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<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Implements methods for natural neighbor interpolation."><title>NaturalNeighbor in spade - Rust</title><script>if(window.location.protocol!=="file:")document.head.insertAdjacentHTML("beforeend","SourceSerif4-Regular-6b053e98.ttf.woff2,FiraSans-Regular-0fe48ade.woff2,FiraSans-Medium-e1aa3f0a.woff2,SourceCodePro-Regular-8badfe75.ttf.woff2,SourceCodePro-Semibold-aa29a496.ttf.woff2".split(",").map(f=>`<link rel="preload" as="font" type="font/woff2" crossorigin href="../static.files/${f}">`).join(""))</script><link rel="stylesheet" href="../static.files/normalize-9960930a.css"><link rel="stylesheet" href="../static.files/rustdoc-42caa33d.css"><meta name="rustdoc-vars" data-root-path="../" data-static-root-path="../static.files/" data-current-crate="spade" data-themes="" data-resource-suffix="" data-rustdoc-version="1.84.0 (9fc6b4312 2025-01-07)" data-channel="1.84.0" data-search-js="search-92e6798f.js" data-settings-js="settings-0f613d39.js" ><script src="../static.files/storage-59e33391.js"></script><script defer src="sidebar-items.js"></script><script defer src="../static.files/main-5f194d8c.js"></script><noscript><link rel="stylesheet" href="../static.files/noscript-893ab5e7.css"></noscript><link rel="alternate icon" type="image/png" href="../static.files/favicon-32x32-6580c154.png"><link rel="icon" type="image/svg+xml" href="../static.files/favicon-044be391.svg"></head><body class="rustdoc struct"><!--[if lte IE 11]><div class="warning">This old browser is unsupported and will most likely display funky things.</div><![endif]--><nav class="mobile-topbar"><button class="sidebar-menu-toggle" title="show sidebar"></button></nav><nav class="sidebar"><div class="sidebar-crate"><h2><a href="../spade/index.html">spade</a><span class="version">2.12.1</span></h2></div><div class="sidebar-elems"><section id="rustdoc-toc"><h2 class="location"><a href="#">Natural<wbr>Neighbor</a></h2><h3><a href="#">Sections</a></h3><ul class="block top-toc"><li><a href="#performance-comparison" title="Performance comparison">Performance comparison</a></li><li><a href="#usage" title="Usage">Usage</a></li><li><a href="#example" title="Example">Example</a></li><li><a href="#visual-comparison-of-interpolation-algorithms" title="Visual comparison of interpolation algorithms">Visual comparison of interpolation algorithms</a></li></ul><h3><a href="#implementations">Methods</a></h3><ul class="block method"><li><a href="#method.get_weights" title="get_weights">get_weights</a></li><li><a href="#method.interpolate" title="interpolate">interpolate</a></li><li><a href="#method.interpolate_gradient" title="interpolate_gradient">interpolate_gradient</a></li></ul><h3><a href="#synthetic-implementations">Auto Trait Implementations</a></h3><ul class="block synthetic-implementation"><li><a href="#impl-Freeze-for-NaturalNeighbor%3C'a,+T%3E" title="!Freeze">!Freeze</a></li><li><a href="#impl-RefUnwindSafe-for-NaturalNeighbor%3C'a,+T%3E" title="!RefUnwindSafe">!RefUnwindSafe</a></li><li><a href="#impl-Sync-for-NaturalNeighbor%3C'a,+T%3E" title="!Sync">!Sync</a></li><li><a href="#impl-Send-for-NaturalNeighbor%3C'a,+T%3E" title="Send">Send</a></li><li><a href="#impl-Unpin-for-NaturalNeighbor%3C'a,+T%3E" title="Unpin">Unpin</a></li><li><a href="#impl-UnwindSafe-for-NaturalNeighbor%3C'a,+T%3E" title="UnwindSafe">UnwindSafe</a></li></ul><h3><a href="#blanket-implementations">Blanket Implementations</a></h3><ul class="block blanket-implementation"><li><a href="#impl-Any-for-T" title="Any">Any</a></li><li><a href="#impl-Borrow%3CT%3E-for-T" title="Borrow&#60;T&#62;">Borrow&#60;T&#62;</a></li><li><a href="#impl-BorrowMut%3CT%3E-for-T" title="BorrowMut&#60;T&#62;">BorrowMut&#60;T&#62;</a></li><li><a href="#impl-From%3CT%3E-for-T" title="From&#60;T&#62;">From&#60;T&#62;</a></li><li><a href="#impl-Into%3CU%3E-for-T" title="Into&#60;U&#62;">Into&#60;U&#62;</a></li><li><a href="#impl-TryFrom%3CU%3E-for-T" title="TryFrom&#60;U&#62;">TryFrom&#60;U&#62;</a></li><li><a href="#impl-TryInto%3CU%3E-for-T" title="TryInto&#60;U&#62;">TryInto&#60;U&#62;</a></li></ul></section><div id="rustdoc-modnav"><h2 class="in-crate"><a href="index.html">In crate spade</a></h2></div></div></nav><div class="sidebar-resizer"></div><main><div class="width-limiter"><rustdoc-search></rustdoc-search><section id="main-content" class="content"><div class="main-heading"><span class="rustdoc-breadcrumbs"><a href="index.html">spade</a></span><h1>Struct <span class="struct">NaturalNeighbor</span><button id="copy-path" title="Copy item path to clipboard">Copy item path</button></h1><rustdoc-toolbar></rustdoc-toolbar><span class="sub-heading"><a class="src" href="../src/spade/delaunay_core/interpolation.rs.html#118-134">Source</a> </span></div><pre class="rust item-decl"><code>pub struct NaturalNeighbor&lt;'a, T&gt;<div class="where">where
T: <a class="trait" href="trait.Triangulation.html" title="trait spade::Triangulation">Triangulation</a>,
T::<a class="associatedtype" href="trait.Triangulation.html#associatedtype.Vertex" title="type spade::Triangulation::Vertex">Vertex</a>: <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>,</div>{ <span class="comment">/* private fields */</span> }</code></pre><details class="toggle top-doc" open><summary class="hideme"><span>Expand description</span></summary><div class="docblock"><p>Implements methods for natural neighbor interpolation.</p>
<p>Natural neighbor interpolation is a spatial interpolation method. For a given set of 2D input points with an
associated value (e.g. a height field of some terrain or temperature measurements at different locations in
a country), natural neighbor interpolation allows to smoothly interpolate the associated value for every
location within the convex hull of the input points.</p>
<p>Spade currently assists with 4 interpolation strategies:</p>
<ul>
<li><strong>Nearest neighbor interpolation:</strong> Fastest. Exhibits too poor quality for many tasks. Not continuous
along the edges of the voronoi diagram. Use <a href="struct.DelaunayTriangulation.html#method.nearest_neighbor" title="method spade::DelaunayTriangulation::nearest_neighbor">DelaunayTriangulation::nearest_neighbor</a>.</li>
<li><strong>Barycentric interpolation:</strong> Fast. Not smooth on the edges of the Delaunay triangulation.
See <a href="struct.Barycentric.html" title="struct spade::Barycentric">Barycentric</a>.</li>
<li><strong>Natural neighbor interpolation:</strong> Slower. Smooth everywhere except the input points. The input points
have no derivative. See <a href="struct.NaturalNeighbor.html#method.interpolate" title="method spade::NaturalNeighbor::interpolate">NaturalNeighbor::interpolate</a></li>
<li><strong>Natural neighbor interpolation with gradients:</strong> Slowest. Smooth everywhere, even at the input points.
See <a href="struct.NaturalNeighbor.html#method.interpolate_gradient" title="method spade::NaturalNeighbor::interpolate_gradient">NaturalNeighbor::interpolate_gradient</a>.</li>
</ul>
<h2 id="performance-comparison"><a class="doc-anchor" href="#performance-comparison">§</a>Performance comparison</h2>
<p>In general, the speed of interpolating random points in a triangulation will be determined by the speed of the
lookup operation after a certain triangulation size is reached. Thus, if random sampling is required, using a
<a href="type.HierarchyHintGenerator.html" title="type spade::HierarchyHintGenerator">crate::HierarchyHintGenerator</a> may be beneficial.</p>
<p>If subsequent queries are close to each other, <a href="struct.LastUsedVertexHintGenerator.html" title="struct spade::LastUsedVertexHintGenerator">crate::LastUsedVertexHintGenerator</a> should also work fine.
In this case, interpolating a single value should result in the following (relative) run times:</p>
<div><table><thead><tr><th>nearest neighbor</th><th>barycentric</th><th>natural neighbor (no gradients)</th><th>natural neighbor (gradients)</th></tr></thead><tbody>
<tr><td>23ns</td><td>103ns</td><td>307ns</td><td>422ns</td></tr>
</tbody></table>
</div><h2 id="usage"><a class="doc-anchor" href="#usage">§</a>Usage</h2>
<p>This type is created by calling <a href="struct.DelaunayTriangulation.html#method.natural_neighbor" title="method spade::DelaunayTriangulation::natural_neighbor">DelaunayTriangulation::natural_neighbor</a>. It contains a few internal buffers
that are used to prevent recurring allocations. For best performance it should be created only once per thread
and then used in all interpolation activities (see example).</p>
<h2 id="example"><a class="doc-anchor" href="#example">§</a>Example</h2>
<div class="example-wrap"><pre class="rust rust-example-rendered"><code><span class="kw">use </span>spade::{Point2, HasPosition, DelaunayTriangulation, InsertionError, Triangulation <span class="kw">as _</span>};
<span class="kw">struct </span>PointWithHeight {
position: Point2&lt;f64&gt;,
height: f64,
}
<span class="kw">impl </span>HasPosition <span class="kw">for </span>PointWithHeight {
<span class="kw">type </span>Scalar = f64;
<span class="kw">fn </span>position(<span class="kw-2">&amp;</span><span class="self">self</span>) -&gt; Point2&lt;f64&gt; {
<span class="self">self</span>.position
}
}
<span class="kw">fn </span>main() -&gt; <span class="prelude-ty">Result</span>&lt;(), InsertionError&gt; {
<span class="kw">let </span><span class="kw-2">mut </span>t = DelaunayTriangulation::&lt;PointWithHeight&gt;::new();
t.insert(PointWithHeight { position: Point2::new(-<span class="number">1.0</span>, -<span class="number">1.0</span>), height: <span class="number">42.0</span>})<span class="question-mark">?</span>;
t.insert(PointWithHeight { position: Point2::new(-<span class="number">1.0</span>, <span class="number">1.0</span>), height: <span class="number">13.37</span>})<span class="question-mark">?</span>;
t.insert(PointWithHeight { position: Point2::new(<span class="number">1.0</span>, -<span class="number">1.0</span>), height: <span class="number">27.18</span>})<span class="question-mark">?</span>;
t.insert(PointWithHeight { position: Point2::new(<span class="number">1.0</span>, <span class="number">1.0</span>), height: <span class="number">31.41</span>})<span class="question-mark">?</span>;
<span class="comment">// Set of query points (would be many more realistically):
</span><span class="kw">let </span>query_points = [Point2::new(<span class="number">0.0</span>, <span class="number">0.1</span>), Point2::new(<span class="number">0.5</span>, <span class="number">0.1</span>)];
<span class="comment">// Good: Re-use interpolation object!
</span><span class="kw">let </span>nn = t.natural_neighbor();
<span class="kw">for </span>p <span class="kw">in </span><span class="kw-2">&amp;</span>query_points {
<span class="macro">println!</span>(<span class="string">"Value at {:?}: {:?}"</span>, p, nn.interpolate(|v| v.data().height, <span class="kw-2">*</span>p));
}
<span class="comment">// Bad (slower): Don't re-use internal buffers
</span><span class="kw">for </span>p <span class="kw">in </span><span class="kw-2">&amp;</span>query_points {
<span class="macro">println!</span>(<span class="string">"Value at {:?}: {:?}"</span>, p, t.natural_neighbor().interpolate(|v| v.data().height, <span class="kw-2">*</span>p));
}
<span class="prelude-val">Ok</span>(())
}</code></pre></div>
<h2 id="visual-comparison-of-interpolation-algorithms"><a class="doc-anchor" href="#visual-comparison-of-interpolation-algorithms">§</a>Visual comparison of interpolation algorithms</h2>
<p><em>Note: All of these images are generated by the “interpolation” example</em></p>
<p>Nearest neighbor interpolation exhibits discontinuities along the voronoi edges:</p>
<img src="data:image/jpg;base64,/9j/4AAQSkZJRgABAgAAAQABAAD/wAARCAIAAgADAREAAhEBAxEB/9sAQwAIBgYHBgUIBwcHCQkICgwUDQwLCwwZEhMPFB0aHx4dGhwcICQuJyAiLCMcHCg3KSwwMTQ0NB8nOT04MjwuMzQy/9sAQwEJCQkMCwwYDQ0YMiEcITIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIy/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDAruPsgoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKALmmMFuiCeWUgfzrzM2i3h7ro/wDgHPiVeBsV80cIUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAZmqsC0S55AJP8An8K97JovlnLpp/X4nXhVo2Z1e0dYUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAE9m+y7iOM/Nj8+K5cdDnw815X+7Uzqq8GbtfInmhQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQBjak+67Ix91QP6/wBa+nyqHLh0+7b/AE/Q78OrQKleibhQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAORzG6uOqkEZqakFODg+ugmrqx0VfEnlBQAUAFABQAUAFABQAUAIWC9SB9aYiJrqBesq/gc0+WXYXMhPtSn7kcr/RD/WnyhzDJLqVAD9nPJwAWGSfpQop9ROT7BHdSvuBtzlThgGGR+FNxXcFJ9h/2pR9+OVPqh/pS5R8wq3UDdJV/E4pcsuwcyJQwboQfpSGLSGFABQAUAFABQAUAFABQBg3bl7uUn+9j8uK+vwUFDDwS7X+/U9KkrQRDXSaBQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAHQwsXgjZjklQT+VfG14qFWUY7Jv8zy5q0mkPrEkOlAiNriFPvSoPxquVsLoZ9riP3d7/AO6hNPkYuZB58jfctpD/ALxAo5V3C77Bm6bokSfUk0e6HvB5Vw33rgD/AHUH9aLx7BZ9w+yg/fllb6vj+VHN5Byii0gH/LJT9ef50ueQcqJVRV+6oH0FK4wZgilmOABkmjcCCFTK/nyDH9xT/CP8TVPTRCWuo6aJiwliwJF/8eHoaSfRg11Q+KVZk3DII4IPUGk1YadxzIrfeUH6ii4ERtID/wAslH04/lT55C5UJ9lA+5LKv0fP86fN5Byh5Vwv3bgH/eQf0ovHsFn3DN0vVIn+hIo90PeDz5F+/bSD/dINHKu4XfYPtcQ+9vT/AHkIo5GHMh63EL/dlQ/jS5Wh3RJ1qQCgYUAc4zF2LMcknJr7aMVCKjHZHqpWVkJVDCgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAazqv3mA+poE2kM+0Rdmz9Bmi5PPEPOJ+7FIfwx/OlcObsg3THpGq/wC83+FGoXl2NmwW4ezT99GuM/dXOOa+ZzJWxMrrt+RwV1L2juWfs7H79xKfoQv8q4ebyMbeYfY4f4lLH/aYmlzsOVEiwxp92NR9BSu2OyH0hhQAUAFABQAUAFAFb/j6l/6Yof8Avo/4Cr+FeZG7LNQWFAEEsbK/nRD5/wCJf7w/xq0+jJa6okjkWVA6ng/pUtWGncfSGFABQAUAFABQAxoY3+9Gp+op3aFZEf2OH+FSp/2WIp87FyoPs7D7lxKPqQ386fN5BbzIrlbhLaX99Gw2n7y4/rW+FSdeFl1RdNS51Ywt0w6xq3+63+NfW6npXl2DziPvRSD8M/youHN3QfaIu7Y+oxTuHPEerq33WB+hoKTTHUDCgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgBCQOpAoEMM8Q6yL+BzRcXPHuJ56n7qSN9FpXFzroHmSnpD/wB9MBQF5dg/fnvGv5mjUPeDypD96Zv+AgCgOV9WH2dD94u31Y0WDkXUVYYl6Rr+VOw1GK6EgGKCgoAKANXSmBgdc8hsn8v/AK1fPZxF+1jLpb9f+CcWJXvJl+vIOYKACgAoAKACgAoAKACgCvMxkfyIzgkZdh/CP8atK2rJfYnRVRQqjCgYAqW7jFpDCgAoAryI0TmaIZz99B39x71ad9GS9NUTI6yIHU5U9Klqwx1IYUAFABQAUAFABQAUAVdRYLZuCeWIA/PNehlkW8Smul/ysbYdXqIxa+oPQCgAIzQBG0MTdY1/KixLjF9BPs6D7pdfoxpWFyLoHlSD7szf8CANAcr6MP347xt+Yo1D3g8yUdYf++WBoC8uweeo+8ki/VaLhzrqKJ4j0kX8TincfPHuPBB6EGgYtAwoAKACgAoAKACgAoAKACgAoAKACgAoAqTTMtxs3FUxyQM0m9TGUmpWJvIDcmSRh/vf4UWL5fMBbxD+AH6807ByR7EgUL0AH0FBVkhaBhQAUAFABQAUAFABQA5EaRwiDLHoKmc404uUnZITaSuzatLUW0ZGcu33jXy2NxbxM77JbHnVanO/IsVxGYUAFABQAUAFABQAUARTy+WoCjMjcKPeqirkt2Fhi8pMZyxOWb1NDdxpWJKkYUAFABQAUAVnU27mVATGfvqO3uKte9oydtSwrBlDKcg9CKkYtIYUAFABQAUAFABQBVvoWnhCpjIbPP0r1st/dT9pPZqx5zzzDYbE+ynd9G10d/6vbbbV7YxBBIIwR1FfRJ31R9LGUZRUou6YlBQUAFABQAUAFABQAUAIVDdQD9RQKyIzbxH+AD6cUWJ5I9g8gD7ski/8Cz/OlYOTsyOKR/P2bi6HOCR3FBMZPmt0LNM1CgAoAKACgAoAKACgAoAKACgAoAqSjE7yf3Nufocg0jGXxXJObc+sJ/8AHf8A61GxXwehP1pmgUAFABQAUAFABQAUAFADkRpHCIMsegqZzjTi5SdkhNpK7Nq0tFtk9ZD1P9BXy2MxksRLtFbL9WefVqub8ixXEZBQAUAFABQAUAFABQA2R1iQuxwBTSvoJuxFBGxYzSD526D+6PSnJ9EJLqyepKCgAoAKACgAoAKBFYg2jFl/1BPzAfwn1+lX8XqTsWQQQCDkGoKCgYUAFABQAUANJrvoYe3vTPlM1zhyfscM7JbtdfTy8+vpuldh82V7m0SdSQAsnZvX61vRrypvyPZyrOauBmlJuVPqu3muz/B9e6yZYnhkKOMEfrXpwnGceaJ+hYTF0sXSVWk7p/en2fn/AFsMqzqCgAoAKACgAoAKACgAoAhkYyN5SHH94+gpESd3yoCoWeFQMAK39KAtaSJqZYUAFABQAUAFABQAUAFABQAUAFAEIUNNMp6FQP50iLXbQsB3QgN1Hyn8KaCGq1G8259Yj/47/wDWpbC+D0J+tM0CgAoAKACgAoAKAHIjSOEQZY9BUznGnFyk7JCbSV2bVpaLbJ6yHqf6CvlsZjJYiXaK2X6s8+rVc35FiuIyCgAoAKACgAoAKACgAoEVl/0mUSH/AFSH5B/ePrVv3VbqLcs1BQUAFABQAUAFABQAUAHWgRW5tGxyYGPH+wf8Kv4vUnb0LNQUFAwoAKaVyZSUU23ZIaTXfRw/K+aW58lmec+3j7KhpHq+/wDwPxe3qldZ8+FABQBHLCkybXXPoe4q4VJQd4nXg8dXwdTnoyt3XR+q6/1Yybi1e3I3cqejCvUpVo1Ntz9Fy3NqGPi+TSS3T/Nd10/NK6IK2PVCgAoAKACgAoAKAI5ZCuFXl26D+tBMnbRbixxiNcdT1J9TQEVZDX/4+Ivo39KOon8SJaCwoAKACgAoAKACgAoAKACgAoAKAIk/4+Jfov8AWjqQviYi/JcsvZxuH170uoLSViYjNMsg5tz6xH/x3/61LYz+D0J+tM0CgAoAKACgByI0jhEGWPQVM5xpxcpOyQm0ldm1aWi2yesh6n+gr5bGYyWIl2itl+rPPq1XN+RYriMgoAKACgAoAKACgAoAKAK8pM8hgU/KP9YR/KrWiuS9dCcAKAAMAdBUDFoGFABQAUAFABQAUAFABQAhAYEEZB6imIrqTauI2P7k8Ix/h9jVfFr1J2LNQWFNK5MpKKbbskNJzXo0KHJq9z4zNc1eJfs6ekF+P/A7L5vySuk8UKACgAoAKAEZQwwwBB7EU02ndGlOpOlJTptprqtGZd1YmLdJHzH6dxXo0MSpWjLc+7yjiCGJ5aGI0n36P/Jvtt23SKddZ9MFABQAUAFADZHEa7j+A9TQS3ZDYkIy7/fbr7e1Aorq9ySgsif/AI+Ivo39KOpD+JEtBYUAFABQAUAFABQAUAFABQAUAFAESf8AHxL9F/rR1IXxMSf5Qkn9w8/TvSYT01JqZYEZoAg5tz6xH/x3/wCtS2M/g9CfrTNAoAKAHIjSOEQZY9BUznGnFyk7JCbSV2bVpaLbJ6yHqf6CvlsZjJYiXaK2X6s8+rVc35FiuIyCgAoAKACgAoAKACgAoAhnlK4jj5kfp7e5qorqyWx8UQijCjnuSe59aTd2NKw+kMKACgAoAKACgAoAKACgAoAKAEZVdSrDIPUU07CK8btA4hkJKn7jn+R96tRc/h3MqlWNGLlUdkupOTmvQo0VTXmfFZhmdTFyaWkO3f1/rT8WlbnlhQAUAFABQAUAFABQBRurAP8APCAp7r0Brso4rl0mfV5TxFKivZYtuS6Pdr17r8VtrpbNIIJBGCOor0U76o+4jKMoqUXdMSgoKAEJCgknAFAiKMGV/NYcfwD+tIlavmZNTLCgCJ/+PiL6N/SjqQ/iRLQWFABQAUAFABQAUAFABQAUAFABQBEn/HxL9F/rR1IXxMkZQylT0IxQU1dWGQMWiAP3l+U/hQiYPQkoLAjNAEHNufWI/wDjv/1qWxn8HoT9aZoORGkcIgyx6CpnONOLlJ2SE2krs2rS0W2T1kPU/wBBXy2MxksRLtFbL9WefVqub8ixXEZBQAUAFABQAUAFABQAUAMllWKMs34Adz6U0rsTdhkETLmST/Wv19h6U5PohJdWTVJQUAFABQAUAFABQAUAFABQAUAFAB0q4Qc3ZHPicTTw1N1Kj0/PyRHKiyoUYZU16VGiqa8z4zMMyni56aRWy/V+f5fe3DFI0biGU5P8Df3h/jW55zV1dE9IgKACgAoAKACgAoAKACgCvc2iTqSAFk7N6/Wt6NeVN+R7OVZzVwM0pNyp9V2812f4Pr3WTLE8MhRxgj9a9OE4zjzRP0LCYuli6Sq0ndP70+z8/wCthlWdRAf374/5ZKef9o/4UtzP4n5E9M0CgAoAif8A4+Ivo39KOpD+JEtBYUAFABQAUAFABQAUAFABQAUAFAESf8fEv0X+tHUhfEyWgshX5Lll7ONw+vel1IWkrE1MsKAAjNAEKq0ThVBaNjwP7v8A9aonONOLlJ6GTfs9XsdDaWqW6ZyGdhy3+FfMY3GSxErbRWy/V/1ocdWq5vyLNcJkFABQAUAFABQAUAFABQAhIAJJwB1oEQRAzyCdvuD/AFan+dW9FYS11LFQUFABQAUAFABQAUAFABQAUAFABQAdKuEHN2Rz4nE08NTdSo9Pz8kMJzXqUqSpqyPhcbjamLqc89ui7f11YVocQyWNZU2t+BHUH1oGnZkcUjBvJl/1g6H+8PWmU11RPSICgAoAKACgAoAKACgAoAjlhSZNrrn0PcVcKkoO8TrweOr4Opz0ZW7ro/Vdf6sYV5BJHJ5IOVP3nHYen1r1KVaNRaH6Jl+a0swjaGkuqf6d10v96QiqFUKBgCtj1UrC0DCgAoAif/j4i+jf0o6kP4kS0FhQAUAFABQAUAFABQAUAFABQAUARJ/x8S/Rf60dSF8TJaCyGf5Qkn9w8/TvSZE9NSamWFADkRpHCIMsegqZzjTi5SdkhNpK7Nm1tEt0OcM7D5j/AEr5fGY2WIlppFbL9X/Wh59Wq5vyE5tD3MB/8c/+tXJ8Xqc/w+haByMjpUFBQMKACgAoAKACgAoAKAKz/wCkymMf6pD859T6Va91X6k7uxZ6VAwoGFABQAUAFABQAUAFABQAUAFAATitKVJ1HZHFjcbTwlPnnv0Xf+urGZzXqQpxgrRPhsTi6uJnz1Xfy6L0/r1CrOYKACgCOWISrg8MOVYdQaCouw2GUsTHJgSr1HqPUU2OS6rYmpEBQAUAFABQAUAFABQBFPKUwiDMjfdHp7mmioq+r2COBEi2EBs8sSOpoUmndFxrThNTg2mu2hn3ViYt0kfMfp3FejQxKlaMtz7rKOIIYnloYjSffo/8m+23bdIp11n0wUAFAET/APHxF9G/pR1IfxIloLCgAoAKACgAoAKACgAoAKACgAoAiT/j4l+i/wBaOpC+JktBYjKGUqehGKBNXVhkDFogD95flP4UImD0JkRpHCIMsegqZzjTi5Sdkim0ldm1aWi2yesh6n+gr5bGYyWIl2itl+rPPq1XN+RYriMgIyMHpQIq82h7mA/+Of8A1qv4vUn4fQtA5GR0qCgoGFABQAUAFABQBBPIxIhiP7xup/uj1qkurJb6IljjWJAijgUm7u40rDqQwoAKACgAoAKACgAoAKACgAoAQnFbUqMqm2x5+OzGlg172snsv8+y/qw3rXpQgoKyPiMTiamJqOpUev5eSCrOcKACgAoAKAIpovMAIO115VvSmiouwQy+ZlWG2RfvL/X6UMJK2xLSJCgAoAKACgAoAZLKIkLH6ADqT6UFJXYyCIrmSTmVuvsPSmxyfRbE1IgKAKN1YB/nhAU916A12UcVy6TPq8p4ilRXssW3JdHu1691+K210tmkEEgjBHUV6Kd9UfcRlGUVKLumJQURP/x8RfRv6UdSH8SJaCwoAKACgAoAKACgAoAKACgAoAKAIk/4+Jfov9aOpC+JktBYUARxIxuzGgyZBkD371E5xpxcpOyM21Fu+x0FpaLbJ6yHqf6Cvl8ZjJYiXaK2X6s4qtVzfkWK4jIKACgAIyMHpQIq82h7mA/+Of8A1qv4vUn4fQtA5GR0qCgoGFABQAUARzSiJM4yx4VfU1SVxN2EgiMalmOZG5Y0SdxJWJakoKACgAoAKACgAoAKACgAoAKAEJxW9Gi6j8jzMxzGGDhZazey/V+X5/e02vTjFRVkfD1as6s3Obu2FMzCgAoAKACgAoAKAIpoi+HQ4lXofX2PtTRUXbR7CwyiVTxtdeGX0NIJKxJQSFABQAUAIzBVLMcAck0DIIlMzidxhR/q1PYetPyKei5UWKRAUAFABQBXubRJ1JACydm9frW9GvKm/I9nKs5q4GaUm5U+q7ea7P8AB9e6yZYnhkKOMEfrXpwnGceaJ+hYTF0sXSVWk7p/en2fn/WxXf8A4+Ivo39Kvqbv4kS0FhQAUAFABQAUAFABQAUAFABQAUARJ/x8S/Rf60dSF8TJaCxyI0jhEGWPQVM5xpxcpOyQm0ldmkLRbONJusiMNzex4IFfM4rGSxMrbR6L9WebXqubv0RoV55mFAwoAKACgAIyMHpQIq82h7mA/wDjn/1qv4vUn4fQtA5GR0qCgoGFACMwRSzHAAyTT3EQQqZX8+QY/uKf4R/iapu2iEtdSxUFBQAUAFABQAUAFABQAUAFABQAhOK6aFH2ju9jxs1zP6pH2dP43+C7/wCX4+ba9FJJWR8ZOcqkuabu/MKZAUAFABQAUAFABQAUAFAEM0TFhLFxIP8Ax4ehplxfR7D4pVlTcMg9CD1BpCasx9BIUAFAFY/6VJj/AJYoef8AaP8AhT2NPhXmWaRmFABQAUAFABQBHLCkybXXPoe4q4VJQd4nXg8dXwdTnoyt3XR+q6/1YxLu1e3uod2CpDYI/CvUpVo1Ntz9Dy7NqOP+DSS3T/Tuv6aWg2tj1woAKACgAoAKACgAoAKACgAoAKAIk/4+Jfov9aOpC+Jk6I0jhEGWPQVM5xpxcpOyRTaSuzatLRbZPWQ9T/QV8tjMZLES7RWy/Vnn1arm/ImdA6Mh6MMGuNOzMSO1ctAob7y/K31FOS1FHYmqSgoAKACgAoACMjB6UCKvNoe5gP8A45/9ar+L1J+H0LQORkdKgoKBlb/j6l/6Yof++j/gKv4V5kbss1BYUAFABQAUAFABQAUAFABQAUAFADW616OE+B+p8dxD/vUf8K/NiV1HghQAUAFABQAUAFABQAUAFABQBBLGyv50Q+f+Jf7w/wAaZaa2ZJHIsqBlPB/SkS007D6BFeZjK/kRnH99h2Hp9afmXFWXMydVCKFUYA4ApEt3FoEFABQAUAFABQAUAV5lDXUIYAgq3BH0qk2tUbUqk6Xv02011WjKV1YmLdJHzH6dxXoUMSpWjLc+5yjiCGJ5aGI0n36P/Jvtt23SKddZ9MFABQAUAFABQAUAFABQAUAFADIUaS7kRBliFwPzqJzjTi5SdkjO6TbZv2lotsnrIep/oK+XxmMliJdorZfqziq1XN+RYriMgoArp+7vHTtIN4+vQ/0q3rEnZlioKCgAoAKACgAoACMjB6UCKvNoe5gP/jn/ANar+L1J+H0HSuZWEER5IyzDsP8AE0JW1Y3roidFVFCqMKBgCpbuMWkMKACgAoAKACgAoAKACgAoAKACgBrda9HCfA/U+O4h/wB6j/hX5sSuo8EKACgAoAKACgAoAKACgAoAKACgCvIjROZohnP30Hf3HvTLTurMV7gFF8nDPJ93/E0WBR11HwxCJNo5J5JPc0hSd2SUEhQAUAFABQAUAFABQBBJ/wAfcH+639KfQtfCyekQUbqwD/PCAp7r0Brso4rl0mfV5TxFKivZYtuS6Pdr17r8VtrpbNIIJBGCOor0U76o+4jKMoqUXdMSgoKACgAoAKACgAoAKAHIjSOEQZY9BUznGnFyk7JCbSV2adjarbXE2cFyq5P58CvmMbi5Yhq2ke3+Z5tSq5yfYv1wEBQAUAV7r5BHN/zzbn6Hg1cexMu5YqBhQMKACgAoAKACgAIyMHpQIyIo2WZ/lYoSSFViDgHHFdDehklqXY4IJU3I8hH++aycmty0kx/2SP8AvSf99mjnY+VB9kj/AL0n/fZo52HKg+yR/wB6T/vs0c7DlQfZI/70n/fZo52HKg+yR/3pP++zRzsOVB9kj/vSf99mjnYcqD7JH/ek/wC+zRzsOVB9kj/vSf8AfZo52HKg+yR/3pP++zRzsOVB9kj/AL0n/fZo52HKg+yR/wB6T/vs0c7DlQfZI/70n/fZo52HKhptI8/ek/77Nd+Ek+R+p8jn75cTFL+VfmxPssf96T/vs11XPD52H2WP+9J/32aLhzsPssf96T/vs0XDnYfZY/70n/fZouHOw+yx/wB6T/vs0XDnYfZY/wC9J/32aLhzsPssf96T/vs0XDnYfZY/70n/AH2aLhzsPssf96T/AL7NFw52H2WP+9J/32aLhzsPssf96T/vs0XDnYfZY/70n/fZouHOxrwRRoXZpAAMn5zRdjUm3YitYzBMNwx5oyM9QfSm9SpvmWnQvVJiFABQAUAFABQAUAFABQBBJ/x9wf7rf0p9C18LJ6RAUAV7m0SdSQAsnZvX61vRrypvyPZyrOauBmlJuVPqu3muz/B9e6yZYnhkKOMEfrXpwnGceaJ+hYTF0sXSVWk7p/en2fn/AFsMqzqCgAoAKACgAoAciNI4RBlj0FTOcacXKTskJtJXZtWlotsnrIep/oK+WxmMliJdorZfqzz6tVzfkLH/AMfk/wDur/WuR/CjBbsnqSgoAKAGugdGQ9GGDTTsxEdq5aBQ33l+VvqKclqKOxNUlBQAUAFABQAUAFAFJVJt/MT78cjMPfk5Fat62M+hKYhJieBtrkZz2b61N7aMq19UOin3N5cg2SD+E9/pSceqBMmqSgoAKACgAoAKACgAoAKACgTaSuyRYievAqXI86vmMIaU9X+H9f1cn+yJJECvDevrWlHFSpOz2PGxdB41e0k/e/Df+v8AglSSJ4mw4+h9a9SlWhVV4s8GtQqUXaaGVqYhQAUAFABQAUAFABQAUAFABQBXP+kT7f8AlnGefdvT8KexfwrzY65UmLcv3kO4fhQgg9SVGDorDoRkUiWrOwtAgoAKACgAoAKACgAoAgk/4+4P91v6U+ha+Fk9IgKACgCOWFJk2uufQ9xVwqSg7xOvB46vg6nPRlbuuj9V1/qxk3Fq9uRu5U9GFepSrRqbbn6LlubUMfF8mklun+a7rp+aV0QVseqFABQAUAORGkcIgyx6CpnONOLlJ2SE2krs2rS0W2T1kPU/0FfLYzGSxEu0Vsv1Z59Wq5vyLFcRkQR/8fk/+6v9ap/CiVuyepKCgAoAKAK6fu7x07SDePr0P9Kt6xJ2ZYqCgoAKACgAoAKACgCC0/1Tf77fzq57kx2Eh/dTPD/CfmT6dxQ9VcFo7EssSSrtcfQ9xUptDauQ+Y9ucTfNH2k9Pr/jVWT2FdrcsggjIORUDCgYUAFABQAUAFAm0ldkixE9eBUuR51fMYQ0p6v8P6/q5KqhegqW7nkVsRUrP33/AJC0jAtRjEa/Ss3ud1NWihWUOpVhkGiMnF80dxzhGcXGSumUprMrlo+R/d716lDHJ2jU37njYnLXG8qWq7df+D/W5UIIODwRXemmro8tpp2YUxBQAUAFABQAUAFABQBFPIUUIn+sc4X/ABpoqKvqx0UYijCDt39aQm7u4+gRBb/u2kh/unK/Q02XLWzJ6RAUAFABQAUAFABQAUAQSf8AH3B/ut/Sn0LXwsnpEBQAUAFACMoYYYAg9iKabTujSnUnSkp02011WjMu6sTFukj5j9O4r0aGJUrRlufd5RxBDE8tDEaT79H/AJN9tu26RTrrPpgoAciNI4RBlj0FTOcacXKTskJtJXZtWlotsnrIep/oK+WxmMliJdorZfqzz6tVzfkWK4jIKAII/wDj8n/3V/rVP4USt2T1JQUAFABQBXuvkEc3/PNufoeDVx7Ey7lioGFAwoAKACgAoAKAILT/AFTf77fzq57kx2FuFJQSJ9+M7h7+opRfQGSIwdAy8gjIpNWGOIzwaQFcxPAd0HKd4z/SrunuK1tiWKVJVyp5HUHqPrUtNDTuPpDCgAoE2krskWInrwKlyPOr5jCGlPV/h/X9XJVUL0FS3c8itiKlZ++/8haRgFABQBbUYUD2rJnoRVopC0DCgCKa3Sbrw3qK6KOJnS21XY5cRg6dfV6Pv/n3KE0Dwnnlc8GvWo4iFVab9jwsRhalB+9t3Iq3OYKACgAoAKACgBGIVSxOAOSaBkMAMjGdxgtwo9FpvsVLT3UT0iAoAgn/AHcsc3bOxvof/r00XHVNE9IgKACgAoAKACgAoAKAIJP+PuD/AHW/pT6Fr4WT0iAoAKACgAoAKAOer3T9lHIjSOEQZY9BUznGnFyk7JCbSV2bVpaLbJ6yHqf6CvlsZjJYiXaK2X6s8+rVc35FiuIyCgAoAgj/AOPyf/dX+tU/hRK3ZPUlBQAUAFADXQOjIejDBpp2YiO1ctAob7y/K31FOS1FHYmqSgoAKACgAoAKAILT/VN/vt/OrnuTHYnqCivD+6meH+E/Mn07irequStHYsVBQUARSwB23odkg6MP6+tUpW0JaEjnO8RSrtkPT0b6UNdULmS+LQtLET14FZuRwV8xhDSnq/w/r+rkqqF6CpbueRWxFSs/ff8AkLSMAoAKACgAoAuVkeiFABQAUABAIweQaE2ndA0mrMpzWQPMXB/uk16NHHPar955OIy1PWj93+RTIKnBBB9DXpqSkrpnjyi4u0lZiUyQoAKACgCvL+/l8kfcXmT+gplr3VcsUiAoAKAGyIJI2Q9GGKBp2dxlu5eEbvvr8rfUU2OSsyWkSFABQAUAFABQAUAQSf8AH3B/ut/Sn0LXwsnpEBQAUAFABQAUAYCI0jhEGWPQV7c5xpxcpOyR+yNpK7Nq0tFtk9ZD1P8AQV8tjMZLES7RWy/Vnn1arm/IsVxGQUAFABQBBH/x+T/7q/1qn8KJW7J6koKACgAoAKAK6fu7x07SDePr0P8ASresSdmWKgoKACgAoAKACgCC0/1Tf77fzq57kx2J6gohuFJQSJ9+M7h7+oqovoSyRGDoGXkEZFJqwx1IG0ldkixE9eBUuR51fMYQ0p6v8P6/q45oI3Qqygg+tLmdzyK2JqVXeT/yIt0lr9/MkP8Ae7r9fWnpLbcy3LCsHUMpBB6EVAhaBBQAUAFADk++v1pPYuHxItVmdwUAFABQAUAFAEckKSj5l59e9a0q86T91mNbD06y99fPqUJrZ4efvL6169DFQq6bPseDicFUoa7x7kNdJxhQBHNL5UeQMsThR6mhFRV2EMXlR4zljyx9TQwk7skoJCgAoAKAIB+6uyP4ZRkf7w/+tT6F7x9CekQFABQAUAFABQAUAQSf8fcH+639KfQtfCyekQFABQAUAFABQBFaWi2yesh6n+grhxmMliJdorZfqz9Uq1XN+RYriMgoAKACgAoAgj/4/J/91f61T+FErdk9SUFABQAUAFAFe6+QRzf8825+h4NXHsTLuWKgYUDCgAoAKACgCC0/1Tf77fzq57kx2J6goKBNpK7I7eIxzNC3Cn50+ncU5Surnm1swhC6p6v8P6/q5dVQvQVk3c8mtiKlZ++/8haRgFABQMrtA0TF7fAzyYz0P09DV8yekh37kkU6y5AyHH3lPUVLi0JokpCCgAoAfEMyCk9jSkrzRZrM7QoAKACgAoAKACgAoArTWavzHhW9O1d1DGyhpPVfiebicuhU96no/wACg6tGcOMH3r1YTjNXi7niTpzpy5ZqzK0X76Uzn7o4jH8zVg9FylikQFABQAUAFAENypMW5fvIdw/Cmi4PUlRg6Kw6EZFIlqzsLQIKACgAoAKACgCCT/j7g/3W/pT6Fr4WT0iAoAKACgAoAKAH14h+nhQAUAFABQAUAQR/8fk/+6v9ap/CiVuyepKCgAoAKACgBroHRkPRhg007MRHauWgUN95flb6inJaijsTVJQUAFABQAUAQWn+qP8Avt/OrnuQmkrstrET14FZORwV8xhDSnq/w/r+rkqqF6CpbueRWxFSs/ff+RFcoSgkQfPGdw9/UU4vWzMUSI4kRXU5BGRUtWdgHUCCgAoAKAIpYFlw2Ssg6OOoqlKw0xiTsjCO4AVj0cfdb/A03G+sR27FioJCgCSEZk+gqZbG1FXkWKg6woAKACgAoAKACgAoAKAKl2ouWFp1B+aQ+i//AF61pTlTfOmRUpwqK01chls2iA8sbkHYdRXp0cbCek9GeHiMvqU3eHvL8SvXaecFABQAUAFABQBBb/u2kh/unK/Q02XLWzJ6RAUAFABQAUAFAEEn/H3B/ut/Sn0LXwsnpEBQAUAFABQAUAPrxD9PCgAoAKACgAoAgj/4/J/91f61T+FErdk9SUFABQAUAFABQBXT93eOnaQbx9eh/pVvWJOzLFQUFABQAUCbSV2SLET14FS5HnV8xhDSnq/w/r+rjLJQsLYH8bfzom7s8itXqVX7z/yLNQYBQAUAV4f3Mzwfwn50+ncVb1Vyn3LFQSFABQAUAFADXRZFKsAVPUGhOwyDElr0zJD6fxL/AIir0l6j3J0dZFDIwKnuKlprcRPAPmJ9qiRtQXvNk9QdQUAFABQAUAFABQAUARzSiGIuecdB6n0ppXdgG20RjQs/Mrnc59/SnJ32AmqQIJrVJckfK/8AP6110MXOno9V/Wxw4nAwq+9HSX9blCSJ4mw4+h9a9WlWhVV4s8OtQqUXaaGVqYhQAUAFAEE/7uWObtnY30P/ANemi46ponpEBQAUAFABQAUAQSf8fcH+639KfQtfCyekQFABQAUAISFBJIAHJJoKjGU5KMVdspT6pBFkJmRvbgfnU8x9DguGcZXadX3I+er+7872NGvGPrAoAKACgAoAKAII/wDj8n/3V/rVP4USt2T1JQUAFABQAUAFAFe6+QRzf8825+h4NXHsTLuWKgYUA2krskWInrwKlyPOr5jCGlPV/h/X9XJVUL0FS3c8itiKlZ++/wDIWkYEFp/qW/32/maqe5TJ6kkKACgCG5QlBIg+eM7h7+oqovWzGiRHEiK6nIIyKlqzsA6gQUAFABQAUAFAEDwFWMkBCueqn7rVSl0ZV+5Ys51kLoQUkHVD1/8Ar1FSNtTooLct1mbhQAUAFABQAUAFABQBVT/SbjzP+WURwnu3c1b91W6sZaqBBQAUAIyh1KsMg04ycXzR3JnCM4uMldMpTWZXLR8j+73r1KGOTtGpv3PGxOWuN5UtV26/8H+tyoQQcHgiu9NNXR5bTTswpiCgBsiCSNkPRhigadncZbuXhG776/K31FNjkrMlpEhQAUAFABQBBJ/x9wf7rf0p9C18LJ6RAUARTXMNvjzZApPbqfypXO7CZdisZf2EG0uuy+92V9dtzOm1ckEQx49Gb/ClzH1eD4SjFqWKnfyW2/fe1t9E/Mz5Z5Z23SuWPb2qT6jC4LD4SPLQgor8X6vd/MjpHUdbXknhhQAUAFABQAUAQR/8fk/+6v8AWqfwolbsnqSgoAKACgAoAKAGugkRkPRhimnbUltJXYWas8C7+GX5W+opTaT0POrZjCCtDV/h/X9XLaqF6Cs27nk1sRUrP33/AJC0jAKACgCC0/1Lf77fzNVPcpk9SSFABQAUAV4f3Mzwfwn50+ncVb1Vyn3LFQSFABQAUAFABQAUAPECTR/NkMD8rDgipcmmddD4QWd4CEueh4WUdD9fQ0cqesTcs1AgoAKACgAoAKAK9y7HbBGcSSdT/dXuaqK6sZMiLGioowqjAqW7u4h1ABQAUAFABQBFNbpN14b1FdFHEzpbarscuIwdOvq9H3/z7lCaB4TzyueDXrUcRCqtN+x4WIwtSg/e27kVbnMFAEA/dXZH8MoyP94f/Wp9C94+hPSICgAoAKACgCCUhbqEkgAK5JP4U+hpCMp+7FXbsQT6pBFkJmRvbgfnUcx7+C4Zxldp1fcj56v7vzvYz5tSuJSQreWp7L/jSuz6rB8OYLDpOceeXd7bdtrdr3a7lOpPeCgAoAKAOtryTwwoAKACgAoAKAII/wDj8n/3V/rVP4USt2T1JQUAFABQAUCbSV2SLET14FS5HnV8xhDSnq/w/r+rkqqF6CpbueRWxFSs/ff+RAn7u8kTtIN4+o4P9Kp6xMuhYqCQoAKACgCC0/1Lf77fzNVPcpk9SSFABQAUAQ3KEoJEHzxncPf1FVF62Y0SI4kRXU5BGRUtWdgHUCCgAoAKACgAoAswjEY96zludtFe4OZQylWAIPUGkaFby5LXmIGSHvH3X6f4Vd1LfcZPFKkybkbI/lUtNbiH0gCgAoAbJIsUbO5wqjJoSu7AQ20bfNNIP3knOP7o7Cqk+iGWKkQUAFABQAUAFABQAEAjB5BoTad0DSasynNZA8xcH+6TXo0cc9qv3nk4jLU9aP3f5FMgqcEEH0NempKSumePKLi7SVmQXKkxbl+8h3D8KpDg9SVGDorDoRkUiWrOwtAgoAimuYbfHmyBSe3U/lSud2Ey7FYy/sINpddl97sr67bmdNq5IIhjx6M3+FLmPq8HwlGLUsVO/ktt++9rb6J+ZlXE8s9zE0rljhse3Sl0PpcNgsPhLRoQUV+L9Xu/mLUnaFABQAUAFABQB1teSeGFABQAUAFABQBBH/x+T/7q/wBap/CiVuyepKCgAoE2krskWInrwKlyPOr5jCGlPV/h/X9XJVUL0FS3c8itiKlZ++/8haRgFAFe7+QRzf8APNsn6Hg1cNdCl2LFQIKBBQAUAQWn+pb/AH2/maqe5TJ6kkKACgAoAKAK0TCCV4WICn5kz+oq3qrlblgEHoQagQtAgoAKACgAoAtRjEa/Ss3ud1NWih1IsKAIJbfL+bC2yX17N9RVKXR7DCK43N5ci+XKP4T39x60OPVbAT1IgoAqt/pNzt/5ZRH5v9pvT8Kv4V5sZaqBBQAUAFABQAUAFABQAUAFAEckKSj5l59e9a0q86T91mNbD06y99fPqUJrZ4efvJ6169DFQq6bPseDicFUoa7ruUYCIjJExwEO4E/3T/k11PuYcsptKKu3p8yGfVIIshMyN7cD86jmPewXDOMrtOr7kfPV/d+d7GfNqVxKSFby1PZf8aV2fVYPhzBYdJzjzy7vbbttbte7Xcp1J7wUARP/AK+P6N/Sn0IfxIlpFhQAUAFABQAUATfarj/nvL/32an2cOyM/ZQ/lX3B9quP+e8v/fZo9nDsg9lD+VfcH2q4/wCe8v8A32aPZw7IPZQ/lX3B9quP+e8v/fZo9nDsg9lD+VfcH2q4/wCe8v8A32aPZw7IPZQ/lX3B9quP+e8v/fZo9nDsg9lD+VfcRJdXH2iX9/L0X+M+9P2cLbEqlDmeiJftVx/z3l/77NL2cOyK9lD+VfcH2q4/57y/99mj2cOyD2UP5V9wq3t0jBhcS5H+0TSdKDVrEVMNRqR5ZxTXoalpr3RLpPbev9R+fT8q5amE6wPAxeQ7yw7+T/R/hr82bMU0c6B4nDr6g1xyi4uzR89Wo1KMuSorMfUmQUANdBIjIejDBpp2dxkdo5a3UN95Plb6inNag9yapEFABQBBaf6lv99v5mqnuUyepJCgAoAKACgCn5KXrO8gyn3U/qfzrS7hoir2IzBFAcTxKU7SqMY+uKfM3sx3vsWBaxEZR5FB6FZDU876iuH2Zx925lH1IP8ASjmXYLh5VwOlwD/vRj+lF49g0D/Sx3hb8CKXuhoHmXI6wKf92T/61O0e4aFpbiVVAe1lGB/CQf61k4p7M7oqySHfbEH3o5l+sZpcjKD7dbf89QPqCP50ckuwD1uYG+7NGf8AgQpcr7CFkijuEw2COxB5H0oTcQIfNktjtn+aPtKB0/3v8aqylsMfcTFUVYiDJJwn+NKK11AkhiWGJUXoO/qfWk3d3EPpAFABQAUAFABQAUAFABQAUAFAEc86W0DzSHCIMn/CqhFzkoo0pU5VZqEd2cDe3Mk9yLiQjk4IHAAPoK96CtHlPq6GDoYRJUYpd/P1EoOwKACgAoAif/Xx/Rv6U+hD+JEtIsKACgAoAKACgAoAKACgAoAKACgCJP8AXyfRf60+hC+JktIsKACgAoAfFNJBIHicow7g0pRUlZoyrUadaPJUV0bFpr3RLpPbev8AUfn0/KuOphOsD57F5DvLDv5P9H+GvzZsxTRzoHicOvqDXHKLi7NHz1ajUoy5Kisx9SZFdP3d5InaQbx9Rwf6Vb1iV0LFQSFABQBBaf6lv99v5mqnuUyepJCgAoAKAIblyEEaH55DtHt6mqitbsaJEQRoqKMBRgVLd3cBxGaAKxheA7rfle8RPH4elXdP4h3vuSxTJMDjIYfeU8EVLi0JokpCCgAoAuVkeiFAARnrQBG0ELfeiQ/VRT5n3AjNjbH/AJYqPpxT55dxiGxiwQrSqPaQ0c7ApBXs77bD8ygBfnPrzgHt0rW6lHUDQiuUlOw5SQdUbg//AF6ycWtRE1SAUAFABQAUAFABQAUAFABQAUAcx4iv/OmW0TO2M7mPqccfkD+tethsNKjd1FZn0GS0oun9YTvzaL5Oz/FGC6h0KnoRXWe01dWEhYtGM/eHB+tDFB3Wo+kUFABQBE/+vj+jf0p9CH8SJaRYUAFABQAUAFABQAUAFABQAUAFAESf6+T6L/Wn0IXxMlpFhQAUAFABQAUAPimkgkDxOUYdwaUoqSs0ZVqNOtHkqK6Ni017ol0ntvX+o/Pp+VcdTCdYHz2LyHeWHfyf6P8ADX5s0JZo5Io7qJw6xsMkeh6/zrlUXFuMkfP1KNSjJwqKzLlZmQUCCgCC0/1Lf77fzNVPcpk9SSFABQAUAV4f30zz/wAI+RPp3NW9FYp9ixUEhQAUARSwLKQwJWQdHHUVSlYaYxZ2jYJcAAno4+6f8DT5b6xHbsWKgQ5Pvr9aT2Kh8SLVZncFABQAUAFABQBTMQmuLpD3VOfQ881d7JMY6NVuodsy/vYztbHBB9RQ7xegBuntvvgzRf3gPmH1HejSXkBPHKkqbo2DL7VDTW4h9ABQAUAFABQAUAFAEbzKnHU+gr0cLldfEWduWPd/ouv5eZxYjH0aOl7vsv1/q/kVnlZ+p49BX1GFy+hhtYLXu9/+B8jwa+MrV9JPTstjltW/5Cc3/Af/AEEVwYz+PL5fkfpXDf8AyLKX/b3/AKUylXKe4RD5Lgjs4z+IquhG0vUlqSwoAKAIn/18f0b+lPoQ/iRLSLCgAoAKACgAoAKACgAoAKACgAoAiT/XyfRf60+hC+JktIsKACgAoAKACgAoAKAHJI8eSjlSRg4Pak4p7mVajTrR5KiujV03X8xJHdryvyl19uOR+vH5VyVcJreB85icibTlh38n+j/z+83Ypo50DxOHX1BrilFxdmjwK1GpRlyVFZj6kyILT/Ut/vt/M1U9ymT1JIUAFAENy5CCND88h2j29TVRWt2NEiII0VFGAowKlu7uA6gQUAFABQAjKHUqwBB6g0DK+yS2/wBXmSL+53H0/wAKu6lvuPcs20iTMrIcj+RqJpoumvfVy5WR2BQAUAFABQAUAV4v+P64/wB1P61T+FANm/cTrOPuNhZP6GmtVYZaqBEElsGfzImMUn95e/1HeqUujAaty0ZCXKhD2cfdP+FPlv8ACMs1AgoAKACgBryLGOTz6V14XBVsVK1Nad3sc+IxVOgrzevbqVnnZ+BwK+nwmUUcO1OXvSX9bf538rHhYjMatZOK0X9dSKvVPPCgDmdW/wCQnN/wH/0EV4WM/jy+X5H6pw3/AMiyl/29/wClMpVynuEcykx5H3lO4U0RNaXQ9WDKGHQjNBSd1cWkMKAIn/18f0b+lPoQ/iRLSLCgAoAKACgAoAKACgAoAKACgAoAiT/XyfRf60+hC+JktIsKACgAoAKACgAoAKACgCJfkuGXsw3D696roQtJNdyxFNJBIHicow7g1EoqSs0TWo060eSoro2LTXuiXSe29f6j8+n5Vx1MJ1gfPYvId5Yd/J/o/wANfmzS0+WOa2LxuGXe3I+tctWLjKzPn61KdKXLUVmWqzMQoAKAK8P76Z5/4R8ifTuat6KxT7FioJCgAoAKACgAoAKAGC23zeZE3lygdR0PsRQ5WVnsbUdZE8dx84imXy5e3o30NQ49UdhYqRBQAUAFABQBXi/4/rj/AHU/rVP4UBM6CRGRhlWGDUp2AhtXIDQOcvFxn1HY1Ul1QyxUiEZQwIYAg9QaAK3kSQc25yveJjx+B7VfMn8QySK5SU7DlJB1RuD/APXpOLWoiVnVBljitKGGq15ctKN/67mVWvTpK83YryXBbhOB619Jg8lhTfNX959un/B/L1PFxOaSn7tLRd+v/AIOte4koqy2PKbbd2FMQUAFAHM6t/yE5v8AgP8A6CK8LGfx5fL8j9U4b/5FlL/t7/0plKuU9wKYEUXylo/7pyPoab7kQ0vElqSwoAif/Xx/Rv6U+hD+JEtIsKACgAoAKACgAoAKACgAoAKACgCJP9fJ9F/rT6EL4mS0iwoAKACgAoAKACgAoAKAIpvlCyf3Dz9O9UuxE9LS7EtIsKQCWc0kHzxOUYMeQfeicVLRo55UadaDhUV0blpr3RLpPbev9R+fT8q4qmE6wPBxeQ7yw7+T/R/hr82bMU0c6B4nDr6g1xyi4uzR89Wo1KMuSorMZcuQgjQ/PIdo9vU0RWt2ZokRBGioowFGBUt3dwHUCCgAoAKACgAoAKAOd1fVZftJhtpXjWPhipKkt359K+2yTJ6So+2xEVJy2vqkumnd/l21PXweHUY88lq/yM1726lXa91Ow95D/jXt/wBnYP8A59R/8BX+R2+zh2I/7R1CHreXLp6+a2RUf2dhI/8ALqNv8K/yFyRXQlXUrxgCL2cg/wDTU/41Sy/BP/l1H/wFf5D5IdkL/aF7/wA/lx/39b/Gn/Z2D/59R/8AAV/kHs4dg/tC9/5/Lj/v63+NH9nYP/n1H/wFf5B7OHYP7Qvf+fy4/wC/rf40f2dg/wDn1H/wFf5B7OHYiTULz7RKftc+SF/5aH396lZfhLteyj/4Cv8AIXJG+xL/AGhe/wDP5cf9/W/xqv7Owf8Az6j/AOAr/Ifs4diKW/vVYTC7uMrwf3h5H51Msvwi19lH/wABX+QnTj2JBqN6RkXlxj/rq3+NV/Z2D/59R/8AAV/kP2cOwv8AaF7/AM/lx/39b/Gj+zsH/wA+o/8AgK/yD2cOwf2he/8AP5cf9/W/xo/s7B/8+o/+Ar/IPZw7D4Wvb+dIvtNwTnO7zD8o9etRUwOChFt0o/cv8jnxVWlh6TqTS/zfY6LdPbgBy00YGA2cuB7+tedCnCmrQikvJWPhqlR1pc0nr/X3E8ciSruRgw9q0MWmtx1MQUAFABQBzOrf8hOb/gP/AKCK8LGfx5fL8j9U4b/5FlL/ALe/9KZSrlPcCgCKX5HSTt90/Q1S7ES0akS1JYUARP8A6+P6N/Sn0IfxIlpFhQAUAFABQAUAFABQAUAFABQAUARJ/r5Pov8AWn0IXxMlpFhQAUAFABQAUAFABQAUAIyhlKnoRimJq6sMhYmIA9V+U/hQ9yYO8SSkWRQfcP8AvH+dUyKexLUlj4ppIJA8TlGHcGlKKkrNGVajTrR5KiujSsNc8yYvdrwBsV1H5kj/AA/KuWrhdPcPncTkd05YZ/J/o/wV/mzfimjnQPE4dfUGuGUXF2aPn61GpRlyVFZj6kyCgAoAKACgAoAzdY1D7JD5Sf62QHBz90ev+Fe7keWfW6vtZ/BFr5vt2t3/AODc7MJh/aS5nsjlq/QD2woAKAImjKktGcHuOxqXG2qFbsOSQMdpG1x/CaFK+gXH1QwoAjT/AF8n0X+tSviYupJVDCgCKL5GMR7cr9KiOj5SV2JasoKAOk0ayEFsJ2/1koz9F7fn1rzcTV5pcq2R8dnONdat7KPwx/F9fu2+/uadcx4pDJbgt5kbGOT1Hf6jvSsWpdHsNFwYyFuF2Hs4+6f8KV+4ct9YliqICgAoA5nVv+QnN/wH/wBBFeFjP48vl+R+qcN/8iyl/wBvf+lMpVynuBQA11DoVPQimJq6sJCxaMZ+8OD9aGKDutR9Ioif/Xx/Rv6U+hD+JEtIsKACgAoAKACgAoAKACgAoAKACgCJP9fJ9F/rT6EL4mS0iwoAKACgAoAKACgAoAKACgCJfkuGXsw3D696roQtJNdyWpLIoPuH/eP86pkU9iWpLI5WO0Kv3m4FNETell1HqoVQo6CgpKysSRTSQSB4nKMO4NTKKkrNGdajTrR5KiujYtNe6JdJ7b1/qPz6flXHUwnWB89i8h3lh38n+j/DX5s2Ypo50DxOHX1BrjlFxdmj56tRqUZclRWY+pMgoAKAIbm5itITLK2FH5k+grpwmEq4qqqVJXb/AA835f1uaU6cqkuWJx9xO9zO80mN7HnAr9Nw2GhhqUaNPZH0FOCpxUY7IircsKACgAoAa8auOe3QjqKTSYmhm9ouJOV7OP61N3HcW25KDkZHSrKI0/18n0X+tSviYupJVDCgCOVSQGX7y8j39qmS6oTHqwdQw6GmndXA0NKs/td0CwBijIL57+grHEVeSOm7PMzbGfVqFl8UtF+r+XTzOoryz4gKACgBCAwIIBB6g0AV/Jkg5tzlf+ebHj8D2qbW2L5k/iJIrhJDtOVkHVG607icWtSWmSczq3/ITm/4D/6CK8LGfx5fL8j9U4b/AORZS/7e/wDSmUq5T3AoAKAIh8lwR2cZ/EVXQjaXqS1JZE/+vj+jf0p9CH8SJaRYUAFABQAUAFABQAUAFABQAUAFAESf6+T6L/Wn0IXxMlpFhQAUAFABQAUAFABQAUAFAEU3yhZP7h5+neqXYielpdiWkWRQfcP+8f502RT2Jaksij+eRpOw+Vap9iI6vmJaksKACgB8U0kEgeJyjDuDSlFSVmjKtRp1o8lRXRsWmvdEuk9t6/1H59PyrjqYTrA+exeQ7yw7+T/R/hr82bMU0c6B4nDr6g1xyi4uzR89Wo1KMuSorMfUmRy2r35u7jy0Y+TGcDnhj6/5/rX6DkeWrCUeea9+X3pdv8/P0R7eEoezhd7v+rGbXuHYFABQAUAFABQAUARFGj+aPp3U9PwqLOOqJtbYbDIJJZCPQcfnSi02wTuyetCgoAKAI41Kz+UoJ3n5APX0qL8r12IclFNt2R2VjaLZ2qxD73Vznq3evLq1HUlc+Dx2LliqzqPbZehZrM4woAKACgAoAZLCkww46dD3FKw1JrYh3TW/3syx/wB4feH1HelqivdltozA1KRZNQlZGBU45H0FeJjP40vl+R+o8OK2WU16/wDpTKlcp7gUAFAEcykx5H3lO4U0RNaXQ9WDKGHQjNBSd1cjf/Xx/Rv6UdCX8SJaRYUAFABQAUAFABQAUAFABQAUAFAESf6+T6L/AFp9CF8TJaRYUAFABQAUAFABQAUAFABQAjKGUqehGKYmrqwyFiYgD1X5T+FD3Jg7xEg+4f8AeP8AOmwp7CysdoVfvNwKSCb0suo9VCqFHQUFJWVhaQwoAKACgAoAfFNJBIHicow7g0pRUlZoyrUadaPJUV0XbjWZ5rJoGUBm4Lg4yPp/n6V25Lgabxim/sq6Xn/wL6efU8GvlNLDzVWD07f8H+n5mXX2xIUAFAB0oAjM0a9XH4c1LnFdRXQnnZ+7G5/DFHN2QrhmY9FRfqc0e8GoeW5+9Kf+AjFHK+rCzF8iPuCx/wBo5o5EOyIxErTOANuAMFeMVPKnJitqP3vH/rBlf7w/qKd2twvbckBDDIORV3uMWgZq6VpzSqbonBX/AFX+96/Tt+dcOMqaciPnc7xqilho9dX6dv1+7ub0MomiD4wehHoa4UfMSVnYkpkhQAUAFABQAUAFAHK6xEDqkzIdjccjvwOteHjH++l/XQ/UuHY3yyk1vr/6UyiJSpxKNv8AtDoa5rdj2+a2kiWkWFIApgRRfKWj/unI+hpvuRDS8Qf/AF8f0b+lLoD+JEtIsKACgAoAKACgAoAKACgAoAKACgCJP9fJ9F/rT6EL4mS0iwoAKACgAoAKACgAoAKACgAoAiX5Lhl7MNw+veq6ELSTXcIPuH/eP86GFPYI/nkaTsPlWh9gjq+YlqSwoAKACgAoAKADpTAieaMDlxn25rvyyfs8VFt2T0/r52OTFuMqTXUj87P3Y3P4Yr7Dm7I8O4ZmboqL9Tml7wah5bn70p/4CMU+V9WFmHkR9wWP+0c0ciHZEgVV+6APoKpJLYBaBhQAUAFAEaf6+T6L/WpXxMXUkqhkRiwd0Z2nuOxqeXqibdia0Rrm5SAjYzHk9setROqoK8jDE4mOHpOpLp+L7HZxRJDGscahUUYAFeVKTk7s+Bq1Z1Zuc3dsiP7i6z/yzl4Ps3/16jZi+KPoWKogKACgAoAKACgAoA5nVv8AkJzf8B/9BFeFjP48vl+R+qcN/wDIspf9vf8ApTKRAIwRkVzHuEXltHzEeP7h6fhTvfcz5XH4RySq5x91u6nrSsUpJ6D6RRFL8jpJ2+6foapdiJaNSB/9fH9G/pS6A/iRLSLCgAoAKACgAoAKACgAoAKACgAoAiT/AF8n0X+tPoQviZLSLCgAoAKACgAoAKACgAoAKACgCKb5Qsn9w8/TvVLsRPS0uwxWPlbV+8zED86fUhP3bLqTqoVQo6CpNUrKwtIYdKYEZmjXq4/DmizJc4rqJ52fuxufwx/OnYXP2QZmboqL9TmjQLzYeW5+9Kf+AjFF0HLJ7sPs8fcFj/tHNF2Hs49SzZqq3cO1QP3i9B71nU+BnNj0lhKtv5ZfkzqK8w/LwoAKACgAoAKACgAoAKAII/8Aj8n/AN1f61T+FDeyJ6kQUAKEWTKuoZSOhr1sm/jv0/VGFdtRVhmya3/1ZMkf9xjyPoa+k1RzXUt9B26K7iZASD3B4KmnuKzg7jreQyR4f/WIdrD3oQpKz0JaZIUAFABQAUAFAHM6t/yE5v8AgP8A6CK8LGfx5fL8j9U4b/5FlL/t7/0plKuU9wKAGvGrjDD6H0p3JcU9xmZIuuXT1HUf409GTeUd9UOO2aMgHIIxS2K0kiJGLPFn7wDA/XimyE7tX8yxUmoUAFABQAUAFABQAUAFABQAUAFAESf6+T6L/Wn0IXxMlpFhQAUAFABQAUAFABQAUAFABQAjAMhB6Ec0xNXWpRgk2ZJVnI4GBVtHNTlbzJvtEjdE2/UE0rI09pJ9A3k/ekk/4CmKAvfdv7gxD3V2P+0CaNQ9zqPEsa/dRh9ENKzKUorZfgL56+j/APfJosP2iDz19H/75NFg9og89fR/++TRYPaIPPX0f/vk0WD2iJrWdBdw8P8AfX+E+tRNe4zkx808JVX92X5M6T7XH/dk/wC+DXm8rPzLlYfa4/7sn/fBo5WHKw+1x/3ZP++DRysOVh9rj/uyf98GjlYcrD7XH/dk/wC+DRysOVh9rj/uyf8AfBo5WHKw+1x/3ZP++DRysOVh9rj/ALsn/fBo5WHKw+1x/wB2T/vg0crDlZClyguZmw+CFH3D71Ti7IpxdkTfa4/7sn/fBqeVk8rD7XH/AHZP++DRysOVjo7uPd92Tp/cNerk6tXfp+qMcRB8pL9rj/uyf9+zX0lzj5GRSyQS4JWVXHRlQgik7FJSRXF00N0HfJRhhm2kZ9Dj1pX1L5Lxsi4LyIjIEmP9w07mXs2L9rj/ALsn/fs07hyMPtcf92T/AL9mi4cjD7XH/dk/79mi4cjD7XH/AHZP+/ZouHIw+1x/3ZP+/ZouHIzm9VuEOpSnD9v4D6CvExavWl/XQ/T+HZKOW00/P/0plLz19H/75Nc1j2/aIPPX0f8A75NFg9og89fR/wDvk0WD2iDz19H/AO+TRYPaIYzITuXejeoU8/WnqQ2t1oyOJma6BK4yD260PYmDbnqXKg6AoAKACgAoAKACgAoAKACgAoAKAIk/18n0X+tPoQviZLSLCgAoAKACgAoAKACgAoAKADpTAqys02FXhGOB7+/0qloYSbnotiRk8rDxjoMEeopXuW48uqJVYMoIOQaRad1dC0hhQAUAFABQAUAFAEtr/wAfcP8A10X+dTU+BnHmH+6Vf8MvyZ09eYfl4UAFABQAUAFABQAUAIzBELHoBk0wIrVSIdzfec7j+NOW45bk1SIKAHx/e/CvWyb+O/T9Uc+I+Ekr6Y4xrusaFmOFAyTSGld2IYUMrGeUckYRT/Cv+JpLuVJ291DebM9zbk/98f8A1qNh/H6loHIyOlUZhQAUAFABQBzOrf8AITm/4D/6CK8LGfx5fL8j9U4b/wCRZS/7e/8ASmUq5T3AoAKACgAoAif/AF8f0b+lPoQ/iRLSLCgAoAKACgAoAKACgAoAKACgAoAiT/XyfRf60+hC+JktIsKACgAoAKACgAoAKACgA6UwIeZz6RD/AMe/+tT2M/j9BY/ncyfwj5V+lDCOruS1JoQ/6l/+mbH/AL5NVuZ/A/ImqTQKACgAoAKACgAoAltf+PuH/rov86mp8DOPMP8AdKv+GX5M6evMPy8KACgAoAKACgAoAKAILn5/LhH8Z5/3R1qo6alR01J6kkKACgB8f3vwr1sm/jv0/VHPiPhJK+mOMrf8fUuf+WKH/vs/4Cp3NPhXmWaozAjIwelAFXmzPc25P/fH/wBap2NPj9S0DkZHSqMwoAKACgDmdW/5Cc3/AAH/ANBFeFjP48vl+R+qcN/8iyl/29/6UylXKe4FABQAUAFAET/6+P6N/Sn0IfxIlpFhQAUAFABQAUAFABQAUAFABQAUARJ/r5Pov9afQhfEyWkWFABQAUAFABQAUAFAB0pgQ8zn0iH/AI9/9ansZ/H6DpThQi8M3A9qSHN6WQ9VCqFHQUFJWVhaQxCAwIIyDTE1fQjjJRvKY/7p9RTfcmLs+VktSWFABQAUAFABQBLa/wDH3D/10X+dTU+BnHmH+6Vf8MvyZ09eYfl4UAFABQAUAFABQAUAV4f3lxLL2HyL+HX9ap6KxT0VixUkhQAUAPj+9+Fetk38d+n6o58R8JHMzSv9njOO7sP4R/ia+kfY5oqy5mTqoRQqjCgYApkt31FpiCgAIyMHpQBV5sz3NuT/AN8f/WqdjT4/UtA5GR0qjMKACgDmdW/5Cc3/AAH/ANBFeFjP48vl+R+qcN/8iyl/29/6UylXKe4FABQAUAFAET/6+P6N/Sn0IfxIlpFhQAUAFABQAUAFABQAUAFABQAUARJ/r5Pov9afQhfEyWkWFAGhpEMc91IkqBl8s8Ee4rswUIzqNSV9D5zifE1sNhYVKMnF8629Hv3Xk9CzdaJ1a2b/AIA39D/j+ddFbAdab+X9f15nlZdxbtDGR/7eXy3X3ttfKJkywyQOUlQq3oRXnThKDtJWPscPiaOJh7SjJSXl+vZ+T1GVBuFAB0pgQ8zn0iH/AI9/9ansZ/H6E3SkaEUfzyNJ2HyrTfYiOr5iWpLCgAoAZIm9cZwRyD6GmmTKN0Eb7wQww68EUNBGV99x9IoKACgAoAKAJbX/AI+4f+ui/wA6mp8DOPMP90q/4Zfkzp68w/LwoAKACgAoAKACgCOeTyoWYfe6D69qcVdjSuxYY/KhVPQc/Whu7uDd2PpCCgAoAY8pjwqDMjcKK9bJ/wCO/T9UY1leOpNDEIUxnLHlm9TX0qRxyldklMkKACgAoACMjB6UAVebM9zbk/8AfH/1qnY0+P1LQORkdKozCgDmdW/5Cc3/AAH/ANBFeFjP48vl+R+qcN/8iyl/29/6UylXKe4FABQAUAFAET/6+P6N/Sn0IfxIlpFhQAUAFABQAUAFABQAUAFABQAUARJ/r5Pov9afQhfEyWkWFAGnof8Ax+v/ANcz/MV35f8AxX6f5HyvF/8AuMP8a/KR0FewfnIyWGOdCkqBl9CKicIzVpK5vh8TWw0/aUZOL8v17ryehkXWidWtm/4A39D/AI/nXn1sB1pv5f1/XmfY5dxbtDGR/wC3l8t197bXyiZMsUkDlJUKMPWvOnCUHaSsfY4fFUcTT9pRkpLy/Xs/J6lbmc+kQ/8AHv8A61LYv4/Qm6UjQjlY7Qq/ebgUIib0suo9VCqFHQUFJWVhaQwoAKACgCORTkSJ94dvUelUuxEk/iW45GDqGHQ0ik01dDqQwoAKACgCW1/4+4f+ui/zqanwM48w/wB0q/4Zfkzp68w/LwoAKACgAoAKACgCvJ+8uo4+yfO317VS0VylorlipJCgAoAbJIsUZdjwKaVwSuNtY2LmaQfvGHA/uj0r18of79pdv1Rjin7iSLdfSHCFABQAUAFABQAEZGD0oAq82Z7m3J/74/8ArVOxp8fqWgcjI6VRmczq3/ITm/4D/wCgivCxn8eXy/I/VOG/+RZS/wC3v/SmUq5T3AoAKACgAoAif/Xx/Rv6U+hD+JEtIsKACgAoAKACgAoAKACgAoAKACgCJP8AXyfRf60+hC+JktIsKANPQ/8Aj9f/AK5n+Yrvy/8Aiv0/yPleL/8AcYf41+UjoK9g/OQoACcDJ4AoApSRLqI2yLm2B7/xn+grOUI1FaS0OqhiKuFlz0pWl5fr39HoUrrQ8Za1b/tm39D/AJ+tcFbAdab+X9f15n12XcW7U8ZH/t5fLdfe216KJkywyQOUlQq3oRXnThKDtJWPscPiaOJh7SjJSXl+vZ+T1K8fzyNJ2HyrSfY0jq+YlqSwoAKACgAoAKAIW/cvvH3G+8PQ+tVuZv3XfoTUjQKQBQAUAWLKN5LyIIpOGDH2ANRVaUHc5Mer4WpFbtNL1aOkrzT8yq0p0pctRWYUGYUAFABQAUABOBk9KAILUblaY9ZDn8O1VLsVLsT1JIUAHSgCsn+kyiQ/6pD8g9T61b91WKeisXI/vfhXp5N/Hfp+qOXEfCSV9McYUAFABQAUAFABQAEZGD0oAq82Z7m3J/74/wDrVOxp8fqYWqnOpSkdPl/9BFeJjP48vl+R+ocN/wDIspf9vf8ApTKdcp7gUAFABQAUARP/AK+P6N/Sn0IfxIlpFhQAUAFABQAUAFABQAUAFABQAUARJ/r5Pov9afQhfEyWkWFAGnof/H6//XM/zFd+X/xX6f5HyvF/+4w/xr8pHQV7B+cgTgZPAFAFXm8PcW4P/ff/ANap3NPg9S0BgYHAFUZhQBWvUWWEQlQzSHaMjp6n8BWc4RmrSVzpwuIq4ep7SlJxa7fl5ry2M240IKubVuAPuN/Q/wCP51wVsB1pv5f1/XmfX5dxZa1PGR/7eXy3X3ttfKJlSwyQOUlQq3oRXnThKDtJWPscPiaOJh7SjJSXl+vZ+T1GVBuFABQAUAFAARkYPSmBEh8p/LP3T9w/0pvXUzj7r5WS1JoFAFuysJLpwSCsXdsdfpWVWqoLzMK1dU15m9BBHbxCOMYA/Mn1NcEpOTuzzJzc3zSJKkznCM1yyV0GKZ5dfJ6FRtw91/h93/BEoPGrZXiaWtrry/y3/AKDzgoAKAILokxiJfvSHb+Hf9KqO9yo73JgAoAHQcCpJFoAKAK8pM8hhU/IP9Yf6Va0Vylork4AAAAwB0qCSSP734V62Tfx36fqjnxHwklfTHGFABQAUAFABQAUAFAEc80cEReQ/L0x6+1JnVg8HWxlZUaKu39yXd+X9bnKXDYuHlK/umPTP3P/AK1ck8PTnLmkj9TwWH+pUI0U7pdfPd/iO2Ie360vqlHt+Z3czF8tfSj6pR7fmHMw8tfSj6pR7fmHMw8tfSj6pR7fmHMw8tfSj6pR7fmHMyJ41+0Rcdm/pS+q0e35kuT5kS+WvpT+qUe35lczDy19KPqlHt+YczDy19KPqlHt+YczDy19KPqlHt+YczDy19KPqlHt+YczDy19KPqlHt+YczIK8U1CgAoAKACgAoAiT/XyfRf60+hC+JktIsKANPQ/+P1/+uZ/mK78v/iv0/yPleL/APcYf41+UjoCcDJ4Ar2D85KvN4e4twf++/8A61TuafB6loDAwOAKozCgAoArw/vpnn/hHyJ9O5qV3LlouUsVRAyWGOdCkqBl9CKicIzVpK5vh8TWw0/aUZOL8v17ryehkXWidWtm/wCAN/Q/4/nXn1sB1pv5f1/XmfY5dxbtDGR/7eXy3X3ttfKJkywyQOUlQq3oRXnThKDtJWPscPiaOJh7SjJSXl+vZ+T1GVBuFABQAUANdA6lT+fpTQpK6sNjcnKP99evv70MmL6Pc2bLSi2JLkfKRwmcH8a5KuI6QOWtiraQ+81wAoAAAA4AFcm5wN31YtIAoAKACgAoOevhaNf+JG/9dwxTPHr5J/z5l9/+a/yEoPGrYatR/iRa/L79iuv727Zv4YxtH1PX+lVtEy2RYqSQoAhnkZcRx/61+nsPWqS6saXVj4o1ijCr+J9T60m7ibuPpAPj+9+Fetk38d+n6o58R8JJX0xxhQAUAFABQAUAFAEc0yW8RkkOAP19qR1YPB1sZWVGirt/cl3fl/W5gXV091LubgD7q+lQ3c/T8ty2jgKPs6ere76t/wCXZdPW7cBGaR6JBzbn1iP/AI7/APWpbGfwehP1pmgUAFABQBE//HxF9G/pR1IfxIloLCgAoAKACgAoArV86bBQAUAFABQAUARJ/r5Pov8AWn0IXxMlpFhQBp6GcXkhPAER/mK78v8A4r9P8j5Xi/8A3GH+NflI1ubw9xbg/wDff/1q9bc/Pfg9S0BgYHAFUZhQAUAQ3LkII0PzyHaPb1NJlwWt30JEQRoqKMADAoJbu7jqYgoAKAGSwxzoUlQMvoRUThGatJXN8Pia2Gn7SjJxfl+vdeT0Mi60Tq1s3/AG/of8fzrz62A6038v6/rzPscu4t2hjI/9vL5br722vlEyZYZIHKSoVb0IrzpwlB2krH2OHxNHEw9pRkpLy/Xs/J6jKg3CgB8cUkz7I0LN6ClKSirsmUlFXkzXh0kQAS5D3C8rkcD2/wDr1xzruWnQ8yvXdT4dDQilEsYYcdiD2PpXO1ZnOncfSGFABQAUAFABQAUAMlkEUTOf4Rmmld2E3ZDbeMxwqG+8eW+ppyd2ebWyqhVWi5X5f5bfkSYpHkV8nr003D3l+P3f8EZLKIkLHnsB6n0ppXPMdOUZcslZjIIiuZJOZX6+3sKbfREt9ETVIgoAfH978K9bJv479P1Rz4j4SSvpjjCgAoAKACgAoAjmmS3iMkhwB+vtSOrB4OtjKyo0Vdv7ku78v63MC6unupdzcAfdX0qG7n6fluW0cBR9nT1b3fVv/LsunrduCkeiFAARmgCDm3PrEf8Ax3/61LYz+D0J+tM0CgAoAif/AI+Ivo39KOpD+JEtBYUAFABQAUAFAFavnTYKACgAoAKACgCJP9fJ9F/rT6EL4mS0iw6UwLeir9qvXByIRGf+Bcj9K7sAv3j9P8j5Tiyb+pRa25l+UjqAMDA4Ar2D86CgAoAKAK8P76Z5/wCEfIn07mpXcuWi5SxVEBQAUAFABQAUAMlhjnQpKgZfQionCM1aSub4fE1sNP2lGTi/L9e68noZF1onVrZv+AN/Q/4/nXn1sB1pv5f1/XmfY5dxbtDGR/7eXy3X3ttfKJSi025kl2NGYwDhmYcfh6/hXlV37F2mtT6yGPw9Wn7SlJSXl+vb0ept21rFaoVjB56k9TXmzqSm7s46lWVR3kTVBmV5QYJDOo+U/wCsA/nVrVWJempOCGAIOQehqBi0DCgAoAKACgAoArz/ALyaKHtne30H/wBerjomyXq7FioKEJCgknAHU0CIIgZ5BO4+Uf6tT/OreisY1KNOsv3iuixipPLxGS05a0nby3X+f5iUHjVsBiKOso6d1r/w3zCg4x8f3vwr1sm/jv0/VHPiPhJK+mOMKACgAoAKAI5pkt4jJIcAfr7UjqweDrYysqNFXb+5Lu/L+tzAurp7qXc3AH3V9Khu5+n5bltHAUfZ09W931b/AMuy6et24KR6IUAFABQAEZoAg5tz6xH/AMd/+tS2M/g9CfrTNAoAif8A4+Ivo39KOpD+JEtBYUAFABQAUAFAFavnTYKACgAoAKACgCJP9fJ9F/rT6EL4mS9KCyHmc+kQ/wDHv/rU9jP4/Q2dC4vX/wCuZ/mK7cv/AIr9P8j5ni//AHGH+JflI6CvYPzkKACgCG5chBGh+eQ7R7eppMuC1u+hIiCNFRRgAYFBLd3cdTEFABQAUAFABQAUAFAEb/er5TNpuWJafRJfr+p93kNNQwSkvtNv8bfoNrzT2QoAKBFZf9GlEZ/1Tn5D/dPpVv3lfqLYs1BQUAFABQAUAFAFe3/ePJN/eOF+gq5aWRK7lioKKzf6TKYx/qkPzn+8fSrXuq/Ujcs1BQUDCgAoOLEZfh6/xRs+60f/AAfmKhw1ellleFGvebsmrfkeBj8kqRpuVF81tbdfl3/DyJa+rPlwoAKACgCOaZLeIySHAH6+1I6sHg62MrKjRV2/uS7vy/rcwLq6e6l3NwB91fSobufp+W5bRwFH2dPVvd9W/wDLsunrduCkeiFABQAUAFABQAEZoAg5tz6xH/x3/wCtS2M/g9CfrTNCJ/8Aj4i+jf0o6kP4kS0FhQAUAFABQAUAVq+dNgoAKACgAoAKAIl4nl+i/wBaroQviYnM59Ih/wCPf/Wo2F8foTdKRoaeh/8AH6//AFzP8xXdl/8AFfp/kfK8X/7jD/GvykdBXsH5yFABQBXh/fTPP/CPkT6dzUruXLRcpYqiAoAKACgAoAKACgAoAKAITyTXw9aaqVZTWzbZ+m4am6VGFOW6SX3IKyNwoAKAGyRrIhRhkGmnbUTVyKB2DGGQ/OvQ/wB4etOS6oSfRk9SUFABQAUAQ3LlYSF++52r9TVRWpMtiREEcaoOijFJu7uNEU7sWEMZ+dup/uj1pxXVib6IljjWJAijAFJu+o0rDqQwoAKACgAoAcrY+lejgsxnh/cesfy9P6+48fMsop4u9SOk+/R+v+f52sSA5r6inVhVjzQd0fEVqFShPkqxs/6/q4VoZEc0yW8RkkOAP19qR1YPB1sZWVGirt/cl3fl/W5gXV091LubgD7q+lQ3c/T8ty2jgKPs6ere76t/5dl09btwUj0QoAKACgAoAKACgAoACM0AQc259Yj/AOO//WpbGfweg5ubiL6N/Sgb+JEtMsKACgAoAKACgCtXzpsFABQAUAFABQBWmjkMpIXchxkA4zirTVjGcZXutgNxIvBtyB9f/rUrLuHtJL7IC6z/AAqPq+P6Ucoe1NbQ5ZTePsiVv3Z/5ae49q7cB/Efp/kfM8WO+Cjf+ZflI3/Muf8An3X/AL+f/Wr19T8+tHuHmXP/AD7r/wB/P/rUahaPcZK108bKsKqSMZ8zp+lLUaUE9xUnESBGglQKMdMj9KLg43d7j1uoHOBKufQnH86d0S4SXQmBz0pkhQAUAFABQAUAFACHgGsMTU9nRlNOzSf39DpwdJVcRCm1dNq/p1/Air4k/SwoAKACgAoAini8xQVOJF5U+9VF2JauLDL5qZxhgcMvoaGrDTuSVIwoAKAK5/e3gH8MQz+J/wDrVe0fUndkk0vlJkDLHhV9TSSuNuwkEXlqSxzI3LH3ok7iSsS1JQUAFABQAUAFABQAoJHSujD4mph5c1N/8E5MXgqOLhy1V6Pqv6+4JbiOGIyOcAdu5+lfUYTHU8Svd0a6HyM8gxarxpQXMn16L17f0ldmDdXT3Uu5uAPur6V0t3Pvsty2jgKPs6ere76t/wCXZdPW7cFI9EKACgAoAKACgAoAKAELBepA+poFdIjNxEP+WgP05ouTzx7ieeG4EcjD/d/xpXDm8hkUbiYHaVjAOATnGaETGLv5FmmahQAUAFABQAUAVq+dNgoAKACgAoAKACgAoAQgHqM0wsX9FtoXvX3Rr/qz047iu7Aa1H6f5HyvFvu4KLX8y/KRu/ZFH3JJU+jn+tevY/POd9UHkzr925J9mQGizDmj2DN2vaF/oSKNQ9wPPlX79s//AAFgaLsOVdGI1xA3EsbD/fjNK6Goy6MjVLJ/9XIqH/YfbRoO81uiUQyjmO5Yj/bAaixPMuqDddJ1SOT/AHWwf1o1C0GH2or/AKyCVffGR+lFw5OzHLdQOcCVc+hOP507oThJdCYHPSmSFADX+7XmZtPlwzXdpfr+h7WQUufGKV/hTf6fqR18qfdBQAUAFABQAUAV5lMb+fGMkDDqP4h/jVp30ZL7onRldQynKkZBqWrDFpDEZgiFj0Ayaa1EQQfu7cyyHBbLtVS1dkJaK7CFDI/nyDBPCKf4R/jQ3bRAu7LFQUFABQAUAFABQAUAFABQBU1FA1mxP8JBH8v616OVzccSkut1+v6G2HdpmNX056AUABOKAI2miXrIv50XJcorqJ9oQ/dDt9FNK4uddA82Q/dhb/gRAoDmfRB+/PaNfzNGoe8HlynrNj/dUCgLS7h9nU/eeRvq1Fg5F1FEEQ/5Zr+IzTsPkj2HgAdABQMWgYUAFABQAUAFABQAUAVq+dNgoAKACgAoAKACgAoAKANPQ/8Aj9f/AK5n+Yrvy/8Aiv0/yPleL/8AcYf41+UjoK9g/OQoAKACgAoAa0Ub/eRW+ozSGm1sRGzg6iPaf9kkfyosivaSE+zEfcnmX6tn+dFg5+6Dy7lek6N/vJ/hRqF49hGE5/1lvFIPZv8AEUtRrl6OxCY41/5dZ4z6xn/A0irvumHmhfu3br7Sp/8Aqov5hy36fcKbiY4wYJf9x8GvDziabhC/d/5fqfTcO0uVVKnL2X+f6C/aSv8ArIZV98ZH6V4nL2Z9NzCrdQOcCVc+h4/nScWg5kTA56VIwoGFABQAUAVv+PWX/pi5/wC+T/gav4l5k7Ms1BRXufn8uEfxnn/dHWrjpqS+wg/0qX/pih/76P8AgKPhXmLcs1BYUAFABQAUAFABQAxpo0+9Io+pp2bFdEf2yD+Fix/2VJquRi5kH2hj9y3lP1AX+dHL5hfyIbo3ElrKDCijaT8z56fSujCS5a8HF63/AD0LpuSmrGHsmPWVV/3V/wAa+s1PStLuHkk/elkP44/lRYOXuw+zxd0yfc5p2DkiPVFX7qgfQUFJJDqBhQAUAFABQAUAFABQAUAFABQAUAFABQAUAVq+dNgoAKACgAoAKACgAoAKANPQ/wDj9f8A65n+Yrvy/wDiv0/yPleL/wDcYf41+UjoK9g/OQoAKACgAoAKACgAoAKACgAoAOtAFeWCF2+aJD/wGvlc1qSeJa7WX6/qfdZFSjHBRl/M2/xt+hF9jiH3N6f7rkV5/Oz2OVDWtC3/AC2Y+zqGo5vIXKRGykX7hjz6jK/yNVzrqLlYm29j6ZI+ob/Cj3GHvB9ruY/9Zbkj1AI/xo5IvZhzPsOXU4Tw6uh9xS9m+g+dE6XUD/dlX8TipcZLoPmRIyrIhU4KkUthkMLGJ/IkOf7jH+If4iqavqhLTQgLNc3ciocAfIW9F7/iTVfDHUndl5VCKFUYAGAKz3LBnVfvMB9TRYCI3duP+Wqn6c/yp8kuwuZCfagfuRSt9Fx/Ony+YcwebcN923A/3n/wotHuF32DbdN1eJPopNHuh7weRI337iT/AICAKOZdgs+4fY4j9/e/+85NHOw5UPW3hT7sSD/gNLmbHZEnSpAKBiEAggjIPUGmm07oNjnSCCQRgjqDX2yaauj1dxKYwoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAK1fOmwUAFABQAUAFABQAUAFAGnof/H6//XM/zFd+X/xX6f5HyvF/+4w/xr8pHQV7B+chQAUAFABQAUAFABQAUAFABQAUAQnkmvh601Uqymtm2z9Nw1N0qMKct0kvuQVkbhQAUAFABQAhUNwQD9RTERNaW7dYk/AYp80u4uVEf2CEfcMkf+61PnfUXKhHtJWAAuW4ORuUEg/WmprsDi+4Q2ckSbRcEAnJ2qMn8aHNPoCi11JPsqn78kr/AFc/0pcw+UVbWBekS/iM0uaXcOVEoULwAB9BSGLSGFABQAUAFABQAUAFABQBg3SlbqUEc7ifz5r6/ByUsPBrsvw0PSpO8EQ10mgUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAFavnTYKACgAoAKACgAoAKACgDT0P8A4/X/AOuZ/mK78v8A4r9P8j5Xi/8A3GH+NflI6CvYPzkKACgAoAKACgAoAKACgAoAKAEPANYYmp7OjKadmk/v6HTg6Sq4iFNq6bV/Tr+BFXxJ+lhQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQBjaihW8Yn+IAj+X9K+oyualhkl0uv1/U78O7wKlegbhQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAVq+dNgoAKACgAoAKACgAoAKANPQ/+P1/+uZ/mK78v/iv0/wAj5Xi//cYf41+UjoK9g/OQoAKACgAoAKACgAoAKACgAoAa/wB2vMzafLhmu7S/X9D2sgpc+MUr/Cm/0/Ujr5U+6CgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgDL1VAHjfuQR+X/669/JptwnDtZ/f/wAMdmFejRn17J1BQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAf/9k" />
<p>Barycentric interpolation, by contrast, is continuous everywhere but has no derivative on the edges of the
Delaunay triangulation. These show up as sharp corners in the color gradients on the drawn edges:</p>
<img src="data:image/jpg;base64,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" />
<p>By contrast, natural neighbor interpolation is smooth on the edges - the previously sharp angles are now rounded
off. However, the vertices themselves are still not continuous and will form sharp “peaks” in the resulting
surface.</p>
<img 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" />
<p>With a gradient, the sharp peaks are gone - the surface will smoothly approximate a linear function as defined
by the gradient in the vicinity of each vertex. In the image below, a gradient of <code>(0.0, 0.0)</code> is used which
leads to a small “disc” around each vertex with values close to the vertex value.</p>
<img 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/>
</div></details><h2 id="implementations" class="section-header">Implementations<a href="#implementations" class="anchor">§</a></h2><div id="implementations-list"><details class="toggle implementors-toggle" open><summary><section id="impl-NaturalNeighbor%3C'a,+DelaunayTriangulation%3CV,+DE,+UE,+F,+L%3E%3E" class="impl"><a class="src rightside" href="../src/spade/delaunay_core/interpolation.rs.html#229-554">Source</a><a href="#impl-NaturalNeighbor%3C'a,+DelaunayTriangulation%3CV,+DE,+UE,+F,+L%3E%3E" class="anchor">§</a><h3 class="code-header">impl&lt;'a, V, DE, UE, F, L&gt; <a class="struct" href="struct.NaturalNeighbor.html" title="struct spade::NaturalNeighbor">NaturalNeighbor</a>&lt;'a, <a class="struct" href="struct.DelaunayTriangulation.html" title="struct spade::DelaunayTriangulation">DelaunayTriangulation</a>&lt;V, DE, UE, F, L&gt;&gt;<div class="where">where
V: <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>,
DE: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/default/trait.Default.html" title="trait core::default::Default">Default</a>,
UE: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/default/trait.Default.html" title="trait core::default::Default">Default</a>,
F: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/default/trait.Default.html" title="trait core::default::Default">Default</a>,
L: <a class="trait" href="trait.HintGenerator.html" title="trait spade::HintGenerator">HintGenerator</a>&lt;&lt;V as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>&gt;,
&lt;V as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>: <a class="trait" href="../num_traits/float/trait.Float.html" title="trait num_traits::float::Float">Float</a>,</div></h3></section></summary><div class="impl-items"><details class="toggle method-toggle" open><summary><section id="method.get_weights" class="method"><a class="src rightside" href="../src/spade/delaunay_core/interpolation.rs.html#266-279">Source</a><h4 class="code-header">pub fn <a href="#method.get_weights" class="fn">get_weights</a>(
&amp;self,
position: <a class="struct" href="struct.Point2.html" title="struct spade::Point2">Point2</a>&lt;&lt;V as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>&gt;,
result: &amp;mut <a class="struct" href="https://doc.rust-lang.org/1.84.0/alloc/vec/struct.Vec.html" title="struct alloc::vec::Vec">Vec</a>&lt;(<a class="type" href="handles/type.FixedVertexHandle.html" title="type spade::handles::FixedVertexHandle">FixedVertexHandle</a>, &lt;V as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>)&gt;,
)</h4></section></summary><div class="docblock"><p>Calculates the natural neighbors and their weights (sibson coordinates) of a given query position.</p>
<p>The neighbors are returned in clockwise order. The weights will add up to 1.0.
The neighbors are stored in the <code>result</code> parameter to prevent unnecessary allocations.
<code>result</code> will be cleared initially.</p>
<p>The number of returned natural neighbors depends on the given query position:</p>
<ul>
<li><code>result</code> will be <strong>empty</strong> if the query position lies outside the triangulation’s convex hull</li>
<li><code>result</code> will contain <strong>exactly one</strong> vertex if the query position is equal to that vertex position.</li>
<li><code>result</code> will contain <strong>exactly two</strong> entries if the query position lies exactly <em>on</em> an edge of the
convex hull.</li>
<li><code>result</code> will contain <strong>at least three</strong> <code>(vertex, weight)</code> tuples if the query point lies on an inner
face or an inner edge.</li>
</ul>
<p><em>Example: The natural neighbors (red vertices) of the query point (blue dot) with their weights.
The elements will be returned in clockwise order as indicated by the indices drawn within the red circles.</em></p>
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</svg></div></details><details class="toggle method-toggle" open><summary><section id="method.interpolate" class="method"><a class="src rightside" href="../src/spade/delaunay_core/interpolation.rs.html#288-307">Source</a><h4 class="code-header">pub fn <a href="#method.interpolate" class="fn">interpolate</a>&lt;I&gt;(
&amp;self,
i: I,
position: <a class="struct" href="struct.Point2.html" title="struct spade::Point2">Point2</a>&lt;&lt;V as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>&gt;,
) -&gt; <a class="enum" href="https://doc.rust-lang.org/1.84.0/core/option/enum.Option.html" title="enum core::option::Option">Option</a>&lt;&lt;V as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>&gt;<div class="where">where
I: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/ops/function/trait.Fn.html" title="trait core::ops::function::Fn">Fn</a>(<a class="type" href="handles/type.VertexHandle.html" title="type spade::handles::VertexHandle">VertexHandle</a>&lt;'_, V, DE, UE, F&gt;) -&gt; &lt;V as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>,</div></h4></section></summary><div class="docblock"><p>Interpolates a value at a given position.</p>
<p>Returns <code>None</code> for any point outside the triangulations convex hull.
The value to interpolate is given by the <code>i</code> parameter. The resulting interpolation will be smooth
everywhere except at the input vertices.</p>
<p>Refer to <a href="struct.NaturalNeighbor.html" title="struct spade::NaturalNeighbor">NaturalNeighbor</a> for an example on how to use this function.</p>
</div></details><details class="toggle method-toggle" open><summary><section id="method.interpolate_gradient" class="method"><a class="src rightside" href="../src/spade/delaunay_core/interpolation.rs.html#361-410">Source</a><h4 class="code-header">pub fn <a href="#method.interpolate_gradient" class="fn">interpolate_gradient</a>&lt;I, G&gt;(
&amp;self,
i: I,
g: G,
flatness: &lt;V as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>,
position: <a class="struct" href="struct.Point2.html" title="struct spade::Point2">Point2</a>&lt;&lt;V as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>&gt;,
) -&gt; <a class="enum" href="https://doc.rust-lang.org/1.84.0/core/option/enum.Option.html" title="enum core::option::Option">Option</a>&lt;&lt;V as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>&gt;<div class="where">where
I: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/ops/function/trait.Fn.html" title="trait core::ops::function::Fn">Fn</a>(<a class="type" href="handles/type.VertexHandle.html" title="type spade::handles::VertexHandle">VertexHandle</a>&lt;'_, V, DE, UE, F&gt;) -&gt; &lt;V as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>,
G: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/ops/function/trait.Fn.html" title="trait core::ops::function::Fn">Fn</a>(<a class="type" href="handles/type.VertexHandle.html" title="type spade::handles::VertexHandle">VertexHandle</a>&lt;'_, V, DE, UE, F&gt;) -&gt; [&lt;V as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>; <a class="primitive" href="https://doc.rust-lang.org/1.84.0/std/primitive.array.html">2</a>],</div></h4></section></summary><div class="docblock"><p>Interpolates a value at a given position.</p>
<p>In contrast to <a href="struct.NaturalNeighbor.html#method.interpolate" title="method spade::NaturalNeighbor::interpolate">Self::interpolate</a>, this method has a well-defined derivative at each vertex and will
approximate a linear function in the proximity of any vertex.</p>
<p>The value to interpolate is given by the <code>i</code> parameter. The gradient that defines the derivative at
each input vertex is given by the <code>g</code> parameter.</p>
<p>The <code>flatness</code> parameter blends between an interpolation that ignores the given gradients (value 0.0)
or adheres to it strongly (values larger than ~2.0) in the vicinity of any vertex. When in doubt, using
a value of 1.0 should result in a good interpolation and is also the fastest.</p>
<p>Returns <code>None</code> for any point outside the triangulation’s convex hull.</p>
<p>Refer to <a href="struct.NaturalNeighbor.html" title="struct spade::NaturalNeighbor">NaturalNeighbor</a> for more information and a visual example.</p>
<h5 id="example-1"><a class="doc-anchor" href="#example-1">§</a>Example</h5>
<div class="example-wrap"><pre class="rust rust-example-rendered"><code><span class="kw">use </span>spade::{DelaunayTriangulation, HasPosition, Point2};
<span class="kw">struct </span>PointWithHeight {
position: Point2&lt;f64&gt;,
height: f64,
}
<span class="kw">impl </span>HasPosition <span class="kw">for </span>PointWithHeight {
<span class="kw">type </span>Scalar = f64;
<span class="kw">fn </span>position(<span class="kw-2">&amp;</span><span class="self">self</span>) -&gt; Point2&lt;f64&gt; { <span class="self">self</span>.position }
}
<span class="kw">let </span><span class="kw-2">mut </span>triangulation: DelaunayTriangulation&lt;PointWithHeight&gt; = Default::default();
<span class="comment">// Insert some points into the triangulation
</span>triangulation.insert(PointWithHeight { position: Point2::new(<span class="number">10.0</span>, <span class="number">10.0</span>), height: <span class="number">0.0 </span>});
triangulation.insert(PointWithHeight { position: Point2::new(<span class="number">10.0</span>, -<span class="number">10.0</span>), height: <span class="number">0.0 </span>});
triangulation.insert(PointWithHeight { position: Point2::new(-<span class="number">10.0</span>, <span class="number">10.0</span>), height: <span class="number">0.0 </span>});
triangulation.insert(PointWithHeight { position: Point2::new(-<span class="number">10.0</span>, -<span class="number">10.0</span>), height: <span class="number">0.0 </span>});
<span class="kw">let </span>nn = triangulation.natural_neighbor();
<span class="comment">// Interpolate point at coordinates (1.0, 2.0). This example uses a fixed gradient of (0.0, 0.0) which
// means that the interpolation will have normal vector parallel to the z-axis at each input point.
// Realistically, the gradient might be stored as an additional property of `PointWithHeight`.
</span><span class="kw">let </span>query_point = Point2::new(<span class="number">1.0</span>, <span class="number">2.0</span>);
<span class="kw">let </span>value: f64 = nn.interpolate_gradient(|v| v.data().height, |<span class="kw">_</span>| [<span class="number">0.0</span>, <span class="number">0.0</span>], <span class="number">1.0</span>, query_point).unwrap();</code></pre></div>
<h5 id="references"><a class="doc-anchor" href="#references">§</a>References</h5>
<p>This method uses the C1 extension proposed by Sibson in
“A brief description of natural neighbor interpolation, R. Sibson, 1981”</p>
</div></details></div></details></div><h2 id="synthetic-implementations" class="section-header">Auto Trait Implementations<a href="#synthetic-implementations" class="anchor">§</a></h2><div id="synthetic-implementations-list"><section id="impl-Freeze-for-NaturalNeighbor%3C'a,+T%3E" class="impl"><a href="#impl-Freeze-for-NaturalNeighbor%3C'a,+T%3E" class="anchor">§</a><h3 class="code-header">impl&lt;'a, T&gt; !<a class="trait" href="https://doc.rust-lang.org/1.84.0/core/marker/trait.Freeze.html" title="trait core::marker::Freeze">Freeze</a> for <a class="struct" href="struct.NaturalNeighbor.html" title="struct spade::NaturalNeighbor">NaturalNeighbor</a>&lt;'a, T&gt;</h3></section><section id="impl-RefUnwindSafe-for-NaturalNeighbor%3C'a,+T%3E" class="impl"><a href="#impl-RefUnwindSafe-for-NaturalNeighbor%3C'a,+T%3E" class="anchor">§</a><h3 class="code-header">impl&lt;'a, T&gt; !<a class="trait" href="https://doc.rust-lang.org/1.84.0/core/panic/unwind_safe/trait.RefUnwindSafe.html" title="trait core::panic::unwind_safe::RefUnwindSafe">RefUnwindSafe</a> for <a class="struct" href="struct.NaturalNeighbor.html" title="struct spade::NaturalNeighbor">NaturalNeighbor</a>&lt;'a, T&gt;</h3></section><section id="impl-Send-for-NaturalNeighbor%3C'a,+T%3E" class="impl"><a href="#impl-Send-for-NaturalNeighbor%3C'a,+T%3E" class="anchor">§</a><h3 class="code-header">impl&lt;'a, T&gt; <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/marker/trait.Send.html" title="trait core::marker::Send">Send</a> for <a class="struct" href="struct.NaturalNeighbor.html" title="struct spade::NaturalNeighbor">NaturalNeighbor</a>&lt;'a, T&gt;<div class="where">where
T: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/marker/trait.Sync.html" title="trait core::marker::Sync">Sync</a>,
&lt;&lt;T as <a class="trait" href="trait.Triangulation.html" title="trait spade::Triangulation">Triangulation</a>&gt;::<a class="associatedtype" href="trait.Triangulation.html#associatedtype.Vertex" title="type spade::Triangulation::Vertex">Vertex</a> as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/marker/trait.Send.html" title="trait core::marker::Send">Send</a>,</div></h3></section><section id="impl-Sync-for-NaturalNeighbor%3C'a,+T%3E" class="impl"><a href="#impl-Sync-for-NaturalNeighbor%3C'a,+T%3E" class="anchor">§</a><h3 class="code-header">impl&lt;'a, T&gt; !<a class="trait" href="https://doc.rust-lang.org/1.84.0/core/marker/trait.Sync.html" title="trait core::marker::Sync">Sync</a> for <a class="struct" href="struct.NaturalNeighbor.html" title="struct spade::NaturalNeighbor">NaturalNeighbor</a>&lt;'a, T&gt;</h3></section><section id="impl-Unpin-for-NaturalNeighbor%3C'a,+T%3E" class="impl"><a href="#impl-Unpin-for-NaturalNeighbor%3C'a,+T%3E" class="anchor">§</a><h3 class="code-header">impl&lt;'a, T&gt; <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/marker/trait.Unpin.html" title="trait core::marker::Unpin">Unpin</a> for <a class="struct" href="struct.NaturalNeighbor.html" title="struct spade::NaturalNeighbor">NaturalNeighbor</a>&lt;'a, T&gt;<div class="where">where
&lt;&lt;T as <a class="trait" href="trait.Triangulation.html" title="trait spade::Triangulation">Triangulation</a>&gt;::<a class="associatedtype" href="trait.Triangulation.html#associatedtype.Vertex" title="type spade::Triangulation::Vertex">Vertex</a> as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/marker/trait.Unpin.html" title="trait core::marker::Unpin">Unpin</a>,</div></h3></section><section id="impl-UnwindSafe-for-NaturalNeighbor%3C'a,+T%3E" class="impl"><a href="#impl-UnwindSafe-for-NaturalNeighbor%3C'a,+T%3E" class="anchor">§</a><h3 class="code-header">impl&lt;'a, T&gt; <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/panic/unwind_safe/trait.UnwindSafe.html" title="trait core::panic::unwind_safe::UnwindSafe">UnwindSafe</a> for <a class="struct" href="struct.NaturalNeighbor.html" title="struct spade::NaturalNeighbor">NaturalNeighbor</a>&lt;'a, T&gt;<div class="where">where
T: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/panic/unwind_safe/trait.RefUnwindSafe.html" title="trait core::panic::unwind_safe::RefUnwindSafe">RefUnwindSafe</a>,
&lt;&lt;T as <a class="trait" href="trait.Triangulation.html" title="trait spade::Triangulation">Triangulation</a>&gt;::<a class="associatedtype" href="trait.Triangulation.html#associatedtype.Vertex" title="type spade::Triangulation::Vertex">Vertex</a> as <a class="trait" href="trait.HasPosition.html" title="trait spade::HasPosition">HasPosition</a>&gt;::<a class="associatedtype" href="trait.HasPosition.html#associatedtype.Scalar" title="type spade::HasPosition::Scalar">Scalar</a>: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/panic/unwind_safe/trait.UnwindSafe.html" title="trait core::panic::unwind_safe::UnwindSafe">UnwindSafe</a>,</div></h3></section></div><h2 id="blanket-implementations" class="section-header">Blanket Implementations<a href="#blanket-implementations" class="anchor">§</a></h2><div id="blanket-implementations-list"><details class="toggle implementors-toggle"><summary><section id="impl-Any-for-T" class="impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/any.rs.html#138">Source</a><a href="#impl-Any-for-T" class="anchor">§</a><h3 class="code-header">impl&lt;T&gt; <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/any/trait.Any.html" title="trait core::any::Any">Any</a> for T<div class="where">where
T: 'static + ?<a class="trait" href="https://doc.rust-lang.org/1.84.0/core/marker/trait.Sized.html" title="trait core::marker::Sized">Sized</a>,</div></h3></section></summary><div class="impl-items"><details class="toggle method-toggle" open><summary><section id="method.type_id" class="method trait-impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/any.rs.html#139">Source</a><a href="#method.type_id" class="anchor">§</a><h4 class="code-header">fn <a href="https://doc.rust-lang.org/1.84.0/core/any/trait.Any.html#tymethod.type_id" class="fn">type_id</a>(&amp;self) -&gt; <a class="struct" href="https://doc.rust-lang.org/1.84.0/core/any/struct.TypeId.html" title="struct core::any::TypeId">TypeId</a></h4></section></summary><div class='docblock'>Gets the <code>TypeId</code> of <code>self</code>. <a href="https://doc.rust-lang.org/1.84.0/core/any/trait.Any.html#tymethod.type_id">Read more</a></div></details></div></details><details class="toggle implementors-toggle"><summary><section id="impl-Borrow%3CT%3E-for-T" class="impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/borrow.rs.html#209">Source</a><a href="#impl-Borrow%3CT%3E-for-T" class="anchor">§</a><h3 class="code-header">impl&lt;T&gt; <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/borrow/trait.Borrow.html" title="trait core::borrow::Borrow">Borrow</a>&lt;T&gt; for T<div class="where">where
T: ?<a class="trait" href="https://doc.rust-lang.org/1.84.0/core/marker/trait.Sized.html" title="trait core::marker::Sized">Sized</a>,</div></h3></section></summary><div class="impl-items"><details class="toggle method-toggle" open><summary><section id="method.borrow" class="method trait-impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/borrow.rs.html#211">Source</a><a href="#method.borrow" class="anchor">§</a><h4 class="code-header">fn <a href="https://doc.rust-lang.org/1.84.0/core/borrow/trait.Borrow.html#tymethod.borrow" class="fn">borrow</a>(&amp;self) -&gt; <a class="primitive" href="https://doc.rust-lang.org/1.84.0/std/primitive.reference.html">&amp;T</a></h4></section></summary><div class='docblock'>Immutably borrows from an owned value. <a href="https://doc.rust-lang.org/1.84.0/core/borrow/trait.Borrow.html#tymethod.borrow">Read more</a></div></details></div></details><details class="toggle implementors-toggle"><summary><section id="impl-BorrowMut%3CT%3E-for-T" class="impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/borrow.rs.html#217">Source</a><a href="#impl-BorrowMut%3CT%3E-for-T" class="anchor">§</a><h3 class="code-header">impl&lt;T&gt; <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/borrow/trait.BorrowMut.html" title="trait core::borrow::BorrowMut">BorrowMut</a>&lt;T&gt; for T<div class="where">where
T: ?<a class="trait" href="https://doc.rust-lang.org/1.84.0/core/marker/trait.Sized.html" title="trait core::marker::Sized">Sized</a>,</div></h3></section></summary><div class="impl-items"><details class="toggle method-toggle" open><summary><section id="method.borrow_mut" class="method trait-impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/borrow.rs.html#218">Source</a><a href="#method.borrow_mut" class="anchor">§</a><h4 class="code-header">fn <a href="https://doc.rust-lang.org/1.84.0/core/borrow/trait.BorrowMut.html#tymethod.borrow_mut" class="fn">borrow_mut</a>(&amp;mut self) -&gt; <a class="primitive" href="https://doc.rust-lang.org/1.84.0/std/primitive.reference.html">&amp;mut T</a></h4></section></summary><div class='docblock'>Mutably borrows from an owned value. <a href="https://doc.rust-lang.org/1.84.0/core/borrow/trait.BorrowMut.html#tymethod.borrow_mut">Read more</a></div></details></div></details><details class="toggle implementors-toggle"><summary><section id="impl-From%3CT%3E-for-T" class="impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/convert/mod.rs.html#765">Source</a><a href="#impl-From%3CT%3E-for-T" class="anchor">§</a><h3 class="code-header">impl&lt;T&gt; <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.From.html" title="trait core::convert::From">From</a>&lt;T&gt; for T</h3></section></summary><div class="impl-items"><details class="toggle method-toggle" open><summary><section id="method.from" class="method trait-impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/convert/mod.rs.html#768">Source</a><a href="#method.from" class="anchor">§</a><h4 class="code-header">fn <a href="https://doc.rust-lang.org/1.84.0/core/convert/trait.From.html#tymethod.from" class="fn">from</a>(t: T) -&gt; T</h4></section></summary><div class="docblock"><p>Returns the argument unchanged.</p>
</div></details></div></details><details class="toggle implementors-toggle"><summary><section id="impl-Into%3CU%3E-for-T" class="impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/convert/mod.rs.html#748-750">Source</a><a href="#impl-Into%3CU%3E-for-T" class="anchor">§</a><h3 class="code-header">impl&lt;T, U&gt; <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.Into.html" title="trait core::convert::Into">Into</a>&lt;U&gt; for T<div class="where">where
U: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.From.html" title="trait core::convert::From">From</a>&lt;T&gt;,</div></h3></section></summary><div class="impl-items"><details class="toggle method-toggle" open><summary><section id="method.into" class="method trait-impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/convert/mod.rs.html#758">Source</a><a href="#method.into" class="anchor">§</a><h4 class="code-header">fn <a href="https://doc.rust-lang.org/1.84.0/core/convert/trait.Into.html#tymethod.into" class="fn">into</a>(self) -&gt; U</h4></section></summary><div class="docblock"><p>Calls <code>U::from(self)</code>.</p>
<p>That is, this conversion is whatever the implementation of
<code><a href="https://doc.rust-lang.org/1.84.0/core/convert/trait.From.html" title="trait core::convert::From">From</a>&lt;T&gt; for U</code> chooses to do.</p>
</div></details></div></details><details class="toggle implementors-toggle"><summary><section id="impl-TryFrom%3CU%3E-for-T" class="impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/convert/mod.rs.html#805-807">Source</a><a href="#impl-TryFrom%3CU%3E-for-T" class="anchor">§</a><h3 class="code-header">impl&lt;T, U&gt; <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryFrom.html" title="trait core::convert::TryFrom">TryFrom</a>&lt;U&gt; for T<div class="where">where
U: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.Into.html" title="trait core::convert::Into">Into</a>&lt;T&gt;,</div></h3></section></summary><div class="impl-items"><details class="toggle" open><summary><section id="associatedtype.Error-1" class="associatedtype trait-impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/convert/mod.rs.html#809">Source</a><a href="#associatedtype.Error-1" class="anchor">§</a><h4 class="code-header">type <a href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryFrom.html#associatedtype.Error" class="associatedtype">Error</a> = <a class="enum" href="https://doc.rust-lang.org/1.84.0/core/convert/enum.Infallible.html" title="enum core::convert::Infallible">Infallible</a></h4></section></summary><div class='docblock'>The type returned in the event of a conversion error.</div></details><details class="toggle method-toggle" open><summary><section id="method.try_from" class="method trait-impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/convert/mod.rs.html#812">Source</a><a href="#method.try_from" class="anchor">§</a><h4 class="code-header">fn <a href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryFrom.html#tymethod.try_from" class="fn">try_from</a>(value: U) -&gt; <a class="enum" href="https://doc.rust-lang.org/1.84.0/core/result/enum.Result.html" title="enum core::result::Result">Result</a>&lt;T, &lt;T as <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryFrom.html" title="trait core::convert::TryFrom">TryFrom</a>&lt;U&gt;&gt;::<a class="associatedtype" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryFrom.html#associatedtype.Error" title="type core::convert::TryFrom::Error">Error</a>&gt;</h4></section></summary><div class='docblock'>Performs the conversion.</div></details></div></details><details class="toggle implementors-toggle"><summary><section id="impl-TryInto%3CU%3E-for-T" class="impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/convert/mod.rs.html#790-792">Source</a><a href="#impl-TryInto%3CU%3E-for-T" class="anchor">§</a><h3 class="code-header">impl&lt;T, U&gt; <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryInto.html" title="trait core::convert::TryInto">TryInto</a>&lt;U&gt; for T<div class="where">where
U: <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryFrom.html" title="trait core::convert::TryFrom">TryFrom</a>&lt;T&gt;,</div></h3></section></summary><div class="impl-items"><details class="toggle" open><summary><section id="associatedtype.Error" class="associatedtype trait-impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/convert/mod.rs.html#794">Source</a><a href="#associatedtype.Error" class="anchor">§</a><h4 class="code-header">type <a href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryInto.html#associatedtype.Error" class="associatedtype">Error</a> = &lt;U as <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryFrom.html" title="trait core::convert::TryFrom">TryFrom</a>&lt;T&gt;&gt;::<a class="associatedtype" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryFrom.html#associatedtype.Error" title="type core::convert::TryFrom::Error">Error</a></h4></section></summary><div class='docblock'>The type returned in the event of a conversion error.</div></details><details class="toggle method-toggle" open><summary><section id="method.try_into" class="method trait-impl"><a class="src rightside" href="https://doc.rust-lang.org/1.84.0/src/core/convert/mod.rs.html#797">Source</a><a href="#method.try_into" class="anchor">§</a><h4 class="code-header">fn <a href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryInto.html#tymethod.try_into" class="fn">try_into</a>(self) -&gt; <a class="enum" href="https://doc.rust-lang.org/1.84.0/core/result/enum.Result.html" title="enum core::result::Result">Result</a>&lt;U, &lt;U as <a class="trait" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryFrom.html" title="trait core::convert::TryFrom">TryFrom</a>&lt;T&gt;&gt;::<a class="associatedtype" href="https://doc.rust-lang.org/1.84.0/core/convert/trait.TryFrom.html#associatedtype.Error" title="type core::convert::TryFrom::Error">Error</a>&gt;</h4></section></summary><div class='docblock'>Performs the conversion.</div></details></div></details></div></section></div></main></body></html>
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