PointCloudSearch generates neighbor lists and window sizes for each target site. More...

Detailed Description

template<typename view_type>
class Compadre::PointCloudSearch< view_type >

PointCloudSearch generates neighbor lists and window sizes for each target site.

Search methods can be run in dry-run mode, or not.

When in dry-run mode:

neighbors_list will be populated with number of neighbors found for each target site.

This allows a user to know memory allocation needed before storage of neighbor indices.

When not in dry-run mode:

neighbors_list_offsets will be populated with offsets for values (dependent on method) determined by neighbor_list. If a 2D view for neighbors_list is used, then $ N(i,j+1) $ will store the $j^{th}$ neighbor of $ i $ , and $ N(i,0) $ will store the number of neighbors for target $ i $ .

Definition at line 151 of file Compadre_PointCloudSearch.hpp.

#include <Compadre_PointCloudSearch.hpp>

Public Types
typedef nanoflann::KDTreeSingleIndexAdaptor < nanoflann::L2_Simple_Adaptor < double, PointCloudSearch < view_type > >, PointCloudSearch < view_type >, 1 >	tree_type_1d

typedef nanoflann::KDTreeSingleIndexAdaptor < nanoflann::L2_Simple_Adaptor < double, PointCloudSearch < view_type > >, PointCloudSearch < view_type >, 2 >	tree_type_2d

typedef nanoflann::KDTreeSingleIndexAdaptor < nanoflann::L2_Simple_Adaptor < double, PointCloudSearch < view_type > >, PointCloudSearch < view_type >, 3 >	tree_type_3d

Public Member Functions
	PointCloudSearch (view_type src_pts_view, const local_index_type dimension=-1, const local_index_type max_leaf=-1)

	~PointCloudSearch ()

template<class BBOX >
bool	kdtree_get_bbox (BBOX &bb) const
	Bounding box query method required by Nanoflann. More...

int	kdtree_get_point_count () const
	Returns the number of source sites. More...

double	kdtree_get_pt (const int idx, int dim) const
	Returns the coordinate value of a point. More...

double	kdtree_distance (const double *queryPt, const int idx, long long sz) const
	Returns the distance between a point and a source site, given its index. More...

void	generateKDTree ()

template<typename trg_view_type , typename neighbor_lists_view_type , typename epsilons_view_type >
size_t	generate2DNeighborListsFromRadiusSearch (bool is_dry_run, trg_view_type trg_pts_view, neighbor_lists_view_type neighbor_lists, epsilons_view_type epsilons, const double uniform_radius=0.0, double max_search_radius=0.0)
	Generates neighbor lists of 2D view by performing a radius search where the radius to be searched is in the epsilons view. If uniform_radius is given, then this overrides the epsilons view radii sizes. Accepts 2D neighbor_lists without number_of_neighbors_list. More...

template<typename trg_view_type , typename neighbor_lists_view_type , typename epsilons_view_type >
size_t	generateCRNeighborListsFromRadiusSearch (bool is_dry_run, trg_view_type trg_pts_view, neighbor_lists_view_type neighbor_lists, neighbor_lists_view_type number_of_neighbors_list, epsilons_view_type epsilons, const double uniform_radius=0.0, double max_search_radius=0.0)
	Generates compressed row neighbor lists by performing a radius search where the radius to be searched is in the epsilons view. If uniform_radius is given, then this overrides the epsilons view radii sizes. Accepts 1D neighbor_lists with 1D number_of_neighbors_list. More...

template<typename trg_view_type , typename neighbor_lists_view_type , typename epsilons_view_type >
size_t	generate2DNeighborListsFromKNNSearch (bool is_dry_run, trg_view_type trg_pts_view, neighbor_lists_view_type neighbor_lists, epsilons_view_type epsilons, const int neighbors_needed, const double epsilon_multiplier=1.6, double max_search_radius=0.0)
	Generates neighbor lists as 2D view by performing a k-nearest neighbor search Only accepts 2D neighbor_lists without number_of_neighbors_list. More...

template<typename trg_view_type , typename neighbor_lists_view_type , typename epsilons_view_type >
size_t	generateCRNeighborListsFromKNNSearch (bool is_dry_run, trg_view_type trg_pts_view, neighbor_lists_view_type neighbor_lists, neighbor_lists_view_type number_of_neighbors_list, epsilons_view_type epsilons, const int neighbors_needed, const double epsilon_multiplier=1.6, double max_search_radius=0.0)
	Generates compressed row neighbor lists by performing a k-nearest neighbor search Only accepts 1D neighbor_lists with 1D number_of_neighbors_list. More...

Static Public Member Functions
static int	getEstimatedNumberNeighborsUpperBound (int unisolvency_size, const int dimension, const double epsilon_multiplier)
	Returns a liberal estimated upper bound on number of neighbors to be returned by a neighbor search for a given choice of dimension, basis size, and epsilon_multiplier. More...

Protected Attributes
view_type	_src_pts_view
	source site coordinates More...

local_index_type	_dim

local_index_type	_max_leaf

std::shared_ptr< tree_type_1d >	_tree_1d

std::shared_ptr< tree_type_2d >	_tree_2d

std::shared_ptr< tree_type_3d >	_tree_3d

Member Typedef Documentation

template<typename view_type>

typedef nanoflann::KDTreeSingleIndexAdaptor<nanoflann::L2_Simple_Adaptor<double, PointCloudSearch<view_type> >, PointCloudSearch<view_type>, 1> Compadre::PointCloudSearch< view_type >::tree_type_1d

Definition at line 156 of file Compadre_PointCloudSearch.hpp.

template<typename view_type>

typedef nanoflann::KDTreeSingleIndexAdaptor<nanoflann::L2_Simple_Adaptor<double, PointCloudSearch<view_type> >, PointCloudSearch<view_type>, 2> Compadre::PointCloudSearch< view_type >::tree_type_2d

Definition at line 158 of file Compadre_PointCloudSearch.hpp.

template<typename view_type>

typedef nanoflann::KDTreeSingleIndexAdaptor<nanoflann::L2_Simple_Adaptor<double, PointCloudSearch<view_type> >, PointCloudSearch<view_type>, 3> Compadre::PointCloudSearch< view_type >::tree_type_3d

Definition at line 160 of file Compadre_PointCloudSearch.hpp.

Constructor & Destructor Documentation

template<typename view_type>

Compadre::PointCloudSearch< view_type >::PointCloudSearch	(	view_type	src_pts_view,
		const local_index_type	dimension = `-1`,
		const local_index_type	max_leaf = `-1`
	)

inline

Definition at line 175 of file Compadre_PointCloudSearch.hpp.

template<typename view_type>

Compadre::PointCloudSearch< view_type >::~PointCloudSearch ( )

inline

Definition at line 184 of file Compadre_PointCloudSearch.hpp.

Member Function Documentation

template<typename view_type>

template<typename trg_view_type , typename neighbor_lists_view_type , typename epsilons_view_type >

size_t Compadre::PointCloudSearch< view_type >::generate2DNeighborListsFromKNNSearch	(	bool	is_dry_run,
		trg_view_type	trg_pts_view,
		neighbor_lists_view_type	neighbor_lists,
		epsilons_view_type	epsilons,
		const int	neighbors_needed,
		const double	epsilon_multiplier = `1.6`,
		double	max_search_radius = `0.0`
	)

inline

Generates neighbor lists as 2D view by performing a k-nearest neighbor search Only accepts 2D neighbor_lists without number_of_neighbors_list.

Parameters

is_dry_run	[in] - whether to do a dry-run (find neighbors, but don't store)
trg_pts_view	[in] - target coordinates from which to seek neighbors
neighbor_lists	[out] - 2D view of neighbor lists to be populated from search
epsilons	[in/out] - radius to search, overwritten if uniform_radius != 0
neighbors_needed	[in] - k neighbors needed as a minimum
epsilon_multiplier	[in] - distance to kth neighbor multiplied by epsilon_multiplier for follow-on radius search
max_search_radius	[in] - largest valid search (useful only for MPI jobs if halo size exists)

Definition at line 518 of file Compadre_PointCloudSearch.hpp.

template<typename view_type>

template<typename trg_view_type , typename neighbor_lists_view_type , typename epsilons_view_type >

size_t Compadre::PointCloudSearch< view_type >::generate2DNeighborListsFromRadiusSearch	(	bool	is_dry_run,
		trg_view_type	trg_pts_view,
		neighbor_lists_view_type	neighbor_lists,
		epsilons_view_type	epsilons,
		const double	uniform_radius = `0.0`,
		double	max_search_radius = `0.0`
	)

inline

Generates neighbor lists of 2D view by performing a radius search where the radius to be searched is in the epsilons view. If uniform_radius is given, then this overrides the epsilons view radii sizes. Accepts 2D neighbor_lists without number_of_neighbors_list.

Parameters

is_dry_run	[in] - whether to do a dry-run (find neighbors, but don't store)
trg_pts_view	[in] - target coordinates from which to seek neighbors
neighbor_lists	[out] - 2D view of neighbor lists to be populated from search
epsilons	[in/out] - radius to search, overwritten if uniform_radius != 0
uniform_radius	[in] - double != 0 determines whether to overwrite all epsilons for uniform search
max_search_radius	[in] - largest valid search (useful only for MPI jobs if halo size exists)

Definition at line 240 of file Compadre_PointCloudSearch.hpp.

template<typename view_type>

template<typename trg_view_type , typename neighbor_lists_view_type , typename epsilons_view_type >

size_t Compadre::PointCloudSearch< view_type >::generateCRNeighborListsFromKNNSearch	(	bool	is_dry_run,
		trg_view_type	trg_pts_view,
		neighbor_lists_view_type	neighbor_lists,
		neighbor_lists_view_type	number_of_neighbors_list,
		epsilons_view_type	epsilons,
		const int	neighbors_needed,
		const double	epsilon_multiplier = `1.6`,
		double	max_search_radius = `0.0`
	)

inline

Generates compressed row neighbor lists by performing a k-nearest neighbor search Only accepts 1D neighbor_lists with 1D number_of_neighbors_list.

Parameters

is_dry_run	[in] - whether to do a dry-run (find neighbors, but don't store)
trg_pts_view	[in] - target coordinates from which to seek neighbors
neighbor_lists	[out] - 1D view of neighbor lists to be populated from search
number_of_neighbors_list	[in/out] - number of neighbors for each target site
epsilons	[in/out] - radius to search, overwritten if uniform_radius != 0
neighbors_needed	[in] - k neighbors needed as a minimum
epsilon_multiplier	[in] - distance to kth neighbor multiplied by epsilon_multiplier for follow-on radius search
max_search_radius	[in] - largest valid search (useful only for MPI jobs if halo size exists)

Definition at line 660 of file Compadre_PointCloudSearch.hpp.

template<typename view_type>

template<typename trg_view_type , typename neighbor_lists_view_type , typename epsilons_view_type >

size_t Compadre::PointCloudSearch< view_type >::generateCRNeighborListsFromRadiusSearch	(	bool	is_dry_run,
		trg_view_type	trg_pts_view,
		neighbor_lists_view_type	neighbor_lists,
		neighbor_lists_view_type	number_of_neighbors_list,
		epsilons_view_type	epsilons,
		const double	uniform_radius = `0.0`,
		double	max_search_radius = `0.0`
	)

inline

Generates compressed row neighbor lists by performing a radius search where the radius to be searched is in the epsilons view. If uniform_radius is given, then this overrides the epsilons view radii sizes. Accepts 1D neighbor_lists with 1D number_of_neighbors_list.

Parameters

is_dry_run	[in] - whether to do a dry-run (find neighbors, but don't store)
trg_pts_view	[in] - target coordinates from which to seek neighbors
neighbor_lists	[out] - 1D view of neighbor lists to be populated from search
number_of_neighbors_list	[in/out] - number of neighbors for each target site
epsilons	[in/out] - radius to search, overwritten if uniform_radius != 0
uniform_radius	[in] - double != 0 determines whether to overwrite all epsilons for uniform search
max_search_radius	[in] - largest valid search (useful only for MPI jobs if halo size exists)

Definition at line 374 of file Compadre_PointCloudSearch.hpp.

template<typename view_type>

void Compadre::PointCloudSearch< view_type >::generateKDTree ( )

inline

Definition at line 215 of file Compadre_PointCloudSearch.hpp.

template<typename view_type>

static int Compadre::PointCloudSearch< view_type >::getEstimatedNumberNeighborsUpperBound	(	int	unisolvency_size,
		const int	dimension,
		const double	epsilon_multiplier
	)

inlinestatic

Returns a liberal estimated upper bound on number of neighbors to be returned by a neighbor search for a given choice of dimension, basis size, and epsilon_multiplier.

Assumes quasiuniform distribution of points. This result can be used to size a preallocated neighbor_lists kokkos view.

Definition at line 189 of file Compadre_PointCloudSearch.hpp.

template<typename view_type>