;\n$carousel-control-width:             15% !default;\n$carousel-control-opacity:           .5 !default;\n$carousel-control-hover-opacity:     .9 !default;\n$carousel-control-transition:        opacity .15s ease !default;\n\n$carousel-indicator-width:           30px !default;\n$carousel-indicator-height:          3px !default;\n$carousel-indicator-hit-area-height: 10px !default;\n$carousel-indicator-spacer:          3px !default;\n$carousel-indicator-opacity:         .5 !default;\n$carousel-indicator-active-bg:       $white !default;\n$carousel-indicator-active-opacity:  1 !default;\n$carousel-indicator-transition:      opacity .6s ease !default;\n\n$carousel-caption-width:             70% !default;\n$carousel-caption-color:             $white !default;\n$carousel-caption-padding-y:         1.25rem !default;\n$carousel-caption-spacer:            1.25rem !default;\n\n$carousel-control-icon-width:        2rem !default;\n\n$carousel-control-prev-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/></svg>\") !default;\n$carousel-control-next-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n\n$carousel-transition-duration:       .6s !default;\n$carousel-transition:                transform $carousel-transition-duration ease-in-out !default; // Define transform transition first if using multiple transitions (e.g., `transform 2s ease, opacity .5s ease-out`)\n// scss-docs-end carousel-variables\n\n// scss-docs-start carousel-dark-variables\n$carousel-dark-indicator-active-bg:  $black !default;\n$carousel-dark-caption-color:        $black !default;\n$carousel-dark-control-icon-filter:  invert(1) grayscale(100) !default;\n// scss-docs-end carousel-dark-variables\n\n\n// Spinners\n\n// scss-docs-start spinner-variables\n$spinner-width:           2rem !default;\n$spinner-height:          $spinner-width !default;\n$spinner-vertical-align:  -.125em !default;\n$spinner-border-width:    .25em !default;\n$spinner-animation-speed: .75s !default;\n\n$spinner-width-sm:        1rem !default;\n$spinner-height-sm:       $spinner-width-sm !default;\n$spinner-border-width-sm: .2em !default;\n// scss-docs-end spinner-variables\n\n\n// Close\n\n// scss-docs-start close-variables\n$btn-close-width:            1em !default;\n$btn-close-height:           $btn-close-width !default;\n$btn-close-padding-x:        .25em !default;\n$btn-close-padding-y:        $btn-close-padding-x !default;\n$btn-close-color:            $black !default;\n$btn-close-bg:               url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$btn-close-color}'><path d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/></svg>\") !default;\n$btn-close-focus-shadow:     $focus-ring-box-shadow !default;\n$btn-close-opacity:          .5 !default;\n$btn-close-hover-opacity:    .75 !default;\n$btn-close-focus-opacity:    1 !default;\n$btn-close-disabled-opacity: .25 !default;\n$btn-close-white-filter:     invert(1) grayscale(100%) brightness(200%) !default;\n// scss-docs-end close-variables\n\n\n// Offcanvas\n\n// scss-docs-start offcanvas-variables\n$offcanvas-padding-y:               $modal-inner-padding !default;\n$offcanvas-padding-x:               $modal-inner-padding !default;\n$offcanvas-horizontal-width:        400px !default;\n$offcanvas-vertical-height:         30vh !default;\n$offcanvas-transition-duration:     .3s !default;\n$offcanvas-border-color:            $modal-content-border-color !default;\n$offcanvas-border-width:            $modal-content-border-width !default;\n$offcanvas-title-line-height:       $modal-title-line-height !default;\n$offcanvas-bg-color:                var(--#{$prefix}body-bg) !default;\n$offcanvas-color:                   var(--#{$prefix}body-color) !default;\n$offcanvas-box-shadow:              $modal-content-box-shadow-xs !default;\n$offcanvas-backdrop-bg:             $modal-backdrop-bg !default;\n$offcanvas-backdrop-opacity:        $modal-backdrop-opacity !default;\n// scss-docs-end offcanvas-variables\n\n// Code\n\n$code-font-size:                    $small-font-size !default;\n$code-color:                        $pink !default;\n\n$kbd-padding-y:                     .1875rem !default;\n$kbd-padding-x:                     .375rem !default;\n$kbd-font-size:                     $code-font-size !default;\n$kbd-color:                         var(--#{$prefix}body-bg) !default;\n$kbd-bg:                            var(--#{$prefix}body-color) !default;\n$nested-kbd-font-weight:            null !default; // Deprecated in v5.2.0, removing in v6\n\n$pre-color:                         null !default;\n\n@import \"variables-dark\"; // TODO: can be removed safely in v6, only here to avoid breaking changes in v5.3\n","// Override bootstrap variables\n$spacer: 1rem;\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1400px,\n);\n$grid-breakpoints: (\n  xs: 0,\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1200px,\n);\n$dropdown-link-hover-bg: var(--pst-color-surface);\n\n// --pst-color-surface can also be assigned to the dark variant because it is\n// scoped to different values depending on light/dark theme\n$dropdown-dark-link-hover-bg: var(--pst-color-surface);\n$dropdown-link-active-bg: var(--pst-color-surface);\n$dropdown-dark-link-active-bg: var(--pst-color-surface);\n$focus-ring-width: 0.1875rem; // 3px at 100% zoom (0.1875 * 16px base font = 3px)\n$focus-ring-opacity: 1;\n$focus-ring-color: var(--pst-color-accent);\n$focus-ring-blur: 0;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color;\n\n// outline creates the same style of focus ring, it just uses CSS outline instead of box shadow\n$focus-ring-outline: $focus-ring-color solid $focus-ring-width;\n$btn-focus-box-shadow: $focus-ring-box-shadow;\n","/***\n * Rules for the UX/UI of skip navigation link btn.\n * It's only visible to people\n * navigating with keyboard for accessibility purposes\n * ref: https://www.youtube.com/watch?v=VUR0I5mqq7I\n ***/\n\n.skip-link {\n  position: fixed;\n  top: 0;\n  left: 0;\n  right: 0;\n  text-align: center;\n  background-color: var(--pst-color-warning);\n  padding: 0.5rem;\n  z-index: $zindex-modal;\n  border-bottom: 1px solid var(--pst-color-border);\n\n  // This shows / hides the button\n  transform: translateY(-100%);\n  transition: transform 150ms ease-in-out;\n\n  &:focus-within {\n    transform: translateY(0%);\n  }\n\n  a {\n    // Ensure we are using a WCAG conformant colour\n    color: var(--pst-color-warning-text) !important;\n\n    &:focus-visible {\n      // use color with sufficient contrast\n      outline-color: $foundation-black;\n    }\n  }\n}\n","/*******************************************************************************\n* master color map. Only the colors that actually differ between light and dark\n* themes are specified separately.\n*\n* To see the full list of colors see https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1234%3A765&t=ifcFT1JtnrSshGfi-1\n*/\n\n@use \"sass:map\";\n@use \"sass:meta\";\n@use \"sass:string\";\n\n/**\n* Function to get items from nested maps\n*/\n// @param {Map} $map - Map\n// @param {Arglist} $keys - Keys to fetch\n// @return {*}\n@function map-deep-get($map, $keys...) {\n  @each $key in $keys {\n    $map: map.get($map, $key);\n  }\n\n  @return $map;\n}\n\n/* Assign base colors for the PyData theme */\n$color-palette: (\n  // Primary color\n  \"teal\":\n    (\n      \"50\": #f4fbfc,\n      \"100\": #e9f6f8,\n      \"200\": #d0ecf1,\n      \"300\": #abdde6,\n      \"400\": #3fb1c5,\n      \"500\": #0a7d91,\n      \"600\": #085d6c,\n      \"700\": #064752,\n      \"800\": #042c33,\n      \"900\": #021b1f,\n    ),\n  // Secondary color\n  \"violet\":\n    (\n      \"50\": #f4eefb,\n      \"100\": #e0c7ff,\n      \"200\": #d5b4fd,\n      \"300\": #b780ff,\n      \"400\": #9c5ffd,\n      \"500\": #8045e5,\n      \"600\": #6432bd,\n      \"700\": #4b258f,\n      \"800\": #341a61,\n      \"900\": #1e0e39,\n    ),\n  // Neutrals\n  \"gray\":\n    (\n      \"50\": #f9f9fa,\n      \"100\": #f3f4f5,\n      \"200\": #e5e7ea,\n      \"300\": #d1d5da,\n      \"400\": #9ca4af,\n      \"500\": #677384,\n      \"600\": #48566b,\n      \"700\": #29313d,\n      \"800\": #222832,\n      \"900\": #14181e,\n    ),\n  // Accent color\n  \"pink\":\n    (\n      \"50\": #fcf8fd,\n      \"100\": #fcf0fa,\n      \"200\": #f8dff5,\n      \"300\": #f3c7ee,\n      \"400\": #e47fd7,\n      \"500\": #c132af,\n      \"600\": #912583,\n      \"700\": #6e1c64,\n      \"800\": #46123f,\n      \"900\": #2b0b27,\n    ),\n  \"foundation\": (\n    \"white\": #ffffff,\n    // gray-900\n    \"black\": #14181e,\n  )\n);\n\n:root {\n  // Add theme colours to the html root element\n  @each $group-color, $color in $color-palette {\n    @each $color-name, $definition in $color {\n      --pst-#{$group-color}-#{$color-name}: #{$definition};\n    }\n  }\n}\n\n// Static SCSS variables used thoroughout the theme\n// Minimum contrast ratio used for the theme.\n// Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n// 4.5 - is for text that is 14pt or less\n$min-contrast-ratio-4: 4.5;\n\n// 3 is for text that is 18pt or bold, or for non-text elements\n$min-contrast-ratio-3: 3;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$foundation-black: #14181e;\n$foundation-white: #fff;\n\n// This is a custom - calculated  color between gray 100 and 200 - to reduce\n// the contrast ratio (avoid a jarring effect)\n$base-light-text: #ced6dd;\n\n// used in sphinx_design - gray 100\n$foundation-light-gray: #f3f4f5;\n\n// used in sphinx_design - gray 700\n$foundation-muted-gray: #29313d;\n\n// used in sphinx_design - gray 800\n$foundation-dark-gray: #222832;\n$pst-semantic-colors: (\n  \"primary\": (\n    \"light\": #{map-deep-get($color-palette, \"teal\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"teal\", \"200\")},\n    \"dark\": #{map-deep-get($color-palette, \"teal\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"teal\", \"800\")},\n  ),\n  \"secondary\": (\n    \"light\": #{map-deep-get($color-palette, \"violet\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"violet\", \"100\")},\n    \"dark\": #{map-deep-get($color-palette, \"violet\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"violet\", \"800\")},\n  ),\n  \"accent\": (\n    \"light\": #{map-deep-get($color-palette, \"pink\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"pink\", \"200\")},\n    \"dark\": #{map-deep-get($color-palette, \"pink\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"pink\", \"800\")},\n  ),\n  \"info\": (\n    \"light\": #276be9,\n    \"bg-light\": #dce7fc,\n    \"dark\": #79a3f2,\n    \"bg-dark\": #06245d,\n  ),\n  \"warning\": (\n    \"light\": #f66a0a,\n    \"bg-light\": #f8e3d0,\n    \"dark\": #ff9245,\n    \"bg-dark\": #652a02,\n  ),\n  \"success\": (\n    \"light\": #00843f,\n    \"bg-light\": #d6ece1,\n    \"dark\": #5fb488,\n    \"bg-dark\": #002f17,\n  ),\n  // This is based on the warning color\n  \"attention\":\n    (\n      \"light\": var(--pst-color-warning),\n      \"bg-light\": var(--pst-color-warning-bg),\n      \"dark\": var(--pst-color-warning),\n      \"bg-dark\": var(--pst-color-warning-bg),\n    ),\n  \"danger\": (\n    \"light\": #d72d47,\n    \"bg-light\": #f9e1e4,\n    \"dark\": #e78894,\n    \"bg-dark\": #4e111b,\n  ),\n  \"text-base\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n    \"dark\": $base-light-text,\n  ),\n  \"text-muted\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"600\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"400\")},\n  ),\n  \"heading-color\": (\n    \"light\": #{$foundation-white},\n    \"dark\": #{$foundation-black},\n  ),\n  \"shadow\": (\n    \"light\": rgba(0, 0, 0, 0.1),\n    \"dark\": rgba(0, 0, 0, 0.2),\n  ),\n  \"border\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"300\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"600\")},\n  ),\n  \"border-muted\": (\n    \"light\": rgba(23, 23, 26, 0.2),\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"700\")},\n  ),\n  \"blockquote-notch\": (\n    // These colors have a contrast ratio > 3.0 against both the background and\n    // surface colors that the notch is sandwiched between\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"500\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"400\")},\n  ),\n  \"inline-code\": (\n    \"light\": #{map-deep-get($color-palette, \"pink\", \"600\")},\n    \"dark\": #{map-deep-get($color-palette, \"pink\", \"300\")},\n  ),\n  \"inline-code-links\": (\n    // need to make sure there is enough contrast against the code bg\n    \"light\": #{map-deep-get($color-palette, \"teal\", \"600\")},\n    // keep primary color for dark mode\n    \"dark\": #{map-deep-get($color-palette, \"teal\", \"400\")},\n  ),\n  \"target\": (\n    \"light\": #f3cf95,\n    \"dark\": #675c04,\n  ),\n  \"table\": (\n    \"light\": #{map-deep-get($color-palette, \"foundation\", \"black\")},\n    \"dark\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n  ),\n  \"table-row-hover\": (\n    \"bg-light\": #{map-deep-get($color-palette, \"violet\", \"300\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"violet\", \"600\")},\n  ),\n  \"table-inner-border\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"200\")},\n    \"dark\": #364150,\n  ),\n  // DEPTH COLORS - you can see the elevation colours and shades\n  // in the Figma file https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1492%3A922&t=sQeQZehkOzposYEg-1\n  // background: color of the canvas / the furthest back layer\n  \"background\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n      \"dark\": #{map-deep-get($color-palette, \"foundation\", \"black\")},\n    ),\n  // on-background: provides slight contrast against background\n  // (by use of shadows in light theme)\n  \"on-background\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n      \"dark\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n    ),\n  \"surface\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"100\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"700\")},\n  ),\n  // on_surface: object on top of surface object (without shadows)\n  \"on-surface\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n      \"dark\": $foundation-light-gray,\n    ),\n);\n\n/*******************************************************************************\n* write the color rules for each theme (light/dark)\n*/\n\n/* NOTE:\n * Mixins enable us to reuse the same definitions for the different modes\n * https://sass-lang.com/documentation/at-rules/mixin\n * #{something} inserts a variable into a CSS selector or property name\n * https://sass-lang.com/documentation/interpolation\n */\n@mixin theme-colors($mode) {\n  // check if this color is defined differently for light/dark\n  @each $col-name, $definition in $pst-semantic-colors {\n    @if meta.type-of($definition) == map {\n      @each $key, $val in $definition {\n        @if string.index($key, $mode) {\n          // since now we define the bg colours in the semantic colours and not\n          // by changing opacity, we need to check if the key contains bg and the\n          // correct mode (light/dark)\n          @if string.index($key, \"bg\") {\n            --pst-color-#{$col-name}-bg: #{$val};\n          } @else {\n            --pst-color-#{$col-name}: #{$val};\n          }\n        }\n      }\n    } @else {\n      --pst-color-#{$col-name}: #{$definition};\n    }\n  }\n\n  // assign the \"duplicate\" colors (ones that just reference other variables)\n  & {\n    --pst-color-link: var(--pst-color-primary);\n    --pst-color-link-hover: var(--pst-color-secondary);\n    --pst-color-table-outer-border: var(--pst-color-surface);\n    --pst-color-table-heading-bg: var(--pst-color-surface);\n    --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n    --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n  }\n\n  // adapt to light/dark-specific content\n  @if $mode == \"light\" {\n    .only-dark,\n    .only-dark ~ figcaption {\n      display: none !important;\n    }\n  } @else {\n    .only-light,\n    .only-light ~ figcaption {\n      display: none !important;\n    }\n\n    /* Adjust images in dark mode (unless they have class .only-dark or\n     * .dark-light, in which case assume they're already optimized for dark\n     * mode).\n     */\n    img:not(.only-dark, .dark-light) {\n      filter: brightness(0.8) contrast(1.2);\n    }\n\n    /* Give images a light background in dark mode in case they have\n    *  transparency and black text (unless they have class .only-dark or .dark-light, in\n    *  which case assume they're already optimized for dark mode).\n    */\n    .bd-content img:not(.only-dark, .dark-light) {\n      background-color: rgb(255 255 255);\n      border-radius: 0.25rem;\n    }\n\n    // MathJax SVG outputs should be filled to same color as text.\n    .MathJax_SVG * {\n      fill: var(--pst-color-text-base);\n    }\n  }\n}\n\n/* Defaults to light mode if data-theme is not set */\nhtml:not([data-theme]) {\n  @include theme-colors(\"light\");\n}\n\n/* NOTE: @each {...} is like a for-loop\n * https://sass-lang.com/documentation/at-rules/control/each\n */\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    @include theme-colors($mode);\n\n    color-scheme: $mode;\n  }\n}\n\n// assign classes too, for runtime use of theme colors\n@each $col-name, $definition in $pst-semantic-colors {\n  .pst-color-#{$col-name} {\n    color: var(--pst-color-#{$col-name});\n  }\n}\n",".bd-container {\n  flex-grow: 1;\n  display: flex;\n  justify-content: center;\n\n  .bd-container__inner {\n    display: flex;\n  }\n}\n\n.bd-page-width {\n  width: 100%;\n\n  @include media-breakpoint-up(lg) {\n    max-width: $breakpoint-page-width;\n  }\n}\n","// Breakpoint viewport sizes and media queries.\n//\n// Breakpoints are defined as a map of (name: minimum width), order from small to large:\n//\n//    (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px)\n//\n// The map defined in the `$grid-breakpoints` global variable is used as the `$breakpoints` argument by default.\n\n// Name of the next breakpoint, or null for the last breakpoint.\n//\n//    >> breakpoint-next(sm)\n//    md\n//    >> breakpoint-next(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    md\n//    >> breakpoint-next(sm, $breakpoint-names: (xs sm md lg xl xxl))\n//    md\n@function breakpoint-next($name, $breakpoints: $grid-breakpoints, $breakpoint-names: map-keys($breakpoints)) {\n  $n: index($breakpoint-names, $name);\n  @if not $n {\n    @error \"breakpoint `#{$name}` not found in `#{$breakpoints}`\";\n  }\n  @return if($n < length($breakpoint-names), nth($breakpoint-names, $n + 1), null);\n}\n\n// Minimum breakpoint width. Null for the smallest (first) breakpoint.\n//\n//    >> breakpoint-min(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    576px\n@function breakpoint-min($name, $breakpoints: $grid-breakpoints) {\n  $min: map-get($breakpoints, $name);\n  @return if($min != 0, $min, null);\n}\n\n// Maximum breakpoint width.\n// The maximum value is reduced by 0.02px to work around the limitations of\n// `min-` and `max-` prefixes and viewports with fractional widths.\n// See https://www.w3.org/TR/mediaqueries-4/#mq-min-max\n// Uses 0.02px rather than 0.01px to work around a current rounding bug in Safari.\n// See https://bugs.webkit.org/show_bug.cgi?id=178261\n//\n//    >> breakpoint-max(md, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    767.98px\n@function breakpoint-max($name, $breakpoints: $grid-breakpoints) {\n  $max: map-get($breakpoints, $name);\n  @return if($max and $max > 0, $max - .02, null);\n}\n\n// Returns a blank string if smallest breakpoint, otherwise returns the name with a dash in front.\n// Useful for making responsive utilities.\n//\n//    >> breakpoint-infix(xs, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"\"  (Returns a blank string)\n//    >> breakpoint-infix(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"-sm\"\n@function breakpoint-infix($name, $breakpoints: $grid-breakpoints) {\n  @return if(breakpoint-min($name, $breakpoints) == null, \"\", \"-#{$name}\");\n}\n\n// Media of at least the minimum breakpoint width. No query for the smallest breakpoint.\n// Makes the @content apply to the given breakpoint and wider.\n@mixin media-breakpoint-up($name, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($name, $breakpoints);\n  @if $min {\n    @media (min-width: $min) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media of at most the maximum breakpoint width. No query for the largest breakpoint.\n// Makes the @content apply to the given breakpoint and narrower.\n@mixin media-breakpoint-down($name, $breakpoints: $grid-breakpoints) {\n  $max: breakpoint-max($name, $breakpoints);\n  @if $max {\n    @media (max-width: $max) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media that spans multiple breakpoint widths.\n// Makes the @content apply between the min and max breakpoints\n@mixin media-breakpoint-between($lower, $upper, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($lower, $breakpoints);\n  $max: breakpoint-max($upper, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($lower, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($upper, $breakpoints) {\n      @content;\n    }\n  }\n}\n\n// Media between the breakpoint's minimum and maximum widths.\n// No minimum for the smallest breakpoint, and no maximum for the largest one.\n// Makes the @content apply only to the given breakpoint, not viewports any wider or narrower.\n@mixin media-breakpoint-only($name, $breakpoints: $grid-breakpoints) {\n  $min:  breakpoint-min($name, $breakpoints);\n  $next: breakpoint-next($name, $breakpoints);\n  $max:  breakpoint-max($next, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($name, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($next, $breakpoints) {\n      @content;\n    }\n  }\n}\n",".pst-async-banner-revealer {\n  // Setting height to 0 and overflow to hidden allows us to add up the heights\n  // of this element's children before revealing them.\n  height: 0;\n  overflow: hidden;\n\n  // Height to be set by JavaScript, which should trigger the following\n  // transition rule (unless the user has set their system to reduce motion).\n  transition: height 300ms ease-in-out;\n\n  @media (prefers-reduced-motion) {\n    transition: none;\n  }\n}\n\n#bd-header-version-warning,\n.bd-header-announcement {\n  min-height: 3rem;\n  width: 100%;\n  display: flex;\n  position: relative;\n  align-items: center;\n  justify-content: center;\n  text-align: center;\n  padding: 0.5rem 12.5%; // Horizontal padding so the width is 75%\n  // One breakpoint less than $breakpoint-sidebar-primary. See variables/_layout.scss for more info.\n  @include media-breakpoint-down(lg) {\n    // Announcements can take a bit more width on mobile\n    padding: 0.5rem 2%;\n  }\n\n  p {\n    font-weight: bold;\n    margin: 0;\n  }\n\n  // Ensure there is enough contrast against the background\n  a {\n    color: var(--pst-color-inline-code-links);\n  }\n\n  // The \"Switch to stable version\" link (styled like a button)\n  .pst-button-link-to-stable-version {\n    @include box-shadow;\n\n    $background-color: var(--pst-color-danger);\n    $hover-background-color: var(--pst-color-danger-highlight);\n    $color: var(--pst-color-danger-text);\n\n    background-color: $background-color;\n    border-color: $background-color;\n    color: $color;\n    border-radius: 0.25rem;\n\n    &:hover {\n      background-color: $hover-background-color;\n      border-color: $hover-background-color;\n      color: $color;\n    }\n\n    &:focus-visible {\n      outline: $focus-ring-width solid $background-color;\n      outline-offset: $focus-ring-width;\n    }\n  }\n}\n\n// Bg color is now defined in the theme color palette - using our secondary color\n.bd-header-announcement {\n  background-color: var(--pst-color-secondary-bg);\n}\n\n#bd-header-version-warning {\n  background-color: var(--pst-color-danger-bg);\n}\n","/*********************************************\n* SASS Mixins\n*********************************************/\n\n/**\n * A consistent box shadow style we apply across elements.\n */\n@mixin box-shadow() {\n  box-shadow:\n    0 0.2rem 0.5rem var(--pst-color-shadow),\n    0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n\n/**\n   * Set background of some cell outputs to white-ish to make sure colors work\n   * This is because many libraries make output that only looks good on white\n   */\n@mixin cell-output-background {\n  color: var(--pst-color-on-background);\n  background-color: var(--pst-color-text-base);\n  border-radius: 0.25rem;\n  padding: 0.5rem;\n}\n\n@mixin table-colors {\n  color: var(--pst-color-table);\n  border: 1px solid var(--pst-color-table-outer-border);\n\n  th,\n  td {\n    ~ th,\n    ~ td {\n      border-left: 1px solid var(--pst-color-table-inner-border);\n    }\n  }\n\n  thead {\n    tr {\n      background-color: var(--pst-color-table-heading-bg);\n      border-bottom: 2px solid var(--pst-color-primary);\n    }\n  }\n\n  tbody {\n    tr {\n      &:nth-child(odd) {\n        background-color: var(--pst-color-table-row-zebra-low-bg);\n      }\n\n      &:nth-child(even) {\n        background-color: var(--pst-color-table-row-zebra-high-bg);\n      }\n\n      &:hover {\n        background-color: var(--pst-color-table-row-hover-bg);\n      }\n    }\n  }\n}\n","/**\n * Main content area\n */\n.bd-main {\n  flex-grow: 1;\n  flex-direction: column;\n  display: flex;\n  min-width: 0;\n\n  .bd-content {\n    display: flex;\n    justify-content: center;\n    height: 100%;\n\n    .bd-article-container {\n      justify-content: start;\n      display: flex;\n      flex-direction: column;\n\n      // Max-width is slightly more than the W3 since our docs often have images.\n      // We shoot for about 100 characters per line instead of 80.\n      // ref: https://www.w3.org/WAI/tutorials/page-structure/styling/#line-length\n      width: 100%;\n      max-width: 60em;\n      overflow-x: auto; // Prevent wide content from pushing off the secondary sidebar\n      padding: 1rem;\n\n      .bd-article {\n        // Give a bit more verticle spacing on wide screens\n        @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n          padding-top: 1.5rem;\n          padding-left: 2rem;\n        }\n      }\n    }\n  }\n}\n",".bd-footer {\n  width: 100%;\n  border-top: 1px solid var(--pst-color-border);\n\n  .bd-footer__inner {\n    display: flex;\n    flex-grow: 1;\n    padding: 1rem;\n    margin: auto;\n  }\n\n  .footer-items__start,\n  .footer-items__center,\n  .footer-items__end {\n    display: flex;\n    flex-direction: column;\n    gap: 0.5rem;\n    justify-content: center;\n    flex-grow: 1;\n  }\n\n  .footer-items__center {\n    text-align: center;\n  }\n\n  .footer-items__end {\n    text-align: end;\n  }\n\n  // So that paragraphs don't take up extra room\n  .footer-item p {\n    margin-bottom: 0;\n  }\n}\n",".bd-footer-article {\n  margin-top: auto;\n\n  .footer-article-items {\n    display: flex;\n    flex-direction: column;\n  }\n}\n",".bd-footer-content {\n  .footer-content-items {\n    display: flex;\n    flex-direction: column;\n    margin-top: auto;\n  }\n}\n","/**\n * Header at the top of the page\n * It includes the announcement bar and the navigation bar.\n */\n\n// Styling for the Icon links can be found in components/_icon-links.scss\n\n// If we want the shadow to only point downward in the future, set\n// box-shadow to: 0 0.125rem 0.25rem -0.125rem rgba(0, 0, 0, 0.11);\n.bd-header {\n  position: sticky;\n  top: 0;\n  z-index: $zindex-fixed;\n\n  // Overrides bootstrap\n  background-color: var(--pst-color-on-background) !important;\n  box-shadow: 0 0.125rem 0.25rem 0 var(--pst-color-shadow);\n  width: 100%;\n  padding: 0;\n  max-width: 100vw;\n  justify-content: center;\n\n  .bd-header__inner {\n    display: flex;\n    align-items: center;\n    height: fit-content;\n    padding-left: 1rem;\n    padding-right: 1rem;\n  }\n\n  :focus-visible {\n    border-radius: $focus-ring-radius;\n  }\n\n  // These items will define the height of the header\n  .navbar-item {\n    height: var(--pst-header-height);\n    max-height: var(--pst-header-height);\n    display: flex;\n    align-items: center;\n  }\n\n  // Hide the navbar header items on mobile because they're in the sidebar\n  .navbar-header-items {\n    display: none;\n    flex-shrink: 1;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: inherit;\n      flex-grow: 1;\n      padding: 0 0 0 0.5rem;\n    }\n  }\n\n  .navbar-header-items__end,\n  .navbar-header-items__center,\n  .navbar-header-items__start {\n    display: flex;\n    align-items: center;\n    flex-flow: wrap;\n\n    // In case we wrap our items to multiple rows on small screens\n    row-gap: 0;\n  }\n\n  .navbar-header-items__end,\n  .navbar-header-items__center {\n    column-gap: 1rem;\n  }\n\n  // A little smaller because this is displayed by default on mobile\n  .navbar-header-items__start {\n    flex-shrink: 0;\n    margin-right: auto;\n    gap: 0.5rem;\n  }\n\n  .navbar-header-items__end {\n    // End navbar items should snap to the right\n    justify-content: end;\n  }\n\n  // Contains the navigation links within the navbar\n  .navbar-nav {\n    display: flex;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      // Center align on wide screens so the dropdown button is centered properly\n      align-items: baseline;\n    }\n\n    li.pst-header-nav-item {\n      margin-inline: 2px; // breathing room so hover and focus styles do not overlap\n      &.dropdown {\n        margin-inline: 4px;\n\n        button {\n          padding-inline: 8px;\n        }\n      }\n\n      a {\n        padding-inline: 6px;\n      }\n\n      @include header-link;\n    }\n\n    li a.nav-link.dropdown-item {\n      @include link-style-text;\n    }\n\n    // Dropdowns for the extra links\n    .dropdown {\n      button {\n        display: unset;\n        border: none;\n\n        &:hover {\n          @include link-style-hover;\n        }\n      }\n\n      .dropdown-menu {\n        z-index: $zindex-popover;\n        border: 1px solid var(--pst-color-border);\n        box-shadow: 0 0 0.3rem 0.1rem var(--pst-color-shadow);\n        background-color: var(--pst-color-on-background);\n        padding: 0.5rem 0;\n        margin: 0.5rem 0;\n        min-width: 20rem;\n\n        .dropdown-item {\n          // Give the items in the dropdown some breathing room but let the hit\n          // and hover area of the items extend to the edges of the menu\n          padding: 0.25rem 1.5rem;\n\n          // Override Bootstrap\n          &:focus:not(:hover, :active) {\n            background-color: inherit;\n          }\n\n          &:focus-visible {\n            z-index: 10; // keep focus ring on top (prevent the hover background of the next dropdown item from covering the ring)\n          }\n        }\n\n        // Hide the menu unless show has been clicked\n        &:not(.show) {\n          display: none;\n        }\n      }\n    }\n  }\n\n  // **************************************************************\n  // Showing and hiding the sidebar toggle buttons and header items\n  // **************************************************************\n\n  // Toggle buttons\n  button.sidebar-toggle {\n    display: flex;\n    cursor: pointer;\n    font-size: var(--pst-font-size-icon);\n    align-items: center;\n    color: var(--pst-color-muted);\n    margin-bottom: 0;\n    padding-bottom: 0.25rem;\n    background-color: inherit;\n    border: none;\n\n    @include icon-navbar-hover;\n  }\n\n  button.primary-toggle {\n    margin-right: 1rem;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  button.secondary-toggle {\n    margin-left: 1rem;\n\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      display: none;\n    }\n  }\n}\n\n// inline the element in the navbar as long as they fit and use display block when collapsing\n@include media-breakpoint-up($breakpoint-sidebar-primary) {\n  .navbar-center-items .navbar-item {\n    display: inline-block;\n  }\n}\n\n.nav-link {\n  &:hover {\n    @include link-style-hover;\n  }\n\n  // Override Bootstrap\n  transition: none;\n\n  &.nav-external::after {\n    font: var(--fa-font-solid);\n    content: var(--pst-icon-external-link);\n    font-size: 0.75em;\n    margin-left: 0.3em;\n  }\n}\n\n.bd-navbar-elements li.nav-item i {\n  font-size: 0.7rem;\n  padding-left: 2px;\n  vertical-align: middle;\n}\n\n// THe elements next to the hamburger menu only show on narrow screens\n.navbar-persistent--mobile {\n  margin-left: auto;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: none;\n  }\n}\n\n// The navbar-persistent content should only show on wide screens\n.navbar-persistent--container {\n  display: none;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: flex;\n  }\n}\n",".header-article__inner {\n  display: flex;\n  padding: 0 0.5rem;\n\n  // The items define the height so that it disappears if there are no items\n  .header-article-item {\n    min-height: var(--pst-header-article-height);\n    height: var(--pst-header-article-height);\n  }\n\n  .header-article-items__start,\n  .header-article-items__end {\n    display: flex;\n    align-items: start;\n    gap: 0.5rem;\n  }\n\n  .header-article-items__end {\n    margin-left: auto;\n  }\n}\n","/**\n * The primary sidebar on the left.\n * e.g., between-pages navigation.\n */\n\n$sidebar-padding-right: 1rem;\n\n.bd-sidebar-primary {\n  display: flex;\n  flex-direction: column;\n  gap: 1rem;\n  max-height: calc(100vh - var(--pst-header-height));\n  position: sticky;\n  top: var(--pst-header-height);\n\n  @include make-col(3);\n\n  // Borders padding and whitespace\n  padding: 2rem $sidebar-padding-right 1rem 1rem;\n  border-right: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n  font-size: var(--pst-sidebar-font-size-mobile);\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    font-size: var(--pst-sidebar-font-size);\n  }\n\n  :focus-visible {\n    border-radius: $focus-ring-radius;\n  }\n\n  // override bootstrap when navlink are displayed in the sidebar\n  .nav-link {\n    font-size: var(--pst-sidebar-font-size-mobile);\n  }\n\n  &.no-sidebar {\n    border-right: 0;\n  }\n\n  &.hide-on-wide {\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  // Headers shouldn't be colored in the sidebars and some extensions add headers\n  h1,\n  h2,\n  h3,\n  h4 {\n    color: var(--pst-color-text-base);\n  }\n\n  .sidebar-primary-items__start,\n  .sidebar-primary-items__end {\n    .sidebar-primary-item {\n      padding: 0.5rem 0;\n    }\n  }\n\n  // Hide the sidebar header items on widescreen since they are visible in the header\n  .sidebar-header-items {\n    display: flex;\n    flex-direction: column;\n\n    .sidebar-header-items__title {\n      font-weight: var(--pst-sidebar-header-font-weight);\n      font-size: var(--pst-sidebar-header-font-size);\n      color: var(--pst-color-text-base);\n      margin-bottom: 0.5rem;\n    }\n\n    // The dropdown toggle for extra links just shows them all instead.\n    .nav-item.dropdown {\n      // On mobile, the dropdown behaves like any other link, no hiding\n      button {\n        display: none;\n      }\n\n      .dropdown-menu {\n        display: flex;\n        flex-direction: column;\n        padding: 0;\n        margin: 0;\n        border: none;\n        background-color: inherit;\n        font-size: inherit;\n\n        .dropdown-item {\n          &:hover,\n          &:focus {\n            // In the mobile sidebar, the dropdown menu is inlined with the\n            // other links, which do not have background-color changes on hover\n            // and focus\n            background-color: unset;\n          }\n        }\n      }\n    }\n\n    .bd-navbar-elements {\n      .nav-link {\n        &:focus-visible {\n          box-shadow: none; // Override Bootstrap\n          outline: $focus-ring-outline;\n          outline-offset: $focus-ring-width;\n        }\n      }\n    }\n\n    // Center header items get displayed vertically, end items are displayed horizontally\n    .sidebar-header-items__center {\n      display: flex;\n      flex-direction: column;\n    }\n\n    // Positioning end items\n    .sidebar-header-items__end {\n      display: flex;\n      align-items: center;\n      gap: 1rem;\n    }\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  .sidebar-primary-items__start {\n    // Add a border on mobile to separate it from the header sidebar area\n    border-top: 1px solid var(--pst-color-border);\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      border-top: none;\n    }\n  }\n\n  .sidebar-primary-items__end {\n    margin-top: auto;\n    margin-bottom: 1em;\n  }\n\n  .list-caption {\n    list-style: none;\n    padding-left: 0;\n\n    // Level 0 TOC heading is put inside the <summary> tag\n    // so let the <summary> tag take up more space\n    li.toctree-l0.has-children {\n      > details {\n        > summary {\n          position: relative;\n          height: auto;\n          width: auto;\n          display: flex;\n          justify-content: space-between;\n          align-items: baseline;\n\n          .toctree-toggle {\n            // Prevent toggle icon from getting squished by summary being a\n            // flexbox\n            flex: 0 0 auto;\n\n            // Make the level 0 chevron icon slightly bigger than descendant\n            // levels\n            .fa-chevron-down {\n              font-size: 1rem;\n            }\n          }\n        }\n      }\n    }\n  }\n\n  li.has-children {\n    $toctree-toggle-width: 30px;\n\n    position: relative;\n\n    > .reference,\n    .caption {\n      margin-right: calc(\n        $toctree-toggle-width + $focus-ring-width\n      ); // keep clear of the toggle icon\n\n      padding-top: 0.25rem; // align caption text with toggle chevron\n    }\n\n    > details {\n      > summary {\n        // Remove browser default toggle icon\n        list-style: none;\n\n        &::-webkit-details-marker {\n          display: none;\n        }\n\n        // The summary element is natively focusable, but delegate the focus state to the toggle icon\n        &:focus-visible {\n          outline: none;\n\n          > .toctree-toggle {\n            outline: $focus-ring-outline;\n            outline-offset: -$focus-ring-width; // Prevent right side of focus ring from disappearing underneath the sidebar's right edge\n          }\n        }\n\n        // Container for expand/collapse chevron icon\n        .toctree-toggle {\n          cursor: pointer;\n\n          // Position it so that it's aligned with the top right corner of the\n          // last positioned element, in this case the li.has-children\n          position: absolute;\n          top: 0;\n          right: 0;\n\n          // Give it dimensions\n          width: $toctree-toggle-width;\n          height: $toctree-toggle-width; // make it square\n\n          // Vertically and horizontally center the icon within the container\n          display: inline-flex;\n          justify-content: center;\n          align-items: center;\n\n          .fa-chevron-down {\n            font-size: 0.75rem;\n          }\n        }\n      }\n\n      // The section is open/expanded, rotate the toggle icon (chevron) so it\n      // points up instead of down\n      &[open] {\n        > summary {\n          .fa-chevron-down {\n            transform: rotate(180deg);\n          }\n        }\n      }\n    }\n  }\n}\n\n/* Between-page links and captions */\nnav.bd-links {\n  margin-right: -$sidebar-padding-right; // align toctree toggle chevrons with right edge of sidebar and allow text to flow closer to the right edge\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: block;\n  }\n\n  ul {\n    list-style: none;\n\n    // Reduce padding of nested `ul` items a bit\n    ul {\n      padding: 0 0 0 1rem;\n    }\n  }\n\n  li > a {\n    display: block;\n    padding: 0.25rem 0.65rem;\n\n    @include link-sidebar;\n\n    box-shadow: none;\n    margin-right: $focus-ring-width; // prevent the right side focus ring from disappearing under the sidebar right edge\n\n    &.reference.external {\n      &::after {\n        font: var(--fa-font-solid);\n        content: var(--pst-icon-external-link);\n        font-size: 0.75em;\n        margin-left: 0.3em;\n      }\n    }\n  }\n\n  .current > a {\n    @include link-sidebar-current;\n\n    background-color: transparent;\n  }\n\n  // Title\n  p.bd-links__title {\n    font-size: var(--pst-sidebar-header-font-size);\n    font-weight: var(--pst-sidebar-header-font-weight);\n    margin-bottom: 0.5rem;\n  }\n\n  // Toctree captions\n  p.caption {\n    font-weight: var(--pst-sidebar-header-font-weight);\n    position: relative;\n    margin-top: 1.25rem;\n    margin-bottom: 0.5rem;\n    color: var(--pst-color-text-base);\n\n    &:first-child {\n      margin-top: 0;\n    }\n\n    font-size: var(--pst-sidebar-font-size-mobile);\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      font-size: var(--pst-sidebar-font-size);\n    }\n  }\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n@mixin make-row($gutter: $grid-gutter-width) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  display: flex;\n  flex-wrap: wrap;\n  // TODO: Revisit calc order after https://github.com/react-bootstrap/react-bootstrap/issues/6039 is fixed\n  margin-top: calc(-1 * var(--#{$prefix}gutter-y)); // stylelint-disable-line function-disallowed-list\n  margin-right: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n}\n\n@mixin make-col-ready() {\n  // Add box sizing if only the grid is loaded\n  box-sizing: if(variable-exists(include-column-box-sizing) and $include-column-box-sizing, border-box, null);\n  // Prevent columns from becoming too narrow when at smaller grid tiers by\n  // always setting `width: 100%;`. This works because we set the width\n  // later on to override this initial width.\n  flex-shrink: 0;\n  width: 100%;\n  max-width: 100%; // Prevent `.col-auto`, `.col` (& responsive variants) from breaking out the grid\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-top: var(--#{$prefix}gutter-y);\n}\n\n@mixin make-col($size: false, $columns: $grid-columns) {\n  @if $size {\n    flex: 0 0 auto;\n    width: percentage(divide($size, $columns));\n\n  } @else {\n    flex: 1 1 0;\n    max-width: 100%;\n  }\n}\n\n@mixin make-col-auto() {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n@mixin make-col-offset($size, $columns: $grid-columns) {\n  $num: divide($size, $columns);\n  margin-left: if($num == 0, 0, percentage($num));\n}\n\n// Row columns\n//\n// Specify on a parent element(e.g., .row) to force immediate children into NN\n// number of columns. Supports wrapping to new lines, but does not do a Masonry\n// style grid.\n@mixin row-cols($count) {\n  > * {\n    flex: 0 0 auto;\n    width: percentage(divide(1, $count));\n  }\n}\n\n// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `$grid-columns`.\n\n@mixin make-grid-columns($columns: $grid-columns, $gutter: $grid-gutter-width, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      // Provide basic `.col-{bp}` classes for equal-width flexbox columns\n      .col#{$infix} {\n        flex: 1 0 0%; // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      }\n\n      .row-cols#{$infix}-auto > * {\n        @include make-col-auto();\n      }\n\n      @if $grid-row-columns > 0 {\n        @for $i from 1 through $grid-row-columns {\n          .row-cols#{$infix}-#{$i} {\n            @include row-cols($i);\n          }\n        }\n      }\n\n      .col#{$infix}-auto {\n        @include make-col-auto();\n      }\n\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .col#{$infix}-#{$i} {\n            @include make-col($i, $columns);\n          }\n        }\n\n        // `$columns - 1` because offsetting by the width of an entire row isn't possible\n        @for $i from 0 through ($columns - 1) {\n          @if not ($infix == \"\" and $i == 0) { // Avoid emitting useless .offset-0\n            .offset#{$infix}-#{$i} {\n              @include make-col-offset($i, $columns);\n            }\n          }\n        }\n      }\n\n      // Gutters\n      //\n      // Make use of `.g-*`, `.gx-*` or `.gy-*` utilities to change spacing between the columns.\n      @each $key, $value in $gutters {\n        .g#{$infix}-#{$key},\n        .gx#{$infix}-#{$key} {\n          --#{$prefix}gutter-x: #{$value};\n        }\n\n        .g#{$infix}-#{$key},\n        .gy#{$infix}-#{$key} {\n          --#{$prefix}gutter-y: #{$value};\n        }\n      }\n    }\n  }\n}\n\n@mixin make-cssgrid($columns: $grid-columns, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .g-col#{$infix}-#{$i} {\n            grid-column: auto / span $i;\n          }\n        }\n\n        // Start with `1` because `0` is an invalid value.\n        // Ends with `$columns - 1` because offsetting by the width of an entire row isn't possible.\n        @for $i from 1 through ($columns - 1) {\n          .g-start#{$infix}-#{$i} {\n            grid-column-start: $i;\n          }\n        }\n      }\n    }\n  }\n}\n","/**\n * Secondary sidebar on the right.\n * e.g., in-page table of contents.\n */\n.bd-sidebar-secondary {\n  display: flex;\n  order: 2;\n  flex-shrink: 0;\n  flex-direction: column;\n  position: sticky;\n  top: var(--pst-header-height);\n  max-height: calc(100vh - var(--pst-header-height));\n  padding: 2rem 1rem 1rem;\n  width: var(--pst-sidebar-secondary);\n  font-size: var(--pst-sidebar-font-size-mobile);\n\n  @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n    font-size: var(--pst-sidebar-font-size);\n  }\n\n  // Color and border\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n}\n\n.sidebar-secondary-item {\n  padding: 0.5rem;\n\n  @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n    border-left: 1px solid var(--pst-color-border);\n    padding-left: 1rem;\n  }\n\n  i {\n    padding-right: 0.5rem;\n  }\n}\n","/*******************************************************************************\n* Rules for the UX/UI of sidebar sliding drawers on mobile\n* Note that this sheet controls styles across many parts of the theme\n* It is aggregated into this one sheet instead of being split across\n* components in order to keep it easier to debug in one place.\n* It is broken up into major sections below.\n*/\n\n/*******************************************************************************\n* Buttons and overlays\n*/\ninput.sidebar-toggle {\n  display: none;\n}\n\n// Background overlays\nlabel.overlay {\n  background-color: black;\n  opacity: 0.5;\n  height: 0;\n  width: 0;\n  position: fixed;\n  top: 0;\n  left: 0;\n  transition: opacity $animation-time ease-out;\n  z-index: $zindex-modal-backdrop;\n}\n\ninput {\n  // Show the correct overlay when its input is checked\n  &#pst-primary-sidebar-checkbox:checked + label.overlay.overlay-primary,\n  &#pst-secondary-sidebar-checkbox:checked + label.overlay.overlay-secondary {\n    height: 100vh;\n    width: 100vw;\n  }\n\n  // Primary sidebar slides in from the left\n  &#pst-primary-sidebar-checkbox:checked ~ .bd-container .bd-sidebar-primary {\n    visibility: visible;\n    margin-left: 0;\n  }\n\n  // Secondary sidebar slides in from the right\n  &#pst-secondary-sidebar-checkbox:checked\n    ~ .bd-container\n    .bd-sidebar-secondary {\n    visibility: visible;\n    margin-right: 0;\n  }\n}\n\n/*******************************************************************************\n* Sidebar drawer behavior\n*/\n\n/**\n * Behavior for sliding drawer elements that will be toggled with an input\n *\n * NOTE: We use this mixin to define the toggle behavior on narrow screens,\n * And the wide-screen behavior of the sections is defined in their own section\n * .scss files.\n */\n@mixin sliding-drawer($side: \"left\") {\n  position: fixed;\n  top: 0;\n  z-index: $zindex-modal;\n  height: 100vh;\n  max-height: 100vh;\n  width: 75%;\n  flex-grow: 0.75;\n  max-width: 350px;\n  transition:\n    visibility $animation-time ease-out,\n    margin $animation-time ease-out;\n  visibility: hidden;\n\n  @if $side == \"right\" {\n    margin-right: -75%;\n    right: 0;\n  } @else {\n    margin-left: -75%;\n    left: 0;\n  }\n}\n\n// Primary sidebar hides/shows at earlier widths\n@include media-breakpoint-up($breakpoint-sidebar-primary) {\n  .sidebar-toggle.primary-toggle {\n    display: none;\n  }\n\n  input#pst-primary-sidebar-checkbox {\n    &:checked + label.overlay.overlay-primary {\n      height: 0;\n      width: 0;\n    }\n  }\n\n  .bd-sidebar-primary {\n    margin-left: 0;\n    visibility: visible;\n  }\n}\n\n.bd-sidebar-primary {\n  @include media-breakpoint-down($breakpoint-sidebar-primary) {\n    @include sliding-drawer(\"left\");\n  }\n}\n\n.bd-sidebar-secondary {\n  @include media-breakpoint-down($breakpoint-sidebar-secondary) {\n    @include sliding-drawer(\"right\");\n  }\n}\n","/**\n * Breadcrumbs for parent pages meant for the article header\n */\nul.bd-breadcrumbs {\n  list-style: none;\n  padding-left: 0;\n  display: flex;\n  flex-wrap: wrap;\n\n  // Font size slightly smaller to avoid cluttering up space too much\n  font-size: 0.8rem;\n\n  li.breadcrumb-item {\n    display: flex;\n    align-items: center;\n\n    // Style should look like heavier in-page links\n    // keeping visited in the default link colour\n    font-weight: bold;\n\n    a {\n      @include link-style-text;\n    }\n\n    // Items that aren't the home have a caret to the left\n    &:not(.breadcrumb-home)::before {\n      font: var(--fa-font-solid);\n      font-size: 0.8rem;\n      content: var(--pst-breadcrumb-divider);\n      color: var(--pst-color-text-muted);\n      padding: 0 0.5rem;\n    }\n  }\n}\n","/**\n * Icon links in the navbar\n */\n\n.navbar-icon-links {\n  display: flex;\n  flex-flow: row wrap;\n  column-gap: 1rem;\n  justify-content: space-evenly;\n\n  // Remove the padding so that we can define it with flexbox gap above\n  li.nav-item a.nav-link {\n    padding-left: 0;\n    padding-right: 0;\n\n    @include icon-navbar-hover;\n\n    &:focus {\n      color: inherit;\n    }\n  }\n\n  // Spacing and centering\n  a span {\n    display: flex;\n    align-items: center;\n  }\n\n  // Icons styling\n  i {\n    &.fa-brands,\n    &.fa-regular,\n    &.fa-solid {\n      vertical-align: middle;\n      font-style: normal;\n      font-size: var(--pst-font-size-icon);\n    }\n\n    /* Social media buttons hard-code the brand color */\n    &.fa-square-twitter::before {\n      color: #55acee;\n    }\n\n    &.fa-square-gitlab::before {\n      color: #548;\n    }\n\n    &.fa-bitbucket::before {\n      color: #0052cc;\n    }\n  }\n\n  // Force images to be icon-sized\n  img.icon-link-image {\n    height: 1.5em;\n    border-radius: 0.2rem;\n  }\n\n  .fa-pydata {\n    stroke: var(--pst-color-background);\n    stroke-linejoin: round;\n    stroke-width: 0.35;\n  }\n}\n","/**\n * Logo in the navbar\n */\n\n.navbar-brand {\n  position: relative;\n  height: var(--pst-header-height);\n  max-height: var(--pst-header-height);\n  padding: 0.5rem 0;\n  width: auto;\n  margin: 0;\n  display: flex;\n\n  // Ensure that the logo stays the same length while other content shrinks\n  flex-shrink: 0;\n  align-items: center;\n  gap: 0.5rem;\n\n  // If there's no logo image, we use a p element w/ the site title\n  p {\n    color: var(--pst-color-text-base);\n    margin-bottom: 0;\n  }\n\n  // If there's a logo, it'll be in an img block\n  img {\n    max-width: 100%;\n    height: 100%;\n    width: auto;\n  }\n\n  &:hover,\n  &:visited:hover {\n    @include link-style-hover;\n\n    color: var(--pst-color-text-base);\n  }\n}\n","/**\n * Navigation text links in the navbar\n */\n.navbar-nav {\n  ul {\n    display: block;\n    list-style: none;\n\n    // Reduce padding of nested `ul` items a bit\n    ul {\n      padding: 0 0 0 1rem;\n    }\n  }\n\n  // Navbar links - do not have an underline by default\n  li {\n    display: flex;\n    flex-direction: column;\n\n    a {\n      display: flex;\n      align-items: center;\n      height: 100%;\n      padding-top: 0.25rem;\n      padding-bottom: 0.25rem;\n\n      @include link-style-text;\n    }\n  }\n}\n","/**\n * The list of in-page TOC links\n */\n.page-toc {\n  .section-nav {\n    padding-left: 0;\n    border-bottom: none;\n\n    ul {\n      padding-left: 1rem;\n    }\n  }\n\n  // override bootstrap settings\n  .nav-link {\n    font-size: var(--pst-sidebar-font-size-mobile);\n\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      font-size: var(--pst-sidebar-font-size);\n    }\n  }\n\n  .onthispage {\n    color: var(--pst-color-text-base);\n    font-weight: var(--pst-sidebar-header-font-weight);\n    margin-bottom: 0.5rem;\n  }\n}\n","/**\n* Previous / Next navigation buttons\n**/\n.prev-next-area {\n  width: 100%;\n\n  p {\n    color: var(--pst-color-text-muted);\n    margin: 0 0.3em;\n    line-height: 1.3em;\n  }\n\n  i {\n    font-size: 1.2em;\n  }\n\n  a {\n    // So that buttons align with icons\n    display: flex;\n    align-items: center;\n    border: none;\n    padding: 10px;\n    max-width: 45%;\n    overflow-x: hidden;\n    color: var(--pst-color-text-muted);\n    text-decoration: none;\n\n    p.prev-next-title {\n      @include link-style-default;\n\n      font-weight: var(--pst-admonition-font-weight-heading);\n      font-size: 1.1em;\n    }\n\n    &:hover,\n    &:visited:hover {\n      p.prev-next-title {\n        @include link-style-hover;\n      }\n    }\n\n    .prev-next-info {\n      flex-direction: column;\n      margin: 0 0.5em;\n\n      .prev-next-subtitle {\n        text-transform: capitalize;\n      }\n    }\n\n    &.left-prev {\n      float: left;\n    }\n\n    &.right-next {\n      float: right;\n\n      div.prev-next-info {\n        text-align: right;\n      }\n    }\n  }\n}\n","/**\n * Search field\n **/\n.bd-search {\n  position: relative;\n  padding-left: 0.5rem;\n  gap: 0.5rem;\n  background-color: var(--pst-color-background);\n  border-radius: $admonition-border-radius;\n  border: 1px solid var(--pst-color-border);\n  color: var(--pst-color-text-base);\n\n  // Background should always be same color regardless of active or not\n  &:active {\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-muted);\n  }\n\n  .icon {\n    position: absolute;\n    color: var(--pst-color-border);\n    left: 25px;\n  }\n\n  .fa-solid.fa-magnifying-glass {\n    position: absolute;\n    left: calc((2.5rem - 0.7em) / 2);\n    color: var(--pst-color-text-muted);\n  }\n\n  input {\n    // Inner-text of the search bar\n    &::placeholder {\n      color: var(--pst-color-text-muted);\n    }\n\n    // Remove the little \"x\" that pops up when you start typing\n    &::-webkit-search-cancel-button,\n    &::-webkit-search-decoration {\n      appearance: none;\n    }\n  }\n\n  // Shows off the keyboard shortcuts for the button\n  .search-button__kbd-shortcut {\n    display: flex;\n    position: absolute;\n    right: 0.5rem;\n    color: var(--pst-color-border);\n  }\n}\n\n.form-control {\n  background-color: var(--pst-color-background);\n  color: var(--pst-color-text-base);\n\n  &:focus,\n  &:focus-visible {\n    border: none;\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-muted);\n  }\n}\n\n/**\n * Search button - located in the navbar\n */\n\n// Search link icon should be a bit bigger since it is separate from icon links\n.search-button {\n  display: flex;\n  align-items: center;\n  align-content: center;\n  color: var(--pst-color-text-muted);\n  padding: 0;\n  border-radius: 0;\n  border: none; // Override Bootstrap button border\n  font-size: 1rem; // Override Bootstrap button font size\n\n  // Override Bootstrap button padding-x. Whitespace in nav bar is controlled\n  // via column gap rule on the container.\n  padding-left: 0;\n  padding-right: 0;\n\n  @include icon-navbar-hover;\n\n  i {\n    font-size: 1.3rem;\n  }\n}\n\n// __search-container will only show up when we use the search pop-up bar\n.search-button__search-container,\n.search-button__overlay {\n  display: none;\n}\n\n.search-button__wrapper.show {\n  .search-button__search-container {\n    display: flex;\n\n    // Center in middle of screen just underneath header\n    position: fixed;\n    z-index: $zindex-modal;\n    top: 30%;\n    left: 50%;\n    transform: translate(-50%, -50%);\n    right: 1rem;\n    margin-top: 0.5rem;\n    width: 90%;\n    max-width: 800px;\n  }\n\n  .search-button__overlay {\n    display: flex;\n    position: fixed;\n    z-index: $zindex-modal-backdrop;\n    background-color: black;\n    opacity: 0.5;\n    width: 100%;\n    height: 100%;\n    top: 0;\n    left: 0;\n  }\n\n  form.bd-search {\n    flex-grow: 1;\n    padding-top: 0;\n    padding-bottom: 0;\n  }\n\n  // Font and input text a bit bigger\n  svg,\n  input {\n    font-size: var(--pst-font-size-icon);\n  }\n}\n\n/**\n * The search button component that looks like a field.\n * Lives at components/search-button-field.html\n */\n.search-button-field {\n  $search-button-border-radius: 1.5em;\n\n  display: inline-flex;\n  align-items: center;\n  border: var(--pst-color-border) solid 1px;\n  border-radius: $search-button-border-radius;\n  color: var(--pst-color-text-muted);\n  padding: 0.5em;\n  background-color: var(--pst-color-surface);\n\n  &:hover {\n    box-shadow: 0 0 0 $focus-ring-width var(--pst-color-link-hover);\n  }\n\n  &:focus-visible {\n    border-radius: $search-button-border-radius;\n  }\n\n  // The keyboard shotcut text\n  .search-button__default-text {\n    font-size: var(--bs-nav-link-font-size);\n    font-weight: var(--bs-nav-link-font-weight);\n    margin-right: 0.5em;\n    margin-left: 0.5em;\n  }\n\n  .kbd-shortcut__modifier {\n    font-size: 0.75em;\n  }\n\n  // Ensures that all the text lines up in the middle\n  > * {\n    align-items: center;\n  }\n\n  // Only the icon should be visible on narrow screens\n  > :not(svg) {\n    display: none;\n\n    @include media-breakpoint-up(lg) {\n      display: flex;\n    }\n  }\n}\n","/**\n * The 'Hide Search Matches' button.\n * This only shows up when a person lands on a page after clicking a search result.\n * Clicking it removes the highlighting of the search term from the page.\n * We want it to behave like a button.\n */\ndiv#searchbox {\n  // Leave `#searchbox` rules empty so that it doesn't show at all when it is empty\n  p.highlight-link {\n    margin: 1rem 0;\n    width: fit-content;\n\n    // A bit more margin on wide screens to mimic article behavior\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      margin-left: 2rem;\n    }\n\n    // Put outer shadow on this one so that we can darken the link w/ an inner shadow\n    @include box-shadow;\n\n    // Style the button to look like a Sphinx Design button\n    a {\n      border-radius: 0.25rem;\n      font-size: 1.25rem;\n      padding: 0.75rem;\n      background-color: var(--pst-color-primary);\n      color: var(--pst-color-primary-text);\n      text-decoration: none;\n\n      // The box shadow is inset so that it darkens the button on hover\n      transition: box-shadow 0.25s ease-out;\n\n      &:hover {\n        box-shadow: inset 0 0 50px 50px rgb(0 0 0 / 25%);\n      }\n\n      &::before {\n        content: var(--pst-icon-search-minus);\n        color: unset;\n        font: var(--fa-font-solid);\n        margin-right: 0.5rem;\n      }\n    }\n  }\n}\n","/**\n * Light/dark theme switcher\n */\n\n.theme-switch-button {\n  color: var(--pst-color-text-muted);\n  border-radius: 0;\n  border: none; // Override Bootstrap button border\n  font-size: 1rem; // Override Bootstrap's button font size\n\n  // Override Bootstrap button padding-x. Whitespace in nav bar is controlled\n  // via column gap rule on the container.\n  padding-left: 0;\n  padding-right: 0;\n\n  &:hover {\n    @include icon-navbar-hover;\n  }\n\n  span {\n    display: none;\n\n    &:active {\n      text-decoration: none;\n      color: var(--pst-color-link-hover);\n    }\n\n    .fa-lg {\n      aspect-ratio: 1 / 1;\n    }\n  }\n}\n\nhtml[data-mode=\"auto\"] .theme-switch-button span[data-mode=\"auto\"] {\n  display: flex;\n}\n\nhtml[data-mode=\"light\"] .theme-switch-button span[data-mode=\"light\"] {\n  display: flex;\n}\n\nhtml[data-mode=\"dark\"] .theme-switch-button span[data-mode=\"dark\"] {\n  display: flex;\n}\n","button.btn.version-switcher__button {\n  border-color: var(--pst-color-border);\n  color: var(--pst-color-text-base);\n\n  // Add a margin on narrow screens to avoid feeling cramped\n  margin-bottom: 1em;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    margin-bottom: unset;\n  }\n\n  &:hover {\n    box-shadow: 0 0 0 $focus-ring-width var(--pst-color-secondary);\n    border-color: transparent;\n  }\n\n  &:active {\n    color: var(--pst-color-text-base);\n    border-color: var(--pst-color-border);\n  }\n\n  &:focus-visible {\n    border-color: transparent;\n  }\n}\n\n.version-switcher__menu {\n  border-color: var(--pst-color-border);\n  border-radius: var(--bs-dropdown-border-radius);\n\n  a.list-group-item {\n    background-color: var(--pst-color-on-background);\n    color: var(--pst-color-text-base);\n    padding: 0.75rem 1.25rem;\n\n    &:not(:last-child) {\n      border-bottom: 1px solid var(--pst-color-border);\n    }\n\n    &:hover {\n      @include link-style-hover;\n\n      background-color: var(--pst-color-surface);\n    }\n\n    &.active {\n      @include link-sidebar-current;\n\n      position: relative;\n      z-index: 1;\n\n      span::before {\n        content: \"\";\n        width: 100%;\n        height: 100%;\n        position: absolute;\n        z-index: -1;\n        left: 0;\n        top: 0;\n      }\n    }\n\n    &:focus-visible {\n      z-index: 10; // keep focus ring on top (prevent the hover background of the next dropdown item from covering the ring)\n    }\n  }\n}\n\n// Font behavior on mobile\nbutton.version-switcher__button,\n.version-switcher__menu {\n  font-size: 1.1em; // A bit smaller than other menu font\n  z-index: $zindex-modal; // higher than the sidebars\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    font-size: unset;\n  }\n}\n","/* Collapsing of the TOC sidebar while scrolling */\n\n/* Nav: hide second level (shown on .active) */\n\nnav.page-toc {\n  // A little extra space before the buttons\n  margin-bottom: 1rem;\n}\n\n.bd-toc .nav {\n  .nav {\n    display: none;\n\n    // So we can manually specify a level as visible in the config\n    &.visible {\n      display: block;\n    }\n  }\n\n  > .active > ul {\n    display: block;\n  }\n}\n\n// Each entry of the in-page TOC\n.toc-entry {\n  display: block;\n\n  a > code {\n    color: var(--pst-color-text-muted);\n  }\n\n  a.nav-link {\n    display: block;\n    padding: 0.125rem 0;\n\n    // Padding w/ negative margin so the top TOC item highlight overlaps w/ the TOC border\n    padding-left: 1rem;\n    margin-left: -1rem;\n\n    @include link-sidebar;\n\n    &.active {\n      @include link-sidebar-current;\n\n      background-color: transparent;\n\n      &:hover {\n        color: var(--pst-color-link-hover);\n      }\n    }\n\n    &:focus-visible {\n      border-radius: $focus-ring-radius;\n    }\n  }\n}\n","div.versionadded,\ndiv.versionchanged,\ndiv.deprecated {\n  vertical-align: middle;\n  margin: 1.5625em auto;\n  padding: 0 0.6rem;\n  overflow: hidden;\n\n  /* break-inside has replaced page-break-inside and is widely usable since 2019 */\n  page-break-inside: avoid;\n  break-inside: avoid;\n  border-left: 0.2rem solid;\n  border-color: var(--pst-color-info);\n  border-radius: $admonition-border-radius;\n  background-color: var(--pst-color-on-background);\n\n  @include box-shadow;\n\n  position: relative;\n\n  > p {\n    margin-bottom: 0.6rem;\n    margin-top: 0.6rem;\n  }\n}\n\ndiv.versionadded {\n  border-color: var(--pst-color-success);\n  background-color: var(--pst-color-success-bg);\n}\n\ndiv.versionchanged {\n  border-color: var(--pst-color-warning);\n  background-color: var(--pst-color-warning-bg);\n}\n\ndiv.deprecated {\n  border-color: var(--pst-color-danger);\n  background-color: var(--pst-color-danger-bg);\n}\n\nspan.versionmodified {\n  font-weight: 600;\n\n  &::before {\n    margin-right: 0.6rem;\n    color: var(--pst-color-info);\n    font: var(--fa-font-solid);\n    content: var(--pst-icon-versionmodified-default);\n  }\n}\n\nspan.versionmodified.added {\n  &::before {\n    color: var(--pst-color-success);\n    content: var(--pst-icon-versionmodified-added);\n  }\n}\n\nspan.versionmodified.changed {\n  &::before {\n    color: var(--pst-color-warning);\n    content: var(--pst-icon-versionmodified-changed);\n  }\n}\n\nspan.versionmodified.deprecated {\n  &::before {\n    color: var(--pst-color-danger);\n    content: var(--pst-icon-versionmodified-deprecated);\n  }\n}\n",".sidebar-indices-items {\n  display: flex;\n  flex-direction: column;\n  border-top: 1px solid var(--pst-color-border);\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    border-top: none;\n  }\n\n  .sidebar-indices-items__title {\n    font-weight: var(--pst-sidebar-header-font-weight);\n    font-size: var(--pst-sidebar-header-font-size);\n    color: var(--pst-color-text-base);\n    margin-bottom: 0.5rem;\n  }\n\n  ul.indices-link {\n    margin-right: -1rem;\n    list-style: none;\n    padding: 0;\n\n    li > a {\n      display: block;\n      padding: 0.25rem 0;\n      color: var(--pst-color-text-muted);\n\n      &:hover {\n        color: var(--pst-color-primary);\n        text-decoration: none;\n        background-color: transparent;\n      }\n    }\n  }\n}\n",".bd-sidebar-primary div#rtd-footer-container {\n  position: sticky;\n  bottom: -1rem;\n  margin: -1rem; // ignore sidebar padding\n\n  .rst-versions.rst-badge {\n    position: unset;\n    font-size: 0.9em;\n    font-family: var(--pst-font-family-base);\n    max-width: unset;\n\n    .rst-current-version {\n      display: flex;\n      align-items: center;\n      gap: 0.2rem;\n      height: 2.5rem;\n      transition: background-color 0.2s ease-out;\n      background-color: var(--pst-color-background);\n      color: var(--pst-color-success);\n      border-top: 1px solid var(--pst-color-border);\n    }\n\n    .fa.fa-book {\n      color: var(--pst-color-text-muted);\n      margin-right: auto;\n\n      &::after {\n        color: var(--pst-color-text-base);\n        content: \"Read The Docs\";\n        font-family: var(--pst-font-family-base);\n        font-weight: var(--pst-admonition-font-weight-heading);\n      }\n    }\n\n    .fa.fa-caret-down {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  .rst-versions.rst-badge.shift-up {\n    .rst-current-version {\n      border-bottom: 1px solid var(--pst-color-border);\n    }\n  }\n\n  .rst-other-versions {\n    background-color: var(--pst-color-surface);\n    color: var(--pst-color-text-base);\n\n    dl {\n      dd a {\n        color: var(--pst-color-text-muted);\n      }\n    }\n\n    hr {\n      background-color: var(--pst-color-border);\n    }\n\n    small a {\n      color: var(--pst-color-link);\n    }\n\n    input {\n      padding-left: 0.5rem;\n      border: 1px solid var(--pst-color-border);\n      background-color: var(--pst-color-surface);\n    }\n  }\n}\n","/**\n * Admonitions and blocks of styled content.\n * Admonitions CSS originally inspired by https://squidfunk.github.io/mkdocs-material/getting-started/\n */\n$admonition-border-radius: 0.25rem;\n$admonition-left-border-width: 0.2rem;\n\ndiv.admonition,\n.admonition {\n  margin: 1.5625em auto;\n  padding: 0 0.6rem 0.8rem;\n  overflow: hidden;\n\n  /* break-inside has replaced page-break-inside and is widely usable since 2019 */\n  page-break-inside: avoid;\n  break-inside: avoid;\n  border-left: $admonition-left-border-width solid;\n  border-color: var(--pst-color-info);\n  border-radius: $admonition-border-radius;\n  background-color: var(--pst-color-on-background);\n\n  @include box-shadow;\n\n  // Last item should have no spacing since we'll control that w/ padding\n  *:last-child {\n    margin-bottom: 0;\n  }\n\n  // Items after the title should be indented\n  p.admonition-title ~ * {\n    margin-left: 1.4rem;\n    margin-right: 1.4rem;\n  }\n\n  // Lists need to have left margin so they don't spill into it\n  > ol,\n  > ul {\n    margin-left: 1em;\n  }\n\n  // Defaults for all admonitions\n  > .admonition-title {\n    margin: 0 -0.6rem;\n    padding: 0.4rem 0.6rem 0.4rem 2rem;\n    font-weight: var(--pst-admonition-font-weight-heading);\n    position: relative;\n\n    @include legacy-backdrop-placeholder;\n\n    background-color: var(--pst-color-info-bg);\n\n    // now that we use solid colors we want the title on top\n    z-index: 1;\n\n    &::after {\n      position: absolute;\n      left: 0.5rem;\n      width: 1rem;\n      height: 1rem;\n      color: var(--pst-color-info);\n      font: var(--fa-font-solid);\n      line-height: inherit;\n      content: var(--pst-icon-admonition-default);\n      opacity: 1;\n    }\n\n    // Next element after title needs some extra upper-space\n    + * {\n      margin-top: 0.4em;\n    }\n  }\n\n  &.attention {\n    border-color: var(--pst-color-attention);\n\n    > .admonition-title {\n      background-color: var(--pst-color-attention-bg);\n\n      &::after {\n        color: var(--pst-color-attention);\n        content: var(--pst-icon-admonition-attention);\n      }\n    }\n  }\n\n  &.caution {\n    border-color: var(--pst-color-warning);\n\n    > .admonition-title {\n      background-color: var(--pst-color-warning-bg);\n\n      &::after {\n        color: var(--pst-color-warning);\n        content: var(--pst-icon-admonition-caution);\n      }\n    }\n  }\n\n  &.warning {\n    border-color: var(--pst-color-warning);\n\n    > .admonition-title {\n      background-color: var(--pst-color-warning-bg);\n\n      &::after {\n        color: var(--pst-color-warning);\n        content: var(--pst-icon-admonition-warning);\n      }\n    }\n  }\n\n  &.danger {\n    border-color: var(--pst-color-danger);\n\n    > .admonition-title {\n      background-color: var(--pst-color-danger-bg);\n\n      &::after {\n        color: var(--pst-color-danger);\n        content: var(--pst-icon-admonition-danger);\n      }\n    }\n  }\n\n  &.error {\n    border-color: var(--pst-color-danger);\n\n    > .admonition-title {\n      background-color: var(--pst-color-danger-bg);\n\n      &::after {\n        color: var(--pst-color-danger);\n        content: var(--pst-icon-admonition-error);\n      }\n    }\n  }\n\n  &.hint {\n    border-color: var(--pst-color-success);\n\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &::after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-hint);\n      }\n    }\n  }\n\n  &.tip {\n    border-color: var(--pst-color-success);\n\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &::after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-tip);\n      }\n    }\n  }\n\n  &.important {\n    border-color: var(--pst-color-attention);\n\n    > .admonition-title {\n      background-color: var(--pst-color-attention-bg);\n\n      &::after {\n        color: var(--pst-color-attention);\n        content: var(--pst-icon-admonition-important);\n      }\n    }\n  }\n\n  &.note {\n    border-color: var(--pst-color-info);\n\n    > .admonition-title {\n      background-color: var(--pst-color-info-bg);\n\n      &::after {\n        color: var(--pst-color-info);\n        content: var(--pst-icon-admonition-note);\n      }\n    }\n  }\n\n  &.seealso {\n    border-color: var(--pst-color-success);\n\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &::after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-seealso);\n      }\n    }\n  }\n\n  &.admonition-todo {\n    border-color: var(--pst-color-secondary);\n\n    > .admonition-title {\n      background-color: var(--pst-color-secondary-bg);\n\n      &::after {\n        color: var(--pst-color-secondary);\n        content: var(--pst-icon-admonition-todo);\n      }\n    }\n  }\n\n  /**\n   * Special-case for a `sidebar` class that makes the admonition float to\n   * the right like the {sidebar} directive.\n   */\n  &.sidebar {\n    max-width: 40%;\n    float: right;\n    clear: both;\n    margin-left: 0.5rem;\n    margin-top: 0;\n\n    // Undo the .sidebar directive border\n    border-width: 0 0 0 $admonition-left-border-width;\n\n    // TODO: these semantic-color-names border-color rules might no longer be\n    //       needed when we drop support for Sphinx 4 / docutils 0.17\n    &.attention,\n    &.important {\n      border-color: var(--pst-color-attention);\n    }\n\n    &.caution,\n    &.warning {\n      border-color: var(--pst-color-warning);\n    }\n\n    &.danger,\n    &.error {\n      border-color: var(--pst-color-danger);\n    }\n\n    &.hint,\n    &.tip,\n    &.seealso {\n      border-color: var(--pst-color-success);\n    }\n\n    &.note,\n    &.todo {\n      border-color: var(--pst-color-info);\n    }\n\n    // No inner margin since we have less horizontal space w/ the sidebar\n    p.admonition-title ~ * {\n      margin-left: 0;\n      margin-right: 0;\n    }\n  }\n}\n\n/**************************************************************\n * Similar content blocks that are not technically admonitions.\n */\n\n/**\n * Topics and the {contents} directive\n */\n// Docutils <= 0.17\ndiv.topic,\ndiv.topic.contents,\n// Docutils >= 0.18\nnav.contents,\naside.topic {\n  display: flex;\n  flex-direction: column;\n  background-color: var(--pst-color-surface);\n  border-color: var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n  padding: 1rem 1.25rem;\n\n  @include box-shadow;\n\n  .topic-title {\n    margin: 0 0 0.5rem;\n  }\n\n  // Over-ride text color to ensure enough contrast\n  p {\n    color: var(--pst-color-on-surface) !important;\n  }\n\n  // Over-ride large default padding\n  ul.simple {\n    padding-left: 1rem;\n\n    ul {\n      // So that sub-lists will have a bit less padding\n      padding-left: 2em;\n    }\n  }\n}\n\n/**\n * Sidebar directive\n */\naside.sidebar {\n  border: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-surface);\n  border-radius: $admonition-border-radius;\n\n  // to match the admonition-styled sidebars:\n  margin-left: 0.5rem;\n  padding: 0;\n\n  > *:last-child {\n    padding-bottom: 1rem;\n  }\n\n  p.sidebar-title {\n    position: relative;\n    margin-bottom: 0;\n    padding-top: 0.5rem;\n    padding-bottom: 0.5rem;\n    border-bottom: 1px solid var(--pst-color-border);\n    font-family: var(--pst-font-family-heading);\n    font-weight: var(--pst-admonition-font-weight-heading);\n  }\n\n  // Add margin to the first non-`.sidebar-title` item\n  > *:not(.sidebar-title):first-child,\n  > p.sidebar-title + * {\n    margin-top: 1rem;\n  }\n\n  > * {\n    padding-left: 1rem;\n    padding-right: 1rem;\n  }\n}\n\n/**\n * Rubrics look kind of like section headers\n */\np.rubric {\n  display: flex;\n  flex-direction: column;\n}\n\n/**\n * Seealso is kind of like a vertically-collapsed admonition\n */\n.seealso dd {\n  margin-top: 0;\n  margin-bottom: 0;\n}\n","/**\n * Miscellaneous color functions and mixins\n**/\n\n@use \"sass:list\";\n@use \"sass:map\";\n@use \"sass:meta\";\n@use \"sass:math\";\n@use \"sass:string\";\n\n// We must add ::before pseudo-element to some theme components (such as admonitions)\n// because users were instructed to customize the background color this way.\n@mixin legacy-backdrop-placeholder {\n  &::before {\n    content: \"\";\n    width: 100%;\n    height: 100%;\n    position: absolute;\n    left: 0;\n    top: 0;\n    z-index: -1;\n\n    // So that hovering over the text within background is still possible.\n    // Otherwise the background overlays the text and you cannot click or select easily.\n    // ref: https://developer.mozilla.org/en-US/docs/Web/CSS/pointer-events\n    pointer-events: none;\n  }\n}\n\n/**\n* Function to get items from nested maps\n*/\n// @param {Map} $map - Map\n// @param {Arglist} $keys - Keys to fetc\n// @return {*}\n@function map-deep-get($map, $keys...) {\n  @each $key in $keys {\n    $map: map.get($map, $key);\n  }\n\n  @return $map;\n}\n\n/**\n  * Function to check if the color needs converting to a \"color\" type\n  * if it is a string we cannot use other color manipulation functions\n  * It is used to create the sphinx-design colours as these are often interpolated\n  */\n// @param {String/Color} $color - Color definition from map\n// @return {Color} - Color type (in hex)\n@function check-color($color) {\n  @if meta.type-of($color) == string {\n    $color: from-hex($color);\n  }\n\n  @return $color;\n}\n\n/**\n  * Function to convert the string representation of a color to a color type (hex)\n  */\n// @param {String} $string - String representation of a color\n@function from-hex($string) {\n  $string-lower: string.to-lower-case($string);\n  $r: \"\";\n  $g: \"\";\n  $b: \"\";\n  $hex: \"0\" \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"a\" \"b\" \"c\" \"d\" \"e\" \"f\";\n  $length: string.length($string);\n  $max: if($length == 4, 1, 2);\n\n  // Check for length accuracy\n  @if $length != 4 and $length != 7 {\n    @return $string;\n  }\n\n  // Loop from the second character (omitting #)\n  @for $i from 2 through $length {\n    $c: string.slice($string-lower, $i, $i);\n\n    // If wrong character, return\n    @if not list.index($hex, $c) {\n      @return $string;\n    }\n\n    @if string.length($r) < $max {\n      $r: $r + $c;\n    } @else if string.length($g) < $max {\n      $g: $g + $c;\n    } @else if string.length($b) < $max {\n      $b: $b + $c;\n    }\n  }\n\n  @if $length == 4 {\n    $r: $r + $r;\n    $g: $g + $g;\n    $b: $b + $b;\n  }\n\n  @return rgb(hex-to-dec($r), hex-to-dec($g), hex-to-dec($b));\n}\n\n@function hex-to-dec($string) {\n  $hex: \"0\" \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"a\" \"b\" \"c\" \"d\" \"e\" \"f\";\n  $string: string.to-lower-case($string);\n  $length: string.length($string);\n  $dec: 0;\n\n  @for $i from 1 through $length {\n    $factor: 1 + (15 * ($length - $i));\n    $index: list.index($hex, string.slice($string, $i, $i));\n    $dec: $dec + $factor * ($index - 1);\n  }\n\n  @return $dec;\n}\n","// Style API docs from sphinx' autodoc / autosummary\n\n/*******************************************************************************\n* Styling for field lists\n*/\n\n/* grey highlighting of 'parameter' and 'returns' field */\ntable.field-list {\n  border-collapse: separate;\n  border-spacing: 10px;\n  margin-left: 1px;\n\n  th.field-name {\n    padding: 1px 8px 1px 5px;\n    white-space: nowrap;\n    background-color: var(--pst-color-surface);\n  }\n\n  /* italic font for parameter types */\n  td.field-body {\n    p {\n      font-style: italic;\n\n      > strong {\n        font-style: normal;\n      }\n    }\n\n    /* reduced space around parameter description */\n    blockquote {\n      border-left: none;\n      margin: 0 0 0.3em;\n      padding-left: 30px;\n    }\n  }\n}\n\n/*******************************************************************************\n* Styling for autosummary tables\n*/\n\n.table.autosummary {\n  // The first column (with the signature) should not wrap\n  td:first-child {\n    white-space: nowrap;\n  }\n}\n\n/* overriding basic.css to use our own monospace font */\n.sig {\n  font-family: var(--pst-font-family-monospace);\n}\n\n/* C++ specific styling - overriding the basic.css to avoid custom colors */\n\n.sig-inline.c-texpr,\n.sig-inline.cpp-texpr {\n  font-family: unset;\n}\n\n.sig.c .k,\n.sig.c .kt,\n.sig.cpp .k,\n.sig.cpp .kt {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .m,\n.sig.cpp .m {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .s,\n.sig.c .sc,\n.sig.cpp .s,\n.sig.cpp .sc {\n  color: var(--pst-color-text-base);\n}\n\n// addition\n\n// .sig.c .sig-name .n,\n// .sig.cpp .sig-name .n {\n//   color: var(--pst-color-inline-code);\n// }\n\n.sig-name {\n  color: var(--pst-color-inline-code);\n}\n\n.sig-param .o,\n.sig-param .default_value {\n  color: var(--pst-color-text-muted);\n  font-weight: normal;\n}\n\n// change target color for dark theme\ndt:target,\nspan.highlighted {\n  background-color: var(--pst-color-target);\n}\n\n.viewcode-back {\n  font-family: var(--pst-font-family-base);\n}\n\n.viewcode-block:target {\n  border-top: 1px solid var(--pst-color-border);\n  border-bottom: 1px solid var(--pst-color-border);\n  position: relative;\n  background-color: var(--pst-color-target);\n}\n\ndl > dt > a:has(.viewcode-link) {\n  // Sphinx applies a `float:right` rule to the .viewcode-line span, which\n  // exposes a browser glitch in the focus ring. It seems the browser creates\n  // two separate boxes, an empty box where the anchor element gets laid out and\n  // then another box around the anchor's contents that have been floated right.\n  // Firefox draws the focus ring around the empty anchor element box. Chrome\n  // draws two focus rings: one around the empty anchor and one around the\n  // floated-right element. To fix the glitch, we apply the float rule on the\n  // parent rather than the child.\n  float: right;\n\n  .viewcode-link {\n    float: none;\n  }\n}\n\n/*******************************************************************************\n* Styling for autosummary titles like \"parameters\" and \"returns\"\n*/\n\n// the API selector\n// from https://github.com/pradyunsg/furo/blob/main/src/furo/assets/styles/content/_api.sass#L6)\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) {\n  // increase margin bottom after the dl elements\n  margin-bottom: 3rem;\n\n  dd {\n    margin-left: 2rem;\n\n    // Fix until this will be solved to Sphinx https://github.com/sphinx-doc/sphinx/issues/10815\n    & > dl.simple > dt {\n      display: flex;\n    }\n  }\n\n  dl.field-list {\n    display: grid;\n    grid-template-columns: unset;\n  }\n\n  dt.field-odd,\n  dt.field-even {\n    margin-top: 0.2rem;\n    margin-bottom: 0.1rem;\n    background-color: var(--pst-color-surface);\n  }\n}\n","/**\n * Code block styling\n * Note that we inherit a lot of styling from Bootstrap so not many rules here.\n */\n\n// General code block behavior\n// Unset bootstrap behavior\ndiv[class*=\"highlight-\"],\ndiv.highlight,\ndiv.literal-block-wrapper {\n  display: flex;\n  flex-direction: column;\n  width: unset;\n  border-radius: $admonition-border-radius;\n  break-inside: avoid;\n}\n\n// Code blocks with captions\n// There's a wrapper when the code block has a title\ndiv.literal-block-wrapper {\n  border: 1px solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n\n  // This is where the title goes\n  div.code-block-caption {\n    margin: 0;\n    border-bottom: 1px solid var(--pst-color-border);\n    padding: 0.5rem;\n    font-size: 1rem;\n    font-weight: var(--pst-font-weight-caption);\n\n    a.headerlink {\n      font-size: inherit;\n    }\n  }\n\n  // Remove the upper border radius since we want it to connect with the title\n  // Remove the box shadow so the wrapper gets the shadow instead\n  div[class*=\"highlight-\"] {\n    margin: 0;\n    border-radius: 0;\n\n    pre {\n      border: none;\n      box-shadow: none;\n    }\n  }\n}\n\n/**\n * In-line code\n */\ncode.literal {\n  padding: 0.1rem 0.25rem;\n  background-color: var(--pst-color-surface);\n  border: 1px solid var(--pst-color-border);\n  border-radius: 0.25rem;\n}\n\na > code {\n  color: var(--pst-color-inline-code-links);\n}\n\n// Fix for Sphinx's \"highlight\" directive - this is an issue with our accessible pygments theme\n// and the colour we are using for the background of the code blocks.\nhtml[data-theme=\"light\"] .highlight .nf {\n  color: #0078a1 !important;\n}\n\n// Minimum opacity needed for linenos to be WCAG AA conformant\nspan.linenos {\n  opacity: 0.8 !important;\n}\n","figure > a,\nfigure > a > img,\nfigure > img,\nfigure > video {\n  display: block;\n  margin-left: auto;\n  margin-right: auto;\n}\n\nfigure {\n  a.headerlink {\n    // So that header link doesn't push caption to be off-center.\n    position: absolute;\n    font-size: inherit;\n  }\n\n  // Default headerlink hover doesn't trigger on figures\n  &:hover a.headerlink {\n    visibility: visible;\n  }\n\n  figcaption {\n    font-family: var(--pst-font-family-heading);\n    font-weight: var(--pst-font-weight-caption);\n    color: var(--pst-color-text-muted);\n    margin-left: auto;\n    margin-right: auto;\n    margin-top: 0.3rem;\n    text-align: center;\n\n    & > p:last-child {\n      // Don't add extra margin to already existing figure bottom margin\n      margin-bottom: 0;\n    }\n\n    p {\n      text-align: start;\n      display: inline-block;\n    }\n\n    table.table {\n      width: fit-content;\n      margin-left: auto;\n      margin-right: auto;\n    }\n  }\n}\n","// For consistency, add bracket around footnotes/citations which are\n// cited more than once. E.g. [Newton](1,2) instead of Newton(1,2)\ndt.label > span.brackets:not(:only-child)::before {\n  content: \"[\";\n}\n\ndt.label > span.brackets:not(:only-child)::after {\n  content: \"]\";\n}\n\n// Make footnote as a superscript\na.footnote-reference {\n  vertical-align: super;\n  font-size: small;\n}\n\n// Docutils 0.18 uses an `aside.footnote` container with different internal structure\naside.footnote {\n  margin-bottom: 0.5rem;\n\n  &:last-child {\n    margin-bottom: 1rem;\n  }\n\n  span.label,\n  span.backrefs {\n    font-weight: bold;\n  }\n\n  &:target {\n    background-color: var(--pst-color-target);\n  }\n}\n","/**\n * Hacky fixes that don't fit cleanly into other sections\n */\n\n// Ensure user highlighting/selecting behaves properly\n// From https://stackoverflow.com/a/34372191\ntable.highlighttable td.linenos,\nspan.linenos,\ndiv.doctest > div.highlight span.gp {\n  /* gp: Generic.Prompt */\n  user-select: none;\n}\n","// Override bootstrap by restoring the basic theme default.\ndd {\n  margin-top: 3px;\n  margin-bottom: 10px;\n  margin-left: 30px;\n}\n\nol,\nul {\n  padding-inline-start: 2rem;\n\n  li > p:first-child {\n    margin-bottom: 0.25rem;\n    margin-top: 0.25rem;\n  }\n}\n","// GitHub blockquote style\nblockquote {\n  padding: 1em;\n  color: var(--pst-color-text-muted);\n  border-left: 0.25em solid var(--pst-color-blockquote-notch);\n  border-radius: $admonition-border-radius;\n  position: relative;\n\n  p {\n    color: var(--pst-color-text-base);\n  }\n\n  // remove padding from included line-block to avoid duplication\n  .line-block {\n    margin: 0;\n  }\n\n  // remove margin bottom for the last p\n  p:last-child {\n    margin-bottom: 0;\n  }\n\n  @include legacy-backdrop-placeholder;\n\n  background-color: var(--pst-color-surface);\n\n  // Ensure there is enough contrast against the background\n  a {\n    color: var(--pst-color-inline-code-links);\n  }\n\n  // hack to make the text in the blockquote selectable\n  &::before {\n    z-index: -1;\n  }\n}\n","/**\n * Span-level styling within content\n */\n\nspan.guilabel {\n  border: 1px solid var(--pst-color-info);\n  font-size: 80%;\n  font-weight: 700;\n  border-radius: 4px;\n  padding: 2.4px 6px;\n  margin: auto 2px;\n  position: relative;\n\n  @include legacy-backdrop-placeholder;\n\n  background-color: var(--pst-color-info-bg);\n}\n\na.reference.download::before {\n  content: var(--pst-icon-download);\n  font: var(--fa-font-solid);\n  font-size: 0.8em;\n  padding: 0 0.25em;\n  color: var(--pst-color-text-muted);\n}\n","/**\n * Tables\n */\n\n// ensure table will fit in the article width and make them y-scrollable\ntable {\n  @include table-colors;\n\n  // default to table-center\n  margin-left: auto;\n  margin-right: auto;\n\n  &.table-right {\n    margin-right: 0;\n  }\n\n  &.table-left {\n    margin-left: 0;\n  }\n}\n\n// customize table caption from bootstrap\n// to display them on top and centered\ntable caption {\n  text-align: center;\n  caption-side: top;\n  color: var(--pst-color-text-muted);\n}\n\n// MyST Markdown tables use these classes to control alignment\nth,\ntd {\n  &.text-left {\n    text-align: left;\n  }\n\n  &.text-right {\n    text-align: right;\n  }\n\n  &.text-center {\n    text-align: center;\n  }\n}\n\n// override bootstrap table colors\n.table {\n  @include table-colors;\n\n  --bs-table-bg: transparent; // background\n  --bs-table-color: var(\n    --pst-color-text-base\n  ); // ensure text and bullets are visible\n}\n\n.pst-scrollable-table-container {\n  // Put a horizontal scrollbar just below tables that are too wide to fit\n  // within the main column\n  overflow-x: auto;\n}\n","/**\n * Style the toctree component in pages (avoid modifying the navbars)\n */\n.toctree-wrapper {\n  p.caption {\n    font-size: 1.5em;\n    margin-bottom: 0;\n  }\n\n  & > ul {\n    padding-left: 0;\n  }\n\n  li[class^=\"toctree-l\"] {\n    list-style: none;\n    margin-bottom: 0.2em;\n\n    & > a {\n      list-style: none;\n      font-size: 1.1em;\n    }\n\n    & > ul {\n      list-style: none;\n      padding-inline-start: 1.5em;\n    }\n  }\n\n  // slightly bigger font for l1\n  .toctree-l1 > a {\n    font-size: 1.3em;\n  }\n}\n\ndiv.topic.contents,  // Docutils <= 0.17\nnav.contents  // Docutils >= 0.18\n{\n  // Style similarly to toctree\n  ul.simple {\n    list-style: none;\n    padding-left: 0;\n  }\n}\n","/**\n * Mathematics via MathJax.\n *\n * This is designed for MathJax v3\n * ref: https://www.sphinx-doc.org/en/master/usage/extensions/math.html#module-sphinx.ext.mathjax\n */\n\n// Applies to all math elements\nspan.math,\ndiv.math {\n  align-items: center;\n  display: flex;\n  max-width: 100%;\n\n  // This will be over-ridden for the y-direction and divs\n  overflow: hidden;\n}\n\n// Inline-only\nspan.math {\n  display: inline-flex;\n}\n\n// Block-level only\ndiv.math {\n  gap: 0.5em;\n\n  // So that the eqno shows up after the equation\n  flex-direction: row-reverse;\n\n  // The equation number / link\n  span.eqno a.headerlink {\n    position: relative;\n    font-size: 1em;\n  }\n\n  // The math container\n  mjx-container {\n    flex-grow: 1;\n    padding-bottom: 0.2rem;\n    overflow: auto;\n\n    // Set height to 0 so that it does not cause scrollbars to appear\n    // ref: https://github.com/mathjax/MathJax/issues/2521\n    mjx-assistive-mml {\n      height: 0;\n    }\n  }\n}\n","/**\n * ABlog\n * ref: https://ablog.readthedocs.io/\n */\n\n/**\n * Sidebar template components\n */\n.ablog-sidebar-item {\n  h2,\n  h3 {\n    font-size: var(--pst-sidebar-header-font-size);\n\n    // Remove unnecessary vertical whitespace\n    margin-top: 0.5rem;\n\n    // The headers are all links, but this makes them hard to parse\n    // So we change the colors to make them look like headers\n    a {\n      color: var(--pst-color-text-base);\n    }\n  }\n\n  ul {\n    // No bullet points for the primary sidebar items\n    list-style: none;\n    padding-left: 0;\n\n    // Otherwise a scrollbar randomly shows up\n    overflow-y: hidden;\n\n    // List of recent post items\n    display: flex;\n    flex-direction: column;\n    gap: 0.5em;\n    margin-bottom: 0;\n\n    // The ablog cloud should move horizontally\n    &.ablog-cloud {\n      flex-flow: row wrap;\n      gap: 0.5rem;\n\n      // Vertical-align tag clouds\n      li {\n        // Center the tag cloud items\n        display: flex;\n        align-items: center;\n      }\n    }\n  }\n}\n\n/**\n * Previous / next buttons at the bottom\n */\n.ablog__prev-next {\n  font-size: 1.2em;\n  display: flex;\n  padding: 1rem 0;\n\n  // The bottom previous / next arrows\n  > span {\n    // To ensure that the whole thing fits on one line even if there are long titles\n    display: flex;\n    max-width: 45%;\n\n    // Links within each span have the collection of icon + text\n    a {\n      display: flex;\n      align-items: center;\n      margin-left: auto;\n      gap: 1rem;\n      line-height: 1.5rem;\n\n      i::before {\n        color: var(--pst-color-text-base);\n      }\n    }\n  }\n\n  // The first span is for the previous page and aligns to the left\n  span.ablog__prev {\n    i.fa-arrow-circle-left::before {\n      content: var(--pst-icon-angle-left);\n    }\n  }\n\n  // The second span is just an empty space so we remove it because we're\n  // positioning with flex\n  span.ablog__spacer {\n    display: none;\n  }\n\n  // The third span is aligned to the right\n  span.ablog__next {\n    margin-left: auto;\n    text-align: right;\n\n    i.fa-arrow-circle-right::before {\n      content: var(--pst-icon-angle-right);\n    }\n  }\n}\n\n/**\n * {postlist} directive and posts page\n */\n.ablog__collection,\n.postlist {\n  padding-left: 0;\n\n  .ablog-post {\n    list-style: none;\n\n    // Post metadata tags (author, links ,etc) should be a bit smaller\n    .ablog-archive {\n      display: flex;\n      flex-flow: row wrap;\n      gap: 1rem;\n      list-style: none;\n      font-size: 0.75rem;\n      padding-left: 0;\n    }\n\n    // Title line should be a bit bigger and bold to stand out\n    .ablog-post-title {\n      margin-top: 0;\n      font-size: 1.25rem;\n\n      a {\n        font-weight: bold;\n      }\n    }\n\n    // Read more button should be a bit bigger\n    .ablog-post-expand {\n      margin-bottom: 0.5rem;\n    }\n  }\n}\n","/**\n * Special cases for Bootstrap functionality\n */\n\n// Bootstrap adds margin to their general container class. However, sphinx/docutils\n// can also generate output with the container class, but in those cases we should\n// not add the margin from bootstrap. Same for max-width.\n.docutils.container {\n  padding-left: unset;\n  padding-right: unset;\n  margin-left: unset;\n  margin-right: unset;\n  max-width: unset;\n  width: unset;\n}\n\n.btn {\n  --bs-btn-focus-box-shadow: #{$btn-focus-box-shadow};\n}\n","/**\n * Sphinx Copybutton\n * ref: https://sphinx-copybutton.readthedocs.io/\n */\n\ndiv.highlight button.copybtn {\n  // Nicer spacing\n  display: flex;\n  align-items: center;\n  justify-content: center;\n\n  // Don't over-ride the success color\n  &:not(.success) {\n    color: var(--pst-color-muted);\n  }\n\n  border: none;\n  background-color: var(--pst-color-surface);\n\n  &:hover {\n    &:not(.success) {\n      color: var(--pst-color-text);\n      background-color: var(--pst-color-shadow);\n    }\n  }\n\n  // Tooltip styling\n  &.o-tooltip--left::after {\n    color: var(--pst-color-text);\n    background-color: var(--pst-color-surface);\n  }\n\n  &:focus {\n    // For keyboard users, make the copy button visible when focussed.\n    opacity: 1;\n  }\n\n  &:focus-visible {\n    outline: $focus-ring-outline;\n  }\n}\n\ndiv.highlight:has(button.copybtn) {\n  // Make sure the code block has enough height for the copy button.\n  // Sphinx-copybutton sets 0.3em top offset plus 1.7em height:\n  // https://github.com/executablebooks/sphinx-copybutton/blob/master/sphinx_copybutton/_static/copybutton.css\n  min-height: 2em;\n}\n","// adapt ethical ad to the theme\n#ethical-ad-placement {\n  .ethical-sidebar a,\n  .ethical-sidebar a:visited,\n  .ethical-sidebar a:hover,\n  .ethical-sidebar a:active,\n  .ethical-footer a,\n  .ethical-footer a:visited,\n  .ethical-footer a:hover,\n  .ethical-footer a:active {\n    color: var(--pst-color-text-base);\n  }\n\n  .ethical-sidebar,\n  .ethical-footer {\n    background-color: var(--pst-color-background);\n    border: 1px solid var(--pst-color-border);\n    border-radius: 5px;\n    color: var(--pst-color-text-base);\n    font-size: 14px;\n    line-height: 20px;\n  }\n}\n","/**\n * Styles for various Sphinx execution libraries.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n\n/******************************************************************************\n * Jupyter Sphinx\n */\n\n.bd-content div.jupyter_container {\n  // We don't want borders around the whole container, just around code cells\n  border: none;\n  background-color: unset;\n  box-shadow: none;\n\n  // Code cells should have the same style as our other code objects\n  div.output,\n  div.highlight {\n    border-radius: 0.25rem;\n  }\n\n  div.highlight {\n    background-color: var(--pst-color-surface);\n  }\n\n  // Ensure the style is the same as our code cells. Jupyter Sphinx makes it tiny.\n  .cell_input,\n  .cell_output {\n    border-radius: 0.25rem;\n\n    pre {\n      padding: 1rem;\n    }\n  }\n}\n","/**\n * Special-cases for packages in the PyData ecosystem\n */\n\n// xarray output display in bootstrap\n.xr-wrap[hidden] {\n  display: block !important;\n}\n\n// ipywidgets\n.jp-OutputArea-output.lm-Widget {\n  // override overflow:hidden rule from Lumino (.lm-Widget) to allow scrolling\n  overflow: auto;\n}\n","/*******************************************************************************\n * Special-cases for the sphinx-design library, mainly to make it compatible\n * with the dark/light themes of pydata-sphinx-theme.\n *\n * NOTE: sphinx-design uses !important quite liberally, so here we must do the\n * same for our overrides to have any effect.\n */\n@use \"../variables/color\" as pst-color;\n@use \"sass:color\";\n@use \"sass:map\";\n@use \"sass:meta\";\n@use \"sass:string\";\n\n/*******************************************************************************\n  * Color and variables\n  *\n  * This is a list of the semantic color names from sphinx-design (we only\n  * need to override variables that sphinx-design has actually defined).\n  * https://github.com/executablebooks/sphinx-design/blob/9226a12a/style/_colors.scss#L31-L43\n  */\n$sd-semantic-color-names: (\n  \"primary\",\n  \"secondary\",\n  \"success\",\n  \"info\",\n  \"warning\",\n  \"danger\",\n  \"light\",\n  \"muted\",\n  \"dark\",\n  \"black\",\n  \"white\"\n);\n\n/**\n  * Here we create some extra --pst-color-* variables and use\n  * them to override the value of the corresponding sphinx-design variables.\n  * This is easier than re-writing the sphinx-design rules. Even easier would be\n  * directly assigning our values to the --sd-color-* variables, but then our\n  * downstream users couldn't override *our* colors and have it affect buttons\n  * and badges.\n  *\n  * First, define the extra keys needed to cover the full range of semantic\n  * color names used in sphinx-design, then merge them with the names we\n  * already define for our own needs.\n  * see https://sphinx-design.readthedocs.io/en/latest/css_variables.html\n  */\n$extra-semantic-colors: (\n  \"white\": $foundation-white,\n  \"light\": (\n    light: $foundation-light-gray,\n    bg-light: color.scale($foundation-light-gray, $lightness: 30%),\n    dark: $foundation-light-gray,\n    bg-dark: color.scale($foundation-light-gray, $lightness: -30%),\n  ),\n  \"muted\": (\n    light: $foundation-muted-gray,\n    bg-light: color.scale($foundation-muted-gray, $lightness: 30%),\n    dark: $foundation-light-gray,\n    bg-dark: color.scale($foundation-muted-gray, $lightness: -30%),\n  ),\n  \"dark\": $foundation-dark-gray,\n  \"black\": $foundation-black,\n);\n$all-colors: map.merge($pst-semantic-colors, $extra-semantic-colors);\n\n@mixin create-sd-colors($value, $name) {\n  // define the pst variables, so that downstream user overrides will work\n  --pst-color-#{$name}: #{$value};\n\n  // we are now using a11y-combination to calculate the text color - this is based\n  // on the WCAG color contrast guidelines\n  --pst-color-#{$name}-text: #{a11y-combination($value)};\n\n  // TODO: highlight seems to be used for buttons @trallard to fix on a11y follow-up work\n  --pst-color-#{$name}-highlight: #{color.adjust($value, $lightness: -15%)};\n\n  // override the sphinx-design variables\n  --sd-color-#{$name}: var(--pst-color-#{$name});\n  --sd-color-#{$name}-text: var(--pst-color-#{$name}-text);\n\n  // TODO: highlight seems to be used for buttons @trallard to fix on a11y follow-up work\n  --sd-color-#{$name}-highlight: var(--pst-color-#{$name}-highlight);\n}\n\n// Now we override the --sd-color-* variables.\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    // check if this color is defined differently for light/dark\n    @each $name in $sd-semantic-color-names {\n      $definition: map.get($all-colors, $name);\n\n      @if meta.type-of($definition) == map {\n        @each $key, $value in $definition {\n          @if string.index($key, $mode) {\n            // since now we define the bg colours in the semantic colours and not\n            // by changing opacity, we need to check if the key contains bg and the\n            // correct mode (light/dark)\n            @if string.index($key, \"bg\") {\n              --sd-color-#{$name}-bg: #{$value};\n\n              // create local variable\n              $value: check-color($value);\n\n              --sd-color-#{$name}-bg-text: #{a11y-combination($value)};\n            } @else {\n              $value: check-color($value);\n\n              @include create-sd-colors($value, $name);\n            }\n          }\n        }\n      } @else {\n        $value: map.get($all-colors, $name);\n\n        @include create-sd-colors($value, $name);\n      }\n    }\n  }\n}\n\n// Make sure the color border variables are set using our variables\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    --sd-color-card-border: var(--pst-color-border);\n  }\n}\n\n/*******************************************************************************\n  * shadows\n  */\nhtml[data-theme=\"light\"] {\n  .sd-shadow-xs,\n  .sd-shadow-sm,\n  .sd-shadow-md,\n  .sd-shadow-lg {\n    @include box-shadow;\n  }\n}\n\n/*******************************************************************************\n  * cards\n  */\n\n.bd-content .sd-card {\n  border: 1px solid var(--pst-color-border);\n\n  // TODO - --pst-color-panel-background is not defined... where is this coming from?\n  .sd-card-header {\n    background-color: var(--pst-color-panel-background);\n    border-bottom: 1px solid var(--pst-color-border);\n  }\n\n  .sd-card-footer {\n    background-color: var(--pst-color-panel-background);\n    border-top: 1px solid var(--pst-color-border);\n  }\n\n  .sd-card-body {\n    background-color: var(--pst-color-panel-background);\n  }\n\n  // Focus ring for link-cards\n  .sd-stretched-link:focus-visible {\n    // Don't put the focus ring on the <a> element (it has zero height in Sphinx Design cards)\n    outline: none;\n\n    // Put the focus ring on the <a> element's ::after pseudo-element\n    &::after {\n      outline: $focus-ring-outline;\n      border-radius: 0.25rem; // copied from Sphinx Design CSS for .sd-card\n    }\n  }\n\n  &.sd-card-hover:hover {\n    border-color: var(--pst-color-link-hover);\n  }\n}\n\n/*******************************************************************************\n  * tabs\n  */\n\n.bd-content .sd-tab-set {\n  > input {\n    // Active tab label\n    &:checked + label {\n      border-style: solid solid none;\n      border-color: var(--pst-color-primary) var(--pst-color-primary)\n        transparent; // top LR bottom\n\n      border-width: 0.125rem 0.125rem 0;\n      border-radius: 0.125rem 0.125rem 0 0;\n      background-color: var(--pst-color-on-background);\n      transform: translateY(0.125rem);\n      color: var(--pst-color-primary);\n    }\n\n    &:focus-visible + label {\n      border: 0.125rem solid var(--pst-color-accent);\n      border-radius: 0.125rem;\n      background-color: var(--pst-color-accent-bg);\n      color: var(--pst-color-on-surface);\n    }\n\n    // Hover label\n    &:not(:checked, :focus-visible) + label:hover {\n      border-color: transparent;\n      color: var(--pst-color-secondary);\n    }\n  }\n\n  // Tab label\n  > label {\n    color: var(--pst-color-on-surface);\n    border: 0.125rem solid transparent;\n    border-radius: 0.125rem 0.125rem 0 0;\n    background-color: var(--pst-color-surface);\n    padding: 0 0.75em;\n    margin-inline-end: 0.25rem;\n    line-height: 1.95;\n\n    html[data-theme=\"dark\"] & {\n      background-color: var(--pst-color-on-background);\n    }\n  }\n\n  // panel\n  .sd-tab-content {\n    border: 0.125rem solid var(--pst-color-primary);\n    border-radius: 0.1875rem;\n    box-shadow: unset;\n    padding: 0.625rem;\n    background-color: var(--pst-color-on-background);\n  }\n}\n\n/*******************************************************************************\n* Dropdowns\n*/\n\ndetails.sd-dropdown {\n  // Remove all borders to over-ride SD behavior, and we'll add our own later\n  border: 0 !important;\n\n  // Drop shadow should behave same as admonitions\n  @include box-shadow;\n\n  // Header is where the \"clickable\" box goes\n  summary.sd-card-header {\n    border: 0 !important;\n    display: flex;\n    align-items: center;\n    position: relative; // So background color works\n    font-weight: 600;\n    padding-top: 0.5rem;\n    padding-bottom: 0.5rem;\n\n    // Set a variable that we can re-use for colors later\n    // We must set this in the current and content sibling container\n    // so that it is defined in both places\n    --pst-sd-dropdown-color: var(--pst-gray-500);\n    --pst-sd-dropdown-bg-color: var(--pst-color-surface);\n\n    & + div.sd-summary-content {\n      border: 0;\n\n      --pst-sd-dropdown-color: var(--sd-color-card-border);\n    }\n\n    @each $name in $sd-semantic-color-names {\n      &.sd-bg-#{$name} {\n        --pst-sd-dropdown-color: var(--sd-color-#{$name});\n        --pst-sd-dropdown-bg-color: var(--sd-color-#{$name}-bg);\n\n        // Otherwise it won't be defined in the sibling element\n        & + div.sd-summary-content {\n          --pst-sd-dropdown-color: var(--sd-color-#{$name});\n          --pst-sd-dropdown-bg-color: var(--sd-color-#{$name}-bg);\n        }\n      }\n      &.sd-bg-text-#{$name} {\n        // Use the WCAG conformant text color\n        color: var(--sd-color-#{$name}-bg-text) !important;\n      }\n    }\n\n    @include legacy-backdrop-placeholder;\n\n    background-color: var(--pst-sd-dropdown-bg-color) !important;\n\n    // Add a left border with the same structure as our admonitions\n    border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n\n    // stylelint-disable-next-line no-duplicate-selectors\n    & + div.sd-summary-content {\n      border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n      border-bottom-left-radius: calc(0.25rem - 1px);\n      background-color: var(--pst-color-on-background);\n    }\n\n    span.sd-summary-icon {\n      display: inline-flex;\n      align-items: center;\n      color: var(--pst-sd-dropdown-color) !important;\n\n      svg {\n        opacity: 1;\n      }\n    }\n\n    // Positioning of the caret\n    .sd-summary-up,\n    .sd-summary-down {\n      top: 0.7rem;\n    }\n\n    // Focus ring\n    &:focus-visible {\n      outline: $focus-ring-outline;\n      outline-offset: -$focus-ring-width;\n    }\n  }\n}\n\n/*******************************************************************************\n* Buttons (which in Sphinx Design are actually links that look like buttons)\n* ref: https://sphinx-design.readthedocs.io/en/pydata-theme/badges_buttons.html#buttons\n*/\nhtml {\n  .sd-btn {\n    min-width: 2.25rem;\n    padding: 0.3125rem 0.75rem 0.4375rem; // 5px 12px 7px\n\n    &:hover {\n      @include link-style-hover; // override Sphinx Design\n\n      text-decoration-thickness: 1px;\n    }\n  }\n\n  @each $name in $sd-semantic-color-names {\n    .sd-btn-#{$name},\n    .sd-btn-outline-#{$name} {\n      &:focus-visible {\n        // Override Sphinx Design's use of -highlight colors. The -highlight\n        // colors are 15% darker, so this would create the effect of darkening\n        // the button when focused but we just want the button to have a focus\n        // ring of the same (non-highlight) color.\n        background-color: var(--sd-color-#{$name}) !important;\n        border-color: var(--sd-color-#{$name}) !important;\n        outline: var(--sd-color-#{$name}) solid $focus-ring-width;\n        outline-offset: $focus-ring-width;\n      }\n    }\n  }\n}\n","/**\n * Sphinx togglebutton\n */\n\n.bd-content {\n  @mixin chevron-down {\n    .toggle-chevron-right {\n      transform: rotate(90deg); // point chevron down\n      transition: none; // match non-animated behavior of other chevrons on site\n    }\n  }\n\n  @mixin chevron-up {\n    .toggle-chevron-right {\n      transform: rotate(-90deg); // point chevron up\n      transition: none; // match non-animated behavior of other chevrons on site\n    }\n  }\n\n  // Admonition toggles\n  .admonition {\n    button.toggle-button {\n      color: inherit;\n\n      // When disclosure widget is closed\n      &.toggle-button-hidden {\n        @include chevron-down;\n      }\n\n      // When open\n      @include chevron-up;\n    }\n\n    // Focus ring\n    // ----------\n    // Sphinx-togglebutton makes the entire admonition header clickable, but\n    // only the button within the header is focusable. We want the entire\n    // clickable area to be surrounded with a focus ring, so that's why we use\n    // the :focus-within selector, rather than a :focus-visible selector on the\n    // button.\n    &:focus-within {\n      overflow: visible;\n\n      // The complicated focus ring styles here are a consequence of the markup\n      // and border styles for this particular admonition class. (For the other\n      // type of admonition on this site, the focus ring style is achieved with\n      // simple `outline` and `outline-offset` rules on the admonition's\n      // header.) The problem is that Sphinx-togglebutton puts the admonition's\n      // left border on the outermost container (rather than separately setting\n      // the left border on the container's children). This makes it complicated\n      // to get the focus ring to simultaneously cover the left border in the\n      // header and align perfectly on the right with the body.\n      .admonition-title:focus-within::before {\n        content: \"\";\n        transform: translateX(\n          -$admonition-left-border-width\n        ); // align left edges of admonition and ring\n\n        width: calc(100% + $admonition-left-border-width); // align right edges\n        height: 100%;\n        border: $focus-ring-outline;\n        border-radius: $focus-ring-width;\n      }\n\n      // When expanded, sharpen the bottom left and right corners of the focus ring\n      &:not(.toggle-hidden) .admonition-title:focus-within::before {\n        border-bottom-left-radius: 0;\n        border-bottom-right-radius: 0;\n      }\n    }\n  }\n\n  // Details buttons\n  details.toggle-details {\n    // Over-ride border color to re-use our primary color\n    summary {\n      border-left: 3px solid var(--pst-color-primary);\n\n      @include chevron-down;\n    }\n\n    // When expanded, sharpen the bottom left and right corners of the focus ring\n    &[open] {\n      @include chevron-up;\n\n      :focus-visible {\n        border-bottom-left-radius: 0;\n        border-bottom-right-radius: 0;\n      }\n    }\n  }\n}\n","/**\n * Styles for various Sphinx execution libraries to display pre-executed notebooks.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n\n/*******************************************************************************\n * nbsphinx\n */\nhtml div.rendered_html,\n// NBsphinx ipywidgets output selector\nhtml .jp-RenderedHTMLCommon {\n  // Add some margin around the element box for the focus ring. Otherwise the\n  // focus ring gets clipped because the containing elements have `overflow:\n  // hidden` applied to them (via the `.lm-Widget` selector)\n  margin: $focus-ring-width;\n\n  table {\n    table-layout: auto;\n  }\n}\n\n.bd-content .nboutput {\n  .output_area {\n    &.rendered_html,\n    .jp-RenderedHTMLCommon {\n      // pandas\n      table.dataframe {\n        @include table-colors;\n      }\n    }\n\n    // Dark theme special-cases\n    html[data-theme=\"dark\"] & {\n      &.rendered_html:not(:has(table.dataframe)),\n      // ipywidgets\n      .widget-subarea {\n        @include cell-output-background;\n      }\n\n      &.stderr {\n        background-color: var(--pst-color-danger);\n      }\n    }\n  }\n}\n\n// Add extra padding to the final item in an nbsphinx container\ndiv.nblast.container {\n  margin-bottom: 1rem;\n}\n\n/*******************************************************************************\n * myst NB\n */\n\ndiv.cell_output .output {\n  max-width: 100%;\n  overflow-x: auto;\n}\n\n.bd-content div.cell_output {\n  // pandas\n  table.dataframe {\n    @include table-colors;\n  }\n\n  html[data-theme=\"dark\"] & {\n    img,\n    .text_html:not(:has(table.dataframe)),\n    // ipywidgets\n    .widget-subarea {\n      @include cell-output-background;\n    }\n  }\n}\n\n// Prevent tables from scrunching together\n.bd-content {\n  div.cell_input {\n    display: flex;\n    flex-direction: column;\n    justify-content: stretch;\n  }\n\n  div.cell_input,\n  div.output {\n    border-radius: $admonition-border-radius;\n  }\n\n  div.output {\n    table {\n      table-layout: auto;\n    }\n  }\n}\n","/**\n * style for the various mapping libs based on leaflet (folium, geemap, ipyleaflet)\n * mainly ensure the good display of the maps in both themes and avoid the customization\n * of the tiles\n */\n\n/**\n * avoid border override from pydata-sphinx-theme\n * minimal selctor to get the priority\n */\nhtml[data-theme=\"dark\"] .bd-content img.leaflet-tile.leaflet-tile-loaded {\n  border-radius: 0;\n  padding: 0;\n}\n","/**\n * /search.html page special-cases\n */\n\n.bd-search-container {\n  /*******************************************\n  * Search results\n  */\n  // Whitespace\n  div#search-results {\n    > h2 {\n      font-size: var(--pst-font-size-icon);\n      margin-top: 1rem;\n    }\n\n    p.search-summary {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  ul.search {\n    margin: 0;\n    list-style: none;\n\n    li {\n      background-image: none;\n      padding: 1rem 0;\n      margin: 1rem 0;\n      border-top: 1px solid var(--pst-color-text-muted);\n\n      // First link is the page title, it should be a bit bigger\n      > a {\n        font-size: 1.2em;\n      }\n\n      div.context,\n      p.context {\n        color: var(--pst-color-text-base);\n        margin: 0.5em 0 0;\n\n        // Add a # before page section titles to make it clear they are sections\n        a::before {\n          content: \"#\";\n          padding-right: 0.2em;\n          color: var(--pst-color-text-muted);\n        }\n      }\n    }\n  }\n}\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/_preview/13/_static/styles/sphinx-book-theme.css b/_preview/13/_static/styles/sphinx-book-theme.css
new file mode 100644
index 0000000..5197540
--- /dev/null
+++ b/_preview/13/_static/styles/sphinx-book-theme.css
@@ -0,0 +1,9 @@
+/*! sphinx-book-theme CSS
+ * BSD 3-Clause License
+ * Copyright (c) 2020, EBP
+ * All rights reserved.
+ *
+ * This follows the 7-1 pattern described here:
+ * https://sass-guidelin.es/#architecture
+ */html[data-theme=dark],html[data-theme=light]{--pst-font-size-base:none;--pst-color-secondary:#e89217}html[data-theme=light]{--sbt-color-announcement:#616161;--pst-color-primary:#176de8}html[data-theme=dark]{--pst-color-primary:#528fe4;--sbt-color-announcement:#616161;--pst-color-background:#121212}html{scroll-padding-top:4rem}.sbt-scroll-pixel-helper{height:0;left:0;position:absolute;top:0;width:0}.d-n,.onlyprint{display:none}@media print{.onlyprint{display:block!important}.noprint{display:none!important}}.bd-article-container h1,.bd-article-container h2,.bd-article-container h3,.bd-article-container h4,.bd-article-container h5,.bd-article-container p.caption{color:var(--pst-color-muted)}.bd-article-container h1,.bd-article-container h2{font-weight:500}a.brackets:before{color:inherit;font-family:inherit;margin-right:0}table{position:relative}@media print{.bd-main .bd-content{height:auto;margin-left:2rem}.bd-main .bd-content #jb-print-docs-body{margin-left:0}.bd-main .bd-content #jb-print-docs-body h1{font-size:3em;margin-bottom:0;text-align:center}.bd-main .bd-content .bd-article{padding-top:0}.bd-main .bd-content .bd-article h1:first-of-type{display:none}.bd-main .bd-content .container{min-width:0!important}.bd-main .bd-content h1{margin-bottom:1em;margin-top:1em}.bd-main .bd-content h1,.bd-main .bd-content h2,.bd-main .bd-content h3,.bd-main .bd-content h4{break-after:avoid;color:#000}.bd-main .bd-content table{break-inside:avoid}.bd-main .bd-content pre{word-wrap:break-word}.bd-main .bd-content a.headerlink{display:none}.bd-main .bd-content aside.margin,.bd-main .bd-content aside.sidebar,.bd-main .bd-content blockquote.epigraph{border:none}.bd-main .bd-content .footer{margin-top:1em}.bd-main .bd-content #jb-print-toc{margin-bottom:1.5rem;margin-left:0}.bd-main .bd-content #jb-print-toc .section-nav{border-left:0!important;list-style-type:disc!important;margin-left:3em!important}.bd-main .bd-content #jb-print-toc .section-nav 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var(--pst-color-shadow),\n    0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n",".bd-header {\n  // Turn off sticky positioning so that it scrolls\n  position: inherit;\n\n  // Turn these off because we'll show it in the article header\n  // The primary sidebar toggle\n  button.sidebar-toggle {\n    display: none;\n  }\n}\n","/*********************************************\n* Left Nav Bar *\n*********************************************/\n\n.bd-sidebar-primary {\n  // So that it sticks to the top of the page instead of the header height\n  top: 0;\n  max-height: 100vh;\n\n  // Tighter padding so the spacing is more equal\n  padding: 1rem;\n\n  // Draw open/close animation\n  transition:\n    margin-left $animation-time ease 0s,\n    opacity $animation-time ease 0s,\n    visibility $animation-time ease 0s;\n\n  @media (max-width: $breakpoint-md) {\n    // So that tooltips don't overlap\n    z-index: $zindex-tooltip + 1;\n  }\n\n  // Remove the top border since there's often no header items above\n  .sidebar-primary-items__start {\n    border-top: none;\n  }\n\n  // On wide screens we have a smaller sidebar width than PST\n  @media (min-width: $breakpoint-lg) {\n    flex-basis: $sidebar-primary-width-widescreen;\n  }\n}\n\n// Default visibility for left sidebar is the reverse of what it is on mobile.\n// It is shown by default, and hidden with a click.\n// Mobile behavior is defined in the pydata sphinx theme\n@media (min-width: $breakpoint-lg) {\n  input#pst-primary-sidebar-checkbox:checked\n    ~ .bd-container\n    .bd-sidebar-primary {\n    margin-left: -$sidebar-primary-width-widescreen;\n    visibility: hidden;\n    opacity: 0;\n  }\n}\n","/*********************************************\n* Table of Contents (right nav bar)          *\n*********************************************/\n\n.bd-sidebar-secondary {\n  // So that it sticks to the top of the page instead of the header height\n  top: 0;\n\n  @media (max-width: $breakpoint-xl) {\n    // So that tooltips don't overlap\n    z-index: $zindex-tooltip + 1;\n  }\n\n  // So that we can center the TOC button with the article header buttons\n  .sidebar-secondary-items {\n    padding: 0;\n    display: flex;\n    gap: 0.5rem;\n\n    // Remove the manual padding since we'll use `gap`\n    .sidebar-secondary-item {\n      padding-top: 0;\n      padding-bottom: 0;\n    }\n  }\n\n  // The 'on this page' title div should take up the full header\n  .onthispage {\n    height: $header-article-height;\n    min-height: $header-article-height;\n    display: flex;\n    gap: 0.5rem;\n    align-items: center;\n    margin: 0;\n    color: var(--pst-color-muted);\n  }\n\n  // Wide screen behavior\n  @media (min-width: $breakpoint-xl) {\n    background: var(--pst-color-background);\n    height: fit-content;\n    transition: max-height 0.4s ease;\n\n    // To make sure it shows above the page content\n    z-index: $zindex-middle;\n\n    // Remove padding so that it's flush on the top and left\n    padding: 0;\n\n    // This is the container div for the secondary sidebar nav\n    .toc-item {\n      // Border is a bit less prominent\n      border-left-color: var(--pst-color-surface);\n      // Flush with the top so that we can fix the height of the toc header div\n      padding-top: 0;\n\n      // The table of contents <ul> after the title div\n      nav.page-toc {\n        // Remove extra margin the PST adds for other sidebar items\n        margin-bottom: 0;\n\n        // Items after \"on this page' should fade in and out\n        transition: opacity 0.4s ease;\n      }\n    }\n\n    // Rules for when sidebar is hidden\n    // the `not:hover` means that hovering over the sidebar will show it again.\n    &.hide:not(:hover) {\n      // Cap the height, and remove the scrollbar\n      max-height: $header-article-height;\n      overflow-y: hidden;\n\n      // Shadow matches what we have in the article header\n      .scrolled & {\n        box-shadow: 0 6px 6px -6px rgba(0, 0, 0, 0.3);\n      }\n\n      // Show a down carot\n      .onthispage:after {\n        opacity: 1;\n        content: \"\\f107\";\n        font-family: \"Font Awesome 5 Free\";\n        font-weight: 900;\n        padding-left: 0.5em;\n        transition: opacity 0.3s ease;\n      }\n\n      // The page-toc list is hidden\n      nav.page-toc {\n        opacity: 0;\n      }\n    }\n  }\n}\n","/*********************************************\n* Footer - content *\n*********************************************/\nfooter {\n  font-size: var(--sbt-font-size-small-1);\n  &.bd-footer-content {\n    display: flex;\n    flex-wrap: wrap;\n    padding: 15px;\n    border-top: 1px solid #ccc;\n    font-size: $sbt-font-size-small-1;\n    .bd-footer-content__inner {\n      padding-left: 0px;\n      p {\n        margin-bottom: 0px;\n      }\n    }\n  }\n}\n",".bd-footer-article {\n  padding: 0rem 1rem;\n  @media (max-width: $breakpoint-md) {\n    // More room for text on mobile\n    padding: 0rem 0.5rem;\n  }\n}\n","#pst-back-to-top {\n  // We should only display the back-to-top button when the TOC is not available.\n  // AKA, on narrow-ish screens.\n  // This over-rides a PST default\n  @media (min-width: $breakpoint-xl) {\n    display: none !important;\n  }\n\n  // Reduce size to be slightly less intrusive on the page\n  font-size: 0.8rem;\n}\n","/**\n * Icon links design for the primary sidebar, where it defaults in this theme.\n */\n.bd-sidebar-primary {\n  .navbar-icon-links {\n    column-gap: 0.5rem;\n\n    .nav-link {\n      // There are few kinds of elements that can be icon links and each is different\n      i,\n      span {\n        font-size: 1.2rem;\n      }\n\n      // Images usually fill more vertical space so we make them a bit smaller\n      img {\n        font-size: 0.8rem;\n      }\n    }\n  }\n}\n",".navbar-brand {\n  height: unset;\n  max-height: unset;\n\n  // Auto-center the image and title and make items vertical\n  flex-direction: column;\n  justify-content: center;\n  gap: 0.25rem;\n\n  // To prevent site text from having underlines\n  &:hover {\n    text-decoration: none;\n  }\n  .logo__title {\n    font-size: 1.25rem;\n    white-space: normal;\n    text-align: center;\n  }\n  // Prevent images from stretching vertically\n  .logo__image {\n    height: unset;\n  }\n}\n","// Clean up the search page so that it has less unnecessary text\n.bd-search-container {\n  margin: 2em;\n\n  #search-results {\n    h2:first-child {\n      display: none;\n    }\n  }\n}\n\n// Sidebar search button field\n.search-button-field {\n  width: 100%;\n  font-size: 0.9rem;\n  // Hide by default and we'll add a rule to show it on wide sreen below\n  display: none;\n\n  .search-button__kbd-shortcut {\n    margin-left: auto;\n  }\n}\n\n// Rules to switch off visibility of the field button and the header button\n@media (min-width: $breakpoint-lg) {\n  .search-button {\n    display: none !important;\n  }\n\n  .search-button-field {\n    display: flex;\n  }\n}\n\n// The \"clear search results\" button\n// Make it line up with our content better\ndiv#searchbox {\n  padding-right: 2rem;\n  padding-left: 2rem;\n  @media (max-width: $breakpoint-md) {\n    padding-right: 1rem;\n    padding-left: 1rem;\n  }\n\n  p.highlight-link {\n    // Remove the extra margin that is added\n    @media (min-width: $breakpoint-md) {\n      margin-left: 0;\n      a {\n        font-size: 1rem;\n      }\n    }\n  }\n}\n","// Images\nimg {\n  max-width: 100%;\n\n  &.align-center {\n    margin-left: auto;\n    margin-right: auto;\n    display: block;\n  }\n\n  &.align-left {\n    clear: left;\n    float: left;\n    margin-right: 1em;\n  }\n\n  &.align-right {\n    clear: right;\n    float: right;\n    margin-left: 1em;\n  }\n}\n\n// Figures\ndiv.figure {\n  width: 100%;\n  margin-bottom: 1em;\n  text-align: center;\n\n  &.align-left {\n    text-align: left;\n    p.caption {\n      margin-left: 0;\n    }\n  }\n  &.align-right {\n    text-align: right;\n    p.caption {\n      margin-right: 0;\n    }\n  }\n\n  p.caption {\n    margin: 0.5em 10%;\n  }\n\n  &.margin,\n  &.margin-caption {\n    p.caption {\n      margin: 0.5em 0;\n    }\n  }\n\n  &.margin-caption p.caption {\n    text-align: left;\n  }\n\n  span.caption-number {\n    font-weight: bold;\n  }\n\n  span {\n    font-size: 0.9rem;\n  }\n}\n","/**\n * Margin content\n * This is the area to the right of the main content\n * normally covered by the in-page table of contents\n * but if `.margin` classes are present, it shows up to the right in the margin\n * and the in-page TOC is hidden.\n * On narrow screens, margin content behaves like a sidebar\n */\n\n/**\n * Variables for calculating margin widths\n */\n// We want our margin content to be 1/3 the width of the main page content\n// after factoring in a small margin\n$margin-width-relative-to-content: 36%;\n$margin-gutter: 3%;\n$margin-width-width-gutter: $margin-width-relative-to-content - $margin-gutter;\n\n// re-subtract the gutter width because we want it to leave a white-space\n// This is how much to offset the margin content.\n$margin-offset: -$margin-width-relative-to-content;\n\n/**\n * This mixin will cause something to \"pop out\" to the margin on wide screens\n */\n@mixin margin-content() {\n  // On narrow screens this is the width of margin content\n  width: 40%;\n  float: right;\n  background-color: unset;\n  font-size: 0.9em;\n  margin-left: 0.5rem;\n\n  // Wide screens and printing should force margin behavior\n  @media (min-width: $breakpoint-lg), print {\n    width: $margin-width-width-gutter;\n    margin: 0 $margin-offset 0 0;\n\n    // Prevent sequential margin content from overlapping\n    clear: right;\n\n    p.sidebar-title {\n      margin-bottom: -1rem;\n      border-bottom: none;\n      padding-left: 0px;\n\n      // Content of admonitions has fewer horizontal white space to save space\n      ~ * {\n        padding-left: 0px;\n        padding-right: 0px;\n      }\n    }\n  }\n}\n\n/**\n * Sidenotes and marginnotes\n * over-rides the \"footnote\" behavior to pop out to the margin instead.\n */\nlabel.margin-toggle {\n  margin-bottom: 0em;\n  &.marginnote-label {\n    display: none;\n  }\n  sup {\n    user-select: none;\n  }\n  @media (max-width: $breakpoint-lg) {\n    cursor: pointer;\n    color: rgb(0, 113, 188);\n    &.marginnote-label {\n      display: inline;\n      &:after {\n        content: \"\\2295\";\n      }\n    }\n  }\n}\n\ninput.margin-toggle {\n  display: none;\n  @media (max-width: $breakpoint-lg) {\n    &:checked + .sidenote,\n    &:checked + .marginnote {\n      display: block;\n      float: left;\n      left: 1rem;\n      clear: both;\n      width: 95%;\n      margin: 1rem 2.5%;\n      position: relative;\n    }\n  }\n}\n\nspan.sidenote,\nspan.marginnote {\n  z-index: $zindex-middle;\n  position: relative;\n  sup {\n    user-select: none;\n  }\n  @include margin-content();\n  border-left: none;\n  @media (max-width: $breakpoint-lg) {\n    display: none;\n  }\n}\n\naside.sidebar {\n  .note {\n    // styling for notes inside sidebars\n    margin: 1rem;\n    padding: 0rem 0rem 1rem 0rem;\n  }\n  // The titles sticking\n  .admonition-title {\n    margin: 0rem -1rem 0rem 0rem;\n  }\n}\n\n/**\n * Sidebar content in the margin.\n * This will be added by the `margin` directive or the `sidebar` directive\n */\naside.sidebar.margin {\n  // Margin content can have an empty title so this prevents an empty title block\n  // from showing up on mobile\n  .sidebar-title:empty {\n    display: none;\n  }\n\n  // On narrow screens padding is added to all sidebar content\n  // This replaces padding w/ margin for admonitions because padding is used\n  // for the title background color\n  .admonition {\n    margin: 0.5rem;\n    padding-left: 0;\n    padding-right: 0;\n\n    // Remove the title margin so it\n    .admonition-title {\n      margin-left: 0;\n      margin-right: 0;\n    }\n  }\n\n  @media (min-width: $breakpoint-lg) {\n    // No border for margin sidebars on wide screen\n    border: none;\n\n    // Admonitions don't need the extra whitespace in the margin\n    .admonition {\n      margin: 1rem 0rem;\n      padding: 0rem 0rem 1rem 0rem;\n    }\n  }\n}\n\ndiv.margin,\nfigure.margin,\naside.margin,\n.margin.docutils.container,\n.cell.tag_popout,\n.cell.tag_margin {\n  z-index: $zindex-middle;\n  position: relative;\n  @include margin-content();\n\n  // Make cell outputs take up more space if they're in the margin\n  div.cell.tag_margin .cell_output {\n    padding-left: 0;\n  }\n}\n\n// A few permutations because docutils 0.18 switched to semantic tags\ndiv.figure.margin-caption p.caption,\ndiv.figure.margin-caption figcaption,\nfigure.margin-caption figcaption {\n  z-index: $zindex-middle;\n  position: relative;\n  @include margin-content();\n}\n\n// Margin captions\n.margin-caption figcaption {\n  text-align: left;\n}\n\n/**\n * Full width content\n */\n\n// Grow 100% by the ratio of the margin to the content width.\n$content-fullwidth-width: 100% + $margin-width-relative-to-content;\n\ndiv.cell.tag_full-width,\ndiv.cell.tag_full_width,\ndiv.full_width,\ndiv.full-width {\n  z-index: $zindex-middle;\n  position: relative;\n  @media (min-width: $breakpoint-lg) {\n    max-width: $content-fullwidth-width;\n    width: $content-fullwidth-width;\n  }\n}\n","/**\n * Epigraphs and other \"special\" quote blocks\n */\n\nblockquote {\n  &.pull-quote,\n  &.epigraph,\n  &.highlights {\n    font-size: 1.25em;\n    border-left: none;\n    background-color: var(--pst-color-background);\n  }\n\n  div > p + p.attribution {\n    font-style: normal;\n    font-size: 0.9em;\n    text-align: right;\n    color: #6c757d;\n    padding-right: 2em;\n  }\n}\n","// Over-ride Sphinx default so that content fills whitespace w/ margin content\npre,\ndiv[class*=\"highlight-\"] {\n  clear: none;\n}\n","// MyST-NB and Jupyter Notebooks\ndiv.cell.tag_output_scroll div.cell_output,\ndiv.cell.tag_scroll-output div.cell_output {\n  max-height: 24em;\n  overflow-y: auto;\n}\n\ndiv.cell.tag_scroll-input div.cell_input {\n  max-height: 24em;\n  overflow-y: auto;\n}\n","/**\n * Comment JavaScript libraries\n  * Libraries supported here are:\n  * - Hypothes.is : https://web.hypothes.is/\n  * - Utteranc.es : https://utteranc.es/\n  */\n// In general we want to hide any comment libraries on print\n@media only print {\n  hypothesis-sidebar,\n  div.utterances {\n    display: none;\n  }\n}\n","/*********************************************\n* Thebe *\n* ref: https://github.com/executablebooks/thebe\n*********************************************/\n.thebelab-cell {\n  border: none !important;\n  margin-right: 0.5em !important;\n}\n\n.thebelab-cell .thebelab-input {\n  padding-left: 10px !important;\n}\n\n.cell.docutils.container {\n  padding-right: 0px !important;\n}\n\nbutton.thebe-launch-button {\n  height: 2.5em;\n  font-size: 1em;\n}\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/_preview/13/_static/styles/theme.css b/_preview/13/_static/styles/theme.css
new file mode 100644
index 0000000..4519dd9
--- /dev/null
+++ b/_preview/13/_static/styles/theme.css
@@ -0,0 +1,2 @@
+/* Provided by Sphinx's 'basic' theme, and included in the final set of assets */
+@import "../basic.css";
diff --git a/_preview/13/_static/togglebutton.css b/_preview/13/_static/togglebutton.css
new file mode 100644
index 0000000..54a6787
--- /dev/null
+++ b/_preview/13/_static/togglebutton.css
@@ -0,0 +1,160 @@
+/**
+ * Admonition-based toggles
+ */
+
+/* Visibility of the target */
+.admonition.toggle .admonition-title ~ * {
+    transition: opacity .3s, height .3s;
+}
+
+/* Toggle buttons inside admonitions so we see the title */
+.admonition.toggle {
+    position: relative;
+}
+
+/* Titles should cut off earlier to avoid overlapping w/ button */
+.admonition.toggle .admonition-title {
+    padding-right: 25%;
+    cursor: pointer;
+}
+
+/* Hovering will cause a slight shift in color to make it feel interactive */
+.admonition.toggle .admonition-title:hover {
+    box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 1%);
+}
+
+/* Hovering will cause a slight shift in color to make it feel interactive */
+.admonition.toggle .admonition-title:active {
+    box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 3%);
+}
+
+/* Remove extra whitespace below the admonition title when hidden */
+.admonition.toggle-hidden {
+    padding-bottom: 0;
+}
+
+.admonition.toggle-hidden .admonition-title {
+    margin-bottom: 0;
+}
+
+/* hides all the content of a page until de-toggled */
+.admonition.toggle-hidden .admonition-title ~ * {
+    height: 0;
+    margin: 0;
+    opacity: 0;
+    visibility: hidden;
+}
+
+/* General button style and position*/
+button.toggle-button {
+    /**
+     * Background and shape. By default there's no background
+     * but users can style as they wish
+     */  
+    background: none;
+    border: none;
+    outline: none;
+
+    /* Positioning just inside the admonition title */
+    position: absolute;
+    right: 0.5em;
+    padding: 0px;
+    border: none;
+    outline: none;
+}
+
+/* Display the toggle hint on wide screens */
+@media (min-width: 768px) {
+    button.toggle-button.toggle-button-hidden:before {
+        content: attr(data-toggle-hint);  /* This will be filled in by JS */
+        font-size: .8em;
+        align-self: center;
+    }
+}
+
+/* Icon behavior */
+.tb-icon {
+    transition: transform .2s ease-out;
+    height: 1.5em;
+    width: 1.5em;
+    stroke: currentColor;  /* So that we inherit the color of other text */
+}
+
+/* The icon should point right when closed, down when open. */
+/* Open */
+.admonition.toggle button .tb-icon {
+    transform: rotate(90deg);
+}
+
+/* Closed */
+.admonition.toggle button.toggle-button-hidden .tb-icon {
+    transform: rotate(0deg);
+}
+
+/* With details toggles, we don't rotate the icon so it points right */
+details.toggle-details .tb-icon {
+    height: 1.4em;
+    width: 1.4em;
+    margin-top: 0.1em;  /* To center the button vertically */
+}
+
+
+/**
+ * Details-based toggles.
+ * In this case, we wrap elements with `.toggle` in a details block.
+ */
+
+/* Details blocks */
+details.toggle-details {
+    margin: 1em 0;
+}
+
+
+details.toggle-details summary {
+    display: flex;
+    align-items: center;
+    cursor: pointer;
+    list-style: none;
+    border-radius: .2em;
+    border-left: 3px solid #1976d2;
+    background-color: rgb(204 204 204 / 10%);
+    padding: 0.2em 0.7em 0.3em 0.5em; /* Less padding on left because the SVG has left margin */
+    font-size: 0.9em;
+}
+
+details.toggle-details summary:hover {
+    background-color: rgb(204 204 204 / 20%);
+}
+
+details.toggle-details summary:active {
+    background: rgb(204 204 204 / 28%);
+}
+
+.toggle-details__summary-text {
+    margin-left: 0.2em;
+}
+
+details.toggle-details[open] summary {
+    margin-bottom: .5em;
+}
+
+details.toggle-details[open] summary .tb-icon {
+    transform: rotate(90deg);
+}
+
+details.toggle-details[open] summary ~ * {
+    animation: toggle-fade-in .3s ease-out;
+}
+
+@keyframes toggle-fade-in {
+  from {opacity: 0%;}
+  to {opacity: 100%;}
+}
+
+/* Print rules - we hide all toggle button elements at print */
+@media print {
+    /* Always hide the summary so the button doesn't show up */
+    details.toggle-details summary {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/_preview/13/_static/togglebutton.js b/_preview/13/_static/togglebutton.js
new file mode 100644
index 0000000..215a7ee
--- /dev/null
+++ b/_preview/13/_static/togglebutton.js
@@ -0,0 +1,187 @@
+/**
+ * Add Toggle Buttons to elements
+ */
+
+let toggleChevron = `
+<svg xmlns="http://www.w3.org/2000/svg" class="tb-icon toggle-chevron-right" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+<path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+<polyline points="9 6 15 12 9 18" />
+</svg>`;
+
+var initToggleItems = () => {
+  var itemsToToggle = document.querySelectorAll(togglebuttonSelector);
+  console.log(`[togglebutton]: Adding toggle buttons to ${itemsToToggle.length} items`)
+  // Add the button to each admonition and hook up a callback to toggle visibility
+  itemsToToggle.forEach((item, index) => {
+    if (item.classList.contains("admonition")) {
+      // If it's an admonition block, then we'll add a button inside
+      // Generate unique IDs for this item
+      var toggleID = `toggle-${index}`;
+      var buttonID = `button-${toggleID}`;
+
+      item.setAttribute('id', toggleID);
+      if (!item.classList.contains("toggle")){
+        item.classList.add("toggle");
+      }
+      // This is the button that will be added to each item to trigger the toggle
+      var collapseButton = `
+        <button type="button" id="${buttonID}" class="toggle-button" data-target="${toggleID}" data-button="${buttonID}" data-toggle-hint="${toggleHintShow}" aria-label="Toggle hidden content">
+            ${toggleChevron}
+        </button>`;
+
+      title = item.querySelector(".admonition-title")
+      title.insertAdjacentHTML("beforeend", collapseButton);
+      thisButton = document.getElementById(buttonID);
+
+      // Add click handlers for the button + admonition title (if admonition)
+      admonitionTitle = document.querySelector(`#${toggleID} > .admonition-title`)
+      if (admonitionTitle) {
+        // If an admonition, then make the whole title block clickable
+        admonitionTitle.addEventListener('click', toggleClickHandler);
+        admonitionTitle.dataset.target = toggleID
+        admonitionTitle.dataset.button = buttonID
+      } else {
+        // If not an admonition then we'll listen for the button click
+        thisButton.addEventListener('click', toggleClickHandler);
+      }
+
+      // Now hide the item for this toggle button unless explicitly noted to show
+      if (!item.classList.contains("toggle-shown")) {
+        toggleHidden(thisButton);
+      }
+    } else {
+      // If not an admonition, wrap the block in a <details> block
+      // Define the structure of the details block and insert it as a sibling
+      var detailsBlock = `
+        <details class="toggle-details">
+            <summary class="toggle-details__summary">
+              ${toggleChevron}
+              <span class="toggle-details__summary-text">${toggleHintShow}</span>
+            </summary>
+        </details>`;
+      item.insertAdjacentHTML("beforebegin", detailsBlock);
+
+      // Now move the toggle-able content inside of the details block
+      details = item.previousElementSibling
+      details.appendChild(item)
+      item.classList.add("toggle-details__container")
+
+      // Set up a click trigger to change the text as needed
+      details.addEventListener('click', (click) => {
+        let parent = click.target.parentElement;
+        if (parent.tagName.toLowerCase() == "details") {
+          summary = parent.querySelector("summary");
+          details = parent;
+        } else {
+          summary = parent;
+          details = parent.parentElement;
+        }
+        // Update the inner text for the proper hint
+        if (details.open) {
+          summary.querySelector("span.toggle-details__summary-text").innerText = toggleHintShow;
+        } else {
+          summary.querySelector("span.toggle-details__summary-text").innerText = toggleHintHide;
+        }
+        
+      });
+
+      // If we have a toggle-shown class, open details block should be open
+      if (item.classList.contains("toggle-shown")) {
+        details.click();
+      }
+    }
+  })
+};
+
+// This should simply add / remove the collapsed class and change the button text
+var toggleHidden = (button) => {
+  target = button.dataset['target']
+  var itemToToggle = document.getElementById(target);
+  if (itemToToggle.classList.contains("toggle-hidden")) {
+    itemToToggle.classList.remove("toggle-hidden");
+    button.classList.remove("toggle-button-hidden");
+  } else {
+    itemToToggle.classList.add("toggle-hidden");
+    button.classList.add("toggle-button-hidden");
+  }
+}
+
+var toggleClickHandler = (click) => {
+  // Be cause the admonition title is clickable and extends to the whole admonition
+  // We only look for a click event on this title to trigger the toggle.
+
+  if (click.target.classList.contains("admonition-title")) {
+    button = click.target.querySelector(".toggle-button");
+  } else if (click.target.classList.contains("tb-icon")) {
+    // We've clicked the icon and need to search up one parent for the button
+    button = click.target.parentElement;
+  } else if (click.target.tagName == "polyline") {
+    // We've clicked the SVG elements inside the button, need to up 2 layers
+    button = click.target.parentElement.parentElement;
+  } else if (click.target.classList.contains("toggle-button")) {
+    // We've clicked the button itself and so don't need to do anything
+    button = click.target;
+  } else {
+    console.log(`[togglebutton]: Couldn't find button for ${click.target}`)
+  }
+  target = document.getElementById(button.dataset['button']);
+  toggleHidden(target);
+}
+
+// If we want to blanket-add toggle classes to certain cells
+var addToggleToSelector = () => {
+  const selector = "";
+  if (selector.length > 0) {
+    document.querySelectorAll(selector).forEach((item) => {
+      item.classList.add("toggle");
+    })
+  }
+}
+
+// Helper function to run when the DOM is finished
+const sphinxToggleRunWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+sphinxToggleRunWhenDOMLoaded(addToggleToSelector)
+sphinxToggleRunWhenDOMLoaded(initToggleItems)
+
+/** Toggle details blocks to be open when printing */
+if (toggleOpenOnPrint == "true") {
+  window.addEventListener("beforeprint", () => {
+    // Open the details
+    document.querySelectorAll("details.toggle-details").forEach((el) => {
+      el.dataset["togglestatus"] = el.open;
+      el.open = true;
+    });
+  
+    // Open the admonitions
+    document.querySelectorAll(".admonition.toggle.toggle-hidden").forEach((el) => {
+      console.log(el);
+      el.querySelector("button.toggle-button").click();
+      el.dataset["toggle_after_print"] = "true";
+    });
+  });
+  window.addEventListener("afterprint", () => {
+    // Re-close the details that were closed
+    document.querySelectorAll("details.toggle-details").forEach((el) => {
+      el.open = el.dataset["togglestatus"] == "true";
+      delete el.dataset["togglestatus"];
+    });
+  
+    // Re-close the admonition toggle buttons
+    document.querySelectorAll(".admonition.toggle").forEach((el) => {
+      if (el.dataset["toggle_after_print"] == "true") {
+        el.querySelector("button.toggle-button").click();
+        delete el.dataset["toggle_after_print"];
+      }
+    });
+  });
+}
diff --git a/_preview/13/_static/vendor/fontawesome/6.5.2/LICENSE.txt b/_preview/13/_static/vendor/fontawesome/6.5.2/LICENSE.txt
new file mode 100644
index 0000000..e69c5e3
--- /dev/null
+++ b/_preview/13/_static/vendor/fontawesome/6.5.2/LICENSE.txt
@@ -0,0 +1,165 @@
+Fonticons, Inc. (https://fontawesome.com)
+
+--------------------------------------------------------------------------------
+
+Font Awesome Free License
+
+Font Awesome Free is free, open source, and GPL friendly. You can use it for
+commercial projects, open source projects, or really almost whatever you want.
+Full Font Awesome Free license: https://fontawesome.com/license/free.
+
+--------------------------------------------------------------------------------
+
+# Icons: CC BY 4.0 License (https://creativecommons.org/licenses/by/4.0/)
+
+The Font Awesome Free download is licensed under a Creative Commons
+Attribution 4.0 International License and applies to all icons packaged
+as SVG and JS file types.
+
+--------------------------------------------------------------------------------
+
+# Fonts: SIL OFL 1.1 License
+
+In the Font Awesome Free download, the SIL OFL license applies to all icons
+packaged as web and desktop font files.
+
+Copyright (c) 2024 Fonticons, Inc. (https://fontawesome.com)
+with Reserved Font Name: "Font Awesome".
+
+This Font Software is licensed under the SIL Open Font License, Version 1.1.
+This license is copied below, and is also available with a FAQ at:
+http://scripts.sil.org/OFL
+
+SIL OPEN FONT LICENSE
+Version 1.1 - 26 February 2007
+
+PREAMBLE
+The goals of the Open Font License (OFL) are to stimulate worldwide
+development of collaborative font projects, to support the font creation
+efforts of academic and linguistic communities, and to provide a free and
+open framework in which fonts may be shared and improved in partnership
+with others.
+
+The OFL allows the licensed fonts to be used, studied, modified and
+redistributed freely as long as they are not sold by themselves. The
+fonts, including any derivative works, can be bundled, embedded,
+redistributed and/or sold with any software provided that any reserved
+names are not used by derivative works. The fonts and derivatives,
+however, cannot be released under any other type of license. The
+requirement for fonts to remain under this license does not apply
+to any document created using the fonts or their derivatives.
+
+DEFINITIONS
+"Font Software" refers to the set of files released by the Copyright
+Holder(s) under this license and clearly marked as such. This may
+include source files, build scripts and documentation.
+
+"Reserved Font Name" refers to any names specified as such after the
+copyright statement(s).
+
+"Original Version" refers to the collection of Font Software components as
+distributed by the Copyright Holder(s).
+
+"Modified Version" refers to any derivative made by adding to, deleting,
+or substituting — in part or in whole — any of the components of the
+Original Version, by changing formats or by porting the Font Software to a
+new environment.
+
+"Author" refers to any designer, engineer, programmer, technical
+writer or other person who contributed to the Font Software.
+
+PERMISSION & CONDITIONS
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of the Font Software, to use, study, copy, merge, embed, modify,
+redistribute, and sell modified and unmodified copies of the Font
+Software, subject to the following conditions:
+
+1) Neither the Font Software nor any of its individual components,
+in Original or Modified Versions, may be sold by itself.
+
+2) Original or Modified Versions of the Font Software may be bundled,
+redistributed and/or sold with any software, provided that each copy
+contains the above copyright notice and this license. These can be
+included either as stand-alone text files, human-readable headers or
+in the appropriate machine-readable metadata fields within text or
+binary files as long as those fields can be easily viewed by the user.
+
+3) No Modified Version of the Font Software may use the Reserved Font
+Name(s) unless explicit written permission is granted by the corresponding
+Copyright Holder. This restriction only applies to the primary font name as
+presented to the users.
+
+4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font
+Software shall not be used to promote, endorse or advertise any
+Modified Version, except to acknowledge the contribution(s) of the
+Copyright Holder(s) and the Author(s) or with their explicit written
+permission.
+
+5) The Font Software, modified or unmodified, in part or in whole,
+must be distributed entirely under this license, and must not be
+distributed under any other license. The requirement for fonts to
+remain under this license does not apply to any document created
+using the Font Software.
+
+TERMINATION
+This license becomes null and void if any of the above conditions are
+not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
+OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE
+COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL
+DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM
+OTHER DEALINGS IN THE FONT SOFTWARE.
+
+--------------------------------------------------------------------------------
+
+# Code: MIT License (https://opensource.org/licenses/MIT)
+
+In the Font Awesome Free download, the MIT license applies to all non-font and
+non-icon files.
+
+Copyright 2024 Fonticons, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in the
+Software without restriction, including without limitation the rights to use, copy,
+modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
+and to permit persons to whom the Software is furnished to do so, subject to the
+following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+--------------------------------------------------------------------------------
+
+# Attribution
+
+Attribution is required by MIT, SIL OFL, and CC BY licenses. Downloaded Font
+Awesome Free files already contain embedded comments with sufficient
+attribution, so you shouldn't need to do anything additional when using these
+files normally.
+
+We've kept attribution comments terse, so we ask that you do not actively work
+to remove them from files, especially code. They're a great way for folks to
+learn about Font Awesome.
+
+--------------------------------------------------------------------------------
+
+# Brand Icons
+
+All brand icons are trademarks of their respective owners. The use of these
+trademarks does not indicate endorsement of the trademark holder by Font
+Awesome, nor vice versa. **Please do not use brand logos for any purpose except
+to represent the company, product, or service to which they refer.**
diff --git a/_preview/13/_static/vendor/fontawesome/6.5.2/css/all.min.css b/_preview/13/_static/vendor/fontawesome/6.5.2/css/all.min.css
new file mode 100644
index 0000000..628b2cc
--- /dev/null
+++ b/_preview/13/_static/vendor/fontawesome/6.5.2/css/all.min.css
@@ -0,0 +1,5 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:Font Awesome\ 6 Free}.fa-brands,.fab{font-family:Font Awesome\ 6 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+ * Copyright 2024 Fonticons, Inc.
+ */
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new file mode 100644
index 0000000000000000000000000000000000000000..f94bec22756c48039ad405859f0d0e542033c1b2
GIT binary patch
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diff --git a/_preview/13/_static/webpack-macros.html b/_preview/13/_static/webpack-macros.html
new file mode 100644
index 0000000..c051fa9
--- /dev/null
+++ b/_preview/13/_static/webpack-macros.html
@@ -0,0 +1,31 @@
+<!--
+  AUTO-GENERATED from webpack.config.js, do **NOT** edit by hand.
+  These are re-used in layout.html
+-->
+{# Load FontAwesome icons #}
+{% macro head_pre_icons() %}
+  <link href="{{ pathto('_static/vendor/fontawesome/6.5.2/css/all.min.css', 1) }}?digest=3ee479438cf8b5e0d341" rel="stylesheet" />
+  <link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.5.2/webfonts/fa-solid-900.woff2', 1) }}" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.5.2/webfonts/fa-brands-400.woff2', 1) }}" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.5.2/webfonts/fa-regular-400.woff2', 1) }}" />
+{% endmacro %}
+
+{% macro head_pre_assets() %}
+  <!-- Loaded before other Sphinx assets -->
+  <link href="{{ pathto('_static/styles/theme.css', 1) }}?digest=3ee479438cf8b5e0d341" rel="stylesheet" />
+<link href="{{ pathto('_static/styles/bootstrap.css', 1) }}?digest=3ee479438cf8b5e0d341" rel="stylesheet" />
+<link href="{{ pathto('_static/styles/pydata-sphinx-theme.css', 1) }}?digest=3ee479438cf8b5e0d341" rel="stylesheet" />
+{% endmacro %}
+
+{% macro head_js_preload() %}
+  <!-- Pre-loaded scripts that we'll load fully later -->
+  <link rel="preload" as="script" href="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=3ee479438cf8b5e0d341" />
+<link rel="preload" as="script" href="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=3ee479438cf8b5e0d341" />
+  <script src="{{ pathto('_static/vendor/fontawesome/6.5.2/js/all.min.js', 1) }}?digest=3ee479438cf8b5e0d341"></script>
+{% endmacro %}
+
+{% macro body_post() %}
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
+  <script src="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=3ee479438cf8b5e0d341"></script>
+<script src="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=3ee479438cf8b5e0d341"></script>
+{% endmacro %}
\ No newline at end of file
diff --git a/_preview/13/genindex.html b/_preview/13/genindex.html
new file mode 100644
index 0000000..29e0898
--- /dev/null
+++ b/_preview/13/genindex.html
@@ -0,0 +1,677 @@
+
+<!DOCTYPE html>
+
+
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+
+  <head>
+    <meta charset="utf-8" />
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+<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.5.2/webfonts/fa-regular-400.woff2" />
+
+    <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
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+  
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+    <script>const THEBE_JS_URL = "https://unpkg.com/thebe@0.8.2/lib/index.js"; const thebe_selector = ".thebe,.cell"; const thebe_selector_input = "pre"; const thebe_selector_output = ".output, .cell_output"</script>
+    <script async="async" src="_static/sphinx-thebe.js?v=c100c467"></script>
+    <script>var togglebuttonSelector = '.toggle, .admonition.dropdown';</script>
+    <script>
+                window.dataLayer = window.dataLayer || [];
+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-T52X8HNYE8');
+            </script>
+    <script>const THEBE_JS_URL = "https://unpkg.com/thebe@0.8.2/lib/index.js"; const thebe_selector = ".thebe,.cell"; const thebe_selector_input = "pre"; const thebe_selector_output = ".output, .cell_output"</script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'genindex';</script>
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+  
+  
+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
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+            <span class="sr-only">MetPy Mondays</span></a>
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+</form></div>
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+<ul class="nav bd-sidenav">
+<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/synoptic/index.html">Synoptic Analysis</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/250hPa_Hemispheric_Plot.html">A 250-hPa Hemispheric Map using Python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/500hPa_Absolute_Vorticity_winds.html">500 hPa Geopotential Heights, Absolute Vorticity, and Winds</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/500hPa_Vorticity_Advection.html">500 hPa Vorticity Advection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/850hPa_Frontogenesis.html">850-hPa Geopotential Heights, Temperature, Frontogenesis, and Winds</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/850hPa_QVectors.html">QVector Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/850hPa_Temperature_Advection.html">850 hPa Temperature Advection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/Ageostrophic_Wind_Example.html">Geostrophic and Ageostrophic Wind</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/Analytic_300hPa_Trough.html">Analytic 300-hPa Trough</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/Differential_Temperature_Advection.html">Differential Temperature Advection with NARR Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/Isentropic_Interpolation.html">Isentropic Analysis</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/PV_baroclinic_isobaric.html">Baroclinic Potential Vorticity Analysis, Divergence, and Winds</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/Upperair_Obs.html">DIFAX Replication</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/Wind_Shear_Vectors_Example.html">Calculating Wind Shear</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/synoptic/xarray_500hPa_map.html">Using Xarray for Data read and selection</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/skewt/index.html">Skew-T Analysis</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/convective/index.html">Convective Calculations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/convective/miller_composite.html">Miller Composite Chart</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/specialty/index.html">Specialty Plotting</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/specialty/Hovmoller_Diagram.html">Hovmoller Diagram Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/specialty/Observational_Data_Cross_Section.html">Real Data Cross-Section Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/specialty/Smoothing_Contours.html">Smoothing Contours</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/declarative/index.html">Simplified Plotting</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
+</ul>
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+                    
+  <div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">## importing all the packages we might need later for our calculation</span>
+<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">metpy.calc</span> <span class="kn">import</span> <span class="n">cape_cin</span><span class="p">,</span> <span class="n">surface_based_cape_cin</span><span class="p">,</span> <span class="n">dewpoint_from_specific_humidity</span><span class="p">,</span> <span class="n">parcel_profile</span><span class="p">,</span><span class="n">relative_humidity_from_specific_humidity</span><span class="p">,</span><span class="n">most_unstable_cape_cin</span><span class="p">,</span> <span class="n">precipitable_water</span> 
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</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">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">## opening NetCDF file using xarray </span>
+
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_mfdataset</span><span class="p">(</span><span class="s2">&quot;/home/jovyan/metpy-cookbook/NETCDF_FILE.nc&quot;</span><span class="p">,</span><span class="n">combine</span> <span class="o">=</span> <span class="s2">&quot;nested&quot;</span><span class="p">,</span> <span class="n">concat_dim</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">,</span><span class="n">decode_times</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">OSError</span><span class="g g-Whitespace">                                   </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">line</span> <span class="mi">3</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1">## opening NetCDF file using xarray </span>
+<span class="ne">----&gt; </span><span class="mi">3</span> <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_mfdataset</span><span class="p">(</span><span class="s2">&quot;/home/jovyan/metpy-cookbook/NETCDF_FILE.nc&quot;</span><span class="p">,</span><span class="n">combine</span> <span class="o">=</span> <span class="s2">&quot;nested&quot;</span><span class="p">,</span> <span class="n">concat_dim</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">,</span><span class="n">decode_times</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/xarray/backends/api.py:1019,</span> in <span class="ni">open_mfdataset</span><span class="nt">(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1016</span> <span class="n">paths</span> <span class="o">=</span> <span class="n">_find_absolute_paths</span><span class="p">(</span><span class="n">paths</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="n">engine</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1018</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">paths</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">1019</span>     <span class="k">raise</span> <span class="ne">OSError</span><span class="p">(</span><span class="s2">&quot;no files to open&quot;</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1021</span> <span class="k">if</span> <span class="n">combine</span> <span class="o">==</span> <span class="s2">&quot;nested&quot;</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1022</span>     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">concat_dim</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">))</span> <span class="ow">or</span> <span class="n">concat_dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+
+<span class="ne">OSError</span>: no files to open
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">ds</span>
+
+<span class="ne">NameError</span>: name &#39;ds&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#### making a function to slice the xarray dataset according to our need.</span>
+<span class="k">def</span> <span class="nf">slicer</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="n">lat1</span><span class="p">,</span> <span class="n">lat2</span><span class="p">,</span> <span class="n">lon1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">time1</span><span class="p">,</span><span class="n">time2</span><span class="p">)</span> <span class="p">:</span>
+    <span class="n">sliced_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lat</span> <span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="n">lat1</span><span class="p">,</span> <span class="n">lat2</span><span class="p">),</span> <span class="n">lon</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">lon1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">),</span><span class="n">time</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">time1</span><span class="p">,</span> <span class="n">time2</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">sliced_data</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#slicing the data for CONUS only</span>
+
+<span class="n">new_data</span> <span class="o">=</span> <span class="n">slicer</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span><span class="mf">23.5</span><span class="p">,</span><span class="mf">50.5</span><span class="p">,</span><span class="o">-</span><span class="mf">125.5</span><span class="p">,</span><span class="o">-</span><span class="mf">66.5</span><span class="p">,</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">line</span> <span class="mi">3</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1">#slicing the data for CONUS only</span>
+<span class="ne">----&gt; </span><span class="mi">3</span> <span class="n">new_data</span> <span class="o">=</span> <span class="n">slicer</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span><span class="mf">23.5</span><span class="p">,</span><span class="mf">50.5</span><span class="p">,</span><span class="o">-</span><span class="mf">125.5</span><span class="p">,</span><span class="o">-</span><span class="mf">66.5</span><span class="p">,</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+<span class="ne">NameError</span>: name &#39;ds&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">new_data</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">new_data</span>
+
+<span class="ne">NameError</span>: name &#39;new_data&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">##extracting temperature, pressure  and specific humidity from the dataset</span>
+<span class="n">p</span> <span class="o">=</span><span class="n">new_data</span><span class="o">.</span><span class="n">lev</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span>
+<span class="n">T</span> <span class="o">=</span> <span class="n">new_data</span><span class="o">.</span><span class="n">T</span>
+<span class="n">sh</span> <span class="o">=</span>  <span class="n">new_data</span><span class="o">.</span><span class="n">QV</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;dimensionless&#39;</span><span class="p">)</span>
+
+
+
+
+<span class="n">CAPE</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="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">60</span><span class="p">),</span><span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+
+
+<span class="c1">###calculating CAPE in multidimension</span>
+
+<span class="k">for</span> <span class="n">h</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">new_data</span><span class="o">.</span><span class="n">time</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="nb">len</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lat</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="nb">len</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lon</span><span class="p">)):</span>
+
+
+            <span class="n">TC</span> <span class="o">=</span> <span class="p">(</span><span class="n">T</span><span class="p">[</span><span class="n">h</span><span class="p">,:,</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">-</span><span class="p">(</span><span class="mf">273.15</span><span class="p">))</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
+            <span class="n">Td</span> <span class="o">=</span> <span class="n">dewpoint_from_specific_humidity</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">p</span><span class="p">)</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">),</span><span class="n">TC</span><span class="p">,</span> <span class="n">sh</span><span class="p">[</span><span class="n">h</span><span class="p">,:,</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">])</span>
+<span class="c1">#      </span>
+            <span class="k">try</span><span class="p">:</span> 
+                <span class="n">MUCAPE</span> <span class="o">=</span> <span class="n">most_unstable_cape_cin</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">p</span><span class="p">)</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</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">TC</span><span class="p">)</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</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">Td</span><span class="p">)</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">))</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">pass</span>
+            <span class="n">CAPE</span><span class="p">[</span><span class="n">h</span><span class="p">,</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">MUCAPE</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">magnitude</span>
+          
+        
+            
+   
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="n">line</span> <span class="mi">2</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1">##extracting temperature, pressure  and specific humidity from the dataset</span>
+<span class="ne">----&gt; </span><span class="mi">2</span> <span class="n">p</span> <span class="o">=</span><span class="n">new_data</span><span class="o">.</span><span class="n">lev</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span>
+<span class="g g-Whitespace">      </span><span class="mi">3</span> <span class="n">T</span> <span class="o">=</span> <span class="n">new_data</span><span class="o">.</span><span class="n">T</span>
+<span class="g g-Whitespace">      </span><span class="mi">4</span> <span class="n">sh</span> <span class="o">=</span>  <span class="n">new_data</span><span class="o">.</span><span class="n">QV</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;dimensionless&#39;</span><span class="p">)</span>
+
+<span class="ne">NameError</span>: name &#39;new_data&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataproj</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span> <span class="n">PlateCarree</span> <span class="p">()</span>
+<span class="c1"># # Plot projection</span>
+<span class="c1"># # The look you want for the view.</span>
+<span class="n">plotproj</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span> <span class="n">PlateCarree</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">15.</span><span class="p">,</span><span class="mf">12.</span><span class="p">))</span>
+
+<span class="n">ax</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span><span class="n">projection</span><span class="o">=</span><span class="n">plotproj</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</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;MUCAPE Calculated Using Metpy (J/Kg)&quot;</span><span class="p">,</span><span class="n">size</span> <span class="o">=</span> <span class="mi">30</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">contourf</span> <span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">new_data</span><span class="o">.</span> <span class="n">lat</span><span class="p">,</span> <span class="n">CAPE</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,:],</span><span class="n">levels</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="mi">2400</span><span class="p">,</span><span class="mi">100</span><span class="p">),</span><span class="n">cmap</span> <span class="o">=</span> <span class="s2">&quot;PuBuGn&quot;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">dataproj</span><span class="p">,</span><span class="n">extend</span> <span class="o">=</span> <span class="s2">&quot;max&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span> <span class="p">(</span><span class="n">orientation</span> <span class="o">=</span> <span class="s2">&quot;horizontal&quot;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">labelsize</span><span class="o">=</span><span class="mi">20</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>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="n">line</span> <span class="mi">12</span>
+<span class="g g-Whitespace">     </span><span class="mi">10</span> <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">11</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;MUCAPE Calculated Using Metpy (J/Kg)&quot;</span><span class="p">,</span><span class="n">size</span> <span class="o">=</span> <span class="mi">30</span><span class="p">)</span>
+<span class="ne">---&gt; </span><span class="mi">12</span> <span class="n">plt</span><span class="o">.</span><span class="n">contourf</span> <span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">new_data</span><span class="o">.</span> <span class="n">lat</span><span class="p">,</span> <span class="n">CAPE</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,:],</span><span class="n">levels</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="mi">2400</span><span class="p">,</span><span class="mi">100</span><span class="p">),</span><span class="n">cmap</span> <span class="o">=</span> <span class="s2">&quot;PuBuGn&quot;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">dataproj</span><span class="p">,</span><span class="n">extend</span> <span class="o">=</span> <span class="s2">&quot;max&quot;</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">13</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span> <span class="p">(</span><span class="n">orientation</span> <span class="o">=</span> <span class="s2">&quot;horizontal&quot;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">labelsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">14</span> <span class="n">plt</span><span class="o">.</span> <span class="n">show</span> <span class="p">()</span>
+
+<span class="ne">NameError</span>: name &#39;new_data&#39; is not defined
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip
+  warnings.warn(f&#39;Downloading: {url}&#39;, DownloadWarning)
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_1_states_provinces_lakes.zip
+  warnings.warn(f&#39;Downloading: {url}&#39;, DownloadWarning)
+</pre></div>
+</div>
+<img alt="../../_images/21c237d3fd230e98f672b9e348752d38f83911a499c7913e454f891a8c90d984.png" src="../../_images/21c237d3fd230e98f672b9e348752d38f83911a499c7913e454f891a8c90d984.png" />
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+  <div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">metpy.calc</span> <span class="kn">import</span> <span class="n">cape_cin</span><span class="p">,</span> <span class="n">surface_based_cape_cin</span><span class="p">,</span> <span class="n">dewpoint_from_specific_humidity</span><span class="p">,</span> <span class="n">parcel_profile</span><span class="p">,</span><span class="n">relative_humidity_from_specific_humidity</span><span class="p">,</span><span class="n">most_unstable_cape_cin</span><span class="p">,</span> <span class="n">precipitable_water</span> 
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</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">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">## opening NetCDF file using xarray </span>
+
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_mfdataset</span><span class="p">(</span><span class="s2">&quot;/home/jovyan/metpy-cookbook/NETCDF_FILE.nc&quot;</span><span class="p">,</span><span class="n">combine</span> <span class="o">=</span> <span class="s2">&quot;nested&quot;</span><span class="p">,</span> <span class="n">concat_dim</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">,</span><span class="n">decode_times</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">OSError</span><span class="g g-Whitespace">                                   </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">line</span> <span class="mi">3</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1">## opening NetCDF file using xarray </span>
+<span class="ne">----&gt; </span><span class="mi">3</span> <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_mfdataset</span><span class="p">(</span><span class="s2">&quot;/home/jovyan/metpy-cookbook/NETCDF_FILE.nc&quot;</span><span class="p">,</span><span class="n">combine</span> <span class="o">=</span> <span class="s2">&quot;nested&quot;</span><span class="p">,</span> <span class="n">concat_dim</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">,</span><span class="n">decode_times</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/xarray/backends/api.py:1019,</span> in <span class="ni">open_mfdataset</span><span class="nt">(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1016</span> <span class="n">paths</span> <span class="o">=</span> <span class="n">_find_absolute_paths</span><span class="p">(</span><span class="n">paths</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="n">engine</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1018</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">paths</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">1019</span>     <span class="k">raise</span> <span class="ne">OSError</span><span class="p">(</span><span class="s2">&quot;no files to open&quot;</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1021</span> <span class="k">if</span> <span class="n">combine</span> <span class="o">==</span> <span class="s2">&quot;nested&quot;</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1022</span>     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">concat_dim</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">))</span> <span class="ow">or</span> <span class="n">concat_dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+
+<span class="ne">OSError</span>: no files to open
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">ds</span>
+
+<span class="ne">NameError</span>: name &#39;ds&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#### making a function to slice the xarray dataset according to our need.</span>
+<span class="k">def</span> <span class="nf">slicer</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="n">lat1</span><span class="p">,</span> <span class="n">lat2</span><span class="p">,</span> <span class="n">lon1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">time1</span><span class="p">,</span><span class="n">time2</span><span class="p">)</span> <span class="p">:</span>
+    <span class="n">sliced_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lat</span> <span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="n">lat1</span><span class="p">,</span> <span class="n">lat2</span><span class="p">),</span> <span class="n">lon</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">lon1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">),</span><span class="n">time</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">time1</span><span class="p">,</span> <span class="n">time2</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">sliced_data</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#slicing the data for CONUS only</span>
+
+<span class="n">new_data</span> <span class="o">=</span> <span class="n">slicer</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span><span class="mf">23.5</span><span class="p">,</span><span class="mf">50.5</span><span class="p">,</span><span class="o">-</span><span class="mf">125.5</span><span class="p">,</span><span class="o">-</span><span class="mf">66.5</span><span class="p">,</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">line</span> <span class="mi">3</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1">#slicing the data for CONUS only</span>
+<span class="ne">----&gt; </span><span class="mi">3</span> <span class="n">new_data</span> <span class="o">=</span> <span class="n">slicer</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span><span class="mf">23.5</span><span class="p">,</span><span class="mf">50.5</span><span class="p">,</span><span class="o">-</span><span class="mf">125.5</span><span class="p">,</span><span class="o">-</span><span class="mf">66.5</span><span class="p">,</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+<span class="ne">NameError</span>: name &#39;ds&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">new_data</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">new_data</span>
+
+<span class="ne">NameError</span>: name &#39;new_data&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">##extracting temperature, pressure  and specific humidity from the dataset</span>
+<span class="n">p</span> <span class="o">=</span><span class="n">new_data</span><span class="o">.</span><span class="n">lev</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span>
+<span class="n">T</span> <span class="o">=</span> <span class="n">new_data</span><span class="o">.</span><span class="n">T</span>
+<span class="n">sh</span> <span class="o">=</span>  <span class="n">new_data</span><span class="o">.</span><span class="n">QV</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;dimensionless&#39;</span><span class="p">)</span>
+
+
+<span class="n">TCPW</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="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">60</span><span class="p">),</span><span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+
+<span class="c1">###calculating TCPW in multidimension</span>
+
+<span class="k">for</span> <span class="n">h</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">new_data</span><span class="o">.</span><span class="n">time</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="nb">len</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lat</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="nb">len</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lon</span><span class="p">)):</span>
+
+
+            <span class="n">TC</span> <span class="o">=</span> <span class="p">(</span><span class="n">T</span><span class="p">[</span><span class="n">h</span><span class="p">,:,</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">-</span><span class="p">(</span><span class="mf">273.15</span><span class="p">))</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
+            <span class="n">Td</span> <span class="o">=</span> <span class="n">dewpoint_from_specific_humidity</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">p</span><span class="p">)</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">),</span><span class="n">TC</span><span class="p">,</span> <span class="n">sh</span><span class="p">[</span><span class="n">h</span><span class="p">,:,</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">])</span>
+
+            <span class="n">TPW_S</span> <span class="o">=</span> <span class="n">precipitable_water</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">p</span><span class="p">)</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</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">Td</span><span class="p">)</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">))</span>
+            <span class="n">TCPW</span><span class="p">[</span><span class="n">h</span><span class="p">,</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">TPW_S</span><span class="o">.</span><span class="n">magnitude</span>
+                
+         
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="n">line</span> <span class="mi">2</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1">##extracting temperature, pressure  and specific humidity from the dataset</span>
+<span class="ne">----&gt; </span><span class="mi">2</span> <span class="n">p</span> <span class="o">=</span><span class="n">new_data</span><span class="o">.</span><span class="n">lev</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span>
+<span class="g g-Whitespace">      </span><span class="mi">3</span> <span class="n">T</span> <span class="o">=</span> <span class="n">new_data</span><span class="o">.</span><span class="n">T</span>
+<span class="g g-Whitespace">      </span><span class="mi">4</span> <span class="n">sh</span> <span class="o">=</span>  <span class="n">new_data</span><span class="o">.</span><span class="n">QV</span><span class="o">*</span><span class="n">units</span><span class="p">(</span><span class="s1">&#39;dimensionless&#39;</span><span class="p">)</span>
+
+<span class="ne">NameError</span>: name &#39;new_data&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">TCPW</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">TCPW</span>
+
+<span class="ne">NameError</span>: name &#39;TCPW&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataproj</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span> <span class="n">PlateCarree</span> <span class="p">()</span>
+<span class="c1"># # Plot projection</span>
+<span class="c1"># # The look you want for the view.</span>
+<span class="n">plotproj</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span> <span class="n">PlateCarree</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">15.</span><span class="p">,</span><span class="mf">12.</span><span class="p">))</span>
+
+<span class="n">ax</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span><span class="n">projection</span><span class="o">=</span><span class="n">plotproj</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</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;Total Column Precipitable Water Calculated Using Metpy (mm)&quot;</span><span class="p">,</span><span class="n">size</span> <span class="o">=</span> <span class="mi">30</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">contourf</span> <span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">new_data</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">TCPW</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,:],</span><span class="n">levels</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="mi">70</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span><span class="n">cmap</span> <span class="o">=</span> <span class="s2">&quot;PuBuGn&quot;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">dataproj</span><span class="p">,</span><span class="n">extend</span> <span class="o">=</span> <span class="s2">&quot;max&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span> <span class="p">(</span><span class="n">orientation</span> <span class="o">=</span> <span class="s2">&quot;horizontal&quot;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">labelsize</span><span class="o">=</span><span class="mi">20</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>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">9</span><span class="p">],</span> <span class="n">line</span> <span class="mi">12</span>
+<span class="g g-Whitespace">     </span><span class="mi">10</span> <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">11</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Total Column Precipitable Water Calculated Using Metpy (mm)&quot;</span><span class="p">,</span><span class="n">size</span> <span class="o">=</span> <span class="mi">30</span><span class="p">)</span>
+<span class="ne">---&gt; </span><span class="mi">12</span> <span class="n">plt</span><span class="o">.</span><span class="n">contourf</span> <span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">new_data</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">TCPW</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,:],</span><span class="n">levels</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="mi">70</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span><span class="n">cmap</span> <span class="o">=</span> <span class="s2">&quot;PuBuGn&quot;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">dataproj</span><span class="p">,</span><span class="n">extend</span> <span class="o">=</span> <span class="s2">&quot;max&quot;</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">13</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span> <span class="p">(</span><span class="n">orientation</span> <span class="o">=</span> <span class="s2">&quot;horizontal&quot;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">labelsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">14</span> <span class="n">plt</span><span class="o">.</span> <span class="n">show</span> <span class="p">()</span>
+
+<span class="ne">NameError</span>: name &#39;new_data&#39; is not defined
+</pre></div>
+</div>
+<img alt="../../_images/f92864b66089223dda94d125544a5a870b4a2b1213eca77387aac4165785b728.png" src="../../_images/f92864b66089223dda94d125544a5a870b4a2b1213eca77387aac4165785b728.png" />
+</div>
+</div>
+
+    <script type="text/x-thebe-config">
+    {
+        requestKernel: true,
+        binderOptions: {
+            repo: "binder-examples/jupyter-stacks-datascience",
+            ref: "master",
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+  <section class="tex2jax_ignore mathjax_ignore" id="miller-composite-chart">
+<h1>Miller Composite Chart<a class="headerlink" href="#miller-composite-chart" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>Create a Miller Composite chart based on Miller 1972 in Python with MetPy and
+Matplotlib.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
+
+<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
+<span class="kn">import</span> <span class="nn">matplotlib.lines</span> <span class="k">as</span> <span class="nn">lines</span>
+<span class="kn">import</span> <span class="nn">matplotlib.patches</span> <span class="k">as</span> <span class="nn">mpatches</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</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">numpy.ma</span> <span class="k">as</span> <span class="nn">ma</span>
+
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
+<span class="kn">from</span> <span class="nn">netCDF4</span> <span class="kn">import</span> <span class="n">num2date</span>
+<span class="kn">from</span> <span class="nn">scipy.ndimage</span> <span class="kn">import</span> <span class="n">gaussian_filter</span>
+<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
+<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Get the data</strong></p>
+<p>This example will use data from the North American Mesoscale Model Analysis for 18 UTC 27 April 2011.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Specify our date/time of product desired</span>
+<span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2011</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
+
+<span class="c1"># Construct the URL for our THREDDS Data Server Catalog,</span>
+<span class="c1"># and access our desired dataset within via NCSS</span>
+<span class="n">base_url</span> <span class="o">=</span> <span class="s1">&#39;https://www.ncei.noaa.gov/thredds/model-namanl-old/&#39;</span>
+<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">base_url</span><span class="si">}{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">/catalog.xml&#39;</span><span class="p">)</span>
+<span class="n">ncss</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;namanl_218_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">00_000.grb&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
+
+<span class="c1"># Create our NCSS query with desired specifications</span>
+<span class="n">query</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
+<span class="n">query</span><span class="o">.</span><span class="n">all_times</span><span class="p">()</span>
+<span class="n">query</span><span class="o">.</span><span class="n">add_lonlat</span><span class="p">()</span>
+<span class="n">query</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="o">-</span><span class="mi">135</span><span class="p">,</span> <span class="o">-</span><span class="mi">60</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">65</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;Temperature_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;Relative_humidity_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;Best_4_layer_lifted_index_layer_between_two_pressure_&#39;</span>
+                <span class="s1">&#39;difference_from_ground_layer&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;Absolute_vorticity_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;Pressure_reduced_to_MSL_msl&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;Dew_point_temperature_height_above_ground&#39;</span><span class="p">)</span>
+
+<span class="c1"># Obtain the data we&#39;ve queried for</span>
+<span class="n">data_18z</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+
+<span class="c1"># Make into an xarray Dataset object</span>
+<span class="n">ds_18z</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data_18z</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+
+<span class="c1"># Assign variable names to collected data</span>
+<span class="n">lat</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">lat</span>
+<span class="n">lon</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">lon</span>
+
+<span class="c1"># Create more useable times for output</span>
+<span class="n">times</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">vtimes</span> <span class="o">=</span> <span class="n">times</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+
+<span class="n">temps</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">uwnd</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">vwnd</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">hgt</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">relh</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Relative_humidity_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">lifted_index</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="p">[</span><span class="s1">&#39;Best_4_layer_lifted_index_layer_between_two_&#39;</span>
+                      <span class="s1">&#39;pressure_difference_from_ground_layer&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">Td_sfc</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Dew_point_temperature_height_above_ground</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">avor</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Absolute_vorticity_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">pmsl</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Pressure_reduced_to_MSL_msl</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Repeat the above process to query for the analysis from 12 hours earlier (06 UTC) to calculate pressure falls and height change.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">td</span> <span class="o">=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
+
+<span class="n">ncss_06z</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;namanl_218_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="o">-</span><span class="n">td</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">00_000.grb&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
+
+<span class="n">query</span> <span class="o">=</span> <span class="n">ncss_06z</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
+<span class="n">query</span><span class="o">.</span><span class="n">all_times</span><span class="p">()</span>
+<span class="n">query</span><span class="o">.</span><span class="n">add_lonlat</span><span class="p">()</span>
+<span class="n">query</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="o">-</span><span class="mi">135</span><span class="p">,</span> <span class="o">-</span><span class="mi">60</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">65</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;Pressure_reduced_to_MSL_msl&#39;</span><span class="p">)</span>
+
+<span class="c1"># Actually getting the data</span>
+<span class="n">data_06z</span> <span class="o">=</span> <span class="n">ncss_06z</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+
+<span class="c1"># Make into an xarray Dataset object</span>
+<span class="n">ds_06z</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data_06z</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+
+<span class="n">hgt_06z</span> <span class="o">=</span> <span class="n">ds_06z</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">pmsl_06z</span> <span class="o">=</span> <span class="n">ds_06z</span><span class="o">.</span><span class="n">Pressure_reduced_to_MSL_msl</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Subset the Data</strong></p>
+<p>With the data pulled in, we will now subset to the specific levels desired</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># 300 hPa</span>
+<span class="n">u_300</span> <span class="o">=</span> <span class="n">uwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">300</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">v_300</span> <span class="o">=</span> <span class="n">vwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">300</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+<span class="c1"># 500 hPa</span>
+<span class="n">avor_500</span> <span class="o">=</span> <span class="n">avor</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
+<span class="n">u_500</span> <span class="o">=</span> <span class="n">uwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">v_500</span> <span class="o">=</span> <span class="n">vwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">hgt_500</span> <span class="o">=</span> <span class="n">hgt</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">hgt_500_06z</span> <span class="o">=</span> <span class="n">hgt_06z</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+<span class="c1"># 700 hPa</span>
+<span class="n">tmp_700</span> <span class="o">=</span> <span class="n">temps</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">700</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">rh_700</span> <span class="o">=</span> <span class="n">relh</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">700</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+<span class="c1"># 850 hPa</span>
+<span class="n">u_850</span> <span class="o">=</span> <span class="n">uwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">850</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">v_850</span> <span class="o">=</span> <span class="n">vwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">850</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Prepare Variables for Plotting</strong></p>
+<p>With the data queried and subset, we will make any needed calculations in preparation for
+plotting.</p>
+<p><strong>The following fields should be plotted:</strong></p>
+<p>500-hPa cyclonic vorticity advection</p>
+<p>Surface-based Lifted Index</p>
+<p>The axis of the 300-hPa, 500-hPa, and 850-hPa jets</p>
+<p>Surface dewpoint</p>
+<p>700-hPa dewpoint depression</p>
+<p>12-hr surface pressure falls and 500-hPa height changes</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># 500 hPa CVA</span>
+<span class="n">vort_adv_500</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">advection</span><span class="p">(</span><span class="n">avor_500</span><span class="p">,</span> <span class="n">u_500</span><span class="p">,</span> <span class="n">v_500</span><span class="p">,)</span> <span class="o">*</span> <span class="mf">1e9</span>
+<span class="n">vort_adv_500_smooth</span> <span class="o">=</span> <span class="n">gaussian_filter</span><span class="p">(</span><span class="n">vort_adv_500</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_2465/1703524492.py:2: UserWarning: Vertical dimension number not found. Defaulting to (..., Z, Y, X) order.
+  vort_adv_500 = mpcalc.advection(avor_500, u_500, v_500,) * 1e9
+/tmp/ipykernel_2465/1703524492.py:2: UserWarning: Latitude and longitude computed on-demand, which may be an expensive operation. To avoid repeating this computation, assign these coordinates ahead of time with .metpy.assign_latitude_longitude().
+  vort_adv_500 = mpcalc.advection(avor_500, u_500, v_500,) * 1e9
+</pre></div>
+</div>
+</div>
+</div>
+<p>For the jet axes, we will calculate the windspeed at each level, and plot the highest values</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">wspd_300</span> <span class="o">=</span> <span class="n">gaussian_filter</span><span class="p">(</span><span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">u_300</span><span class="p">,</span> <span class="n">v_300</span><span class="p">),</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">wspd_500</span> <span class="o">=</span> <span class="n">gaussian_filter</span><span class="p">(</span><span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">u_500</span><span class="p">,</span> <span class="n">v_500</span><span class="p">),</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">wspd_850</span> <span class="o">=</span> <span class="n">gaussian_filter</span><span class="p">(</span><span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">u_850</span><span class="p">,</span> <span class="n">v_850</span><span class="p">),</span> <span class="mi">5</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>700-hPa dewpoint depression will be calculated from Temperature_isobaric and RH</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Td_dep_700</span> <span class="o">=</span> <span class="n">tmp_700</span> <span class="o">-</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">dewpoint_from_relative_humidity</span><span class="p">(</span><span class="n">tmp_700</span><span class="p">,</span> <span class="n">rh_700</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>12-hr surface pressure falls and 500-hPa height changes</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pmsl_change</span> <span class="o">=</span> <span class="n">pmsl</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">quantify</span><span class="p">()</span> <span class="o">-</span> <span class="n">pmsl_06z</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">quantify</span><span class="p">()</span>
+<span class="n">hgt_500_change</span> <span class="o">=</span> <span class="n">hgt_500</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">quantify</span><span class="p">()</span> <span class="o">-</span> <span class="n">hgt_500_06z</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">quantify</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>To plot the jet axes, we will mask the wind fields below the upper 1/3 of windspeed.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># 500 hPa</span>
+<span class="n">u_500_masked</span> <span class="o">=</span> <span class="n">u_500</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wspd_500</span> <span class="o">&gt;</span> <span class="mf">0.66</span> <span class="o">*</span> <span class="n">wspd_500</span><span class="o">.</span><span class="n">max</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">v_500_masked</span> <span class="o">=</span> <span class="n">v_500</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wspd_500</span> <span class="o">&gt;</span> <span class="mf">0.66</span> <span class="o">*</span> <span class="n">wspd_500</span><span class="o">.</span><span class="n">max</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="c1"># 300 hPa</span>
+<span class="n">u_300_masked</span> <span class="o">=</span> <span class="n">u_300</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wspd_300</span> <span class="o">&gt;</span> <span class="mf">0.66</span> <span class="o">*</span> <span class="n">wspd_300</span><span class="o">.</span><span class="n">max</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">v_300_masked</span> <span class="o">=</span> <span class="n">v_300</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wspd_300</span> <span class="o">&gt;</span> <span class="mf">0.66</span> <span class="o">*</span> <span class="n">wspd_300</span><span class="o">.</span><span class="n">max</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="c1"># 850 hPa</span>
+<span class="n">u_850_masked</span> <span class="o">=</span> <span class="n">u_850</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wspd_850</span> <span class="o">&gt;</span> <span class="mf">0.66</span> <span class="o">*</span> <span class="n">wspd_850</span><span class="o">.</span><span class="n">max</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">v_850_masked</span> <span class="o">=</span> <span class="n">v_850</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wspd_850</span> <span class="o">&gt;</span> <span class="mf">0.66</span> <span class="o">*</span> <span class="n">wspd_850</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Create the Plot</strong></p>
+<p>With the data now ready, we will create the plot</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up our projection</span>
+<span class="n">crs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">central_latitude</span><span class="o">=</span><span class="mf">45.0</span><span class="p">)</span>
+
+<span class="c1"># Coordinates to limit map area</span>
+<span class="n">bounds</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">122.</span><span class="p">,</span> <span class="o">-</span><span class="mf">75.</span><span class="p">,</span> <span class="mf">25.</span><span class="p">,</span> <span class="mf">50.</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Plot the composite</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">17</span><span class="p">,</span> <span class="mi">12</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="mi">1</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">projection</span><span class="o">=</span><span class="n">crs</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
+
+<span class="c1"># Plot Lifted Index</span>
+<span class="n">cs1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">lifted_index</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+                 <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
+<span class="n">cs1</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
+           <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Plot Surface pressure falls</span>
+<span class="n">cs2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">pmsl_change</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+                 <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+                 <span class="n">colors</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">0.75</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
+<span class="n">cs2</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
+           <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Plot 500-hPa height falls</span>
+<span class="n">cs3</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">hgt_500_change</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">60</span><span class="p">,</span> <span class="o">-</span><span class="mi">29</span><span class="p">,</span> <span class="mi">15</span><span class="p">),</span>
+                 <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span> <span class="n">colors</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">0.75</span><span class="p">,</span>
+                 <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+<span class="n">cs3</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
+           <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Plot surface pressure</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">pmsl</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="mi">990</span><span class="p">,</span> <span class="mi">1011</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+            <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;yellow&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Plot surface dewpoint</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">Td_sfc</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degF&#39;</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="mi">65</span><span class="p">,</span> <span class="mi">76</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+            <span class="n">colors</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;green&#39;</span><span class="p">],</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="c1"># Plot 700-hPa dewpoint depression</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">Td_dep_700</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">46</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+            <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;tan&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+
+<span class="c1"># Plot Vorticity Advection</span>
+<span class="n">purple</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">vort_adv_500_smooth</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">106</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+            <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;BlueViolet&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+
+<span class="c1"># Define a skip to reduce the barb point density</span>
+<span class="n">skip_300</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
+<span class="n">skip_500</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+<span class="n">skip_850</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+
+<span class="c1"># 300-hPa wind barbs</span>
+<span class="n">jet300</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">skip_300</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lat</span><span class="p">[</span><span class="n">skip_300</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+                  <span class="n">u_300_masked</span><span class="p">[</span><span class="n">skip_300</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">v_300_masked</span><span class="p">[</span><span class="n">skip_300</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+                  <span class="n">length</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
+                  <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+                  <span class="n">color</span><span class="o">=</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;300-hPa Jet Core Winds (kt)&#39;</span><span class="p">)</span>
+
+
+<span class="c1"># 500-hPa wind barbs</span>
+<span class="n">jet500</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">skip_500</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lat</span><span class="p">[</span><span class="n">skip_500</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+                  <span class="n">u_500_masked</span><span class="p">[</span><span class="n">skip_500</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">v_500_masked</span><span class="p">[</span><span class="n">skip_500</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+                  <span class="n">length</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
+                  <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+                  <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;500-hPa Jet Core Winds (kt)&#39;</span><span class="p">)</span>
+
+<span class="c1"># 850-hPa wind barbs</span>
+<span class="n">jet850</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">skip_850</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lat</span><span class="p">[</span><span class="n">skip_850</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+                  <span class="n">u_850_masked</span><span class="p">[</span><span class="n">skip_850</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">v_850_masked</span><span class="p">[</span><span class="n">skip_850</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+                  <span class="n">length</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
+                  <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+                  <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;850-hPa Jet Core Winds (kt)&#39;</span><span class="p">)</span>
+
+<span class="c1"># Legend</span>
+<span class="n">purple</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;BlueViolet&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Cyclonic Absolute Vorticity Advection&#39;</span><span class="p">)</span>
+<span class="n">yellow</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;yellow&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Surface MSLP &lt; 1010 hPa&#39;</span><span class="p">)</span>
+<span class="n">green</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Surface Td &gt; 65 F&#39;</span><span class="p">)</span>
+<span class="n">tan</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;tan&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;700 hPa Dewpoint Depression &gt; 15 C&#39;</span><span class="p">)</span>
+<span class="n">red_line</span> <span class="o">=</span> <span class="n">lines</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Best Lifted Index (C)&#39;</span><span class="p">)</span>
+<span class="n">dashed_black_line</span> <span class="o">=</span> <span class="n">lines</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span>
+                                 <span class="n">label</span><span class="o">=</span><span class="s1">&#39;12-hr Surface Pressure Falls (hPa)&#39;</span><span class="p">)</span>
+<span class="n">black_line</span> <span class="o">=</span> <span class="n">lines</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span>
+                          <span class="n">label</span><span class="o">=</span><span class="s1">&#39;12-hr 500-hPa Height Falls (m)&#39;</span><span class="p">)</span>
+<span class="n">leg</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="p">[</span><span class="n">jet300</span><span class="p">,</span> <span class="n">jet500</span><span class="p">,</span> <span class="n">jet850</span><span class="p">,</span> <span class="n">dashed_black_line</span><span class="p">,</span> <span class="n">black_line</span><span class="p">,</span> <span class="n">red_line</span><span class="p">,</span>
+                          <span class="n">purple</span><span class="p">,</span> <span class="n">tan</span><span class="p">,</span> <span class="n">green</span><span class="p">,</span> <span class="n">yellow</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+                 <span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;Composite Analysis Valid: </span><span class="si">{</span><span class="n">vtimes</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span>
+                 <span class="n">framealpha</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">leg</span><span class="o">.</span><span class="n">set_zorder</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip
+  warnings.warn(f&#39;Downloading: {url}&#39;, DownloadWarning)
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_cultural/ne_50m_admin_1_states_provinces_lakes.zip
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+  <section class="tex2jax_ignore mathjax_ignore" id="skew-t-analysis">
+<h1>Skew-T Analysis<a class="headerlink" href="#skew-t-analysis" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>The Skew-T Log-P diagram, or “Skew-T” for short, is a type of plot specific
+to meteorology that is frequently used in the analysis of vertical profiles of
+temperature and humidity. For instance, the data collected by weather balloons
+in 12-hourly soundings are often shown with such plots, as they foster an
+understanding of e.g. the stability of the atmosphere above a location.</p>
+<p>The units in this chapter show ways to use MetPy to generate skew-T plots and
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+<li class="toctree-l2"><a class="reference internal" href="../synoptic/850hPa_Temperature_Advection.html">850 hPa Temperature Advection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../synoptic/Ageostrophic_Wind_Example.html">Geostrophic and Ageostrophic Wind</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../synoptic/Analytic_300hPa_Trough.html">Analytic 300-hPa Trough</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../synoptic/Differential_Temperature_Advection.html">Differential Temperature Advection with NARR Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../synoptic/Isentropic_Interpolation.html">Isentropic Analysis</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../synoptic/PV_baroclinic_isobaric.html">Baroclinic Potential Vorticity Analysis, Divergence, and Winds</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../synoptic/Upperair_Obs.html">DIFAX Replication</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../synoptic/Wind_Shear_Vectors_Example.html">Calculating Wind Shear</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../synoptic/xarray_500hPa_map.html">Using Xarray for Data read and selection</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../skewt/index.html">Skew-T Analysis</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../convective/index.html">Convective Calculations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="../convective/miller_composite.html">Miller Composite Chart</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Specialty Plotting</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Hovmoller Diagram Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Observational_Data_Cross_Section.html">Real Data Cross-Section Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Smoothing_Contours.html">Smoothing Contours</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../declarative/index.html">Simplified Plotting</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
+</ul>
+</details></li>
+</ul>
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+                    
+  <section class="tex2jax_ignore mathjax_ignore" id="hovmoller-diagram-example">
+<h1>Hovmoller Diagram Example<a class="headerlink" href="#hovmoller-diagram-example" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>By: Kevin Goebbert</p>
+<p>Northern Hemispheric v-wind component over the mid-latitudes in a
+Hovmoller diagram. This diagram can be used to illustrate upper-level
+wave and energy propogation (e.g., downstream baroclinic development)</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
+<span class="kn">import</span> <span class="nn">matplotlib.gridspec</span> <span class="k">as</span> <span class="nn">gridspec</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</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">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="get-the-data">
+<h2>Get the data<a class="headerlink" href="#get-the-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Using NCEP/NCAR reanalysis data via xarray remote access using the
+OPeNDAP protocol.</p>
+<p>Set the time range, parameter, and level to desired values</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create time slice from dates</span>
+<span class="n">start_time</span> <span class="o">=</span> <span class="s1">&#39;2011-01-20&#39;</span>
+<span class="n">end_time</span> <span class="o">=</span> <span class="s1">&#39;2011-02-06&#39;</span>
+
+<span class="c1"># Select NCEP/NCAR parameter and level</span>
+<span class="n">param</span> <span class="o">=</span> <span class="s1">&#39;vwnd&#39;</span>
+<span class="n">level</span> <span class="o">=</span> <span class="mi">250</span>
+
+<span class="c1"># Remote get dataset using OPeNDAP method via xarray</span>
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/&#39;</span>
+                     <span class="sa">f</span><span class="s1">&#39;ncep.reanalysis/pressure/</span><span class="si">{</span><span class="n">param</span><span class="si">}</span><span class="s1">.</span><span class="si">{</span><span class="n">start_time</span><span class="p">[:</span><span class="mi">4</span><span class="p">]</span><span class="si">}</span><span class="s1">.nc&#39;</span><span class="p">)</span>
+
+<span class="c1"># Create slice variables subset domain</span>
+<span class="n">time_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">start_time</span><span class="p">,</span> <span class="n">end_time</span><span class="p">)</span>
+<span class="n">lat_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">60</span><span class="p">,</span> <span class="mi">40</span><span class="p">)</span>
+<span class="n">lon_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">360</span><span class="p">)</span>
+
+<span class="c1"># Get data, selecting time, level, lat/lon slice</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="n">param</span><span class="p">]</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">time</span><span class="o">=</span><span class="n">time_slice</span><span class="p">,</span>
+                     <span class="n">level</span><span class="o">=</span><span class="n">level</span><span class="p">,</span>
+                     <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span>
+                     <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span>
+
+<span class="c1"># Compute weights and take weighted average over latitude dimension</span>
+<span class="n">weights</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">data</span><span class="o">.</span><span class="n">lat</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+<span class="n">avg_data</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span> <span class="o">*</span> <span class="n">weights</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:,</span> <span class="kc">None</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="s1">&#39;lat&#39;</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">weights</span><span class="p">)</span>
+
+<span class="c1"># Get times and make array of datetime objects</span>
+<span class="n">vtimes</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+
+<span class="c1"># Specify longitude values for chosen domain</span>
+<span class="n">lons</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">lon</span><span class="o">.</span><span class="n">values</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="make-the-hovmoller-plot">
+<h2>Make the Hovmoller Plot<a class="headerlink" href="#make-the-hovmoller-plot" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Pretty simple to use common matplotlib/cartopy to create the diagram.
+Cartopy is used to create a geographic reference map to highlight the
+area being averaged as well as the visual reference for longitude.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Start figure</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">10</span><span class="p">,</span> <span class="mi">13</span><span class="p">))</span>
+
+<span class="c1"># Use gridspec to help size elements of plot; small top plot</span>
+<span class="c1"># and big bottom plot</span>
+<span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">height_ratios</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.03</span><span class="p">)</span>
+
+<span class="c1"># Tick labels</span>
+<span class="n">x_tick_labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">u</span><span class="s1">&#39;0</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">E&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;90</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">E&#39;</span><span class="p">,</span>
+                 <span class="sa">u</span><span class="s1">&#39;180</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">E&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;90</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">W&#39;</span><span class="p">,</span>
+                 <span class="sa">u</span><span class="s1">&#39;0</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">E&#39;</span><span class="p">]</span>
+
+<span class="c1"># Top plot for geographic reference (makes small map)</span>
+<span class="n">ax1</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">gs</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">projection</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=</span><span class="mi">180</span><span class="p">))</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mf">357.5</span><span class="p">,</span> <span class="mi">35</span><span class="p">,</span> <span class="mi">65</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=</span><span class="mi">180</span><span class="p">))</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">])</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">u</span><span class="s1">&#39;40</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">N&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;60</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">N&#39;</span><span class="p">])</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span> <span class="o">-</span><span class="mi">90</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">90</span><span class="p">,</span> <span class="mi">180</span><span class="p">])</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">x_tick_labels</span><span class="p">)</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="c1"># Add geopolitical boundaries for map reference</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">LAKES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+                <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+<span class="c1"># Set some titles</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Hovmoller Diagram&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="s1">&#39;NCEP/NCAR Reanalysis&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+
+<span class="c1"># Bottom plot for Hovmoller diagram</span>
+<span class="n">ax2</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">gs</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">ax2</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>  <span class="c1"># Reverse the time order to do oldest first</span>
+
+<span class="c1"># Plot of chosen variable averaged over latitude and slightly smoothed</span>
+<span class="n">clevs</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="o">-</span><span class="mi">50</span><span class="p">,</span> <span class="mi">51</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">vtimes</span><span class="p">,</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span>
+    <span class="n">avg_data</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">clevs</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">cm</span><span class="o">.</span><span class="n">bwr</span><span class="p">,</span> <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">vtimes</span><span class="p">,</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span>
+    <span class="n">avg_data</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">clevs</span><span class="p">,</span> <span class="n">colors</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="mi">1</span><span class="p">)</span>
+<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">pad</span><span class="o">=</span><span class="mf">0.04</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+                    <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;m $s^{-1}$&#39;</span><span class="p">)</span>
+
+<span class="c1"># Make some ticks and tick labels</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">90</span><span class="p">,</span> <span class="mi">180</span><span class="p">,</span> <span class="mi">270</span><span class="p">,</span> <span class="mf">357.5</span><span class="p">])</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">x_tick_labels</span><span class="p">)</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">4</span><span class="p">::</span><span class="mi">8</span><span class="p">])</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">4</span><span class="p">::</span><span class="mi">8</span><span class="p">])</span>
+
+<span class="c1"># Set some titles</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;250-hPa V-wind&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</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="sa">f</span><span class="s1">&#39;Time Range: </span><span class="si">{</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s1">%Y%m%d %HZ</span><span class="si">}</span><span class="s1"> - </span><span class="si">{</span><span class="n">vtimes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s1">%Y%m%d %HZ</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span>
+          <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(1.0, 1.0, &#39;Time Range: 20110120 00Z - 20110206 18Z&#39;)
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_lakes.zip
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+  <section class="tex2jax_ignore mathjax_ignore" id="real-data-cross-section-example">
+<h1>Real Data Cross-Section Example<a class="headerlink" href="#real-data-cross-section-example" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>Cross-section using real data from soundings.</p>
+<p>This example uses actual soundings to create a cross-section. There are
+two functions defined to help interpolate radiosonde observations, which
+won’t all be at the same level, to a standard grid. The vertical
+interpolation assumes a log-linear relationship. Each radisosonde
+vertical profile is interpolated first, then the
+<code class="docutils literal notranslate"><span class="pre">scipy.interpolate.griddata</span></code> function is used to generate a full 2D
+(x, p) grid between each station. Pyproj is used to calculate the
+distance between each station and the standard atmosphere is used to
+convert the elevation of each station to a pressure value for plotting
+purposes.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">siphon.simplewebservice.wyoming</span> <span class="kn">import</span> <span class="n">WyomingUpperAir</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="vertical-interpolation-function">
+<h2>Vertical Interpolation Function<a class="headerlink" href="#vertical-interpolation-function" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Function interpolates to given pressure level data to set grid.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">vertical_interpolate</span><span class="p">(</span><span class="n">vcoord_data</span><span class="p">,</span> <span class="n">interp_var</span><span class="p">,</span> <span class="n">interp_levels</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A function to interpolate sounding data from each station to</span>
+<span class="sd">    every millibar. Assumes a log-linear relationship.</span>
+
+<span class="sd">    Input</span>
+<span class="sd">    -----</span>
+<span class="sd">    vcoord_data : A 1D array of vertical level values (e.g., pressure from a radiosonde)</span>
+<span class="sd">    interp_var : A 1D array of the variable to be interpolated to all pressure levels</span>
+<span class="sd">    vcoord_interp_levels : A 1D array containing veritcal levels to interpolate to</span>
+
+<span class="sd">    Return</span>
+<span class="sd">    ------</span>
+<span class="sd">    interp_data : A 1D array that contains the interpolated variable on the interp_levels</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Make veritcal coordinate data and grid level log variables</span>
+    <span class="n">lnp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">vcoord_data</span><span class="p">)</span>
+    <span class="n">lnp_intervals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">interp_levels</span><span class="p">)</span>
+
+    <span class="c1"># Use numpy to interpolate from observed levels to grid levels</span>
+    <span class="n">interp_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">lnp_intervals</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">lnp</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">interp_var</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="c1"># Mask for missing data (generally only near the surface)</span>
+    <span class="n">mask_low</span> <span class="o">=</span> <span class="n">interp_levels</span> <span class="o">&gt;</span> <span class="n">vcoord_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">mask_high</span> <span class="o">=</span> <span class="n">interp_levels</span> <span class="o">&lt;</span> <span class="n">vcoord_data</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">interp_data</span><span class="p">[</span><span class="n">mask_low</span><span class="p">]</span> <span class="o">=</span> <span class="n">interp_var</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">interp_data</span><span class="p">[</span><span class="n">mask_high</span><span class="p">]</span> <span class="o">=</span> <span class="n">interp_var</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">interp_data</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="radiosonde-observation-interpolation-function">
+<h2>Radiosonde Observation Interpolation Function<a class="headerlink" href="#radiosonde-observation-interpolation-function" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>This function interpolates given radiosonde data into a 2D array for all
+meteorological variables given in dataframe. Returns a dictionary that
+will have requesite data for plotting a cross section.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">radiosonde_cross_section</span><span class="p">(</span><span class="n">stns</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;This function takes a list of radiosonde observation sites with a</span>
+<span class="sd">    dictionary of Pandas Dataframes with the requesite data for each station.</span>
+
+<span class="sd">    Input</span>
+<span class="sd">    -----</span>
+<span class="sd">    stns : List of statition three-letter identifiers</span>
+<span class="sd">    data : A dictionary of Pandas Dataframes containing the radiosonde observations</span>
+<span class="sd">    for the stations</span>
+<span class="sd">    start : interpolation start value, optional (default = 1000 hPa)</span>
+<span class="sd">    end : Interpolation end value, optional (default = 100 hPa)</span>
+<span class="sd">    step : Interpolation interval, option (default = 10 hPa)</span>
+
+<span class="sd">    Return</span>
+<span class="sd">    ------</span>
+<span class="sd">    cross_section : A dictionary that contains the following variables</span>
+
+<span class="sd">        grid_data : An interpolated grid with 100 points between the first and last station,</span>
+<span class="sd">        with the corresponding number of vertical points based on start, end, and interval</span>
+<span class="sd">        (default is 90)</span>
+<span class="sd">        obs_distance : An array of distances between each radiosonde observation location</span>
+<span class="sd">        x_grid : A 2D array of horizontal direction grid points</span>
+<span class="sd">        p_grid : A 2D array of vertical pressure levels</span>
+<span class="sd">        ground_elevation : A representation of the terrain between radiosonde observation sites</span>
+<span class="sd">        based on the elevation of each station converted to pressure using the standard</span>
+<span class="sd">        atmosphere</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">pyproj</span> <span class="kn">import</span> <span class="n">Geod</span>
+    <span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">griddata</span>
+    
+    <span class="n">data_units</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;pressure&#39;</span><span class="p">:</span> <span class="s1">&#39;hPa&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;height&#39;</span><span class="p">:</span> <span class="s1">&#39;meter&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;temperature&#39;</span><span class="p">:</span> <span class="s1">&#39;degC&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;dewpoint&#39;</span><span class="p">:</span> <span class="s1">&#39;degC&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;direction&#39;</span><span class="p">:</span> <span class="s1">&#39;degrees&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;speed&#39;</span><span class="p">:</span> <span class="s1">&#39;knot&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;u_wind&#39;</span><span class="p">:</span> <span class="s1">&#39;knot&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;v_wind&#39;</span><span class="p">:</span> <span class="s1">&#39;knot&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;station&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+                  <span class="s1">&#39;station_number&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+                  <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+                  <span class="s1">&#39;latitude&#39;</span><span class="p">:</span> <span class="s1">&#39;degrees&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;longitude&#39;</span><span class="p">:</span> <span class="s1">&#39;degrees&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;elevation&#39;</span><span class="p">:</span> <span class="s1">&#39;meter&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;pw&#39;</span><span class="p">:</span> <span class="s1">&#39;millimeter&#39;</span><span class="p">}</span>
+    <span class="c1"># Set up vertical grid, largest value first (high pressure nearest surface)</span>
+    <span class="n">vertical_levels</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="n">start</span><span class="p">,</span> <span class="n">end</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="n">step</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="n">data_units</span><span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">])</span>
+
+    <span class="c1"># Number of vertical levels and stations</span>
+    <span class="n">plevs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertical_levels</span><span class="p">)</span>
+    <span class="n">nstns</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">stns</span><span class="p">)</span>
+
+    <span class="c1"># Create dictionary of interpolated values and include neccsary attribute data</span>
+    <span class="c1"># including lat, lon, and elevation of each station</span>
+    <span class="n">lats</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">lons</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">elev</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">keys</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">stns</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">keys</span><span class="p">()[:</span><span class="mi">8</span><span class="p">]</span>
+    <span class="n">tmp_grid</span> <span class="o">=</span> <span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span>
+
+    <span class="c1"># Interpolate all variables for each radiosonde observation</span>
+    <span class="c1"># Temperature, Dewpoint, U-wind, V-wind</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">tmp_grid</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+        <span class="n">tmp_grid</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">nstns</span><span class="p">,</span> <span class="n">plevs</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">station</span><span class="p">,</span> <span class="n">loc</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">stns</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="n">nstns</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;pressure&#39;</span><span class="p">:</span>
+                <span class="n">lats</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">]</span><span class="o">.</span><span class="n">latitude</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                <span class="n">lons</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">]</span><span class="o">.</span><span class="n">longitude</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                <span class="n">elev</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">]</span><span class="o">.</span><span class="n">elevation</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                <span class="n">tmp_grid</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">loc</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">vertical_levels</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">tmp_grid</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">loc</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">vertical_interpolate</span><span class="p">(</span>
+                    <span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">][</span><span class="s1">&#39;pressure&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">][</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+                    <span class="n">vertical_levels</span><span class="o">.</span><span class="n">m</span><span class="p">)</span>
+
+    <span class="c1"># Compute distance between each station using Pyproj</span>
+    <span class="n">g</span> <span class="o">=</span> <span class="n">Geod</span><span class="p">(</span><span class="n">ellps</span><span class="o">=</span><span class="s1">&#39;sphere&#39;</span><span class="p">)</span>
+    <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">dist</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">nstns</span><span class="o">*</span><span class="p">[</span><span class="n">lons</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">nstns</span><span class="o">*</span><span class="p">[</span><span class="n">lats</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">lons</span><span class="p">[:],</span> <span class="n">lats</span><span class="p">[:])</span>
+
+    <span class="c1"># Compute sudo ground elevation in pressure from standard atmsophere and the elevation</span>
+    <span class="c1"># of each station</span>
+    <span class="n">ground_elevation</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">height_to_pressure_std</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">elev</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;meters&#39;</span><span class="p">))</span>
+
+    <span class="c1"># Set up grid for 2D interpolation</span>
+    <span class="n">grid</span> <span class="o">=</span> <span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span>
+    <span class="n">x</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="n">dist</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dist</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">100</span><span class="p">)</span>
+    <span class="n">nx</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+
+    <span class="n">pp</span><span class="p">,</span> <span class="n">xx</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">vertical_levels</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+    <span class="n">pdist</span><span class="p">,</span> <span class="n">ddist</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">vertical_levels</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="n">dist</span><span class="p">)</span>
+
+    <span class="c1"># Interpolate to 2D grid using scipy.interpolate.griddata</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">grid</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+        <span class="n">grid</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">nx</span><span class="p">,</span> <span class="n">plevs</span><span class="p">))</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="n">data_units</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+        <span class="n">grid</span><span class="p">[</span><span class="n">key</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">griddata</span><span class="p">((</span><span class="n">ddist</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">pdist</span><span class="o">.</span><span class="n">flatten</span><span class="p">()),</span>
+                                <span class="n">tmp_grid</span><span class="p">[</span><span class="n">key</span><span class="p">][:]</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
+                                <span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">pp</span><span class="p">),</span>
+                                <span class="n">method</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="n">data_units</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+
+    <span class="c1"># Gather needed data in dictionary for return</span>
+    <span class="n">cross_section</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;grid_data&#39;</span><span class="p">:</span> <span class="n">grid</span><span class="p">,</span> <span class="s1">&#39;obs_distance&#39;</span><span class="p">:</span> <span class="n">dist</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">meter</span><span class="p">,</span>
+                     <span class="s1">&#39;x_grid&#39;</span><span class="p">:</span> <span class="n">xx</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">meter</span><span class="p">,</span> <span class="s1">&#39;p_grid&#39;</span><span class="p">:</span> <span class="n">pp</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="s1">&#39;elevation&#39;</span><span class="p">:</span> <span class="n">ground_elevation</span><span class="p">}</span>
+    <span class="k">return</span> <span class="n">cross_section</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="stations-and-time">
+<h2>Stations and Time<a class="headerlink" href="#stations-and-time" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Select cross section stations by creating a list of three-letter
+identifiers and choose a date by creating a datetime object</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># A roughly east-west cross section</span>
+<span class="n">stn_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;DNR&#39;</span><span class="p">,</span> <span class="s1">&#39;LBF&#39;</span><span class="p">,</span> <span class="s1">&#39;OAX&#39;</span><span class="p">,</span> <span class="s1">&#39;DVN&#39;</span><span class="p">,</span> <span class="s1">&#39;DTX&#39;</span><span class="p">,</span> <span class="s1">&#39;BUF&#39;</span><span class="p">]</span>
+
+<span class="c1"># Set a date and hour of your choosing</span>
+<span class="n">date</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2019</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="get-radiosonde-data">
+<h2>Get Radiosonde Data<a class="headerlink" href="#get-radiosonde-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>This example is built around the data from the University of Wyoming
+sounding archive and using the Siphon package to remotely access that
+data.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up empty dictionary to fill with Wyoming Sounding data</span>
+<span class="n">df</span> <span class="o">=</span> <span class="p">{}</span>
+
+<span class="c1"># Loop over stations to get data and put into dictionary</span>
+<span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">stn_list</span><span class="p">:</span>
+    <span class="n">df</span><span class="p">[</span><span class="n">station</span><span class="p">]</span> <span class="o">=</span> <span class="n">WyomingUpperAir</span><span class="o">.</span><span class="n">request_data</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">station</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">HTTPError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">line</span> <span class="mi">6</span>
+<span class="g g-Whitespace">      </span><span class="mi">4</span> <span class="c1"># Loop over stations to get data and put into dictionary</span>
+<span class="g g-Whitespace">      </span><span class="mi">5</span> <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">stn_list</span><span class="p">:</span>
+<span class="ne">----&gt; </span><span class="mi">6</span>     <span class="n">df</span><span class="p">[</span><span class="n">station</span><span class="p">]</span> <span class="o">=</span> <span class="n">WyomingUpperAir</span><span class="o">.</span><span class="n">request_data</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">station</span><span class="p">)</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/simplewebservice/wyoming.py:50,</span> in <span class="ni">WyomingUpperAir.request_data</span><span class="nt">(cls, time, site_id, **kwargs)</span>
+<span class="g g-Whitespace">     </span><span class="mi">30</span><span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Retrieve upper air observations from the Wyoming archive.</span>
+<span class="g g-Whitespace">     </span><span class="mi">31</span><span class="sd"> </span>
+<span class="g g-Whitespace">     </span><span class="mi">32</span><span class="sd"> Parameters</span>
+<span class="sd">   (...)</span>
+<span class="g g-Whitespace">     </span><span class="mi">47</span><span class="sd"> </span>
+<span class="g g-Whitespace">     </span><span class="mi">48</span><span class="sd"> &quot;&quot;&quot;</span>
+<span class="g g-Whitespace">     </span><span class="mi">49</span> <span class="n">endpoint</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">()</span>
+<span class="ne">---&gt; </span><span class="mi">50</span> <span class="n">df</span> <span class="o">=</span> <span class="n">endpoint</span><span class="o">.</span><span class="n">_get_data</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">site_id</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">51</span> <span class="k">return</span> <span class="n">df</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/simplewebservice/wyoming.py:70,</span> in <span class="ni">WyomingUpperAir._get_data</span><span class="nt">(self, time, site_id)</span>
+<span class="g g-Whitespace">     </span><span class="mi">53</span> <span class="k">def</span> <span class="nf">_get_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">site_id</span><span class="p">):</span>
+<span class="g g-Whitespace">     </span><span class="mi">54</span><span class="w">     </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Download and parse upper air observations from an online archive.</span>
+<span class="g g-Whitespace">     </span><span class="mi">55</span><span class="sd"> </span>
+<span class="g g-Whitespace">     </span><span class="mi">56</span><span class="sd">     Parameters</span>
+<span class="sd">   (...)</span>
+<span class="g g-Whitespace">     </span><span class="mi">68</span><span class="sd"> </span>
+<span class="g g-Whitespace">     </span><span class="mi">69</span><span class="sd">     &quot;&quot;&quot;</span>
+<span class="ne">---&gt; </span><span class="mi">70</span>     <span class="n">raw_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_data_raw</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">site_id</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">71</span>     <span class="n">soup</span> <span class="o">=</span> <span class="n">BeautifulSoup</span><span class="p">(</span><span class="n">raw_data</span><span class="p">,</span> <span class="s1">&#39;html.parser&#39;</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">72</span>     <span class="n">tabular_data</span> <span class="o">=</span> <span class="n">StringIO</span><span class="p">(</span><span class="n">soup</span><span class="o">.</span><span class="n">find_all</span><span class="p">(</span><span class="s1">&#39;pre&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">contents</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/simplewebservice/wyoming.py:144,</span> in <span class="ni">WyomingUpperAir._get_data_raw</span><span class="nt">(self, time, site_id)</span>
+<span class="g g-Whitespace">    </span><span class="mi">126</span><span class="w"> </span><span class="sd">&quot;&quot;&quot;Download data from the University of Wyoming&#39;s upper air archive.</span>
+<span class="g g-Whitespace">    </span><span class="mi">127</span><span class="sd"> </span>
+<span class="g g-Whitespace">    </span><span class="mi">128</span><span class="sd"> Parameters</span>
+<span class="sd">   (...)</span>
+<span class="g g-Whitespace">    </span><span class="mi">138</span><span class="sd"> </span>
+<span class="g g-Whitespace">    </span><span class="mi">139</span><span class="sd"> &quot;&quot;&quot;</span>
+<span class="g g-Whitespace">    </span><span class="mi">140</span> <span class="n">path</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;?region=naconf&amp;TYPE=TEXT%3ALIST&#39;</span>
+<span class="g g-Whitespace">    </span><span class="mi">141</span>         <span class="s1">&#39;&amp;YEAR={time:%Y}&amp;MONTH={time:%m}&amp;FROM={time:</span><span class="si">%d</span><span class="s1">%H}&amp;TO={time:</span><span class="si">%d</span><span class="s1">%H}&#39;</span>
+<span class="g g-Whitespace">    </span><span class="mi">142</span>         <span class="s1">&#39;&amp;STNM=</span><span class="si">{stid}</span><span class="s1">&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">=</span><span class="n">time</span><span class="p">,</span> <span class="n">stid</span><span class="o">=</span><span class="n">site_id</span><span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">144</span> <span class="n">resp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">145</span> <span class="c1"># See if the return is valid, but has no data</span>
+<span class="g g-Whitespace">    </span><span class="mi">146</span> <span class="k">if</span> <span class="n">resp</span><span class="o">.</span><span class="n">text</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s2">&quot;Can&#39;t&quot;</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/http_util.py:457,</span> in <span class="ni">HTTPEndPoint.get_path</span><span class="nt">(self, path, query)</span>
+<span class="g g-Whitespace">    </span><span class="mi">434</span> <span class="k">def</span> <span class="nf">get_path</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">query</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="g g-Whitespace">    </span><span class="mi">435</span><span class="w">     </span><span class="sd">&quot;&quot;&quot;Make a GET request, optionally including a query, to a relative path.</span>
+<span class="g g-Whitespace">    </span><span class="mi">436</span><span class="sd"> </span>
+<span class="g g-Whitespace">    </span><span class="mi">437</span><span class="sd">     The path of the request includes a path on top of the base URL</span>
+<span class="sd">   (...)</span>
+<span class="g g-Whitespace">    </span><span class="mi">455</span><span class="sd"> </span>
+<span class="g g-Whitespace">    </span><span class="mi">456</span><span class="sd">     &quot;&quot;&quot;</span>
+<span class="ne">--&gt; </span><span class="mi">457</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">url_path</span><span class="p">(</span><span class="n">path</span><span class="p">),</span> <span class="n">query</span><span class="p">)</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/http_util.py:492,</span> in <span class="ni">HTTPEndPoint.get</span><span class="nt">(self, path, params)</span>
+<span class="g g-Whitespace">    </span><span class="mi">490</span>     <span class="k">else</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">491</span>         <span class="n">text</span> <span class="o">=</span> <span class="n">resp</span><span class="o">.</span><span class="n">text</span>
+<span class="ne">--&gt; </span><span class="mi">492</span>     <span class="k">raise</span> <span class="n">requests</span><span class="o">.</span><span class="n">HTTPError</span><span class="p">(</span><span class="s1">&#39;Error accessing </span><span class="si">{0}</span><span class="se">\n</span><span class="s1">&#39;</span>
+<span class="g g-Whitespace">    </span><span class="mi">493</span>                              <span class="s1">&#39;Server Error (</span><span class="si">{1:d}</span><span class="s1">: </span><span class="si">{2}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">resp</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">url</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">494</span>                                                                 <span class="n">resp</span><span class="o">.</span><span class="n">status_code</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">495</span>                                                                 <span class="n">text</span><span class="p">))</span>
+<span class="g g-Whitespace">    </span><span class="mi">496</span> <span class="k">return</span> <span class="n">resp</span>
+
+<span class="ne">HTTPError</span>: Error accessing http://weather.uwyo.edu/cgi-bin/sounding/?region=naconf&amp;TYPE=TEXT%3ALIST&amp;YEAR=2019&amp;MONTH=06&amp;FROM=0100&amp;TO=0100&amp;STNM=OAX
+<span class="n">Server</span> <span class="n">Error</span> <span class="p">(</span><span class="mi">503</span><span class="p">:</span> <span class="n">Sorry</span><span class="p">,</span> <span class="n">the</span> <span class="n">server</span> <span class="ow">is</span> <span class="n">too</span> <span class="n">busy</span> <span class="n">to</span> <span class="n">process</span> <span class="n">your</span> <span class="n">request</span><span class="o">.</span>
+<span class="n">Please</span> <span class="k">try</span> <span class="n">again</span> <span class="n">later</span><span class="o">.</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="create-interpolated-fields">
+<h2>Create Interpolated fields<a class="headerlink" href="#create-interpolated-fields" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Use the function <code class="docutils literal notranslate"><span class="pre">radisonde_cross_section</span></code> to generate the 2D grid (x,
+p) for all radiosonde variables including, Temperature, Dewpoint,
+u-component of the wind, and v-component of the wind.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">xsect</span> <span class="o">=</span> <span class="n">radiosonde_cross_section</span><span class="p">(</span><span class="n">stn_list</span><span class="p">,</span> <span class="n">df</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">KeyError</span><span class="g g-Whitespace">                                  </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">xsect</span> <span class="o">=</span> <span class="n">radiosonde_cross_section</span><span class="p">(</span><span class="n">stn_list</span><span class="p">,</span> <span class="n">df</span><span class="p">)</span>
+
+<span class="nn">Cell In[3], line 68,</span> in <span class="ni">radiosonde_cross_section</span><span class="nt">(stns, data, start, end, step)</span>
+<span class="g g-Whitespace">     </span><span class="mi">66</span> <span class="k">for</span> <span class="n">station</span><span class="p">,</span> <span class="n">loc</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">stns</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="n">nstns</span><span class="p">)):</span>
+<span class="g g-Whitespace">     </span><span class="mi">67</span>     <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;pressure&#39;</span><span class="p">:</span>
+<span class="ne">---&gt; </span><span class="mi">68</span>         <span class="n">lats</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">]</span><span class="o">.</span><span class="n">latitude</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="g g-Whitespace">     </span><span class="mi">69</span>         <span class="n">lons</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">]</span><span class="o">.</span><span class="n">longitude</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="g g-Whitespace">     </span><span class="mi">70</span>         <span class="n">elev</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">]</span><span class="o">.</span><span class="n">elevation</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+<span class="ne">KeyError</span>: &#39;OAX&#39;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="calculate-variables-for-plotting">
+<h2>Calculate Variables for Plotting<a class="headerlink" href="#calculate-variables-for-plotting" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Use MetPy to calculate common variables for plotting a cross section,
+specifically potential temperature and mixing ratio</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">potemp</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">potential_temperature</span><span class="p">(</span>
+    <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;p_grid&#39;</span><span class="p">],</span> <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;temperature&#39;</span><span class="p">])</span>
+
+<span class="n">relhum</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">relative_humidity_from_dewpoint</span><span class="p">(</span>
+    <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;temperature&#39;</span><span class="p">],</span>
+    <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">])</span>
+
+<span class="n">mixrat</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">mixing_ratio_from_relative_humidity</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;p_grid&#39;</span><span class="p">],</span>
+                                                    <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;temperature&#39;</span><span class="p">],</span>
+                                                    <span class="n">relhum</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="n">line</span> <span class="mi">2</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="n">potemp</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">potential_temperature</span><span class="p">(</span>
+<span class="ne">----&gt; </span><span class="mi">2</span>     <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;p_grid&#39;</span><span class="p">],</span> <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;temperature&#39;</span><span class="p">])</span>
+<span class="g g-Whitespace">      </span><span class="mi">4</span> <span class="n">relhum</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">relative_humidity_from_dewpoint</span><span class="p">(</span>
+<span class="g g-Whitespace">      </span><span class="mi">5</span>     <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;temperature&#39;</span><span class="p">],</span>
+<span class="g g-Whitespace">      </span><span class="mi">6</span>     <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">])</span>
+<span class="g g-Whitespace">      </span><span class="mi">8</span> <span class="n">mixrat</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">mixing_ratio_from_relative_humidity</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;p_grid&#39;</span><span class="p">],</span>
+<span class="g g-Whitespace">      </span><span class="mi">9</span>                                                     <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;temperature&#39;</span><span class="p">],</span>
+<span class="g g-Whitespace">     </span><span class="mi">10</span>                                                     <span class="n">relhum</span><span class="p">)</span>
+
+<span class="ne">NameError</span>: name &#39;xsect&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="plot-cross-section">
+<h2>Plot Cross Section<a class="headerlink" href="#plot-cross-section" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Use standard Matplotlib to plot the now 2D cross section grid using the
+data from xsect and those calculated above. Additionally, the actualy
+radiosonde wind observations are plotted as barbs on this plot.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Start Figure, set big size for cross section</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">17</span><span class="p">,</span> <span class="mi">11</span><span class="p">))</span>
+
+<span class="c1"># Specify plotting axis (single panel)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
+
+<span class="c1"># Set y-scale to be log since pressure decreases exponentially with height</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+
+<span class="c1"># Set limits, tickmarks, and ticklabels for y-axis</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mi">1030</span><span class="p">,</span> <span class="mi">101</span><span class="p">])</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">101</span><span class="p">,</span> <span class="o">-</span><span class="mi">100</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">101</span><span class="p">,</span> <span class="o">-</span><span class="mi">100</span><span class="p">))</span>
+
+<span class="c1"># Invert the y-axis since pressure decreases with increasing height</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">yaxis_inverted</span><span class="p">()</span>
+
+<span class="c1"># Plot the sudo elevation on the cross section</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;obs_distance&#39;</span><span class="p">],</span> <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;elevation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="mi">1030</span><span class="p">,</span>
+                <span class="n">where</span><span class="o">=</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;elevation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">m</span> <span class="o">&lt;=</span> <span class="mi">1030</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span>
+                <span class="n">interpolate</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="c1"># Don&#39;t plot xticks</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([],</span> <span class="p">[])</span>
+
+<span class="c1"># Plot wind barbs for each sounding location</span>
+<span class="k">for</span> <span class="n">stn</span><span class="p">,</span> <span class="n">stn_name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">stn_list</span><span class="p">)),</span> <span class="n">stn_list</span><span class="p">):</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;obs_distance&#39;</span><span class="p">][</span><span class="n">stn</span><span class="p">],</span> <span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ymax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+               <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;obs_distance&#39;</span><span class="p">][</span><span class="n">stn</span><span class="p">],</span> <span class="mi">1100</span><span class="p">,</span> <span class="n">stn_name</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;obs_distance&#39;</span><span class="p">][</span><span class="n">stn</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="n">stn_name</span><span class="p">][</span><span class="s1">&#39;pressure&#39;</span><span class="p">][::</span><span class="mi">2</span><span class="p">],</span>
+             <span class="n">df</span><span class="p">[</span><span class="n">stn_name</span><span class="p">][</span><span class="s1">&#39;u_wind&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[::</span><span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
+             <span class="n">df</span><span class="p">[</span><span class="n">stn_name</span><span class="p">][</span><span class="s1">&#39;v_wind&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[::</span><span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
+
+<span class="c1"># Plot smoothed potential temperature grid (K)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;x_grid&#39;</span><span class="p">],</span> <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;p_grid&#39;</span><span class="p">],</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_gaussian</span><span class="p">(</span><span class="n">potemp</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+                <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot smoothed mixing ratio grid (g/kg)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;x_grid&#39;</span><span class="p">],</span> <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;p_grid&#39;</span><span class="p">],</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_gaussian</span><span class="p">(</span><span class="n">mixrat</span><span class="o">*</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+                <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">41</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;tab:green&#39;</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="c1"># Add some informative titles</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Cross-Section from DNR to BUF Potential Temp. &#39;</span>
+          <span class="s1">&#39;(K; red) and Mix. Rat. (g/kg; green)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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">date</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="n">line</span> <span class="mi">19</span>
+<span class="g g-Whitespace">     </span><span class="mi">16</span> <span class="n">ax</span><span class="o">.</span><span class="n">yaxis_inverted</span><span class="p">()</span>
+<span class="g g-Whitespace">     </span><span class="mi">18</span> <span class="c1"># Plot the sudo elevation on the cross section</span>
+<span class="ne">---&gt; </span><span class="mi">19</span> <span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;obs_distance&#39;</span><span class="p">],</span> <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;elevation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="mi">1030</span><span class="p">,</span>
+<span class="g g-Whitespace">     </span><span class="mi">20</span>                 <span class="n">where</span><span class="o">=</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;elevation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">m</span> <span class="o">&lt;=</span> <span class="mi">1030</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span>
+<span class="g g-Whitespace">     </span><span class="mi">21</span>                 <span class="n">interpolate</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">22</span> <span class="c1"># Don&#39;t plot xticks</span>
+<span class="g g-Whitespace">     </span><span class="mi">23</span> <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([],</span> <span class="p">[])</span>
+
+<span class="ne">NameError</span>: name &#39;xsect&#39; is not defined
+</pre></div>
+</div>
+<img alt="../../_images/6f0321e2d35497901ee209dbaf8d3a1712be4edc195bb182e05f0ab96cf5d35f.png" src="../../_images/6f0321e2d35497901ee209dbaf8d3a1712be4edc195bb182e05f0ab96cf5d35f.png" />
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+</ul>
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+<h1>Smoothing Contours<a class="headerlink" href="#smoothing-contours" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>Demonstrate how to smooth contour values from a higher resolution
+model field.</p>
+<p>By: Kevin Goebbert</p>
+<p>Date: 13 April 2017</p>
+<p>Do the needed imports</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
+<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
+<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Set up netCDF Subset Service link</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Specify our date/time of product desired</span>
+<span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">()</span>
+
+<span class="c1"># Construct the URL for our THREDDS Data Server Catalog,</span>
+<span class="c1"># and access our desired dataset within via NCSS</span>
+<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/catalog/grib/&#39;</span>
+                 <span class="s1">&#39;NCEP/NAM/CONUS_12km/latest.xml&#39;</span><span class="p">)</span>
+<span class="n">ncss</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
+
+<span class="c1"># Create our NCSS query with desired specifications</span>
+<span class="n">query</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
+<span class="n">query</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">dt</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">add_lonlat</span><span class="p">()</span>
+<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
+
+<span class="c1"># Obtain the data we&#39;ve queried for</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+
+<span class="c1"># Make into an xarray Dataset object</span>
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Pull apart the data</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get dimension names to pull appropriate variables</span>
+<span class="c1"># dtime = ds.Geopotential_height_isobaric.dims[0]</span>
+<span class="c1"># dlev = ds.Geopotential_height_isobaric.dims[1]</span>
+<span class="c1"># dlat = ds.Geopotential_height_isobaric.dims[2]</span>
+<span class="c1"># dlon = ds.Geopotential_height_isobaric.dims[3]</span>
+
+<span class="c1"># Get lat and lon data, as well as time data and metadata</span>
+<span class="n">lats</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span>
+<span class="n">lons</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span>
+
+<span class="c1"># Need 2D lat/lons for plotting, do so if necessary</span>
+<span class="k">if</span> <span class="n">lats</span><span class="o">.</span><span class="n">ndim</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span>
+    <span class="n">lons</span><span class="p">,</span> <span class="n">lats</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">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">)</span>
+
+<span class="c1"># Determine the level of 500 hPa</span>
+<span class="n">lev_500</span> <span class="o">=</span> <span class="mi">500</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
+
+<span class="c1"># Create more useable times for output</span>
+<span class="n">times</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">vtimes</span> <span class="o">=</span> <span class="n">times</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+
+<span class="c1"># Pull out the 500 hPa Heights</span>
+<span class="n">hght_500</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">lev_500</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">uwnd_500</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">lev_500</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">vwnd_500</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">lev_500</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+<span class="c1"># Calculate the magnitude of the wind speed in kts</span>
+<span class="n">sped</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">uwnd_500</span><span class="p">,</span> <span class="n">vwnd_500</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;knots&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Set up the projection for LCC</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plotcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mf">100.0</span><span class="p">,</span>
+                                <span class="n">central_latitude</span><span class="o">=</span><span class="mf">45.0</span><span class="p">)</span>
+<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Subset and smooth</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Smooth the 500-hPa geopotential height field</span>
+<span class="c1"># Be sure to only smooth the 2D field</span>
+<span class="n">Z_500</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_gaussian</span><span class="p">(</span><span class="n">hght_500</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Plot the contours</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Start plot with new figure and axis</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="mf">17.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</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="mi">1</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">plotcrs</span><span class="p">)</span>
+
+<span class="c1"># Add some titles to make the plot readable by someone else</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;500-hPa Geo Heights (m; black), Smoothed 500-hPa Geo. &#39;</span>
+          <span class="s1">&#39;Heights (m; red)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;VALID: </span><span class="si">{</span><span class="n">vtimes</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+
+<span class="c1"># Set GAREA and add map features</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mf">125.</span><span class="p">,</span> <span class="o">-</span><span class="mf">67.</span><span class="p">,</span> <span class="mf">22.</span><span class="p">,</span> <span class="mf">52.</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+<span class="c1"># Set the CINT</span>
+<span class="n">clev500</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">5100</span><span class="p">,</span> <span class="mi">6000</span><span class="p">,</span> <span class="mi">60</span><span class="p">)</span>
+
+<span class="c1"># Plot smoothed 500-hPa contours</span>
+<span class="n">cs2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">Z_500</span><span class="p">,</span> <span class="n">clev500</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+                 <span class="n">linewidths</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">c2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs2</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
+                <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Contour the 500 hPa heights with labels</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_500</span><span class="p">,</span> <span class="n">clev500</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span>
+                <span class="n">linewidths</span><span class="o">=</span><span class="mf">2.5</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">cl</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
+                <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
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+<li class="toctree-l2"><a class="reference internal" href="850hPa_Frontogenesis.html">850-hPa Geopotential Heights, Temperature, Frontogenesis, and Winds</a></li>
+<li class="toctree-l2"><a class="reference internal" href="850hPa_QVectors.html">QVector Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="850hPa_Temperature_Advection.html">850 hPa Temperature Advection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Ageostrophic_Wind_Example.html">Geostrophic and Ageostrophic Wind</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Analytic_300hPa_Trough.html">Analytic 300-hPa Trough</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Differential_Temperature_Advection.html">Differential Temperature Advection with NARR Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Isentropic_Interpolation.html">Isentropic Analysis</a></li>
+<li class="toctree-l2"><a class="reference internal" href="PV_baroclinic_isobaric.html">Baroclinic Potential Vorticity Analysis, Divergence, and Winds</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Upperair_Obs.html">DIFAX Replication</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Wind_Shear_Vectors_Example.html">Calculating Wind Shear</a></li>
+<li class="toctree-l2"><a class="reference internal" href="xarray_500hPa_map.html">Using Xarray for Data read and selection</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../skewt/index.html">Skew-T Analysis</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../convective/index.html">Convective Calculations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="../convective/miller_composite.html">Miller Composite Chart</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../specialty/index.html">Specialty Plotting</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../specialty/Smoothing_Contours.html">Smoothing Contours</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../declarative/index.html">Simplified Plotting</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
+</ul>
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+  <section class="tex2jax_ignore mathjax_ignore" id="a-250-hpa-hemispheric-map-using-python">
+<h1>A 250-hPa Hemispheric Map using Python<a class="headerlink" href="#a-250-hpa-hemispheric-map-using-python" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>This example plots a hemispheric plot of GFS 250-hPa Geopotential Heights and
+wind speed in knots.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
+<span class="kn">import</span> <span class="nn">cartopy.util</span> <span class="k">as</span> <span class="nn">cutil</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
+<span class="kn">from</span> <span class="nn">siphon.ncss</span> <span class="kn">import</span> <span class="n">NCSS</span>
+<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+
+<span class="c1"># Latest GFS Dataset</span>
+<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="s1">&#39;http://thredds.ucar.edu/thredds/catalog/grib/&#39;</span>
+                 <span class="s1">&#39;NCEP/GFS/Global_0p5deg/latest.xml&#39;</span><span class="p">)</span>
+<span class="n">best_ds</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">values</span><span class="p">())[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">ncss</span> <span class="o">=</span> <span class="n">NCSS</span><span class="p">(</span><span class="n">best_ds</span><span class="o">.</span><span class="n">access_urls</span><span class="p">[</span><span class="s1">&#39;NetcdfSubset&#39;</span><span class="p">])</span>
+
+<span class="c1"># Set the time to current</span>
+<span class="n">now</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">()</span>
+
+<span class="c1"># Query for Latest GFS Run</span>
+<span class="n">gfsdata_hght</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">now</span><span class="p">)</span><span class="o">.</span><span class="n">accept</span><span class="p">(</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
+<span class="n">gfsdata_hght</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">)</span>
+
+<span class="c1"># Set the lat/lon box for the data you want to pull in.</span>
+<span class="c1"># lonlat_box(north_lat,south_lat,east_lon,west_lon)</span>
+<span class="n">gfsdata_hght</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">359.9</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">90</span><span class="p">)</span>
+
+<span class="c1"># Set desired level 50000 = 50000 Pa = 500 hPa</span>
+<span class="n">gfsdata_hght</span><span class="o">.</span><span class="n">vertical_level</span><span class="p">(</span><span class="mi">25000</span><span class="p">)</span>
+
+<span class="c1"># Actually getting the data</span>
+<span class="n">data_hght</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">gfsdata_hght</span><span class="p">)</span>
+
+<span class="c1"># Make into an xarray Dataset object</span>
+<span class="n">ds_hght</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data_hght</span><span class="p">))</span>
+
+<span class="c1"># Query for Latest GFS Run</span>
+<span class="n">gfsdata_wind</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">now</span><span class="p">)</span><span class="o">.</span><span class="n">accept</span><span class="p">(</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
+<span class="n">gfsdata_wind</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
+                       <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
+
+<span class="c1"># Set the lat/lon box for the data you want to pull in.</span>
+<span class="c1"># lonlat_box(north_lat,south_lat,east_lon,west_lon)</span>
+<span class="n">gfsdata_wind</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">359.9</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">90</span><span class="p">)</span>
+
+<span class="c1"># Set desired level 50000 = 50000 Pa = 500 hPa</span>
+<span class="n">gfsdata_wind</span><span class="o">.</span><span class="n">vertical_level</span><span class="p">(</span><span class="mi">25000</span><span class="p">)</span>
+
+<span class="c1"># Actually getting the data</span>
+<span class="n">data_wind</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">gfsdata_wind</span><span class="p">)</span>
+
+<span class="c1"># Make into an xarray Dataset object</span>
+<span class="n">ds_wind</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data_wind</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The next cell will take the downloaded data and parse it to different variables
+for use later on. Add a cyclic point using the cartopy utility add_cyclic_point
+to the longitudes (the cyclic dimension) as well as any data that is being
+contoured or filled.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lat</span> <span class="o">=</span> <span class="n">ds_hght</span><span class="o">.</span><span class="n">latitude</span><span class="o">.</span><span class="n">values</span>
+<span class="n">lon</span> <span class="o">=</span> <span class="n">ds_hght</span><span class="o">.</span><span class="n">longitude</span><span class="o">.</span><span class="n">values</span>
+
+<span class="c1"># Converting times using the num2date function available through netCDF4</span>
+<span class="n">vtimes</span> <span class="o">=</span> <span class="n">ds_hght</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+
+<span class="c1"># Smooth the 250-hPa heights using a gaussian filter from scipy.ndimage</span>
+<span class="n">hgt</span> <span class="o">=</span> <span class="n">ds_hght</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">hgt_250</span><span class="p">,</span> <span class="n">lon</span> <span class="o">=</span> <span class="n">cutil</span><span class="o">.</span><span class="n">add_cyclic_point</span><span class="p">(</span><span class="n">hgt</span><span class="p">,</span> <span class="n">coord</span><span class="o">=</span><span class="n">lon</span><span class="p">)</span>
+
+<span class="n">Z_250</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">hgt_250</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+
+<span class="c1"># Calculate wind speed from u and v components, add cyclic point,</span>
+<span class="c1"># and smooth slightly</span>
+<span class="n">u250</span> <span class="o">=</span> <span class="n">ds_wind</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">v250</span> <span class="o">=</span> <span class="n">ds_wind</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+<span class="n">wspd250</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">u250</span><span class="p">,</span> <span class="n">v250</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;knots&#39;</span><span class="p">)</span>
+<span class="n">wspd250</span> <span class="o">=</span> <span class="n">cutil</span><span class="o">.</span><span class="n">add_cyclic_point</span><span class="p">(</span><span class="n">wspd250</span><span class="p">)</span>
+
+<span class="n">smooth_wspd250</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">wspd250</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The next cell sets up the geographic details for the plot that we are going to do later.
+This is done using the Cartopy package. We will also bring in some geographic data to
+geo-reference the image for us.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
+<span class="n">plotcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">NorthPolarStereo</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mf">100.0</span><span class="p">)</span>
+
+<span class="c1"># Make a grid of lat/lon values to use for plotting.</span>
+<span class="n">lons</span><span class="p">,</span> <span class="n">lats</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">lon</span><span class="p">,</span> <span class="n">lat</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">12.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">plotcrs</span><span class="p">)</span>
+
+<span class="c1"># Set some titles for the plots</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;250-hPa Geopotential Heights (m)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;VALID: </span><span class="si">{</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+
+<span class="c1"># Set the extent of the image for the NH and add</span>
+<span class="c1">#   ax.set_extent([west long, east long, south lat, north lat])</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span> <span class="mi">180</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">90</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">),</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+               <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">),</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+<span class="c1"># Add geopotential height contours every 120 m</span>
+<span class="n">clev250</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">9000</span><span class="p">,</span> <span class="mi">12000</span><span class="p">,</span> <span class="mi">120</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">Z_250</span><span class="p">,</span> <span class="n">clev250</span><span class="p">,</span> <span class="n">colors</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">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span>
+           <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Add colorfilled wind speed in knots every 20 kts</span>
+<span class="n">clevsped250</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">50</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">20</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">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;BuPu&#39;</span><span class="p">)</span>
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">smooth_wspd250</span><span class="p">,</span> <span class="n">clevsped250</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">extend</span><span class="o">=</span><span class="s1">&#39;max&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+                    <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_2589/1982158916.py:30: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
+  cmap = plt.cm.get_cmap(&#39;BuPu&#39;)
+</pre></div>
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diff --git a/_preview/13/notebooks/synoptic/500hPa_Absolute_Vorticity_winds.html b/_preview/13/notebooks/synoptic/500hPa_Absolute_Vorticity_winds.html
new file mode 100644
index 0000000..c530ed0
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+  <section class="tex2jax_ignore mathjax_ignore" id="hpa-geopotential-heights-absolute-vorticity-and-winds">
+<h1>500 hPa Geopotential Heights, Absolute Vorticity, and Winds<a class="headerlink" href="#hpa-geopotential-heights-absolute-vorticity-and-winds" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>Classic 500-hPa absolute vorticity plot using NAM analysis file.</p>
+<p>This example uses example data from the NAM analysis for 12 UTC 31
+October 2016 and uses xarray as the main read source with using MetPy to
+calculate absolute vorticity and wind speed with geographic plotting
+using Cartopy for a CONUS view of the 500-hPa geopotential heights,
+absolute vorticity, and wind barbs.</p>
+<p>Note: NAM winds are grid relative and this example introduces a simple
+function to convert the grid-relative wind components to an
+earth-relative (north/east oriented winds) to enable easier plotting of
+wind barbs and calculation of absolute vorticity.</p>
+<p>Import the needed modules</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</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">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="function-to-compute-earth-relative-winds">
+<h2>Function to Compute Earth-Relative Winds<a class="headerlink" href="#function-to-compute-earth-relative-winds" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>This function takes a data array with relevant information about the
+projection of the wind component data, along with the grid-relative
+components of the wind. It outputs the earth-relative components of the
+wind.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">earth_relative_wind_components</span><span class="p">(</span><span class="n">ugrd</span><span class="p">,</span> <span class="n">vgrd</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Calculate the north-relative components of the</span>
+<span class="sd">    wind from the grid-relative components using Cartopy</span>
+<span class="sd">    transform_vectors.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">        ugrd : Xarray DataArray (M, N)</span>
+<span class="sd">            grid relative u-component of the wind</span>
+<span class="sd">        vgrd : Xarray DataArray (M, N)</span>
+<span class="sd">            grid relative v-component of the wind</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">        unr, vnr : tuple of array-like Quantity</span>
+<span class="sd">            The north-relative wind components in the X (East-West)</span>
+<span class="sd">            and Y (North-South) directions, respectively.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="s1">&#39;metpy_crs&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">coords</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;No CRS in coordinate, be sure to&#39;</span>
+                         <span class="s1">&#39;use the MetPy accessor parse_cf()&#39;</span><span class="p">)</span>
+
+    <span class="n">data_crs</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">cartopy_crs</span>
+
+    <span class="n">x</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">values</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">values</span>
+
+    <span class="n">xx</span><span class="p">,</span> <span class="n">yy</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="n">ut</span><span class="p">,</span> <span class="n">vt</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span><span class="o">.</span><span class="n">transform_vectors</span><span class="p">(</span><span class="n">data_crs</span><span class="p">,</span> <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span>
+                                                  <span class="n">ugrd</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">vgrd</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+
+    <span class="c1"># Make a copy of u and v component DataArrays</span>
+    <span class="n">uer</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">ver</span> <span class="o">=</span> <span class="n">vgrd</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+    <span class="c1"># Update values with transformed winds</span>
+    <span class="n">uer</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">ut</span>
+    <span class="n">ver</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">vt</span>
+
+    <span class="k">return</span> <span class="n">uer</span><span class="p">,</span> <span class="n">ver</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The following code reads the example data using the xarray open_dataset
+function and prints the coordinate values that are associated with the
+various variables contained within the file</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/casestudies/&#39;</span>
+                     <span class="s1">&#39;python-gallery/NAM_20161031_1200.nc&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="data-retrieval">
+<h2>Data Retrieval<a class="headerlink" href="#data-retrieval" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>This code retrieves the necessary data from the file and completes some
+smoothing of the geopotential height and wind fields using the SciPy
+function gaussian_filter. A nicely formated valid time (vtime) variable
+is also created</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Grab lat/lon values (NAM will be 2D)</span>
+<span class="n">lats</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span><span class="o">.</span><span class="n">data</span>
+<span class="n">lons</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span><span class="o">.</span><span class="n">data</span>
+
+<span class="c1"># Grab x, y data and make 2D for wind component plotting because</span>
+<span class="c1"># u- and v-components are grid relative</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">x</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">y</span>
+
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</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"># Grab Cartopy CRS from metadata for plotting wind barbs</span>
+<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">cartopy_crs</span>
+
+<span class="c1"># Select and grab 500-hPa geopotential heights and smooth with n-point smoother</span>
+<span class="n">level</span> <span class="o">=</span> <span class="mi">500</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
+<span class="n">hght_500</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+
+<span class="c1"># Select and grab 500-hPa wind components</span>
+<span class="n">uwnd_500</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">assign_latitude_longitude</span><span class="p">()</span>
+<span class="n">vwnd_500</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">assign_latitude_longitude</span><span class="p">()</span>
+
+<span class="c1"># Compute north-relative wind components for plotting purposes</span>
+<span class="n">uwnd_er</span><span class="p">,</span> <span class="n">vwnd_er</span> <span class="o">=</span> <span class="n">earth_relative_wind_components</span><span class="p">(</span><span class="n">uwnd_500</span><span class="p">,</span> <span class="n">vwnd_500</span><span class="p">)</span>
+
+<span class="c1"># Smooth wind components as desired</span>
+<span class="n">uwnd_er</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">uwnd_er</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+<span class="n">vwnd_er</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">vwnd_er</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+
+<span class="c1"># Create a clean datetime object for plotting based on time</span>
+<span class="c1"># of Geopotential heights</span>
+<span class="n">vtime</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="metpy-absolute-vorticity-calculation">
+<h2>MetPy Absolute Vorticity Calculation<a class="headerlink" href="#metpy-absolute-vorticity-calculation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>This code calculates <code class="docutils literal notranslate"><span class="pre">absolute_vorticity()</span></code>
+using the wind components and makes use of the
+xarray data object features to complete the necessary
+grid-aware calculation.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate absolute vorticity from MetPy function</span>
+<span class="n">avor_500</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">absolute_vorticity</span><span class="p">(</span><span class="n">uwnd_er</span><span class="p">,</span> <span class="n">vwnd_er</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="map-creation">
+<h2>Map Creation<a class="headerlink" href="#map-creation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>This next set of code creates the plot and draws contours on a Lambert
+Conformal map centered on -100 E longitude. The main view is over the
+CONUS with geopotential heights contoured every 60 m and absolute
+vorticity colorshaded (<code class="docutils literal notranslate"><span class="pre">*10^5</span></code>).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up the projection that will be used for plotting</span>
+<span class="n">mapcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span> <span class="n">central_latitude</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span>
+                               <span class="n">standard_parallels</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
+
+<span class="c1"># Set up the projection of the data;</span>
+<span class="c1"># if lat/lon then PlateCarree is what you want</span>
+<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
+
+<span class="c1"># Start the figure and create plot axes with proper projection</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">55</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+<span class="c1"># Add geopolitical boundaries for map reference</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+
+<span class="c1"># Absolute Vorticity colors</span>
+<span class="c1"># Use two different colormaps from matplotlib and combine into one color set</span>
+<span class="n">clevs_500_avor</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">8</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="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">46</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">colors1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">YlOrRd</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</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="mi">48</span><span class="p">))</span>
+<span class="n">colors2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">BuPu</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+<span class="n">colors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">colors2</span><span class="p">,</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="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">colors1</span><span class="p">))</span>
+
+<span class="c1"># Plot absolute vorticity values (multiplying by 10^5 to scale appropriately)</span>
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">avor_500</span><span class="o">*</span><span class="mf">1e5</span><span class="p">,</span> <span class="n">clevs_500_avor</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span>
+                 <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;max&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+                  <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Abs. Vorticity ($s^{-1}$)&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot 500-hPa Geopotential Heights in meters</span>
+<span class="n">clevs_500_hght</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="mi">8000</span><span class="p">,</span> <span class="mi">60</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_500</span><span class="p">,</span> <span class="n">clevs_500_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="c1"># Set up a 2D slice to reduce the number of wind barbs plotted (every 20th)</span>
+<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
+         <span class="n">uwnd_er</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+         <span class="n">vwnd_er</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+         <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+
+<span class="c1"># Plot two titles, one on right and left side</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;500-hPa NAM Geopotential Heights (m)&#39;</span>
+          <span class="s1">&#39; and Wind Barbs (kt)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;Valid Time: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(1.0, 1.0, &#39;Valid Time: 2016-10-31 12:00:00&#39;)
+</pre></div>
+</div>
+<img alt="../../_images/bc8bba4565a06490278c04e332a46216117fbcbcc2109fb150e142e6233168eb.png" src="../../_images/bc8bba4565a06490278c04e332a46216117fbcbcc2109fb150e142e6233168eb.png" />
+</div>
+</div>
+</section>
+</section>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#function-to-compute-earth-relative-winds">Function to Compute Earth-Relative Winds</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#data-retrieval">Data Retrieval</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#metpy-absolute-vorticity-calculation">MetPy Absolute Vorticity Calculation</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="PV_baroclinic_isobaric.html">Baroclinic Potential Vorticity Analysis, Divergence, and Winds</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Upperair_Obs.html">DIFAX Replication</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Wind_Shear_Vectors_Example.html">Calculating Wind Shear</a></li>
+<li class="toctree-l2"><a class="reference internal" href="xarray_500hPa_map.html">Using Xarray for Data read and selection</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../skewt/index.html">Skew-T Analysis</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../convective/index.html">Convective Calculations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="../convective/miller_composite.html">Miller Composite Chart</a></li>
+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../specialty/index.html">Specialty Plotting</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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+</ul>
+</details></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../declarative/index.html">Simplified Plotting</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
+</ul>
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+  <section class="tex2jax_ignore mathjax_ignore" id="hpa-vorticity-advection">
+<h1>500 hPa Vorticity Advection<a class="headerlink" href="#hpa-vorticity-advection" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>Plot an 500-hPa map with calculating vorticity advection using MetPy calculations.</p>
+<p>Beyond just plotting 500-hPa level data, this uses calculations from <code class="docutils literal notranslate"><span class="pre">metpy.calc</span></code> to find
+the vorticity and vorticity advection. Currently, this needs an extra helper function to
+calculate the distance between lat/lon grid points.</p>
+<p>Imports</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
+<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="function-to-compute-earth-relative-winds">
+<h2>Function to Compute Earth-Relative Winds<a class="headerlink" href="#function-to-compute-earth-relative-winds" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>This function takes a data array with relevant information about the
+projection of the wind component data, along with the grid-relative
+components of the wind. It outputs the earth-relative components of the
+wind.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">earth_relative_wind_components</span><span class="p">(</span><span class="n">ugrd</span><span class="p">,</span> <span class="n">vgrd</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Calculate the north-relative components of the</span>
+<span class="sd">    wind from the grid-relative components using Cartopy</span>
+<span class="sd">    transform_vectors.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">        ugrd : Xarray DataArray (M, N)</span>
+<span class="sd">            grid relative u-component of the wind</span>
+<span class="sd">        vgrd : Xarray DataArray (M, N)</span>
+<span class="sd">            grid relative v-component of the wind</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">        unr, vnr : tuple of array-like Quantity</span>
+<span class="sd">            The north-relative wind components in the X (East-West)</span>
+<span class="sd">            and Y (North-South) directions, respectively.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="s1">&#39;metpy_crs&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">coords</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;No CRS in coordinate, be sure to use&#39;</span>
+                         <span class="s1">&#39;the MetPy accessor parse_cf()&#39;</span><span class="p">)</span>
+
+    <span class="n">data_crs</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">cartopy_crs</span>
+
+    <span class="n">x</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">values</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">values</span>
+
+    <span class="n">xx</span><span class="p">,</span> <span class="n">yy</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="n">ut</span><span class="p">,</span> <span class="n">vt</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span><span class="o">.</span><span class="n">transform_vectors</span><span class="p">(</span><span class="n">data_crs</span><span class="p">,</span> <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span>
+                                                  <span class="n">ugrd</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">vgrd</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+
+    <span class="c1"># Make a copy of u and v component DataArrays</span>
+    <span class="n">uer</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">ver</span> <span class="o">=</span> <span class="n">vgrd</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+    <span class="c1"># Update values with transformed winds</span>
+    <span class="n">uer</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">ut</span>
+    <span class="n">ver</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">vt</span>
+
+    <span class="k">return</span> <span class="n">uer</span><span class="p">,</span> <span class="n">ver</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="data-aquisition">
+<h2>Data Aquisition<a class="headerlink" href="#data-aquisition" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
+
+<span class="c1"># Assemble our URL to the THREDDS Data Server catalog,</span>
+<span class="c1"># and access our desired dataset within via NCSS</span>
+<span class="n">base_url</span> <span class="o">=</span> <span class="s1">&#39;https://www.ncei.noaa.gov/thredds/catalog/model-namanl-old/&#39;</span>
+<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">base_url</span><span class="si">}{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">/catalog.xml&#39;</span><span class="p">)</span>
+<span class="n">ncss</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;namanl_218_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">00_000.grb&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
+
+<span class="c1"># Query for Latest GFS Run</span>
+<span class="n">query</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
+
+<span class="n">query</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">dt</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">accept</span><span class="p">(</span><span class="s1">&#39;netcdf&#39;</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
+
+<span class="c1"># Obtain our queried data</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+<span class="n">ds_data</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+<span class="n">ds</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">assign_latitude_longitude</span><span class="p">()</span>
+
+<span class="n">times</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span>
+<span class="n">vtime</span> <span class="o">=</span> <span class="n">times</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+
+<span class="n">lev_500</span> <span class="o">=</span> <span class="mi">500</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
+
+<span class="n">hght_500</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">lev_500</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">hght_500</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_gaussian</span><span class="p">(</span><span class="n">hght_500</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+
+<span class="n">uwnd_500</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">lev_500</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">vwnd_500</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">lev_500</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+<span class="c1"># Compute north-relative wind components for calculation purposes</span>
+<span class="n">uwnd_500er</span><span class="p">,</span> <span class="n">vwnd_500er</span> <span class="o">=</span> <span class="n">earth_relative_wind_components</span><span class="p">(</span><span class="n">uwnd_500</span><span class="p">,</span> <span class="n">vwnd_500</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="begin-data-calculations">
+<h2>Begin Data Calculations<a class="headerlink" href="#begin-data-calculations" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">avor</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">vorticity</span><span class="p">(</span><span class="n">uwnd_500er</span><span class="p">,</span> <span class="n">vwnd_500er</span><span class="p">)</span>
+
+<span class="n">avor</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">avor</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1e5</span>
+
+<span class="n">vort_adv</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">advection</span><span class="p">(</span><span class="n">avor</span><span class="p">,</span> <span class="n">uwnd_500er</span><span class="p">,</span> <span class="n">vwnd_500er</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1e4</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_2647/3729036084.py:5: UserWarning: Vertical dimension number not found. Defaulting to (..., Z, Y, X) order.
+  vort_adv = mpcalc.advection(avor, uwnd_500er, vwnd_500er) * 1e4
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="map-creation">
+<h2>Map Creation<a class="headerlink" href="#map-creation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up Coordinate System for Plot and Transforms</span>
+<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">cartopy_crs</span>
+<span class="n">plotcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_latitude</span><span class="o">=</span><span class="mf">45.</span><span class="p">,</span> <span class="n">central_longitude</span><span class="o">=-</span><span class="mf">100.</span><span class="p">,</span>
+                                <span class="n">standard_parallels</span><span class="o">=</span><span class="p">[</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">12.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">plotcrs</span><span class="p">)</span>
+
+<span class="c1"># Plot Titles</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;500-hPa Heights (m), AVOR$*10^5$ ($s^{-1}$),&#39;</span>
+          <span class="s1">&#39;AVOR Adv$*10^9$ ($s^{-2}$)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;VALID: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot Background</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="mf">235.</span><span class="p">,</span> <span class="mf">290.</span><span class="p">,</span> <span class="mf">20.</span><span class="p">,</span> <span class="mf">58.</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
+
+<span class="c1"># Plot Height Contours</span>
+<span class="n">clev500</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">5100</span><span class="p">,</span> <span class="mi">6061</span><span class="p">,</span> <span class="mi">60</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">hght_500</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span> <span class="n">hght_500</span><span class="o">.</span><span class="n">latitude</span><span class="p">,</span>
+                <span class="n">hght_500</span><span class="p">,</span> <span class="n">clev500</span><span class="p">,</span>
+                <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#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">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span>
+           <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Plot Absolute Vorticity Contours</span>
+<span class="n">clevvort500</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="o">-</span><span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">cs2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">avor</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span> <span class="n">avor</span><span class="o">.</span><span class="n">latitude</span><span class="p">,</span>
+                 <span class="n">avor</span><span class="p">,</span> <span class="n">clevvort500</span><span class="p">,</span>
+                 <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.25</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span>
+                 <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs2</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span>
+           <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Plot Colorfill of Vorticity Advection</span>
+<span class="n">clev_avoradv</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="o">-</span><span class="mi">30</span><span class="p">,</span> <span class="mi">31</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">vort_adv</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span> <span class="n">vort_adv</span><span class="o">.</span><span class="n">latitude</span><span class="p">,</span> <span class="n">vort_adv</span><span class="p">,</span>
+                 <span class="n">clev_avoradv</span><span class="p">[</span><span class="n">clev_avoradv</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">],</span> <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span>
+                 <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;bwr&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+                  <span class="n">extendrect</span><span class="o">=</span><span class="s1">&#39;True&#39;</span><span class="p">,</span> <span class="n">ticks</span><span class="o">=</span><span class="n">clev_avoradv</span><span class="p">)</span>
+<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$1/s^2$&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot Wind Barbs</span>
+<span class="c1"># Transform Vectors and plot wind barbs.</span>
+<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</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">uwnd_500</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span>
+                     <span class="n">uwnd_500</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">uwnd_500</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span> <span class="n">vwnd_500</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
+         <span class="n">length</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.quiver.Barbs at 0x7ff051a81210&gt;
+</pre></div>
+</div>
+<img alt="../../_images/b53d0ec6077dcd0cef13e5c86c2fe66c173a67bd7f3409cd077b0881dd62644d.png" src="../../_images/b53d0ec6077dcd0cef13e5c86c2fe66c173a67bd7f3409cd077b0881dd62644d.png" />
+</div>
+</div>
+</section>
+</section>
+
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+  <section class="tex2jax_ignore mathjax_ignore" id="hpa-geopotential-heights-temperature-frontogenesis-and-winds">
+<h1>850-hPa Geopotential Heights, Temperature, Frontogenesis, and Winds<a class="headerlink" href="#hpa-geopotential-heights-temperature-frontogenesis-and-winds" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>Frontogenesis at 850-hPa with Geopotential Heights, Temperature, and
+Winds</p>
+<p>This example uses example data from the GFS analysis for 12 UTC 26
+October 2010 and uses xarray as the main read source with using MetPy to
+calculate frontogenesis and wind speed with geographic plotting using
+Cartopy for a CONUS view.</p>
+<p>Import the needed modules.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</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">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Use Xarray to access GFS data from THREDDS resource and uses
+metpy accessor to parse file to make it easy to pull data using
+common coordinate names (e.g., vertical) and attach units.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/casestudies/&#39;</span>
+                     <span class="s1">&#39;python-gallery/GFS_20101026_1200.nc&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+</div>
+</div>
+<p>Subset data based on latitude and longitude values, calculate potential
+temperature for frontogenesis calculation.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set subset slice for the geographic extent of data to limit download</span>
+<span class="n">lon_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">350</span><span class="p">)</span>
+<span class="n">lat_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">85</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+
+<span class="c1"># Grab lat/lon values (GFS will be 1D)</span>
+<span class="n">lats</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
+<span class="n">lons</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
+
+<span class="n">level</span> <span class="o">=</span> <span class="mi">850</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
+<span class="n">hght_850</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">tmpk_850</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">uwnd_850</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">vwnd_850</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+<span class="c1"># Convert temperatures to degree Celsius for plotting purposes</span>
+<span class="n">tmpc_850</span> <span class="o">=</span> <span class="n">tmpk_850</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
+
+<span class="c1"># Calculate potential temperature for frontogenesis calculation</span>
+<span class="n">thta_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">potential_temperature</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">tmpk_850</span><span class="p">)</span>
+
+<span class="c1"># Get a sensible datetime format</span>
+<span class="n">vtime</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="calculate-frontogenesis">
+<h2>Calculate frontogenesis<a class="headerlink" href="#calculate-frontogenesis" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Frontogenesis calculation in MetPy requires temperature, wind
+components, and grid spacings. First compute the grid deltas using MetPy
+functionality, then put it all together in the frontogenesis function.</p>
+<p>Note: MetPy will give the output with SI units, but typically
+frontogenesis (read: GEMPAK) output this variable with units of K per
+100 km per 3 h; a conversion factor is included here to use at plot time
+to reflect those units.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fronto_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">frontogenesis</span><span class="p">(</span><span class="n">thta_850</span><span class="p">,</span> <span class="n">uwnd_850</span><span class="p">,</span> <span class="n">vwnd_850</span><span class="p">)</span>
+
+<span class="c1"># A conversion factor to get frontogensis units of K per 100 km per 3 h</span>
+<span class="n">convert_to_per_100km_3h</span> <span class="o">=</span> <span class="mi">1000</span><span class="o">*</span><span class="mi">100</span><span class="o">*</span><span class="mi">3600</span><span class="o">*</span><span class="mi">3</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/pint/facets/plain/quantity.py:1006: RuntimeWarning: invalid value encountered in divide
+  magnitude = magnitude_op(new_self._magnitude, other._magnitude)
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="plotting-frontogenesis">
+<h2>Plotting Frontogenesis<a class="headerlink" href="#plotting-frontogenesis" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Using a Lambert Conformal projection from Cartopy to plot 850-hPa
+variables including frontogenesis.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set map projection</span>
+<span class="n">mapcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span> <span class="n">central_latitude</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span>
+                               <span class="n">standard_parallels</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
+
+<span class="c1"># Set projection of the data (GFS is lat/lon)</span>
+<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
+
+<span class="c1"># Start figure and limit the graphical area extent</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">55</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+<span class="c1"># Add map features of Coastlines and States</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+
+<span class="c1"># Plot 850-hPa Frontogenesis</span>
+<span class="n">clevs_tmpc</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="o">-</span><span class="mi">40</span><span class="p">,</span> <span class="mi">41</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">fronto_850</span><span class="o">*</span><span class="n">convert_to_per_100km_3h</span><span class="p">,</span>
+                 <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">,</span> <span class="mf">8.5</span><span class="p">,</span> <span class="mf">0.5</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">cm</span><span class="o">.</span><span class="n">bwr</span><span class="p">,</span> <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span>
+                 <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+                  <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Frontogenesis K / 100 km / 3 h&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot 850-hPa Temperature in Celsius</span>
+<span class="n">csf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_850</span><span class="p">,</span> <span class="n">clevs_tmpc</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span>
+                 <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">csf</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot 850-hPa Geopotential Heights</span>
+<span class="n">clevs_850_hght</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="mi">8000</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_850</span><span class="p">,</span> <span class="n">clevs_850_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot 850-hPa Wind Barbs only plotting every fifth barb</span>
+<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span>
+         <span class="n">uwnd_850</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+         <span class="n">vwnd_850</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+         <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+
+<span class="c1"># Plot some titles</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;GFS 850-hPa Geopotential Heights (m), Temp (C), and Winds&#39;</span><span class="p">,</span>
+          <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;Valid Time: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(1.0, 1.0, &#39;Valid Time: 2010-10-26 12:00:00&#39;)
+</pre></div>
+</div>
+<img alt="../../_images/16374ab4270821fd7362b34950f7be0b0cfed3497610ad31e2b1702c11cabccb.png" src="../../_images/16374ab4270821fd7362b34950f7be0b0cfed3497610ad31e2b1702c11cabccb.png" />
+</div>
+</div>
+</section>
+</section>
+
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+  <section class="tex2jax_ignore mathjax_ignore" id="qvector-example">
+<h1>QVector Example<a class="headerlink" href="#qvector-example" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>Computing Q-vectors and Q-vector divergence for a real case.</p>
+<p>By: Kevin Goebbert</p>
+<p>This example uses GFS output to compute the 850-hPa Q-vectors and
+Q-vector divergence for 12 UTC 26 October 2010.</p>
+<p>Import needed modules</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">from</span> <span class="nn">metpy.plots</span> <span class="kn">import</span> <span class="n">named_areas</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</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">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Use Xarray to access GFS data from THREDDS resource and uses
+metpy accessor to parse file to make it easy to pull data using
+common coordinate names (e.g., vertical) and attach units.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/casestudies/&#39;</span>
+                     <span class="s1">&#39;python-gallery/GFS_20101026_1200.nc&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Subset data based on latitude and longitude values and select only data
+from 850 hPa</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set subset slice for the geographic extent of data to limit download</span>
+<span class="n">lon_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">350</span><span class="p">)</span>
+<span class="n">lat_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">85</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+
+<span class="c1"># Grab data and smooth using a nine-point filter applied 50 times to grab</span>
+<span class="c1"># the synoptic signal</span>
+<span class="n">level</span> <span class="o">=</span> <span class="mi">850</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
+<span class="n">hght_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+<span class="n">tmpk_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
+<span class="n">uwnd_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+<span class="n">vwnd_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+
+<span class="c1"># Grab lat/lon values (GFS will be 1D) from a subset variable</span>
+<span class="n">lons</span><span class="p">,</span> <span class="n">lats</span> <span class="o">=</span> <span class="n">hght_850</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">hght_850</span><span class="o">.</span><span class="n">lat</span>
+
+<span class="c1"># Convert temperatures to degree Celsius for plotting purposes</span>
+<span class="n">tmpc_850</span> <span class="o">=</span> <span class="n">tmpk_850</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
+
+<span class="c1"># Get a sensible datetime format</span>
+<span class="n">vtime</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="compute-q-vectors">
+<h2>Compute Q-vectors<a class="headerlink" href="#compute-q-vectors" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Use the MetPy module to compute Q-vectors from requisite data and
+additionally compute the Q-vector divergence (and multiply by -2) to
+calculate the right hand side forcing of the Q-G Omega equation.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Compute grid spacings for data</span>
+<span class="c1">#dx, dy = mpcalc.lat_lon_grid_deltas(lons, lats)</span>
+
+<span class="c1"># Compute the Q-vector components</span>
+<span class="n">uqvect</span><span class="p">,</span> <span class="n">vqvect</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">q_vector</span><span class="p">(</span><span class="n">uwnd_850</span><span class="p">,</span> <span class="n">vwnd_850</span><span class="p">,</span> <span class="n">tmpk_850</span><span class="p">,</span> <span class="mi">850</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
+
+<span class="c1"># Compute the divergence of the Q-vectors calculated above</span>
+<span class="n">q_div</span> <span class="o">=</span> <span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">mpcalc</span><span class="o">.</span><span class="n">divergence</span><span class="p">(</span><span class="n">uqvect</span><span class="p">,</span> <span class="n">vqvect</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="plot-data">
+<h2>Plot Data<a class="headerlink" href="#plot-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Use Cartopy to plot data on a map using a Lambert Conformal projection.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set the map projection (how the data will be displayed)</span>
+<span class="n">mapcrs</span> <span class="o">=</span> <span class="n">named_areas</span><span class="p">[</span><span class="s1">&#39;uslcc&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">projection</span>
+
+<span class="c1"># Set the data project (GFS is lat/lon format)</span>
+<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
+
+<span class="c1"># Start the figure and set an extent to only display a smaller graphics area</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">(</span><span class="n">named_areas</span><span class="p">[</span><span class="s1">&#39;uslcc&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">bounds</span><span class="p">,</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+<span class="c1"># Add map features to plot coastlines and state boundaries</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+
+<span class="c1"># Plot 850-hPa Q-Vector Divergence and scale</span>
+<span class="n">clevs_850_tmpc</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="o">-</span><span class="mi">40</span><span class="p">,</span> <span class="mi">41</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">clevs_qdiv</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">35</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span><span class="o">+</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">36</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">q_div</span><span class="o">*</span><span class="mf">1e18</span><span class="p">,</span> <span class="n">clevs_qdiv</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">cm</span><span class="o">.</span><span class="n">bwr</span><span class="p">,</span>
+                 <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+                  <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ticks</span><span class="o">=</span><span class="n">clevs_qdiv</span><span class="p">)</span>
+<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Q-Vector Div. (*10$^</span><span class="si">{18}</span><span class="s1">$ m s$^{-1}$ kg$^{-1}$)&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot 850-hPa Temperatures</span>
+<span class="n">csf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_850</span><span class="p">,</span> <span class="n">clevs_850_tmpc</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span>
+                 <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">csf</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot 850-hPa Geopotential Heights</span>
+<span class="n">clevs_850_hght</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="mi">8000</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_850</span><span class="p">,</span> <span class="n">clevs_850_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot 850-hPa Q-vectors, scale to get nice sized arrows</span>
+<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span>
+          <span class="n">uqvect</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">vqvect</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;mid&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+          <span class="n">scale</span><span class="o">=</span><span class="mf">5e-12</span><span class="p">,</span> <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;inches&#39;</span><span class="p">,</span>
+          <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+
+<span class="c1"># Add some titles</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;850-hPa GFS Geo. Heights (m), Temp (C),&#39;</span>
+          <span class="s1">&#39; and Q-Vectors (m$^2$ kg$^{-1}$ s$^{-1}$)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;Valid Time: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(1.0, 1.0, &#39;Valid Time: 2010-10-26 12:00:00&#39;)
+</pre></div>
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+<li class="toctree-l2"><a class="reference internal" href="250hPa_Hemispheric_Plot.html">A 250-hPa Hemispheric Map using Python</a></li>
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+  <section class="tex2jax_ignore mathjax_ignore" id="hpa-temperature-advection">
+<h1>850 hPa Temperature Advection<a class="headerlink" href="#hpa-temperature-advection" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>Plot an 850 hPa map with calculating advection using MetPy.</p>
+<p>Beyond just plotting 850-hPa level data, this uses calculations from <code class="docutils literal notranslate"><span class="pre">metpy.calc</span></code> to find
+the temperature advection. Currently, this needs an extra helper function to calculate
+the distance between lat/lon grid points.</p>
+<p>Imports</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
+<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
+<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="create-ncss-object-to-access-the-netcdfsubset">
+<h2>Create NCSS object to access the NetcdfSubset<a class="headerlink" href="#create-ncss-object-to-access-the-netcdfsubset" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Data from NCEI GFS 0.5 deg Analysis Archive</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2017</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
+
+<span class="c1"># Assemble our URL to the THREDDS Data Server catalog,</span>
+<span class="c1"># and access our desired dataset within via NCSS</span>
+<span class="n">base_url</span> <span class="o">=</span> <span class="s1">&#39;https://www.ncei.noaa.gov/thredds/model-gfs-g4-anl-files-old/&#39;</span>
+<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">base_url</span><span class="si">}{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">/catalog.xml&#39;</span><span class="p">)</span>
+<span class="n">ncss</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;gfsanl_4_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">00_000.grb2&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
+
+<span class="c1"># Create NCSS query for our desired time, region, and data variables</span>
+<span class="n">query</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
+
+<span class="n">query</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">dt</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="n">north</span><span class="o">=</span><span class="mi">65</span><span class="p">,</span> <span class="n">south</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">east</span><span class="o">=</span><span class="mi">310</span><span class="p">,</span> <span class="n">west</span><span class="o">=</span><span class="mi">220</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">accept</span><span class="p">(</span><span class="s1">&#39;netcdf&#39;</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;Temperature_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
+
+<span class="c1"># Obtain the queried data</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+
+<span class="c1"># Make into an xarray Dataset object</span>
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+
+<span class="c1"># Pull out variables you want to use</span>
+<span class="n">level</span> <span class="o">=</span> <span class="mi">850</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
+<span class="n">hght_850</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">temp_850</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">u_wind_850</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">v_wind_850</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">time</span> <span class="o">=</span> <span class="n">hght_850</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span>
+<span class="n">lat</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span><span class="o">.</span><span class="n">values</span>
+<span class="n">lon</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span><span class="o">.</span><span class="n">values</span>
+
+<span class="c1"># Convert number of hours since the reference time into an actual date</span>
+<span class="n">vtime</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+
+<span class="c1"># Combine 1D latitude and longitudes into a 2D grid of locations</span>
+<span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</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">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="begin-data-calculations">
+<h2>Begin data calculations<a class="headerlink" href="#begin-data-calculations" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate temperature advection using metpy function</span>
+<span class="n">adv</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">advection</span><span class="p">(</span><span class="n">temp_850</span><span class="p">,</span> <span class="n">u_wind_850</span><span class="p">,</span> <span class="n">v_wind_850</span><span class="p">)</span>
+
+<span class="c1"># Smooth heights and advection a little</span>
+<span class="c1"># Be sure to only put in a 2D lat/lon or Y/X array for smoothing</span>
+<span class="n">Z_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_gaussian</span><span class="p">(</span><span class="n">hght_850</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">adv</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_gaussian</span><span class="p">(</span><span class="n">adv</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_2738/72829660.py:2: UserWarning: Vertical dimension number not found. Defaulting to (..., Z, Y, X) order.
+  adv = mpcalc.advection(temp_850, u_wind_850, v_wind_850)
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="begin-map-creation">
+<h2>Begin map creation<a class="headerlink" href="#begin-map-creation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set Projection of Data</span>
+<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
+
+<span class="c1"># Set Projection of Plot</span>
+<span class="n">plotcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_latitude</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span>
+                                <span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span> <span class="n">standard_parallels</span><span class="o">=</span><span class="p">[</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">])</span>
+
+<span class="c1"># Create new figure</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">14</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
+
+<span class="c1"># Add the map and set the extent</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">plotcrs</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="sa">f</span><span class="s1">&#39;850mb Temperature Advection for </span><span class="si">{</span><span class="n">vtime</span><span class="si">:</span><span class="s1">%d %B %Y %H:%MZ</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span>
+          <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="mf">235.</span><span class="p">,</span> <span class="mf">290.</span><span class="p">,</span> <span class="mf">20.</span><span class="p">,</span> <span class="mf">55.</span><span class="p">])</span>
+
+<span class="c1"># Add state/country boundaries to plot</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">BORDERS</span><span class="p">)</span>
+
+<span class="c1"># Plot Height Contours</span>
+<span class="n">clev850</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">900</span><span class="p">,</span> <span class="mi">3000</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</span><span class="p">,</span> <span class="n">Z_850</span><span class="p">,</span> <span class="n">clev850</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span>
+                <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span>
+           <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Plot Temperature Contours</span>
+<span class="n">clevtemp850</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="o">-</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">cs2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</span><span class="p">,</span> <span class="n">temp_850</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">),</span>
+                 <span class="n">clevtemp850</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.25</span><span class="p">,</span>
+                 <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs2</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span>
+           <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Plot Colorfill of Temperature Advection</span>
+<span class="n">cint</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="o">-</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">)</span>
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</span><span class="p">,</span> <span class="mi">3</span><span class="o">*</span><span class="n">adv</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;delta_degC/hour&#39;</span><span class="p">),</span>
+                 <span class="n">cint</span><span class="p">[</span><span class="n">cint</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">],</span>
+                 <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;bwr&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+                  <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ticks</span><span class="o">=</span><span class="n">cint</span><span class="p">)</span>
+<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$^</span><span class="si">{o}</span><span class="s1">C/3h$&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+
+<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+<span class="c1"># Plot Wind Barbs</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
+         <span class="n">u_wind_850</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
+         <span class="n">v_wind_850</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
+         <span class="n">length</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_cultural/ne_50m_admin_0_boundary_lines_land.zip
+  warnings.warn(f&#39;Downloading: {url}&#39;, DownloadWarning)
+</pre></div>
+</div>
+<img alt="../../_images/fdbedc177954ec89ac026d4d50d1319536d9c9a8bf314bfc4bd26dc21b700747.png" src="../../_images/fdbedc177954ec89ac026d4d50d1319536d9c9a8bf314bfc4bd26dc21b700747.png" />
+</div>
+</div>
+</section>
+</section>
+
+    <script type="text/x-thebe-config">
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+        binderOptions: {
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diff --git a/_preview/13/notebooks/synoptic/Ageostrophic_Wind_Example.html b/_preview/13/notebooks/synoptic/Ageostrophic_Wind_Example.html
new file mode 100644
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+  <section class="tex2jax_ignore mathjax_ignore" id="geostrophic-and-ageostrophic-wind">
+<h1>Geostrophic and Ageostrophic Wind<a class="headerlink" href="#geostrophic-and-ageostrophic-wind" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>Plot a 1000-hPa map calculating the geostrophic from MetPy and finding the
+ageostrophic wind from the total wind and the geostrophic wind.</p>
+<p>This uses the geostrophic wind calculation from <code class="docutils literal notranslate"><span class="pre">metpy.calc</span></code> to find
+the geostrophic wind, then performs the simple subtraction to find the ageostrophic
+wind. Currently, this needs an extra helper function to calculate
+the distance between lat/lon grid points.</p>
+<p>Additionally, we utilize the <code class="docutils literal notranslate"><span class="pre">ndimage.zoom</span></code> method for smoothing the 1000-hPa
+height contours without smoothing the data.</p>
+<p>Imports</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
+
+<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">ndimage</span>
+<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
+<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Set up access to the data</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">22</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
+<span class="n">forecast_hour</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="n">h</span> <span class="o">=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">forecast_hour</span><span class="p">)</span>
+
+<span class="c1"># Assemble our URL to the THREDDS Data Server catalog,</span>
+<span class="c1"># and access our desired dataset within via NCSS</span>
+<span class="n">base_url</span> <span class="o">=</span> <span class="s1">&#39;https://www.ncei.noaa.gov/thredds/model-gfs-g4-anl-files-old/&#39;</span>
+<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">base_url</span><span class="si">}{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">/catalog.xml&#39;</span><span class="p">)</span>
+<span class="n">ncss</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;gfsanl_4_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">&#39;</span>
+                    <span class="sa">f</span><span class="s1">&#39;00_00</span><span class="si">{</span><span class="n">forecast_hour</span><span class="si">}</span><span class="s1">.grb2&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
+
+<span class="c1"># Create NCSS query for our desired time, region, and data variables</span>
+<span class="n">query</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
+
+<span class="n">query</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="n">north</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">south</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">east</span><span class="o">=-</span><span class="mi">80</span><span class="p">,</span> <span class="n">west</span><span class="o">=-</span><span class="mi">115</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">dt</span> <span class="o">+</span> <span class="n">h</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">vertical_level</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Pull out variables you want to use</span>
+<span class="n">height</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">u_wind</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">quantify</span><span class="p">()</span>
+<span class="n">v_wind</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">quantify</span><span class="p">()</span>
+
+<span class="n">vtime</span> <span class="o">=</span> <span class="n">height</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+
+<span class="n">lat</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span>
+<span class="n">lon</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Combine 1D latitude and longitudes into a 2D grid of locations</span>
+<span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</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">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">)</span>
+
+<span class="c1"># Smooth height data</span>
+<span class="n">height</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+
+<span class="c1"># Compute the geostrophic wind</span>
+<span class="n">geo_wind_u</span><span class="p">,</span> <span class="n">geo_wind_v</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">geostrophic_wind</span><span class="p">(</span><span class="n">height</span><span class="p">)</span>
+
+<span class="c1"># Calculate ageostrophic wind components</span>
+<span class="n">ageo_wind_u</span> <span class="o">=</span> <span class="n">u_wind</span> <span class="o">-</span> <span class="n">geo_wind_u</span>
+<span class="n">ageo_wind_v</span> <span class="o">=</span> <span class="n">v_wind</span> <span class="o">-</span> <span class="n">geo_wind_v</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create new figure</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">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
+
+<span class="c1"># Add the map and set the extent</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mf">105.</span><span class="p">,</span> <span class="o">-</span><span class="mf">93.</span><span class="p">,</span> <span class="mf">35.</span><span class="p">,</span> <span class="mf">43.</span><span class="p">])</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">patch</span><span class="o">.</span><span class="n">set_fill</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Add state boundaries to plot</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="c1"># Contour the heights every 10 m</span>
+<span class="n">contours</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">10</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+<span class="c1"># Because we have a very local graphics area, the contours have joints</span>
+<span class="c1"># to smooth those out we can use `ndimage.zoom`</span>
+<span class="n">zoom_500</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">zoom_xarray</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">c</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">zoom_500</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">zoom_500</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">zoom_500</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="n">contours</span><span class="p">,</span>
+               <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="c1"># Set up parameters for quiver plot. The slices below are used to</span>
+<span class="c1"># subset the data (here taking every 4th point in x and y). The</span>
+<span class="c1"># quiver_kwargs are parameters to control the appearance of the</span>
+<span class="c1"># quiver so that they stay consistent between the calls.</span>
+<span class="n">quiver_slices</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+<span class="n">quiver_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;headlength&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s1">&#39;headwidth&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">&#39;angles&#39;</span><span class="p">:</span> <span class="s1">&#39;uv&#39;</span><span class="p">,</span>
+                 <span class="s1">&#39;scale_units&#39;</span><span class="p">:</span> <span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="s1">&#39;scale&#39;</span><span class="p">:</span> <span class="mi">20</span><span class="p">}</span>
+
+<span class="c1"># Plot the wind vectors</span>
+<span class="n">wind</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span>
+                 <span class="n">u_wind</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span> <span class="n">v_wind</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span>
+                 <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">quiver_kwargs</span><span class="p">)</span>
+<span class="n">geo</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span>
+                <span class="n">geo_wind_u</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span> <span class="n">geo_wind_v</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span>
+                <span class="n">color</span><span class="o">=</span><span class="s1">&#39;darkorchid&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">quiver_kwargs</span><span class="p">)</span>
+<span class="n">ageo</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span>
+                 <span class="n">ageo_wind_u</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span> <span class="n">ageo_wind_v</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span>
+                 <span class="n">color</span><span class="o">=</span><span class="s1">&#39;lime&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">quiver_kwargs</span><span class="p">)</span>
+
+<span class="c1"># Add a title to the plot</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;1000mb Geopotential Heights(m), Wind(blue), &#39;</span>
+          <span class="s1">&#39;Geostrophic Wind(purple), and </span><span class="se">\n</span><span class="s1"> Ageostrophic Wind(green) &#39;</span>
+          <span class="sa">f</span><span class="s1">&#39;for </span><span class="si">{</span><span class="n">vtime</span><span class="si">:</span><span class="s1">%d %B %Y %H:%MZ</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0.5, 1.0, &#39;1000mb Geopotential Heights(m), Wind(blue), Geostrophic Wind(purple), and \n Ageostrophic Wind(green) for 22 August 2016 21:00Z&#39;)
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_1_states_provinces_lakes.zip
+  warnings.warn(f&#39;Downloading: {url}&#39;, DownloadWarning)
+</pre></div>
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+  <section class="tex2jax_ignore mathjax_ignore" id="analytic-300-hpa-trough">
+<h1>Analytic 300-hPa Trough<a class="headerlink" href="#analytic-300-hpa-trough" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Below are three definitions to create an analytic 300-hPa trough roughly
+based on the Sanders Analytic Model with modified coefficients to create
+different style waves.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">single_300hPa_trough</span><span class="p">(</span><span class="n">parameter</span><span class="o">=</span><span class="s1">&#39;hght&#39;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot; Single trough with heights and Temperatures based on Sanders Analytic Model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">X</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="mf">.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">101</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">linspace</span><span class="p">(</span><span class="mf">.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</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">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
+
+    <span class="n">p</span> <span class="o">=</span> <span class="mi">4</span>
+    <span class="n">q</span> <span class="o">=</span> <span class="mi">2</span>
+
+    <span class="k">if</span> <span class="n">parameter</span> <span class="o">==</span> <span class="s1">&#39;hght&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">(</span><span class="mi">9240</span> <span class="o">+</span> <span class="mi">100</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">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">pi</span><span class="p">)</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">q</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">pi</span><span class="p">)</span>
+                <span class="o">+</span> <span class="mi">200</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">y</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="mi">300</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">x</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">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="n">parameter</span> <span class="o">==</span> <span class="s1">&#39;temp&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">50</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">cos</span><span class="p">(</span><span class="n">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">pi</span><span class="p">)</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">q</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">pi</span><span class="p">)</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">cos</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">pi</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.5</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">x</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">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
+
+
+<span class="k">def</span> <span class="nf">lifting_300hPa_trough</span><span class="p">(</span><span class="n">parameter</span><span class="o">=</span><span class="s1">&#39;hght&#39;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot; Lifting trough with heights and Temperatures based on Sanders Analytic Model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">X</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="mf">.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">101</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">linspace</span><span class="p">(</span><span class="mf">.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</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">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
+
+    <span class="n">p</span> <span class="o">=</span> <span class="mi">4</span>
+    <span class="n">q</span> <span class="o">=</span> <span class="mi">2</span>
+
+    <span class="k">if</span> <span class="n">parameter</span> <span class="o">==</span> <span class="s1">&#39;hght&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">(</span><span class="mi">9240</span> <span class="o">+</span> <span class="mi">150</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">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">pi</span><span class="p">)</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">q</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">pi</span><span class="p">)</span>
+                <span class="o">+</span> <span class="mi">200</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">y</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="mi">400</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">x</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">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="n">parameter</span> <span class="o">==</span> <span class="s1">&#39;temp&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">50</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">cos</span><span class="p">(</span><span class="n">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">pi</span><span class="p">)</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">q</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">pi</span><span class="p">)</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">cos</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">pi</span><span class="p">)</span> <span class="o">+</span> <span class="mi">5</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">x</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">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span>
+
+
+<span class="k">def</span> <span class="nf">digging_300hPa_trough</span><span class="p">(</span><span class="n">parameter</span><span class="o">=</span><span class="s1">&#39;hght&#39;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot; Digging trough with heights and Temperatures based on Sanders Analytic Model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">X</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="mf">.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">101</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">linspace</span><span class="p">(</span><span class="mf">.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</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">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
+
+    <span class="n">p</span> <span class="o">=</span> <span class="mi">4</span>
+    <span class="n">q</span> <span class="o">=</span> <span class="mi">2</span>
+
+    <span class="k">if</span> <span class="n">parameter</span> <span class="o">==</span> <span class="s1">&#39;hght&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">(</span><span class="mi">9240</span> <span class="o">+</span> <span class="mi">150</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">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">pi</span><span class="p">)</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">q</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">pi</span><span class="p">)</span>
+                <span class="o">+</span> <span class="mi">200</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">y</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="mi">400</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">x</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="mi">5</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="mi">2</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="n">parameter</span> <span class="o">==</span> <span class="s1">&#39;temp&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">50</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">cos</span><span class="p">(</span><span class="n">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">pi</span><span class="p">)</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">q</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">pi</span><span class="p">)</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">cos</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">pi</span><span class="p">)</span> <span class="o">+</span> <span class="mi">5</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">x</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">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Call the appropriate definition to develop the desired wave.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Single Trough</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">single_300hPa_trough</span><span class="p">(</span><span class="n">parameter</span><span class="o">=</span><span class="s1">&#39;hght&#39;</span><span class="p">)</span>
+<span class="n">T</span> <span class="o">=</span> <span class="n">single_300hPa_trough</span><span class="p">(</span><span class="n">parameter</span><span class="o">=</span><span class="s1">&#39;temp&#39;</span><span class="p">)</span>
+
+<span class="c1"># Lifting Trough</span>
+<span class="c1"># Z = lifting_300hPa_trough(parameter=&#39;hght&#39;)</span>
+<span class="c1"># T = lifting_300hPa_trough(parameter=&#39;temp&#39;)</span>
+
+<span class="c1"># Digging Trough</span>
+<span class="c1"># Z = digging_300hPa_trough(parameter=&#39;hght&#39;)</span>
+<span class="c1"># T = digging_300hPa_trough(parameter=&#39;temp&#39;)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Set geographic parameters for analytic grid to then</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lats</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">35</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</span>
+<span class="n">lons</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">260</span><span class="p">,</span> <span class="mi">290</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</span>
+<span class="n">lon</span><span class="p">,</span> <span class="n">lat</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">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">)</span>
+
+<span class="c1"># Coriolis Parameter</span>
+<span class="n">f</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">coriolis_parameter</span><span class="p">(</span><span class="n">lat</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degrees</span><span class="p">)</span>
+
+<span class="c1"># Calculate Geostrophic Wind from Analytic Heights</span>
+<span class="n">dx</span><span class="p">,</span> <span class="n">dy</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">lat_lon_grid_deltas</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">)</span>
+<span class="n">ugeo</span><span class="p">,</span> <span class="n">vgeo</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">geostrophic_wind</span><span class="p">(</span><span class="n">Z</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">meter</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="p">,</span> <span class="n">latitude</span><span class="o">=</span><span class="n">lat</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degrees</span><span class="p">)</span>
+
+<span class="c1"># Get the wind direction for each point</span>
+<span class="n">wdir</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_direction</span><span class="p">(</span><span class="n">ugeo</span><span class="p">,</span> <span class="n">vgeo</span><span class="p">)</span>
+
+<span class="c1"># Compute the Gradient Wind via an approximation</span>
+<span class="n">dydx</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">first_derivative</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">delta</span><span class="o">=</span><span class="n">dx</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">d2ydx2</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">first_derivative</span><span class="p">(</span><span class="n">dydx</span><span class="p">,</span> <span class="n">delta</span><span class="o">=</span><span class="n">dx</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">R</span> <span class="o">=</span> <span class="p">((</span><span class="mi">1</span> <span class="o">+</span> <span class="n">dydx</span><span class="o">.</span><span class="n">m</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mf">3.</span> <span class="o">/</span> <span class="mf">2.</span><span class="p">))</span> <span class="o">/</span> <span class="n">d2ydx2</span><span class="o">.</span><span class="n">m</span>
+
+<span class="n">geo_mag</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">ugeo</span><span class="p">,</span> <span class="n">vgeo</span><span class="p">)</span>
+<span class="n">grad_mag</span> <span class="o">=</span> <span class="n">geo_mag</span><span class="o">.</span><span class="n">m</span> <span class="o">-</span> <span class="p">(</span><span class="n">geo_mag</span><span class="o">.</span><span class="n">m</span><span class="o">**</span><span class="mi">2</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">magnitude</span> <span class="o">*</span> <span class="n">R</span><span class="p">)</span>
+
+<span class="n">ugrad</span><span class="p">,</span> <span class="n">vgrad</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_components</span><span class="p">(</span><span class="n">grad_mag</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;m/s&#39;</span><span class="p">),</span> <span class="n">wdir</span><span class="p">)</span>
+
+<span class="c1"># Calculate Ageostrophic wind</span>
+<span class="n">uageo</span> <span class="o">=</span> <span class="n">ugrad</span> <span class="o">-</span> <span class="n">ugeo</span>
+<span class="n">vageo</span> <span class="o">=</span> <span class="n">vgrad</span> <span class="o">-</span> <span class="n">vgeo</span>
+
+<span class="c1"># Compute QVectors</span>
+<span class="n">uqvect</span><span class="p">,</span> <span class="n">vqvect</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">q_vector</span><span class="p">(</span><span class="n">ugeo</span><span class="p">,</span> <span class="n">vgeo</span><span class="p">,</span> <span class="n">T</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span><span class="p">,</span> <span class="mi">500</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="p">)</span>
+
+<span class="c1"># Calculate divergence of the ageostrophic wind</span>
+<span class="n">div</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">divergence</span><span class="p">(</span><span class="n">uageo</span><span class="p">,</span> <span class="n">vageo</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="n">dy</span><span class="p">)</span>
+
+<span class="c1"># Calculate Relative Vorticity Advection</span>
+<span class="n">relvor</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">vorticity</span><span class="p">(</span><span class="n">ugeo</span><span class="p">,</span> <span class="n">vgeo</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="n">dy</span><span class="p">)</span>
+<span class="n">adv</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">advection</span><span class="p">(</span><span class="n">relvor</span><span class="p">,</span> <span class="n">ugeo</span><span class="p">,</span> <span class="n">vgeo</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="n">dy</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Create figure containing Geopotential Heights, Temperature, Divergence
+of the Ageostrophic Wind, Relative Vorticity Advection (shaded),
+geostrphic wind barbs, and Q-vectors.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></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">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
+
+<span class="c1"># Plot Geopotential Height Contours</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">12000</span><span class="p">,</span> <span class="mi">120</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot Temperature Contours</span>
+<span class="n">cs2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">50</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs2</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot Divergence of Ageo Wind Contours</span>
+<span class="n">cs3</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">div</span><span class="o">*</span><span class="mi">10</span><span class="o">**</span><span class="mi">9</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">25</span><span class="p">,</span> <span class="mi">26</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span>
+                 <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs3</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot Rel. Vor. Adv. colorfilled</span>
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">adv</span><span class="o">*</span><span class="mi">10</span><span class="o">**</span><span class="mi">9</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">20</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">1</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">cm</span><span class="o">.</span><span class="n">bwr</span><span class="p">)</span>
+<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">pad</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+<span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Rel. Vor. Adv.&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot Geostrophic Wind Barbs</span>
+<span class="n">wind_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
+         <span class="n">ugeo</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">,</span> <span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="n">vgeo</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">,</span> <span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">m</span><span class="p">)</span>
+
+<span class="c1"># Plot Ageostrophic Wind Vectors</span>
+<span class="c1"># ageo_slice = slice(None, None, 10)</span>
+<span class="c1"># ax.quiver(lons[ageo_slice], lats[ageo_slice],</span>
+<span class="c1">#           uageo[ageo_slice, ageo_slice].m, vageo[ageo_slice, ageo_slice].m,</span>
+<span class="c1">#           color=&#39;blue&#39;, pivot=&#39;mid&#39;)</span>
+
+<span class="c1"># Plot QVectors</span>
+<span class="n">qvec_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">qvec_slice</span><span class="p">],</span> <span class="n">lats</span><span class="p">[</span><span class="n">qvec_slice</span><span class="p">],</span>
+          <span class="n">uqvect</span><span class="p">[</span><span class="n">qvec_slice</span><span class="p">,</span> <span class="n">qvec_slice</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="n">vqvect</span><span class="p">[</span><span class="n">qvec_slice</span><span class="p">,</span> <span class="n">qvec_slice</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="p">,</span>
+          <span class="n">color</span><span class="o">=</span><span class="s1">&#39;darkcyan&#39;</span><span class="p">,</span> <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;mid&#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="s1">&#39;300-hPa Geo Heights (black), Q-Vector (dark cyan), Divergence (grey; dashed)&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0.5, 1.0, &#39;300-hPa Geo Heights (black), Q-Vector (dark cyan), Divergence (grey; dashed)&#39;)
+</pre></div>
+</div>
+<img alt="../../_images/67cc747389a2e46fb6c9eb3a4f270e7fc3d82f365f981c079fae47d6a330ba62.png" src="../../_images/67cc747389a2e46fb6c9eb3a4f270e7fc3d82f365f981c079fae47d6a330ba62.png" />
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+</ul>
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+  <section class="tex2jax_ignore mathjax_ignore" id="differential-temperature-advection-with-narr-data">
+<h1>Differential Temperature Advection with NARR Data<a class="headerlink" href="#differential-temperature-advection-with-narr-data" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>By: Kevin Goebbert</p>
+<p>This example creates a four-panel plot to illustrate the difference
+between single level temperature advection and a computed differential
+temperature advection between two layers. This example makes use of NARR
+output.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
+<span class="kn">import</span> <span class="nn">matplotlib.gridspec</span> <span class="k">as</span> <span class="nn">gridspec</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</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">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="data-input">
+<h2>Data Input<a class="headerlink" href="#data-input" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Use Xarray to access GFS data from THREDDS resource and uses
+metpy accessor to parse file to make it easy to pull data using
+common coordinate names (e.g., vertical) and attach units.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/casestudies/&#39;</span>
+                     <span class="s1">&#39;python-gallery/NARR_19930313_1800.nc&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+<span class="n">ds</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">assign_latitude_longitude</span><span class="p">()</span>
+
+<span class="c1"># Get lat/lon data from file</span>
+<span class="n">lats</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span>
+<span class="n">lons</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span>
+
+<span class="c1"># Get 700-hPa data and smooth</span>
+<span class="n">level</span> <span class="o">=</span> <span class="mi">700</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
+<span class="n">hght_700</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
+<span class="n">tmpk_700</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;Temperature_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
+<span class="n">uwnd_700</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span>
+    <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
+<span class="n">vwnd_700</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span>
+    <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
+
+<span class="c1"># Get 300-hPa data and</span>
+<span class="n">level</span> <span class="o">=</span> <span class="mi">300</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
+<span class="n">hght_300</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
+<span class="n">tmpk_300</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;Temperature_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
+<span class="n">uwnd_300</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span>
+    <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
+<span class="n">vwnd_300</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span>
+    <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
+
+<span class="c1"># Convert Temperatures to degC</span>
+<span class="n">tmpc_700</span> <span class="o">=</span> <span class="n">tmpk_700</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
+<span class="n">tmpc_300</span> <span class="o">=</span> <span class="n">tmpk_300</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
+
+<span class="c1"># Get time in a nice datetime object format</span>
+<span class="n">vtime</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="differential-temperature-advection-calculation">
+<h2>Differential Temperature Advection Calculation<a class="headerlink" href="#differential-temperature-advection-calculation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Use MetPy advection funtion to calculate temperature advection at 700
+and 300 hPa, then manually compute the differential between those two
+layers. The differential temperature advection is then valid at 500 hPa
+(due to centered differencing) and is the same level that height changes
+due to absolute vorticity advection is commonly assessed.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Use MetPy advection function to calculate temperature advection at two levels</span>
+<span class="n">tadv_700</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">advection</span><span class="p">(</span><span class="n">tmpk_700</span><span class="p">,</span> <span class="n">uwnd_700</span><span class="p">,</span> <span class="n">vwnd_700</span><span class="p">)</span>
+<span class="n">tadv_300</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">advection</span><span class="p">(</span><span class="n">tmpk_300</span><span class="p">,</span> <span class="n">uwnd_300</span><span class="p">,</span> <span class="n">vwnd_300</span><span class="p">)</span>
+
+<span class="c1"># Centered finite difference to calculate differential temperature advection</span>
+<span class="n">diff_tadv</span> <span class="o">=</span> <span class="p">((</span><span class="n">tadv_700</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">tadv_300</span><span class="o">.</span><span class="n">data</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">400</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">))</span><span class="o">.</span><span class="n">to_base_units</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_2880/3635212219.py:2: UserWarning: Vertical dimension number not found. Defaulting to (..., Z, Y, X) order.
+  tadv_700 = mpcalc.advection(tmpk_700, uwnd_700, vwnd_700)
+/tmp/ipykernel_2880/3635212219.py:3: UserWarning: Vertical dimension number not found. Defaulting to (..., Z, Y, X) order.
+  tadv_300 = mpcalc.advection(tmpk_300, uwnd_300, vwnd_300)
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="make-four-panel-plot">
+<h2>Make Four Panel Plot<a class="headerlink" href="#make-four-panel-plot" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>A four panel plot is produced to illustrate the temperature fields at
+two levels (700 and 300 hPa), which are common fields to be plotted.
+Then a panel containing the evaluated temperature advection at 700 hPa
+and differential temperature advection between 700 and 300 hPa. Of
+meteorological significance is the difference between these two
+advection plots. For the QG Height Tendency equation, the forcing term
+is proportional to the differential temperature advection, which paints
+a slightly different picture than just the 700-hPa temperature advection
+alone.</p>
+<p>To create the four panel plot it takes a bit of code at this point. The
+following code is segmented into Upper-left, Lower-left, Upper-right,
+Lower-right panels using matplotlib’s gridspec to help with spacing.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up plot crs (mapcrs) and the data crs, will need to</span>
+<span class="c1"># transform all variables</span>
+<span class="n">mapcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span>
+                               <span class="n">central_latitude</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span>
+                               <span class="n">standard_parallels</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
+<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
+
+<span class="c1"># Set some common contour interval levels</span>
+<span class="n">clevs_700_tmpc</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="o">-</span><span class="mi">40</span><span class="p">,</span> <span class="mi">41</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">clevs_700_hght</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="mi">8000</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+<span class="n">clevs_300_hght</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="mi">10000</span><span class="p">,</span> <span class="mi">120</span><span class="p">)</span>
+
+<span class="c1"># Create slice to reduce number of wind barbs at plot time</span>
+<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+
+<span class="c1"># Start figure</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">22</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
+
+<span class="c1"># Use gridspec to help size elements of plot; small top plot</span>
+<span class="c1"># and big bottom plot</span>
+<span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">height_ratios</span><span class="o">=</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">hspace</span><span class="o">=</span><span class="mf">0.03</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="mf">0.03</span><span class="p">)</span>
+
+
+<span class="c1"># Upper-left panel (700-hPa TMPC)</span>
+<span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</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">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">49</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_700</span><span class="p">,</span> <span class="n">clevs_700_tmpc</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">cm</span><span class="o">.</span><span class="n">coolwarm</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$^{\circ}$C&#39;</span><span class="p">)</span>
+
+<span class="n">csf</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_700</span><span class="p">,</span> <span class="n">clevs_700_tmpc</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span>
+                  <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">csf</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_700</span><span class="p">,</span> <span class="n">clevs_700_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+                 <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="n">ax1</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">uwnd_700</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">vwnd_700</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</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="s1">&#39;700-hPa NARR HGHT (m), TMPC, and Wind (kt)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;Valid: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+
+
+<span class="c1"># Lower-left panel (300-hPa TMPC)</span>
+<span class="n">ax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</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">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">49</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_300</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">60</span><span class="p">,</span> <span class="o">-</span><span class="mi">24</span><span class="p">,</span> <span class="mi">2</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">cm</span><span class="o">.</span><span class="n">BuPu_r</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$^{\circ}$C&#39;</span><span class="p">)</span>
+
+<span class="n">csf</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_300</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">60</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">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">csf</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_300</span><span class="p">,</span> <span class="n">clevs_300_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+                 <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="n">ax2</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">uwnd_300</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">vwnd_300</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</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="s1">&#39;300-hPa NARR HGHT (m), TMPC, and Wind (kt)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;Valid: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+
+
+<span class="c1"># Upper-right panel (700-hPa TMPC Adv)</span>
+<span class="n">ax3</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</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">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">49</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax3</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tadv_700</span><span class="o">*</span><span class="mi">3600</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">1</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">cm</span><span class="o">.</span><span class="n">coolwarm</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;TMPC ADV ($^{\circ}$C h$^{-1}$)&#39;</span><span class="p">)</span>
+
+<span class="n">csf</span> <span class="o">=</span> <span class="n">ax3</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_700</span><span class="p">,</span> <span class="n">clevs_700_tmpc</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span>
+                  <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">csf</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax3</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_700</span><span class="p">,</span> <span class="n">clevs_700_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+                 <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="n">ax3</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">uwnd_700</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">vwnd_700</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</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="s1">&#39;700-hPa NARR HGHT (m), TMP ADV (C/h), and Wind (kt)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;Valid: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+
+
+<span class="c1"># Lower-right panel (diff TMPC)</span>
+<span class="n">ax4</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</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">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
+<span class="n">ax4</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">49</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">ax4</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+<span class="n">ax4</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax4</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">diff_tadv</span><span class="o">*</span><span class="mf">1e9</span><span class="p">,</span> <span class="n">clevs_700_tmpc</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">cm</span><span class="o">.</span><span class="n">coolwarm</span><span class="p">,</span> <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+                  <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;dTMPC ($10^9$ $^{\circ}$C s$^{-1}$ Pa$^{-1}$)&#39;</span><span class="p">)</span>
+
+<span class="n">csf</span> <span class="o">=</span> <span class="n">ax4</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_700</span><span class="p">,</span> <span class="n">clevs_700_tmpc</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span>
+                  <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">csf</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax4</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_700</span><span class="p">,</span> <span class="n">clevs_700_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+                 <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="n">ax4</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">uwnd_700</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">vwnd_700</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+          <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</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="s1">&#39;700-hPa NARR HGHT (m), Diff. TMP ADV (C/s/Pa), and Wind (kt)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;Valid: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(1.0, 1.0, &#39;Valid: 1993-03-13 18:00:00&#39;)
+</pre></div>
+</div>
+<img alt="../../_images/9e6822df19649397cc745aac356ea92826f3b2fb6f207ef794d9954937d33f21.png" src="../../_images/9e6822df19649397cc745aac356ea92826f3b2fb6f207ef794d9954937d33f21.png" />
+</div>
+</div>
+</section>
+</section>
+
+    <script type="text/x-thebe-config">
+    {
+        requestKernel: true,
+        binderOptions: {
+            repo: "binder-examples/jupyter-stacks-datascience",
+            ref: "master",
+        },
+        codeMirrorConfig: {
+            theme: "abcdef",
+            mode: "python"
+        },
+        kernelOptions: {
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+    }
+    </script>
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+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">metpy.calc</span> <span class="kn">import</span> <span class="n">geostrophic_wind</span>
+<span class="kn">from</span> <span class="nn">metpy.calc</span> <span class="kn">import</span> <span class="n">q_vector</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</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">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
+<span class="kn">from</span> <span class="nn">scipy.ndimage.filters</span> <span class="kn">import</span> <span class="n">gaussian_filter</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_2907/2639708157.py:10: DeprecationWarning: Please import `gaussian_filter` from the `scipy.ndimage` namespace; the `scipy.ndimage.filters` namespace is deprecated and will be removed in SciPy 2.0.0.
+  from scipy.ndimage.filters import gaussian_filter
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">## opening NetCDF file using xarray </span>
+
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_mfdataset</span><span class="p">(</span><span class="s2">&quot;/home/jovyan/metpy-cookbook/NETCDF_FILE.nc&quot;</span><span class="p">,</span><span class="n">combine</span> <span class="o">=</span> <span class="s2">&quot;nested&quot;</span><span class="p">,</span> <span class="n">concat_dim</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">,</span><span class="n">decode_times</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">OSError</span><span class="g g-Whitespace">                                   </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">line</span> <span class="mi">3</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1">## opening NetCDF file using xarray </span>
+<span class="ne">----&gt; </span><span class="mi">3</span> <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_mfdataset</span><span class="p">(</span><span class="s2">&quot;/home/jovyan/metpy-cookbook/NETCDF_FILE.nc&quot;</span><span class="p">,</span><span class="n">combine</span> <span class="o">=</span> <span class="s2">&quot;nested&quot;</span><span class="p">,</span> <span class="n">concat_dim</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">,</span><span class="n">decode_times</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/xarray/backends/api.py:1019,</span> in <span class="ni">open_mfdataset</span><span class="nt">(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1016</span> <span class="n">paths</span> <span class="o">=</span> <span class="n">_find_absolute_paths</span><span class="p">(</span><span class="n">paths</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="n">engine</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1018</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">paths</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">1019</span>     <span class="k">raise</span> <span class="ne">OSError</span><span class="p">(</span><span class="s2">&quot;no files to open&quot;</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1021</span> <span class="k">if</span> <span class="n">combine</span> <span class="o">==</span> <span class="s2">&quot;nested&quot;</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1022</span>     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">concat_dim</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">))</span> <span class="ow">or</span> <span class="n">concat_dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+
+<span class="ne">OSError</span>: no files to open
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">ds</span>
+
+<span class="ne">NameError</span>: name &#39;ds&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#### making a function to slice the xarray dataset according to our need.</span>
+<span class="k">def</span> <span class="nf">slicer</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="n">lat1</span><span class="p">,</span> <span class="n">lat2</span><span class="p">,</span> <span class="n">lon1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">time1</span><span class="p">,</span><span class="n">time2</span><span class="p">)</span> <span class="p">:</span>
+    <span class="n">sliced_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lat</span> <span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="n">lat1</span><span class="p">,</span> <span class="n">lat2</span><span class="p">),</span> <span class="n">lon</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">lon1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">),</span><span class="n">time</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">time1</span><span class="p">,</span> <span class="n">time2</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">sliced_data</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#slicing the data for CONUS only</span>
+
+<span class="n">new_data</span> <span class="o">=</span> <span class="n">slicer</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span><span class="mf">23.5</span><span class="p">,</span><span class="mf">50.5</span><span class="p">,</span><span class="o">-</span><span class="mf">125.5</span><span class="p">,</span><span class="o">-</span><span class="mf">66.5</span><span class="p">,</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">line</span> <span class="mi">3</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1">#slicing the data for CONUS only</span>
+<span class="ne">----&gt; </span><span class="mi">3</span> <span class="n">new_data</span> <span class="o">=</span> <span class="n">slicer</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span><span class="mf">23.5</span><span class="p">,</span><span class="mf">50.5</span><span class="p">,</span><span class="o">-</span><span class="mf">125.5</span><span class="p">,</span><span class="o">-</span><span class="mf">66.5</span><span class="p">,</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+<span class="ne">NameError</span>: name &#39;ds&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">new_data</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">new_data</span>
+
+<span class="ne">NameError</span>: name &#39;new_data&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">###extracting temperature, pressure, and geopotential from the dataset</span>
+<span class="n">gph</span> <span class="o">=</span> <span class="n">new_data</span><span class="o">.</span><span class="n">H</span>
+<span class="n">p</span> <span class="o">=</span><span class="n">new_data</span><span class="o">.</span><span class="n">lev</span>
+<span class="n">T</span> <span class="o">=</span> <span class="n">new_data</span><span class="o">.</span><span class="n">T</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="n">line</span> <span class="mi">2</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1">###extracting temperature, pressure, and geopotential from the dataset</span>
+<span class="ne">----&gt; </span><span class="mi">2</span> <span class="n">gph</span> <span class="o">=</span> <span class="n">new_data</span><span class="o">.</span><span class="n">H</span>
+<span class="g g-Whitespace">      </span><span class="mi">3</span> <span class="n">p</span> <span class="o">=</span><span class="n">new_data</span><span class="o">.</span><span class="n">lev</span>
+<span class="g g-Whitespace">      </span><span class="mi">4</span> <span class="n">T</span> <span class="o">=</span> <span class="n">new_data</span><span class="o">.</span><span class="n">T</span>
+
+<span class="ne">NameError</span>: name &#39;new_data&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">gph</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">gph</span>
+
+<span class="ne">NameError</span>: name &#39;gph&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">U</span><span class="p">,</span><span class="n">V</span> <span class="o">=</span> <span class="n">geostrophic_wind</span><span class="p">(</span><span class="n">gph</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">9</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">U</span><span class="p">,</span><span class="n">V</span> <span class="o">=</span> <span class="n">geostrophic_wind</span><span class="p">(</span><span class="n">gph</span><span class="p">)</span>
+
+<span class="ne">NameError</span>: name &#39;gph&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">U</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">U</span><span class="p">)</span>
+
+<span class="ne">NameError</span>: name &#39;U&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataproj</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span> <span class="n">PlateCarree</span> <span class="p">()</span>
+<span class="c1"># # Plot projection</span>
+<span class="c1"># # The look you want for the view.</span>
+<span class="n">plotproj</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span> <span class="n">PlateCarree</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">15.</span><span class="p">,</span><span class="mf">12.</span><span class="p">))</span>
+
+<span class="n">ax</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span><span class="n">projection</span><span class="o">=</span><span class="n">plotproj</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</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;Geostrophic Wind Calculated Using Metpy&quot;</span><span class="p">,</span><span class="n">size</span> <span class="o">=</span> <span class="mi">30</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">quiver</span> <span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">new_data</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">U</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">12</span><span class="p">,:,:],</span><span class="n">V</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">12</span><span class="p">,:,:],</span><span class="n">minlength</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span><span class="n">units</span><span class="o">=</span><span class="s1">&#39;width&#39;</span><span class="p">)</span>
+<span class="c1"># plt.colorbar (orientation = &quot;horizontal&quot;, pad=0.01).ax.tick_params(labelsize=20)</span>
+<span class="n">plt</span><span class="o">.</span> <span class="n">show</span> <span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">11</span><span class="p">],</span> <span class="n">line</span> <span class="mi">12</span>
+<span class="g g-Whitespace">     </span><span class="mi">10</span> <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">11</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Geostrophic Wind Calculated Using Metpy&quot;</span><span class="p">,</span><span class="n">size</span> <span class="o">=</span> <span class="mi">30</span><span class="p">)</span>
+<span class="ne">---&gt; </span><span class="mi">12</span> <span class="n">plt</span><span class="o">.</span><span class="n">quiver</span> <span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">new_data</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">U</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">12</span><span class="p">,:,:],</span><span class="n">V</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">12</span><span class="p">,:,:],</span><span class="n">minlength</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span><span class="n">units</span><span class="o">=</span><span class="s1">&#39;width&#39;</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">13</span> <span class="c1"># plt.colorbar (orientation = &quot;horizontal&quot;, pad=0.01).ax.tick_params(labelsize=20)</span>
+<span class="g g-Whitespace">     </span><span class="mi">14</span> <span class="n">plt</span><span class="o">.</span> <span class="n">show</span> <span class="p">()</span>
+
+<span class="ne">NameError</span>: name &#39;new_data&#39; is not defined
+</pre></div>
+</div>
+<img alt="../../_images/1e97a632b8c610d73f63fe562c004b432c6f555fb6d80732064800c3daabebb6.png" src="../../_images/1e97a632b8c610d73f63fe562c004b432c6f555fb6d80732064800c3daabebb6.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">qx</span><span class="p">,</span> <span class="n">qy</span> <span class="o">=</span> <span class="n">q_vector</span><span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="n">V</span><span class="p">,</span><span class="n">T</span><span class="p">,</span><span class="n">p</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">12</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">qx</span><span class="p">,</span> <span class="n">qy</span> <span class="o">=</span> <span class="n">q_vector</span><span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="n">V</span><span class="p">,</span><span class="n">T</span><span class="p">,</span><span class="n">p</span><span class="p">)</span>
+
+<span class="ne">NameError</span>: name &#39;U&#39; is not defined
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataproj</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span> <span class="n">PlateCarree</span> <span class="p">()</span>
+<span class="c1"># # Plot projection</span>
+<span class="c1"># # The look you want for the view.</span>
+<span class="n">plotproj</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span> <span class="n">PlateCarree</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">15.</span><span class="p">,</span><span class="mf">12.</span><span class="p">))</span>
+
+<span class="n">ax</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span><span class="n">projection</span><span class="o">=</span><span class="n">plotproj</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</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;Q-vector Calculated Using Metpy&quot;</span><span class="p">,</span><span class="n">size</span> <span class="o">=</span> <span class="mi">30</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">new_data</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span><span class="n">gaussian_filter</span><span class="p">(</span><span class="n">gph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">12</span><span class="p">,:,:],</span><span class="mi">1</span><span class="p">),</span> <span class="n">colors</span> <span class="o">=</span> <span class="s2">&quot;black&quot;</span><span class="p">)</span>
+<span class="c1"># plt.contourf(new_data.lon, new_data.lat, new_data.OMEGA[0,12,:,:],levels =np.arange(-2,2,0.2),cmap = &quot;RdBu&quot;, transform=dataproj,extend = &quot;both&quot; )</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span> <span class="p">(</span><span class="n">orientation</span> <span class="o">=</span> <span class="s2">&quot;horizontal&quot;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">labelsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+<span class="c1"># plt.colorbar (orientation = &quot;horizontal&quot;, pad=0.01).ax.tick_params(labelsize=20)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">quiver</span> <span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">new_data</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">qx</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">12</span><span class="p">,:,:],</span><span class="n">gaussian_filter</span><span class="p">(</span><span class="n">qy</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">12</span><span class="p">,:,:],</span><span class="mf">0.7</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span><span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;mid&#39;</span><span class="p">,</span> 
+          <span class="n">scale</span><span class="o">=</span><span class="mf">1e-11</span><span class="p">,</span> <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;inches&#39;</span><span class="p">,</span>
+          <span class="n">transform</span><span class="o">=</span><span class="n">dataproj</span><span class="p">)</span>
+
+<span class="c1"># gaussian_filter(data, sigma)</span>
+<span class="n">plt</span><span class="o">.</span> <span class="n">show</span> <span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">13</span><span class="p">],</span> <span class="n">line</span> <span class="mi">13</span>
+<span class="g g-Whitespace">     </span><span class="mi">10</span> <span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">11</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Q-vector Calculated Using Metpy&quot;</span><span class="p">,</span><span class="n">size</span> <span class="o">=</span> <span class="mi">30</span><span class="p">)</span>
+<span class="ne">---&gt; </span><span class="mi">13</span> <span class="n">plt</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">new_data</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span><span class="n">gaussian_filter</span><span class="p">(</span><span class="n">gph</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">12</span><span class="p">,:,:],</span><span class="mi">1</span><span class="p">),</span> <span class="n">colors</span> <span class="o">=</span> <span class="s2">&quot;black&quot;</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">14</span> <span class="c1"># plt.contourf(new_data.lon, new_data.lat, new_data.OMEGA[0,12,:,:],levels =np.arange(-2,2,0.2),cmap = &quot;RdBu&quot;, transform=dataproj,extend = &quot;both&quot; )</span>
+<span class="g g-Whitespace">     </span><span class="mi">15</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span> <span class="p">(</span><span class="n">orientation</span> <span class="o">=</span> <span class="s2">&quot;horizontal&quot;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">labelsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+
+<span class="ne">NameError</span>: name &#39;new_data&#39; is not defined
+</pre></div>
+</div>
+<img alt="../../_images/9afd6dc84cab18996a538d3b3e397922d61084707424d45bbe056f9037001c08.png" src="../../_images/9afd6dc84cab18996a538d3b3e397922d61084707424d45bbe056f9037001c08.png" />
+</div>
+</div>
+
+    <script type="text/x-thebe-config">
+    {
+        requestKernel: true,
+        binderOptions: {
+            repo: "binder-examples/jupyter-stacks-datascience",
+            ref: "master",
+        },
+        codeMirrorConfig: {
+            theme: "abcdef",
+            mode: "python"
+        },
+        kernelOptions: {
+            name: "python3",
+            path: "./notebooks/synoptic"
+        },
+        predefinedOutput: true
+    }
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+<li class="toctree-l2"><a class="reference internal" href="850hPa_Temperature_Advection.html">850 hPa Temperature Advection</a></li>
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+  <section class="tex2jax_ignore mathjax_ignore" id="isentropic-analysis">
+<h1>Isentropic Analysis<a class="headerlink" href="#isentropic-analysis" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>The MetPy function <code class="docutils literal notranslate"><span class="pre">metpy.calc.isentropic_interpolation</span></code> allows for isentropic analysis from
+model analysis data in isobaric coordinates.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
+
+<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
+<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Getting the data</p>
+<p>In this example, the latest GFS forecasts data from the National Centers for
+Environmental Information (https://nomads.ncdc.noaa.gov) will be used, courtesy of the
+Univeristy Corporation for Atmospheric Research Thredds Data Server.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Latest GFS Dataset</span>
+<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="s1">&#39;http://thredds.ucar.edu/thredds/catalog/grib/&#39;</span>
+                 <span class="s1">&#39;NCEP/GFS/Global_0p5deg/catalog.xml&#39;</span><span class="p">)</span>
+<span class="n">ncss</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">latest</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
+
+<span class="c1"># Find the start of the model run and define time range</span>
+<span class="n">start_time</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">time_span</span><span class="p">[</span><span class="s1">&#39;begin&#39;</span><span class="p">]</span>
+<span class="n">start</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">start_time</span><span class="p">,</span> <span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1">T%H:%M:%Sz&#39;</span><span class="p">)</span>
+<span class="n">end</span> <span class="o">=</span> <span class="n">start</span> <span class="o">+</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
+
+<span class="c1"># Query for Latest GFS Run</span>
+<span class="n">gfsdata</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span><span class="o">.</span><span class="n">time_range</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">)</span><span class="o">.</span><span class="n">accept</span><span class="p">(</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
+
+<span class="n">gfsdata</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Temperature_isobaric&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;Relative_humidity_isobaric&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">add_lonlat</span><span class="p">()</span>
+
+<span class="c1"># Set the lat/lon box for the data you want to pull in.</span>
+<span class="c1"># lonlat_box(north_lat,south_lat,east_lon,west_lon)</span>
+<span class="n">gfsdata</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="o">-</span><span class="mi">150</span><span class="p">,</span> <span class="o">-</span><span class="mi">50</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">65</span><span class="p">)</span>
+
+<span class="c1"># Actually getting the data</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">gfsdata</span><span class="p">)</span>
+
+<span class="c1"># Make into an xarray Dataset object</span>
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+
+<span class="n">dlev_hght</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">dims</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">dlev_uwnd</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">dims</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">lat</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">latitude</span>
+<span class="n">lon</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">longitude</span>
+
+<span class="n">lev_hght</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="n">dlev_hght</span><span class="p">]</span>
+<span class="n">lev_uwnd</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="n">dlev_uwnd</span><span class="p">]</span>
+
+<span class="c1"># Due to a different number of vertical levels find where they are common</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">common_ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">intersect1d</span><span class="p">(</span><span class="n">lev_uwnd</span><span class="p">,</span> <span class="n">lev_hght</span><span class="p">,</span> <span class="n">return_indices</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">common_levels</span> <span class="o">=</span> <span class="n">lev_hght</span><span class="p">[</span><span class="n">common_ind</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">unit_array</span>
+<span class="n">times</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span>
+<span class="n">vtimes</span> <span class="o">=</span> <span class="n">times</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+<span class="n">temps</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">unit_array</span>
+<span class="n">uwnd</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">unit_array</span>
+<span class="n">vwnd</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">unit_array</span>
+<span class="n">relh</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Relative_humidity_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">unit_array</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>To properly interpolate to isentropic coordinates, the function must know the desired output
+isentropic levels. An array with these levels will be created below.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">isentlevs</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">310</span><span class="p">,</span> <span class="mi">316</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">kelvin</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Conversion to Isentropic Coordinates</p>
+<p>Once model data in isobaric coordinates has been pulled and the desired isentropic levels
+created, the conversion to isentropic coordinates can begin. Data will be passed to the
+function as below. The function requires that isentropic levels, isobaric levels, and
+temperature be input. Any additional inputs (in this case relative humidity, u, and v wind
+components) will be linearly interpolated to isentropic space.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">isent_anal</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">isentropic_interpolation</span><span class="p">(</span><span class="n">isentlevs</span><span class="p">,</span>
+                                             <span class="n">common_levels</span><span class="p">,</span>
+                                             <span class="n">temps</span><span class="p">[:,</span> <span class="n">common_ind</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:],</span>
+                                             <span class="n">relh</span><span class="p">,</span>
+                                             <span class="n">uwnd</span><span class="p">,</span>
+                                             <span class="n">vwnd</span><span class="p">,</span>
+                                             <span class="n">vertical_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_2931/414936893.py:1: UserWarning: Interpolation point out of data bounds encountered
+  isent_anal = mpcalc.isentropic_interpolation(isentlevs,
+</pre></div>
+</div>
+</div>
+</div>
+<p>The output is a list, so now we will separate the variables to different names before
+plotting.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">isentprs</span><span class="p">,</span> <span class="n">isentrh</span><span class="p">,</span> <span class="n">isentu</span><span class="p">,</span> <span class="n">isentv</span> <span class="o">=</span> <span class="n">isent_anal</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>A quick look at the shape of these variables will show that the data is now in isentropic
+coordinates, with the number of vertical levels as specified above.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">isentprs</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">isentrh</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">isentu</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">isentv</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(4, 2, 101, 201)
+(4, 2, 101, 201)
+(4, 2, 101, 201)
+(4, 2, 101, 201)
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Plotting the Isentropic Analysis</strong></p>
+<p>Set up our projection</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">crs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">central_latitude</span><span class="o">=</span><span class="mf">45.0</span><span class="p">)</span>
+
+<span class="c1"># Set up our array of latitude and longitude values and transform to</span>
+<span class="c1"># the desired projection.</span>
+<span class="n">clons</span><span class="p">,</span> <span class="n">clats</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">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">)</span>
+
+<span class="c1"># Get data to plot state and province boundaries</span>
+<span class="n">states_provinces</span> <span class="o">=</span> <span class="n">cfeature</span><span class="o">.</span><span class="n">NaturalEarthFeature</span><span class="p">(</span>
+    <span class="n">category</span><span class="o">=</span><span class="s1">&#39;cultural&#39;</span><span class="p">,</span>
+    <span class="n">name</span><span class="o">=</span><span class="s1">&#39;admin_1_states_provinces_lakes&#39;</span><span class="p">,</span>
+    <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;50m&#39;</span><span class="p">,</span>
+    <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
+
+<span class="c1"># Choose Isentropic Level index value</span>
+<span class="n">level</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># [310, 315]</span>
+
+<span class="c1"># Choose forecast hour index value</span>
+<span class="n">FH</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># [0, 3, 6, 9]</span>
+
+<span class="c1"># Start Figure</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">14.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">crs</span><span class="p">)</span>
+
+<span class="c1"># Set plot extent</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">((</span><span class="o">-</span><span class="mf">121.</span><span class="p">,</span> <span class="o">-</span><span class="mf">74.</span><span class="p">,</span> <span class="mf">25.</span><span class="p">,</span> <span class="mf">50.</span><span class="p">),</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">states_provinces</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+<span class="c1"># Plot the 300K surface</span>
+<span class="n">clevisent</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="mi">1000</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">clons</span><span class="p">,</span> <span class="n">clats</span><span class="p">,</span>
+                <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">isentprs</span><span class="p">[</span><span class="n">FH</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:],</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+                <span class="n">clevisent</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+                <span class="n">colors</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">1.5</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
+           <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">clons</span><span class="p">,</span> <span class="n">clats</span><span class="p">,</span>
+                 <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">isentrh</span><span class="p">[</span><span class="n">FH</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:],</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+                 <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">101</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+                 <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</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">cm</span><span class="o">.</span><span class="n">YlGn</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</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> <span class="n">aspect</span><span class="o">=</span><span class="mi">65</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">extendrect</span><span class="o">=</span><span class="s1">&#39;True&#39;</span><span class="p">)</span>
+
+<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="n">FH</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">clons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">2</span><span class="p">:]],</span> <span class="n">clats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">2</span><span class="p">:]],</span>
+         <span class="n">isentu</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="n">isentv</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="n">length</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
+         <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+<span class="c1"># Make some titles</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">isentlevs</span><span class="p">[</span><span class="n">level</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="si">:</span><span class="s1">.0f</span><span class="si">}</span><span class="s1"> K Isentropic Level&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;VALID: </span><span class="si">{</span><span class="n">vtimes</span><span class="p">[</span><span class="n">FH</span><span class="p">]</span><span class="si">}</span><span class="s1"> UTC&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(1.0, 1.0, &#39;VALID: 2024-06-14 12:00:00 UTC&#39;)
+</pre></div>
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+</div>
+</div>
+</section>
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+  <section class="tex2jax_ignore mathjax_ignore" id="baroclinic-potential-vorticity-analysis-divergence-and-winds">
+<h1>Baroclinic Potential Vorticity Analysis, Divergence, and Winds<a class="headerlink" href="#baroclinic-potential-vorticity-analysis-divergence-and-winds" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>Classic baroclinic potential vorticity plot at 250 hPa using GFS
+analysis file.</p>
+<p>This example uses example data from the GFS analysis for 12 UTC 31
+October 2016 and uses xarray as the main read source with using MetPy to
+calculate the baroclinic potential vorticity, divergence and wind speed
+with geographic plotting using Cartopy for a CONUS view of the 250-hPa
+surface with divergence and wind barbs.</p>
+<p>By: Kevin Goebbert</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</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">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The following code reads the example data using the xarray open_dataset
+function and prints the coordinate values that are associated with the
+various variables contained within the file.</p>
+<p>This code uses the metpy accessor to parse file to make it easy to pull
+data using common coordinate names (e.g., vertical) and attach units.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/casestudies&#39;</span>
+                     <span class="s1">&#39;/python-gallery/GFS_20101026_1200.nc&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
+</pre></div>
+</div>
+</div>
+</div>
+<section id="data-retrieval">
+<h2>Data Retrieval<a class="headerlink" href="#data-retrieval" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>This code retrieves the necessary data from the file and completes some
+smoothing of the temperature, and wind fields using the MetPy function
+smooth_n_point. A nicely formated valid time (vtime) variable is also
+created.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set subset slice for the geographic extent of data to limit download</span>
+<span class="n">lon_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">350</span><span class="p">)</span>
+<span class="n">lat_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">85</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+
+<span class="c1"># Grab lat/lon values (GFS will be 1D)</span>
+<span class="n">lats</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
+<span class="n">lons</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
+
+
+<span class="c1"># Grab the pressure levels and select the data to be imported</span>
+<span class="c1"># Need all pressure levels for Temperatures, U and V Wind,</span>
+<span class="c1"># and Rel. Humidity</span>
+<span class="c1"># Smooth with the gaussian filter from scipy</span>
+<span class="n">pres</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">vertical</span>
+
+<span class="n">tmpk_var</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;Temperature_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">tmpk</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">tmpk_var</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">thta</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">potential_temperature</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">tmpk</span><span class="p">)</span>
+
+<span class="n">uwnd_var</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">vwnd_var</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">uwnd</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">uwnd_var</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">vwnd</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">vwnd_var</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+
+<span class="c1"># Create a clean datetime object for plotting based on time of</span>
+<span class="c1"># Geopotential heights</span>
+<span class="n">vtime</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Use MetPy to compute the baroclinic potential vorticity on all isobaric
+levels and other variables</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Comput the PV on all isobaric surfaces</span>
+<span class="n">pv</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">potential_vorticity_baroclinic</span><span class="p">(</span><span class="n">thta</span><span class="p">,</span> <span class="n">pres</span><span class="p">,</span> <span class="n">uwnd</span><span class="p">,</span> <span class="n">vwnd</span><span class="p">)</span>
+
+<span class="c1"># Use MetPy to compute the divergence on the pressure surfaces</span>
+<span class="n">div</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">divergence</span><span class="p">(</span><span class="n">uwnd</span><span class="p">,</span> <span class="n">vwnd</span><span class="p">)</span>
+
+<span class="c1"># Select the 250-hPa surface</span>
+<span class="n">pv_250</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">250</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
+<span class="n">div_250</span> <span class="o">=</span> <span class="n">div</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">250</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
+<span class="n">uwnd_250</span> <span class="o">=</span> <span class="n">uwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">250</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
+<span class="n">vwnd_250</span> <span class="o">=</span> <span class="n">vwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">250</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="map-creation">
+<h2>Map Creation<a class="headerlink" href="#map-creation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>This next set of code creates the plot and draws contours on a Lambert
+Conformal map centered on -100 E longitude. The main view is over the
+CONUS with isobaric PV map with PV contoured every 1 PVU and divergence
+colorshaded.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up the projection that will be used for plotting</span>
+<span class="n">mapcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span>
+                               <span class="n">central_latitude</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span>
+                               <span class="n">standard_parallels</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
+
+<span class="c1"># Set up the projection of the data;</span>
+<span class="c1"># if lat/lon then PlateCarree is what you want</span>
+<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
+
+<span class="c1"># Start the figure and create plot axes with proper projection</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">55</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+<span class="c1"># Add geopolitical boundaries for map reference</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
+
+<span class="c1"># Plot the contours of PV at 250 hPa, scaling 10^6 every 1 PVU</span>
+<span class="n">clevs_pv</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="mi">25</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">cs1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">pv_250</span><span class="o">*</span><span class="mf">1e6</span><span class="p">,</span> <span class="n">clevs_pv</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+                 <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs1</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot the colorfill of divergence, scaled 10^5 every 1 s^1</span>
+<span class="n">clevs_div</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="o">-</span><span class="mi">15</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">cs1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">div_250</span><span class="o">*</span><span class="mf">1e5</span><span class="p">,</span> <span class="n">clevs_div</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">cm</span><span class="o">.</span><span class="n">PuOr</span><span class="p">,</span>
+                  <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cs1</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> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Plot the wind barbs at 250 hPa</span>
+<span class="n">wind_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</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">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
+         <span class="n">uwnd_250</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">,</span> <span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+         <span class="n">vwnd_250</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">,</span> <span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+         <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">length</span><span class="o">=</span><span class="mf">6.5</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+
+<span class="c1"># Plot some titles to tell people what is on the map</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;250-hPa GFS PV (PVU), Divergence ($10^5$ s$^{-1}$),&#39;</span>
+          <span class="s1">&#39; and Wind Barbs (kt)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;Valid Time: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(1.0, 1.0, &#39;Valid Time: 2010-10-26 12:00:00&#39;)
+</pre></div>
+</div>
+<img alt="../../_images/a4228b68bb3b1a94ac05d9e451ab85702236c1dbc44b53a4d43f2e527d815ab4.png" src="../../_images/a4228b68bb3b1a94ac05d9e451ab85702236c1dbc44b53a4d43f2e527d815ab4.png" />
+</div>
+</div>
+</section>
+</section>
+
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new file mode 100644
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+  <section class="tex2jax_ignore mathjax_ignore" id="difax-replication">
+<h1>DIFAX Replication<a class="headerlink" href="#difax-replication" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>This example replicates the traditional DIFAX images for upper-level
+observations.</p>
+<p>By: Kevin Goebbert</p>
+<p>Observation data comes from Iowa State Archive, accessed through the
+Siphon package. Contour data comes from the GFS 0.5 degree analysis.
+Classic upper-level data of Geopotential Height and Temperature are
+plotted.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">urllib.request</span>
+
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
+
+<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</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">matplotlib.ticker</span> <span class="kn">import</span> <span class="n">FixedLocator</span>
+<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</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">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+
+<span class="kn">from</span> <span class="nn">metpy.plots</span> <span class="kn">import</span> <span class="n">StationPlot</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
+<span class="kn">from</span> <span class="nn">siphon.simplewebservice.iastate</span> <span class="kn">import</span> <span class="n">IAStateUpperAir</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="plotting-high-low-symbols">
+<h2>Plotting High/Low Symbols<a class="headerlink" href="#plotting-high-low-symbols" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>A helper function to plot a text symbol (e.g., H, L) for relative
+maximum/minimum for a given field (e.g., geopotential height).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_maxmin_points</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">extrema</span><span class="p">,</span> <span class="n">nsize</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span>
+                       <span class="n">plotValue</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    This function will find and plot relative maximum and minimum for a 2D grid. The function</span>
+<span class="sd">    can be used to plot an H for maximum values (e.g., High pressure) and an L for minimum</span>
+<span class="sd">    values (e.g., low pressue). It is best to used filetered data to obtain  a synoptic scale</span>
+<span class="sd">    max/min value. The symbol text can be set to a string value and optionally the color of the</span>
+<span class="sd">    symbol and any plotted value can be set with the parameter color.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">        lon : 2D array</span>
+<span class="sd">            Plotting longitude values</span>
+<span class="sd">        lat : 2D array</span>
+<span class="sd">            Plotting latitude values</span>
+<span class="sd">        data : 2D array</span>
+<span class="sd">            Data that you wish to plot the max/min symbol placement</span>
+<span class="sd">        extrema : str</span>
+<span class="sd">            Either a value of max for Maximum Values or min for Minimum Values</span>
+<span class="sd">        nsize : int</span>
+<span class="sd">            Size of the grid box to filter the max and min values to plot a reasonable number</span>
+<span class="sd">        symbol : str</span>
+<span class="sd">            Text to be placed at location of max/min value</span>
+<span class="sd">        color : str</span>
+<span class="sd">            Name of matplotlib colorname to plot the symbol (and numerical value, if plotted)</span>
+<span class="sd">        plot_value : Boolean (True/False)</span>
+<span class="sd">            Whether to plot the numeric value of max/min point</span>
+
+<span class="sd">    Return</span>
+<span class="sd">    ------</span>
+<span class="sd">        The max/min symbol will be plotted on the current axes within the bounding frame</span>
+<span class="sd">        (e.g., clip_on=True)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">scipy.ndimage</span> <span class="kn">import</span> <span class="n">maximum_filter</span><span class="p">,</span> <span class="n">minimum_filter</span>
+
+    <span class="k">if</span> <span class="p">(</span><span class="n">extrema</span> <span class="o">==</span> <span class="s1">&#39;max&#39;</span><span class="p">):</span>
+        <span class="n">data_ext</span> <span class="o">=</span> <span class="n">maximum_filter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">nsize</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="p">(</span><span class="n">extrema</span> <span class="o">==</span> <span class="s1">&#39;min&#39;</span><span class="p">):</span>
+        <span class="n">data_ext</span> <span class="o">=</span> <span class="n">minimum_filter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">nsize</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Value for hilo must be either max or min&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">lon</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">lon</span><span class="p">,</span> <span class="n">lat</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">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">)</span>
+
+    <span class="n">mxx</span><span class="p">,</span> <span class="n">mxy</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">data_ext</span> <span class="o">==</span> <span class="n">data</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="nb">len</span><span class="p">(</span><span class="n">mxy</span><span class="p">)):</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">lat</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">36</span><span class="p">,</span>
+                <span class="n">clip_on</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">lat</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span>
+                <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</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</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">clip_on</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">&#39;bold&#39;</span><span class="p">,</span>
+                <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#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">transform</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">lat</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">markeredgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+                <span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">lat</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span>
+                <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="station-information">
+<h2>Station Information<a class="headerlink" href="#station-information" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>A helper function for obtaining radiosonde station information (e.g.,
+latitude/longitude) requried to plot data obtained from each station.
+Original code by github user sgdecker.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">station_info</span><span class="p">(</span><span class="n">stid</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Provide information about weather stations.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    stid: str or iterable object containing strs</span>
+<span class="sd">         The ICAO or IATA code(s) for which station information is requested.</span>
+<span class="sd">    with_units: bool</span>
+<span class="sd">         Whether to include units for values that have them. Default True.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    info: dict</span>
+<span class="sd">         Information about the station(s) within a dictionary with these keys:</span>
+<span class="sd">             &#39;state&#39;: Two-character ID of the state/province where the station is located,</span>
+<span class="sd">                       if applicable</span>
+<span class="sd">             &#39;name&#39;: The name of the station</span>
+<span class="sd">             &#39;lat&#39;: The latitude of the station [deg]</span>
+<span class="sd">             &#39;lon&#39;: The longitude of the station [deg]</span>
+<span class="sd">             &#39;elevation&#39;: The elevation of the station [m]</span>
+<span class="sd">             &#39;country&#39;: Two-character ID of the country where the station is located</span>
+
+<span class="sd">    Modified code from Steven Decker, Rutgers University</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Provide a helper function for later usage</span>
+    <span class="k">def</span> <span class="nf">str2latlon</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
+        <span class="n">deg</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">s</span><span class="p">[:</span><span class="mi">3</span><span class="p">])</span>
+        <span class="n">mn</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;S&#39;</span> <span class="ow">or</span> <span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;W&#39;</span><span class="p">:</span>
+            <span class="n">deg</span> <span class="o">=</span> <span class="o">-</span><span class="n">deg</span>
+            <span class="n">mn</span> <span class="o">=</span> <span class="o">-</span><span class="n">mn</span>
+        <span class="k">return</span> <span class="n">deg</span> <span class="o">+</span> <span class="n">mn</span> <span class="o">/</span> <span class="mf">60.</span>
+
+    <span class="c1"># Various constants describing the underlying data</span>
+    <span class="n">url</span> <span class="o">=</span> <span class="s1">&#39;https://www.aviationweather.gov/docs/metar/stations.txt&#39;</span>
+    <span class="c1"># file = &#39;stations.txt&#39;</span>
+    <span class="n">state_bnds</span> <span class="o">=</span> <span class="nb">slice</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">name_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">19</span><span class="p">)</span>
+    <span class="n">icao_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">24</span><span class="p">)</span>
+    <span class="n">iata_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">26</span><span class="p">,</span> <span class="mi">29</span><span class="p">)</span>
+    <span class="n">lat_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">39</span><span class="p">,</span> <span class="mi">45</span><span class="p">)</span>
+    <span class="n">lon_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">47</span><span class="p">,</span> <span class="mi">54</span><span class="p">)</span>
+    <span class="n">z_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">55</span><span class="p">,</span> <span class="mi">59</span><span class="p">)</span>
+    <span class="n">cntry_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">81</span><span class="p">,</span> <span class="mi">83</span><span class="p">)</span>
+
+    <span class="c1"># Generalize to any number of IDs</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">stid</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="n">stid</span> <span class="o">=</span> <span class="p">[</span><span class="n">stid</span><span class="p">]</span>
+
+    <span class="c1"># Get the station dataset</span>
+    <span class="n">infile</span> <span class="o">=</span> <span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">urlopen</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">infile</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+
+    <span class="n">state</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">lat</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">lon</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">z</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">cntry</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">stid</span><span class="p">:</span>
+        <span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">line_bytes</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
+            <span class="n">line</span> <span class="o">=</span> <span class="n">line_bytes</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;UTF-8&#39;</span><span class="p">)</span>
+            <span class="n">icao</span> <span class="o">=</span> <span class="n">line</span><span class="p">[</span><span class="n">icao_bnds</span><span class="p">]</span>
+            <span class="n">iata</span> <span class="o">=</span> <span class="n">line</span><span class="p">[</span><span class="n">iata_bnds</span><span class="p">]</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">iata</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span> <span class="ow">and</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">icao</span><span class="p">:</span>
+                <span class="n">state</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">state_bnds</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span>
+                <span class="n">name</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">name_bnds</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span>
+                <span class="n">lat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">str2latlon</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">lat_bnds</span><span class="p">]))</span>
+                <span class="n">lon</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">str2latlon</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">lon_bnds</span><span class="p">]))</span>
+                <span class="n">z</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">z_bnds</span><span class="p">]))</span>
+                <span class="n">cntry</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">cntry_bnds</span><span class="p">])</span>
+
+                <span class="k">break</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">state</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;NA&#39;</span><span class="p">)</span>
+            <span class="n">name</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;NA&#39;</span><span class="p">)</span>
+            <span class="n">lat</span><span class="o">.</span><span class="n">append</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">lon</span><span class="o">.</span><span class="n">append</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">z</span><span class="o">.</span><span class="n">append</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">cntry</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;NA&#39;</span><span class="p">)</span>
+
+    <span class="n">infile</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;state&#39;</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">state</span><span class="p">),</span> <span class="s1">&#39;name&#39;</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">name</span><span class="p">),</span> <span class="s1">&#39;lat&#39;</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">lat</span><span class="p">),</span>
+            <span class="s1">&#39;lon&#39;</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">lon</span><span class="p">),</span> <span class="s1">&#39;elevation&#39;</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">z</span><span class="p">),</span> <span class="s1">&#39;country&#39;</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">cntry</span><span class="p">),</span>
+            <span class="s1">&#39;units&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;lat&#39;</span><span class="p">:</span> <span class="s1">&#39;deg&#39;</span><span class="p">,</span> <span class="s1">&#39;lon&#39;</span><span class="p">:</span> <span class="s1">&#39;deg&#39;</span><span class="p">,</span> <span class="s1">&#39;z&#39;</span><span class="p">:</span> <span class="s1">&#39;m&#39;</span><span class="p">}}</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="observation-data">
+<h2>Observation Data<a class="headerlink" href="#observation-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Set a date and time for upper-air observations (should only be 00 or 12
+UTC for the hour).</p>
+<p>Request all data from Iowa State using the Siphon package. The result is
+a pandas DataFrame containing all of the sounding data from all
+available stations.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set date for desired UPA data</span>
+<span class="n">today</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">()</span>
+
+<span class="c1"># Go back one day to ensure data availability</span>
+<span class="n">date</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="n">today</span><span class="o">.</span><span class="n">year</span><span class="p">,</span> <span class="n">today</span><span class="o">.</span><span class="n">month</span><span class="p">,</span> <span class="n">today</span><span class="o">.</span><span class="n">day</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">-</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Request data using Siphon request for data from Iowa State Archive</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">IAStateUpperAir</span><span class="o">.</span><span class="n">request_all_data</span><span class="p">(</span><span class="n">date</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="subset-observational-data">
+<h2>Subset Observational Data<a class="headerlink" href="#subset-observational-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>From the request above will give all levels from all radisonde sites
+available through the service. For plotting a pressure surface map there
+is only need to have the data from that level. Below the data is subset
+and a few parameters set based on the level chosen. Additionally, the
+station information is obtained and latitude and longitude data is added
+to the DataFrame.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">level</span> <span class="o">=</span> <span class="mi">500</span>
+
+<span class="k">if</span> <span class="p">(</span><span class="n">level</span> <span class="o">==</span> <span class="mi">925</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">level</span> <span class="o">==</span> <span class="mi">850</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">level</span> <span class="o">==</span> <span class="mi">700</span><span class="p">):</span>
+    <span class="n">cint</span> <span class="o">=</span> <span class="mi">30</span>
+    <span class="k">def</span> <span class="nf">hght_format</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> <span class="k">return</span> <span class="nb">format</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="s1">&#39;.0f&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]</span>
+<span class="k">elif</span> <span class="n">level</span> <span class="o">==</span> <span class="mi">500</span><span class="p">:</span>
+    <span class="n">cint</span> <span class="o">=</span> <span class="mi">60</span>
+    <span class="k">def</span> <span class="nf">hght_format</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> <span class="k">return</span> <span class="nb">format</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="s1">&#39;.0f&#39;</span><span class="p">)[:</span><span class="mi">3</span><span class="p">]</span>
+<span class="k">elif</span> <span class="n">level</span> <span class="o">==</span> <span class="mi">300</span><span class="p">:</span>
+    <span class="n">cint</span> <span class="o">=</span> <span class="mi">120</span>
+    <span class="k">def</span> <span class="nf">hght_format</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> <span class="k">return</span> <span class="nb">format</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="s1">&#39;.0f&#39;</span><span class="p">)[:</span><span class="mi">3</span><span class="p">]</span>
+<span class="k">elif</span> <span class="n">level</span> <span class="o">&lt;</span> <span class="mi">300</span><span class="p">:</span>
+    <span class="n">cint</span> <span class="o">=</span> <span class="mi">120</span>
+    <span class="k">def</span> <span class="nf">hght_format</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> <span class="k">return</span> <span class="nb">format</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="s1">&#39;.0f&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>
+
+<span class="c1"># Create subset of all data for a given level</span>
+<span class="n">data_subset</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">pressure</span> <span class="o">==</span> <span class="n">level</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">data_subset</span><span class="p">]</span>
+
+<span class="c1"># Get station lat/lon from look-up file; add to Dataframe</span>
+<span class="n">stn_info</span> <span class="o">=</span> <span class="n">station_info</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">station</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+<span class="n">df</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="s1">&#39;latitude&#39;</span><span class="p">,</span> <span class="n">stn_info</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">])</span>
+<span class="n">df</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="s1">&#39;longitude&#39;</span><span class="p">,</span> <span class="n">stn_info</span><span class="p">[</span><span class="s1">&#39;lon&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">HTTPError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">line</span> <span class="mi">21</span>
+<span class="g g-Whitespace">     </span><span class="mi">18</span> <span class="n">df</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">data_subset</span><span class="p">]</span>
+<span class="g g-Whitespace">     </span><span class="mi">20</span> <span class="c1"># Get station lat/lon from look-up file; add to Dataframe</span>
+<span class="ne">---&gt; </span><span class="mi">21</span> <span class="n">stn_info</span> <span class="o">=</span> <span class="n">station_info</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">station</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+<span class="g g-Whitespace">     </span><span class="mi">22</span> <span class="n">df</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="s1">&#39;latitude&#39;</span><span class="p">,</span> <span class="n">stn_info</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">])</span>
+<span class="g g-Whitespace">     </span><span class="mi">23</span> <span class="n">df</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="s1">&#39;longitude&#39;</span><span class="p">,</span> <span class="n">stn_info</span><span class="p">[</span><span class="s1">&#39;lon&#39;</span><span class="p">])</span>
+
+<span class="nn">Cell In[3], line 52,</span> in <span class="ni">station_info</span><span class="nt">(stid)</span>
+<span class="g g-Whitespace">     </span><span class="mi">49</span>     <span class="n">stid</span> <span class="o">=</span> <span class="p">[</span><span class="n">stid</span><span class="p">]</span>
+<span class="g g-Whitespace">     </span><span class="mi">51</span> <span class="c1"># Get the station dataset</span>
+<span class="ne">---&gt; </span><span class="mi">52</span> <span class="n">infile</span> <span class="o">=</span> <span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">urlopen</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">53</span> <span class="n">data</span> <span class="o">=</span> <span class="n">infile</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+<span class="g g-Whitespace">     </span><span class="mi">55</span> <span class="n">state</span> <span class="o">=</span> <span class="p">[]</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/urllib/request.py:216,</span> in <span class="ni">urlopen</span><span class="nt">(url, data, timeout, cafile, capath, cadefault, context)</span>
+<span class="g g-Whitespace">    </span><span class="mi">214</span> <span class="k">else</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">215</span>     <span class="n">opener</span> <span class="o">=</span> <span class="n">_opener</span>
+<span class="ne">--&gt; </span><span class="mi">216</span> <span class="k">return</span> <span class="n">opener</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">timeout</span><span class="p">)</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/urllib/request.py:525,</span> in <span class="ni">OpenerDirector.open</span><span class="nt">(self, fullurl, data, timeout)</span>
+<span class="g g-Whitespace">    </span><span class="mi">523</span> <span class="k">for</span> <span class="n">processor</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">process_response</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="p">[]):</span>
+<span class="g g-Whitespace">    </span><span class="mi">524</span>     <span class="n">meth</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">processor</span><span class="p">,</span> <span class="n">meth_name</span><span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">525</span>     <span class="n">response</span> <span class="o">=</span> <span class="n">meth</span><span class="p">(</span><span class="n">req</span><span class="p">,</span> <span class="n">response</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">527</span> <span class="k">return</span> <span class="n">response</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/urllib/request.py:634,</span> in <span class="ni">HTTPErrorProcessor.http_response</span><span class="nt">(self, request, response)</span>
+<span class="g g-Whitespace">    </span><span class="mi">631</span> <span class="c1"># According to RFC 2616, &quot;2xx&quot; code indicates that the client&#39;s</span>
+<span class="g g-Whitespace">    </span><span class="mi">632</span> <span class="c1"># request was successfully received, understood, and accepted.</span>
+<span class="g g-Whitespace">    </span><span class="mi">633</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="mi">200</span> <span class="o">&lt;=</span> <span class="n">code</span> <span class="o">&lt;</span> <span class="mi">300</span><span class="p">):</span>
+<span class="ne">--&gt; </span><span class="mi">634</span>     <span class="n">response</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">635</span>         <span class="s1">&#39;http&#39;</span><span class="p">,</span> <span class="n">request</span><span class="p">,</span> <span class="n">response</span><span class="p">,</span> <span class="n">code</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="n">hdrs</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">637</span> <span class="k">return</span> <span class="n">response</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/urllib/request.py:563,</span> in <span class="ni">OpenerDirector.error</span><span class="nt">(self, proto, *args)</span>
+<span class="g g-Whitespace">    </span><span class="mi">561</span> <span class="k">if</span> <span class="n">http_err</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">562</span>     <span class="n">args</span> <span class="o">=</span> <span class="p">(</span><span class="nb">dict</span><span class="p">,</span> <span class="s1">&#39;default&#39;</span><span class="p">,</span> <span class="s1">&#39;http_error_default&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">orig_args</span>
+<span class="ne">--&gt; </span><span class="mi">563</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_chain</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/urllib/request.py:496,</span> in <span class="ni">OpenerDirector._call_chain</span><span class="nt">(self, chain, kind, meth_name, *args)</span>
+<span class="g g-Whitespace">    </span><span class="mi">494</span> <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">handlers</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">495</span>     <span class="n">func</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">meth_name</span><span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">496</span>     <span class="n">result</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">497</span>     <span class="k">if</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">498</span>         <span class="k">return</span> <span class="n">result</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/urllib/request.py:643,</span> in <span class="ni">HTTPDefaultErrorHandler.http_error_default</span><span class="nt">(self, req, fp, code, msg, hdrs)</span>
+<span class="g g-Whitespace">    </span><span class="mi">642</span> <span class="k">def</span> <span class="nf">http_error_default</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">req</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">code</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="n">hdrs</span><span class="p">):</span>
+<span class="ne">--&gt; </span><span class="mi">643</span>     <span class="k">raise</span> <span class="n">HTTPError</span><span class="p">(</span><span class="n">req</span><span class="o">.</span><span class="n">full_url</span><span class="p">,</span> <span class="n">code</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="n">hdrs</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span>
+
+<span class="ne">HTTPError</span>: HTTP Error 308: Permanent Redirect
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="gridded-data">
+<h2>Gridded Data<a class="headerlink" href="#gridded-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Obtain GFS gridded output for contour plotting. Specifically,
+geopotential height and temperature data for the given level and subset
+for over North America. Data are smoothed for aesthetic reasons.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get GFS data and subset to North America for Geopotential Height and Temperature</span>
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/grib/NCEP/GFS/Global_0p5deg_ana/&#39;</span>
+                     <span class="s1">&#39;GFS_Global_0p5deg_ana_{0:%Y%m</span><span class="si">%d</span><span class="s1">}_{0:%H}00.grib2&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                         <span class="n">date</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+
+<span class="c1"># Geopotential height and smooth</span>
+<span class="n">hght</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="n">date</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">70</span><span class="p">,</span> <span class="mi">15</span><span class="p">),</span> <span class="n">lon</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">360</span><span class="o">-</span><span class="mi">145</span><span class="p">,</span> <span class="mi">360</span><span class="o">-</span><span class="mi">50</span><span class="p">))</span>
+<span class="n">smooth_hght</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">hght</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+
+<span class="c1"># Temperature, smooth, and convert to Celsius</span>
+<span class="n">tmpk</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
+    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="n">date</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">70</span><span class="p">,</span> <span class="mi">15</span><span class="p">),</span> <span class="n">lon</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">360</span><span class="o">-</span><span class="mi">145</span><span class="p">,</span> <span class="mi">360</span><span class="o">-</span><span class="mi">50</span><span class="p">))</span>
+<span class="n">smooth_tmpc</span> <span class="o">=</span> <span class="p">(</span><span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">tmpk</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="create-difax-replication">
+<h2>Create DIFAX Replication<a class="headerlink" href="#create-difax-replication" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<p>Plot the observational data and contours on a Lambert Conformal map and
+add features that resemble the historic DIFAX maps.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up map coordinate reference system</span>
+<span class="n">mapcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span>
+    <span class="n">central_latitude</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span> <span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span> <span class="n">standard_parallels</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
+
+<span class="c1"># Set up station locations for plotting observations</span>
+<span class="n">point_locs</span> <span class="o">=</span> <span class="n">mapcrs</span><span class="o">.</span><span class="n">transform_points</span><span class="p">(</span>
+    <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;longitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;latitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+
+<span class="c1"># Start figure and set graphics extent</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">17</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">125</span><span class="p">,</span> <span class="o">-</span><span class="mi">70</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">55</span><span class="p">])</span>
+
+<span class="c1"># Add map features for geographic reference</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">),</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">LAND</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">),</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">),</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot plus signs every degree lat/lon</span>
+<span class="n">plus_lat</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">plus_lon</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">other_lat</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">other_lon</span> <span class="o">=</span> <span class="p">[]</span>
+
+<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">hght</span><span class="o">.</span><span class="n">lon</span><span class="o">.</span><span class="n">values</span><span class="p">[::</span><span class="mi">2</span><span class="p">]:</span>
+    <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">hght</span><span class="o">.</span><span class="n">lat</span><span class="o">.</span><span class="n">values</span><span class="p">[::</span><span class="mi">2</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">x</span> <span class="o">%</span> <span class="mi">5</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">y</span> <span class="o">%</span> <span class="mi">5</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+            <span class="n">plus_lon</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="n">plus_lat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">other_lon</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="n">other_lat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">other_lon</span><span class="p">,</span> <span class="n">other_lat</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span>
+           <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">plus_lon</span><span class="p">,</span> <span class="n">plus_lat</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
+           <span class="n">color</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">)</span>
+
+<span class="c1"># Add gridlines for every 5 degree lat/lon</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">ylocs</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">71</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">xlocs</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">150</span><span class="p">,</span> <span class="o">-</span><span class="mi">49</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+
+<span class="c1"># Start the station plot by specifying the axes to draw on, as well as the</span>
+<span class="c1"># lon/lat of the stations (with transform). We also the fontsize to 10 pt.</span>
+<span class="n">stationplot</span> <span class="o">=</span> <span class="n">StationPlot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;longitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;latitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">clip_on</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                          <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+
+<span class="c1"># Plot the temperature and dew point to the upper and lower left, respectively, of</span>
+<span class="c1"># the center point.</span>
+<span class="n">stationplot</span><span class="o">.</span><span class="n">plot_parameter</span><span class="p">(</span><span class="s1">&#39;NW&#39;</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;temperature&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
+<span class="n">stationplot</span><span class="o">.</span><span class="n">plot_parameter</span><span class="p">(</span><span class="s1">&#39;SW&#39;</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
+
+<span class="c1"># A more complex example uses a custom formatter to control how the geopotential height</span>
+<span class="c1"># values are plotted. This is set in an earlier if-statement to work appropriate for</span>
+<span class="c1"># different levels.</span>
+<span class="n">stationplot</span><span class="o">.</span><span class="n">plot_parameter</span><span class="p">(</span><span class="s1">&#39;NE&#39;</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;height&#39;</span><span class="p">],</span> <span class="n">formatter</span><span class="o">=</span><span class="n">hght_format</span><span class="p">)</span>
+
+<span class="c1"># Add wind barbs</span>
+<span class="n">stationplot</span><span class="o">.</span><span class="n">plot_barb</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;u_wind&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;v_wind&#39;</span><span class="p">],</span> <span class="n">length</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;tip&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot Solid Contours of Geopotential Height</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">hght</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">hght</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">smooth_hght</span><span class="p">,</span>
+                <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20000</span><span class="p">,</span> <span class="n">cint</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">clabels</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Contour labels with black boxes and white text</span>
+<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">cs</span><span class="o">.</span><span class="n">labelTexts</span><span class="p">:</span>
+    <span class="n">t</span><span class="o">.</span><span class="n">set_bbox</span><span class="p">({</span><span class="s1">&#39;facecolor&#39;</span><span class="p">:</span> <span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="s1">&#39;pad&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">})</span>
+    <span class="n">t</span><span class="o">.</span><span class="n">set_fontweight</span><span class="p">(</span><span class="s1">&#39;heavy&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot Dashed Contours of Temperature</span>
+<span class="n">cs2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">hght</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">hght</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">smooth_tmpc</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">60</span><span class="p">,</span> <span class="mi">51</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span>
+                 <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">clabels</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs2</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Set longer dashes than default</span>
+<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">cs2</span><span class="o">.</span><span class="n">collections</span><span class="p">:</span>
+    <span class="n">c</span><span class="o">.</span><span class="n">set_dashes</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span> <span class="p">(</span><span class="mf">5.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">))])</span>
+
+<span class="c1"># Contour labels with black boxes and white text</span>
+<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">cs</span><span class="o">.</span><span class="n">labelTexts</span><span class="p">:</span>
+    <span class="n">t</span><span class="o">.</span><span class="n">set_bbox</span><span class="p">({</span><span class="s1">&#39;facecolor&#39;</span><span class="p">:</span> <span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="s1">&#39;pad&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">})</span>
+    <span class="n">t</span><span class="o">.</span><span class="n">set_fontweight</span><span class="p">(</span><span class="s1">&#39;heavy&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot filled circles for Radiosonde Obs</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;longitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;latitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+           <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+<span class="c1"># Use definition to plot H/L symbols</span>
+<span class="n">plot_maxmin_points</span><span class="p">(</span><span class="n">hght</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">hght</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">smooth_hght</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="s1">&#39;max&#39;</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span>
+                   <span class="n">symbol</span><span class="o">=</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">plot_maxmin_points</span><span class="p">(</span><span class="n">hght</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">hght</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">smooth_hght</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="s1">&#39;min&#39;</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span>
+                   <span class="n">symbol</span><span class="o">=</span><span class="s1">&#39;L&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+<span class="c1"># Add titles</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Upper-air Observations at </span><span class="si">{</span><span class="n">level</span><span class="si">}</span><span class="s1">-hPa Analysis Heights/Temperature&#39;</span><span class="p">,</span>
+          <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;Valid: </span><span class="si">{</span><span class="n">date</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">KeyError</span><span class="g g-Whitespace">                                  </span>Traceback (most recent call last)
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805,</span> in <span class="ni">Index.get_loc</span><span class="nt">(self, key)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3804</span> <span class="k">try</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">3805</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">casted_key</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3806</span> <span class="k">except</span> <span class="ne">KeyError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+
+<span class="nn">File index.pyx:167,</span> in <span class="ni">pandas._libs.index.IndexEngine.get_loc</span><span class="nt">()</span>
+
+<span class="nn">File index.pyx:196,</span> in <span class="ni">pandas._libs.index.IndexEngine.get_loc</span><span class="nt">()</span>
+
+<span class="nn">File pandas/_libs/hashtable_class_helper.pxi:7081,</span> in <span class="ni">pandas._libs.hashtable.PyObjectHashTable.get_item</span><span class="nt">()</span>
+
+<span class="nn">File pandas/_libs/hashtable_class_helper.pxi:7089,</span> in <span class="ni">pandas._libs.hashtable.PyObjectHashTable.get_item</span><span class="nt">()</span>
+
+<span class="ne">KeyError</span>: &#39;longitude&#39;
+
+<span class="n">The</span> <span class="n">above</span> <span class="n">exception</span> <span class="n">was</span> <span class="n">the</span> <span class="n">direct</span> <span class="n">cause</span> <span class="n">of</span> <span class="n">the</span> <span class="n">following</span> <span class="n">exception</span><span class="p">:</span>
+
+<span class="ne">KeyError</span><span class="g g-Whitespace">                                  </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="n">line</span> <span class="mi">7</span>
+<span class="g g-Whitespace">      </span><span class="mi">2</span> <span class="n">mapcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span>
+<span class="g g-Whitespace">      </span><span class="mi">3</span>     <span class="n">central_latitude</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span> <span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span> <span class="n">standard_parallels</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
+<span class="g g-Whitespace">      </span><span class="mi">5</span> <span class="c1"># Set up station locations for plotting observations</span>
+<span class="g g-Whitespace">      </span><span class="mi">6</span> <span class="n">point_locs</span> <span class="o">=</span> <span class="n">mapcrs</span><span class="o">.</span><span class="n">transform_points</span><span class="p">(</span>
+<span class="ne">----&gt; </span><span class="mi">7</span>     <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;longitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;latitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+<span class="g g-Whitespace">      </span><span class="mi">9</span> <span class="c1"># Start figure and set graphics extent</span>
+<span class="g g-Whitespace">     </span><span class="mi">10</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="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">17</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/pandas/core/frame.py:4102,</span> in <span class="ni">DataFrame.__getitem__</span><span class="nt">(self, key)</span>
+<span class="g g-Whitespace">   </span><span class="mi">4100</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">nlevels</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">4101</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getitem_multilevel</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="ne">-&gt; </span><span class="mi">4102</span> <span class="n">indexer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">4103</span> <span class="k">if</span> <span class="n">is_integer</span><span class="p">(</span><span class="n">indexer</span><span class="p">):</span>
+<span class="g g-Whitespace">   </span><span class="mi">4104</span>     <span class="n">indexer</span> <span class="o">=</span> <span class="p">[</span><span class="n">indexer</span><span class="p">]</span>
+
+<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812,</span> in <span class="ni">Index.get_loc</span><span class="nt">(self, key)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3807</span>     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">casted_key</span><span class="p">,</span> <span class="nb">slice</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span>
+<span class="g g-Whitespace">   </span><span class="mi">3808</span>         <span class="nb">isinstance</span><span class="p">(</span><span class="n">casted_key</span><span class="p">,</span> <span class="n">abc</span><span class="o">.</span><span class="n">Iterable</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3809</span>         <span class="ow">and</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">slice</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">casted_key</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3810</span>     <span class="p">):</span>
+<span class="g g-Whitespace">   </span><span class="mi">3811</span>         <span class="k">raise</span> <span class="n">InvalidIndexError</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="ne">-&gt; </span><span class="mi">3812</span>     <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span>
+<span class="g g-Whitespace">   </span><span class="mi">3813</span> <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">3814</span>     <span class="c1"># If we have a listlike key, _check_indexing_error will raise</span>
+<span class="g g-Whitespace">   </span><span class="mi">3815</span>     <span class="c1">#  InvalidIndexError. Otherwise we fall through and re-raise</span>
+<span class="g g-Whitespace">   </span><span class="mi">3816</span>     <span class="c1">#  the TypeError.</span>
+<span class="g g-Whitespace">   </span><span class="mi">3817</span>     <span class="bp">self</span><span class="o">.</span><span class="n">_check_indexing_error</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+
+<span class="ne">KeyError</span>: &#39;longitude&#39;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+
+    <script type="text/x-thebe-config">
+    {
+        requestKernel: true,
+        binderOptions: {
+            repo: "binder-examples/jupyter-stacks-datascience",
+            ref: "master",
+        },
+        codeMirrorConfig: {
+            theme: "abcdef",
+            mode: "python"
+        },
+        kernelOptions: {
+            name: "python3",
+            path: "./notebooks/synoptic"
+        },
+        predefinedOutput: true
+    }
+    </script>
+    <script>kernelName = 'python3'</script>
+
+                  </article>
+
+                
+                
+
+                
+                
+                  <footer class="prev-next-footer">
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diff --git a/_preview/13/notebooks/synoptic/Wind_Shear_Vectors_Example.html b/_preview/13/notebooks/synoptic/Wind_Shear_Vectors_Example.html
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+  <section class="tex2jax_ignore mathjax_ignore" id="calculating-wind-shear">
+<h1>Calculating Wind Shear<a class="headerlink" href="#calculating-wind-shear" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>This example plots calculates the 850-500 hPa Bulk Wind Shear</p>
+<p>Plotting over a regional domain, accessing the Best Collection of GFS from the
+Unidata Thredds server, plots MSLP (hPa), 850-hPa Wind Vector (m/s), 500-hPa
+Wind Vector (m/s), and the Wind Shear between the two layers (m/s)</p>
+<section id="import-necessary-packages">
+<h2>Import necessary packages<a class="headerlink" href="#import-necessary-packages" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</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">metpy.units</span> <span class="kn">import</span> <span class="n">units</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">scipy.ndimage</span> <span class="k">as</span> <span class="nn">ndimage</span>
+<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
+<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="obtain-data">
+<h2>Obtain data<a class="headerlink" href="#obtain-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Construct a TDSCatalog instance pointing to the gfs dataset</span>
+<span class="n">best_gfs</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="s1">&#39;http://thredds.ucar.edu/thredds/catalog/grib/&#39;</span>
+                      <span class="s1">&#39;NCEP/GFS/Global_0p5deg/catalog.xml&#39;</span><span class="p">)</span>
+
+<span class="c1"># Interface with the Best GFS Half Degree Forecast Time Series dataset</span>
+<span class="c1"># via NCSS directly</span>
+<span class="n">ncss</span> <span class="o">=</span> <span class="n">best_gfs</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="s1">&#39;Best GFS Half Degree Forecast Time Series&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="first-query-for-mslp">
+<h2>First Query for MSLP<a class="headerlink" href="#first-query-for-mslp" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create our NCSS query with desired specifications</span>
+<span class="n">query</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
+<span class="n">query</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="n">north</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">south</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">east</span><span class="o">=-</span><span class="mi">80</span><span class="p">,</span> <span class="n">west</span><span class="o">=-</span><span class="mi">115</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">())</span>
+<span class="n">query</span><span class="o">.</span><span class="n">accept</span><span class="p">(</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;MSLP_Eta_model_reduction_msl&#39;</span><span class="p">)</span>
+
+<span class="c1"># Obtain the data we&#39;ve queried for as a netcdf4-python dataset</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+
+<span class="c1"># Make into an xarray Dataset object</span>
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+
+<span class="c1"># Pull out the variables you want to use</span>
+<span class="n">mslp_var</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">MSLP_Eta_model_reduction_msl</span>
+<span class="n">time_var</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">MSLP_Eta_model_reduction_msl</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span>
+<span class="n">lat_var</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">latitude</span>
+<span class="n">lon_var</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">longitude</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="second-query-for-850-hpa-data">
+<h2>Second Query for 850-hPa data<a class="headerlink" href="#second-query-for-850-hpa-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Request data for 850-hPa winds</span>
+<span class="c1"># First clear the query&#39;s variables from previous query for MSLP</span>
+<span class="n">query</span><span class="o">.</span><span class="n">var</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+<span class="n">query</span><span class="o">.</span><span class="n">vertical_level</span><span class="p">(</span><span class="mi">85000</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+
+<span class="c1"># Make into an xarray Dataset object</span>
+<span class="n">ds_wind</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+
+<span class="n">u_wind_var850</span> <span class="o">=</span> <span class="n">ds_wind</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span>
+<span class="n">v_wind_var850</span> <span class="o">=</span> <span class="n">ds_wind</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Third Query for 500-hPa data</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Request data for 500-hPa winds</span>
+<span class="c1"># First clear the query&#39;s variables from previous query for 850-hPa data</span>
+<span class="n">query</span><span class="o">.</span><span class="n">var</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+<span class="n">query</span><span class="o">.</span><span class="n">vertical_level</span><span class="p">(</span><span class="mi">50000</span><span class="p">)</span>
+<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+
+<span class="c1"># Make into an xarray Dataset object</span>
+<span class="n">ds_wind</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+
+<span class="n">u_wind_var500</span> <span class="o">=</span> <span class="n">ds_wind</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span>
+<span class="n">v_wind_var500</span> <span class="o">=</span> <span class="n">ds_wind</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="data-manipulation">
+<h2>Data Manipulation<a class="headerlink" href="#data-manipulation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get actual data values and remove any size 1 dimensions</span>
+<span class="n">lat</span> <span class="o">=</span> <span class="n">lat_var</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">lon</span> <span class="o">=</span> <span class="n">lon_var</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">mslp</span> <span class="o">=</span> <span class="n">mslp_var</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">u_wind850</span> <span class="o">=</span> <span class="n">u_wind_var850</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">v_wind850</span> <span class="o">=</span> <span class="n">v_wind_var850</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">u_wind500</span> <span class="o">=</span> <span class="n">u_wind_var500</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="n">v_wind500</span> <span class="o">=</span> <span class="n">v_wind_var500</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+<span class="c1"># Convert number of hours since the reference time into an actual date</span>
+<span class="n">time</span> <span class="o">=</span> <span class="n">time_var</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+<span class="c1"># Combine 1D latitude and longitudes into a 2D grid of locations</span>
+<span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</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">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">)</span>
+
+<span class="c1"># Smooth mslp data</span>
+<span class="n">mslp</span> <span class="o">=</span> <span class="n">ndimage</span><span class="o">.</span><span class="n">gaussian_filter</span><span class="p">(</span><span class="n">mslp</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Begin making figure</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create new figure</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">15</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
+
+<span class="c1"># Add the map and set the extent</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mf">108.</span><span class="p">,</span> <span class="o">-</span><span class="mf">91.</span><span class="p">,</span> <span class="mf">33.</span><span class="p">,</span> <span class="mf">45.</span><span class="p">])</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">patch</span><span class="o">.</span><span class="n">set_fill</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Add state boundaries to plot</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="c1"># Contour the MSLP</span>
+<span class="n">c</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</span><span class="p">,</span> <span class="n">mslp</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;lime&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="n">wslice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="c1"># Plot 850-hPa wind vectors</span>
+<span class="n">vectors850</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
+                       <span class="n">u_wind850</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span> <span class="n">v_wind850</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
+                       <span class="n">headlength</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span>
+                       <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;gold&#39;</span><span class="p">,</span>
+                       <span class="n">label</span><span class="o">=</span><span class="s1">&#39;850mb wind&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot 500-hPa wind vectors</span>
+<span class="n">vectors500</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
+                       <span class="n">u_wind500</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span> <span class="n">v_wind500</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
+                       <span class="n">headlength</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span>
+                       <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">12</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">label</span><span class="o">=</span><span class="s1">&#39;500mb wind&#39;</span><span class="p">)</span>
+
+<span class="c1"># Plot 500-850 shear</span>
+<span class="n">shear</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
+                  <span class="n">u_wind500</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">]</span> <span class="o">-</span> <span class="n">u_wind850</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
+                  <span class="n">v_wind500</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">]</span> <span class="o">-</span> <span class="n">v_wind850</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
+                  <span class="n">headlength</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span>
+                  <span class="n">scale</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;deeppink&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;500-850mb shear&#39;</span><span class="p">)</span>
+
+<span class="c1"># Add a legend</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="s1">&#39;850mb wind&#39;</span><span class="p">,</span> <span class="s1">&#39;500mb wind&#39;</span><span class="p">,</span> <span class="s1">&#39;500-850mb shear&#39;</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+
+<span class="c1"># Manually set colors for legend</span>
+<span class="n">legend</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend</span><span class="p">()</span>
+<span class="n">legend</span><span class="o">.</span><span class="n">legendHandles</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">&#39;gold&#39;</span><span class="p">)</span>
+<span class="n">legend</span><span class="o">.</span><span class="n">legendHandles</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">&#39;cornflowerblue&#39;</span><span class="p">)</span>
+<span class="n">legend</span><span class="o">.</span><span class="n">legendHandles</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">&#39;deeppink&#39;</span><span class="p">)</span>
+
+<span class="c1"># Add a title to the plot</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;MSLP, 850mb Wind, 500mb Wind, and 500-850mb Vertical Wind Shear </span><span class="se">\n</span><span class="s1">&#39;</span>
+          <span class="sa">f</span><span class="s1">&#39; for </span><span class="si">{</span><span class="n">time</span><span class="si">:</span><span class="s1">%d %B %Y %H:%MZ</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_3013/1356670571.py:43: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.
+  legend.legendHandles[0].set_color(&#39;gold&#39;)
+/tmp/ipykernel_3013/1356670571.py:44: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.
+  legend.legendHandles[1].set_color(&#39;cornflowerblue&#39;)
+/tmp/ipykernel_3013/1356670571.py:45: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.
+  legend.legendHandles[2].set_color(&#39;deeppink&#39;)
+</pre></div>
+</div>
+<img alt="../../_images/3e070bab759e211260f072e1586f38ae0350e628d828c985c87bdda9a94535bb.png" src="../../_images/3e070bab759e211260f072e1586f38ae0350e628d828c985c87bdda9a94535bb.png" />
+</div>
+</div>
+</section>
+</section>
+
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+  <section class="tex2jax_ignore mathjax_ignore" id="using-xarray-for-data-read-and-selection">
+<h1>Using Xarray for Data read and selection<a class="headerlink" href="#using-xarray-for-data-read-and-selection" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
+<p>Use Xarray module to read in model data from nomads server.</p>
+<p>This example uses the xarray module to access data from the nomads server for
+archive NAM analysis data via OPeNDAP. Xarray makes it easier to select times
+and levels, although you still have to know the coordinate variable name. A
+simple 500 hPa plot is created after selecting with xarray.</p>
+<p>Import all of our needed modules</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</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">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
+<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</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">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="accessing-data-using-xarray">
+<h2>Accessing data using Xarray<a class="headerlink" href="#accessing-data-using-xarray" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Specify our date/time of product desired</span>
+<span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
+
+<span class="c1"># Construct our OPeNDAP access URL</span>
+<span class="n">base_url</span> <span class="o">=</span> <span class="s1">&#39;https://www.ncei.noaa.gov/thredds/dodsC/model-namanl-old/&#39;</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">base_url</span><span class="si">}{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">/&#39;</span>
+                       <span class="sa">f</span><span class="s1">&#39;namanl_218_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">00_000.grb&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>NAM data is in a projected coordinate and you get back the projection
+X and Y values in km</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create a 2-d meshgrid of our x, y coordinates</span>
+<span class="c1"># manually converted to meters (km * 1000)</span>
+<span class="c1">#x, y = np.meshgrid(data[&#39;x&#39;].values * 1000, data[&#39;y&#39;].values * 1000)</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;meter&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;meter&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Getting the valid times in a more useable format</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get the valid times from the file</span>
+<span class="n">vtimes</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">vtimes</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[datetime.datetime(2016, 4, 16, 18, 0)]
+</pre></div>
+</div>
+</div>
+</div>
+<p>Xarray has some nice functionality to choose the time and level that
+you specifically want to use. In this example the time variable is ‘time’ and
+the level variable is ‘isobaric1’. Unfortunately, these can be different with
+each file you use, so you’ll always need to check what they are by listing
+the coordinate variable names</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print(data.Geopotential_height.coords)</span>
+<span class="n">hght_500</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">time1</span><span class="o">=</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
+<span class="n">uwnd_500</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">time1</span><span class="o">=</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
+<span class="n">vwnd_500</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">time1</span><span class="o">=</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="now-make-the-500-hpa-map">
+<h2>Now make the 500-hPa map<a class="headerlink" href="#now-make-the-500-hpa-map" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Must set data projection, NAM is LCC projection</span>
+<span class="n">datacrs</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">cartopy_crs</span>
+
+<span class="c1"># A different LCC projection for the plot.</span>
+<span class="n">plotcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_latitude</span><span class="o">=</span><span class="mf">45.</span><span class="p">,</span> <span class="n">central_longitude</span><span class="o">=-</span><span class="mf">100.</span><span class="p">,</span>
+                                <span class="n">standard_parallels</span><span class="o">=</span><span class="p">[</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</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">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">17.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="n">plotcrs</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">67</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+<span class="n">clev500</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">5100</span><span class="p">,</span> <span class="mi">6000</span><span class="p">,</span> <span class="mi">60</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</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">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">hght_500</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">clev500</span><span class="p">,</span>
+                <span class="n">colors</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">2.5</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
+          <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Here we put boxes around the clabels with a black boarder white facecolor</span>
+<span class="c1"># `labelTexts` necessary as ~cartopy.mpl.contour.GeoContourSet.clabel</span>
+<span class="c1"># does not return list of texts as of 0.18</span>
+<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">cs</span><span class="o">.</span><span class="n">labelTexts</span><span class="p">:</span>
+    <span class="n">t</span><span class="o">.</span><span class="n">set_bbox</span><span class="p">({</span><span class="s1">&#39;fc&#39;</span><span class="p">:</span> <span class="s1">&#39;w&#39;</span><span class="p">})</span>
+
+<span class="c1"># Transform Vectors before plotting, then plot wind barbs.</span>
+<span class="n">wind_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
+         <span class="n">uwnd_500</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">,</span> <span class="n">wind_slice</span><span class="p">],</span> <span class="n">vwnd_500</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">,</span> <span class="n">wind_slice</span><span class="p">],</span>
+         <span class="n">length</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
+
+<span class="c1"># Add some titles to make the plot readable by someone else</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;500-hPa Geopotential Heights (m)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#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="sa">f</span><span class="s1">&#39;VALID: </span><span class="si">{</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">);</span>
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diff --git a/_preview/13/searchindex.js b/_preview/13/searchindex.js
new file mode 100644
index 0000000..f6dba3d
--- /dev/null
+++ b/_preview/13/searchindex.js
@@ -0,0 +1 @@
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\ No newline at end of file