* order $\log(n)$, where $n$ is the number of points stored in this class.
*
* The wrapper provides methods that give access to some of the functionalities
- * of the nanoflann library, like searching the $p$ nearest neighbors of
- * a given point, or
- * searching the points that fall within a radius of a target point.
+ * of the nanoflann library, like searching the $p$ nearest neighbors of a given
+ * point, or searching the points that fall within a radius of a target point.
*
* > From wikipedia (https://en.wikipedia.org/wiki/K-d_tree):
* >
* > Every non-leaf node can be thought of as implicitly generating a splitting
* > hyperplane that divides the space into two parts, known as half-spaces.
* > Points to the left of this hyperplane are represented by the left subtree
- * of > that node and points right of the hyperplane are represented by the
- * right > subtree. The hyperplane direction is chosen in the following way:
- * every node > in the tree is associated with one of the $k$-dimensions, with
- * the hyperplane > perpendicular to that dimension's axis. So, for example, if
- * for a particular > split the "x" axis is chosen, all points in the subtree
- * with a smaller "x" > value than the node will appear in the left subtree and
- * all points with > larger "x" value will be in the right subtree. In such a
- * case, the > hyperplane would be set by the $x$-value of the point, and its
- * normal would be > the unit $x$-axis.
+ * > of that node and points right of the hyperplane are represented by the
+ * > right subtree. The hyperplane direction is chosen in the following way:
+ * > every node in the tree is associated with one of the $k$-dimensions, with
+ * > the hyperplane perpendicular to that dimension's axis. So, for example, if
+ * > for a particular split the "x" axis is chosen, all points in the subtree
+ * > with a smaller "x" value than the node will appear in the left subtree and
+ * > all points with larger "x" value will be in the right subtree. In such a
+ * > case, the hyperplane would be set by the $x$-value of the point, and its
+ * > normal would be the unit $x$-axis.
*
* @author Luca Heltai, 2017.
*/
* @param[in] max_leaf_size A number denoting how many points per leaf
* are used in the kdtree algorithm.
*
- * @param[in] pts A vector of points that are to be represented by
- * the current object. If no points are passed to this constructor
- * (or if the default value of the argument is used), then you have
- * to pass them later to this object by calling the set_points()
- * method.
+ * @param[in] pts A vector of points that are to be represented by the current
+ * object. If no points are passed to this constructor (or if the default
+ * value of the argument is used), then you have to pass them later to this
+ * object by calling the set_points() method.
*
- * Access to any of the methods without first passing a reference to
- * a vector of points will result in an exception. Only a reference
- * to the points is stored, so you should make sure that the life of
- * the vector you pass is longer than the life of this class, or
- * you will get undefined behaviour.
+ * Access to any of the methods without first passing a reference to a vector
+ * of points will result in an exception. Only a reference to the points is
+ * stored, so you should make sure that the life of the vector you pass is
+ * longer than the life of this class, or you will get undefined behaviour.
*
- * @warning If you change the contents of the vector of points that you
- * passed either to the constructor or to set_points(), remember to call
- * the set_points() method again. The tree and the index are
- * constructed only once when you pass the points (either at
- * construction time, or when you call set_points()). If you update
- * your points, and do not call set_points() again, then all following results
- * will likely be wrong.
+ * @warning If you change the contents of the vector of points that you passed
+ * either to the constructor or to set_points(), remember to call the
+ * set_points() method again. The tree and the index are constructed only once
+ * when you pass the points (either at construction time, or when you call
+ * set_points()). If you update your points, and do not call set_points()
+ * again, then all following results will likely be wrong.
*/
KDTree(const unsigned int max_leaf_size = 10,
const std::vector<Point<dim>> &pts = std::vector<Point<dim>>());
/**
* Adaptor class used internally by nanoflann. This class stores a reference
- * to the vector of points, and generates some helper functions for
- * nanoflann.
+ * to the vector of points, and generates some helper functions for nanoflann.
*/
struct PointCloudAdaptor
{
/**
* Optional bounding-box computation: return false to default to a
- * standard bbox computation loop. Return true if the BBOX was
+ * standard bbox computation loop. Return true if the BBOX was
* already computed by the class and returned in "bb" so it can be
- * avoided to redo it again. Look at bb.size() to find out the
+ * avoided to redo it again. Look at bb.size() to find out the
* expected dimensionality (e.g. 2 or 3 for point clouds).
*/
template <class BBOX>
/**
- * Store a reference to the passed points. After you called this
- * method, you can call the value() method to compute the minimum
- * distance between an evaluation point and the collection of points
- * you passed to this method, or the get_points_within_ball() and
- * the get_closest_points() methods.
+ * Store a reference to the passed points. After you called this method, you
+ * can call the value() method to compute the minimum distance between an
+ * evaluation point and the collection of points you passed to this method, or
+ * the get_points_within_ball() and the get_closest_points() methods.
*
* Notice that the constructor calls this method internally if you
* pass it a non-empty vector of points.
*
* @param[in] target The target point
* @param[in] radius The radius of the ball
- * @param[in] sorted If @p true, sort the output results in ascending order with respect to distance
+ * @param[in] sorted If @p true, sort the output results in ascending order
+ * with respect to distance
*
- * @return A vector of indices and distances to @p target of the matching points
+ * @return A vector of indices and distances to @p target
+ * of the matching points
*/
std::vector<std::pair<unsigned int, double>>
get_points_within_ball(const Point<dim> &target,
const bool sorted = false) const;
/**
- * Fill and return a vector with the indices and distances of the closest @p n_points
- * points to the given target point.
+ * Fill and return a vector with the indices and distances of the closest
+ * @p n_points points to the given target point.
*
* @param[in] target The target point
* @param[in] n_points The number of requested points
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