Line data    Source code

       1             : //* This file is part of the MOOSE framework
       2             : //* https://mooseframework.inl.gov
       3             : //*
       4             : //* All rights reserved, see COPYRIGHT for full restrictions
       5             : //* https://github.com/idaholab/moose/blob/master/COPYRIGHT
       6             : //*
       7             : //* Licensed under LGPL 2.1, please see LICENSE for details
       8             : //* https://www.gnu.org/licenses/lgpl-2.1.html
       9             : 
      10             : #include "KDTree.h"
      11             : #include "MooseError.h"
      12             : 
      13             : #include "libmesh/nanoflann.hpp"
      14             : #include "libmesh/point.h"
      15             : 
      16             : // Make newer nanoflann API compatible with older nanoflann versions
      17             : #if NANOFLANN_VERSION < 0x150
      18             : namespace nanoflann
      19             : {
      20             : typedef SearchParams SearchParameters;
      21             : 
      22             : template <typename T, typename U>
      23             : using ResultItem = std::pair<T, U>;
      24             : }
      25             : #endif
      26             : 
      27       68674 : KDTree::KDTree(const std::vector<Point> & master_points, unsigned int max_leaf_size)
      28       68674 :   : _point_list_adaptor(master_points.begin(), master_points.end()),
      29       68674 :     _kd_tree(std::make_unique<KdTreeT>(
      30      137348 :         LIBMESH_DIM, _point_list_adaptor, nanoflann::KDTreeSingleIndexAdaptorParams(max_leaf_size)))
      31             : {
      32             :   mooseAssert(_kd_tree != nullptr, "KDTree was not properly initialized.");
      33             : 
      34       68674 :   _kd_tree->buildIndex();
      35       68674 : }
      36             : 
      37             : void
      38      414220 : KDTree::neighborSearch(const Point & query_point,
      39             :                        unsigned int patch_size,
      40             :                        std::vector<std::size_t> & return_index)
      41             : {
      42      414220 :   std::vector<Real> return_dist_sqr(patch_size);
      43      414220 :   neighborSearch(query_point, patch_size, return_index, return_dist_sqr);
      44      414220 : }
      45             : 
      46             : void
      47     6641816 : KDTree::neighborSearch(const Point & query_point,
      48             :                        unsigned int patch_size,
      49             :                        std::vector<std::size_t> & return_index,
      50             :                        std::vector<Real> & return_dist_sqr)
      51             : {
      52     6641816 :   return_index.resize(patch_size);
      53             : 
      54             :   std::size_t n_result =
      55     6641816 :       _kd_tree->knnSearch(&query_point(0), patch_size, return_index.data(), return_dist_sqr.data());
      56             : 
      57     6641816 :   if (n_result == 0)
      58           0 :     mooseError("Unable to find closest node!");
      59             : 
      60     6641816 :   return_index.resize(n_result);
      61     6641816 :   return_dist_sqr.resize(n_result);
      62     6641816 : }
      63             : 
      64             : void
      65           3 : KDTree::radiusSearch(const Point & query_point,
      66             :                      Real radius,
      67             :                      std::vector<nanoflann::ResultItem<std::size_t, Real>> & indices_dist)
      68             : {
      69           3 :   nanoflann::SearchParameters sp;
      70           3 :   _kd_tree->radiusSearch(&query_point(0), radius * radius, indices_dist, sp);
      71           3 : }
      72             : 
      73             : std::size_t
      74     6242997 : KDTree::numberCandidatePoints()
      75             : {
      76     6242997 :   return _point_list_adaptor.kdtree_get_point_count();
      77             : }