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.
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| | PointCloudSearch (view_type src_pts_view, const local_index_type dimension=-1, const local_index_type max_leaf=-1) |
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| | ~PointCloudSearch () |
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| template<class BBOX > |
| bool | kdtree_get_bbox (BBOX &bb) const |
| | Bounding box query method required by Nanoflann. More...
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| int | kdtree_get_point_count () const |
| | Returns the number of source sites. More...
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| double | kdtree_get_pt (const int idx, int dim) const |
| | Returns the coordinate value of a point. More...
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| 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...
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| void | generateKDTree () |
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| 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...
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| 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...
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| 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...
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| 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...
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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 |
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bool |
is_dry_run, |
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trg_view_type |
trg_pts_view, |
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neighbor_lists_view_type |
neighbor_lists, |
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epsilons_view_type |
epsilons, |
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const int |
neighbors_needed, |
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const double |
epsilon_multiplier = 1.6, |
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double |
max_search_radius = 0.0 |
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) |
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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
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| 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 |
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bool |
is_dry_run, |
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trg_view_type |
trg_pts_view, |
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neighbor_lists_view_type |
neighbor_lists, |
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epsilons_view_type |
epsilons, |
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const double |
uniform_radius = 0.0, |
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double |
max_search_radius = 0.0 |
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) |
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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
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| 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, |
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trg_view_type |
trg_pts_view, |
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neighbor_lists_view_type |
neighbor_lists, |
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neighbor_lists_view_type |
number_of_neighbors_list, |
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epsilons_view_type |
epsilons, |
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const int |
neighbors_needed, |
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const double |
epsilon_multiplier = 1.6, |
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double |
max_search_radius = 0.0 |
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) |
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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, |
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trg_view_type |
trg_pts_view, |
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neighbor_lists_view_type |
neighbor_lists, |
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neighbor_lists_view_type |
number_of_neighbors_list, |
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epsilons_view_type |
epsilons, |
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const double |
uniform_radius = 0.0, |
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double |
max_search_radius = 0.0 |
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) |
| |
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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 >
| static int Compadre::PointCloudSearch< view_type >::getEstimatedNumberNeighborsUpperBound |
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int |
unisolvency_size, |
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const int |
dimension, |
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const double |
epsilon_multiplier |
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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.
