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<body>
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<main>
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<article id="content">
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<header>
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<h1 class="title">Module <code>underworld3.ckdtree</code></h1>
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</header>
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<section id="section-intro">
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="underworld3.ckdtree.cKDTree"><code class="flex name class">
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<span>class <span class="ident">cKDTree</span></span>
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<span>(</span><span>...)</span>
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</code></dt>
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<dd>
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<div class="desc"><p>KD-Tree indexes are data structures and algorithms for the efficient
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determination of nearest neighbours.</p>
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<p>This class generates a kd-tree index for the provided points, and provides
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the necessary methods for finding which points are closest to a given query
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location.</p>
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<p>This class utilises <code>nanoflann</code> for kd-tree functionality.</p>
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<h2 id="parameters">Parameters</h2>
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<p>points:
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The points for which the kd-tree index will be build. This
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should be a 2-dimensional array of size (n_points,dim).</p>
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<h2 id="example">Example</h2>
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<pre><code class="language-python-repl">&gt;&gt;&gt; import numpy as np
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&gt;&gt;&gt; import underworld3 as uw
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</code></pre>
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<p>Generate a random set of points</p>
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<pre><code class="language-python-repl">&gt;&gt;&gt; pts = np.random.random( size=(100,2) )
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</code></pre>
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<p>Build the index on the points</p>
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<pre><code class="language-python-repl">&gt;&gt;&gt; index = uw.algorithms.KDTree(pts)
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&gt;&gt;&gt; index.build_index()
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</code></pre>
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<p>Search the index for a coordinate</p>
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<pre><code class="language-python-repl">&gt;&gt;&gt; coord = np.zeros((1,2))
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&gt;&gt;&gt; coord[0] = (0.5,0.5)
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&gt;&gt;&gt; indices, dist_sqr, found = index.find_closest_point(coord)
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</code></pre>
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<p>Confirm that a point has been found</p>
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<pre><code class="language-python-repl">&gt;&gt;&gt; found[0]
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True
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</code></pre></div>
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<h3>Methods</h3>
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<dl>
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<dt id="underworld3.ckdtree.cKDTree.build_index"><code class="name flex">
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<span>def <span class="ident">build_index</span></span>(<span>self)</span>
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</code></dt>
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<dd>
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<div class="desc"><p>Build the kd-tree index.</p></div>
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</dd>
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<dt id="underworld3.ckdtree.cKDTree.find_closest_n_points"><code class="name flex">
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<span>def <span class="ident">find_closest_n_points</span></span>(<span>self, nCount: numpy.int64, coords: numpy.ndarray)</span>
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</code></dt>
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<dd>
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<div class="desc"><p>Find the n points closest to the provided coordinates.</p>
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<h2 id="parameters">Parameters</h2>
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<p>nCount:
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The number of nearest neighbour points to find for each <code>coords</code>.</p>
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<p>coords:
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Coordinates of the points for which the kd-tree index will be searched for nearest
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neighbours. This should be a 2-dimensional array of size (n_coords,dim).</p>
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<h2 id="returns">Returns</h2>
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<dl>
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<dt><code>indices:</code></dt>
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<dd>An integer array of indices into the <code>points</code> array (passed into the constructor) corresponding to
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the nearest neighbour for the search coordinates. It will be of size (n_coords).</dd>
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<dt><code>dist_sqr:</code></dt>
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<dd>A float array of squred distances between the provided coords and the nearest neighbouring
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points. It will be of size (n_coords).</dd>
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</dl></div>
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</dd>
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<dt id="underworld3.ckdtree.cKDTree.find_closest_point"><code class="name flex">
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<span>def <span class="ident">find_closest_point</span></span>(<span>self, coords: numpy.ndarray)</span>
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</code></dt>
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<dd>
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<div class="desc"><p>Find the points closest to the provided set of coordinates.</p>
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<h2 id="parameters">Parameters</h2>
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<p>coords:
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An array of coordinates for which the kd-tree index will be searched for nearest
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neighbours. This should be a 2-dimensional array of size (n_coords,dim).</p>
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<h2 id="returns">Returns</h2>
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<dl>
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<dt><code>indices:</code></dt>
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<dd>An integer array of indices into the <code>points</code> array (passed into the constructor) corresponding to
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the nearest neighbour for the search coordinates. It will be of size (n_coords).</dd>
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<dt><code>dist_sqr:</code></dt>
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<dd>A float array of squared distances between the provided coords and the nearest neighbouring
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points. It will be of size (n_coords).</dd>
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<dt><code>found:</code></dt>
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<dd>A bool array of flags which signals whether a nearest neighbour has been found for a given
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coordinate. It will be of size (n_coords).</dd>
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</dl></div>
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</dd>
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<dt id="underworld3.ckdtree.cKDTree.kdtree_points"><code class="name flex">
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<span>def <span class="ident">kdtree_points</span></span>(<span>self)</span>
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</code></dt>
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<dd>
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<div class="desc"><p>Returns a view of the points used to define the kd-tree</p></div>
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</dd>
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<dt id="underworld3.ckdtree.cKDTree.query"><code class="name flex">
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<span>def <span class="ident">query</span></span>(<span>self, coords: numpy.ndarray, k: numpy.int64, sqr_dists=True)</span>
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</code></dt>
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<dd>
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<div class="desc"><p>Find the n points closest to the provided coordinates.</p></div>
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</dd>
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<dt id="underworld3.ckdtree.cKDTree.rbf_interpolator_local"><code class="name flex">
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<span>def <span class="ident">rbf_interpolator_local</span></span>(<span>self, coords, data, nnn=4, verbose=False)</span>
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</code></dt>
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<dd>
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<div class="desc"></div>
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</dd>
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<dt id="underworld3.ckdtree.cKDTree.rbf_interpolator_local_from_kdtree"><code class="name flex">
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<span>def <span class="ident">rbf_interpolator_local_from_kdtree</span></span>(<span>self, coords, data, nnn=4, verbose=False)</span>
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</code></dt>
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<dd>
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<div class="desc"><p>An inverse (squared) distance weighted mapping of a numpy array from the
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set of coordinates defined by the kd-tree to the set of input points specified.
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This assumes all points are local to the same processor.
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If that is not the case, it is best to use a particle swarm
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to manage the distributed data.</p></div>
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</dd>
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<dt id="underworld3.ckdtree.cKDTree.rbf_interpolator_local_to_kdtree"><code class="name flex">
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<span>def <span class="ident">rbf_interpolator_local_to_kdtree</span></span>(<span>self, coords, data, nnn=4, verbose=False, weights=None)</span>
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</code></dt>
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<dd>
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<div class="desc"><p>An inverse (squared) distance weighted mapping of a numpy array to the
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set of coordinates defined by the kd-tree from the set of input points specified.
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This assumes all points are local to the same processor.
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If that is not the case, it is sensible to use a particle swarm
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to manage the distributed data.</p></div>
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</dd>
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</dl>
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</dd>
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</dl>
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</section>
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</article>
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<nav id="sidebar">
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<div class="toc">
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<ul></ul>
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</div>
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<ul id="index">
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<li><h3>Super-module</h3>
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<ul>
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<li><code><a title="underworld3" href="index.html">underworld3</a></code></li>
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</ul>
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</li>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<ul>
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<li>
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<h4><code><a title="underworld3.ckdtree.cKDTree" href="#underworld3.ckdtree.cKDTree">cKDTree</a></code></h4>
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<ul class="">
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<li><code><a title="underworld3.ckdtree.cKDTree.build_index" href="#underworld3.ckdtree.cKDTree.build_index">build_index</a></code></li>
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<li><code><a title="underworld3.ckdtree.cKDTree.find_closest_n_points" href="#underworld3.ckdtree.cKDTree.find_closest_n_points">find_closest_n_points</a></code></li>
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<li><code><a title="underworld3.ckdtree.cKDTree.find_closest_point" href="#underworld3.ckdtree.cKDTree.find_closest_point">find_closest_point</a></code></li>
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<li><code><a title="underworld3.ckdtree.cKDTree.kdtree_points" href="#underworld3.ckdtree.cKDTree.kdtree_points">kdtree_points</a></code></li>
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<li><code><a title="underworld3.ckdtree.cKDTree.query" href="#underworld3.ckdtree.cKDTree.query">query</a></code></li>
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<li><code><a title="underworld3.ckdtree.cKDTree.rbf_interpolator_local" href="#underworld3.ckdtree.cKDTree.rbf_interpolator_local">rbf_interpolator_local</a></code></li>
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<li><code><a title="underworld3.ckdtree.cKDTree.rbf_interpolator_local_from_kdtree" href="#underworld3.ckdtree.cKDTree.rbf_interpolator_local_from_kdtree">rbf_interpolator_local_from_kdtree</a></code></li>
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<li><code><a title="underworld3.ckdtree.cKDTree.rbf_interpolator_local_to_kdtree" href="#underworld3.ckdtree.cKDTree.rbf_interpolator_local_to_kdtree">rbf_interpolator_local_to_kdtree</a></code></li>
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</ul>
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</li>
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</ul>
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</li>
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</ul>
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</nav>
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</main>
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<footer id="footer">
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