<?xml version="1.0" encoding="ascii"?> <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> <head> <title>Bio.KDTree.KDTree.KDTree</title> <link rel="stylesheet" href="epydoc.css" type="text/css" /> <script type="text/javascript" src="epydoc.js"></script> </head> <body bgcolor="white" text="black" link="blue" vlink="#204080" alink="#204080"> <!-- ==================== NAVIGATION BAR ==================== --> <table class="navbar" border="0" width="100%" cellpadding="0" bgcolor="#a0c0ff" cellspacing="0"> <tr valign="middle"> <!-- Tree link --> <th> <a href="module-tree.html">Trees</a> </th> <!-- Index link --> <th> <a href="identifier-index.html">Indices</a> </th> <!-- Help link --> <th> <a href="help.html">Help</a> </th> <th class="navbar" width="100%"></th> </tr> </table> <table width="100%" cellpadding="0" cellspacing="0"> <tr valign="top"> <td width="100%"> <span class="breadcrumbs"> Bio :: KDTree :: KDTree :: KDTree :: Class KDTree </span> </td> <td> <table cellpadding="0" cellspacing="0"> <!-- hide/show private --> <tr><td align="right"><span class="options">[<a href="javascript:void(0);" class="privatelink" onclick="toggle_private();">hide private</a>]</span></td></tr> <tr><td align="right"><span class="options" >[<a href="frames.html" target="_top">frames</a >] | <a href="Bio.KDTree.KDTree.KDTree-class.html" target="_top">no frames</a>]</span></td></tr> </table> </td> </tr> </table> <!-- ==================== CLASS DESCRIPTION ==================== --> <h1 class="epydoc">Class KDTree</h1><p class="nomargin-top"><span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree">source code</a></span></p> <p>KD tree implementation (C++, SWIG python wrapper)</p> <p>The KD tree data structure can be used for all kinds of searches that involve N-dimensional vectors, e.g. neighbor searches (find all points within a radius of a given point) or finding all point pairs in a set that are within a certain radius of each other.</p> <p>Reference:</p> <p>Computational Geometry: Algorithms and Applications Second Edition Mark de Berg, Marc van Kreveld, Mark Overmars, Otfried Schwarzkopf published by Springer-Verlag 2nd rev. ed. 2000. ISBN: 3-540-65620-0</p> <p>The KD tree data structure is described in chapter 5, pg. 99.</p> <p>The following article made clear to me that the nodes should contain more than one point (this leads to dramatic speed improvements for the "all fixed radius neighbor search", see below):</p> <p>JL Bentley, "Kd trees for semidynamic point sets," in Sixth Annual ACM Symposium on Computational Geometry, vol. 91. San Francisco, 1990</p> <p>This KD implementation also performs a "all fixed radius neighbor search", i.e. it can find all point pairs in a set that are within a certain radius of each other. As far as I know the algorithm has not been published.</p> <!-- ==================== INSTANCE METHODS ==================== --> <a name="section-InstanceMethods"></a> <table class="summary" border="1" cellpadding="3" cellspacing="0" width="100%" bgcolor="white"> <tr bgcolor="#70b0f0" class="table-header"> <td colspan="2" class="table-header"> <table border="0" cellpadding="0" cellspacing="0" width="100%"> <tr valign="top"> <td align="left"><span class="table-header">Instance Methods</span></td> <td align="right" valign="top" ><span class="options">[<a href="#section-InstanceMethods" class="privatelink" onclick="toggle_private();" >hide private</a>]</span></td> </tr> </table> </td> </tr> <tr> <td width="15%" align="right" valign="top" class="summary"> <span class="summary-type"> </span> </td><td class="summary"> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr> <td><span class="summary-sig"><a name="__init__"></a><span class="summary-sig-name">__init__</span>(<span class="summary-sig-arg">self</span>, <span class="summary-sig-arg">dim</span>, <span class="summary-sig-arg">bucket_size</span>=<span class="summary-sig-default">1</span>)</span></td> <td align="right" valign="top"> <span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.__init__">source code</a></span> </td> </tr> </table> </td> </tr> <tr> <td width="15%" align="right" valign="top" class="summary"> <span class="summary-type"> </span> </td><td class="summary"> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr> <td><span class="summary-sig"><a href="Bio.KDTree.KDTree.KDTree-class.html#set_coords" class="summary-sig-name">set_coords</a>(<span class="summary-sig-arg">self</span>, <span class="summary-sig-arg">coords</span>)</span><br /> Add the coordinates of the points.</td> <td align="right" valign="top"> <span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.set_coords">source code</a></span> </td> </tr> </table> </td> </tr> <tr> <td width="15%" align="right" valign="top" class="summary"> <span class="summary-type"> </span> </td><td class="summary"> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr> <td><span class="summary-sig"><a href="Bio.KDTree.KDTree.KDTree-class.html#search" class="summary-sig-name">search</a>(<span class="summary-sig-arg">self</span>, <span class="summary-sig-arg">center</span>, <span class="summary-sig-arg">radius</span>)</span><br /> Search all points within radius of center.</td> <td align="right" valign="top"> <span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.search">source code</a></span> </td> </tr> </table> </td> </tr> <tr> <td width="15%" align="right" valign="top" class="summary"> <span class="summary-type"> </span> </td><td class="summary"> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr> <td><span class="summary-sig"><a href="Bio.KDTree.KDTree.KDTree-class.html#get_radii" class="summary-sig-name">get_radii</a>(<span class="summary-sig-arg">self</span>)</span><br /> Return radii.</td> <td align="right" valign="top"> <span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.get_radii">source code</a></span> </td> </tr> </table> </td> </tr> <tr> <td width="15%" align="right" valign="top" class="summary"> <span class="summary-type"> </span> </td><td class="summary"> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr> <td><span class="summary-sig"><a href="Bio.KDTree.KDTree.KDTree-class.html#get_indices" class="summary-sig-name">get_indices</a>(<span class="summary-sig-arg">self</span>)</span><br /> Return the list of indices.</td> <td align="right" valign="top"> <span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.get_indices">source code</a></span> </td> </tr> </table> </td> </tr> <tr> <td width="15%" align="right" valign="top" class="summary"> <span class="summary-type"> </span> </td><td class="summary"> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr> <td><span class="summary-sig"><a href="Bio.KDTree.KDTree.KDTree-class.html#all_search" class="summary-sig-name">all_search</a>(<span class="summary-sig-arg">self</span>, <span class="summary-sig-arg">radius</span>)</span><br /> All fixed neighbor search.</td> <td align="right" valign="top"> <span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.all_search">source code</a></span> </td> </tr> </table> </td> </tr> <tr> <td width="15%" align="right" valign="top" class="summary"> <span class="summary-type"> </span> </td><td class="summary"> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr> <td><span class="summary-sig"><a href="Bio.KDTree.KDTree.KDTree-class.html#all_get_indices" class="summary-sig-name">all_get_indices</a>(<span class="summary-sig-arg">self</span>)</span><br /> Return All Fixed Neighbor Search results.</td> <td align="right" valign="top"> <span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.all_get_indices">source code</a></span> </td> </tr> </table> </td> </tr> <tr> <td width="15%" align="right" valign="top" class="summary"> <span class="summary-type"> </span> </td><td class="summary"> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr> <td><span class="summary-sig"><a href="Bio.KDTree.KDTree.KDTree-class.html#all_get_radii" class="summary-sig-name">all_get_radii</a>(<span class="summary-sig-arg">self</span>)</span><br /> Return All Fixed Neighbor Search results.</td> <td align="right" valign="top"> <span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.all_get_radii">source code</a></span> </td> </tr> </table> </td> </tr> </table> <!-- ==================== METHOD DETAILS ==================== --> <a name="section-MethodDetails"></a> <table class="details" border="1" cellpadding="3" cellspacing="0" width="100%" bgcolor="white"> <tr bgcolor="#70b0f0" class="table-header"> <td colspan="2" class="table-header"> <table border="0" cellpadding="0" cellspacing="0" width="100%"> <tr valign="top"> <td align="left"><span class="table-header">Method Details</span></td> <td align="right" valign="top" ><span class="options">[<a href="#section-MethodDetails" class="privatelink" onclick="toggle_private();" >hide private</a>]</span></td> </tr> </table> </td> </tr> </table> <a name="set_coords"></a> <div> <table class="details" border="1" cellpadding="3" cellspacing="0" width="100%" bgcolor="white"> <tr><td> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr valign="top"><td> <h3 class="epydoc"><span class="sig"><span class="sig-name">set_coords</span>(<span class="sig-arg">self</span>, <span class="sig-arg">coords</span>)</span> </h3> </td><td align="right" valign="top" ><span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.set_coords">source code</a></span> </td> </tr></table> <p>Add the coordinates of the points.</p> <p>o coords - two dimensional Numeric array of type "f". E.g. if the points have dimensionality D and there are N points, the coords array should be NxD dimensional.</p> <dl class="fields"> </dl> </td></tr></table> </div> <a name="search"></a> <div> <table class="details" border="1" cellpadding="3" cellspacing="0" width="100%" bgcolor="white"> <tr><td> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr valign="top"><td> <h3 class="epydoc"><span class="sig"><span class="sig-name">search</span>(<span class="sig-arg">self</span>, <span class="sig-arg">center</span>, <span class="sig-arg">radius</span>)</span> </h3> </td><td align="right" valign="top" ><span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.search">source code</a></span> </td> </tr></table> <p>Search all points within radius of center.</p> <p>o center - one dimensional Numeric array of type "f". E.g. if the points have dimensionality D, the center array should be D dimensional. o radius - float>0</p> <dl class="fields"> </dl> </td></tr></table> </div> <a name="get_radii"></a> <div> <table class="details" border="1" cellpadding="3" cellspacing="0" width="100%" bgcolor="white"> <tr><td> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr valign="top"><td> <h3 class="epydoc"><span class="sig"><span class="sig-name">get_radii</span>(<span class="sig-arg">self</span>)</span> </h3> </td><td align="right" valign="top" ><span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.get_radii">source code</a></span> </td> </tr></table> <p>Return radii.</p> <p>Return the list of distances from center after a neighbor search.</p> <dl class="fields"> </dl> </td></tr></table> </div> <a name="get_indices"></a> <div> <table class="details" border="1" cellpadding="3" cellspacing="0" width="100%" bgcolor="white"> <tr><td> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr valign="top"><td> <h3 class="epydoc"><span class="sig"><span class="sig-name">get_indices</span>(<span class="sig-arg">self</span>)</span> </h3> </td><td align="right" valign="top" ><span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.get_indices">source code</a></span> </td> </tr></table> <p>Return the list of indices.</p> <p>Return the list of indices after a neighbor search. The indices refer to the original coords Numeric array. The coordinates with these indices were within radius of center.</p> <p>For an index pair, the first index<second index.</p> <dl class="fields"> </dl> </td></tr></table> </div> <a name="all_search"></a> <div> <table class="details" border="1" cellpadding="3" cellspacing="0" width="100%" bgcolor="white"> <tr><td> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr valign="top"><td> <h3 class="epydoc"><span class="sig"><span class="sig-name">all_search</span>(<span class="sig-arg">self</span>, <span class="sig-arg">radius</span>)</span> </h3> </td><td align="right" valign="top" ><span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.all_search">source code</a></span> </td> </tr></table> <p>All fixed neighbor search.</p> <p>Search all point pairs that are within radius.</p> <p>o radius - float (>0)</p> <dl class="fields"> </dl> </td></tr></table> </div> <a name="all_get_indices"></a> <div> <table class="details" border="1" cellpadding="3" cellspacing="0" width="100%" bgcolor="white"> <tr><td> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr valign="top"><td> <h3 class="epydoc"><span class="sig"><span class="sig-name">all_get_indices</span>(<span class="sig-arg">self</span>)</span> </h3> </td><td align="right" valign="top" ><span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.all_get_indices">source code</a></span> </td> </tr></table> <p>Return All Fixed Neighbor Search results.</p> <p>Return a Nx2 dim Numeric array containing the indices of the point pairs, where N is the number of neighbor pairs.</p> <dl class="fields"> </dl> </td></tr></table> </div> <a name="all_get_radii"></a> <div> <table class="details" border="1" cellpadding="3" cellspacing="0" width="100%" bgcolor="white"> <tr><td> <table width="100%" cellpadding="0" cellspacing="0" border="0"> <tr valign="top"><td> <h3 class="epydoc"><span class="sig"><span class="sig-name">all_get_radii</span>(<span class="sig-arg">self</span>)</span> </h3> </td><td align="right" valign="top" ><span class="codelink"><a href="Bio.KDTree.KDTree-pysrc.html#KDTree.all_get_radii">source code</a></span> </td> </tr></table> <p>Return All Fixed Neighbor Search results.</p> <p>Return an N-dim array containing the distances of all the point pairs, where N is the number of neighbor pairs..</p> <dl class="fields"> </dl> </td></tr></table> </div> <br /> <!-- ==================== NAVIGATION BAR ==================== --> <table class="navbar" border="0" width="100%" cellpadding="0" bgcolor="#a0c0ff" cellspacing="0"> <tr valign="middle"> <!-- Tree link --> <th> <a href="module-tree.html">Trees</a> </th> <!-- Index link --> <th> <a href="identifier-index.html">Indices</a> </th> <!-- Help link --> <th> <a href="help.html">Help</a> </th> <th class="navbar" width="100%"></th> </tr> </table> <table border="0" cellpadding="0" cellspacing="0" width="100%%"> <tr> <td align="left" class="footer"> Generated by Epydoc 3.0.1 on Mon Sep 15 09:26:35 2008 </td> <td align="right" class="footer"> <a target="mainFrame" href="http://epydoc.sourceforge.net" >http://epydoc.sourceforge.net</a> </td> </tr> </table> <script type="text/javascript"> <!-- // Private objects are initially displayed (because if // javascript is turned off then we want them to be // visible); but by default, we want to hide them. 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