diff options
Diffstat (limited to 'src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp')
| -rw-r--r-- | src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp | 85 |
1 files changed, 36 insertions, 49 deletions
diff --git a/src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp b/src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp index 217e251f..9bd51106 100644 --- a/src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp +++ b/src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp @@ -1,5 +1,5 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ * library for computational topology. * * Author(s): Clément Maria @@ -33,7 +33,7 @@ #include <string> #include <vector> -#include <limits> // infinity +#include <limits> // infinity #include <utility> // for pair #include <map> @@ -50,15 +50,14 @@ using Vertex_handle = Simplex_tree::Vertex_handle; using Simplex_handle = Simplex_tree::Simplex_handle; using Filtration_value = Simplex_tree::Filtration_value; using Siblings = Simplex_tree::Siblings; -using Graph_t = boost::adjacency_list < boost::vecS, boost::vecS, boost::undirectedS -, boost::property < Gudhi::vertex_filtration_t, Filtration_value > -, boost::property < Gudhi::edge_filtration_t, Filtration_value > ->; -using Edge_t = std::pair< Vertex_handle, Vertex_handle >; +using Graph_t = boost::adjacency_list<boost::vecS, boost::vecS, boost::undirectedS, + boost::property<Gudhi::vertex_filtration_t, Filtration_value>, + boost::property<Gudhi::edge_filtration_t, Filtration_value> >; +using Edge_t = std::pair<Vertex_handle, Vertex_handle>; -using Kernel = CGAL::Epick_d< CGAL::Dimension_tag<3> >; +using Kernel = CGAL::Epick_d<CGAL::Dimension_tag<3> >; using Point = Kernel::Point_d; -using Traits = CGAL::Min_sphere_of_points_d_traits_d<Kernel,Filtration_value,3>; +using Traits = CGAL::Min_sphere_of_points_d_traits_d<Kernel, Filtration_value, 3>; using Min_sphere = CGAL::Min_sphere_of_spheres_d<Traits>; using Points_off_reader = Gudhi::Points_off_reader<Point>; @@ -76,7 +75,7 @@ class Cech_blocker { std::cout << vertex << ", "; #endif // DEBUG_TRACES } - Min_sphere ms(points.begin(),points.end()); + Min_sphere ms(points.begin(), points.end()); Filtration_value radius = ms.radius(); #if DEBUG_TRACES std::cout << "] - radius = " << radius << " - returns " << (radius > threshold_) << std::endl; @@ -85,24 +84,20 @@ class Cech_blocker { return (radius > threshold_); } Cech_blocker(Simplex_tree& simplex_tree, Filtration_value threshold, const std::vector<Point>& point_cloud) - : simplex_tree_(simplex_tree), - threshold_(threshold), - point_cloud_(point_cloud) { } + : simplex_tree_(simplex_tree), threshold_(threshold), point_cloud_(point_cloud) {} + private: Simplex_tree simplex_tree_; Filtration_value threshold_; std::vector<Point> point_cloud_; }; -template< typename InputPointRange> -Graph_t compute_proximity_graph(InputPointRange &points, Filtration_value threshold); +template <typename InputPointRange> +Graph_t compute_proximity_graph(InputPointRange& points, Filtration_value threshold); -void program_options(int argc, char * argv[] - , std::string & off_file_points - , Filtration_value & threshold - , int & dim_max); +void program_options(int argc, char* argv[], std::string& off_file_points, Filtration_value& threshold, int& dim_max); -int main(int argc, char * argv[]) { +int main(int argc, char* argv[]) { std::string off_file_points; Filtration_value threshold; int dim_max; @@ -115,7 +110,7 @@ int main(int argc, char * argv[]) { // Compute the proximity graph of the points Graph_t prox_graph = compute_proximity_graph(off_reader.get_point_cloud(), threshold); - //Min_sphere sph1(off_reader.get_point_cloud()[0], off_reader.get_point_cloud()[1], off_reader.get_point_cloud()[2]); + // Min_sphere sph1(off_reader.get_point_cloud()[0], off_reader.get_point_cloud()[1], off_reader.get_point_cloud()[2]); // Construct the Rips complex in a Simplex Tree Simplex_tree st; // insert the proximity graph in the simplex tree @@ -135,7 +130,8 @@ int main(int argc, char * argv[]) { std::cout << "* The complex contains " << st.num_simplices() << " simplices - dimension=" << st.dimension() << "\n"; std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " << "[" << st.filtration(f_simplex) << "] "; + std::cout << " " + << "[" << st.filtration(f_simplex) << "] "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { std::cout << static_cast<int>(vertex) << " "; } @@ -145,24 +141,19 @@ int main(int argc, char * argv[]) { return 0; } -void program_options(int argc, char * argv[] - , std::string & off_file_points - , Filtration_value & threshold - , int & dim_max) { +void program_options(int argc, char* argv[], std::string& off_file_points, Filtration_value& threshold, int& dim_max) { namespace po = boost::program_options; po::options_description hidden("Hidden options"); - hidden.add_options() - ("input-file", po::value<std::string>(&off_file_points), - "Name of an OFF file containing a 3d point set.\n"); + hidden.add_options()("input-file", po::value<std::string>(&off_file_points), + "Name of an OFF file containing a 3d point set.\n"); po::options_description visible("Allowed options", 100); - visible.add_options() - ("help,h", "produce help message") - ("max-edge-length,r", - po::value<Filtration_value>(&threshold)->default_value(std::numeric_limits<Filtration_value>::infinity()), - "Maximal length of an edge for the Cech complex construction.") - ("cpx-dimension,d", po::value<int>(&dim_max)->default_value(1), - "Maximal dimension of the Cech complex we want to compute."); + visible.add_options()("help,h", "produce help message")( + "max-edge-length,r", + po::value<Filtration_value>(&threshold)->default_value(std::numeric_limits<Filtration_value>::infinity()), + "Maximal length of an edge for the Cech complex construction.")( + "cpx-dimension,d", po::value<int>(&dim_max)->default_value(1), + "Maximal dimension of the Cech complex we want to compute."); po::positional_options_description pos; pos.add("input-file", 1); @@ -171,8 +162,7 @@ void program_options(int argc, char * argv[] all.add(visible).add(hidden); po::variables_map vm; - po::store(po::command_line_parser(argc, argv). - options(all).positional(pos).run(), vm); + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { @@ -194,10 +184,10 @@ void program_options(int argc, char * argv[] * The type PointCloud furnishes .begin() and .end() methods, that return * iterators with value_type Point. */ -template< typename InputPointRange> -Graph_t compute_proximity_graph(InputPointRange &points, Filtration_value threshold) { - std::vector< Edge_t > edges; - std::vector< Filtration_value > edges_fil; +template <typename InputPointRange> +Graph_t compute_proximity_graph(InputPointRange& points, Filtration_value threshold) { + std::vector<Edge_t> edges; + std::vector<Filtration_value> edges_fil; Kernel k; Vertex_handle idx_u, idx_v; @@ -217,16 +207,13 @@ Graph_t compute_proximity_graph(InputPointRange &points, Filtration_value thresh ++idx_u; } - Graph_t skel_graph(edges.begin() - , edges.end() - , edges_fil.begin() - , idx_u); // number of points labeled from 0 to idx_u-1 + Graph_t skel_graph(edges.begin(), edges.end(), edges_fil.begin(), + idx_u); // number of points labeled from 0 to idx_u-1 auto vertex_prop = boost::get(Gudhi::vertex_filtration_t(), skel_graph); boost::graph_traits<Graph_t>::vertex_iterator vi, vi_end; - for (std::tie(vi, vi_end) = boost::vertices(skel_graph); - vi != vi_end; ++vi) { + for (std::tie(vi, vi_end) = boost::vertices(skel_graph); vi != vi_end; ++vi) { boost::put(vertex_prop, *vi, 0.); } |