diff options
Diffstat (limited to 'src')
197 files changed, 7787 insertions, 5234 deletions
diff --git a/src/Alpha_complex/include/gudhi/Alpha_complex.h b/src/Alpha_complex/include/gudhi/Alpha_complex.h index 1ff95c3d..5f7d7622 100644 --- a/src/Alpha_complex/include/gudhi/Alpha_complex.h +++ b/src/Alpha_complex/include/gudhi/Alpha_complex.h @@ -268,8 +268,6 @@ class Alpha_complex { return false; // ----- >> } - complex.set_dimension(triangulation_->maximal_dimension()); - // -------------------------------------------------------------------------------------------- // Simplex_tree construction from loop on triangulation finite full cells list if (triangulation_->number_of_vertices() > 0) { diff --git a/src/Alpha_complex/test/Alpha_complex_unit_test.cpp b/src/Alpha_complex/test/Alpha_complex_unit_test.cpp index 7380547f..166373fe 100644 --- a/src/Alpha_complex/test/Alpha_complex_unit_test.cpp +++ b/src/Alpha_complex/test/Alpha_complex_unit_test.cpp @@ -159,7 +159,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_from_points) { BOOST_CHECK(simplex_tree.num_simplices() == 15); std::cout << "simplex_tree.dimension()=" << simplex_tree.dimension() << std::endl; - BOOST_CHECK(simplex_tree.dimension() == 4); + BOOST_CHECK(simplex_tree.dimension() == 3); std::cout << "simplex_tree.num_vertices()=" << simplex_tree.num_vertices() << std::endl; BOOST_CHECK(simplex_tree.num_vertices() == 4); @@ -232,7 +232,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_from_points) { BOOST_CHECK(simplex_tree.num_simplices() == 10); std::cout << "simplex_tree.dimension()=" << simplex_tree.dimension() << std::endl; - BOOST_CHECK(simplex_tree.dimension() == 4); + BOOST_CHECK(simplex_tree.dimension() == 1); std::cout << "simplex_tree.num_vertices()=" << simplex_tree.num_vertices() << std::endl; BOOST_CHECK(simplex_tree.num_vertices() == 4); diff --git a/src/Alpha_complex/utilities/CMakeLists.txt b/src/Alpha_complex/utilities/CMakeLists.txt new file mode 100644 index 00000000..79d9e7dd --- /dev/null +++ b/src/Alpha_complex/utilities/CMakeLists.txt @@ -0,0 +1,65 @@ +cmake_minimum_required(VERSION 2.6) +project(Alpha_complex_utilities) + +if(CGAL_FOUND) + add_executable(alpha_complex_3d_persistence alpha_complex_3d_persistence.cpp) + target_link_libraries(alpha_complex_3d_persistence ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + add_executable(exact_alpha_complex_3d_persistence exact_alpha_complex_3d_persistence.cpp) + target_link_libraries(exact_alpha_complex_3d_persistence ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + add_executable(weighted_alpha_complex_3d_persistence weighted_alpha_complex_3d_persistence.cpp) + target_link_libraries(weighted_alpha_complex_3d_persistence ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + + if (TBB_FOUND) + target_link_libraries(alpha_complex_3d_persistence ${TBB_LIBRARIES}) + target_link_libraries(exact_alpha_complex_3d_persistence ${TBB_LIBRARIES}) + target_link_libraries(weighted_alpha_complex_3d_persistence ${TBB_LIBRARIES}) + endif(TBB_FOUND) + + add_test(NAME Alpha_complex_utilities_alpha_complex_3d_persistence COMMAND $<TARGET_FILE:alpha_complex_3d_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "-p" "2" "-m" "0.45") + add_test(NAME Alpha_complex_utilities_exact_alpha_complex_3d COMMAND $<TARGET_FILE:exact_alpha_complex_3d_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "-p" "2" "-m" "0.45") + add_test(NAME Alpha_complex_utilities_weighted_alpha_complex_3d COMMAND $<TARGET_FILE:weighted_alpha_complex_3d_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.weights" "-p" "2" "-m" "0.45") + + install(TARGETS alpha_complex_3d_persistence DESTINATION bin) + install(TARGETS exact_alpha_complex_3d_persistence DESTINATION bin) + install(TARGETS weighted_alpha_complex_3d_persistence DESTINATION bin) + + if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) + add_executable (alpha_complex_persistence alpha_complex_persistence.cpp) + target_link_libraries(alpha_complex_persistence ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + + add_executable(periodic_alpha_complex_3d_persistence periodic_alpha_complex_3d_persistence.cpp) + target_link_libraries(periodic_alpha_complex_3d_persistence ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + + if (TBB_FOUND) + target_link_libraries(alpha_complex_persistence ${TBB_LIBRARIES}) + target_link_libraries(periodic_alpha_complex_3d_persistence ${TBB_LIBRARIES}) + endif(TBB_FOUND) + add_test(NAME Alpha_complex_utilities_alpha_complex_persistence COMMAND $<TARGET_FILE:alpha_complex_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "-p" "2" "-m" "0.45") + add_test(NAME Alpha_complex_utilities_periodic_alpha_complex_3d_persistence COMMAND $<TARGET_FILE:periodic_alpha_complex_3d_persistence> + "${CMAKE_SOURCE_DIR}/data/points/grid_10_10_10_in_0_1.off" "${CMAKE_SOURCE_DIR}/data/points/iso_cuboid_3_in_0_1.txt" "-p" "2" "-m" "0") + + install(TARGETS alpha_complex_persistence DESTINATION bin) + install(TARGETS periodic_alpha_complex_3d_persistence DESTINATION bin) + + endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) + + if (NOT CGAL_VERSION VERSION_LESS 4.11.0) + add_executable(weighted_periodic_alpha_complex_3d_persistence weighted_periodic_alpha_complex_3d_persistence.cpp) + target_link_libraries(weighted_periodic_alpha_complex_3d_persistence ${CGAL_LIBRARY}) + if (TBB_FOUND) + target_link_libraries(weighted_periodic_alpha_complex_3d_persistence ${TBB_LIBRARIES}) + endif(TBB_FOUND) + + add_test(NAME Persistent_cohomology_example_weigted_periodic_alpha_complex_3d COMMAND $<TARGET_FILE:weighted_periodic_alpha_complex_3d_persistence> + "${CMAKE_SOURCE_DIR}/data/points/grid_10_10_10_in_0_1.off" "${CMAKE_SOURCE_DIR}/data/points/grid_10_10_10_in_0_1.weights" + "${CMAKE_SOURCE_DIR}/data/points/iso_cuboid_3_in_0_1.txt" "3" "1.0") + + install(TARGETS weighted_periodic_alpha_complex_3d_persistence DESTINATION bin) + + endif (NOT CGAL_VERSION VERSION_LESS 4.11.0) + +endif(CGAL_FOUND) diff --git a/src/Alpha_complex/utilities/README b/src/Alpha_complex/utilities/README new file mode 100644 index 00000000..1cd2ca95 --- /dev/null +++ b/src/Alpha_complex/utilities/README @@ -0,0 +1,177 @@ +# Alpha_complex #
+
+## `alpha_complex_3d_persistence` ##
+This program computes the persistent homology with coefficient field Z/pZ of the 3D alpha complex built from a 3D point cloud. The output diagram contains one bar per line, written with the convention:
+
+`p dim birth death`
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
+
+**Usage**
+`alpha_complex_3d_persistence [options] <input OFF file>`
+where
+`<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-p [ --field-charac ]` (default=11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+
+**Example**
+`alpha_complex_3d_persistence ../../data/points/tore3D_300.off -p 2 -m 0.45`
+
+outputs:
+```
+Simplex_tree dim: 3
+2 0 0 inf
+2 1 0.0682162 1.0001
+2 1 0.0934117 1.00003
+2 2 0.56444 1.03938
+```
+
+Here we retrieve expected Betti numbers on a tore 3D:
+```
+Betti numbers[0] = 1
+Betti numbers[1] = 2
+Betti numbers[2] = 1
+```
+
+N.B.:
+* `alpha_complex_3d_persistence` only accepts OFF files in dimension 3.
+* Filtration values are alpha square values.
+
+
+
+## `exact_alpha_complex_3d_persistence` ##
+Same as `alpha_complex_3d_persistence`, but using exact computation. It is slower, but it is necessary when points are on a grid for instance.
+
+
+
+## `weighted_alpha_complex_3d_persistence` ##
+Same as `alpha_complex_3d_persistence`, but using weighted points.
+
+**Usage**
+`weighted_alpha_complex_3d_persistence [options] <input OFF file> <weights input file>`
+where
+`<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+`<input weights file>` is the path to the file containing the weights of the points (one value per line).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-p [ --field-charac ]` (default=11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+
+**Example**
+`weighted_alpha_complex_3d_persistence ../../data/points/tore3D_300.off ../../data/points/tore3D_300.weights -p 2 -m 0.45`
+
+outputs:
+```
+Simplex_tree dim: 3
+2 0 -1 inf
+2 1 -0.931784 0.000103311
+2 1 -0.906588 2.60165e-05
+2 2 -0.43556 0.0393798
+```
+
+N.B.:
+* Weights values are explained on CGAL [Alpha shape](https://doc.cgal.org/latest/Alpha_shapes_3/index.html#title0)
+and [Regular triangulation](https://doc.cgal.org/latest/Triangulation_3/index.html#Triangulation3secclassRegulartriangulation) documentation.
+* Filtration values are alpha square values.
+
+
+## `periodic_alpha_complex_3d_persistence` ##
+Same as `alpha_complex_3d_persistence`, but using periodic alpha shape 3d.
+Refer to the [CGAL's 3D Periodic Triangulations User Manual](https://doc.cgal.org/latest/Periodic_3_triangulation_3/index.html) for more details.
+
+**Usage**
+`periodic_alpha_complex_3d_persistence [options] <input OFF file> <cuboid file>`
+where
+`<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+`<cuboid file>` is the path to the file describing the periodic domain. It must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsIsoCuboid).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-p [ --field-charac ]` (default=11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals
+
+
+**Example**
+`periodic_alpha_complex_3d_persistence ../../data/points/grid_10_10_10_in_0_1.off ../../data/points/iso_cuboid_3_in_0_1.txt -p 3 -m 1.0`
+
+outputs:
+```
+Periodic Delaunay computed.
+Simplex_tree dim: 3
+3 0 0 inf
+3 1 0.0025 inf
+3 1 0.0025 inf
+3 1 0.0025 inf
+3 2 0.005 inf
+3 2 0.005 inf
+3 2 0.005 inf
+3 3 0.0075 inf
+```
+
+Here we retrieve expected Betti numbers on an 3D iso-oriented cuboids:
+```
+Betti numbers[0] = 1
+Betti numbers[1] = 3
+Betti numbers[2] = 3
+Betti numbers[3] = 1
+```
+
+N.B.:
+* Cuboid file must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsIsoCuboid).
+* Filtration values are alpha square values.
+
+
+
+
+## `alpha_complex_persistence` ##
+This program computes the persistent homology with coefficient field Z/pZ of the dD alpha complex built from a dD point cloud. The output diagram contains one bar per line, written with the convention:
+
+`p dim birth death`
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
+
+**Usage**
+`alpha_complex_persistence [options] <input OFF file>`
+where
+`<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-r [ --max-alpha-square-value ]` (default = inf) Maximal alpha square value for the Alpha complex construction.
+* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+
+**Example**
+`alpha_complex_persistence -r 32 -p 2 -m 0.45 ../../data/points/tore3D_300.off`
+
+outputs:
+```
+Alpha complex is of dimension 3 - 9273 simplices - 300 vertices.
+Simplex_tree dim: 3
+2 0 0 inf
+2 1 0.0682162 1.0001
+2 1 0.0934117 1.00003
+2 2 0.56444 1.03938
+```
+
+Here we retrieve expected Betti numbers on a tore 3D:
+```
+Betti numbers[0] = 1
+Betti numbers[1] = 2
+Betti numbers[2] = 1
+```
+
+N.B.:
+* Filtration values are alpha square values.
diff --git a/src/Persistent_cohomology/example/alpha_complex_3d_helper.h b/src/Alpha_complex/utilities/alpha_complex_3d_helper.h index 7865e4ec..6b3b7d5d 100644 --- a/src/Persistent_cohomology/example/alpha_complex_3d_helper.h +++ b/src/Alpha_complex/utilities/alpha_complex_3d_helper.h @@ -23,7 +23,7 @@ #ifndef ALPHA_COMPLEX_3D_HELPER_H_ #define ALPHA_COMPLEX_3D_HELPER_H_ -template<class Vertex_list, class Cell_handle> +template <class Vertex_list, class Cell_handle> Vertex_list from_cell(const Cell_handle& ch) { Vertex_list the_list; for (auto i = 0; i < 4; i++) { @@ -35,7 +35,7 @@ Vertex_list from_cell(const Cell_handle& ch) { return the_list; } -template<class Vertex_list, class Facet> +template <class Vertex_list, class Facet> Vertex_list from_facet(const Facet& fct) { Vertex_list the_list; for (auto i = 0; i < 4; i++) { @@ -49,7 +49,7 @@ Vertex_list from_facet(const Facet& fct) { return the_list; } -template<class Vertex_list, class Edge_3> +template <class Vertex_list, class Edge_3> Vertex_list from_edge(const Edge_3& edg) { Vertex_list the_list; for (auto i = 0; i < 4; i++) { @@ -63,7 +63,7 @@ Vertex_list from_edge(const Edge_3& edg) { return the_list; } -template<class Vertex_list, class Vertex_handle> +template <class Vertex_list, class Vertex_handle> Vertex_list from_vertex(const Vertex_handle& vh) { Vertex_list the_list; #ifdef DEBUG_TRACES diff --git a/src/Persistent_cohomology/example/alpha_complex_3d_persistence.cpp b/src/Alpha_complex/utilities/alpha_complex_3d_persistence.cpp index fd227b82..0a021a0f 100644 --- a/src/Persistent_cohomology/example/alpha_complex_3d_persistence.cpp +++ b/src/Alpha_complex/utilities/alpha_complex_3d_persistence.cpp @@ -20,6 +20,7 @@ * along with this program. If not, see <http://www.gnu.org/licenses/>. */ +#include <boost/program_options.hpp> #include <boost/variant.hpp> #include <gudhi/Simplex_tree.h> @@ -39,6 +40,7 @@ #include <utility> #include <list> #include <vector> +#include <cstdlib> #include "alpha_complex_3d_helper.h" @@ -56,10 +58,10 @@ using Point_3 = Kernel::Point_3; // filtration with alpha values needed type definition using Alpha_value_type = Alpha_shape_3::FT; using Object = CGAL::Object; -using Dispatch = CGAL::Dispatch_output_iterator< - CGAL::cpp11::tuple<Object, Alpha_value_type>, - CGAL::cpp11::tuple<std::back_insert_iterator< std::vector<Object> >, - std::back_insert_iterator< std::vector<Alpha_value_type> > > >; +using Dispatch = + CGAL::Dispatch_output_iterator<CGAL::cpp11::tuple<Object, Alpha_value_type>, + CGAL::cpp11::tuple<std::back_insert_iterator<std::vector<Object> >, + std::back_insert_iterator<std::vector<Alpha_value_type> > > >; using Cell_handle = Alpha_shape_3::Cell_handle; using Facet = Alpha_shape_3::Facet; using Edge_3 = Alpha_shape_3::Edge; @@ -70,41 +72,29 @@ using Vertex_list = std::list<Alpha_shape_3::Vertex_handle>; using ST = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; using Filtration_value = ST::Filtration_value; using Simplex_tree_vertex = ST::Vertex_handle; -using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex >; +using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; using Alpha_shape_simplex_tree_pair = std::pair<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; -using Simplex_tree_vector_vertex = std::vector< Simplex_tree_vertex >; -using PCOH = Gudhi::persistent_cohomology::Persistent_cohomology< ST, Gudhi::persistent_cohomology::Field_Zp >; +using Simplex_tree_vector_vertex = std::vector<Simplex_tree_vertex>; +using Persistent_cohomology = + Gudhi::persistent_cohomology::Persistent_cohomology<ST, Gudhi::persistent_cohomology::Field_Zp>; -void usage(const std::string& progName) { - std::cerr << "Usage: " << progName << - " path_to_file_graph coeff_field_characteristic[integer > 0] min_persistence[float >= -1.0]\n"; - exit(-1); -} - -int main(int argc, char * const argv[]) { - // program args management - if (argc != 4) { - std::cerr << "Error: Number of arguments (" << argc << ") is not correct\n"; - usage(argv[0]); - } +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &output_file_diag, + int &coeff_field_characteristic, Filtration_value &min_persistence); - int coeff_field_characteristic = atoi(argv[2]); +int main(int argc, char **argv) { + std::string off_file_points; + std::string output_file_diag; + int coeff_field_characteristic; + Filtration_value min_persistence; - Filtration_value min_persistence = 0.0; - int returnedScanValue = sscanf(argv[3], "%f", &min_persistence); - if ((returnedScanValue == EOF) || (min_persistence < -1.0)) { - std::cerr << "Error: " << argv[3] << " is not correct\n"; - usage(argv[0]); - } + program_options(argc, argv, off_file_points, output_file_diag, coeff_field_characteristic, min_persistence); - // Read points from file - std::string offInputFile(argv[1]); // Read the OFF file (input file name given as parameter) and triangulate points - Gudhi::Points_3D_off_reader<Point_3> off_reader(offInputFile); + Gudhi::Points_3D_off_reader<Point_3> off_reader(off_file_points); // Check the read operation was correct if (!off_reader.is_valid()) { - std::cerr << "Unable to read file " << offInputFile << std::endl; - usage(argv[0]); + std::cerr << "Unable to read file " << off_file_points << std::endl; + exit(-1); } // Retrieve the triangulation @@ -142,28 +132,28 @@ int main(int argc, char * const argv[]) { Filtration_value filtration_max = 0.0; for (auto object_iterator : the_objects) { // Retrieve Alpha shape vertex list from object - if (const Cell_handle * cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { + if (const Cell_handle* cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { vertex_list = from_cell<Vertex_list, Cell_handle>(*cell); count_cells++; if (dim_max < 3) { // Cell is of dim 3 dim_max = 3; } - } else if (const Facet * facet = CGAL::object_cast<Facet>(&object_iterator)) { + } else if (const Facet* facet = CGAL::object_cast<Facet>(&object_iterator)) { vertex_list = from_facet<Vertex_list, Facet>(*facet); count_facets++; if (dim_max < 2) { // Facet is of dim 2 dim_max = 2; } - } else if (const Edge_3 * edge = CGAL::object_cast<Edge_3>(&object_iterator)) { + } else if (const Edge_3* edge = CGAL::object_cast<Edge_3>(&object_iterator)) { vertex_list = from_edge<Vertex_list, Edge_3>(*edge); count_edges++; if (dim_max < 1) { // Edge_3 is of dim 1 dim_max = 1; } - } else if (const Vertex_handle * vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { + } else if (const Vertex_handle* vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { count_vertices++; vertex_list = from_vertex<Vertex_list, Vertex_handle>(*vertex); } @@ -202,8 +192,6 @@ int main(int argc, char * const argv[]) { else std::cout << "This shall not happen" << std::endl; } - simplex_tree.set_filtration(filtration_max); - simplex_tree.set_dimension(dim_max); #ifdef DEBUG_TRACES std::cout << "vertices \t\t" << count_vertices << std::endl; @@ -211,12 +199,10 @@ int main(int argc, char * const argv[]) { std::cout << "facets \t\t" << count_facets << std::endl; std::cout << "cells \t\t" << count_cells << std::endl; - std::cout << "Information of the Simplex Tree: " << std::endl; std::cout << " Number of vertices = " << simplex_tree.num_vertices() << " "; std::cout << " Number of simplices = " << simplex_tree.num_simplices() << std::endl << std::endl; std::cout << " Dimension = " << simplex_tree.dimension() << " "; - std::cout << " filtration = " << simplex_tree.filtration() << std::endl << std::endl; #endif // DEBUG_TRACES #ifdef DEBUG_TRACES @@ -231,13 +217,64 @@ int main(int argc, char * const argv[]) { std::cout << "Simplex_tree dim: " << simplex_tree.dimension() << std::endl; // Compute the persistence diagram of the complex - PCOH pcoh(simplex_tree); + Persistent_cohomology pcoh(simplex_tree, true); // initializes the coefficient field for homology pcoh.init_coefficients(coeff_field_characteristic); pcoh.compute_persistent_cohomology(min_persistence); - pcoh.output_diagram(); + // Output the diagram in filediag + if (output_file_diag.empty()) { + pcoh.output_diagram(); + } else { + std::cout << "Result in file: " << output_file_diag << std::endl; + std::ofstream out(output_file_diag); + pcoh.output_diagram(out); + out.close(); + } return 0; } + +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &output_file_diag, + int &coeff_field_characteristic, Filtration_value &min_persistence) { + 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 file containing a point set. Format is one point per line: X1 ... Xd "); + + po::options_description visible("Allowed options", 100); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "field-charac,p", po::value<int>(&coeff_field_characteristic)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); + + po::positional_options_description pos; + pos.add("input-file", 1); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map 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")) { + std::cout << std::endl; + std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::cout << "of a 3D Alpha complex defined on a set of input points.\n \n"; + std::cout << "The output diagram contains one bar per line, written with the convention: \n"; + std::cout << " p dim b d \n"; + std::cout << "where dim is the dimension of the homological feature,\n"; + std::cout << "b and d are respectively the birth and death of the feature and \n"; + std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} diff --git a/src/Persistent_cohomology/example/alpha_complex_persistence.cpp b/src/Alpha_complex/utilities/alpha_complex_persistence.cpp index 9e84e91f..2105220a 100644 --- a/src/Persistent_cohomology/example/alpha_complex_persistence.cpp +++ b/src/Alpha_complex/utilities/alpha_complex_persistence.cpp @@ -14,12 +14,9 @@ using Simplex_tree = Gudhi::Simplex_tree<>; using Filtration_value = Simplex_tree::Filtration_value; -void program_options(int argc, char * argv[] - , std::string & off_file_points - , std::string & output_file_diag - , Filtration_value & alpha_square_max_value - , int & coeff_field_characteristic - , Filtration_value & min_persistence); +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &output_file_diag, + Filtration_value &alpha_square_max_value, int &coeff_field_characteristic, + Filtration_value &min_persistence); int main(int argc, char **argv) { std::string off_file_points; @@ -28,13 +25,13 @@ int main(int argc, char **argv) { int coeff_field_characteristic; Filtration_value min_persistence; - program_options(argc, argv, off_file_points, output_file_diag, alpha_square_max_value, - coeff_field_characteristic, min_persistence); + program_options(argc, argv, off_file_points, output_file_diag, alpha_square_max_value, coeff_field_characteristic, + min_persistence); // ---------------------------------------------------------------------------- // Init of an alpha complex from an OFF file // ---------------------------------------------------------------------------- - using Kernel = CGAL::Epick_d< CGAL::Dynamic_dimension_tag >; + using Kernel = CGAL::Epick_d<CGAL::Dynamic_dimension_tag>; Gudhi::alpha_complex::Alpha_complex<Kernel> alpha_complex_from_file(off_file_points); Simplex_tree simplex; @@ -42,17 +39,16 @@ int main(int argc, char **argv) { // ---------------------------------------------------------------------------- // Display information about the alpha complex // ---------------------------------------------------------------------------- - std::cout << "Simplicial complex is of dimension " << simplex.dimension() << - " - " << simplex.num_simplices() << " simplices - " << - simplex.num_vertices() << " vertices." << std::endl; + std::cout << "Simplicial complex is of dimension " << simplex.dimension() << " - " << simplex.num_simplices() + << " simplices - " << simplex.num_vertices() << " vertices." << std::endl; // Sort the simplices in the order of the filtration simplex.initialize_filtration(); std::cout << "Simplex_tree dim: " << simplex.dimension() << std::endl; // Compute the persistence diagram of the complex - Gudhi::persistent_cohomology::Persistent_cohomology< Simplex_tree, - Gudhi::persistent_cohomology::Field_Zp > pcoh(simplex); + Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Gudhi::persistent_cohomology::Field_Zp> pcoh( + simplex); // initializes the coefficient field for homology pcoh.init_coefficients(coeff_field_characteristic); @@ -72,30 +68,26 @@ int main(int argc, char **argv) { return 0; } -void program_options(int argc, char * argv[] - , std::string & off_file_points - , std::string & output_file_diag - , Filtration_value & alpha_square_max_value - , int & coeff_field_characteristic - , Filtration_value & min_persistence) { +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &output_file_diag, + Filtration_value &alpha_square_max_value, int &coeff_field_characteristic, + Filtration_value &min_persistence) { 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 file containing a point set. Format is one point per line: X1 ... Xd "); + hidden.add_options()("input-file", po::value<std::string>(&off_file_points), + "Name of file containing a point set. Format is one point per line: X1 ... Xd "); po::options_description visible("Allowed options", 100); - visible.add_options() - ("help,h", "produce help message") - ("output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout") - ("max-alpha-square-value,r", - po::value<Filtration_value>(&alpha_square_max_value)->default_value(std::numeric_limits<Filtration_value>::infinity()), - "Maximal alpha square value for the Alpha complex construction.") - ("field-charac,p", po::value<int>(&coeff_field_characteristic)->default_value(11), - "Characteristic p of the coefficient field Z/pZ for computing homology.") - ("min-persistence,m", po::value<Filtration_value>(&min_persistence), - "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length intervals"); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "max-alpha-square-value,r", po::value<Filtration_value>(&alpha_square_max_value) + ->default_value(std::numeric_limits<Filtration_value>::infinity()), + "Maximal alpha square value for the Alpha complex construction.")( + "field-charac,p", po::value<int>(&coeff_field_characteristic)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); po::positional_options_description pos; pos.add("input-file", 1); @@ -104,8 +96,7 @@ 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")) { diff --git a/src/Persistent_cohomology/example/exact_alpha_complex_3d_persistence.cpp b/src/Alpha_complex/utilities/exact_alpha_complex_3d_persistence.cpp index 8a335075..9a266418 100644 --- a/src/Persistent_cohomology/example/exact_alpha_complex_3d_persistence.cpp +++ b/src/Alpha_complex/utilities/exact_alpha_complex_3d_persistence.cpp @@ -4,7 +4,7 @@ * * Author(s): Vincent Rouvreau * - * Copyright (C) 2014 INRIA Saclay (France) + * Copyright (C) 2014 INRIA * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by @@ -20,6 +20,7 @@ * along with this program. If not, see <http://www.gnu.org/licenses/>. */ +#include <boost/program_options.hpp> #include <boost/variant.hpp> #include <gudhi/Simplex_tree.h> @@ -39,6 +40,7 @@ #include <utility> #include <list> #include <vector> +#include <cstdlib> #include "alpha_complex_3d_helper.h" @@ -57,10 +59,10 @@ using Point_3 = Kernel::Point_3; // filtration with alpha values needed type definition using Alpha_value_type = Alpha_shape_3::FT; using Object = CGAL::Object; -using Dispatch = CGAL::Dispatch_output_iterator< - CGAL::cpp11::tuple<Object, Alpha_value_type>, - CGAL::cpp11::tuple<std::back_insert_iterator< std::vector<Object> >, - std::back_insert_iterator< std::vector<Alpha_value_type> > > >; +using Dispatch = + CGAL::Dispatch_output_iterator<CGAL::cpp11::tuple<Object, Alpha_value_type>, + CGAL::cpp11::tuple<std::back_insert_iterator<std::vector<Object> >, + std::back_insert_iterator<std::vector<Alpha_value_type> > > >; using Cell_handle = Alpha_shape_3::Cell_handle; using Facet = Alpha_shape_3::Facet; using Edge_3 = Alpha_shape_3::Edge; @@ -71,41 +73,29 @@ using Vertex_list = std::list<Vertex_handle>; using ST = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; using Filtration_value = ST::Filtration_value; using Simplex_tree_vertex = ST::Vertex_handle; -using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex >; +using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; using Alpha_shape_simplex_tree_pair = std::pair<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; -using Simplex_tree_vector_vertex = std::vector< Simplex_tree_vertex >; -using PCOH = Gudhi::persistent_cohomology::Persistent_cohomology< ST, Gudhi::persistent_cohomology::Field_Zp >; +using Simplex_tree_vector_vertex = std::vector<Simplex_tree_vertex>; +using Persistent_cohomology = + Gudhi::persistent_cohomology::Persistent_cohomology<ST, Gudhi::persistent_cohomology::Field_Zp>; -void usage(char * const progName) { - std::cerr << "Usage: " << progName << - " path_to_file_graph coeff_field_characteristic[integer > 0] min_persistence[float >= -1.0]\n"; - exit(-1); -} - -int main(int argc, char * const argv[]) { - // program args management - if (argc != 4) { - std::cerr << "Error: Number of arguments (" << argc << ") is not correct\n"; - usage(argv[0]); - } +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &output_file_diag, + int &coeff_field_characteristic, Filtration_value &min_persistence); - int coeff_field_characteristic = atoi(argv[2]); +int main(int argc, char **argv) { + std::string off_file_points; + std::string output_file_diag; + int coeff_field_characteristic; + Filtration_value min_persistence; - Filtration_value min_persistence = 0.0; - int returnedScanValue = sscanf(argv[3], "%f", &min_persistence); - if ((returnedScanValue == EOF) || (min_persistence < -1.0)) { - std::cerr << "Error: " << argv[3] << " is not correct\n"; - usage(argv[0]); - } + program_options(argc, argv, off_file_points, output_file_diag, coeff_field_characteristic, min_persistence); - // Read points from file - std::string offInputFile(argv[1]); // Read the OFF file (input file name given as parameter) and triangulate points - Gudhi::Points_3D_off_reader<Point_3> off_reader(offInputFile); + Gudhi::Points_3D_off_reader<Point_3> off_reader(off_file_points); // Check the read operation was correct if (!off_reader.is_valid()) { - std::cerr << "Unable to read file " << offInputFile << std::endl; - usage(argv[0]); + std::cerr << "Unable to read file " << off_file_points << std::endl; + exit(-1); } // Retrieve the triangulation @@ -143,28 +133,28 @@ int main(int argc, char * const argv[]) { Filtration_value filtration_max = 0.0; for (auto object_iterator : the_objects) { // Retrieve Alpha shape vertex list from object - if (const Cell_handle * cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { + if (const Cell_handle* cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { vertex_list = from_cell<Vertex_list, Cell_handle>(*cell); count_cells++; if (dim_max < 3) { // Cell is of dim 3 dim_max = 3; } - } else if (const Facet * facet = CGAL::object_cast<Facet>(&object_iterator)) { + } else if (const Facet* facet = CGAL::object_cast<Facet>(&object_iterator)) { vertex_list = from_facet<Vertex_list, Facet>(*facet); count_facets++; if (dim_max < 2) { // Facet is of dim 2 dim_max = 2; } - } else if (const Edge_3 * edge = CGAL::object_cast<Edge_3>(&object_iterator)) { + } else if (const Edge_3* edge = CGAL::object_cast<Edge_3>(&object_iterator)) { vertex_list = from_edge<Vertex_list, Edge_3>(*edge); count_edges++; if (dim_max < 1) { // Edge_3 is of dim 1 dim_max = 1; } - } else if (const Vertex_handle * vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { + } else if (const Vertex_handle* vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { count_vertices++; vertex_list = from_vertex<Vertex_list, Vertex_handle>(*vertex); } @@ -204,8 +194,6 @@ int main(int argc, char * const argv[]) { else std::cout << "This shall not happen" << std::endl; } - simplex_tree.set_filtration(filtration_max); - simplex_tree.set_dimension(dim_max); #ifdef DEBUG_TRACES std::cout << "vertices \t\t" << count_vertices << std::endl; @@ -213,12 +201,10 @@ int main(int argc, char * const argv[]) { std::cout << "facets \t\t" << count_facets << std::endl; std::cout << "cells \t\t" << count_cells << std::endl; - std::cout << "Information of the Simplex Tree: " << std::endl; std::cout << " Number of vertices = " << simplex_tree.num_vertices() << " "; std::cout << " Number of simplices = " << simplex_tree.num_simplices() << std::endl << std::endl; std::cout << " Dimension = " << simplex_tree.dimension() << " "; - std::cout << " filtration = " << simplex_tree.filtration() << std::endl << std::endl; #endif // DEBUG_TRACES #ifdef DEBUG_TRACES @@ -233,13 +219,64 @@ int main(int argc, char * const argv[]) { std::cout << "Simplex_tree dim: " << simplex_tree.dimension() << std::endl; // Compute the persistence diagram of the complex - PCOH pcoh(simplex_tree); + Persistent_cohomology pcoh(simplex_tree, true); // initializes the coefficient field for homology pcoh.init_coefficients(coeff_field_characteristic); pcoh.compute_persistent_cohomology(min_persistence); - pcoh.output_diagram(); + // Output the diagram in filediag + if (output_file_diag.empty()) { + pcoh.output_diagram(); + } else { + std::cout << "Result in file: " << output_file_diag << std::endl; + std::ofstream out(output_file_diag); + pcoh.output_diagram(out); + out.close(); + } return 0; } + +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &output_file_diag, + int &coeff_field_characteristic, Filtration_value &min_persistence) { + 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 file containing a point set. Format is one point per line: X1 ... Xd "); + + po::options_description visible("Allowed options", 100); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "field-charac,p", po::value<int>(&coeff_field_characteristic)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); + + po::positional_options_description pos; + pos.add("input-file", 1); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map 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")) { + std::cout << std::endl; + std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::cout << "of a 3D Alpha complex defined on a set of input points.\n \n"; + std::cout << "The output diagram contains one bar per line, written with the convention: \n"; + std::cout << " p dim b d \n"; + std::cout << "where dim is the dimension of the homological feature,\n"; + std::cout << "b and d are respectively the birth and death of the feature and \n"; + std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} diff --git a/src/Alpha_complex/utilities/periodic_alpha_complex_3d_persistence.cpp b/src/Alpha_complex/utilities/periodic_alpha_complex_3d_persistence.cpp new file mode 100644 index 00000000..186a58f8 --- /dev/null +++ b/src/Alpha_complex/utilities/periodic_alpha_complex_3d_persistence.cpp @@ -0,0 +1,308 @@ +/* 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): Vincent Rouvreau + * + * Copyright (C) 2014 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <boost/program_options.hpp> +#include <boost/variant.hpp> + +#include <gudhi/Simplex_tree.h> +#include <gudhi/Persistent_cohomology.h> +#include <gudhi/Points_3D_off_io.h> + +#include <CGAL/Exact_predicates_inexact_constructions_kernel.h> +#include <CGAL/Periodic_3_Delaunay_triangulation_traits_3.h> +#include <CGAL/Periodic_3_Delaunay_triangulation_3.h> +#include <CGAL/Alpha_shape_3.h> +#include <CGAL/iterator.h> + +#include <fstream> +#include <cmath> +#include <string> +#include <tuple> +#include <map> +#include <utility> +#include <list> +#include <vector> +#include <cstdlib> + +#include "alpha_complex_3d_helper.h" + +// Traits +using K = CGAL::Exact_predicates_inexact_constructions_kernel; +using PK = CGAL::Periodic_3_Delaunay_triangulation_traits_3<K>; +// Vertex type +using DsVb = CGAL::Periodic_3_triangulation_ds_vertex_base_3<>; +using Vb = CGAL::Triangulation_vertex_base_3<PK, DsVb>; +using AsVb = CGAL::Alpha_shape_vertex_base_3<PK, Vb>; +// Cell type +using DsCb = CGAL::Periodic_3_triangulation_ds_cell_base_3<>; +using Cb = CGAL::Triangulation_cell_base_3<PK, DsCb>; +using AsCb = CGAL::Alpha_shape_cell_base_3<PK, Cb>; +using Tds = CGAL::Triangulation_data_structure_3<AsVb, AsCb>; +using P3DT3 = CGAL::Periodic_3_Delaunay_triangulation_3<PK, Tds>; +using Alpha_shape_3 = CGAL::Alpha_shape_3<P3DT3>; +using Point_3 = PK::Point_3; + +// filtration with alpha values needed type definition +using Alpha_value_type = Alpha_shape_3::FT; +using Object = CGAL::Object; +using Dispatch = + CGAL::Dispatch_output_iterator<CGAL::cpp11::tuple<Object, Alpha_value_type>, + CGAL::cpp11::tuple<std::back_insert_iterator<std::vector<Object> >, + std::back_insert_iterator<std::vector<Alpha_value_type> > > >; +using Cell_handle = Alpha_shape_3::Cell_handle; +using Facet = Alpha_shape_3::Facet; +using Edge_3 = Alpha_shape_3::Edge; +using Vertex_handle = Alpha_shape_3::Vertex_handle; +using Vertex_list = std::list<Alpha_shape_3::Vertex_handle>; + +// gudhi type definition +using ST = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; +using Filtration_value = ST::Filtration_value; +using Simplex_tree_vertex = ST::Vertex_handle; +using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; +using Alpha_shape_simplex_tree_pair = std::pair<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; +using Simplex_tree_vector_vertex = std::vector<Simplex_tree_vertex>; +using Persistent_cohomology = + Gudhi::persistent_cohomology::Persistent_cohomology<ST, Gudhi::persistent_cohomology::Field_Zp>; + +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &cuboid_file, + std::string &output_file_diag, int &coeff_field_characteristic, Filtration_value &min_persistence); + +int main(int argc, char **argv) { + std::string off_file_points; + std::string cuboid_file; + std::string output_file_diag; + int coeff_field_characteristic; + Filtration_value min_persistence; + + program_options(argc, argv, off_file_points, cuboid_file, output_file_diag, coeff_field_characteristic, + min_persistence); + + // Read the OFF file (input file name given as parameter) and triangulate points + Gudhi::Points_3D_off_reader<Point_3> off_reader(off_file_points); + // Check the read operation was correct + if (!off_reader.is_valid()) { + std::cerr << "Unable to read OFF file " << off_file_points << std::endl; + exit(-1); + } + + // Read iso_cuboid_3 information from file + std::ifstream iso_cuboid_str(cuboid_file); + double x_min, y_min, z_min, x_max, y_max, z_max; + if (iso_cuboid_str.good()) { + iso_cuboid_str >> x_min >> y_min >> z_min >> x_max >> y_max >> z_max; + } else { + std::cerr << "Unable to read file " << cuboid_file << std::endl; + exit(-1); + } + + // Retrieve the triangulation + std::vector<Point_3> lp = off_reader.get_point_cloud(); + + // Define the periodic cube + P3DT3 pdt(PK::Iso_cuboid_3(x_min, y_min, z_min, x_max, y_max, z_max)); + // Heuristic for inserting large point sets (if pts is reasonably large) + pdt.insert(lp.begin(), lp.end(), true); + // As pdt won't be modified anymore switch to 1-sheeted cover if possible + if (pdt.is_triangulation_in_1_sheet()) pdt.convert_to_1_sheeted_covering(); + std::cout << "Periodic Delaunay computed." << std::endl; + + // alpha shape construction from points. CGAL has a strange behavior in REGULARIZED mode. This is the default mode + // Maybe need to set it to GENERAL mode + Alpha_shape_3 as(pdt, 0, Alpha_shape_3::GENERAL); + + // filtration with alpha values from alpha shape + std::vector<Object> the_objects; + std::vector<Alpha_value_type> the_alpha_values; + + Dispatch disp = CGAL::dispatch_output<Object, Alpha_value_type>(std::back_inserter(the_objects), + std::back_inserter(the_alpha_values)); + + as.filtration_with_alpha_values(disp); +#ifdef DEBUG_TRACES + std::cout << "filtration_with_alpha_values returns : " << the_objects.size() << " objects" << std::endl; +#endif // DEBUG_TRACES + + Alpha_shape_3::size_type count_vertices = 0; + Alpha_shape_3::size_type count_edges = 0; + Alpha_shape_3::size_type count_facets = 0; + Alpha_shape_3::size_type count_cells = 0; + + // Loop on objects vector + Vertex_list vertex_list; + ST simplex_tree; + Alpha_shape_simplex_tree_map map_cgal_simplex_tree; + std::vector<Alpha_value_type>::iterator the_alpha_value_iterator = the_alpha_values.begin(); + int dim_max = 0; + Filtration_value filtration_max = 0.0; + for (auto object_iterator : the_objects) { + // Retrieve Alpha shape vertex list from object + if (const Cell_handle* cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { + vertex_list = from_cell<Vertex_list, Cell_handle>(*cell); + count_cells++; + if (dim_max < 3) { + // Cell is of dim 3 + dim_max = 3; + } + } else if (const Facet* facet = CGAL::object_cast<Facet>(&object_iterator)) { + vertex_list = from_facet<Vertex_list, Facet>(*facet); + count_facets++; + if (dim_max < 2) { + // Facet is of dim 2 + dim_max = 2; + } + } else if (const Edge_3* edge = CGAL::object_cast<Edge_3>(&object_iterator)) { + vertex_list = from_edge<Vertex_list, Edge_3>(*edge); + count_edges++; + if (dim_max < 1) { + // Edge_3 is of dim 1 + dim_max = 1; + } + } else if (const Vertex_handle* vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { + count_vertices++; + vertex_list = from_vertex<Vertex_list, Vertex_handle>(*vertex); + } + // Construction of the vector of simplex_tree vertex from list of alpha_shapes vertex + Simplex_tree_vector_vertex the_simplex_tree; + for (auto the_alpha_shape_vertex : vertex_list) { + Alpha_shape_simplex_tree_map::iterator the_map_iterator = map_cgal_simplex_tree.find(the_alpha_shape_vertex); + if (the_map_iterator == map_cgal_simplex_tree.end()) { + // alpha shape not found + Simplex_tree_vertex vertex = map_cgal_simplex_tree.size(); +#ifdef DEBUG_TRACES + std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] not found - insert " << vertex << std::endl; +#endif // DEBUG_TRACES + the_simplex_tree.push_back(vertex); + map_cgal_simplex_tree.insert(Alpha_shape_simplex_tree_pair(the_alpha_shape_vertex, vertex)); + } else { + // alpha shape found + Simplex_tree_vertex vertex = the_map_iterator->second; +#ifdef DEBUG_TRACES + std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] found in " << vertex << std::endl; +#endif // DEBUG_TRACES + the_simplex_tree.push_back(vertex); + } + } + // Construction of the simplex_tree + Filtration_value filtr = /*std::sqrt*/ (*the_alpha_value_iterator); +#ifdef DEBUG_TRACES + std::cout << "filtration = " << filtr << std::endl; +#endif // DEBUG_TRACES + if (filtr > filtration_max) { + filtration_max = filtr; + } + simplex_tree.insert_simplex(the_simplex_tree, filtr); + if (the_alpha_value_iterator != the_alpha_values.end()) + ++the_alpha_value_iterator; + else + std::cout << "This shall not happen" << std::endl; + } + +#ifdef DEBUG_TRACES + std::cout << "vertices \t\t" << count_vertices << std::endl; + std::cout << "edges \t\t" << count_edges << std::endl; + std::cout << "facets \t\t" << count_facets << std::endl; + std::cout << "cells \t\t" << count_cells << std::endl; + + std::cout << "Information of the Simplex Tree: " << std::endl; + std::cout << " Number of vertices = " << simplex_tree.num_vertices() << " "; + std::cout << " Number of simplices = " << simplex_tree.num_simplices() << std::endl << std::endl; + std::cout << " Dimension = " << simplex_tree.dimension() << " "; +#endif // DEBUG_TRACES + +#ifdef DEBUG_TRACES + std::cout << "Iterator on vertices: " << std::endl; + for (auto vertex : simplex_tree.complex_vertex_range()) { + std::cout << vertex << " "; + } +#endif // DEBUG_TRACES + + // Sort the simplices in the order of the filtration + simplex_tree.initialize_filtration(); + + std::cout << "Simplex_tree dim: " << simplex_tree.dimension() << std::endl; + // Compute the persistence diagram of the complex + Persistent_cohomology pcoh(simplex_tree, true); + // initializes the coefficient field for homology + pcoh.init_coefficients(coeff_field_characteristic); + + pcoh.compute_persistent_cohomology(min_persistence); + + // Output the diagram in filediag + if (output_file_diag.empty()) { + pcoh.output_diagram(); + } else { + std::cout << "Result in file: " << output_file_diag << std::endl; + std::ofstream out(output_file_diag); + pcoh.output_diagram(out); + out.close(); + } + + return 0; +} + +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &cuboid_file, + std::string &output_file_diag, int &coeff_field_characteristic, + Filtration_value &min_persistence) { + 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 file containing a point set. Format is one point per line: X1 ... Xd ")( + "cuboid-file", po::value<std::string>(&cuboid_file), + "Name of file describing the periodic domain. Format is: min_hx min_hy min_hz\nmax_hx max_hy max_hz"); + + po::options_description visible("Allowed options", 100); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "field-charac,p", po::value<int>(&coeff_field_characteristic)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); + + po::positional_options_description pos; + pos.add("input-file", 1); + pos.add("cuboid-file", 2); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map 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") || !vm.count("cuboid-file")) { + std::cout << std::endl; + std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::cout << "of a periodic 3D Alpha complex defined on a set of input points.\n \n"; + std::cout << "The output diagram contains one bar per line, written with the convention: \n"; + std::cout << " p dim b d \n"; + std::cout << "where dim is the dimension of the homological feature,\n"; + std::cout << "b and d are respectively the birth and death of the feature and \n"; + std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::cout << "Usage: " << argv[0] << " [options] input-file cuboid-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} diff --git a/src/Persistent_cohomology/example/weighted_alpha_complex_3d_persistence.cpp b/src/Alpha_complex/utilities/weighted_alpha_complex_3d_persistence.cpp index 34b90933..0e73a99b 100644 --- a/src/Persistent_cohomology/example/weighted_alpha_complex_3d_persistence.cpp +++ b/src/Alpha_complex/utilities/weighted_alpha_complex_3d_persistence.cpp @@ -20,18 +20,24 @@ * along with this program. If not, see <http://www.gnu.org/licenses/>. */ +#include <boost/program_options.hpp> #include <boost/variant.hpp> #include <gudhi/Simplex_tree.h> #include <gudhi/Persistent_cohomology.h> #include <gudhi/Points_3D_off_io.h> +#include <CGAL/config.h> #include <CGAL/Exact_predicates_inexact_constructions_kernel.h> -#include <CGAL/Regular_triangulation_euclidean_traits_3.h> #include <CGAL/Regular_triangulation_3.h> #include <CGAL/Alpha_shape_3.h> #include <CGAL/iterator.h> +// For CGAL < 4.11 +#if CGAL_VERSION_NR < 1041100000 +#include <CGAL/Regular_triangulation_euclidean_traits_3.h> +#endif // CGAL_VERSION_NR < 1041100000 + #include <fstream> #include <cmath> #include <string> @@ -44,26 +50,44 @@ #include "alpha_complex_3d_helper.h" -// Traits +// Alpha_shape_3 templates type definitions using Kernel = CGAL::Exact_predicates_inexact_constructions_kernel; + +// For CGAL < 4.11 +#if CGAL_VERSION_NR < 1041100000 using Gt = CGAL::Regular_triangulation_euclidean_traits_3<Kernel>; using Vb = CGAL::Alpha_shape_vertex_base_3<Gt>; using Fb = CGAL::Alpha_shape_cell_base_3<Gt>; using Tds = CGAL::Triangulation_data_structure_3<Vb, Fb>; using Triangulation_3 = CGAL::Regular_triangulation_3<Gt, Tds>; -using Alpha_shape_3 = CGAL::Alpha_shape_3<Triangulation_3>; // From file type definition using Point_3 = Gt::Bare_point; using Weighted_point_3 = Gt::Weighted_point; +// For CGAL >= 4.11 +#else // CGAL_VERSION_NR < 1041100000 +using Rvb = CGAL::Regular_triangulation_vertex_base_3<Kernel>; +using Vb = CGAL::Alpha_shape_vertex_base_3<Kernel,Rvb>; +using Rcb = CGAL::Regular_triangulation_cell_base_3<Kernel>; +using Cb = CGAL::Alpha_shape_cell_base_3<Kernel,Rcb>; +using Tds = CGAL::Triangulation_data_structure_3<Vb,Cb>; +using Triangulation_3 = CGAL::Regular_triangulation_3<Kernel,Tds>; + +// From file type definition +using Point_3 = Triangulation_3::Bare_point; +using Weighted_point_3 = Triangulation_3::Weighted_point; +#endif // CGAL_VERSION_NR < 1041100000 + +using Alpha_shape_3 = CGAL::Alpha_shape_3<Triangulation_3>; + // filtration with alpha values needed type definition using Alpha_value_type = Alpha_shape_3::FT; using Object = CGAL::Object; -using Dispatch = CGAL::Dispatch_output_iterator< - CGAL::cpp11::tuple<Object, Alpha_value_type>, - CGAL::cpp11::tuple<std::back_insert_iterator< std::vector<Object> >, - std::back_insert_iterator< std::vector<Alpha_value_type> > > >; +using Dispatch = + CGAL::Dispatch_output_iterator<CGAL::cpp11::tuple<Object, Alpha_value_type>, + CGAL::cpp11::tuple<std::back_insert_iterator<std::vector<Object> >, + std::back_insert_iterator<std::vector<Alpha_value_type> > > >; using Cell_handle = Alpha_shape_3::Cell_handle; using Facet = Alpha_shape_3::Facet; using Edge_3 = Alpha_shape_3::Edge; @@ -74,63 +98,59 @@ using Vertex_list = std::list<Alpha_shape_3::Vertex_handle>; using ST = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; using Filtration_value = ST::Filtration_value; using Simplex_tree_vertex = ST::Vertex_handle; -using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex >; +using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; using Alpha_shape_simplex_tree_pair = std::pair<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; -using Simplex_tree_vector_vertex = std::vector< Simplex_tree_vertex >; -using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology< - ST, Gudhi::persistent_cohomology::Field_Zp >; - -void usage(char * const progName) { - std::cerr << "Usage: " << progName << - " path_to_file_graph path_to_weight_file coeff_field_characteristic[integer > 0] min_persistence[float >= -1.0]\n"; - exit(-1); -} +using Simplex_tree_vector_vertex = std::vector<Simplex_tree_vertex>; +using Persistent_cohomology = + Gudhi::persistent_cohomology::Persistent_cohomology<ST, Gudhi::persistent_cohomology::Field_Zp>; -int main(int argc, char * const argv[]) { - // program args management - if (argc != 5) { - std::cerr << "Error: Number of arguments (" << argc << ") is not correct\n"; - usage(argv[0]); - } +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &weight_file, + std::string &output_file_diag, int &coeff_field_characteristic, Filtration_value &min_persistence); + +int main(int argc, char **argv) { + std::string off_file_points; + std::string weight_file; + std::string output_file_diag; + int coeff_field_characteristic; + Filtration_value min_persistence; - int coeff_field_characteristic = atoi(argv[3]); - Filtration_value min_persistence = strtof(argv[4], nullptr); + program_options(argc, argv, off_file_points, weight_file, output_file_diag, coeff_field_characteristic, + min_persistence); - // Read points from file - std::string offInputFile(argv[1]); // Read the OFF file (input file name given as parameter) and triangulate points - Gudhi::Points_3D_off_reader<Point_3> off_reader(offInputFile); + Gudhi::Points_3D_off_reader<Point_3> off_reader(off_file_points); // Check the read operation was correct if (!off_reader.is_valid()) { - std::cerr << "Unable to read file " << offInputFile << std::endl; - usage(argv[0]); + std::cerr << "Unable to read OFF file " << off_file_points << std::endl; + exit(-1); } // Retrieve the triangulation std::vector<Point_3> lp = off_reader.get_point_cloud(); // Read weights information from file - std::ifstream weights_ifstr(argv[2]); + std::ifstream weights_ifstr(weight_file); std::vector<Weighted_point_3> wp; if (weights_ifstr.good()) { double weight = 0.0; std::size_t index = 0; + wp.reserve(lp.size()); // Attempt read the weight in a double format, return false if it fails while ((weights_ifstr >> weight) && (index < lp.size())) { wp.push_back(Weighted_point_3(lp[index], weight)); index++; } if (index != lp.size()) { - std::cerr << "Bad number of weights in file " << argv[2] << std::endl; - usage(argv[0]); + std::cerr << "Bad number of weights in file " << weight_file << std::endl; + exit(-1); } } else { - std::cerr << "Unable to read file " << argv[2] << std::endl; - usage(argv[0]); + std::cerr << "Unable to read weights file " << weight_file << std::endl; + exit(-1); } // alpha shape construction from points. CGAL has a strange behavior in REGULARIZED mode. - Alpha_shape_3 as(lp.begin(), lp.end(), 0, Alpha_shape_3::GENERAL); + Alpha_shape_3 as(wp.begin(), wp.end(), 0, Alpha_shape_3::GENERAL); #ifdef DEBUG_TRACES std::cout << "Alpha shape computed in GENERAL mode" << std::endl; #endif // DEBUG_TRACES @@ -161,29 +181,28 @@ int main(int argc, char * const argv[]) { Filtration_value filtration_max = 0.0; for (auto object_iterator : the_objects) { // Retrieve Alpha shape vertex list from object - if (const Cell_handle * cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { + if (const Cell_handle* cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { vertex_list = from_cell<Vertex_list, Cell_handle>(*cell); count_cells++; if (dim_max < 3) { // Cell is of dim 3 dim_max = 3; } - } else if (const Facet * facet = CGAL::object_cast<Facet>(&object_iterator)) { + } else if (const Facet* facet = CGAL::object_cast<Facet>(&object_iterator)) { vertex_list = from_facet<Vertex_list, Facet>(*facet); count_facets++; if (dim_max < 2) { // Facet is of dim 2 dim_max = 2; } - } else if (const Edge_3 * edge = CGAL::object_cast<Edge_3>(&object_iterator)) { + } else if (const Edge_3* edge = CGAL::object_cast<Edge_3>(&object_iterator)) { vertex_list = from_edge<Vertex_list, Edge_3>(*edge); count_edges++; if (dim_max < 1) { // Edge_3 is of dim 1 dim_max = 1; } - } else if (const Alpha_shape_3::Vertex_handle * vertex = - CGAL::object_cast<Alpha_shape_3::Vertex_handle>(&object_iterator)) { + } else if (const Vertex_handle* vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { count_vertices++; vertex_list = from_vertex<Vertex_list, Vertex_handle>(*vertex); } @@ -209,7 +228,7 @@ int main(int argc, char * const argv[]) { } } // Construction of the simplex_tree - Filtration_value filtr = /*std::sqrt*/(*the_alpha_value_iterator); + Filtration_value filtr = /*std::sqrt*/ (*the_alpha_value_iterator); #ifdef DEBUG_TRACES std::cout << "filtration = " << filtr << std::endl; #endif // DEBUG_TRACES @@ -222,8 +241,6 @@ int main(int argc, char * const argv[]) { else std::cout << "This shall not happen" << std::endl; } - simplex_tree.set_filtration(filtration_max); - simplex_tree.set_dimension(dim_max); #ifdef DEBUG_TRACES std::cout << "vertices \t\t" << count_vertices << std::endl; @@ -231,12 +248,10 @@ int main(int argc, char * const argv[]) { std::cout << "facets \t\t" << count_facets << std::endl; std::cout << "cells \t\t" << count_cells << std::endl; - std::cout << "Information of the Simplex Tree: " << std::endl; std::cout << " Number of vertices = " << simplex_tree.num_vertices() << " "; std::cout << " Number of simplices = " << simplex_tree.num_simplices() << std::endl << std::endl; std::cout << " Dimension = " << simplex_tree.dimension() << " "; - std::cout << " filtration = " << simplex_tree.filtration() << std::endl << std::endl; #endif // DEBUG_TRACES #ifdef DEBUG_TRACES @@ -257,7 +272,62 @@ int main(int argc, char * const argv[]) { pcoh.compute_persistent_cohomology(min_persistence); - pcoh.output_diagram(); + // Output the diagram in filediag + if (output_file_diag.empty()) { + pcoh.output_diagram(); + } else { + std::cout << "Result in file: " << output_file_diag << std::endl; + std::ofstream out(output_file_diag); + pcoh.output_diagram(out); + out.close(); + } return 0; } + +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &weight_file, + std::string &output_file_diag, int &coeff_field_characteristic, + Filtration_value &min_persistence) { + 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 file containing a point set. Format is one point per line: X1 ... Xd ")( + "weight-file", po::value<std::string>(&weight_file), + "Name of file containing a point weights. Format is one weigt per line: W1\n...\nWn "); + + po::options_description visible("Allowed options", 100); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "field-charac,p", po::value<int>(&coeff_field_characteristic)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); + + po::positional_options_description pos; + pos.add("input-file", 1); + pos.add("weight-file", 2); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map 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") || !vm.count("weight-file")) { + std::cout << std::endl; + std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::cout << "of a weighted 3D Alpha complex defined on a set of input points.\n \n"; + std::cout << "The output diagram contains one bar per line, written with the convention: \n"; + std::cout << " p dim b d \n"; + std::cout << "where dim is the dimension of the homological feature,\n"; + std::cout << "b and d are respectively the birth and death of the feature and \n"; + std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::cout << "Usage: " << argv[0] << " [options] input-file weight-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} diff --git a/src/Persistent_cohomology/example/periodic_alpha_complex_3d_persistence.cpp b/src/Alpha_complex/utilities/weighted_periodic_alpha_complex_3d_persistence.cpp index 8928cfc2..13634ff7 100644 --- a/src/Persistent_cohomology/example/periodic_alpha_complex_3d_persistence.cpp +++ b/src/Alpha_complex/utilities/weighted_periodic_alpha_complex_3d_persistence.cpp @@ -27,8 +27,8 @@ #include <gudhi/Points_3D_off_io.h> #include <CGAL/Exact_predicates_inexact_constructions_kernel.h> -#include <CGAL/Periodic_3_Delaunay_triangulation_traits_3.h> -#include <CGAL/Periodic_3_Delaunay_triangulation_3.h> +#include <CGAL/Periodic_3_regular_triangulation_traits_3.h> +#include <CGAL/Periodic_3_regular_triangulation_3.h> #include <CGAL/Alpha_shape_3.h> #include <CGAL/iterator.h> @@ -45,28 +45,31 @@ #include "alpha_complex_3d_helper.h" // Traits -using K = CGAL::Exact_predicates_inexact_constructions_kernel; -using PK = CGAL::Periodic_3_Delaunay_triangulation_traits_3<K>; +using Kernel = CGAL::Exact_predicates_inexact_constructions_kernel; +using PK = CGAL::Periodic_3_regular_triangulation_traits_3<Kernel>; + // Vertex type using DsVb = CGAL::Periodic_3_triangulation_ds_vertex_base_3<>; -using Vb = CGAL::Triangulation_vertex_base_3<PK, DsVb>; -using AsVb = CGAL::Alpha_shape_vertex_base_3<PK, Vb>; +using Vb = CGAL::Regular_triangulation_vertex_base_3<PK,DsVb>; +using AsVb = CGAL::Alpha_shape_vertex_base_3<PK,Vb>; // Cell type using DsCb = CGAL::Periodic_3_triangulation_ds_cell_base_3<>; -using Cb = CGAL::Triangulation_cell_base_3<PK, DsCb>; -using AsCb = CGAL::Alpha_shape_cell_base_3<PK, Cb>; -using Tds = CGAL::Triangulation_data_structure_3<AsVb, AsCb>; -using P3DT3 = CGAL::Periodic_3_Delaunay_triangulation_3<PK, Tds>; -using Alpha_shape_3 = CGAL::Alpha_shape_3<P3DT3>; -using Point_3 = PK::Point_3; +using Cb = CGAL::Regular_triangulation_cell_base_3<PK,DsCb>; +using AsCb = CGAL::Alpha_shape_cell_base_3<PK,Cb>; +using Tds = CGAL::Triangulation_data_structure_3<AsVb,AsCb>; +using P3RT3 = CGAL::Periodic_3_regular_triangulation_3<PK,Tds>; +using Alpha_shape_3 = CGAL::Alpha_shape_3<P3RT3>; + +using Point_3 = P3RT3::Bare_point; +using Weighted_point_3 = P3RT3::Weighted_point; // filtration with alpha values needed type definition using Alpha_value_type = Alpha_shape_3::FT; using Object = CGAL::Object; -using Dispatch = CGAL::Dispatch_output_iterator< - CGAL::cpp11::tuple<Object, Alpha_value_type>, - CGAL::cpp11::tuple<std::back_insert_iterator< std::vector<Object> >, - std::back_insert_iterator< std::vector<Alpha_value_type> > > >; +using Dispatch = + CGAL::Dispatch_output_iterator<CGAL::cpp11::tuple<Object, Alpha_value_type>, + CGAL::cpp11::tuple<std::back_insert_iterator<std::vector<Object> >, + std::back_insert_iterator<std::vector<Alpha_value_type> > > >; using Cell_handle = Alpha_shape_3::Cell_handle; using Facet = Alpha_shape_3::Facet; using Edge_3 = Alpha_shape_3::Edge; @@ -77,27 +80,27 @@ using Vertex_list = std::list<Alpha_shape_3::Vertex_handle>; using ST = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; using Filtration_value = ST::Filtration_value; using Simplex_tree_vertex = ST::Vertex_handle; -using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex >; +using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; using Alpha_shape_simplex_tree_pair = std::pair<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; -using Simplex_tree_vector_vertex = std::vector< Simplex_tree_vertex >; -using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology< - ST, Gudhi::persistent_cohomology::Field_Zp >; +using Simplex_tree_vector_vertex = std::vector<Simplex_tree_vertex>; +using Persistent_cohomology = + Gudhi::persistent_cohomology::Persistent_cohomology<ST, Gudhi::persistent_cohomology::Field_Zp>; -void usage(char * const progName) { - std::cerr << "Usage: " << progName << - " path_to_file_graph path_to_iso_cuboid_3_file coeff_field_characteristic[integer > 0] min_persistence[float >= -1.0]\n"; +void usage(const std::string& progName) { + std::cerr << "Usage: " << progName << " path_to_the_OFF_file path_to_weight_file path_to_the_cuboid_file " + "coeff_field_characteristic[integer > 0] min_persistence[float >= -1.0]\n"; exit(-1); } -int main(int argc, char * const argv[]) { +int main(int argc, char* const argv[]) { // program args management - if (argc != 5) { + if (argc != 6) { std::cerr << "Error: Number of arguments (" << argc << ") is not correct\n"; usage(argv[0]); } - int coeff_field_characteristic = atoi(argv[3]); - Filtration_value min_persistence = strtof(argv[4], nullptr); + int coeff_field_characteristic = atoi(argv[4]); + Filtration_value min_persistence = strtof(argv[5], nullptr); // Read points from file std::string offInputFile(argv[1]); @@ -109,30 +112,51 @@ int main(int argc, char * const argv[]) { usage(argv[0]); } + // Retrieve the triangulation + std::vector<Point_3> lp = off_reader.get_point_cloud(); + + // Read weights information from file + std::ifstream weights_ifstr(argv[2]); + std::vector<Weighted_point_3> wp; + if (weights_ifstr.good()) { + double weight = 0.0; + std::size_t index = 0; + wp.reserve(lp.size()); + // Attempt read the weight in a double format, return false if it fails + while ((weights_ifstr >> weight) && (index < lp.size())) { + wp.push_back(Weighted_point_3(lp[index], weight)); + index++; + } + if (index != lp.size()) { + std::cerr << "Bad number of weights in file " << argv[2] << std::endl; + usage(argv[0]); + } + } else { + std::cerr << "Unable to read file " << argv[2] << std::endl; + usage(argv[0]); + } + // Read iso_cuboid_3 information from file - std::ifstream iso_cuboid_str(argv[2]); + std::ifstream iso_cuboid_str(argv[3]); double x_min, y_min, z_min, x_max, y_max, z_max; if (iso_cuboid_str.good()) { iso_cuboid_str >> x_min >> y_min >> z_min >> x_max >> y_max >> z_max; } else { - std::cerr << "Unable to read file " << argv[2] << std::endl; + std::cerr << "Unable to read file " << argv[3] << std::endl; usage(argv[0]); } - // Retrieve the triangulation - std::vector<Point_3> lp = off_reader.get_point_cloud(); - // Define the periodic cube - P3DT3 pdt(PK::Iso_cuboid_3(x_min, y_min, z_min, x_max, y_max, z_max)); + P3RT3 prt(PK::Iso_cuboid_3(x_min, y_min, z_min, x_max, y_max, z_max)); // Heuristic for inserting large point sets (if pts is reasonably large) - pdt.insert(lp.begin(), lp.end(), true); - // As pdt won't be modified anymore switch to 1-sheeted cover if possible - if (pdt.is_triangulation_in_1_sheet()) pdt.convert_to_1_sheeted_covering(); + prt.insert(wp.begin(), wp.end(), true); + // As prt won't be modified anymore switch to 1-sheeted cover if possible + if (prt.is_triangulation_in_1_sheet()) prt.convert_to_1_sheeted_covering(); std::cout << "Periodic Delaunay computed." << std::endl; // alpha shape construction from points. CGAL has a strange behavior in REGULARIZED mode. This is the default mode // Maybe need to set it to GENERAL mode - Alpha_shape_3 as(pdt, 0, Alpha_shape_3::GENERAL); + Alpha_shape_3 as(prt, 0, Alpha_shape_3::GENERAL); // filtration with alpha values from alpha shape std::vector<Object> the_objects; @@ -160,29 +184,28 @@ int main(int argc, char * const argv[]) { Filtration_value filtration_max = 0.0; for (auto object_iterator : the_objects) { // Retrieve Alpha shape vertex list from object - if (const Cell_handle * cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { + if (const Cell_handle* cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { vertex_list = from_cell<Vertex_list, Cell_handle>(*cell); count_cells++; if (dim_max < 3) { // Cell is of dim 3 dim_max = 3; } - } else if (const Facet * facet = CGAL::object_cast<Facet>(&object_iterator)) { + } else if (const Facet* facet = CGAL::object_cast<Facet>(&object_iterator)) { vertex_list = from_facet<Vertex_list, Facet>(*facet); count_facets++; if (dim_max < 2) { // Facet is of dim 2 dim_max = 2; } - } else if (const Edge_3 * edge = CGAL::object_cast<Edge_3>(&object_iterator)) { + } else if (const Edge_3* edge = CGAL::object_cast<Edge_3>(&object_iterator)) { vertex_list = from_edge<Vertex_list, Edge_3>(*edge); count_edges++; if (dim_max < 1) { // Edge_3 is of dim 1 dim_max = 1; } - } else if (const Alpha_shape_3::Vertex_handle * vertex = - CGAL::object_cast<Alpha_shape_3::Vertex_handle>(&object_iterator)) { + } else if (const Vertex_handle* vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { count_vertices++; vertex_list = from_vertex<Vertex_list, Vertex_handle>(*vertex); } @@ -208,7 +231,7 @@ int main(int argc, char * const argv[]) { } } // Construction of the simplex_tree - Filtration_value filtr = /*std::sqrt*/(*the_alpha_value_iterator); + Filtration_value filtr = /*std::sqrt*/ (*the_alpha_value_iterator); #ifdef DEBUG_TRACES std::cout << "filtration = " << filtr << std::endl; #endif // DEBUG_TRACES @@ -221,8 +244,6 @@ int main(int argc, char * const argv[]) { else std::cout << "This shall not happen" << std::endl; } - simplex_tree.set_filtration(filtration_max); - simplex_tree.set_dimension(dim_max); #ifdef DEBUG_TRACES std::cout << "vertices \t\t" << count_vertices << std::endl; @@ -230,12 +251,10 @@ int main(int argc, char * const argv[]) { std::cout << "facets \t\t" << count_facets << std::endl; std::cout << "cells \t\t" << count_cells << std::endl; - std::cout << "Information of the Simplex Tree: " << std::endl; std::cout << " Number of vertices = " << simplex_tree.num_vertices() << " "; std::cout << " Number of simplices = " << simplex_tree.num_simplices() << std::endl << std::endl; std::cout << " Dimension = " << simplex_tree.dimension() << " "; - std::cout << " filtration = " << simplex_tree.filtration() << std::endl << std::endl; #endif // DEBUG_TRACES #ifdef DEBUG_TRACES diff --git a/src/Bitmap_cubical_complex/doc/Gudhi_Cubical_Complex_doc.h b/src/Bitmap_cubical_complex/doc/Gudhi_Cubical_Complex_doc.h index 5963caa3..ee84e201 100644 --- a/src/Bitmap_cubical_complex/doc/Gudhi_Cubical_Complex_doc.h +++ b/src/Bitmap_cubical_complex/doc/Gudhi_Cubical_Complex_doc.h @@ -63,7 +63,7 @@ namespace cubical_complex { * For further details and theory of cubical complexes, please consult \cite kaczynski2004computational as well as the * following paper \cite peikert2012topological . * - * \section cubicalcomplexdatastructure Data structure. + * \section cubicalcomplexdatastructure Data structure * * The implementation of Cubical complex provides a representation of complexes that occupy a rectangular region in * \f$\mathbb{R}^n\f$. This extra assumption allows for a memory efficient way of storing cubical complexes in a form @@ -85,37 +85,14 @@ namespace cubical_complex { * present in the product that gives the cube \f$C\f$. In a similar way, we can compute boundary and the coboundary of * each cube. Further details can be found in the literature. * - * \section inputformat Input Format. + * \section inputformat Input Format * * In the current implantation, filtration is given at the maximal cubes, and it is then extended by the lower star * filtration to all cubes. There are a number of constructors that can be used to construct cubical complex by users * who want to use the code directly. They can be found in the \a Bitmap_cubical_complex class. * Currently one input from a text file is used. It uses a format used already in Perseus software - * (http://www.sas.upenn.edu/~vnanda/perseus/) by Vidit Nanda. - * Below we are providing a description of the format. The first line contains a number d begin the dimension of the - * bitmap (2 in the example below). Next d lines are the numbers of top dimensional cubes in each dimensions (3 and 3 - * in the example below). Next, in lexicographical order, the filtration of top dimensional cubes is given (1 4 6 8 - * 20 4 7 6 5 in the example below). - * - * - * \image html "exampleBitmap.png" "Example of a input data." - * - * The input file for the following complex is: - * \verbatim -2 -3 -3 -1 -4 -6 -8 -20 -4 -7 -6 -5 -\endverbatim - + * (http://www.sas.upenn.edu/~vnanda/perseus/) by Vidit Nanda. The file format is described here: \ref FileFormatsPerseus. + * * \section PeriodicBoundaryConditions Periodic boundary conditions * Often one would like to impose periodic boundary conditions to the cubical complex. Let \f$ I_1\times ... \times * I_n \f$ be a box that is decomposed with a cubical complex \f$ \mathcal{K} \f$. Imposing periodic boundary @@ -123,28 +100,10 @@ namespace cubical_complex { * considered the same. In particular, if for a bitmap \f$ \mathcal{K} \f$ periodic boundary conditions are imposed * in all directions, then complex \f$ \mathcal{K} \f$ became n-dimensional torus. One can use various constructors * from the file Bitmap_cubical_complex_periodic_boundary_conditions_base.h to construct cubical complex with periodic - * boundary conditions. One can also use Perseus style input files. To indicate periodic boundary conditions in a - * given direction, then number of top dimensional cells in this direction have to be multiplied by -1. For instance: - - *\verbatim -2 --3 -3 -1 -4 -6 -8 -20 -4 -7 -6 -5 -\endverbatim - - * Indicate that we have imposed periodic boundary conditions in the direction x, but not in the direction y. - + * boundary conditions. One can also use Perseus style input files (see \ref FileFormatsPerseus). + * * \section BitmapExamples Examples - * End user programs are available in example/Bitmap_cubical_complex folder. + * End user programs are available in example/Bitmap_cubical_complex and utilities/Bitmap_cubical_complex folders. * * \copyright GNU General Public License v3. */ diff --git a/src/Bitmap_cubical_complex/example/CMakeLists.txt b/src/Bitmap_cubical_complex/example/CMakeLists.txt index a0401619..99304aa4 100644 --- a/src/Bitmap_cubical_complex/example/CMakeLists.txt +++ b/src/Bitmap_cubical_complex/example/CMakeLists.txt @@ -1,17 +1,6 @@ cmake_minimum_required(VERSION 2.6) project(Bitmap_cubical_complex_examples) -add_executable ( Bitmap_cubical_complex Bitmap_cubical_complex.cpp ) -if (TBB_FOUND) - target_link_libraries(Bitmap_cubical_complex ${TBB_LIBRARIES}) -endif() - -add_test(NAME Bitmap_cubical_complex_example_persistence_one_sphere COMMAND $<TARGET_FILE:Bitmap_cubical_complex> - "${CMAKE_SOURCE_DIR}/data/bitmap/CubicalOneSphere.txt") - -add_test(NAME Bitmap_cubical_complex_example_persistence_two_sphere COMMAND $<TARGET_FILE:Bitmap_cubical_complex> - "${CMAKE_SOURCE_DIR}/data/bitmap/CubicalTwoSphere.txt") - add_executable ( Random_bitmap_cubical_complex Random_bitmap_cubical_complex.cpp ) if (TBB_FOUND) target_link_libraries(Random_bitmap_cubical_complex ${TBB_LIBRARIES}) @@ -19,19 +8,4 @@ endif() add_test(NAME Bitmap_cubical_complex_example_random COMMAND $<TARGET_FILE:Random_bitmap_cubical_complex> "2" "100" "100") -add_executable ( Bitmap_cubical_complex_periodic_boundary_conditions Bitmap_cubical_complex_periodic_boundary_conditions.cpp ) -if (TBB_FOUND) - target_link_libraries(Bitmap_cubical_complex_periodic_boundary_conditions ${TBB_LIBRARIES}) -endif() - -add_test(NAME Bitmap_cubical_complex_example_periodic_boundary_conditions_2d_torus - COMMAND $<TARGET_FILE:Bitmap_cubical_complex_periodic_boundary_conditions> - "${CMAKE_SOURCE_DIR}/data/bitmap/2d_torus.txt") - -add_test(NAME Bitmap_cubical_complex_example_periodic_boundary_conditions_3d_torus - COMMAND $<TARGET_FILE:Bitmap_cubical_complex_periodic_boundary_conditions> - "${CMAKE_SOURCE_DIR}/data/bitmap/3d_torus.txt") - -install(TARGETS Bitmap_cubical_complex DESTINATION bin) install(TARGETS Random_bitmap_cubical_complex DESTINATION bin) -install(TARGETS Bitmap_cubical_complex_periodic_boundary_conditions DESTINATION bin) diff --git a/src/Bitmap_cubical_complex/example/Random_bitmap_cubical_complex.cpp b/src/Bitmap_cubical_complex/example/Random_bitmap_cubical_complex.cpp index 16ad65a0..f70558f2 100644 --- a/src/Bitmap_cubical_complex/example/Random_bitmap_cubical_complex.cpp +++ b/src/Bitmap_cubical_complex/example/Random_bitmap_cubical_complex.cpp @@ -20,7 +20,6 @@ * along with this program. If not, see <http://www.gnu.org/licenses/>. */ - // for persistence algorithm #include <gudhi/reader_utils.h> #include <gudhi/Bitmap_cubical_complex.h> @@ -34,10 +33,11 @@ int main(int argc, char** argv) { srand(time(0)); - std::cout << "This program computes persistent homology, by using bitmap_cubical_complex class, of cubical " << - "complexes. The first parameter of the program is the dimension D of the bitmap. The next D parameters are " << - "number of top dimensional cubes in each dimension of the bitmap. The program will create random cubical " << - "complex of that sizes and compute persistent homology of it." << std::endl; + std::cout + << "This program computes persistent homology, by using bitmap_cubical_complex class, of cubical " + << "complexes. The first parameter of the program is the dimension D of the bitmap. The next D parameters are " + << "number of top dimensional cubes in each dimension of the bitmap. The program will create random cubical " + << "complex of that sizes and compute persistent homology of it." << std::endl; int p = 2; double min_persistence = 0; @@ -47,16 +47,16 @@ int main(int argc, char** argv) { return 1; } - size_t dimensionOfBitmap = (size_t) atoi(argv[1]); - std::vector< unsigned > sizes; + size_t dimensionOfBitmap = (size_t)atoi(argv[1]); + std::vector<unsigned> sizes; size_t multipliers = 1; for (size_t dim = 0; dim != dimensionOfBitmap; ++dim) { - unsigned sizeInThisDimension = (unsigned) atoi(argv[2 + dim]); + unsigned sizeInThisDimension = (unsigned)atoi(argv[2 + dim]); sizes.push_back(sizeInThisDimension); multipliers *= sizeInThisDimension; } - std::vector< double > data; + std::vector<double> data; for (size_t i = 0; i != multipliers; ++i) { data.push_back(rand() / static_cast<double>(RAND_MAX)); } @@ -80,4 +80,3 @@ int main(int argc, char** argv) { return 0; } - diff --git a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h index f395de65..969daba6 100644 --- a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h +++ b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h @@ -31,10 +31,11 @@ #endif #include <limits> -#include <utility> // for pair<> +#include <utility> // for pair<> #include <algorithm> // for sort #include <vector> #include <numeric> // for iota +#include <cstddef> namespace Gudhi { @@ -43,7 +44,8 @@ namespace cubical_complex { // global variable, was used just for debugging. const bool globalDbg = false; -template <typename T> class is_before_in_filtration; +template <typename T> +class is_before_in_filtration; /** * @brief Cubical complex represented as a bitmap. @@ -60,11 +62,10 @@ class Bitmap_cubical_complex : public T { //*********************************************// // Typedefs and typenames //*********************************************// - typedef size_t Simplex_key; + typedef std::size_t Simplex_key; typedef typename T::filtration_type Filtration_value; typedef Simplex_key Simplex_handle; - //*********************************************// // Constructors //*********************************************// @@ -77,12 +78,12 @@ class Bitmap_cubical_complex : public T { /** * Constructor form a Perseus-style file. **/ - Bitmap_cubical_complex(const char* perseus_style_file) : - T(perseus_style_file), key_associated_to_simplex(this->total_number_of_cells + 1) { + Bitmap_cubical_complex(const char* perseus_style_file) + : T(perseus_style_file), key_associated_to_simplex(this->total_number_of_cells + 1) { if (globalDbg) { std::cerr << "Bitmap_cubical_complex( const char* perseus_style_file )\n"; } - for (size_t i = 0; i != this->total_number_of_cells; ++i) { + for (std::size_t i = 0; i != this->total_number_of_cells; ++i) { this->key_associated_to_simplex[i] = i; } // we initialize this only once, in each constructor, when the bitmap is constructed. @@ -97,10 +98,9 @@ class Bitmap_cubical_complex : public T { * with filtration on top dimensional cells. **/ Bitmap_cubical_complex(const std::vector<unsigned>& dimensions, - const std::vector<Filtration_value>& top_dimensional_cells) : - T(dimensions, top_dimensional_cells), - key_associated_to_simplex(this->total_number_of_cells + 1) { - for (size_t i = 0; i != this->total_number_of_cells; ++i) { + const std::vector<Filtration_value>& top_dimensional_cells) + : T(dimensions, top_dimensional_cells), key_associated_to_simplex(this->total_number_of_cells + 1) { + for (std::size_t i = 0; i != this->total_number_of_cells; ++i) { this->key_associated_to_simplex[i] = i; } // we initialize this only once, in each constructor, when the bitmap is constructed. @@ -118,10 +118,10 @@ class Bitmap_cubical_complex : public T { **/ Bitmap_cubical_complex(const std::vector<unsigned>& dimensions, const std::vector<Filtration_value>& top_dimensional_cells, - std::vector< bool > directions_in_which_periodic_b_cond_are_to_be_imposed) : - T(dimensions, top_dimensional_cells, directions_in_which_periodic_b_cond_are_to_be_imposed), - key_associated_to_simplex(this->total_number_of_cells + 1) { - for (size_t i = 0; i != this->total_number_of_cells; ++i) { + std::vector<bool> directions_in_which_periodic_b_cond_are_to_be_imposed) + : T(dimensions, top_dimensional_cells, directions_in_which_periodic_b_cond_are_to_be_imposed), + key_associated_to_simplex(this->total_number_of_cells + 1) { + for (std::size_t i = 0; i != this->total_number_of_cells; ++i) { this->key_associated_to_simplex[i] = i; } // we initialize this only once, in each constructor, when the bitmap is constructed. @@ -142,9 +142,7 @@ class Bitmap_cubical_complex : public T { /** * Returns number of all cubes in the complex. **/ - size_t num_simplices()const { - return this->total_number_of_cells; - } + std::size_t num_simplices() const { return this->total_number_of_cells; } /** * Returns a Simplex_handle to a cube that do not exist in this complex. @@ -159,14 +157,12 @@ class Bitmap_cubical_complex : public T { /** * Returns dimension of the complex. **/ - inline size_t dimension()const { - return this->sizes.size(); - } + inline std::size_t dimension() const { return this->sizes.size(); } /** * Return dimension of a cell pointed by the Simplex_handle. **/ - inline unsigned dimension(Simplex_handle sh)const { + inline unsigned dimension(Simplex_handle sh) const { if (globalDbg) { std::cerr << "unsigned dimension(const Simplex_handle& sh)\n"; } @@ -199,7 +195,7 @@ class Bitmap_cubical_complex : public T { /** * Return the key of a cube pointed by the Simplex_handle. **/ - Simplex_key key(Simplex_handle sh)const { + Simplex_key key(Simplex_handle sh) const { if (globalDbg) { std::cerr << "Simplex_key key(const Simplex_handle& sh)\n"; } @@ -217,7 +213,7 @@ class Bitmap_cubical_complex : public T { std::cerr << "Simplex_handle simplex(Simplex_key key)\n"; } if (key != null_key()) { - return this->simplex_associated_to_key[ key ]; + return this->simplex_associated_to_key[key]; } return null_simplex(); } @@ -246,8 +242,8 @@ class Bitmap_cubical_complex : public T { /** * Boundary_simplex_range class provides ranges for boundary iterators. **/ - typedef typename std::vector< Simplex_handle >::iterator Boundary_simplex_iterator; - typedef typename std::vector< Simplex_handle > Boundary_simplex_range; + typedef typename std::vector<Simplex_handle>::iterator Boundary_simplex_iterator; + typedef typename std::vector<Simplex_handle> Boundary_simplex_range; /** * Filtration_simplex_iterator class provides an iterator though the whole structure in the order of filtration. @@ -257,13 +253,13 @@ class Bitmap_cubical_complex : public T { **/ class Filtration_simplex_range; - class Filtration_simplex_iterator : std::iterator< std::input_iterator_tag, Simplex_handle > { + class Filtration_simplex_iterator : std::iterator<std::input_iterator_tag, Simplex_handle> { // Iterator over all simplices of the complex in the order of the indexing scheme. // 'value_type' must be 'Simplex_handle'. public: - Filtration_simplex_iterator(Bitmap_cubical_complex* b) : b(b), position(0) { } + Filtration_simplex_iterator(Bitmap_cubical_complex* b) : b(b), position(0) {} - Filtration_simplex_iterator() : b(NULL), position(0) { } + Filtration_simplex_iterator() : b(NULL), position(0) {} Filtration_simplex_iterator operator++() { if (globalDbg) { @@ -288,14 +284,14 @@ class Bitmap_cubical_complex : public T { return (*this); } - bool operator==(const Filtration_simplex_iterator& rhs)const { + bool operator==(const Filtration_simplex_iterator& rhs) const { if (globalDbg) { std::cerr << "bool operator == ( const Filtration_simplex_iterator& rhs )\n"; } - return ( this->position == rhs.position); + return (this->position == rhs.position); } - bool operator!=(const Filtration_simplex_iterator& rhs)const { + bool operator!=(const Filtration_simplex_iterator& rhs) const { if (globalDbg) { std::cerr << "bool operator != ( const Filtration_simplex_iterator& rhs )\n"; } @@ -306,14 +302,14 @@ class Bitmap_cubical_complex : public T { if (globalDbg) { std::cerr << "Simplex_handle operator*()\n"; } - return this->b->simplex_associated_to_key[ this->position ]; + return this->b->simplex_associated_to_key[this->position]; } friend class Filtration_simplex_range; private: Bitmap_cubical_complex<T>* b; - size_t position; + std::size_t position; }; /** @@ -326,7 +322,7 @@ class Bitmap_cubical_complex : public T { typedef Filtration_simplex_iterator const_iterator; typedef Filtration_simplex_iterator iterator; - Filtration_simplex_range(Bitmap_cubical_complex<T>* b) : b(b) { } + Filtration_simplex_range(Bitmap_cubical_complex<T>* b) : b(b) {} Filtration_simplex_iterator begin() { if (globalDbg) { @@ -348,8 +344,6 @@ class Bitmap_cubical_complex : public T { Bitmap_cubical_complex<T>* b; }; - - //*********************************************// // Methods to access iterators from the container: @@ -357,9 +351,7 @@ class Bitmap_cubical_complex : public T { * boundary_simplex_range creates an object of a Boundary_simplex_range class * that provides ranges for the Boundary_simplex_iterator. **/ - Boundary_simplex_range boundary_simplex_range(Simplex_handle sh) { - return this->get_boundary_of_a_cell(sh); - } + Boundary_simplex_range boundary_simplex_range(Simplex_handle sh) { return this->get_boundary_of_a_cell(sh); } /** * filtration_simplex_range creates an object of a Filtration_simplex_range class @@ -374,8 +366,6 @@ class Bitmap_cubical_complex : public T { } //*********************************************// - - //*********************************************// // Elements which are in Gudhi now, but I (and in all the cases I asked also Marc) do not understand why they are // there. @@ -390,43 +380,41 @@ class Bitmap_cubical_complex : public T { * Function needed for compatibility with Gudhi. Not useful for other purposes. **/ std::pair<Simplex_handle, Simplex_handle> endpoints(Simplex_handle sh) { - std::vector< size_t > bdry = this->get_boundary_of_a_cell(sh); + std::vector<std::size_t> bdry = this->get_boundary_of_a_cell(sh); if (globalDbg) { std::cerr << "std::pair<Simplex_handle, Simplex_handle> endpoints( Simplex_handle sh )\n"; std::cerr << "bdry.size() : " << bdry.size() << std::endl; } // this method returns two first elements from the boundary of sh. if (bdry.size() < 2) - throw("Error in endpoints in Bitmap_cubical_complex class. The cell have less than two elements in the " - "boundary."); + throw( + "Error in endpoints in Bitmap_cubical_complex class. The cell have less than two elements in the " + "boundary."); return std::make_pair(bdry[0], bdry[1]); } - /** * Class needed for compatibility with Gudhi. Not useful for other purposes. **/ class Skeleton_simplex_range; - class Skeleton_simplex_iterator : std::iterator< std::input_iterator_tag, Simplex_handle > { + class Skeleton_simplex_iterator : std::iterator<std::input_iterator_tag, Simplex_handle> { // Iterator over all simplices of the complex in the order of the indexing scheme. // 'value_type' must be 'Simplex_handle'. public: - Skeleton_simplex_iterator(Bitmap_cubical_complex* b, size_t d) : b(b), dimension(d) { + Skeleton_simplex_iterator(Bitmap_cubical_complex* b, std::size_t d) : b(b), dimension(d) { if (globalDbg) { - std::cerr << "Skeleton_simplex_iterator ( Bitmap_cubical_complex* b , size_t d )\n"; + std::cerr << "Skeleton_simplex_iterator ( Bitmap_cubical_complex* b , std::size_t d )\n"; } // find the position of the first simplex of a dimension d this->position = 0; - while ( - (this->position != b->data.size()) && - (this->b->get_dimension_of_a_cell(this->position) != this->dimension) - ) { + while ((this->position != b->data.size()) && + (this->b->get_dimension_of_a_cell(this->position) != this->dimension)) { ++this->position; } } - Skeleton_simplex_iterator() : b(NULL), position(0), dimension(0) { } + Skeleton_simplex_iterator() : b(NULL), position(0), dimension(0) {} Skeleton_simplex_iterator operator++() { if (globalDbg) { @@ -434,10 +422,8 @@ class Bitmap_cubical_complex : public T { } // increment the position as long as you did not get to the next element of the dimension dimension. ++this->position; - while ( - (this->position != this->b->data.size()) && - (this->b->get_dimension_of_a_cell(this->position) != this->dimension) - ) { + while ((this->position != this->b->data.size()) && + (this->b->get_dimension_of_a_cell(this->position) != this->dimension)) { ++this->position; } return (*this); @@ -459,14 +445,14 @@ class Bitmap_cubical_complex : public T { return (*this); } - bool operator==(const Skeleton_simplex_iterator& rhs)const { + bool operator==(const Skeleton_simplex_iterator& rhs) const { if (globalDbg) { std::cerr << "bool operator ==\n"; } - return ( this->position == rhs.position); + return (this->position == rhs.position); } - bool operator!=(const Skeleton_simplex_iterator& rhs)const { + bool operator!=(const Skeleton_simplex_iterator& rhs) const { if (globalDbg) { std::cerr << "bool operator != ( const Skeleton_simplex_iterator& rhs )\n"; } @@ -481,9 +467,10 @@ class Bitmap_cubical_complex : public T { } friend class Skeleton_simplex_range; + private: Bitmap_cubical_complex<T>* b; - size_t position; + std::size_t position; unsigned dimension; }; @@ -497,7 +484,7 @@ class Bitmap_cubical_complex : public T { typedef Skeleton_simplex_iterator const_iterator; typedef Skeleton_simplex_iterator iterator; - Skeleton_simplex_range(Bitmap_cubical_complex<T>* b, unsigned dimension) : b(b), dimension(dimension) { } + Skeleton_simplex_range(Bitmap_cubical_complex<T>* b, unsigned dimension) : b(b), dimension(dimension) {} Skeleton_simplex_iterator begin() { if (globalDbg) { @@ -533,8 +520,8 @@ class Bitmap_cubical_complex : public T { friend class is_before_in_filtration<T>; protected: - std::vector< size_t > key_associated_to_simplex; - std::vector< size_t > simplex_associated_to_key; + std::vector<std::size_t> key_associated_to_simplex; + std::vector<std::size_t> simplex_associated_to_key; }; // Bitmap_cubical_complex template <typename T> @@ -542,7 +529,7 @@ void Bitmap_cubical_complex<T>::initialize_simplex_associated_to_key() { if (globalDbg) { std::cerr << "void Bitmap_cubical_complex<T>::initialize_elements_ordered_according_to_filtration() \n"; } - this->simplex_associated_to_key = std::vector<size_t>(this->data.size()); + this->simplex_associated_to_key = std::vector<std::size_t>(this->data.size()); std::iota(std::begin(simplex_associated_to_key), std::end(simplex_associated_to_key), 0); #ifdef GUDHI_USE_TBB tbb::parallel_sort(simplex_associated_to_key.begin(), simplex_associated_to_key.end(), @@ -552,16 +539,15 @@ void Bitmap_cubical_complex<T>::initialize_simplex_associated_to_key() { #endif // we still need to deal here with a key_associated_to_simplex: - for ( size_t i = 0 ; i != simplex_associated_to_key.size() ; ++i ) { - this->key_associated_to_simplex[ simplex_associated_to_key[i] ] = i; + for (std::size_t i = 0; i != simplex_associated_to_key.size(); ++i) { + this->key_associated_to_simplex[simplex_associated_to_key[i]] = i; } } template <typename T> class is_before_in_filtration { public: - explicit is_before_in_filtration(Bitmap_cubical_complex<T> * CC) - : CC_(CC) { } + explicit is_before_in_filtration(Bitmap_cubical_complex<T>* CC) : CC_(CC) {} bool operator()(const typename Bitmap_cubical_complex<T>::Simplex_handle& sh1, const typename Bitmap_cubical_complex<T>::Simplex_handle& sh2) const { @@ -573,8 +559,8 @@ class is_before_in_filtration { return fil1 < fil2; } // in this case they are on the same filtration level, so the dimension decide. - size_t dim1 = CC_->get_dimension_of_a_cell(sh1); - size_t dim2 = CC_->get_dimension_of_a_cell(sh2); + std::size_t dim1 = CC_->get_dimension_of_a_cell(sh1); + std::size_t dim2 = CC_->get_dimension_of_a_cell(sh2); if (dim1 != dim2) { return dim1 < dim2; } diff --git a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex/counter.h b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex/counter.h index 4b072f10..705b68a0 100644 --- a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex/counter.h +++ b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex/counter.h @@ -25,6 +25,7 @@ #include <iostream> #include <vector> +#include <cstddef> namespace Gudhi { @@ -63,14 +64,14 @@ class counter { * If the value of the function is false, that means, that the counter have reached its end-value. **/ bool increment() { - size_t i = 0; + std::size_t i = 0; while ((i != this->end.size()) && (this->current[i] == this->end[i])) { ++i; } if (i == this->end.size())return false; ++this->current[i]; - for (size_t j = 0; j != i; ++j) { + for (std::size_t j = 0; j != i; ++j) { this->current[j] = this->begin[j]; } return true; @@ -80,7 +81,7 @@ class counter { * Function to check if we are at the end of counter. **/ bool isFinal() { - for (size_t i = 0; i != this->current.size(); ++i) { + for (std::size_t i = 0; i != this->current.size(); ++i) { if (this->current[i] == this->end[i])return true; } return false; @@ -93,7 +94,7 @@ class counter { **/ std::vector< unsigned > find_opposite(const std::vector< bool >& directionsForPeriodicBCond) { std::vector< unsigned > result; - for (size_t i = 0; i != this->current.size(); ++i) { + for (std::size_t i = 0; i != this->current.size(); ++i) { if ((this->current[i] == this->end[i]) && (directionsForPeriodicBCond[i] == true)) { result.push_back(this->begin[i]); } else { @@ -108,7 +109,7 @@ class counter { **/ std::vector< bool > directions_of_finals() { std::vector< bool > result; - for (size_t i = 0; i != this->current.size(); ++i) { + for (std::size_t i = 0; i != this->current.size(); ++i) { if (this->current[i] == this->end[i]) { result.push_back(true); } else { @@ -123,7 +124,7 @@ class counter { **/ friend std::ostream& operator<<(std::ostream& out, const counter& c) { // std::cerr << "c.current.size() : " << c.current.size() << endl; - for (size_t i = 0; i != c.current.size(); ++i) { + for (std::size_t i = 0; i != c.current.size(); ++i) { out << c.current[i] << " "; } return out; diff --git a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h index 0442ac34..bf257be1 100644 --- a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h +++ b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h @@ -32,7 +32,9 @@ #include <algorithm> #include <iterator> #include <limits> -#include <utility> // for pair<> +#include <utility> +#include <stdexcept> +#include <cstddef> namespace Gudhi { @@ -65,8 +67,7 @@ class Bitmap_cubical_complex_base { /** *Default constructor **/ - Bitmap_cubical_complex_base() : - total_number_of_cells(0) { } + Bitmap_cubical_complex_base() : total_number_of_cells(0) {} /** * There are a few constructors of a Bitmap_cubical_complex_base class. * First one, that takes vector<unsigned>, creates an empty bitmap of a dimension equal @@ -90,7 +91,7 @@ class Bitmap_cubical_complex_base { /** * Destructor of the Bitmap_cubical_complex_base class. **/ - virtual ~Bitmap_cubical_complex_base() { } + virtual ~Bitmap_cubical_complex_base() {} /** * The functions get_boundary_of_a_cell, get_coboundary_of_a_cell, get_dimension_of_a_cell @@ -100,8 +101,10 @@ class Bitmap_cubical_complex_base { * non-negative integer, indicating a position of a cube in the data structure. * In the case of functions that compute (co)boundary, the output is a vector if non-negative integers pointing to * the positions of (co)boundary element of the input cell. + * The boundary elements are guaranteed to be returned so that the + * incidence coefficients of boundary elements are alternating. */ - virtual inline std::vector< size_t > get_boundary_of_a_cell(size_t cell)const; + virtual inline std::vector<std::size_t> get_boundary_of_a_cell(std::size_t cell) const; /** * The functions get_coboundary_of_a_cell, get_coboundary_of_a_cell, * get_dimension_of_a_cell and get_cell_data are the basic @@ -112,21 +115,81 @@ class Bitmap_cubical_complex_base { * In the case of functions that compute (co)boundary, the output is a vector if * non-negative integers pointing to the * positions of (co)boundary element of the input cell. + * Note that unlike in the case of boundary, over here the elements are + * not guaranteed to be returned with alternating incidence numbers. + * **/ - virtual inline std::vector< size_t > get_coboundary_of_a_cell(size_t cell)const; + virtual inline std::vector<std::size_t> get_coboundary_of_a_cell(std::size_t cell) const; + /** - * In the case of get_dimension_of_a_cell function, the output is a non-negative integer - * indicating the dimension of a cell. - **/ - inline unsigned get_dimension_of_a_cell(size_t cell)const; + * This procedure compute incidence numbers between cubes. For a cube \f$A\f$ of + * dimension n and a cube \f$B \subset A\f$ of dimension n-1, an incidence + * between \f$A\f$ and \f$B\f$ is the integer with which \f$B\f$ appears in the boundary of \f$A\f$. + * Note that first parameter is a cube of dimension n, + * and the second parameter is an adjusted cube in dimension n-1. + * Given \f$A = [b_1,e_1] \times \ldots \ [b_{j-1},e_{j-1}] \times [b_{j},e_{j}] \times [b_{j+1},e_{j+1}] \times \ldots + *\times [b_{n},e_{n}] \f$ + * such that \f$ b_{j} \neq e_{j} \f$ + * and \f$B = [b_1,e_1] \times \ldots \ [b_{j-1},e_{j-1}] \times [a,a] \times [b_{j+1},e_{j+1}] \times \ldots \times + *[b_{n},e_{n}] \f$ + * where \f$ a = b_{j}\f$ or \f$ a = e_{j}\f$, the incidence between \f$A\f$ and \f$B\f$ + * computed by this procedure is given by formula: + * \f$ c\ (-1)^{\sum_{i=1}^{j-1} dim [b_{i},e_{i}]} \f$ + * Where \f$ dim [b_{i},e_{i}] = 0 \f$ if \f$ b_{i}=e_{i} \f$ and 1 in other case. + * c is -1 if \f$ a = b_{j}\f$ and 1 if \f$ a = e_{j}\f$. + * @exception std::logic_error In case when the cube \f$B\f$ is not n-1 + * dimensional face of a cube \f$A\f$. + **/ + virtual int compute_incidence_between_cells(std::size_t coface, std::size_t face) const { + // first get the counters for coface and face: + std::vector<unsigned> coface_counter = this->compute_counter_for_given_cell(coface); + std::vector<unsigned> face_counter = this->compute_counter_for_given_cell(face); + + // coface_counter and face_counter should agree at all positions except from one: + int number_of_position_in_which_counters_do_not_agree = -1; + std::size_t number_of_full_faces_that_comes_before = 0; + for (std::size_t i = 0; i != coface_counter.size(); ++i) { + if ((coface_counter[i] % 2 == 1) && (number_of_position_in_which_counters_do_not_agree == -1)) { + ++number_of_full_faces_that_comes_before; + } + if (coface_counter[i] != face_counter[i]) { + if (number_of_position_in_which_counters_do_not_agree != -1) { + std::cout << "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face.\n"; + throw std::logic_error( + "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face."); + } + number_of_position_in_which_counters_do_not_agree = i; + } + } + + int incidence = 1; + if (number_of_full_faces_that_comes_before % 2) incidence = -1; + // if the face cell is on the right from coface cell: + if (coface_counter[number_of_position_in_which_counters_do_not_agree] + 1 == + face_counter[number_of_position_in_which_counters_do_not_agree]) { + incidence *= -1; + } + + return incidence; + } + + /** +* In the case of get_dimension_of_a_cell function, the output is a non-negative integer +* indicating the dimension of a cell. +* Note that unlike in the case of boundary, over here the elements are +* not guaranteed to be returned with alternating incidence numbers. +* To compute incidence between cells use compute_incidence_between_cells +* procedure +**/ + inline unsigned get_dimension_of_a_cell(std::size_t cell) const; + /** * In the case of get_cell_data, the output parameter is a reference to the value of a cube in a given position. * This allows reading and changing the value of filtration. Note that if the value of a filtration is changed, the * code do not check if we have a filtration or not. i.e. it do not check if the value of a filtration of a cell is * not smaller than the value of a filtration of its boundary and not greater than the value of its coboundary. **/ - inline T& get_cell_data(size_t cell); - + inline T& get_cell_data(std::size_t cell); /** * Typical input used to construct a baseBitmap class is a filtration given at the top dimensional cells. @@ -141,33 +204,29 @@ class Bitmap_cubical_complex_base { /** * Returns dimension of a complex. **/ - inline unsigned dimension()const { - return sizes.size(); - } + inline unsigned dimension() const { return sizes.size(); } /** * Returns number of all cubes in the data structure. **/ - inline unsigned size()const { - return this->data.size(); - } + inline unsigned size() const { return this->data.size(); } /** * Writing to stream operator. By using it we get the values T of cells in order in which they are stored in the * structure. This procedure is used for debugging purposes. **/ template <typename K> - friend std::ostream& operator<<(std::ostream & os, const Bitmap_cubical_complex_base<K>& b); + friend std::ostream& operator<<(std::ostream& os, const Bitmap_cubical_complex_base<K>& b); /** * Function that put the input data to bins. By putting data to bins we mean rounding them to a sequence of values * equally distributed in the range of data. * Sometimes if most of the cells have different birth-death times, the performance of the algorithms to compute * persistence gets worst. When dealing with this type of data, one may want to put different values on cells to - * some number of bins. The function put_data_to_bins( size_t number_of_bins ) is designed for that purpose. + * some number of bins. The function put_data_to_bins( std::size_t number_of_bins ) is designed for that purpose. * The parameter of the function is the number of bins (distinct values) we want to have in the cubical complex. **/ - void put_data_to_bins(size_t number_of_bins); + void put_data_to_bins(std::size_t number_of_bins); /** * Function that put the input data to bins. By putting data to bins we mean rounding them to a sequence of values @@ -184,7 +243,7 @@ class Bitmap_cubical_complex_base { /** * Functions to find min and max values of filtration. **/ - std::pair< T, T > min_max_filtration(); + std::pair<T, T> min_max_filtration(); // ITERATORS @@ -192,11 +251,9 @@ class Bitmap_cubical_complex_base { * @brief Iterator through all cells in the complex (in order they appear in the structure -- i.e. * in lexicographical order). **/ - class All_cells_iterator : std::iterator< std::input_iterator_tag, T > { + class All_cells_iterator : std::iterator<std::input_iterator_tag, T> { public: - All_cells_iterator() { - this->counter = 0; - } + All_cells_iterator() { this->counter = 0; } All_cells_iterator operator++() { // first find first element of the counter that can be increased: @@ -215,14 +272,12 @@ class Bitmap_cubical_complex_base { return *this; } - bool operator==(const All_cells_iterator& rhs)const { - if (this->counter != rhs.counter)return false; + bool operator==(const All_cells_iterator& rhs) const { + if (this->counter != rhs.counter) return false; return true; } - bool operator!=(const All_cells_iterator& rhs)const { - return !(*this == rhs); - } + bool operator!=(const All_cells_iterator& rhs) const { return !(*this == rhs); } /* * The operator * returns position of a cube in the structure of cubical complex. This position can be then used as @@ -231,12 +286,11 @@ class Bitmap_cubical_complex_base { * boundary and coboundary and dimension * and in function get_cell_data to get a filtration of a cell. */ - size_t operator*() { - return this->counter; - } + std::size_t operator*() { return this->counter; } friend class Bitmap_cubical_complex_base; + protected: - size_t counter; + std::size_t counter; }; /** @@ -261,71 +315,61 @@ class Bitmap_cubical_complex_base { **/ class All_cells_range { public: - All_cells_range(Bitmap_cubical_complex_base* b) : b(b) { } + All_cells_range(Bitmap_cubical_complex_base* b) : b(b) {} - All_cells_iterator begin() { - return b->all_cells_iterator_begin(); - } + All_cells_iterator begin() { return b->all_cells_iterator_begin(); } + + All_cells_iterator end() { return b->all_cells_iterator_end(); } - All_cells_iterator end() { - return b->all_cells_iterator_end(); - } private: Bitmap_cubical_complex_base<T>* b; }; - All_cells_range all_cells_range() { - return All_cells_range(this); - } - + All_cells_range all_cells_range() { return All_cells_range(this); } /** * Boundary_range class provides ranges for boundary iterators. **/ - typedef typename std::vector< size_t >::const_iterator Boundary_iterator; - typedef typename std::vector< size_t > Boundary_range; + typedef typename std::vector<std::size_t>::const_iterator Boundary_iterator; + typedef typename std::vector<std::size_t> Boundary_range; /** * boundary_simplex_range creates an object of a Boundary_simplex_range class * that provides ranges for the Boundary_simplex_iterator. **/ - Boundary_range boundary_range(size_t sh) { - return this->get_boundary_of_a_cell(sh); - } + Boundary_range boundary_range(std::size_t sh) { return this->get_boundary_of_a_cell(sh); } /** * Coboundary_range class provides ranges for boundary iterators. **/ - typedef typename std::vector< size_t >::const_iterator Coboundary_iterator; - typedef typename std::vector< size_t > Coboundary_range; + typedef typename std::vector<std::size_t>::const_iterator Coboundary_iterator; + typedef typename std::vector<std::size_t> Coboundary_range; /** * boundary_simplex_range creates an object of a Boundary_simplex_range class * that provides ranges for the Boundary_simplex_iterator. **/ - Coboundary_range coboundary_range(size_t sh) { - return this->get_coboundary_of_a_cell(sh); - } + Coboundary_range coboundary_range(std::size_t sh) { return this->get_coboundary_of_a_cell(sh); } /** * @brief Iterator through top dimensional cells of the complex. The cells appear in order they are stored * in the structure (i.e. in lexicographical order) **/ - class Top_dimensional_cells_iterator : std::iterator< std::input_iterator_tag, T > { + class Top_dimensional_cells_iterator : std::iterator<std::input_iterator_tag, T> { public: Top_dimensional_cells_iterator(Bitmap_cubical_complex_base& b) : b(b) { - this->counter = std::vector<size_t>(b.dimension()); + this->counter = std::vector<std::size_t>(b.dimension()); // std::fill( this->counter.begin() , this->counter.end() , 0 ); } Top_dimensional_cells_iterator operator++() { // first find first element of the counter that can be increased: - size_t dim = 0; - while ((dim != this->b.dimension()) && (this->counter[dim] == this->b.sizes[dim] - 1))++dim; + std::size_t dim = 0; + while ((dim != this->b.dimension()) && (this->counter[dim] == this->b.sizes[dim] - 1)) ++dim; if (dim != this->b.dimension()) { ++this->counter[dim]; - for (size_t i = 0; i != dim; ++i) { + for (std::size_t i = 0; i != dim; ++i) { this->counter[i] = 0; } } else { @@ -346,18 +390,16 @@ class Bitmap_cubical_complex_base { return *this; } - bool operator==(const Top_dimensional_cells_iterator& rhs)const { - if (&this->b != &rhs.b)return false; - if (this->counter.size() != rhs.counter.size())return false; - for (size_t i = 0; i != this->counter.size(); ++i) { - if (this->counter[i] != rhs.counter[i])return false; + bool operator==(const Top_dimensional_cells_iterator& rhs) const { + if (&this->b != &rhs.b) return false; + if (this->counter.size() != rhs.counter.size()) return false; + for (std::size_t i = 0; i != this->counter.size(); ++i) { + if (this->counter[i] != rhs.counter[i]) return false; } return true; } - bool operator!=(const Top_dimensional_cells_iterator& rhs)const { - return !(*this == rhs); - } + bool operator!=(const Top_dimensional_cells_iterator& rhs) const { return !(*this == rhs); } /* * The operator * returns position of a cube in the structure of cubical complex. This position can be then used as @@ -366,26 +408,25 @@ class Bitmap_cubical_complex_base { * boundary and coboundary and dimension * and in function get_cell_data to get a filtration of a cell. */ - size_t operator*() { - return this->compute_index_in_bitmap(); - } + std::size_t operator*() { return this->compute_index_in_bitmap(); } - size_t compute_index_in_bitmap()const { - size_t index = 0; - for (size_t i = 0; i != this->counter.size(); ++i) { + std::size_t compute_index_in_bitmap() const { + std::size_t index = 0; + for (std::size_t i = 0; i != this->counter.size(); ++i) { index += (2 * this->counter[i] + 1) * this->b.multipliers[i]; } return index; } - void print_counter()const { - for (size_t i = 0; i != this->counter.size(); ++i) { + void print_counter() const { + for (std::size_t i = 0; i != this->counter.size(); ++i) { std::cout << this->counter[i] << " "; } } friend class Bitmap_cubical_complex_base; + protected: - std::vector< size_t > counter; + std::vector<std::size_t> counter; Bitmap_cubical_complex_base& b; }; @@ -402,7 +443,7 @@ class Bitmap_cubical_complex_base { **/ Top_dimensional_cells_iterator top_dimensional_cells_iterator_end() { Top_dimensional_cells_iterator a(*this); - for (size_t i = 0; i != this->dimension(); ++i) { + for (std::size_t i = 0; i != this->dimension(); ++i) { a.counter[i] = this->sizes[i] - 1; } a.counter[0]++; @@ -414,32 +455,24 @@ class Bitmap_cubical_complex_base { **/ class Top_dimensional_cells_range { public: - Top_dimensional_cells_range(Bitmap_cubical_complex_base* b) : b(b) { } + Top_dimensional_cells_range(Bitmap_cubical_complex_base* b) : b(b) {} - Top_dimensional_cells_iterator begin() { - return b->top_dimensional_cells_iterator_begin(); - } + Top_dimensional_cells_iterator begin() { return b->top_dimensional_cells_iterator_begin(); } + + Top_dimensional_cells_iterator end() { return b->top_dimensional_cells_iterator_end(); } - Top_dimensional_cells_iterator end() { - return b->top_dimensional_cells_iterator_end(); - } private: Bitmap_cubical_complex_base<T>* b; }; - Top_dimensional_cells_range top_dimensional_cells_range() { - return Top_dimensional_cells_range(this); - } - + Top_dimensional_cells_range top_dimensional_cells_range() { return Top_dimensional_cells_range(this); } //****************************************************************************************************************// //****************************************************************************************************************// //****************************************************************************************************************// //****************************************************************************************************************// - inline size_t number_cells()const { - return this->total_number_of_cells; - } + inline std::size_t number_cells() const { return this->total_number_of_cells; } //****************************************************************************************************************// //****************************************************************************************************************// @@ -450,11 +483,11 @@ class Bitmap_cubical_complex_base { std::vector<unsigned> sizes; std::vector<unsigned> multipliers; std::vector<T> data; - size_t total_number_of_cells; + std::size_t total_number_of_cells; void set_up_containers(const std::vector<unsigned>& sizes) { unsigned multiplier = 1; - for (size_t i = 0; i != sizes.size(); ++i) { + for (std::size_t i = 0; i != sizes.size(); ++i) { this->sizes.push_back(sizes[i]); this->multipliers.push_back(multiplier); multiplier *= 2 * sizes[i] + 1; @@ -463,18 +496,18 @@ class Bitmap_cubical_complex_base { this->total_number_of_cells = multiplier; } - size_t compute_position_in_bitmap(const std::vector< unsigned >& counter) { - size_t position = 0; - for (size_t i = 0; i != this->multipliers.size(); ++i) { + std::size_t compute_position_in_bitmap(const std::vector<unsigned>& counter) { + std::size_t position = 0; + for (std::size_t i = 0; i != this->multipliers.size(); ++i) { position += this->multipliers[i] * counter[i]; } return position; } - std::vector<unsigned> compute_counter_for_given_cell(size_t cell)const { + std::vector<unsigned> compute_counter_for_given_cell(std::size_t cell) const { std::vector<unsigned> counter; counter.reserve(this->sizes.size()); - for (size_t dim = this->sizes.size(); dim != 0; --dim) { + for (std::size_t dim = this->sizes.size(); dim != 0; --dim) { counter.push_back(cell / this->multipliers[dim - 1]); cell = cell % this->multipliers[dim - 1]; } @@ -486,96 +519,94 @@ class Bitmap_cubical_complex_base { const std::vector<T>& top_dimensional_cells); Bitmap_cubical_complex_base(const char* perseus_style_file, std::vector<bool> directions); Bitmap_cubical_complex_base(const std::vector<unsigned>& sizes, std::vector<bool> directions); - Bitmap_cubical_complex_base(const std::vector<unsigned>& dimensions, - const std::vector<T>& top_dimensional_cells, + Bitmap_cubical_complex_base(const std::vector<unsigned>& dimensions, const std::vector<T>& top_dimensional_cells, std::vector<bool> directions); }; template <typename T> -void Bitmap_cubical_complex_base<T>::put_data_to_bins(size_t number_of_bins) { - bool bdg = false; +void Bitmap_cubical_complex_base<T>::put_data_to_bins(std::size_t number_of_bins) { + bool dbg = false; - std::pair< T, T > min_max = this->min_max_filtration(); - T dx = (min_max.second - min_max.first) / (T) number_of_bins; + std::pair<T, T> min_max = this->min_max_filtration(); + T dx = (min_max.second - min_max.first) / (T)number_of_bins; // now put the data into the appropriate bins: - for (size_t i = 0; i != this->data.size(); ++i) { - if (bdg) { + for (std::size_t i = 0; i != this->data.size(); ++i) { + if (dbg) { std::cerr << "Before binning : " << this->data[i] << std::endl; } this->data[i] = min_max.first + dx * (this->data[i] - min_max.first) / number_of_bins; - if (bdg) { + if (dbg) { std::cerr << "After binning : " << this->data[i] << std::endl; - getchar(); } } } template <typename T> void Bitmap_cubical_complex_base<T>::put_data_to_bins(T diameter_of_bin) { - bool bdg = false; - std::pair< T, T > min_max = this->min_max_filtration(); + bool dbg = false; + std::pair<T, T> min_max = this->min_max_filtration(); - size_t number_of_bins = (min_max.second - min_max.first) / diameter_of_bin; + std::size_t number_of_bins = (min_max.second - min_max.first) / diameter_of_bin; // now put the data into the appropriate bins: - for (size_t i = 0; i != this->data.size(); ++i) { - if (bdg) { + for (std::size_t i = 0; i != this->data.size(); ++i) { + if (dbg) { std::cerr << "Before binning : " << this->data[i] << std::endl; } this->data[i] = min_max.first + diameter_of_bin * (this->data[i] - min_max.first) / number_of_bins; - if (bdg) { + if (dbg) { std::cerr << "After binning : " << this->data[i] << std::endl; - getchar(); } } } template <typename T> -std::pair< T, T > Bitmap_cubical_complex_base<T>::min_max_filtration() { - std::pair< T, T > min_max(std::numeric_limits<T>::max(), std::numeric_limits<T>::min()); - for (size_t i = 0; i != this->data.size(); ++i) { - if (this->data[i] < min_max.first)min_max.first = this->data[i]; - if (this->data[i] > min_max.second)min_max.second = this->data[i]; +std::pair<T, T> Bitmap_cubical_complex_base<T>::min_max_filtration() { + std::pair<T, T> min_max(std::numeric_limits<T>::max(), std::numeric_limits<T>::min()); + for (std::size_t i = 0; i != this->data.size(); ++i) { + if (this->data[i] < min_max.first) min_max.first = this->data[i]; + if (this->data[i] > min_max.second) min_max.second = this->data[i]; } return min_max; } template <typename K> -std::ostream& operator<<(std::ostream & out, const Bitmap_cubical_complex_base<K>& b) { - for (typename Bitmap_cubical_complex_base<K>::all_cells_const_iterator - it = b.all_cells_const_begin(); it != b.all_cells_const_end(); ++it) { +std::ostream& operator<<(std::ostream& out, const Bitmap_cubical_complex_base<K>& b) { + for (typename Bitmap_cubical_complex_base<K>::all_cells_const_iterator it = b.all_cells_const_begin(); + it != b.all_cells_const_end(); ++it) { out << *it << " "; } return out; } template <typename T> -Bitmap_cubical_complex_base<T>::Bitmap_cubical_complex_base -(const std::vector<unsigned>& sizes) { +Bitmap_cubical_complex_base<T>::Bitmap_cubical_complex_base(const std::vector<unsigned>& sizes) { this->set_up_containers(sizes); } template <typename T> -void Bitmap_cubical_complex_base<T>::setup_bitmap_based_on_top_dimensional_cells_list(const std::vector<unsigned>& sizes_in_following_directions, - const std::vector<T>& top_dimensional_cells) { +void Bitmap_cubical_complex_base<T>::setup_bitmap_based_on_top_dimensional_cells_list( + const std::vector<unsigned>& sizes_in_following_directions, const std::vector<T>& top_dimensional_cells) { this->set_up_containers(sizes_in_following_directions); - size_t number_of_top_dimensional_elements = 1; - for (size_t i = 0; i != sizes_in_following_directions.size(); ++i) { + std::size_t number_of_top_dimensional_elements = 1; + for (std::size_t i = 0; i != sizes_in_following_directions.size(); ++i) { number_of_top_dimensional_elements *= sizes_in_following_directions[i]; } if (number_of_top_dimensional_elements != top_dimensional_cells.size()) { - std::cerr << "Error in constructor Bitmap_cubical_complex_base ( std::vector<size_t> sizes_in_following_directions" - << ", std::vector<T> top_dimensional_cells ). Number of top dimensional elements that follow from " - << "sizes_in_following_directions vector is different than the size of top_dimensional_cells vector." - << std::endl; - throw("Error in constructor Bitmap_cubical_complex_base( std::vector<size_t> sizes_in_following_directions," - "std::vector<T> top_dimensional_cells ). Number of top dimensional elements that follow from " - "sizes_in_following_directions vector is different than the size of top_dimensional_cells vector."); + std::cerr << "Error in constructor Bitmap_cubical_complex_base ( std::vector<std::size_t> " + << "sizes_in_following_directions, std::vector<T> top_dimensional_cells ). Number of top dimensional " + << "elements that follow from sizes_in_following_directions vector is different than the size of " + << "top_dimensional_cells vector." + << std::endl; + throw( + "Error in constructor Bitmap_cubical_complex_base( std::vector<std::size_t> sizes_in_following_directions," + "std::vector<T> top_dimensional_cells ). Number of top dimensional elements that follow from " + "sizes_in_following_directions vector is different than the size of top_dimensional_cells vector."); } Bitmap_cubical_complex_base<T>::Top_dimensional_cells_iterator it(*this); - size_t index = 0; + std::size_t index = 0; for (it = this->top_dimensional_cells_iterator_begin(); it != this->top_dimensional_cells_iterator_end(); ++it) { this->get_cell_data(*it) = top_dimensional_cells[index]; ++index; @@ -584,8 +615,8 @@ void Bitmap_cubical_complex_base<T>::setup_bitmap_based_on_top_dimensional_cells } template <typename T> -Bitmap_cubical_complex_base<T>::Bitmap_cubical_complex_base -(const std::vector<unsigned>& sizes_in_following_directions, const std::vector<T>& top_dimensional_cells) { +Bitmap_cubical_complex_base<T>::Bitmap_cubical_complex_base(const std::vector<unsigned>& sizes_in_following_directions, + const std::vector<T>& top_dimensional_cells) { this->setup_bitmap_based_on_top_dimensional_cells_list(sizes_in_following_directions, top_dimensional_cells); } @@ -599,15 +630,17 @@ void Bitmap_cubical_complex_base<T>::read_perseus_style_file(const char* perseus if (dbg) { std::cerr << "dimensionOfData : " << dimensionOfData << std::endl; - getchar(); } std::vector<unsigned> sizes; sizes.reserve(dimensionOfData); - for (size_t i = 0; i != dimensionOfData; ++i) { + // all dimensions multiplied + std::size_t dimensions = 1; + for (std::size_t i = 0; i != dimensionOfData; ++i) { unsigned size_in_this_dimension; inFiltration >> size_in_this_dimension; sizes.push_back(size_in_this_dimension); + dimensions *= size_in_this_dimension; if (dbg) { std::cerr << "size_in_this_dimension : " << size_in_this_dimension << std::endl; } @@ -617,19 +650,20 @@ void Bitmap_cubical_complex_base<T>::read_perseus_style_file(const char* perseus Bitmap_cubical_complex_base<T>::Top_dimensional_cells_iterator it(*this); it = this->top_dimensional_cells_iterator_begin(); - while (!inFiltration.eof()) { - T filtrationLevel; - inFiltration >> filtrationLevel; + T filtrationLevel; + for (std::size_t i = 0; i < dimensions; ++i) { + if (!(inFiltration >> filtrationLevel) || (inFiltration.eof())) { + throw std::ios_base::failure("Bad Perseus file format."); + } if (dbg) { - std::cerr << "Cell of an index : " - << it.compute_index_in_bitmap() - << " and dimension: " - << this->get_dimension_of_a_cell(it.compute_index_in_bitmap()) - << " get the value : " << filtrationLevel << std::endl; + std::cerr << "Cell of an index : " << it.compute_index_in_bitmap() + << " and dimension: " << this->get_dimension_of_a_cell(it.compute_index_in_bitmap()) + << " get the value : " << filtrationLevel << std::endl; } this->get_cell_data(*it) = filtrationLevel; ++it; } + inFiltration.close(); this->impose_lower_star_filtration(); } @@ -668,37 +702,44 @@ Bitmap_cubical_complex_base<T>::Bitmap_cubical_complex_base(const char* perseus_ } template <typename T> -std::vector< size_t > Bitmap_cubical_complex_base<T>::get_boundary_of_a_cell(size_t cell)const { - std::vector< size_t > boundary_elements; +std::vector<std::size_t> Bitmap_cubical_complex_base<T>::get_boundary_of_a_cell(std::size_t cell) const { + std::vector<std::size_t> boundary_elements; // Speed traded of for memory. Check if it is better in practice. - boundary_elements.reserve(this->dimension()*2); + boundary_elements.reserve(this->dimension() * 2); - size_t cell1 = cell; - for (size_t i = this->multipliers.size(); i != 0; --i) { + std::size_t sum_of_dimensions = 0; + std::size_t cell1 = cell; + for (std::size_t i = this->multipliers.size(); i != 0; --i) { unsigned position = cell1 / this->multipliers[i - 1]; if (position % 2 == 1) { - boundary_elements.push_back(cell - this->multipliers[ i - 1 ]); - boundary_elements.push_back(cell + this->multipliers[ i - 1 ]); + if (sum_of_dimensions % 2) { + boundary_elements.push_back(cell + this->multipliers[i - 1]); + boundary_elements.push_back(cell - this->multipliers[i - 1]); + } else { + boundary_elements.push_back(cell - this->multipliers[i - 1]); + boundary_elements.push_back(cell + this->multipliers[i - 1]); + } + ++sum_of_dimensions; } cell1 = cell1 % this->multipliers[i - 1]; } + return boundary_elements; } template <typename T> -std::vector< size_t > Bitmap_cubical_complex_base<T>::get_coboundary_of_a_cell(size_t cell)const { +std::vector<std::size_t> Bitmap_cubical_complex_base<T>::get_coboundary_of_a_cell(std::size_t cell) const { std::vector<unsigned> counter = this->compute_counter_for_given_cell(cell); - std::vector< size_t > coboundary_elements; - size_t cell1 = cell; - for (size_t i = this->multipliers.size(); i != 0; --i) { + std::vector<std::size_t> coboundary_elements; + std::size_t cell1 = cell; + for (std::size_t i = this->multipliers.size(); i != 0; --i) { unsigned position = cell1 / this->multipliers[i - 1]; if (position % 2 == 0) { if ((cell > this->multipliers[i - 1]) && (counter[i - 1] != 0)) { coboundary_elements.push_back(cell - this->multipliers[i - 1]); } - if ( - (cell + this->multipliers[i - 1] < this->data.size()) && (counter[i - 1] != 2 * this->sizes[i - 1])) { + if ((cell + this->multipliers[i - 1] < this->data.size()) && (counter[i - 1] != 2 * this->sizes[i - 1])) { coboundary_elements.push_back(cell + this->multipliers[i - 1]); } } @@ -708,11 +749,11 @@ std::vector< size_t > Bitmap_cubical_complex_base<T>::get_coboundary_of_a_cell(s } template <typename T> -unsigned Bitmap_cubical_complex_base<T>::get_dimension_of_a_cell(size_t cell)const { +unsigned Bitmap_cubical_complex_base<T>::get_dimension_of_a_cell(std::size_t cell) const { bool dbg = false; if (dbg) std::cerr << "\n\n\n Computing position o a cell of an index : " << cell << std::endl; unsigned dimension = 0; - for (size_t i = this->multipliers.size(); i != 0; --i) { + for (std::size_t i = this->multipliers.size(); i != 0; --i) { unsigned position = cell / this->multipliers[i - 1]; if (dbg) { @@ -720,7 +761,6 @@ unsigned Bitmap_cubical_complex_base<T>::get_dimension_of_a_cell(size_t cell)con std::cerr << "cell : " << cell << std::endl; std::cerr << "position : " << position << std::endl; std::cerr << "multipliers[" << i - 1 << "] = " << this->multipliers[i - 1] << std::endl; - getchar(); } if (position % 2 == 1) { @@ -733,7 +773,7 @@ unsigned Bitmap_cubical_complex_base<T>::get_dimension_of_a_cell(size_t cell)con } template <typename T> -inline T& Bitmap_cubical_complex_base<T>::get_cell_data(size_t cell) { +inline T& Bitmap_cubical_complex_base<T>::get_cell_data(std::size_t cell) { return this->data[cell]; } @@ -744,12 +784,12 @@ void Bitmap_cubical_complex_base<T>::impose_lower_star_filtration() { // this vector will be used to check which elements have already been taken care of in imposing lower star filtration std::vector<bool> is_this_cell_considered(this->data.size(), false); - size_t size_to_reserve = 1; - for (size_t i = 0; i != this->multipliers.size(); ++i) { - size_to_reserve *= (size_t) ((this->multipliers[i] - 1) / 2); + std::size_t size_to_reserve = 1; + for (std::size_t i = 0; i != this->multipliers.size(); ++i) { + size_to_reserve *= (std::size_t)((this->multipliers[i] - 1) / 2); } - std::vector<size_t> indices_to_consider; + std::vector<std::size_t> indices_to_consider; indices_to_consider.reserve(size_to_reserve); // we assume here that we already have a filtration on the top dimensional cells and // we have to extend it to lower ones. @@ -761,32 +801,29 @@ void Bitmap_cubical_complex_base<T>::impose_lower_star_filtration() { while (indices_to_consider.size()) { if (dbg) { std::cerr << "indices_to_consider in this iteration \n"; - for (size_t i = 0; i != indices_to_consider.size(); ++i) { + for (std::size_t i = 0; i != indices_to_consider.size(); ++i) { std::cout << indices_to_consider[i] << " "; } - getchar(); } - std::vector<size_t> new_indices_to_consider; - for (size_t i = 0; i != indices_to_consider.size(); ++i) { - std::vector<size_t> bd = this->get_boundary_of_a_cell(indices_to_consider[i]); - for (size_t boundaryIt = 0; boundaryIt != bd.size(); ++boundaryIt) { + std::vector<std::size_t> new_indices_to_consider; + for (std::size_t i = 0; i != indices_to_consider.size(); ++i) { + std::vector<std::size_t> bd = this->get_boundary_of_a_cell(indices_to_consider[i]); + for (std::size_t boundaryIt = 0; boundaryIt != bd.size(); ++boundaryIt) { if (dbg) { - std::cerr << "filtration of a cell : " << bd[boundaryIt] << " is : " << this->data[ bd[boundaryIt] ] - << " while of a cell: " << indices_to_consider[i] << " is: " << this->data[ indices_to_consider[i] ] - << std::endl; - getchar(); + std::cerr << "filtration of a cell : " << bd[boundaryIt] << " is : " << this->data[bd[boundaryIt]] + << " while of a cell: " << indices_to_consider[i] << " is: " << this->data[indices_to_consider[i]] + << std::endl; } - if (this->data[ bd[boundaryIt] ] > this->data[ indices_to_consider[i] ]) { - this->data[ bd[boundaryIt] ] = this->data[ indices_to_consider[i] ]; + if (this->data[bd[boundaryIt]] > this->data[indices_to_consider[i]]) { + this->data[bd[boundaryIt]] = this->data[indices_to_consider[i]]; if (dbg) { - std::cerr << "Setting the value of a cell : " << bd[boundaryIt] << " to : " - << this->data[ indices_to_consider[i] ] << std::endl; - getchar(); + std::cerr << "Setting the value of a cell : " << bd[boundaryIt] + << " to : " << this->data[indices_to_consider[i]] << std::endl; } } - if (is_this_cell_considered[ bd[boundaryIt] ] == false) { + if (is_this_cell_considered[bd[boundaryIt]] == false) { new_indices_to_consider.push_back(bd[boundaryIt]); - is_this_cell_considered[ bd[boundaryIt] ] = true; + is_this_cell_considered[bd[boundaryIt]] = true; } } } @@ -795,8 +832,8 @@ void Bitmap_cubical_complex_base<T>::impose_lower_star_filtration() { } template <typename T> -bool compareFirstElementsOfTuples(const std::pair< std::pair< T, size_t >, char >& first, - const std::pair< std::pair< T, size_t >, char >& second) { +bool compareFirstElementsOfTuples(const std::pair<std::pair<T, std::size_t>, char>& first, + const std::pair<std::pair<T, std::size_t>, char>& second) { if (first.first.first < second.first.first) { return true; } else { diff --git a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h index c3cc93dd..4a0d1c74 100644 --- a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h +++ b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h @@ -28,6 +28,8 @@ #include <cmath> #include <limits> // for numeric_limits<> #include <vector> +#include <stdexcept> +#include <cstddef> namespace Gudhi { @@ -41,7 +43,8 @@ namespace cubical_complex { /** * @brief Cubical complex with periodic boundary conditions represented as a bitmap. * @ingroup cubical_complex - * @details This is a class implementing a bitmap data structure with periodic boundary conditions. Most of the functions are + * @details This is a class implementing a bitmap data structure with periodic boundary conditions. Most of the + * functions are * identical to the functions from Bitmap_cubical_complex_base. * The ones that needed to be updated are the constructors and get_boundary_of_a_cell and get_coboundary_of_a_cell. */ @@ -53,7 +56,7 @@ class Bitmap_cubical_complex_periodic_boundary_conditions_base : public Bitmap_c /** * Default constructor of Bitmap_cubical_complex_periodic_boundary_conditions_base class. */ - Bitmap_cubical_complex_periodic_boundary_conditions_base() { } + Bitmap_cubical_complex_periodic_boundary_conditions_base() {} /** * A constructor of Bitmap_cubical_complex_periodic_boundary_conditions_base class that takes the following * parameters: (1) vector with numbers of top dimensional cells in all dimensions and (2) vector of booleans. If @@ -61,8 +64,9 @@ class Bitmap_cubical_complex_periodic_boundary_conditions_base : public Bitmap_c * imposed in this direction. In case of false, the periodic boundary conditions will not be imposed in the direction * i. */ - Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& sizes, - const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed); + Bitmap_cubical_complex_periodic_boundary_conditions_base( + const std::vector<unsigned>& sizes, + const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed); /** * A constructor of Bitmap_cubical_complex_periodic_boundary_conditions_base class that takes the name of Perseus * style file as an input. Please consult the documentation about the specification of the file. @@ -75,9 +79,9 @@ class Bitmap_cubical_complex_periodic_boundary_conditions_base : public Bitmap_c * value, that means that periodic boundary conditions are to be imposed in this direction. In case of false, the * periodic boundary conditions will not be imposed in the direction i. */ - Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& dimensions, - const std::vector<T>& topDimensionalCells, - const std::vector< bool >& directions_in_which_periodic_b_cond_are_to_be_imposed); + Bitmap_cubical_complex_periodic_boundary_conditions_base( + const std::vector<unsigned>& dimensions, const std::vector<T>& topDimensionalCells, + const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed); /** * Destructor of the Bitmap_cubical_complex_periodic_boundary_conditions_base class. @@ -88,21 +92,81 @@ class Bitmap_cubical_complex_periodic_boundary_conditions_base : public Bitmap_c /** * A version of a function that return boundary of a given cell for an object of * Bitmap_cubical_complex_periodic_boundary_conditions_base class. + * The boundary elements are guaranteed to be returned so that the + * incidence coefficients are alternating. */ - virtual std::vector< size_t > get_boundary_of_a_cell(size_t cell) const; + virtual std::vector<std::size_t> get_boundary_of_a_cell(std::size_t cell) const; /** * A version of a function that return coboundary of a given cell for an object of * Bitmap_cubical_complex_periodic_boundary_conditions_base class. + * Note that unlike in the case of boundary, over here the elements are + * not guaranteed to be returned with alternating incidence numbers. + * To compute incidence between cells use compute_incidence_between_cells + * procedure */ - virtual std::vector< size_t > get_coboundary_of_a_cell(size_t cell) const; + virtual std::vector<std::size_t> get_coboundary_of_a_cell(std::size_t cell) const; + + /** + * This procedure compute incidence numbers between cubes. For a cube \f$A\f$ of + * dimension n and a cube \f$B \subset A\f$ of dimension n-1, an incidence + * between \f$A\f$ and \f$B\f$ is the integer with which \f$B\f$ appears in the boundary of \f$A\f$. + * Note that first parameter is a cube of dimension n, + * and the second parameter is an adjusted cube in dimension n-1. + * Given \f$A = [b_1,e_1] \times \ldots \ [b_{j-1},e_{j-1}] \times [b_{j},e_{j}] \times [b_{j+1},e_{j+1}] \times \ldots + *\times [b_{n},e_{n}] \f$ + * such that \f$ b_{j} \neq e_{j} \f$ + * and \f$B = [b_1,e_1] \times \ldots \ [b_{j-1},e_{j-1}] \times [a,a] \times [b_{j+1},e_{j+1}] \times \ldots \times + *[b_{n},e_{n}]s \f$ + * where \f$ a = b_{j}\f$ or \f$ a = e_{j}\f$, the incidence between \f$A\f$ and \f$B\f$ + * computed by this procedure is given by formula: + * \f$ c\ (-1)^{\sum_{i=1}^{j-1} dim [b_{i},e_{i}]} \f$ + * Where \f$ dim [b_{i},e_{i}] = 0 \f$ if \f$ b_{i}=e_{i} \f$ and 1 in other case. + * c is -1 if \f$ a = b_{j}\f$ and 1 if \f$ a = e_{j}\f$. + * @exception std::logic_error In case when the cube \f$B\f$ is not n-1 + * dimensional face of a cube \f$A\f$. + **/ + virtual int compute_incidence_between_cells(std::size_t coface, std::size_t face) { + // first get the counters for coface and face: + std::vector<unsigned> coface_counter = this->compute_counter_for_given_cell(coface); + std::vector<unsigned> face_counter = this->compute_counter_for_given_cell(face); + + // coface_counter and face_counter should agree at all positions except from one: + int number_of_position_in_which_counters_do_not_agree = -1; + std::size_t number_of_full_faces_that_comes_before = 0; + for (std::size_t i = 0; i != coface_counter.size(); ++i) { + if ((coface_counter[i] % 2 == 1) && (number_of_position_in_which_counters_do_not_agree == -1)) { + ++number_of_full_faces_that_comes_before; + } + if (coface_counter[i] != face_counter[i]) { + if (number_of_position_in_which_counters_do_not_agree != -1) { + std::cout << "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face.\n"; + throw std::logic_error( + "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face."); + } + number_of_position_in_which_counters_do_not_agree = i; + } + } + + int incidence = 1; + if (number_of_full_faces_that_comes_before % 2) incidence = -1; + // if the face cell is on the right from coface cell: + if ((coface_counter[number_of_position_in_which_counters_do_not_agree] + 1 == + face_counter[number_of_position_in_which_counters_do_not_agree]) || + ((coface_counter[number_of_position_in_which_counters_do_not_agree] != 1) && + (face_counter[number_of_position_in_which_counters_do_not_agree] == 0))) { + incidence *= -1; + } + + return incidence; + } protected: - std::vector< bool > directions_in_which_periodic_b_cond_are_to_be_imposed; + std::vector<bool> directions_in_which_periodic_b_cond_are_to_be_imposed; void set_up_containers(const std::vector<unsigned>& sizes) { unsigned multiplier = 1; - for (size_t i = 0; i != sizes.size(); ++i) { + for (std::size_t i = 0; i != sizes.size(); ++i) { this->sizes.push_back(sizes[i]); this->multipliers.push_back(multiplier); @@ -119,19 +183,23 @@ class Bitmap_cubical_complex_periodic_boundary_conditions_base : public Bitmap_c Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& sizes); Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& dimensions, const std::vector<T>& topDimensionalCells); - void construct_complex_based_on_top_dimensional_cells(const std::vector<unsigned>& dimensions, - const std::vector<T>& topDimensionalCells, - const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed); + + /** + * A procedure used to construct the data structures in the class. + **/ + void construct_complex_based_on_top_dimensional_cells( + const std::vector<unsigned>& dimensions, const std::vector<T>& topDimensionalCells, + const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed); }; template <typename T> -void Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::construct_complex_based_on_top_dimensional_cells(const std::vector<unsigned>& dimensions, - const std::vector<T>& topDimensionalCells, - const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed) { +void Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::construct_complex_based_on_top_dimensional_cells( + const std::vector<unsigned>& dimensions, const std::vector<T>& topDimensionalCells, + const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed) { this->directions_in_which_periodic_b_cond_are_to_be_imposed = directions_in_which_periodic_b_cond_are_to_be_imposed; this->set_up_containers(dimensions); - size_t i = 0; + std::size_t i = 0; for (auto it = this->top_dimensional_cells_iterator_begin(); it != this->top_dimensional_cells_iterator_end(); ++it) { this->get_cell_data(*it) = topDimensionalCells[i]; ++i; @@ -140,14 +208,16 @@ void Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::construct_comp } template <typename T> -Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& sizes, - const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed) { +Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base( + const std::vector<unsigned>& sizes, + const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed) { this->directions_in_which_periodic_b_cond_are_to_be_imposed(directions_in_which_periodic_b_cond_are_to_be_imposed); this->set_up_containers(sizes); } template <typename T> -Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base(const char* perseus_style_file) { +Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base( + const char* perseus_style_file) { // for Perseus style files: bool dbg = false; @@ -160,7 +230,7 @@ Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_comp std::vector<unsigned> sizes; sizes.reserve(dimensionOfData); - for (size_t i = 0; i != dimensionOfData; ++i) { + for (std::size_t i = 0; i != dimensionOfData; ++i) { int size_in_this_dimension; inFiltration >> size_in_this_dimension; if (size_in_this_dimension < 0) { @@ -176,14 +246,12 @@ Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_comp while (!inFiltration.eof()) { double filtrationLevel; inFiltration >> filtrationLevel; - if (inFiltration.eof())break; + if (inFiltration.eof()) break; if (dbg) { - std::cerr << "Cell of an index : " - << it.compute_index_in_bitmap() - << " and dimension: " - << this->get_dimension_of_a_cell(it.compute_index_in_bitmap()) - << " get the value : " << filtrationLevel << std::endl; + std::cerr << "Cell of an index : " << it.compute_index_in_bitmap() + << " and dimension: " << this->get_dimension_of_a_cell(it.compute_index_in_bitmap()) + << " get the value : " << filtrationLevel << std::endl; } this->get_cell_data(*it) = filtrationLevel; ++it; @@ -193,24 +261,24 @@ Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_comp } template <typename T> -Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& sizes) { +Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base( + const std::vector<unsigned>& sizes) { this->directions_in_which_periodic_b_cond_are_to_be_imposed = std::vector<bool>(sizes.size(), false); this->set_up_containers(sizes); } template <typename T> -Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& dimensions, - const std::vector<T>& topDimensionalCells) { +Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base( + const std::vector<unsigned>& dimensions, const std::vector<T>& topDimensionalCells) { std::vector<bool> directions_in_which_periodic_b_cond_are_to_be_imposed = std::vector<bool>(dimensions.size(), false); this->construct_complex_based_on_top_dimensional_cells(dimensions, topDimensionalCells, directions_in_which_periodic_b_cond_are_to_be_imposed); } template <typename T> -Bitmap_cubical_complex_periodic_boundary_conditions_base<T>:: -Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& dimensions, - const std::vector<T>& topDimensionalCells, - const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed) { +Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base( + const std::vector<unsigned>& dimensions, const std::vector<T>& topDimensionalCells, + const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed) { this->construct_complex_based_on_top_dimensional_cells(dimensions, topDimensionalCells, directions_in_which_periodic_b_cond_are_to_be_imposed); } @@ -218,46 +286,65 @@ Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsig // ***********************Methods************************ // template <typename T> -std::vector< size_t > Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::get_boundary_of_a_cell(size_t cell) const { +std::vector<std::size_t> Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::get_boundary_of_a_cell( + std::size_t cell) const { bool dbg = false; if (dbg) { std::cerr << "Computations of boundary of a cell : " << cell << std::endl; } - std::vector< size_t > boundary_elements; - size_t cell1 = cell; - for (size_t i = this->multipliers.size(); i != 0; --i) { + std::vector<std::size_t> boundary_elements; + boundary_elements.reserve(this->dimension() * 2); + std::size_t cell1 = cell; + std::size_t sum_of_dimensions = 0; + + for (std::size_t i = this->multipliers.size(); i != 0; --i) { unsigned position = cell1 / this->multipliers[i - 1]; // this cell have a nonzero length in this direction, therefore we can compute its boundary in this direction. - if (position % 2 == 1) { // if there are no periodic boundary conditions in this direction, we do not have to do anything. if (!directions_in_which_periodic_b_cond_are_to_be_imposed[i - 1]) { // std::cerr << "A\n"; - boundary_elements.push_back(cell - this->multipliers[ i - 1 ]); - boundary_elements.push_back(cell + this->multipliers[ i - 1 ]); + if (sum_of_dimensions % 2) { + boundary_elements.push_back(cell - this->multipliers[i - 1]); + boundary_elements.push_back(cell + this->multipliers[i - 1]); + } else { + boundary_elements.push_back(cell + this->multipliers[i - 1]); + boundary_elements.push_back(cell - this->multipliers[i - 1]); + } if (dbg) { - std::cerr << cell - this->multipliers[ i - 1 ] << " " << cell + this->multipliers[ i - 1 ] << " "; + std::cerr << cell - this->multipliers[i - 1] << " " << cell + this->multipliers[i - 1] << " "; } } else { // in this direction we have to do boundary conditions. Therefore, we need to check if we are not at the end. - if (position != 2 * this->sizes[ i - 1 ] - 1) { + if (position != 2 * this->sizes[i - 1] - 1) { // std::cerr << "B\n"; - boundary_elements.push_back(cell - this->multipliers[ i - 1 ]); - boundary_elements.push_back(cell + this->multipliers[ i - 1 ]); + if (sum_of_dimensions % 2) { + boundary_elements.push_back(cell - this->multipliers[i - 1]); + boundary_elements.push_back(cell + this->multipliers[i - 1]); + } else { + boundary_elements.push_back(cell + this->multipliers[i - 1]); + boundary_elements.push_back(cell - this->multipliers[i - 1]); + } if (dbg) { - std::cerr << cell - this->multipliers[ i - 1 ] << " " << cell + this->multipliers[ i - 1 ] << " "; + std::cerr << cell - this->multipliers[i - 1] << " " << cell + this->multipliers[i - 1] << " "; } } else { // std::cerr << "C\n"; - boundary_elements.push_back(cell - this->multipliers[ i - 1 ]); - boundary_elements.push_back(cell - (2 * this->sizes[ i - 1 ] - 1) * this->multipliers[ i - 1 ]); + if (sum_of_dimensions % 2) { + boundary_elements.push_back(cell - this->multipliers[i - 1]); + boundary_elements.push_back(cell - (2 * this->sizes[i - 1] - 1) * this->multipliers[i - 1]); + } else { + boundary_elements.push_back(cell - (2 * this->sizes[i - 1] - 1) * this->multipliers[i - 1]); + boundary_elements.push_back(cell - this->multipliers[i - 1]); + } if (dbg) { - std::cerr << cell - this->multipliers[ i - 1 ] << " " << - cell - (2 * this->sizes[ i - 1 ] - 1) * this->multipliers[ i - 1 ] << " "; + std::cerr << cell - this->multipliers[i - 1] << " " + << cell - (2 * this->sizes[i - 1] - 1) * this->multipliers[i - 1] << " "; } } } + ++sum_of_dimensions; } cell1 = cell1 % this->multipliers[i - 1]; } @@ -265,11 +352,12 @@ std::vector< size_t > Bitmap_cubical_complex_periodic_boundary_conditions_base<T } template <typename T> -std::vector< size_t > Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::get_coboundary_of_a_cell(size_t cell) const { +std::vector<std::size_t> Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::get_coboundary_of_a_cell( + std::size_t cell) const { std::vector<unsigned> counter = this->compute_counter_for_given_cell(cell); - std::vector< size_t > coboundary_elements; - size_t cell1 = cell; - for (size_t i = this->multipliers.size(); i != 0; --i) { + std::vector<std::size_t> coboundary_elements; + std::size_t cell1 = cell; + for (std::size_t i = this->multipliers.size(); i != 0; --i) { unsigned position = cell1 / this->multipliers[i - 1]; // if the cell has zero length in this direction, then it will have cbd in this direction. if (position % 2 == 0) { @@ -289,7 +377,7 @@ std::vector< size_t > Bitmap_cubical_complex_periodic_boundary_conditions_base<T } else { // in this case counter[i-1] == 0. coboundary_elements.push_back(cell + this->multipliers[i - 1]); - coboundary_elements.push_back(cell + (2 * this->sizes[ i - 1 ] - 1) * this->multipliers[i - 1]); + coboundary_elements.push_back(cell + (2 * this->sizes[i - 1] - 1) * this->multipliers[i - 1]); } } } diff --git a/src/Bitmap_cubical_complex/test/Bitmap_test.cpp b/src/Bitmap_cubical_complex/test/Bitmap_test.cpp index db90eb94..4af699e9 100644 --- a/src/Bitmap_cubical_complex/test/Bitmap_test.cpp +++ b/src/Bitmap_cubical_complex/test/Bitmap_test.cpp @@ -33,17 +33,16 @@ #include <sstream> #include <vector> - typedef Gudhi::cubical_complex::Bitmap_cubical_complex_base<double> Bitmap_cubical_complex_base; typedef Gudhi::cubical_complex::Bitmap_cubical_complex<Bitmap_cubical_complex_base> Bitmap_cubical_complex; typedef Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double> -Bitmap_cubical_complex_periodic_boundary_conditions_base; + Bitmap_cubical_complex_periodic_boundary_conditions_base; typedef Gudhi::cubical_complex::Bitmap_cubical_complex<Bitmap_cubical_complex_periodic_boundary_conditions_base> -Bitmap_cubical_complex_periodic_boundary_conditions; + Bitmap_cubical_complex_periodic_boundary_conditions; BOOST_AUTO_TEST_CASE(check_dimension) { - std::vector< double > increasingFiltrationOfTopDimensionalCells({1, 2, 3, 4, 5, 6, 7, 8, 9}); + std::vector<double> increasingFiltrationOfTopDimensionalCells({1, 2, 3, 4, 5, 6, 7, 8, 9}); std::vector<unsigned> dimensions({3, 3}); @@ -52,29 +51,28 @@ BOOST_AUTO_TEST_CASE(check_dimension) { } BOOST_AUTO_TEST_CASE(topDimensionalCellsIterator_test) { - std::vector< double > expectedFiltrationValues1({0, 0, 0, 0, 100, 0, 0, 0, 0}); + std::vector<double> expectedFiltrationValues1({0, 0, 0, 0, 100, 0, 0, 0, 0}); - std::vector< double > expectedFiltrationValues2({1, 2, 3, 4, 5, 6, 7, 8, 9}); + std::vector<double> expectedFiltrationValues2({1, 2, 3, 4, 5, 6, 7, 8, 9}); - std::vector< double > increasingFiltrationOfTopDimensionalCells({1, 2, 3, 4, 5, 6, 7, 8, 9}); + std::vector<double> increasingFiltrationOfTopDimensionalCells({1, 2, 3, 4, 5, 6, 7, 8, 9}); - std::vector< double > oneDimensionalCycle({0, 0, 0, 0, 100, 0, 0, 0, 0}); + std::vector<double> oneDimensionalCycle({0, 0, 0, 0, 100, 0, 0, 0, 0}); std::vector<unsigned> dimensions({3, 3}); Bitmap_cubical_complex increasing(dimensions, increasingFiltrationOfTopDimensionalCells); Bitmap_cubical_complex hole(dimensions, oneDimensionalCycle); - int i = 0; - for (Bitmap_cubical_complex::Top_dimensional_cells_iterator - it = increasing.top_dimensional_cells_iterator_begin(); it != increasing.top_dimensional_cells_iterator_end(); ++it) { + for (Bitmap_cubical_complex::Top_dimensional_cells_iterator it = increasing.top_dimensional_cells_iterator_begin(); + it != increasing.top_dimensional_cells_iterator_end(); ++it) { BOOST_CHECK(increasing.get_cell_data(*it) == expectedFiltrationValues2[i]); ++i; } i = 0; - for (Bitmap_cubical_complex::Top_dimensional_cells_iterator - it = hole.top_dimensional_cells_iterator_begin(); it != hole.top_dimensional_cells_iterator_end(); ++it) { + for (Bitmap_cubical_complex::Top_dimensional_cells_iterator it = hole.top_dimensional_cells_iterator_begin(); + it != hole.top_dimensional_cells_iterator_end(); ++it) { BOOST_CHECK(hole.get_cell_data(*it) == expectedFiltrationValues1[i]); ++i; } @@ -100,24 +98,24 @@ BOOST_AUTO_TEST_CASE(compute_boundary_test_1) { std::vector<double> boundary8; boundary8.push_back(1); boundary8.push_back(15); - boundary8.push_back(7); boundary8.push_back(9); + boundary8.push_back(7); std::vector<double> boundary9; boundary9.push_back(2); boundary9.push_back(16); std::vector<double> boundary10; boundary10.push_back(3); boundary10.push_back(17); - boundary10.push_back(9); boundary10.push_back(11); + boundary10.push_back(9); std::vector<double> boundary11; boundary11.push_back(4); boundary11.push_back(18); std::vector<double> boundary12; boundary12.push_back(5); boundary12.push_back(19); - boundary12.push_back(11); boundary12.push_back(13); + boundary12.push_back(11); std::vector<double> boundary13; boundary13.push_back(6); boundary13.push_back(20); @@ -140,24 +138,24 @@ BOOST_AUTO_TEST_CASE(compute_boundary_test_1) { std::vector<double> boundary22; boundary22.push_back(15); boundary22.push_back(29); - boundary22.push_back(21); boundary22.push_back(23); + boundary22.push_back(21); std::vector<double> boundary23; boundary23.push_back(16); boundary23.push_back(30); std::vector<double> boundary24; boundary24.push_back(17); boundary24.push_back(31); - boundary24.push_back(23); boundary24.push_back(25); + boundary24.push_back(23); std::vector<double> boundary25; boundary25.push_back(18); boundary25.push_back(32); std::vector<double> boundary26; boundary26.push_back(19); boundary26.push_back(33); - boundary26.push_back(25); boundary26.push_back(27); + boundary26.push_back(25); std::vector<double> boundary27; boundary27.push_back(20); boundary27.push_back(34); @@ -180,24 +178,24 @@ BOOST_AUTO_TEST_CASE(compute_boundary_test_1) { std::vector<double> boundary36; boundary36.push_back(29); boundary36.push_back(43); - boundary36.push_back(35); boundary36.push_back(37); + boundary36.push_back(35); std::vector<double> boundary37; boundary37.push_back(30); boundary37.push_back(44); std::vector<double> boundary38; boundary38.push_back(31); boundary38.push_back(45); - boundary38.push_back(37); boundary38.push_back(39); + boundary38.push_back(37); std::vector<double> boundary39; boundary39.push_back(32); boundary39.push_back(46); std::vector<double> boundary40; boundary40.push_back(33); boundary40.push_back(47); - boundary40.push_back(39); boundary40.push_back(41); + boundary40.push_back(39); std::vector<double> boundary41; boundary41.push_back(34); boundary41.push_back(48); @@ -214,7 +212,7 @@ BOOST_AUTO_TEST_CASE(compute_boundary_test_1) { boundary47.push_back(46); boundary47.push_back(48); std::vector<double> boundary48; - std::vector< std::vector<double> > boundaries; + std::vector<std::vector<double> > boundaries; boundaries.push_back(boundary0); boundaries.push_back(boundary1); boundaries.push_back(boundary2); @@ -265,15 +263,13 @@ BOOST_AUTO_TEST_CASE(compute_boundary_test_1) { boundaries.push_back(boundary47); boundaries.push_back(boundary48); - - - std::vector< double > increasingFiltrationOfTopDimensionalCells({1, 2, 3, 4, 5, 6, 7, 8, 9}); + std::vector<double> increasingFiltrationOfTopDimensionalCells({1, 2, 3, 4, 5, 6, 7, 8, 9}); std::vector<unsigned> dimensions({3, 3}); Bitmap_cubical_complex increasing(dimensions, increasingFiltrationOfTopDimensionalCells); for (size_t i = 0; i != increasing.size(); ++i) { - std::vector< size_t > bd = increasing.get_boundary_of_a_cell(i); + std::vector<size_t> bd = increasing.get_boundary_of_a_cell(i); for (size_t j = 0; j != bd.size(); ++j) { BOOST_CHECK(boundaries[i][j] == bd[j]); } @@ -281,13 +277,12 @@ BOOST_AUTO_TEST_CASE(compute_boundary_test_1) { } BOOST_AUTO_TEST_CASE(compute_boundary_test_2) { - std::vector< double > increasingFiltrationOfTopDimensionalCells({1, 2, 3, 4, 5, 6, 7, 8, 9}); + std::vector<double> increasingFiltrationOfTopDimensionalCells({1, 2, 3, 4, 5, 6, 7, 8, 9}); std::vector<unsigned> dimensions({3, 3}); Bitmap_cubical_complex increasing(dimensions, increasingFiltrationOfTopDimensionalCells); - std::vector<double> coboundaryElements; coboundaryElements.push_back(7); coboundaryElements.push_back(1); @@ -373,9 +368,10 @@ BOOST_AUTO_TEST_CASE(compute_boundary_test_2) { coboundaryElements.push_back(40); coboundaryElements.push_back(41); coboundaryElements.push_back(47); + size_t number = 0; for (size_t i = 0; i != increasing.size(); ++i) { - std::vector< size_t > bd = increasing.get_coboundary_of_a_cell(i); + std::vector<size_t> bd = increasing.get_coboundary_of_a_cell(i); for (size_t j = 0; j != bd.size(); ++j) { BOOST_CHECK(coboundaryElements[number] == bd[j]); ++number; @@ -384,7 +380,7 @@ BOOST_AUTO_TEST_CASE(compute_boundary_test_2) { } BOOST_AUTO_TEST_CASE(compute_boundary_test_3) { - std::vector< double > increasingFiltrationOfTopDimensionalCells({1, 2, 3, 4, 5, 6, 7, 8, 9}); + std::vector<double> increasingFiltrationOfTopDimensionalCells({1, 2, 3, 4, 5, 6, 7, 8, 9}); std::vector<unsigned> dimensions({3, 3}); @@ -447,13 +443,13 @@ BOOST_AUTO_TEST_CASE(compute_boundary_test_3) { } BOOST_AUTO_TEST_CASE(Filtration_simplex_iterator_test) { - std::vector< double > increasingFiltrationOfTopDimensionalCells({1, 2, 3, 4, 5, 6, 7, 8, 9}); + std::vector<double> increasingFiltrationOfTopDimensionalCells({1, 2, 3, 4, 5, 6, 7, 8, 9}); std::vector<unsigned> dimensions({3, 3}); Bitmap_cubical_complex increasing(dimensions, increasingFiltrationOfTopDimensionalCells); - std::vector< unsigned > dim; + std::vector<unsigned> dim; dim.push_back(0); dim.push_back(0); dim.push_back(0); @@ -555,7 +551,6 @@ BOOST_AUTO_TEST_CASE(Filtration_simplex_iterator_test) { fil.push_back(9); fil.push_back(9); - Bitmap_cubical_complex::Filtration_simplex_range range = increasing.filtration_simplex_range(); size_t position = 0; for (Bitmap_cubical_complex::Filtration_simplex_iterator it = range.begin(); it != range.end(); ++it) { @@ -566,7 +561,7 @@ BOOST_AUTO_TEST_CASE(Filtration_simplex_iterator_test) { } BOOST_AUTO_TEST_CASE(boudary_operator_2d_bitmap_with_periodic_bcond) { - std::vector< double > filtration({0, 0, 0, 0}); + std::vector<double> filtration({0, 0, 0, 0}); std::vector<unsigned> dimensions({2, 2}); @@ -575,57 +570,56 @@ BOOST_AUTO_TEST_CASE(boudary_operator_2d_bitmap_with_periodic_bcond) { Bitmap_cubical_complex_periodic_boundary_conditions cmplx(dimensions, filtration, periodic_directions); BOOST_CHECK(cmplx.dimension() == 2); - std::vector<double> boundary0; std::vector<double> boundary1; - boundary1.push_back(0); boundary1.push_back(2); + boundary1.push_back(0); std::vector<double> boundary2; std::vector<double> boundary3; - boundary3.push_back(2); boundary3.push_back(0); + boundary3.push_back(2); std::vector<double> boundary4; - boundary4.push_back(0); boundary4.push_back(8); + boundary4.push_back(0); std::vector<double> boundary5; - boundary5.push_back(1); boundary5.push_back(9); + boundary5.push_back(1); boundary5.push_back(4); boundary5.push_back(6); std::vector<double> boundary6; - boundary6.push_back(2); boundary6.push_back(10); + boundary6.push_back(2); std::vector<double> boundary7; - boundary7.push_back(3); boundary7.push_back(11); + boundary7.push_back(3); boundary7.push_back(6); boundary7.push_back(4); std::vector<double> boundary8; std::vector<double> boundary9; - boundary9.push_back(8); boundary9.push_back(10); + boundary9.push_back(8); std::vector<double> boundary10; std::vector<double> boundary11; - boundary11.push_back(10); boundary11.push_back(8); + boundary11.push_back(10); std::vector<double> boundary12; - boundary12.push_back(8); boundary12.push_back(0); + boundary12.push_back(8); std::vector<double> boundary13; - boundary13.push_back(9); boundary13.push_back(1); + boundary13.push_back(9); boundary13.push_back(12); boundary13.push_back(14); std::vector<double> boundary14; - boundary14.push_back(10); boundary14.push_back(2); + boundary14.push_back(10); std::vector<double> boundary15; - boundary15.push_back(11); boundary15.push_back(3); + boundary15.push_back(11); boundary15.push_back(14); boundary15.push_back(12); - std::vector< std::vector<double> > boundaries; + std::vector<std::vector<double> > boundaries; boundaries.push_back(boundary0); boundaries.push_back(boundary1); boundaries.push_back(boundary2); @@ -644,7 +638,7 @@ BOOST_AUTO_TEST_CASE(boudary_operator_2d_bitmap_with_periodic_bcond) { boundaries.push_back(boundary15); for (size_t i = 0; i != cmplx.size(); ++i) { - std::vector< size_t > bd = cmplx.get_boundary_of_a_cell(i); + std::vector<size_t> bd = cmplx.get_boundary_of_a_cell(i); for (size_t j = 0; j != bd.size(); ++j) { BOOST_CHECK(boundaries[i][j] == bd[j]); } @@ -652,7 +646,7 @@ BOOST_AUTO_TEST_CASE(boudary_operator_2d_bitmap_with_periodic_bcond) { } BOOST_AUTO_TEST_CASE(coboudary_operator_2d_bitmap_with_periodic_bcond) { - std::vector< double > filtration({0, 0, 0, 0}); + std::vector<double> filtration({0, 0, 0, 0}); std::vector<unsigned> dimensions({2, 2}); @@ -661,7 +655,6 @@ BOOST_AUTO_TEST_CASE(coboudary_operator_2d_bitmap_with_periodic_bcond) { Bitmap_cubical_complex_periodic_boundary_conditions cmplx(dimensions, filtration, periodic_directions); BOOST_CHECK(cmplx.dimension() == 2); - std::vector<double> coboundary0; coboundary0.push_back(4); coboundary0.push_back(12); @@ -711,7 +704,7 @@ BOOST_AUTO_TEST_CASE(coboudary_operator_2d_bitmap_with_periodic_bcond) { coboundary14.push_back(15); std::vector<double> coboundary15; - std::vector< std::vector<double> > coboundaries; + std::vector<std::vector<double> > coboundaries; coboundaries.push_back(coboundary0); coboundaries.push_back(coboundary1); coboundaries.push_back(coboundary2); @@ -730,7 +723,7 @@ BOOST_AUTO_TEST_CASE(coboudary_operator_2d_bitmap_with_periodic_bcond) { coboundaries.push_back(coboundary15); for (size_t i = 0; i != cmplx.size(); ++i) { - std::vector< size_t > cbd = cmplx.get_coboundary_of_a_cell(i); + std::vector<size_t> cbd = cmplx.get_coboundary_of_a_cell(i); for (size_t j = 0; j != cbd.size(); ++j) { BOOST_CHECK(coboundaries[i][j] == cbd[j]); } @@ -738,7 +731,7 @@ BOOST_AUTO_TEST_CASE(coboudary_operator_2d_bitmap_with_periodic_bcond) { } BOOST_AUTO_TEST_CASE(bitmap_2d_with_periodic_bcond_filtration) { - std::vector< double > filtrationOrg({0, 1, 2, 3}); + std::vector<double> filtrationOrg({0, 1, 2, 3}); std::vector<unsigned> dimensions({2, 2}); @@ -747,7 +740,6 @@ BOOST_AUTO_TEST_CASE(bitmap_2d_with_periodic_bcond_filtration) { Bitmap_cubical_complex_periodic_boundary_conditions cmplx(dimensions, filtrationOrg, periodic_directions); BOOST_CHECK(cmplx.dimension() == 2); - std::vector<double> filtration; filtration.push_back(0); // 0 filtration.push_back(0); // 1 @@ -766,613 +758,821 @@ BOOST_AUTO_TEST_CASE(bitmap_2d_with_periodic_bcond_filtration) { filtration.push_back(2); // 14 filtration.push_back(3); // 15 - for (size_t i = 0; i != cmplx.size(); ++i) { BOOST_CHECK(filtration[i] == cmplx.get_cell_data(i)); } } -BOOST_AUTO_TEST_CASE(all_cells_iterator_and_boundary_iterators_in_Bitmap_cubical_complex_base_check) -{ - std::vector< double > expected_filtration; - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(1); - expected_filtration.push_back(1); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(1); - expected_filtration.push_back(1); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(1); - expected_filtration.push_back(1); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(3); - expected_filtration.push_back(3); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(3); - expected_filtration.push_back(3); - - std::vector<unsigned> expected_dimension; - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(2); - expected_dimension.push_back(1); - expected_dimension.push_back(2); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(2); - expected_dimension.push_back(1); - expected_dimension.push_back(2); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - - std::vector<size_t> expected_boundary; - expected_boundary.push_back(0); - expected_boundary.push_back(2); - expected_boundary.push_back(2); - expected_boundary.push_back(4); - expected_boundary.push_back(0); - expected_boundary.push_back(10); - expected_boundary.push_back(1); - expected_boundary.push_back(11); - expected_boundary.push_back(5); - expected_boundary.push_back(7); - expected_boundary.push_back(2); - expected_boundary.push_back(12); - expected_boundary.push_back(3); - expected_boundary.push_back(13); - expected_boundary.push_back(7); - expected_boundary.push_back(9); - expected_boundary.push_back(4); - expected_boundary.push_back(14); - expected_boundary.push_back(10); - expected_boundary.push_back(12); - expected_boundary.push_back(12); - expected_boundary.push_back(14); - expected_boundary.push_back(10); - expected_boundary.push_back(20); - expected_boundary.push_back(11); - expected_boundary.push_back(21); - expected_boundary.push_back(15); - expected_boundary.push_back(17); - expected_boundary.push_back(12); - expected_boundary.push_back(22); - expected_boundary.push_back(13); - expected_boundary.push_back(23); - expected_boundary.push_back(17); - expected_boundary.push_back(19); - expected_boundary.push_back(14); - expected_boundary.push_back(24); - expected_boundary.push_back(20); - expected_boundary.push_back(22); - expected_boundary.push_back(22); - expected_boundary.push_back(24); - - - std::vector<size_t> expected_coboundary; - expected_coboundary.push_back(5); - expected_coboundary.push_back(1); - expected_coboundary.push_back(6); - expected_coboundary.push_back(7); - expected_coboundary.push_back(1); - expected_coboundary.push_back(3); - expected_coboundary.push_back(8); - expected_coboundary.push_back(9); - expected_coboundary.push_back(3); - expected_coboundary.push_back(6); - expected_coboundary.push_back(6); - expected_coboundary.push_back(8); - expected_coboundary.push_back(8); - expected_coboundary.push_back(5); - expected_coboundary.push_back(15); - expected_coboundary.push_back(11); - expected_coboundary.push_back(6); - expected_coboundary.push_back(16); - expected_coboundary.push_back(7); - expected_coboundary.push_back(17); - expected_coboundary.push_back(11); - expected_coboundary.push_back(13); - expected_coboundary.push_back(8); - expected_coboundary.push_back(18); - expected_coboundary.push_back(9); - expected_coboundary.push_back(19); - expected_coboundary.push_back(13); - expected_coboundary.push_back(16); - expected_coboundary.push_back(16); - expected_coboundary.push_back(18); - expected_coboundary.push_back(18); - expected_coboundary.push_back(15); - expected_coboundary.push_back(21); - expected_coboundary.push_back(16); - expected_coboundary.push_back(17); - expected_coboundary.push_back(21); - expected_coboundary.push_back(23); - expected_coboundary.push_back(18); - expected_coboundary.push_back(19); - expected_coboundary.push_back(23); - - - - std::vector< unsigned > sizes(2); - sizes[0] = 2; - sizes[1] = 2; - - std::vector< double > data(4); - data[0] = 0; - data[1] = 1; - data[2] = 2; - data[3] = 3; - - Bitmap_cubical_complex_base ba( sizes , data ); - int i = 0; - int bd_it = 0; - int cbd_it = 0; - for ( Bitmap_cubical_complex_base::All_cells_iterator it = ba.all_cells_iterator_begin() ; it != ba.all_cells_iterator_end() ; ++it ) - { - BOOST_CHECK( expected_filtration[i] == ba.get_cell_data( *it ) ); - BOOST_CHECK( expected_dimension[i] == ba.get_dimension_of_a_cell( *it ) ); - - Bitmap_cubical_complex_base::Boundary_range bdrange = ba.boundary_range(*it); - for ( Bitmap_cubical_complex_base::Boundary_iterator bd = bdrange.begin() ; bd != bdrange.end() ; ++bd ) - { - BOOST_CHECK( expected_boundary[bd_it] == *bd ); - ++bd_it; - } - - Bitmap_cubical_complex_base::Coboundary_range cbdrange = ba.coboundary_range(*it); - for ( Bitmap_cubical_complex_base::Coboundary_iterator cbd = cbdrange.begin() ; cbd != cbdrange.end() ; ++cbd ) - { - BOOST_CHECK( expected_coboundary[cbd_it] == *cbd ); - ++cbd_it; - } - ++i; + +BOOST_AUTO_TEST_CASE(all_cells_iterator_and_boundary_iterators_in_Bitmap_cubical_complex_base_check) { + std::vector<double> expected_filtration; + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(1); + expected_filtration.push_back(1); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(1); + expected_filtration.push_back(1); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(1); + expected_filtration.push_back(1); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(3); + expected_filtration.push_back(3); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(3); + expected_filtration.push_back(3); + + std::vector<unsigned> expected_dimension; + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(2); + expected_dimension.push_back(1); + expected_dimension.push_back(2); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(2); + expected_dimension.push_back(1); + expected_dimension.push_back(2); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + + std::vector<size_t> expected_boundary; + expected_boundary.push_back(0); + expected_boundary.push_back(2); + expected_boundary.push_back(2); + expected_boundary.push_back(4); + expected_boundary.push_back(0); + expected_boundary.push_back(10); + expected_boundary.push_back(1); + expected_boundary.push_back(11); + expected_boundary.push_back(7); + expected_boundary.push_back(5); + expected_boundary.push_back(2); + expected_boundary.push_back(12); + expected_boundary.push_back(3); + expected_boundary.push_back(13); + expected_boundary.push_back(9); + expected_boundary.push_back(7); + expected_boundary.push_back(4); + expected_boundary.push_back(14); + expected_boundary.push_back(10); + expected_boundary.push_back(12); + expected_boundary.push_back(12); + expected_boundary.push_back(14); + expected_boundary.push_back(10); + expected_boundary.push_back(20); + expected_boundary.push_back(11); + expected_boundary.push_back(21); + expected_boundary.push_back(17); + expected_boundary.push_back(15); + expected_boundary.push_back(12); + expected_boundary.push_back(22); + expected_boundary.push_back(13); + expected_boundary.push_back(23); + expected_boundary.push_back(19); + expected_boundary.push_back(17); + expected_boundary.push_back(14); + expected_boundary.push_back(24); + expected_boundary.push_back(20); + expected_boundary.push_back(22); + expected_boundary.push_back(22); + expected_boundary.push_back(24); + + std::vector<size_t> expected_coboundary; + expected_coboundary.push_back(5); + expected_coboundary.push_back(1); + expected_coboundary.push_back(6); + expected_coboundary.push_back(7); + expected_coboundary.push_back(1); + expected_coboundary.push_back(3); + expected_coboundary.push_back(8); + expected_coboundary.push_back(9); + expected_coboundary.push_back(3); + expected_coboundary.push_back(6); + expected_coboundary.push_back(6); + expected_coboundary.push_back(8); + expected_coboundary.push_back(8); + expected_coboundary.push_back(5); + expected_coboundary.push_back(15); + expected_coboundary.push_back(11); + expected_coboundary.push_back(6); + expected_coboundary.push_back(16); + expected_coboundary.push_back(7); + expected_coboundary.push_back(17); + expected_coboundary.push_back(11); + expected_coboundary.push_back(13); + expected_coboundary.push_back(8); + expected_coboundary.push_back(18); + expected_coboundary.push_back(9); + expected_coboundary.push_back(19); + expected_coboundary.push_back(13); + expected_coboundary.push_back(16); + expected_coboundary.push_back(16); + expected_coboundary.push_back(18); + expected_coboundary.push_back(18); + expected_coboundary.push_back(15); + expected_coboundary.push_back(21); + expected_coboundary.push_back(16); + expected_coboundary.push_back(17); + expected_coboundary.push_back(21); + expected_coboundary.push_back(23); + expected_coboundary.push_back(18); + expected_coboundary.push_back(19); + expected_coboundary.push_back(23); + + std::vector<unsigned> sizes(2); + sizes[0] = 2; + sizes[1] = 2; + + std::vector<double> data(4); + data[0] = 0; + data[1] = 1; + data[2] = 2; + data[3] = 3; + + Bitmap_cubical_complex_base ba(sizes, data); + int i = 0; + int bd_it = 0; + int cbd_it = 0; + for (Bitmap_cubical_complex_base::All_cells_iterator it = ba.all_cells_iterator_begin(); + it != ba.all_cells_iterator_end(); ++it) { + BOOST_CHECK(expected_filtration[i] == ba.get_cell_data(*it)); + BOOST_CHECK(expected_dimension[i] == ba.get_dimension_of_a_cell(*it)); + + Bitmap_cubical_complex_base::Boundary_range bdrange = ba.boundary_range(*it); + for (Bitmap_cubical_complex_base::Boundary_iterator bd = bdrange.begin(); bd != bdrange.end(); ++bd) { + BOOST_CHECK(expected_boundary[bd_it] == *bd); + ++bd_it; } + + Bitmap_cubical_complex_base::Coboundary_range cbdrange = ba.coboundary_range(*it); + for (Bitmap_cubical_complex_base::Coboundary_iterator cbd = cbdrange.begin(); cbd != cbdrange.end(); ++cbd) { + BOOST_CHECK(expected_coboundary[cbd_it] == *cbd); + ++cbd_it; + } + ++i; + } } +BOOST_AUTO_TEST_CASE(all_cells_iterator_and_boundary_iterators_in_Bitmap_cubical_complex_base_check_range_check_2) { + std::vector<double> expected_filtration; + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(1); + expected_filtration.push_back(1); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(1); + expected_filtration.push_back(1); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(1); + expected_filtration.push_back(1); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(3); + expected_filtration.push_back(3); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(3); + expected_filtration.push_back(3); + + std::vector<unsigned> expected_dimension; + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(2); + expected_dimension.push_back(1); + expected_dimension.push_back(2); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(2); + expected_dimension.push_back(1); + expected_dimension.push_back(2); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + + std::vector<size_t> expected_boundary; + expected_boundary.push_back(0); + expected_boundary.push_back(2); + expected_boundary.push_back(2); + expected_boundary.push_back(4); + expected_boundary.push_back(0); + expected_boundary.push_back(10); + expected_boundary.push_back(1); + expected_boundary.push_back(11); + expected_boundary.push_back(7); + expected_boundary.push_back(5); + expected_boundary.push_back(2); + expected_boundary.push_back(12); + expected_boundary.push_back(3); + expected_boundary.push_back(13); + expected_boundary.push_back(9); + expected_boundary.push_back(7); + expected_boundary.push_back(4); + expected_boundary.push_back(14); + expected_boundary.push_back(10); + expected_boundary.push_back(12); + expected_boundary.push_back(12); + expected_boundary.push_back(14); + expected_boundary.push_back(10); + expected_boundary.push_back(20); + expected_boundary.push_back(11); + expected_boundary.push_back(21); + expected_boundary.push_back(17); + expected_boundary.push_back(15); + expected_boundary.push_back(12); + expected_boundary.push_back(22); + expected_boundary.push_back(13); + expected_boundary.push_back(23); + expected_boundary.push_back(19); + expected_boundary.push_back(17); + expected_boundary.push_back(14); + expected_boundary.push_back(24); + expected_boundary.push_back(20); + expected_boundary.push_back(22); + expected_boundary.push_back(22); + expected_boundary.push_back(24); + + std::vector<size_t> expected_coboundary; + expected_coboundary.push_back(5); + expected_coboundary.push_back(1); + expected_coboundary.push_back(6); + expected_coboundary.push_back(7); + expected_coboundary.push_back(1); + expected_coboundary.push_back(3); + expected_coboundary.push_back(8); + expected_coboundary.push_back(9); + expected_coboundary.push_back(3); + expected_coboundary.push_back(6); + expected_coboundary.push_back(6); + expected_coboundary.push_back(8); + expected_coboundary.push_back(8); + expected_coboundary.push_back(5); + expected_coboundary.push_back(15); + expected_coboundary.push_back(11); + expected_coboundary.push_back(6); + expected_coboundary.push_back(16); + expected_coboundary.push_back(7); + expected_coboundary.push_back(17); + expected_coboundary.push_back(11); + expected_coboundary.push_back(13); + expected_coboundary.push_back(8); + expected_coboundary.push_back(18); + expected_coboundary.push_back(9); + expected_coboundary.push_back(19); + expected_coboundary.push_back(13); + expected_coboundary.push_back(16); + expected_coboundary.push_back(16); + expected_coboundary.push_back(18); + expected_coboundary.push_back(18); + expected_coboundary.push_back(15); + expected_coboundary.push_back(21); + expected_coboundary.push_back(16); + expected_coboundary.push_back(17); + expected_coboundary.push_back(21); + expected_coboundary.push_back(23); + expected_coboundary.push_back(18); + expected_coboundary.push_back(19); + expected_coboundary.push_back(23); + + std::vector<unsigned> sizes(2); + sizes[0] = 2; + sizes[1] = 2; + + std::vector<double> data(4); + data[0] = 0; + data[1] = 1; + data[2] = 2; + data[3] = 3; + + Bitmap_cubical_complex_base ba(sizes, data); + int i = 0; + int bd_it = 0; + int cbd_it = 0; + + Bitmap_cubical_complex_base::All_cells_range range(&ba); + for (Bitmap_cubical_complex_base::All_cells_iterator it = range.begin(); it != range.end(); ++it) { + BOOST_CHECK(expected_filtration[i] == ba.get_cell_data(*it)); + BOOST_CHECK(expected_dimension[i] == ba.get_dimension_of_a_cell(*it)); + + Bitmap_cubical_complex_base::Boundary_range bdrange = ba.boundary_range(*it); + for (Bitmap_cubical_complex_base::Boundary_iterator bd = bdrange.begin(); bd != bdrange.end(); ++bd) { + BOOST_CHECK(expected_boundary[bd_it] == *bd); + ++bd_it; + } + Bitmap_cubical_complex_base::Coboundary_range cbdrange = ba.coboundary_range(*it); + for (Bitmap_cubical_complex_base::Coboundary_iterator cbd = cbdrange.begin(); cbd != cbdrange.end(); ++cbd) { + BOOST_CHECK(expected_coboundary[cbd_it] == *cbd); + ++cbd_it; + } + ++i; + } +} +BOOST_AUTO_TEST_CASE(all_cells_iterator_and_boundary_iterators_in_Bitmap_cubical_complex_base_check_range_check) { + std::vector<double> expected_filtration; + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(1); + expected_filtration.push_back(1); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(1); + expected_filtration.push_back(1); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(1); + expected_filtration.push_back(1); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(3); + expected_filtration.push_back(3); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(3); + expected_filtration.push_back(3); + + std::vector<unsigned> expected_dimension; + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(2); + expected_dimension.push_back(1); + expected_dimension.push_back(2); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(2); + expected_dimension.push_back(1); + expected_dimension.push_back(2); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + expected_dimension.push_back(1); + expected_dimension.push_back(0); + + std::vector<size_t> expected_boundary; + expected_boundary.push_back(0); + expected_boundary.push_back(2); + expected_boundary.push_back(2); + expected_boundary.push_back(4); + expected_boundary.push_back(0); + expected_boundary.push_back(10); + expected_boundary.push_back(1); + expected_boundary.push_back(11); + expected_boundary.push_back(7); + expected_boundary.push_back(5); + expected_boundary.push_back(2); + expected_boundary.push_back(12); + expected_boundary.push_back(3); + expected_boundary.push_back(13); + expected_boundary.push_back(9); + expected_boundary.push_back(7); + expected_boundary.push_back(4); + expected_boundary.push_back(14); + expected_boundary.push_back(10); + expected_boundary.push_back(12); + expected_boundary.push_back(12); + expected_boundary.push_back(14); + expected_boundary.push_back(10); + expected_boundary.push_back(20); + expected_boundary.push_back(11); + expected_boundary.push_back(21); + expected_boundary.push_back(17); + expected_boundary.push_back(15); + expected_boundary.push_back(12); + expected_boundary.push_back(22); + expected_boundary.push_back(13); + expected_boundary.push_back(23); + expected_boundary.push_back(19); + expected_boundary.push_back(17); + expected_boundary.push_back(14); + expected_boundary.push_back(24); + expected_boundary.push_back(20); + expected_boundary.push_back(22); + expected_boundary.push_back(22); + expected_boundary.push_back(24); + + std::vector<size_t> expected_coboundary; + expected_coboundary.push_back(5); + expected_coboundary.push_back(1); + expected_coboundary.push_back(6); + expected_coboundary.push_back(7); + expected_coboundary.push_back(1); + expected_coboundary.push_back(3); + expected_coboundary.push_back(8); + expected_coboundary.push_back(9); + expected_coboundary.push_back(3); + expected_coboundary.push_back(6); + expected_coboundary.push_back(6); + expected_coboundary.push_back(8); + expected_coboundary.push_back(8); + expected_coboundary.push_back(5); + expected_coboundary.push_back(15); + expected_coboundary.push_back(11); + expected_coboundary.push_back(6); + expected_coboundary.push_back(16); + expected_coboundary.push_back(7); + expected_coboundary.push_back(17); + expected_coboundary.push_back(11); + expected_coboundary.push_back(13); + expected_coboundary.push_back(8); + expected_coboundary.push_back(18); + expected_coboundary.push_back(9); + expected_coboundary.push_back(19); + expected_coboundary.push_back(13); + expected_coboundary.push_back(16); + expected_coboundary.push_back(16); + expected_coboundary.push_back(18); + expected_coboundary.push_back(18); + expected_coboundary.push_back(15); + expected_coboundary.push_back(21); + expected_coboundary.push_back(16); + expected_coboundary.push_back(17); + expected_coboundary.push_back(21); + expected_coboundary.push_back(23); + expected_coboundary.push_back(18); + expected_coboundary.push_back(19); + expected_coboundary.push_back(23); + + std::vector<unsigned> sizes(2); + sizes[0] = 2; + sizes[1] = 2; + + std::vector<double> data(4); + data[0] = 0; + data[1] = 1; + data[2] = 2; + data[3] = 3; + + Bitmap_cubical_complex_base ba(sizes, data); + int i = 0; + int bd_it = 0; + int cbd_it = 0; + + Bitmap_cubical_complex_base::All_cells_range range = ba.all_cells_range(); + for (Bitmap_cubical_complex_base::All_cells_iterator it = range.begin(); it != range.end(); ++it) { + BOOST_CHECK(expected_filtration[i] == ba.get_cell_data(*it)); + BOOST_CHECK(expected_dimension[i] == ba.get_dimension_of_a_cell(*it)); + + Bitmap_cubical_complex_base::Boundary_range bdrange = ba.boundary_range(*it); + for (Bitmap_cubical_complex_base::Boundary_iterator bd = bdrange.begin(); bd != bdrange.end(); ++bd) { + BOOST_CHECK(expected_boundary[bd_it] == *bd); + ++bd_it; + } + Bitmap_cubical_complex_base::Coboundary_range cbdrange = ba.coboundary_range(*it); + for (Bitmap_cubical_complex_base::Coboundary_iterator cbd = cbdrange.begin(); cbd != cbdrange.end(); ++cbd) { + BOOST_CHECK(expected_coboundary[cbd_it] == *cbd); + ++cbd_it; + } + ++i; + } +} +BOOST_AUTO_TEST_CASE(Top_dimensional_cells_iterator_range_check) { + std::vector<double> expected_filtration; + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(1); + expected_filtration.push_back(1); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(1); + expected_filtration.push_back(1); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(0); + expected_filtration.push_back(1); + expected_filtration.push_back(1); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(3); + expected_filtration.push_back(3); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(2); + expected_filtration.push_back(3); + expected_filtration.push_back(3); + + std::vector<unsigned> sizes(2); + sizes[0] = 2; + sizes[1] = 2; + + std::vector<double> data(4); + data[0] = 0; + data[1] = 1; + data[2] = 2; + data[3] = 3; + + Bitmap_cubical_complex_base ba(sizes, data); + int i = 0; + Bitmap_cubical_complex_base::Top_dimensional_cells_range range = ba.top_dimensional_cells_range(); + for (Bitmap_cubical_complex_base::Top_dimensional_cells_iterator it = range.begin(); it != range.end(); ++it) { + BOOST_CHECK(data[i] == ba.get_cell_data(*it)); + BOOST_CHECK(ba.get_dimension_of_a_cell(*it) == 2); + ++i; + } +} +BOOST_AUTO_TEST_CASE(check_if_boundary_of_boundary_is_zero_non_periodic_case_3_d) { + std::vector<unsigned> sizes(3); + sizes[0] = 3; + sizes[1] = 3; + sizes[2] = 3; + + std::vector<double> data(27, 0); + + int number_of_all_elements = (2 * sizes[0] + 1) * (2 * sizes[1] + 1) * (2 * sizes[2] + 1); + std::vector<int> elems_in_boundary(number_of_all_elements, 0); + Bitmap_cubical_complex_base ba(sizes, data); + for (Bitmap_cubical_complex_base::All_cells_iterator it = ba.all_cells_iterator_begin(); + it != ba.all_cells_iterator_end(); ++it) { + int i = 1; + Bitmap_cubical_complex_base::Boundary_range bdrange = ba.boundary_range(*it); + for (Bitmap_cubical_complex_base::Boundary_iterator bd = bdrange.begin(); bd != bdrange.end(); ++bd) { + Bitmap_cubical_complex_base::Boundary_range second_bdrange = ba.boundary_range(*bd); + int j = 1; + for (Bitmap_cubical_complex_base::Boundary_iterator bd2 = second_bdrange.begin(); bd2 != second_bdrange.end(); + ++bd2) { + elems_in_boundary[*bd2] += i * j; + j *= -1; + } + i *= -1; + } + // check if there is anything nonzero in elems_in_boundary + for (size_t i = 0; i != elems_in_boundary.size(); ++i) { + BOOST_CHECK(elems_in_boundary[i] == 0); + } + } +} -BOOST_AUTO_TEST_CASE(all_cells_iterator_and_boundary_iterators_in_Bitmap_cubical_complex_base_check_range_check_2) -{ - std::vector< double > expected_filtration; - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(1); - expected_filtration.push_back(1); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(1); - expected_filtration.push_back(1); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(1); - expected_filtration.push_back(1); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(3); - expected_filtration.push_back(3); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(3); - expected_filtration.push_back(3); - - std::vector<unsigned> expected_dimension; - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(2); - expected_dimension.push_back(1); - expected_dimension.push_back(2); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(2); - expected_dimension.push_back(1); - expected_dimension.push_back(2); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - - std::vector<size_t> expected_boundary; - expected_boundary.push_back(0); - expected_boundary.push_back(2); - expected_boundary.push_back(2); - expected_boundary.push_back(4); - expected_boundary.push_back(0); - expected_boundary.push_back(10); - expected_boundary.push_back(1); - expected_boundary.push_back(11); - expected_boundary.push_back(5); - expected_boundary.push_back(7); - expected_boundary.push_back(2); - expected_boundary.push_back(12); - expected_boundary.push_back(3); - expected_boundary.push_back(13); - expected_boundary.push_back(7); - expected_boundary.push_back(9); - expected_boundary.push_back(4); - expected_boundary.push_back(14); - expected_boundary.push_back(10); - expected_boundary.push_back(12); - expected_boundary.push_back(12); - expected_boundary.push_back(14); - expected_boundary.push_back(10); - expected_boundary.push_back(20); - expected_boundary.push_back(11); - expected_boundary.push_back(21); - expected_boundary.push_back(15); - expected_boundary.push_back(17); - expected_boundary.push_back(12); - expected_boundary.push_back(22); - expected_boundary.push_back(13); - expected_boundary.push_back(23); - expected_boundary.push_back(17); - expected_boundary.push_back(19); - expected_boundary.push_back(14); - expected_boundary.push_back(24); - expected_boundary.push_back(20); - expected_boundary.push_back(22); - expected_boundary.push_back(22); - expected_boundary.push_back(24); - - - std::vector<size_t> expected_coboundary; - expected_coboundary.push_back(5); - expected_coboundary.push_back(1); - expected_coboundary.push_back(6); - expected_coboundary.push_back(7); - expected_coboundary.push_back(1); - expected_coboundary.push_back(3); - expected_coboundary.push_back(8); - expected_coboundary.push_back(9); - expected_coboundary.push_back(3); - expected_coboundary.push_back(6); - expected_coboundary.push_back(6); - expected_coboundary.push_back(8); - expected_coboundary.push_back(8); - expected_coboundary.push_back(5); - expected_coboundary.push_back(15); - expected_coboundary.push_back(11); - expected_coboundary.push_back(6); - expected_coboundary.push_back(16); - expected_coboundary.push_back(7); - expected_coboundary.push_back(17); - expected_coboundary.push_back(11); - expected_coboundary.push_back(13); - expected_coboundary.push_back(8); - expected_coboundary.push_back(18); - expected_coboundary.push_back(9); - expected_coboundary.push_back(19); - expected_coboundary.push_back(13); - expected_coboundary.push_back(16); - expected_coboundary.push_back(16); - expected_coboundary.push_back(18); - expected_coboundary.push_back(18); - expected_coboundary.push_back(15); - expected_coboundary.push_back(21); - expected_coboundary.push_back(16); - expected_coboundary.push_back(17); - expected_coboundary.push_back(21); - expected_coboundary.push_back(23); - expected_coboundary.push_back(18); - expected_coboundary.push_back(19); - expected_coboundary.push_back(23); - - - - std::vector< unsigned > sizes(2); - sizes[0] = 2; - sizes[1] = 2; - - std::vector< double > data(4); - data[0] = 0; - data[1] = 1; - data[2] = 2; - data[3] = 3; - - Bitmap_cubical_complex_base ba( sizes , data ); - int i = 0; - int bd_it = 0; - int cbd_it = 0; - - Bitmap_cubical_complex_base::All_cells_range range(&ba); - for ( Bitmap_cubical_complex_base::All_cells_iterator it = range.begin() ; it != range.end() ; ++it ) - { - BOOST_CHECK( expected_filtration[i] == ba.get_cell_data( *it ) ); - BOOST_CHECK( expected_dimension[i] == ba.get_dimension_of_a_cell( *it ) ); - - Bitmap_cubical_complex_base::Boundary_range bdrange = ba.boundary_range(*it); - for ( Bitmap_cubical_complex_base::Boundary_iterator bd = bdrange.begin() ; bd != bdrange.end() ; ++bd ) - { - BOOST_CHECK( expected_boundary[bd_it] == *bd ); - ++bd_it; - } - - Bitmap_cubical_complex_base::Coboundary_range cbdrange = ba.coboundary_range(*it); - for ( Bitmap_cubical_complex_base::Coboundary_iterator cbd = cbdrange.begin() ; cbd != cbdrange.end() ; ++cbd ) - { - BOOST_CHECK( expected_coboundary[cbd_it] == *cbd ); - ++cbd_it; - } - ++i; +BOOST_AUTO_TEST_CASE(check_if_boundary_of_boundary_is_zero_non_periodic_case_4_d) { + std::vector<unsigned> sizes(4); + sizes[0] = 3; + sizes[1] = 3; + sizes[2] = 3; + sizes[3] = 3; + + std::vector<double> data(81, 0); + + int number_of_all_elements = (2 * sizes[0] + 1) * (2 * sizes[1] + 1) * (2 * sizes[2] + 1) * (2 * sizes[3] + 1); + std::vector<int> elems_in_boundary(number_of_all_elements, 0); + Bitmap_cubical_complex_base ba(sizes, data); + for (Bitmap_cubical_complex_base::All_cells_iterator it = ba.all_cells_iterator_begin(); + it != ba.all_cells_iterator_end(); ++it) { + int i = 1; + Bitmap_cubical_complex_base::Boundary_range bdrange = ba.boundary_range(*it); + for (Bitmap_cubical_complex_base::Boundary_iterator bd = bdrange.begin(); bd != bdrange.end(); ++bd) { + Bitmap_cubical_complex_base::Boundary_range second_bdrange = ba.boundary_range(*bd); + int j = 1; + for (Bitmap_cubical_complex_base::Boundary_iterator bd2 = second_bdrange.begin(); bd2 != second_bdrange.end(); + ++bd2) { + elems_in_boundary[*bd2] += i * j; + j *= -1; + } + i *= -1; } + // check if there is anything nonzero in elems_in_boundary + for (size_t i = 0; i != elems_in_boundary.size(); ++i) { + BOOST_CHECK(elems_in_boundary[i] == 0); + } + } } +BOOST_AUTO_TEST_CASE(check_if_boundary_of_boundary_is_zero_periodic_case_2d) { + std::vector<unsigned> sizes(2); + sizes[0] = 3; + sizes[1] = 3; + + std::vector<bool> directions_of_periodicity(2, true); + std::vector<double> data(9, 0); + + int number_of_all_elements = (2 * sizes[0]) * (2 * sizes[1]); // *(2*sizes[2]); + std::vector<int> elems_in_boundary(number_of_all_elements, 0); + Bitmap_cubical_complex_periodic_boundary_conditions ba(sizes, data, directions_of_periodicity); + for (Bitmap_cubical_complex_periodic_boundary_conditions::All_cells_iterator it = ba.all_cells_iterator_begin(); + it != ba.all_cells_iterator_end(); ++it) { + int i = 1; + + // std::cout << "Element : " << *it << std::endl; + + Bitmap_cubical_complex_periodic_boundary_conditions_base::Boundary_range bdrange = ba.boundary_range(*it); + for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd = bdrange.begin(); + bd != bdrange.end(); ++bd) { + // std::cout << *bd << " "; + Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_range second_bdrange = ba.boundary_range(*bd); + int j = 1; + for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd2 = second_bdrange.begin(); + bd2 != second_bdrange.end(); ++bd2) { + elems_in_boundary[*bd2] += i * j; + j *= -1; + } + i *= -1; + } + // getchar(); + // check if there is anything nonzero in elems_in_boundary + for (size_t i = 0; i != elems_in_boundary.size(); ++i) { + BOOST_CHECK(elems_in_boundary[i] == 0); + } + } +} +BOOST_AUTO_TEST_CASE(check_if_boundary_of_boundary_is_zero_periodic_case_3d) { + std::vector<unsigned> sizes(3); + sizes[0] = 3; + sizes[1] = 3; + sizes[2] = 3; + + std::vector<bool> directions_of_periodicity(3, true); + + std::vector<double> data(27, 0); + + int number_of_all_elements = (2 * sizes[0]) * (2 * sizes[1]) * (2 * sizes[2]); + Bitmap_cubical_complex_periodic_boundary_conditions ba(sizes, data, directions_of_periodicity); + std::vector<int> elems_in_boundary(number_of_all_elements, 0); + for (Bitmap_cubical_complex_periodic_boundary_conditions::All_cells_iterator it = ba.all_cells_iterator_begin(); + it != ba.all_cells_iterator_end(); ++it) { + // std::cout << "Element : " << *it << std::endl; + + int i = 1; + + Bitmap_cubical_complex_periodic_boundary_conditions_base::Boundary_range bdrange = ba.boundary_range(*it); + for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd = bdrange.begin(); + bd != bdrange.end(); ++bd) { + Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_range second_bdrange = ba.boundary_range(*bd); + // std::cout << *bd << " "; + int j = 1; + for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd2 = second_bdrange.begin(); + bd2 != second_bdrange.end(); ++bd2) { + elems_in_boundary[*bd2] += i * j; + j *= -1; + } + i *= -1; + } + // check if there is anything nonzero in elems_in_boundary + for (size_t i = 0; i != elems_in_boundary.size(); ++i) { + BOOST_CHECK(elems_in_boundary[i] == 0); + } + } +} +BOOST_AUTO_TEST_CASE(check_if_boundary_of_boundary_is_zero_periodic_case_4d) { + std::vector<unsigned> sizes(4); + sizes[0] = 3; + sizes[1] = 3; + sizes[2] = 3; + sizes[3] = 3; + + std::vector<bool> directions_of_periodicity(4, true); + + std::vector<double> data(81, 0); + + int number_of_all_elements = (2 * sizes[0]) * (2 * sizes[1]) * (2 * sizes[2]) * (2 * sizes[3]); + std::vector<int> elems_in_boundary(number_of_all_elements, 0); + Bitmap_cubical_complex_periodic_boundary_conditions ba(sizes, data, directions_of_periodicity); + for (Bitmap_cubical_complex_periodic_boundary_conditions::All_cells_iterator it = ba.all_cells_iterator_begin(); + it != ba.all_cells_iterator_end(); ++it) { + int i = 1; + + Bitmap_cubical_complex_periodic_boundary_conditions_base::Boundary_range bdrange = ba.boundary_range(*it); + for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd = bdrange.begin(); + bd != bdrange.end(); ++bd) { + Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_range second_bdrange = ba.boundary_range(*bd); + int j = 1; + for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd2 = second_bdrange.begin(); + bd2 != second_bdrange.end(); ++bd2) { + elems_in_boundary[*bd2] += i * j; + j *= -1; + } + i *= -1; + } -BOOST_AUTO_TEST_CASE(all_cells_iterator_and_boundary_iterators_in_Bitmap_cubical_complex_base_check_range_check) -{ - std::vector< double > expected_filtration; - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(1); - expected_filtration.push_back(1); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(1); - expected_filtration.push_back(1); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(1); - expected_filtration.push_back(1); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(3); - expected_filtration.push_back(3); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(3); - expected_filtration.push_back(3); - - std::vector<unsigned> expected_dimension; - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(2); - expected_dimension.push_back(1); - expected_dimension.push_back(2); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(2); - expected_dimension.push_back(1); - expected_dimension.push_back(2); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - expected_dimension.push_back(1); - expected_dimension.push_back(0); - - std::vector<size_t> expected_boundary; - expected_boundary.push_back(0); - expected_boundary.push_back(2); - expected_boundary.push_back(2); - expected_boundary.push_back(4); - expected_boundary.push_back(0); - expected_boundary.push_back(10); - expected_boundary.push_back(1); - expected_boundary.push_back(11); - expected_boundary.push_back(5); - expected_boundary.push_back(7); - expected_boundary.push_back(2); - expected_boundary.push_back(12); - expected_boundary.push_back(3); - expected_boundary.push_back(13); - expected_boundary.push_back(7); - expected_boundary.push_back(9); - expected_boundary.push_back(4); - expected_boundary.push_back(14); - expected_boundary.push_back(10); - expected_boundary.push_back(12); - expected_boundary.push_back(12); - expected_boundary.push_back(14); - expected_boundary.push_back(10); - expected_boundary.push_back(20); - expected_boundary.push_back(11); - expected_boundary.push_back(21); - expected_boundary.push_back(15); - expected_boundary.push_back(17); - expected_boundary.push_back(12); - expected_boundary.push_back(22); - expected_boundary.push_back(13); - expected_boundary.push_back(23); - expected_boundary.push_back(17); - expected_boundary.push_back(19); - expected_boundary.push_back(14); - expected_boundary.push_back(24); - expected_boundary.push_back(20); - expected_boundary.push_back(22); - expected_boundary.push_back(22); - expected_boundary.push_back(24); - - - std::vector<size_t> expected_coboundary; - expected_coboundary.push_back(5); - expected_coboundary.push_back(1); - expected_coboundary.push_back(6); - expected_coboundary.push_back(7); - expected_coboundary.push_back(1); - expected_coboundary.push_back(3); - expected_coboundary.push_back(8); - expected_coboundary.push_back(9); - expected_coboundary.push_back(3); - expected_coboundary.push_back(6); - expected_coboundary.push_back(6); - expected_coboundary.push_back(8); - expected_coboundary.push_back(8); - expected_coboundary.push_back(5); - expected_coboundary.push_back(15); - expected_coboundary.push_back(11); - expected_coboundary.push_back(6); - expected_coboundary.push_back(16); - expected_coboundary.push_back(7); - expected_coboundary.push_back(17); - expected_coboundary.push_back(11); - expected_coboundary.push_back(13); - expected_coboundary.push_back(8); - expected_coboundary.push_back(18); - expected_coboundary.push_back(9); - expected_coboundary.push_back(19); - expected_coboundary.push_back(13); - expected_coboundary.push_back(16); - expected_coboundary.push_back(16); - expected_coboundary.push_back(18); - expected_coboundary.push_back(18); - expected_coboundary.push_back(15); - expected_coboundary.push_back(21); - expected_coboundary.push_back(16); - expected_coboundary.push_back(17); - expected_coboundary.push_back(21); - expected_coboundary.push_back(23); - expected_coboundary.push_back(18); - expected_coboundary.push_back(19); - expected_coboundary.push_back(23); - - - - std::vector< unsigned > sizes(2); - sizes[0] = 2; - sizes[1] = 2; - - std::vector< double > data(4); - data[0] = 0; - data[1] = 1; - data[2] = 2; - data[3] = 3; - - Bitmap_cubical_complex_base ba( sizes , data ); - int i = 0; - int bd_it = 0; - int cbd_it = 0; - - Bitmap_cubical_complex_base::All_cells_range range = ba.all_cells_range(); - for ( Bitmap_cubical_complex_base::All_cells_iterator it = range.begin() ; it != range.end() ; ++it ) - { - BOOST_CHECK( expected_filtration[i] == ba.get_cell_data( *it ) ); - BOOST_CHECK( expected_dimension[i] == ba.get_dimension_of_a_cell( *it ) ); - - Bitmap_cubical_complex_base::Boundary_range bdrange = ba.boundary_range(*it); - for ( Bitmap_cubical_complex_base::Boundary_iterator bd = bdrange.begin() ; bd != bdrange.end() ; ++bd ) - { - BOOST_CHECK( expected_boundary[bd_it] == *bd ); - ++bd_it; - } - - Bitmap_cubical_complex_base::Coboundary_range cbdrange = ba.coboundary_range(*it); - for ( Bitmap_cubical_complex_base::Coboundary_iterator cbd = cbdrange.begin() ; cbd != cbdrange.end() ; ++cbd ) - { - BOOST_CHECK( expected_coboundary[cbd_it] == *cbd ); - ++cbd_it; - } - ++i; + // check if there is anything nonzero in elems_in_boundary + for (size_t i = 0; i != elems_in_boundary.size(); ++i) { + BOOST_CHECK(elems_in_boundary[i] == 0); } + } } -BOOST_AUTO_TEST_CASE(Top_dimensional_cells_iterator_range_check) -{ - std::vector< double > expected_filtration; - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(1); - expected_filtration.push_back(1); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(1); - expected_filtration.push_back(1); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(0); - expected_filtration.push_back(1); - expected_filtration.push_back(1); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(3); - expected_filtration.push_back(3); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(2); - expected_filtration.push_back(3); - expected_filtration.push_back(3); - - - std::vector< unsigned > sizes(2); - sizes[0] = 2; - sizes[1] = 2; - - std::vector< double > data(4); - data[0] = 0; - data[1] = 1; - data[2] = 2; - data[3] = 3; - - Bitmap_cubical_complex_base ba( sizes , data ); - int i = 0; - - Bitmap_cubical_complex_base::Top_dimensional_cells_range range = ba.top_dimensional_cells_range(); - for ( Bitmap_cubical_complex_base::Top_dimensional_cells_iterator it = range.begin() ; it != range.end() ; ++it ) - { - BOOST_CHECK( data[i] == ba.get_cell_data( *it ) ); - BOOST_CHECK( ba.get_dimension_of_a_cell( *it ) == 2 ); - ++i; +BOOST_AUTO_TEST_CASE(compute_incidence_between_cells_test) { + std::vector<unsigned> sizes(3); + sizes[0] = 3; + sizes[1] = 3; + sizes[2] = 3; + + std::vector<double> data(27, 0); + + int number_of_all_elements = (2 * sizes[0] + 1) * (2 * sizes[1] + 1) * (2 * sizes[1] + 1); + Bitmap_cubical_complex_base ba(sizes, data); + std::vector<int> elems_in_boundary(number_of_all_elements, 0); + for (Bitmap_cubical_complex_base::All_cells_iterator it = ba.all_cells_iterator_begin(); + it != ba.all_cells_iterator_end(); ++it) { + Bitmap_cubical_complex_base::Boundary_range bdrange = ba.boundary_range(*it); + for (Bitmap_cubical_complex_base::Boundary_iterator bd = bdrange.begin(); bd != bdrange.end(); ++bd) { + Bitmap_cubical_complex_base::Boundary_range second_bdrange = ba.boundary_range(*bd); + for (Bitmap_cubical_complex_base::Boundary_iterator bd2 = second_bdrange.begin(); bd2 != second_bdrange.end(); + ++bd2) { + elems_in_boundary[*bd2] += + ba.compute_incidence_between_cells(*it, *bd) * ba.compute_incidence_between_cells(*bd, *bd2); + } } + // check if there is anything nonzero in elems_in_boundary + for (size_t i = 0; i != elems_in_boundary.size(); ++i) { + BOOST_CHECK(elems_in_boundary[i] == 0); + } + } } +BOOST_AUTO_TEST_CASE(compute_incidence_between_cells_test_periodic_boundary_conditions) { + std::vector<unsigned> sizes(3); + sizes[0] = 3; + sizes[1] = 3; + sizes[2] = 3; + + std::vector<bool> directions_of_periodicity(3, true); + std::vector<double> data(27, 0); + + int number_of_all_elements = (2 * sizes[0]) * (2 * sizes[1]) * (2 * sizes[2]); + Bitmap_cubical_complex_periodic_boundary_conditions ba(sizes, data, directions_of_periodicity); + + std::vector<int> elems_in_boundary(number_of_all_elements, 0); + for (Bitmap_cubical_complex_periodic_boundary_conditions::All_cells_iterator it = ba.all_cells_iterator_begin(); + it != ba.all_cells_iterator_end(); ++it) { + Bitmap_cubical_complex_periodic_boundary_conditions_base::Boundary_range bdrange = ba.boundary_range(*it); + for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd = bdrange.begin(); + bd != bdrange.end(); ++bd) { + // std::cout << *bd << " "; + Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_range second_bdrange = ba.boundary_range(*bd); + for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd2 = second_bdrange.begin(); + bd2 != second_bdrange.end(); ++bd2) { + elems_in_boundary[*bd2] += + ba.compute_incidence_between_cells(*it, *bd) * ba.compute_incidence_between_cells(*bd, *bd2); + } + } + // check if there is anything nonzero in elems_in_boundary + for (size_t i = 0; i != elems_in_boundary.size(); ++i) { + BOOST_CHECK(elems_in_boundary[i] == 0); + } + } +} diff --git a/src/Bitmap_cubical_complex/utilities/CMakeLists.txt b/src/Bitmap_cubical_complex/utilities/CMakeLists.txt new file mode 100644 index 00000000..676a730a --- /dev/null +++ b/src/Bitmap_cubical_complex/utilities/CMakeLists.txt @@ -0,0 +1,29 @@ +cmake_minimum_required(VERSION 2.6) +project(Bitmap_cubical_complex_utilities) + +add_executable ( cubical_complex_persistence cubical_complex_persistence.cpp ) +if (TBB_FOUND) + target_link_libraries(cubical_complex_persistence ${TBB_LIBRARIES}) +endif() + +add_test(NAME Bitmap_cubical_complex_utility_persistence_one_sphere COMMAND $<TARGET_FILE:cubical_complex_persistence> + "${CMAKE_SOURCE_DIR}/data/bitmap/CubicalOneSphere.txt") + +add_test(NAME Bitmap_cubical_complex_utility_persistence_two_sphere COMMAND $<TARGET_FILE:cubical_complex_persistence> + "${CMAKE_SOURCE_DIR}/data/bitmap/CubicalTwoSphere.txt") + +add_executable ( periodic_cubical_complex_persistence periodic_cubical_complex_persistence.cpp ) +if (TBB_FOUND) + target_link_libraries(periodic_cubical_complex_persistence ${TBB_LIBRARIES}) +endif() + +add_test(NAME Bitmap_cubical_complex_utility_periodic_boundary_conditions_2d_torus + COMMAND $<TARGET_FILE:periodic_cubical_complex_persistence> + "${CMAKE_SOURCE_DIR}/data/bitmap/2d_torus.txt") + +add_test(NAME Bitmap_cubical_complex_utility_periodic_boundary_conditions_3d_torus + COMMAND $<TARGET_FILE:periodic_cubical_complex_persistence> + "${CMAKE_SOURCE_DIR}/data/bitmap/3d_torus.txt") + +install(TARGETS cubical_complex_persistence DESTINATION bin) +install(TARGETS periodic_cubical_complex_persistence DESTINATION bin) diff --git a/src/Bitmap_cubical_complex/utilities/README b/src/Bitmap_cubical_complex/utilities/README new file mode 100644 index 00000000..ddff7034 --- /dev/null +++ b/src/Bitmap_cubical_complex/utilities/README @@ -0,0 +1,18 @@ +# Bitmap_cubical_complex #
+
+## `cubical_complex_persistence` ##
+This program computes persistent homology, by using the Bitmap_cubical_complex class, of cubical complexes provided in text files in Perseus style. See [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsPerseus) for a description of the file format.
+
+Example:
+
+* Create a Cubical Complex from the Perseus style file `CubicalTwoSphere.txt`, computes Persistence cohomology from it and writes the results in a persistence file `CubicalTwoSphere.txt_persistence`:
+`cubical_complex_persistence data/bitmap/CubicalTwoSphere.txt`
+
+## `periodic_cubical_complex_persistence` ##
+
+Same as above, but with periodic boundary conditions.
+
+Example:
+
+* Create a Periodical Cubical Complex from the Perseus style file `3d_torus.txt`, computes Persistence cohomology from it and writes the results in a persistence file `3d_torus.txt_persistence`:
+`periodic_cubical_complex_persistence data/bitmap/3d_torus.txt`
diff --git a/src/Bitmap_cubical_complex/example/Bitmap_cubical_complex.cpp b/src/Bitmap_cubical_complex/utilities/cubical_complex_persistence.cpp index 67735ba1..9d1bc08c 100644 --- a/src/Bitmap_cubical_complex/example/Bitmap_cubical_complex.cpp +++ b/src/Bitmap_cubical_complex/utilities/cubical_complex_persistence.cpp @@ -20,7 +20,6 @@ * along with this program. If not, see <http://www.gnu.org/licenses/>. */ - #include <gudhi/reader_utils.h> #include <gudhi/Bitmap_cubical_complex.h> #include <gudhi/Persistent_cohomology.h> @@ -32,16 +31,18 @@ #include <cstddef> int main(int argc, char** argv) { - std::cout << "This program computes persistent homology, by using bitmap_cubical_complex class, of cubical " << - "complexes provided in text files in Perseus style (the only numbered in the first line is a dimension D of a" << - "bitmap. In the lines I between 2 and D+1 there are numbers of top dimensional cells in the direction I. Let " << - "N denote product of the numbers in the lines between 2 and D. In the lines D+2 to D+2+N there are " << - "filtrations of top dimensional cells. We assume that the cells are in the lexicographical order. See " << - "CubicalOneSphere.txt or CubicalTwoSphere.txt for example.\n" << std::endl; + std::cout + << "This program computes persistent homology, by using bitmap_cubical_complex class, of cubical " + << "complexes provided in text files in Perseus style (the only numbered in the first line is a dimension D of a" + << "bitmap. In the lines I between 2 and D+1 there are numbers of top dimensional cells in the direction I. Let " + << "N denote product of the numbers in the lines between 2 and D. In the lines D+2 to D+2+N there are " + << "filtrations of top dimensional cells. We assume that the cells are in the lexicographical order. See " + << "CubicalOneSphere.txt or CubicalTwoSphere.txt for example.\n" + << std::endl; if (argc != 2) { - std::cerr << "Wrong number of parameters. Please provide the name of a file with a Perseus style bitmap at " << - "the input. The program will now terminate.\n"; + std::cerr << "Wrong number of parameters. Please provide the name of a file with a Perseus style bitmap at " + << "the input. The program will now terminate.\n"; return 1; } @@ -54,7 +55,7 @@ int main(int argc, char** argv) { // Compute the persistence diagram of the complex Persistent_cohomology pcoh(b); - int p = 2; + int p = 11; double min_persistence = 0; pcoh.init_coefficients(p); // initializes the coefficient field for homology @@ -66,7 +67,7 @@ int main(int argc, char** argv) { std::size_t last_in_path = output_file_name.find_last_of("/\\"); if (last_in_path != std::string::npos) { - output_file_name = output_file_name.substr(last_in_path+1); + output_file_name = output_file_name.substr(last_in_path + 1); } std::ofstream out(output_file_name.c_str()); @@ -77,4 +78,3 @@ int main(int argc, char** argv) { return 0; } - diff --git a/src/Bitmap_cubical_complex/example/Bitmap_cubical_complex_periodic_boundary_conditions.cpp b/src/Bitmap_cubical_complex/utilities/periodic_cubical_complex_persistence.cpp index f8754345..c812cb3a 100644 --- a/src/Bitmap_cubical_complex/example/Bitmap_cubical_complex_periodic_boundary_conditions.cpp +++ b/src/Bitmap_cubical_complex/utilities/periodic_cubical_complex_persistence.cpp @@ -20,7 +20,6 @@ * along with this program. If not, see <http://www.gnu.org/licenses/>. */ - #include <gudhi/reader_utils.h> #include <gudhi/Bitmap_cubical_complex.h> #include <gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h> @@ -30,24 +29,27 @@ #include <iostream> #include <sstream> #include <vector> +#include <string> int main(int argc, char** argv) { - std::cout << "This program computes persistent homology, by using " << - "Bitmap_cubical_complex_periodic_boundary_conditions class, of cubical complexes provided in text files in " << - "Perseus style (the only numbered in the first line is a dimension D of a bitmap. In the lines I between 2 " << - "and D+1 there are numbers of top dimensional cells in the direction I. Let N denote product of the numbers " << - "in the lines between 2 and D. In the lines D+2 to D+2+N there are filtrations of top dimensional cells. We " << - "assume that the cells are in the lexicographical order. See CubicalOneSphere.txt or CubicalTwoSphere.txt for" << - " example.\n" << std::endl; + std::cout + << "This program computes persistent homology, by using " + << "Bitmap_cubical_complex_periodic_boundary_conditions class, of cubical complexes provided in text files in " + << "Perseus style (the only numbered in the first line is a dimension D of a bitmap. In the lines I between 2 " + << "and D+1 there are numbers of top dimensional cells in the direction I. Let N denote product of the numbers " + << "in the lines between 2 and D. In the lines D+2 to D+2+N there are filtrations of top dimensional cells. We " + << "assume that the cells are in the lexicographical order. See CubicalOneSphere.txt or CubicalTwoSphere.txt for" + << " example.\n" + << std::endl; if (argc != 2) { - std::cerr << "Wrong number of parameters. Please provide the name of a file with a Perseus style bitmap at " << - "the input. The program will now terminate.\n"; + std::cerr << "Wrong number of parameters. Please provide the name of a file with a Perseus style bitmap at " + << "the input. The program will now terminate.\n"; return 1; } typedef Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double> Bitmap_base; - typedef Gudhi::cubical_complex::Bitmap_cubical_complex< Bitmap_base > Bitmap_cubical_complex; + typedef Gudhi::cubical_complex::Bitmap_cubical_complex<Bitmap_base> Bitmap_cubical_complex; Bitmap_cubical_complex b(argv[1]); @@ -56,7 +58,7 @@ int main(int argc, char** argv) { // Compute the persistence diagram of the complex Persistent_cohomology pcoh(b, true); - int p = 2; + int p = 11; double min_persistence = 0; pcoh.init_coefficients(p); // initializes the coefficient field for homology pcoh.compute_persistent_cohomology(min_persistence); @@ -67,7 +69,7 @@ int main(int argc, char** argv) { std::size_t last_in_path = output_file_name.find_last_of("/\\"); if (last_in_path != std::string::npos) { - output_file_name = output_file_name.substr(last_in_path+1); + output_file_name = output_file_name.substr(last_in_path + 1); } std::ofstream out(output_file_name.c_str()); @@ -78,4 +80,3 @@ int main(int argc, char** argv) { return 0; } - diff --git a/src/Bottleneck_distance/benchmark/CMakeLists.txt b/src/Bottleneck_distance/benchmark/CMakeLists.txt index 170081ce..20a4e47b 100644 --- a/src/Bottleneck_distance/benchmark/CMakeLists.txt +++ b/src/Bottleneck_distance/benchmark/CMakeLists.txt @@ -1,9 +1,9 @@ cmake_minimum_required(VERSION 2.6) project(Bottleneck_distance_benchmark) -if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) +if (NOT CGAL_VERSION VERSION_LESS 4.8.1) add_executable ( bottleneck_chrono bottleneck_chrono.cpp ) if (TBB_FOUND) target_link_libraries(bottleneck_chrono ${TBB_LIBRARIES}) endif(TBB_FOUND) -endif(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) +endif(NOT CGAL_VERSION VERSION_LESS 4.8.1) diff --git a/src/Bottleneck_distance/example/CMakeLists.txt b/src/Bottleneck_distance/example/CMakeLists.txt index dc1da31c..6095d6eb 100644 --- a/src/Bottleneck_distance/example/CMakeLists.txt +++ b/src/Bottleneck_distance/example/CMakeLists.txt @@ -1,30 +1,22 @@ cmake_minimum_required(VERSION 2.6) project(Bottleneck_distance_examples) -if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) - add_executable (bottleneck_read_file_example bottleneck_read_file_example.cpp) +if (NOT CGAL_VERSION VERSION_LESS 4.8.1) add_executable (bottleneck_basic_example bottleneck_basic_example.cpp) - add_executable (alpha_rips_persistence_bottleneck_distance alpha_rips_persistence_bottleneck_distance.cpp) target_link_libraries(alpha_rips_persistence_bottleneck_distance ${Boost_PROGRAM_OPTIONS_LIBRARY}) + if (TBB_FOUND) - target_link_libraries(bottleneck_read_file_example ${TBB_LIBRARIES}) - target_link_libraries(bottleneck_basic_example ${TBB_LIBRARIES}) target_link_libraries(alpha_rips_persistence_bottleneck_distance ${TBB_LIBRARIES}) + target_link_libraries(bottleneck_basic_example ${TBB_LIBRARIES}) endif(TBB_FOUND) - + add_test(NAME Bottleneck_distance_example_basic COMMAND $<TARGET_FILE:bottleneck_basic_example>) - add_test(NAME Bottleneck_distance_example_alpha_rips_persistence_bottleneck COMMAND $<TARGET_FILE:alpha_rips_persistence_bottleneck_distance> "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-r" "0.15" "-m" "0.12" "-d" "3" "-p" "3") - add_test(NAME Bottleneck_read_file_example - COMMAND $<TARGET_FILE:bottleneck_read_file_example> - "${CMAKE_SOURCE_DIR}/data/persistence_diagram/first.pers" "${CMAKE_SOURCE_DIR}/data/persistence_diagram/second.pers") - - install(TARGETS bottleneck_read_file_example DESTINATION bin) install(TARGETS bottleneck_basic_example DESTINATION bin) install(TARGETS alpha_rips_persistence_bottleneck_distance DESTINATION bin) - -endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + +endif (NOT CGAL_VERSION VERSION_LESS 4.8.1) diff --git a/src/Bottleneck_distance/example/README b/src/Bottleneck_distance/example/README new file mode 100644 index 00000000..01bcd74a --- /dev/null +++ b/src/Bottleneck_distance/example/README @@ -0,0 +1,19 @@ +# Bottleneck_distance #
+
+## `alpha_rips_persistence_bottleneck_distance` ##
+This program computes the persistent homology with coefficient field Z/pZ of a Rips complex defined on a set of input points. The output diagram contains one bar per line, written with the convention:
+
+`p dim birth death`
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
+
+Usage:
+`alpha_rips_persistence_bottleneck_distance [options] <OFF input file>`
+
+Allowed options:
+
+* `-h [ --help ]` Produce help message
+* `-r [ --max-edge-length ]` (default = inf) Maximal length of an edge for the Rips complex construction.`
+* `-d [ --cpx-dimension ]` (default = 1) Maximal dimension of the Rips complex we want to compute.`
+* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
diff --git a/src/Bottleneck_distance/include/gudhi/Neighbors_finder.h b/src/Bottleneck_distance/include/gudhi/Neighbors_finder.h index bdc47578..a6b9b021 100644 --- a/src/Bottleneck_distance/include/gudhi/Neighbors_finder.h +++ b/src/Bottleneck_distance/include/gudhi/Neighbors_finder.h @@ -44,16 +44,16 @@ struct Square_query { typedef Internal_point Point_d; typedef double FT; bool contains(Point_d p) const { - return std::abs(p.x()-c.x())<=size && std::abs(p.y()-c.y())<=size; + return std::abs(p.x()-c.x()) <= size && std::abs(p.y()-c.y()) <= size; } - bool inner_range_intersects(CGAL::Kd_tree_rectangle<FT,D> const&r) const { + bool inner_range_intersects(CGAL::Kd_tree_rectangle<FT, D> const&r) const { return r.max_coord(0) >= c.x() - size && r.min_coord(0) <= c.x() + size && r.max_coord(1) >= c.y() - size && r.min_coord(1) <= c.y() + size; } - bool outer_range_contains(CGAL::Kd_tree_rectangle<FT,D> const&r) const { + bool outer_range_contains(CGAL::Kd_tree_rectangle<FT, D> const&r) const { return r.min_coord(0) >= c.x() - size && r.max_coord(0) <= c.x() + size && @@ -146,7 +146,7 @@ inline int Neighbors_finder::pull_near(int u_point_index) { // Is the query point near to a V point in the plane ? Internal_point u_point = g.get_u_point(u_point_index); auto neighbor = kd_t.search_any_point(Square_query{u_point, r}); - if(!neighbor) + if (!neighbor) return null_point_index(); tmp = neighbor->point_index; auto point = g.get_v_point(tmp); diff --git a/src/Bottleneck_distance/test/CMakeLists.txt b/src/Bottleneck_distance/test/CMakeLists.txt index a165d472..2676b82c 100644 --- a/src/Bottleneck_distance/test/CMakeLists.txt +++ b/src/Bottleneck_distance/test/CMakeLists.txt @@ -1,7 +1,7 @@ cmake_minimum_required(VERSION 2.6) project(Bottleneck_distance_tests) -if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) +if (NOT CGAL_VERSION VERSION_LESS 4.8.1) include(GUDHI_test_coverage) add_executable ( Bottleneck_distance_test_unit bottleneck_unit_test.cpp ) @@ -12,4 +12,4 @@ if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) gudhi_add_coverage_test(Bottleneck_distance_test_unit) -endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) +endif (NOT CGAL_VERSION VERSION_LESS 4.8.1) diff --git a/src/Bottleneck_distance/utilities/CMakeLists.txt b/src/Bottleneck_distance/utilities/CMakeLists.txt new file mode 100644 index 00000000..d19e3b1c --- /dev/null +++ b/src/Bottleneck_distance/utilities/CMakeLists.txt @@ -0,0 +1,16 @@ +cmake_minimum_required(VERSION 2.6) +project(Bottleneck_distance_utilities) + +if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + add_executable (bottleneck_distance bottleneck_distance.cpp) + if (TBB_FOUND) + target_link_libraries(bottleneck_distance ${TBB_LIBRARIES}) + endif(TBB_FOUND) + + add_test(NAME Bottleneck_distance_utilities_Bottleneck_read_file + COMMAND $<TARGET_FILE:bottleneck_distance> + "${CMAKE_SOURCE_DIR}/data/persistence_diagram/first.pers" "${CMAKE_SOURCE_DIR}/data/persistence_diagram/second.pers") + + install(TARGETS bottleneck_distance DESTINATION bin) + +endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) diff --git a/src/Bottleneck_distance/utilities/README b/src/Bottleneck_distance/utilities/README new file mode 100644 index 00000000..d9fdd252 --- /dev/null +++ b/src/Bottleneck_distance/utilities/README @@ -0,0 +1,10 @@ +# Bottleneck_distance #
+
+## `bottleneck_read_file_example` ##
+This program computes the Bottleneck distance between two persistence diagram files.
+
+Usage:
+`bottleneck_read_file_example <file_1.pers> <file_2.pers> [<tolerance>]`
+where
+`<file_1.pers>` and `<file_2.pers>` must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsPers).
+`<tolerance>` is an error bound on the bottleneck distance (set by default to the smallest positive double value).
diff --git a/src/Bottleneck_distance/example/bottleneck_read_file_example.cpp b/src/Bottleneck_distance/utilities/bottleneck_distance.cpp index 238d99ad..9dd52b31 100644 --- a/src/Bottleneck_distance/example/bottleneck_read_file_example.cpp +++ b/src/Bottleneck_distance/utilities/bottleneck_distance.cpp @@ -25,21 +25,21 @@ #include <iostream> #include <vector> #include <utility> // for pair -#include <fstream> -#include <sstream> #include <string> +#include <limits> // for numeric_limits int main(int argc, char** argv) { if (argc < 3) { - std::cout << "To run this program please provide as an input two files with persistence diagrams. Each file " << - "should contain a birth-death pair per line. Third, optional parameter is an error bound on a bottleneck" << - " distance (set by default to zero). The program will now terminate \n"; + std::cout << "To run this program please provide as an input two files with persistence diagrams. Each file" << + " should contain a birth-death pair per line. Third, optional parameter is an error bound on the bottleneck" << + " distance (set by default to the smallest positive double value). If you set the error bound to 0, be" << + " aware this version is exact but expensive. The program will now terminate \n"; return -1; } - std::vector<std::pair<double, double>> diag1 = read_persistence_intervals_in_dimension(argv[1]); - std::vector<std::pair<double, double>> diag2 = read_persistence_intervals_in_dimension(argv[2]); + std::vector<std::pair<double, double>> diag1 = Gudhi::read_persistence_intervals_in_dimension(argv[1]); + std::vector<std::pair<double, double>> diag2 = Gudhi::read_persistence_intervals_in_dimension(argv[2]); - double tolerance = 0.; + double tolerance = std::numeric_limits<double>::min(); if (argc == 4) { tolerance = atof(argv[3]); } diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index e02d3a2c..94587044 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -25,6 +25,7 @@ add_gudhi_module(Spatial_searching) add_gudhi_module(Subsampling) add_gudhi_module(Tangential_complex) add_gudhi_module(Witness_complex) +add_gudhi_module(Nerve_GIC) message("++ GUDHI_MODULES list is:\"${GUDHI_MODULES}\"") diff --git a/src/Contraction/example/CMakeLists.txt b/src/Contraction/example/CMakeLists.txt index 83594c0e..a92d1685 100644 --- a/src/Contraction/example/CMakeLists.txt +++ b/src/Contraction/example/CMakeLists.txt @@ -1,9 +1,10 @@ cmake_minimum_required(VERSION 2.6) project(Contraction_examples) - add_executable(RipsContraction Rips_contraction.cpp) + add_executable(GarlandHeckbert Garland_heckbert.cpp) +target_link_libraries(GarlandHeckbert ${Boost_TIMER_LIBRARY}) add_test(NAME Contraction_example_tore3D_0.2 COMMAND $<TARGET_FILE:RipsContraction> "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "0.2") diff --git a/src/Contraction/example/Garland_heckbert.cpp b/src/Contraction/example/Garland_heckbert.cpp index f0cde95e..2b0dc973 100644 --- a/src/Contraction/example/Garland_heckbert.cpp +++ b/src/Contraction/example/Garland_heckbert.cpp @@ -29,7 +29,6 @@ #include <gudhi/Edge_contraction.h> #include <gudhi/Skeleton_blocker.h> #include <gudhi/Off_reader.h> -#include <gudhi/Clock.h> #include <iostream> @@ -165,8 +164,6 @@ int main(int argc, char *argv[]) { int num_contractions = atoi(argv[3]); - Gudhi::Clock contraction_chrono("Time to simplify and enumerate simplices"); - // constructs the contractor object with Garland Heckbert policies. Complex_contractor contractor(complex, new GH_cost(complex), @@ -182,8 +179,6 @@ int main(int argc, char *argv[]) { complex.num_edges() << " edges and " << complex.num_triangles() << " triangles." << std::endl; - std::cout << contraction_chrono; - // write simplified complex Gudhi::skeleton_blocker::Skeleton_blocker_off_writer<Complex> off_writer(argv[2], complex); diff --git a/src/Doxyfile b/src/Doxyfile index eb0b3e9e..429bf6a1 100644 --- a/src/Doxyfile +++ b/src/Doxyfile @@ -38,7 +38,7 @@ PROJECT_NAME = "GUDHI" # could be handy for archiving the generated documentation or if some version # control system is used. -PROJECT_NUMBER = "2.0.0" +PROJECT_NUMBER = "2.0.1" # Using the PROJECT_BRIEF tag one can provide an optional one line description # for a project that appears at the top of each page and should give viewer a @@ -819,7 +819,8 @@ EXCLUDE_SYMBOLS = # command). EXAMPLE_PATH = biblio/ \ - example/ + example/ \ + utilities/ # If the value of the EXAMPLE_PATH tag contains directories, you can use the # EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and @@ -852,6 +853,7 @@ IMAGE_PATH = doc/Skeleton_blocker/ \ doc/Spatial_searching/ \ doc/Tangential_complex/ \ doc/Bottleneck_distance/ \ + doc/Nerve_GIC/ \ doc/Persistence_representations/ # The INPUT_FILTER tag can be used to specify a program that doxygen should @@ -2116,7 +2118,7 @@ COLLABORATION_GRAPH = NO # The default value is: YES. # This tag requires that the tag HAVE_DOT is set to YES. -GROUP_GRAPHS = NO +GROUP_GRAPHS = YES # If the UML_LOOK tag is set to YES doxygen will generate inheritance and # collaboration diagrams in a style similar to the OMG's Unified Modeling diff --git a/src/GudhUI/CMakeLists.txt b/src/GudhUI/CMakeLists.txt index 57861946..374195d0 100644 --- a/src/GudhUI/CMakeLists.txt +++ b/src/GudhUI/CMakeLists.txt @@ -5,28 +5,6 @@ find_package(Qt5 COMPONENTS Widgets Xml OpenGL) find_package(QGLViewer) find_package(OpenGL) -if (CGAL_VERSION VERSION_EQUAL 4.8.0) - message(ERROR " GudhUI does not compile with CGAL 4.8.0. 4.8.1, 4.8.2 and 4.9 are OK.") -endif() - -if (NOT CGAL_FOUND) - message(ERROR " GudhUI requires CGAL and will not be compiled.") -endif() - -if (NOT Qt5_FOUND) - message(ERROR " GudhUI requires Qt5 and will not be compiled.") -endif() - -if (NOT OPENGL_FOUND) - message(ERROR " GudhUI requires OpenGL and will not be compiled.") -endif() - -if (NOT QGLVIEWER_FOUND) - message(ERROR " GudhUI requires QGLViewer and will not be compiled.") -endif() - - - if ( CGAL_FOUND AND Qt5_FOUND AND OPENGL_FOUND AND QGLVIEWER_FOUND AND NOT CGAL_VERSION VERSION_EQUAL 4.8.0) set(CMAKE_AUTOMOC ON) @@ -37,8 +15,6 @@ if ( CGAL_FOUND AND Qt5_FOUND AND OPENGL_FOUND AND QGLVIEWER_FOUND AND NOT CGAL_ SET(Boost_USE_MULTITHREAD OFF) include_directories (${QGLVIEWER_INCLUDE_DIR}) - ##################################################################### - add_executable ( GudhUI gui/gudhui.cpp gui/MainWindow.cpp @@ -52,14 +28,10 @@ if ( CGAL_FOUND AND Qt5_FOUND AND OPENGL_FOUND AND QGLVIEWER_FOUND AND NOT CGAL_ target_link_libraries( GudhUI Qt5::Widgets Qt5::Xml Qt5::OpenGL ) target_link_libraries( GudhUI ${QGLVIEWER_LIBRARIES} ) target_link_libraries( GudhUI ${OPENGL_gl_LIBRARY} ${OPENGL_glu_LIBRARY} ) -if (TBB_FOUND) - target_link_libraries( GudhUI ${TBB_LIBRARIES}) -endif() + if (TBB_FOUND) + target_link_libraries( GudhUI ${TBB_LIBRARIES}) + endif() install(TARGETS GudhUI DESTINATION bin) -############################################################################### - -else() - message(STATUS "NOTICE: GudhUI requires CGAL, the QGLViewer, OpenGL and Qt5, and will not be compiled.") endif() diff --git a/src/Hasse_complex/include/gudhi/Hasse_complex.h b/src/Hasse_complex/include/gudhi/Hasse_complex.h index 8b06b771..e67f7609 100644 --- a/src/Hasse_complex/include/gudhi/Hasse_complex.h +++ b/src/Hasse_complex/include/gudhi/Hasse_complex.h @@ -30,6 +30,7 @@ #include <algorithm> #include <utility> // for pair #include <vector> +#include <limits> // for infinity value #ifdef GUDHI_USE_TBB #include <tbb/parallel_for.h> @@ -104,7 +105,6 @@ class Hasse_complex { Hasse_complex(Complex_ds & cpx) : complex_(cpx.num_simplices()) , vertices_() - , threshold_(cpx.filtration()) , num_vertices_() , dim_max_(cpx.dimension()) { int size = complex_.size(); @@ -125,7 +125,6 @@ class Hasse_complex { Hasse_complex() : complex_() , vertices_() - , threshold_(0) , num_vertices_(0) , dim_max_(-1) { } @@ -157,15 +156,11 @@ class Hasse_complex { Filtration_value filtration(Simplex_handle sh) { if (sh == null_simplex()) { - return filtration(); + return std::numeric_limits<Filtration_value>::infinity(); } return complex_[sh].filtration_; } - Filtration_value filtration() { - return threshold_; - } - int dimension(Simplex_handle sh) { if (complex_[sh].boundary_.empty()) return 0; return complex_[sh].boundary_.size() - 1; @@ -206,7 +201,6 @@ class Hasse_complex { std::vector< Hasse_simp, Gudhi::no_init_allocator<Hasse_simp> > complex_; std::vector<Simplex_handle> vertices_; - Filtration_value threshold_; size_t num_vertices_; int dim_max_; }; @@ -245,7 +239,6 @@ std::istream& operator>>(std::istream & is } hcpx.dim_max_ = max_dim; - hcpx.threshold_ = max_fil; return is; } diff --git a/src/Nerve_GIC/doc/COPYRIGHT b/src/Nerve_GIC/doc/COPYRIGHT new file mode 100644 index 00000000..0c36a526 --- /dev/null +++ b/src/Nerve_GIC/doc/COPYRIGHT @@ -0,0 +1,19 @@ +The files of this directory are part of the Gudhi Library. The Gudhi library +(Geometric Understanding in Higher Dimensions) is a generic C++ library for +computational topology. + +Author(s): Mathieu Carrière + +Copyright (C) 2017 INRIA + +This program is free software: you can redistribute it and/or modify it under +the terms of the GNU General Public License as published by the Free Software +Foundation, either version 3 of the License, or (at your option) any later +version. + +This program is distributed in the hope that it will be useful, but WITHOUT +ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS +FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along with +this program. If not, see <http://www.gnu.org/licenses/>. diff --git a/src/Nerve_GIC/doc/GIC.jpg b/src/Nerve_GIC/doc/GIC.jpg Binary files differnew file mode 100644 index 00000000..cb1b9b7f --- /dev/null +++ b/src/Nerve_GIC/doc/GIC.jpg diff --git a/src/Nerve_GIC/doc/GIC.pdf b/src/Nerve_GIC/doc/GIC.pdf Binary files differnew file mode 100644 index 00000000..30525745 --- /dev/null +++ b/src/Nerve_GIC/doc/GIC.pdf diff --git a/src/Nerve_GIC/doc/Intro_graph_induced_complex.h b/src/Nerve_GIC/doc/Intro_graph_induced_complex.h new file mode 100644 index 00000000..3a0d8154 --- /dev/null +++ b/src/Nerve_GIC/doc/Intro_graph_induced_complex.h @@ -0,0 +1,216 @@ +/* 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): Mathieu Carriere + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef DOC_COVER_COMPLEX_INTRO_COVER_COMPLEX_H_ +#define DOC_COVER_COMPLEX_INTRO_COVER_COMPLEX_H_ + +namespace Gudhi { + +namespace cover_complex { + +/** \defgroup cover_complex Cover complex + * + * \author Mathieu Carrière + * + * @{ + * + * Visualizations of the simplicial complexes can be done with either + * neato (from <a target="_blank" href="http://www.graphviz.org/">graphviz</a>), + * <a target="_blank" href="http://www.geomview.org/">geomview</a>, + * <a target="_blank" href="https://github.com/MLWave/kepler-mapper">KeplerMapper</a>. + * Input point clouds are assumed to be + * <a target="_blank" href="http://www.geomview.org/docs/html/OFF.html">OFF files</a>. + * + * \section covers Covers + * + * Nerves and Graph Induced Complexes require a cover C of the input point cloud P, + * that is a set of subsets of P whose union is P itself. + * Very often, this cover is obtained from the preimage of a family of intervals covering + * the image of some scalar-valued function f defined on P. This family is parameterized + * by its resolution, which can be either the number or the length of the intervals, + * and its gain, which is the overlap percentage between consecutive intervals (ordered by their first values). + * + * \section nerves Nerves + * + * \subsection nervedefinition Nerve definition + * + * Assume you are given a cover C of your point cloud P. Then, the Nerve of this cover + * is the simplicial complex that has one k-simplex per k-fold intersection of cover elements. + * See also <a target="_blank" href="https://en.wikipedia.org/wiki/Nerve_of_a_covering"> Wikipedia </a>. + * + * \image html "nerve.png" "Nerve of a double torus" + * + * \subsection nerveexample Example + * + * This example builds the Nerve of a point cloud sampled on a 3D human shape (human.off). + * The cover C comes from the preimages of intervals (10 intervals with gain 0.3) + * covering the height function (coordinate 2), + * which are then refined into their connected components using the triangulation of the .OFF file. + * + * \include Nerve_GIC/Nerve.cpp + * + * When launching: + * + * \code $> ./Nerve ../../../../data/points/human.off 2 10 0.3 --v + * \endcode + * + * the program output is: + * + * \include Nerve_GIC/Nerve.txt + * + * The program also writes a file SC.txt. The first three lines in this file are the location of the input point cloud + * and the function used to compute the cover. + * The fourth line contains the number of vertices nv and edges ne of the Nerve. + * The next nv lines represent the vertices. Each line contains the vertex ID, + * the number of data points it contains, and their average color function value. + * Finally, the next ne lines represent the edges, characterized by the ID of their vertices. + * + * Using KeplerMapper, one can obtain the following visualization: + * + * \image html "nervevisu.jpg" "Visualization with KeplerMapper" + * + * \section gic Graph Induced Complexes (GIC) + * + * \subsection gicdefinition GIC definition + * + * Again, assume you are given a cover C of your point cloud P. Moreover, assume + * you are also given a graph G built on top of P. Then, for any clique in G + * whose nodes all belong to different elements of C, the GIC includes a corresponding + * simplex, whose dimension is the number of nodes in the clique minus one. + * See \cite Dey13 for more details. + * + * \image html "GIC.jpg" "GIC of a point cloud." + * + * \subsection gicexamplevor Example with cover from Voronoï + * + * This example builds the GIC of a point cloud sampled on a 3D human shape (human.off). + * We randomly subsampled 100 points in the point cloud, which act as seeds of + * a geodesic Voronoï diagram. Each cell of the diagram is then an element of C. + * The graph G (used to compute both the geodesics for Voronoï and the GIC) + * comes from the triangulation of the human shape. Note that the resulting simplicial complex is in dimension 3 + * in this example. + * + * \include Nerve_GIC/VoronoiGIC.cpp + * + * When launching: + * + * \code $> ./VoronoiGIC ../../../../data/points/human.off 700 --v + * \endcode + * + * the program outputs SC.off. Using e.g. + * + * \code $> geomview SC.off + * \endcode + * + * one can obtain the following visualization: + * + * \image html "gicvoronoivisu.jpg" "Visualization with Geomview" + * + * \subsection functionalGICdefinition Functional GIC + * + * If one restricts to the cliques in G whose nodes all belong to preimages of consecutive + * intervals (assuming the cover of the height function is minimal, i.e. no more than + * two intervals can intersect at a time), the GIC is of dimension one, i.e. a graph. + * We call this graph the functional GIC. See \cite Carriere16 for more details. + * + * \subsection functionalGICexample Example + * + * Functional GIC comes with automatic selection of the Rips threshold, + * the resolution and the gain of the function cover. See \cite Carriere17c for more details. In this example, + * we compute the functional GIC of a Klein bottle embedded in R^5, + * where the graph G comes from a Rips complex with automatic threshold, + * and the cover C comes from the preimages of intervals covering the first coordinate, + * with automatic resolution and gain. Note that automatic threshold, resolution and gain + * can be computed as well for the Nerve. + * + * \include Nerve_GIC/CoordGIC.cpp + * + * When launching: + * + * \code $> ./CoordGIC ../../../../data/points/KleinBottle5D.off 0 --v + * \endcode + * + * the program outputs SC.dot. Using e.g. + * + * \code $> neato SC.dot -Tpdf -o SC.pdf + * \endcode + * + * one can obtain the following visualization: + * + * \image html "coordGICvisu2.jpg" "Visualization with Neato" + * + * where nodes are colored by the filter function values and, for each node, the first number is its ID + * and the second is the number of data points that its contain. + * + * We also provide an example on a set of 72 pictures taken around the same object (lucky_cat.off). + * The function is now the first eigenfunction given by PCA, whose values + * are written in a file (lucky_cat_PCA1). Threshold, resolution and gain are automatically selected as before. + * + * \include Nerve_GIC/FuncGIC.cpp + * + * When launching: + * + * \code $> ./FuncGIC ../../data/points/COIL_database/lucky_cat.off ../../data/points/COIL_database/lucky_cat_PCA1 --v + * \endcode + * + * the program outputs again SC.dot which gives the following visualization after using neato: + * + * \image html "funcGICvisu.jpg" "Visualization with neato" + * + * \copyright GNU General Public License v3. + * \verbatim Contact: gudhi-users@lists.gforge.inria.fr \endverbatim + */ +/** @} */ // end defgroup cover_complex + +} // namespace cover_complex + +} // namespace Gudhi + +#endif // DOC_COVER_COMPLEX_INTRO_COVER_COMPLEX_H_ + + +/* * \subsection gicexample Example with cover from function + * + * This example builds the GIC of a point cloud sampled on a 3D human shape (human.off). + * The cover C comes from the preimages of intervals (with length 0.075 and gain 0) + * covering the height function (coordinate 2), + * and the graph G comes from a Rips complex built with threshold 0.075. + * Note that if the gain is too big, the number of cliques increases a lot, + * which make the computation time much larger. + * + * \include Nerve_GIC/GIC.cpp + * + * When launching: + * + * \code $> ./GIC ../../../../data/points/human.off 0.075 2 0.075 0 --v + * \endcode + * + * the program outputs SC.txt, which can be visualized with python and firefox as before: + * + * \image html "gicvisu.jpg" "Visualization with KeplerMapper" + * */ + + +/* * Using e.g. + * + * \code $> python KeplerMapperVisuFromTxtFile.py && firefox SC.html + * \endcode */ diff --git a/src/Nerve_GIC/doc/coordGICvisu.pdf b/src/Nerve_GIC/doc/coordGICvisu.pdf Binary files differnew file mode 100644 index 00000000..313aa1b5 --- /dev/null +++ b/src/Nerve_GIC/doc/coordGICvisu.pdf diff --git a/src/Nerve_GIC/doc/coordGICvisu2.jpg b/src/Nerve_GIC/doc/coordGICvisu2.jpg Binary files differnew file mode 100644 index 00000000..046feb2a --- /dev/null +++ b/src/Nerve_GIC/doc/coordGICvisu2.jpg diff --git a/src/Nerve_GIC/doc/funcGICvisu.jpg b/src/Nerve_GIC/doc/funcGICvisu.jpg Binary files differnew file mode 100644 index 00000000..f3da45ac --- /dev/null +++ b/src/Nerve_GIC/doc/funcGICvisu.jpg diff --git a/src/Nerve_GIC/doc/gicvisu.jpg b/src/Nerve_GIC/doc/gicvisu.jpg Binary files differnew file mode 100644 index 00000000..576dae47 --- /dev/null +++ b/src/Nerve_GIC/doc/gicvisu.jpg diff --git a/src/Nerve_GIC/doc/gicvoronoivisu.jpg b/src/Nerve_GIC/doc/gicvoronoivisu.jpg Binary files differnew file mode 100644 index 00000000..cd86c411 --- /dev/null +++ b/src/Nerve_GIC/doc/gicvoronoivisu.jpg diff --git a/src/Nerve_GIC/doc/nerve.png b/src/Nerve_GIC/doc/nerve.png Binary files differnew file mode 100644 index 00000000..b66da4a4 --- /dev/null +++ b/src/Nerve_GIC/doc/nerve.png diff --git a/src/Nerve_GIC/doc/nervevisu.jpg b/src/Nerve_GIC/doc/nervevisu.jpg Binary files differnew file mode 100644 index 00000000..67ae1d7e --- /dev/null +++ b/src/Nerve_GIC/doc/nervevisu.jpg diff --git a/src/Nerve_GIC/example/CMakeLists.txt b/src/Nerve_GIC/example/CMakeLists.txt new file mode 100644 index 00000000..461b6db2 --- /dev/null +++ b/src/Nerve_GIC/example/CMakeLists.txt @@ -0,0 +1,29 @@ +cmake_minimum_required(VERSION 2.6) +project(Nerve_GIC_examples) + +add_executable ( Nerve Nerve.cpp ) +add_executable ( CoordGIC CoordGIC.cpp ) +add_executable ( FuncGIC FuncGIC.cpp ) +add_executable ( VoronoiGIC VoronoiGIC.cpp ) + +if (TBB_FOUND) + target_link_libraries(Nerve ${TBB_LIBRARIES}) + target_link_libraries(CoordGIC ${TBB_LIBRARIES}) + target_link_libraries(FuncGIC ${TBB_LIBRARIES}) + target_link_libraries(VoronoiGIC ${TBB_LIBRARIES}) +endif() + +file(COPY KeplerMapperVisuFromTxtFile.py km.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) + +add_test(NAME Nerve_GIC_example_nerve COMMAND $<TARGET_FILE:Nerve> + "${CMAKE_SOURCE_DIR}/data/points/human.off" "2" "10" "0.3") + +add_test(NAME Nerve_GIC_example_VoronoiGIC COMMAND $<TARGET_FILE:VoronoiGIC> + "${CMAKE_SOURCE_DIR}/data/points/human.off" "100") + +add_test(NAME Nerve_GIC_example_CoordGIC COMMAND $<TARGET_FILE:CoordGIC> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "0") + +add_test(NAME Nerve_GIC_example_FuncGIC COMMAND $<TARGET_FILE:FuncGIC> + "${CMAKE_SOURCE_DIR}/data/points/COIL_database/lucky_cat.off" + "${CMAKE_SOURCE_DIR}/data/points/COIL_database/lucky_cat_PCA1") diff --git a/src/Nerve_GIC/example/CoordGIC.cpp b/src/Nerve_GIC/example/CoordGIC.cpp new file mode 100644 index 00000000..c03fcbb3 --- /dev/null +++ b/src/Nerve_GIC/example/CoordGIC.cpp @@ -0,0 +1,93 @@ +/* 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): Mathieu Carrière + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/GIC.h> + +#include <string> +#include <vector> + +void usage(int nbArgs, char *const progName) { + std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n"; + std::cerr << "Usage: " << progName << " filename.off coordinate [--v] \n"; + std::cerr << " i.e.: " << progName << " ../../data/points/human.off 2 --v \n"; + exit(-1); // ----- >> +} + +int main(int argc, char **argv) { + if ((argc != 3) && (argc != 4)) usage(argc, argv[0]); + + using Point = std::vector<float>; + + std::string off_file_name(argv[1]); + int coord = atoi(argv[2]); + bool verb = 0; + if (argc == 4) verb = 1; + + // ----------------------------------------- + // Init of a functional GIC from an OFF file + // ----------------------------------------- + + Gudhi::cover_complex::Cover_complex<Point> GIC; + GIC.set_verbose(verb); + + bool check = GIC.read_point_cloud(off_file_name); + + if (!check) { + std::cout << "Incorrect OFF file." << std::endl; + } else { + GIC.set_type("GIC"); + + GIC.set_color_from_coordinate(coord); + GIC.set_function_from_coordinate(coord); + + GIC.set_graph_from_automatic_rips(Gudhi::Euclidean_distance()); + GIC.set_automatic_resolution(); + GIC.set_gain(); + GIC.set_cover_from_function(); + + GIC.find_simplices(); + + GIC.plot_DOT(); + + Gudhi::Simplex_tree<> stree; + GIC.create_complex(stree); + + // -------------------------------------------- + // Display information about the functional GIC + // -------------------------------------------- + + if (verb) { + std::cout << "Functional GIC is of dimension " << stree.dimension() << " - " << stree.num_simplices() + << " simplices - " << stree.num_vertices() << " vertices." << std::endl; + + std::cout << "Iterator on functional GIC simplices" << std::endl; + for (auto f_simplex : stree.filtration_simplex_range()) { + for (auto vertex : stree.simplex_vertex_range(f_simplex)) { + std::cout << vertex << " "; + } + std::cout << std::endl; + } + } + } + + return 0; +} diff --git a/src/Nerve_GIC/example/FuncGIC.cpp b/src/Nerve_GIC/example/FuncGIC.cpp new file mode 100644 index 00000000..3762db4e --- /dev/null +++ b/src/Nerve_GIC/example/FuncGIC.cpp @@ -0,0 +1,94 @@ +/* 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): Mathieu Carrière + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/GIC.h> + +#include <string> +#include <vector> + +void usage(int nbArgs, char *const progName) { + std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n"; + std::cerr << "Usage: " << progName << " filename.off function [--v] \n"; + std::cerr << " i.e.: " << progName << " ../../data/points/COIL_database/lucky_cat.off " + "../../data/points/COIL_database/lucky_cat_PCA1 --v \n"; + exit(-1); // ----- >> +} + +int main(int argc, char **argv) { + if ((argc != 3) && (argc != 4)) usage(argc, argv[0]); + + using Point = std::vector<float>; + + std::string off_file_name(argv[1]); + std::string func_file_name = argv[2]; + bool verb = 0; + if (argc == 4) verb = 1; + + // ----------------------------------------- + // Init of a functional GIC from an OFF file + // ----------------------------------------- + + Gudhi::cover_complex::Cover_complex<Point> GIC; + GIC.set_verbose(verb); + + bool check = GIC.read_point_cloud(off_file_name); + + if (!check) { + std::cout << "Incorrect OFF file." << std::endl; + } else { + GIC.set_type("GIC"); + + GIC.set_color_from_file(func_file_name); + GIC.set_function_from_file(func_file_name); + + GIC.set_graph_from_automatic_rips(Gudhi::Euclidean_distance()); + GIC.set_automatic_resolution(); + GIC.set_gain(); + GIC.set_cover_from_function(); + + GIC.find_simplices(); + + GIC.plot_DOT(); + + Gudhi::Simplex_tree<> stree; + GIC.create_complex(stree); + + // -------------------------------------------- + // Display information about the functional GIC + // -------------------------------------------- + + if (verb) { + std::cout << "Functional GIC is of dimension " << stree.dimension() << " - " << stree.num_simplices() + << " simplices - " << stree.num_vertices() << " vertices." << std::endl; + + std::cout << "Iterator on functional GIC simplices" << std::endl; + for (auto f_simplex : stree.filtration_simplex_range()) { + for (auto vertex : stree.simplex_vertex_range(f_simplex)) { + std::cout << vertex << " "; + } + std::cout << std::endl; + } + } + } + + return 0; +} diff --git a/src/Nerve_GIC/example/GIC.cpp b/src/Nerve_GIC/example/GIC.cpp new file mode 100644 index 00000000..2bc24a4d --- /dev/null +++ b/src/Nerve_GIC/example/GIC.cpp @@ -0,0 +1,95 @@ +/* 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): Mathieu Carrière + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/GIC.h> + +#include <string> +#include <vector> + +void usage(int nbArgs, char *const progName) { + std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n"; + std::cerr << "Usage: " << progName << " filename.off threshold coordinate resolution gain [--v] \n"; + std::cerr << " i.e.: " << progName << " ../../data/points/human.off 0.075 2 0.075 0 --v \n"; + exit(-1); // ----- >> +} + +int main(int argc, char **argv) { + if ((argc != 6) && (argc != 7)) usage(argc, argv[0]); + + using Point = std::vector<float>; + + std::string off_file_name(argv[1]); + double threshold = atof(argv[2]); + int coord = atoi(argv[3]); + double resolution = atof(argv[4]); + double gain = atof(argv[5]); + bool verb = 0; + if (argc == 7) verb = 1; + + // ---------------------------------------------------------------------------- + // Init of a graph induced complex from an OFF file + // ---------------------------------------------------------------------------- + + Gudhi::graph_induced_complex::Graph_induced_complex<Point> GIC; + GIC.set_verbose(verb); + + bool check = GIC.read_point_cloud(off_file_name); + + if (!check) { + std::cout << "Incorrect OFF file." << std::endl; + } else { + GIC.set_color_from_coordinate(coord); + GIC.set_function_from_coordinate(coord); + + GIC.set_graph_from_rips(threshold, Gudhi::Euclidean_distance()); + + GIC.set_resolution_with_interval_length(resolution); + GIC.set_gain(gain); + GIC.set_cover_from_function(); + + GIC.find_GIC_simplices(); + + GIC.plot_TXT_for_KeplerMapper(); + + Gudhi::Simplex_tree<> stree; + GIC.create_complex(stree); + + // ---------------------------------------------------------------------------- + // Display information about the graph induced complex + // ---------------------------------------------------------------------------- + + if (verb) { + std::cout << "Graph induced complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() + << " simplices - " << stree.num_vertices() << " vertices." << std::endl; + + std::cout << "Iterator on graph induced complex simplices" << std::endl; + for (auto f_simplex : stree.filtration_simplex_range()) { + for (auto vertex : stree.simplex_vertex_range(f_simplex)) { + std::cout << vertex << " "; + } + std::cout << std::endl; + } + } + } + + return 0; +} diff --git a/src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py b/src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py new file mode 100755 index 00000000..d2897774 --- /dev/null +++ b/src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python + +import km +import numpy as np +from collections import defaultdict + +"""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): Mathieu Carriere + + Copyright (C) 2017 INRIA + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see <http://www.gnu.org/licenses/>. +""" + +__author__ = "Mathieu Carriere" +__copyright__ = "Copyright (C) 2017 INRIA" +__license__ = "GPL v3" + +network = {} +mapper = km.KeplerMapper(verbose=0) +data = np.zeros((3,3)) +projected_data = mapper.fit_transform( data, projection="sum", scaler=None ) + +f = open('SC.txt','r') +nodes = defaultdict(list) +links = defaultdict(list) +custom = defaultdict(list) + +dat = f.readline() +lens = f.readline() +color = f.readline(); +param = [float(i) for i in f.readline().split(" ")] + +nums = [int(i) for i in f.readline().split(" ")] +num_nodes = nums[0] +num_edges = nums[1] + +for i in range(0,num_nodes): + point = [float(j) for j in f.readline().split(" ")] + nodes[ str(int(point[0])) ] = [ int(point[0]), point[1], int(point[2]) ] + links[ str(int(point[0])) ] = [] + custom[ int(point[0]) ] = point[1] + +m = min([custom[i] for i in range(0,num_nodes)]) +M = max([custom[i] for i in range(0,num_nodes)]) + +for i in range(0,num_edges): + edge = [int(j) for j in f.readline().split(" ")] + links[ str(edge[0]) ].append( str(edge[1]) ) + links[ str(edge[1]) ].append( str(edge[0]) ) + +network["nodes"] = nodes +network["links"] = links +network["meta"] = lens + +mapper.visualize(network, color_function = color, path_html="SC.html", title=dat, +graph_link_distance=30, graph_gravity=0.1, graph_charge=-120, custom_tooltips=custom, width_html=0, +height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=param[0],gain=param[1], minimum=m,maximum=M) diff --git a/src/Nerve_GIC/example/Nerve.cpp b/src/Nerve_GIC/example/Nerve.cpp new file mode 100644 index 00000000..4d5b009b --- /dev/null +++ b/src/Nerve_GIC/example/Nerve.cpp @@ -0,0 +1,95 @@ +/* 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): Mathieu Carrière + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/GIC.h> + +#include <string> +#include <vector> + +void usage(int nbArgs, char *const progName) { + std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n"; + std::cerr << "Usage: " << progName << " filename.off coordinate resolution gain [--v] \n"; + std::cerr << " i.e.: " << progName << " ../../data/points/human.off 2 10 0.3 --v \n"; + exit(-1); // ----- >> +} + +int main(int argc, char **argv) { + if ((argc != 5) && (argc != 6)) usage(argc, argv[0]); + + using Point = std::vector<float>; + + std::string off_file_name(argv[1]); + int coord = atoi(argv[2]); + int resolution = atoi(argv[3]); + double gain = atof(argv[4]); + bool verb = 0; + if (argc == 6) verb = 1; + + // -------------------------------- + // Init of a Nerve from an OFF file + // -------------------------------- + + Gudhi::cover_complex::Cover_complex<Point> SC; + SC.set_verbose(verb); + + bool check = SC.read_point_cloud(off_file_name); + + if (!check) { + std::cout << "Incorrect OFF file." << std::endl; + } else { + SC.set_type("Nerve"); + + SC.set_color_from_coordinate(coord); + SC.set_function_from_coordinate(coord); + + SC.set_graph_from_OFF(); + SC.set_resolution_with_interval_number(resolution); + SC.set_gain(gain); + SC.set_cover_from_function(); + + SC.find_simplices(); + + SC.write_info(); + + Gudhi::Simplex_tree<> stree; + SC.create_complex(stree); + + // ---------------------------------------------------------------------------- + // Display information about the graph induced complex + // ---------------------------------------------------------------------------- + + if (verb) { + std::cout << "Nerve is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " + << stree.num_vertices() << " vertices." << std::endl; + + std::cout << "Iterator on Nerve simplices" << std::endl; + for (auto f_simplex : stree.filtration_simplex_range()) { + for (auto vertex : stree.simplex_vertex_range(f_simplex)) { + std::cout << vertex << " "; + } + std::cout << std::endl; + } + } + } + + return 0; +} diff --git a/src/Nerve_GIC/example/Nerve.txt b/src/Nerve_GIC/example/Nerve.txt new file mode 100644 index 00000000..2a861c5f --- /dev/null +++ b/src/Nerve_GIC/example/Nerve.txt @@ -0,0 +1,43 @@ +Nerve is of dimension 1 - 41 simplices - 21 vertices. +Iterator on Nerve simplices +0 +1 +2 +2 0 +3 +3 1 +4 +4 3 +5 +5 2 +6 +6 4 +7 +7 5 +8 +9 +9 6 +10 +10 7 +11 +12 +12 8 +13 +13 9 +13 10 +14 +14 11 +15 +15 13 +16 +16 12 +17 +17 14 +18 +18 15 +18 16 +18 17 +19 +19 18 +20 +20 19 diff --git a/src/Nerve_GIC/example/VoronoiGIC.cpp b/src/Nerve_GIC/example/VoronoiGIC.cpp new file mode 100644 index 00000000..32431cc2 --- /dev/null +++ b/src/Nerve_GIC/example/VoronoiGIC.cpp @@ -0,0 +1,90 @@ +/* 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): Mathieu Carrière + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/GIC.h> + +#include <string> +#include <vector> + +void usage(int nbArgs, char *const progName) { + std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n"; + std::cerr << "Usage: " << progName << " filename.off N [--v] \n"; + std::cerr << " i.e.: " << progName << " ../../data/points/human.off 100 --v \n"; + exit(-1); // ----- >> +} + +int main(int argc, char **argv) { + if ((argc != 3) && (argc != 4)) usage(argc, argv[0]); + + using Point = std::vector<float>; + + std::string off_file_name(argv[1]); + int m = atoi(argv[2]); + bool verb = 0; + if (argc == 4) verb = 1; + + // ---------------------------------------------------------------------------- + // Init of a graph induced complex from an OFF file + // ---------------------------------------------------------------------------- + + Gudhi::cover_complex::Cover_complex<Point> GIC; + GIC.set_verbose(verb); + + bool check = GIC.read_point_cloud(off_file_name); + + if (!check) { + std::cout << "Incorrect OFF file." << std::endl; + } else { + GIC.set_type("GIC"); + + GIC.set_color_from_coordinate(); + + GIC.set_graph_from_OFF(); + GIC.set_cover_from_Voronoi(Gudhi::Euclidean_distance(), m); + + GIC.find_simplices(); + + GIC.plot_OFF(); + + Gudhi::Simplex_tree<> stree; + GIC.create_complex(stree); + + // ---------------------------------------------------------------------------- + // Display information about the graph induced complex + // ---------------------------------------------------------------------------- + + if (verb) { + std::cout << "Graph induced complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() + << " simplices - " << stree.num_vertices() << " vertices." << std::endl; + + std::cout << "Iterator on graph induced complex simplices" << std::endl; + for (auto f_simplex : stree.filtration_simplex_range()) { + for (auto vertex : stree.simplex_vertex_range(f_simplex)) { + std::cout << vertex << " "; + } + std::cout << std::endl; + } + } + } + + return 0; +} diff --git a/src/Nerve_GIC/example/km.py b/src/Nerve_GIC/example/km.py new file mode 100755 index 00000000..53024aab --- /dev/null +++ b/src/Nerve_GIC/example/km.py @@ -0,0 +1,390 @@ +from __future__ import division +import numpy as np +from collections import defaultdict +import json +import itertools +from sklearn import cluster, preprocessing, manifold +from datetime import datetime +import sys + +class KeplerMapper(object): + # With this class you can build topological networks from (high-dimensional) data. + # + # 1) Fit a projection/lens/function to a dataset and transform it. + # For instance "mean_of_row(x) for x in X" + # 2) Map this projection with overlapping intervals/hypercubes. + # Cluster the points inside the interval + # (Note: we cluster on the inverse image/original data to lessen projection loss). + # If two clusters/nodes have the same members (due to the overlap), then: + # connect these with an edge. + # 3) Visualize the network using HTML and D3.js. + # + # functions + # --------- + # fit_transform: Create a projection (lens) from a dataset + # map: Apply Mapper algorithm on this projection and build a simplicial complex + # visualize: Turns the complex dictionary into a HTML/D3.js visualization + + def __init__(self, verbose=2): + self.verbose = verbose + + self.chunk_dist = [] + self.overlap_dist = [] + self.d = [] + self.nr_cubes = 0 + self.overlap_perc = 0 + self.clusterer = False + + def fit_transform(self, X, projection="sum", scaler=preprocessing.MinMaxScaler()): + # Creates the projection/lens from X. + # + # Input: X. Input features as a numpy array. + # Output: projected_X. original data transformed to a projection (lens). + # + # parameters + # ---------- + # projection: Projection parameter is either a string, + # a scikit class with fit_transform, like manifold.TSNE(), + # or a list of dimension indices. + # scaler: if None, do no scaling, else apply scaling to the projection + # Default: Min-Max scaling + + self.scaler = scaler + self.projection = str(projection) + + # Detect if projection is a class (for scikit-learn) + #if str(type(projection))[1:6] == "class": #TODO: de-ugly-fy + # reducer = projection + # if self.verbose > 0: + # try: + # projection.set_params(**{"verbose":self.verbose}) + # except: + # pass + # print("\n..Projecting data using: \n\t%s\n"%str(projection)) + # X = reducer.fit_transform(X) + + # Detect if projection is a string (for standard functions) + if isinstance(projection, str): + if self.verbose > 0: + print("\n..Projecting data using: %s"%(projection)) + # Stats lenses + if projection == "sum": # sum of row + X = np.sum(X, axis=1).reshape((X.shape[0],1)) + if projection == "mean": # mean of row + X = np.mean(X, axis=1).reshape((X.shape[0],1)) + if projection == "median": # mean of row + X = np.median(X, axis=1).reshape((X.shape[0],1)) + if projection == "max": # max of row + X = np.max(X, axis=1).reshape((X.shape[0],1)) + if projection == "min": # min of row + X = np.min(X, axis=1).reshape((X.shape[0],1)) + if projection == "std": # std of row + X = np.std(X, axis=1).reshape((X.shape[0],1)) + + if projection == "dist_mean": # Distance of x to mean of X + X_mean = np.mean(X, axis=0) + X = np.sum(np.sqrt((X - X_mean)**2), axis=1).reshape((X.shape[0],1)) + + # Detect if projection is a list (with dimension indices) + if isinstance(projection, list): + if self.verbose > 0: + print("\n..Projecting data using: %s"%(str(projection))) + X = X[:,np.array(projection)] + + # Scaling + if scaler is not None: + if self.verbose > 0: + print("\n..Scaling with: %s\n"%str(scaler)) + X = scaler.fit_transform(X) + + return X + + def map(self, projected_X, inverse_X=None, clusterer=cluster.DBSCAN(eps=0.5,min_samples=3), nr_cubes=10, overlap_perc=0.1): + # This maps the data to a simplicial complex. Returns a dictionary with nodes and links. + # + # Input: projected_X. A Numpy array with the projection/lens. + # Output: complex. A dictionary with "nodes", "links" and "meta information" + # + # parameters + # ---------- + # projected_X projected_X. A Numpy array with the projection/lens. Required. + # inverse_X Numpy array or None. If None then the projection itself is used for clustering. + # clusterer Scikit-learn API compatible clustering algorithm. Default: DBSCAN + # nr_cubes Int. The number of intervals/hypercubes to create. + # overlap_perc Float. The percentage of overlap "between" the intervals/hypercubes. + + start = datetime.now() + + # Helper function + def cube_coordinates_all(nr_cubes, nr_dimensions): + # Helper function to get origin coordinates for our intervals/hypercubes + # Useful for looping no matter the number of cubes or dimensions + # Example: if there are 4 cubes per dimension and 3 dimensions + # return the bottom left (origin) coordinates of 64 hypercubes, + # as a sorted list of Numpy arrays + # TODO: elegance-ify... + l = [] + for x in range(nr_cubes): + l += [x] * nr_dimensions + return [np.array(list(f)) for f in sorted(set(itertools.permutations(l,nr_dimensions)))] + + nodes = defaultdict(list) + links = defaultdict(list) + complex = {} + self.nr_cubes = nr_cubes + self.clusterer = clusterer + self.overlap_perc = overlap_perc + + if self.verbose > 0: + print("Mapping on data shaped %s using dimensions\n"%(str(projected_X.shape))) + + # If inverse image is not provided, we use the projection as the inverse image (suffer projection loss) + if inverse_X is None: + inverse_X = projected_X + + # We chop up the min-max column ranges into 'nr_cubes' parts + self.chunk_dist = (np.max(projected_X, axis=0) - np.min(projected_X, axis=0))/nr_cubes + + # We calculate the overlapping windows distance + self.overlap_dist = self.overlap_perc * self.chunk_dist + + # We find our starting point + self.d = np.min(projected_X, axis=0) + + # Use a dimension index array on the projected X + # (For now this uses the entire dimensionality, but we keep for experimentation) + di = np.array([x for x in range(projected_X.shape[1])]) + + # Prefix'ing the data with ID's + ids = np.array([x for x in range(projected_X.shape[0])]) + projected_X = np.c_[ids,projected_X] + inverse_X = np.c_[ids,inverse_X] + + # Subdivide the projected data X in intervals/hypercubes with overlap + if self.verbose > 0: + total_cubes = len(cube_coordinates_all(nr_cubes,projected_X.shape[1])) + print("Creating %s hypercubes."%total_cubes) + + for i, coor in enumerate(cube_coordinates_all(nr_cubes,di.shape[0])): + # Slice the hypercube + hypercube = projected_X[ np.invert(np.any((projected_X[:,di+1] >= self.d[di] + (coor * self.chunk_dist[di])) & + (projected_X[:,di+1] < self.d[di] + (coor * self.chunk_dist[di]) + self.chunk_dist[di] + self.overlap_dist[di]) == False, axis=1 )) ] + + if self.verbose > 1: + print("There are %s points in cube_%s / %s with starting range %s"% + (hypercube.shape[0],i,total_cubes,self.d[di] + (coor * self.chunk_dist[di]))) + + # If at least one sample inside the hypercube + if hypercube.shape[0] > 0: + # Cluster the data point(s) in the cube, skipping the id-column + # Note that we apply clustering on the inverse image (original data samples) that fall inside the cube. + inverse_x = inverse_X[[int(nn) for nn in hypercube[:,0]]] + + clusterer.fit(inverse_x[:,1:]) + + if self.verbose > 1: + print("Found %s clusters in cube_%s\n"%(np.unique(clusterer.labels_[clusterer.labels_ > -1]).shape[0],i)) + + #Now for every (sample id in cube, predicted cluster label) + for a in np.c_[hypercube[:,0],clusterer.labels_]: + if a[1] != -1: #if not predicted as noise + cluster_id = str(coor[0])+"_"+str(i)+"_"+str(a[1])+"_"+str(coor)+"_"+str(self.d[di] + (coor * self.chunk_dist[di])) # TODO: de-rudimentary-ify + nodes[cluster_id].append( int(a[0]) ) # Append the member id's as integers + else: + if self.verbose > 1: + print("Cube_%s is empty.\n"%(i)) + + # Create links when clusters from different hypercubes have members with the same sample id. + candidates = itertools.combinations(nodes.keys(),2) + for candidate in candidates: + # if there are non-unique members in the union + if len(nodes[candidate[0]]+nodes[candidate[1]]) != len(set(nodes[candidate[0]]+nodes[candidate[1]])): + links[candidate[0]].append( candidate[1] ) + + # Reporting + if self.verbose > 0: + nr_links = 0 + for k in links: + nr_links += len(links[k]) + print("\ncreated %s edges and %s nodes in %s."%(nr_links,len(nodes),str(datetime.now()-start))) + + complex["nodes"] = nodes + complex["links"] = links + complex["meta"] = self.projection + + return complex + + def visualize(self, complex, color_function="", path_html="mapper_visualization_output.html", title="My Data", + graph_link_distance=30, graph_gravity=0.1, graph_charge=-120, custom_tooltips=None, width_html=0, + height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=0,gain=0,minimum=0,maximum=0): + # Turns the dictionary 'complex' in a html file with d3.js + # + # Input: complex. Dictionary (output from calling .map()) + # Output: a HTML page saved as a file in 'path_html'. + # + # parameters + # ---------- + # color_function string. Not fully implemented. Default: "" (distance to origin) + # path_html file path as string. Where to save the HTML page. + # title string. HTML page document title and first heading. + # graph_link_distance int. Edge length. + # graph_gravity float. "Gravity" to center of layout. + # graph_charge int. charge between nodes. + # custom_tooltips None or Numpy Array. You could use "y"-label array for this. + # width_html int. Width of canvas. Default: 0 (full width) + # height_html int. Height of canvas. Default: 0 (full height) + # show_tooltips bool. default:True + # show_title bool. default:True + # show_meta bool. default:True + + # Format JSON for D3 graph + json_s = {} + json_s["nodes"] = [] + json_s["links"] = [] + k2e = {} # a key to incremental int dict, used for id's when linking + + for e, k in enumerate(complex["nodes"]): + # Tooltip and node color formatting, TODO: de-mess-ify + if custom_tooltips is not None: + tooltip_s = "<h2>Cluster %s</h2>"%k + " ".join(str(custom_tooltips[complex["nodes"][k][0]]).split(" ")) + if maximum == minimum: + tooltip_i = 0 + else: + tooltip_i = int(30*(custom_tooltips[complex["nodes"][k][0]]-minimum)/(maximum-minimum)) + json_s["nodes"].append({"name": str(k), "tooltip": tooltip_s, "group": 2 * int(np.log(complex["nodes"][k][2])), "color": tooltip_i}) + else: + tooltip_s = "<h2>Cluster %s</h2>Contains %s members."%(k,len(complex["nodes"][k])) + json_s["nodes"].append({"name": str(k), "tooltip": tooltip_s, "group": 2 * int(np.log(len(complex["nodes"][k]))), "color": str(k.split("_")[0])}) + k2e[k] = e + for k in complex["links"]: + for link in complex["links"][k]: + json_s["links"].append({"source": k2e[k], "target":k2e[link],"value":1}) + + # Width and height of graph in HTML output + if width_html == 0: + width_css = "100%" + width_js = 'document.getElementById("holder").offsetWidth-20' + else: + width_css = "%spx" % width_html + width_js = "%s" % width_html + if height_html == 0: + height_css = "100%" + height_js = 'document.getElementById("holder").offsetHeight-20' + else: + height_css = "%spx" % height_html + height_js = "%s" % height_html + + # Whether to show certain UI elements or not + if show_tooltips == False: + tooltips_display = "display: none;" + else: + tooltips_display = "" + + if show_meta == False: + meta_display = "display: none;" + else: + meta_display = "" + + if show_title == False: + title_display = "display: none;" + else: + title_display = "" + + with open(path_html,"wb") as outfile: + html = """<!DOCTYPE html> + <meta charset="utf-8"> + <meta name="generator" content="KeplerMapper"> + <title>%s | KeplerMapper</title> + <link href='https://fonts.googleapis.com/css?family=Roboto:700,300' rel='stylesheet' type='text/css'> + <style> + * {margin: 0; padding: 0;} + html { height: 100%%;} + body {background: #111; height: 100%%; font: 100 16px Roboto, Sans-serif;} + .link { stroke: #999; stroke-opacity: .333; } + .divs div { border-radius: 50%%; background: red; position: absolute; } + .divs { position: absolute; top: 0; left: 0; } + #holder { position: relative; width: %s; height: %s; background: #111; display: block;} + h1 { %s padding: 20px; color: #fafafa; text-shadow: 0px 1px #000,0px -1px #000; position: absolute; font: 300 30px Roboto, Sans-serif;} + h2 { text-shadow: 0px 1px #000,0px -1px #000; font: 700 16px Roboto, Sans-serif;} + .meta { position: absolute; opacity: 0.9; width: 220px; top: 80px; left: 20px; display: block; %s background: #000; line-height: 25px; color: #fafafa; border: 20px solid #000; font: 100 16px Roboto, Sans-serif;} + div.tooltip { position: absolute; width: 380px; display: block; %s padding: 20px; background: #000; border: 0px; border-radius: 3px; pointer-events: none; z-index: 999; color: #FAFAFA;} + } + </style> + <body> + <div id="holder"> + <h1>%s</h1> + <p class="meta"> + <b>Lens</b><br>%s<br><br> + <b>Length of intervals</b><br>%s<br><br> + <b>Overlap percentage</b><br>%s%%<br><br> + <b>Color Function</b><br>%s + </p> + </div> + <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js"></script> + <script> + var width = %s, + height = %s; + var color = d3.scale.ordinal() + .domain(["0","1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30"]) + .range(["#FF0000","#FF1400","#FF2800","#FF3c00","#FF5000","#FF6400","#FF7800","#FF8c00","#FFa000","#FFb400","#FFc800","#FFdc00","#FFf000","#fdff00","#b0ff00","#65ff00","#17ff00","#00ff36","#00ff83","#00ffd0","#00e4ff","#00c4ff","#00a4ff","#00a4ff","#0084ff","#0064ff","#0044ff","#0022ff","#0002ff","#0100ff","#0300ff","#0500ff"]); + var force = d3.layout.force() + .charge(%s) + .linkDistance(%s) + .gravity(%s) + .size([width, height]); + var svg = d3.select("#holder").append("svg") + .attr("width", width) + .attr("height", height); + + var div = d3.select("#holder").append("div") + .attr("class", "tooltip") + .style("opacity", 0.0); + + var divs = d3.select('#holder').append('div') + .attr('class', 'divs') + .attr('style', function(d) { return 'overflow: hidden; width: ' + width + 'px; height: ' + height + 'px;'; }); + + graph = %s; + force + .nodes(graph.nodes) + .links(graph.links) + .start(); + var link = svg.selectAll(".link") + .data(graph.links) + .enter().append("line") + .attr("class", "link") + .style("stroke-width", function(d) { return Math.sqrt(d.value); }); + var node = divs.selectAll('div') + .data(graph.nodes) + .enter().append('div') + .on("mouseover", function(d) { + div.transition() + .duration(200) + .style("opacity", .9); + div .html(d.tooltip + "<br/>") + .style("left", (d3.event.pageX + 100) + "px") + .style("top", (d3.event.pageY - 28) + "px"); + }) + .on("mouseout", function(d) { + div.transition() + .duration(500) + .style("opacity", 0); + }) + .call(force.drag); + + node.append("title") + .text(function(d) { return d.name; }); + force.on("tick", function() { + link.attr("x1", function(d) { return d.source.x; }) + .attr("y1", function(d) { return d.source.y; }) + .attr("x2", function(d) { return d.target.x; }) + .attr("y2", function(d) { return d.target.y; }); + node.attr("cx", function(d) { return d.x; }) + .attr("cy", function(d) { return d.y; }) + .attr('style', function(d) { return 'width: ' + (d.group * 2) + 'px; height: ' + (d.group * 2) + 'px; ' + 'left: '+(d.x-(d.group))+'px; ' + 'top: '+(d.y-(d.group))+'px; background: '+color(d.color)+'; box-shadow: 0px 0px 3px #111; box-shadow: 0px 0px 33px '+color(d.color)+', inset 0px 0px 5px rgba(0, 0, 0, 0.2);'}) + ; + }); + </script>"""%(title,width_css, height_css, title_display, meta_display, tooltips_display, title,complex["meta"],res,gain*100,color_function,width_js,height_js,graph_charge,graph_link_distance,graph_gravity,json.dumps(json_s)) + outfile.write(html.encode("utf-8")) + if self.verbose > 0: + print("\nWrote d3.js graph to '%s'"%path_html) diff --git a/src/Nerve_GIC/example/km.py.COPYRIGHT b/src/Nerve_GIC/example/km.py.COPYRIGHT new file mode 100644 index 00000000..bef7b121 --- /dev/null +++ b/src/Nerve_GIC/example/km.py.COPYRIGHT @@ -0,0 +1,26 @@ +km.py is a fork of https://github.com/MLWave/kepler-mapper. +Only the visualization part has been kept (Mapper part has been removed). + +This file has te following Copyright : + +The MIT License (MIT) + +Copyright (c) 2015 Triskelion - HJ van Veen - info@mlwave.com + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. diff --git a/src/Nerve_GIC/include/gudhi/GIC.h b/src/Nerve_GIC/include/gudhi/GIC.h new file mode 100644 index 00000000..9f107a7e --- /dev/null +++ b/src/Nerve_GIC/include/gudhi/GIC.h @@ -0,0 +1,1166 @@ +/* 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: Mathieu Carriere + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef GIC_H_ +#define GIC_H_ + +#include <gudhi/Debug_utils.h> +#include <gudhi/graph_simplicial_complex.h> +#include <gudhi/reader_utils.h> +#include <gudhi/Simplex_tree.h> +#include <gudhi/Rips_complex.h> +#include <gudhi/Points_off_io.h> +#include <gudhi/distance_functions.h> + +#include <iostream> +#include <vector> +#include <map> +#include <string> +#include <limits> // for numeric_limits +#include <utility> // for pair<> +#include <algorithm> // for std::max +#include <random> +#include <cassert> + +namespace Gudhi { + +namespace cover_complex { + +using Simplex_tree = Gudhi::Simplex_tree<>; +using Filtration_value = Simplex_tree::Filtration_value; +using Rips_complex = Gudhi::rips_complex::Rips_complex<Filtration_value>; + +/** + * \class Cover_complex + * \brief Cover complex data structure. + * + * \ingroup cover_complex + * + * \details + * The data structure is a simplicial complex, representing a + * Graph Induced simplicial Complex (GIC) or a Nerve, + * and whose simplices are computed with a cover C of a point + * cloud P, which often comes from the preimages of intervals + * covering the image of a function f defined on P. + * These intervals are parameterized by their resolution + * (either their length or their number) + * and their gain (percentage of overlap). + * To compute a GIC, one also needs a graph G built on top of P, + * whose cliques with vertices belonging to different elements of C + * correspond to the simplices of the GIC. + * + */ +template <typename Point> +class Cover_complex { + private: + // Graph_induced_complex(std::map<int, double> fun){func = fun;} + bool verbose = false; // whether to display information. + std::vector<Point> point_cloud; + std::vector<std::vector<int> > one_skeleton; + typedef int Cover_t; // elements of cover C are indexed by integers. + std::vector<std::vector<Cover_t> > simplices; + std::map<int, std::vector<Cover_t> > cover; + std::map<Cover_t, std::vector<int> > cover_back; + int maximal_dim; // maximal dimension of output simplicial complex. + int data_dimension; // dimension of input data. + int n; // number of points. + std::map<Cover_t, int> + cover_fct; // integer-valued function that allows to state if two elements of the cover are consecutive or not. + std::map<Cover_t, std::pair<int, double> > + cover_color; // size and coloring of the vertices of the output simplicial complex. + Simplex_tree st; + + std::map<int, std::vector<int> > adjacency_matrix; + std::vector<std::vector<double> > distances; + + int resolution_int = -1; + double resolution_double = -1; + double gain = -1; + double rate_constant = 10; // Constant in the subsampling. + double rate_power = 0.001; // Power in the subsampling. + int mask = 0; // Ignore nodes containing less than mask points. + + std::map<int, double> func; + std::map<int, double> func_color; + std::vector<int> voronoi_subsamples; + std::string cover_name; + std::string point_cloud_name; + std::string color_name; + std::string type; // Nerve or GIC + bool functional_cover = false; // whether we use a cover with preimages of a function or not + + // Point comparator + struct Less { + Less(std::map<int, double> func) { Fct = func; } + std::map<int, double> Fct; + bool operator()(int a, int b) { + if (Fct[a] == Fct[b]) + return a < b; + else + return Fct[a] < Fct[b]; + } + }; + + // DFS + private: + void dfs(std::map<int, std::vector<int> >& G, int p, std::vector<int>& cc, std::map<int, bool>& visit) { + cc.push_back(p); + visit[p] = true; + int neighb = G[p].size(); + for (int i = 0; i < neighb; i++) + if (visit.find(G[p][i]) != visit.end()) + if (!(visit[G[p][i]])) dfs(G, G[p][i], cc, visit); + } + + // Find random number in [0,1]. + double GetUniform() { + static std::default_random_engine re; + static std::uniform_real_distribution<double> Dist(0, 1); + return Dist(re); + } + + // Subsample points. + void SampleWithoutReplacement(int populationSize, int sampleSize, std::vector<int>& samples) { + int t = 0; + int m = 0; + double u; + while (m < sampleSize) { + u = GetUniform(); + if ((populationSize - t) * u >= sampleSize - m) { + t++; + } else { + samples[m] = t; + t++; + m++; + } + } + } + + private: + void fill_adjacency_matrix_from_st() { + std::vector<int> empty; + for (int i = 0; i < n; i++) adjacency_matrix[i] = empty; + for (auto simplex : st.complex_simplex_range()) { + if (st.dimension(simplex) == 1) { + std::vector<int> vertices; + for (auto vertex : st.simplex_vertex_range(simplex)) vertices.push_back(vertex); + adjacency_matrix[vertices[0]].push_back(vertices[1]); + adjacency_matrix[vertices[1]].push_back(vertices[0]); + } + } + } + + public: + /** \brief Specifies whether the type of the output simplicial complex. + * + * @param[in] t string (either "GIC" or "Nerve"). + * + */ + void set_type(const std::string& t) { type = t; } + + public: + /** \brief Specifies whether the program should display information or not. + * + * @param[in] verb boolean (true = display info, false = do not display info). + * + */ + void set_verbose(bool verb = false) { verbose = verb; } + + public: + /** \brief Sets the constants used to subsample the data set. These constants are + * explained in \cite Carriere17c. + * + * @param[in] constant double. + * @param[in] power double. + * + */ + void set_subsampling(double constant, double power) { + rate_constant = constant; + rate_power = power; + } + + public: + /** \brief Sets the mask, which is a threshold integer such that nodes in the complex that contain a number of data + * points which is less than or equal to + * this threshold are not displayed. + * + * @param[in] nodemask integer. + * + */ + void set_mask(int nodemask) { mask = nodemask; } + + public: + /** \brief Reads and stores the input point cloud. + * + * @param[in] off_file_name name of the input .OFF or .nOFF file. + * + */ + bool read_point_cloud(const std::string& off_file_name) { + point_cloud_name = off_file_name; + std::ifstream input(off_file_name); + std::string line; + + char comment = '#'; + while (comment == '#') { + getline(input, line); + if (!line.empty() && !std::all_of(line.begin(), line.end(), isspace)) comment = line[line.find_first_not_of(' ')]; + } + if (std::strcmp((char*)line.c_str(), "nOFF") == 0) { + comment = '#'; + while (comment == '#') { + getline(input, line); + if (!line.empty() && !std::all_of(line.begin(), line.end(), isspace)) + comment = line[line.find_first_not_of(' ')]; + } + std::stringstream stream(line); + stream >> data_dimension; + } else { + data_dimension = 3; + } + + comment = '#'; + int numedges, numfaces, i, num; + while (comment == '#') { + getline(input, line); + if (!line.empty() && !std::all_of(line.begin(), line.end(), isspace)) comment = line[line.find_first_not_of(' ')]; + } + std::stringstream stream(line); + stream >> n; + stream >> numfaces; + stream >> numedges; + + i = 0; + while (i < n) { + getline(input, line); + if (!line.empty() && line[line.find_first_not_of(' ')] != '#' && + !std::all_of(line.begin(), line.end(), isspace)) { + std::vector<double> point; + std::istringstream iss(line); + point.assign(std::istream_iterator<double>(iss), std::istream_iterator<double>()); + point_cloud.emplace_back(point.begin(), point.begin() + data_dimension); + i++; + } + } + + i = 0; + while (i < numfaces) { + getline(input, line); + if (!line.empty() && line[line.find_first_not_of(' ')] != '#' && + !std::all_of(line.begin(), line.end(), isspace)) { + std::vector<int> simplex; + std::istringstream iss(line); + simplex.assign(std::istream_iterator<int>(iss), std::istream_iterator<int>()); + num = simplex[0]; + std::vector<int> edge(2); + for (int j = 1; j <= num; j++) { + for (int k = j + 1; k <= num; k++) { + edge[0] = simplex[j]; + edge[1] = simplex[k]; + one_skeleton.push_back(edge); + } + } + i++; + } + } + + return input.is_open(); + } + + // ******************************************************************************************************************* + // Graphs. + // ******************************************************************************************************************* + + public: // Set graph from file. + /** \brief Creates a graph G from a file containing the edges. + * + * @param[in] graph_file_name name of the input graph file. + * The graph file contains one edge per line, + * each edge being represented by the IDs of its two nodes. + * + */ + void set_graph_from_file(const std::string& graph_file_name) { + int neighb; + std::ifstream input(graph_file_name); + std::string line; + int edge[2]; + int n = 0; + while (std::getline(input, line)) { + std::stringstream stream(line); + stream >> edge[0]; + while (stream >> neighb) { + edge[1] = neighb; + st.insert_simplex_and_subfaces(edge); + } + n++; + } + + fill_adjacency_matrix_from_st(); + } + + public: // Set graph from OFF file. + /** \brief Creates a graph G from the triangulation given by the input .OFF file. + * + */ + void set_graph_from_OFF() { + int num_edges = one_skeleton.size(); + if (num_edges > 0) { + for (int i = 0; i < num_edges; i++) st.insert_simplex_and_subfaces(one_skeleton[i]); + fill_adjacency_matrix_from_st(); + } else { + std::cout << "No triangulation read in OFF file!" << std::endl; + } + } + + public: // Set graph from Rips complex. + /** \brief Creates a graph G from a Rips complex. + * + * @param[in] threshold threshold value for the Rips complex. + * @param[in] distance distance used to compute the Rips complex. + * + */ + template <typename Distance> + void set_graph_from_rips(double threshold, Distance distance) { + Rips_complex rips_complex_from_points(point_cloud, threshold, distance); + rips_complex_from_points.create_complex(st, 1); + fill_adjacency_matrix_from_st(); + } + + public: // Pairwise distances. + /** \private \brief Computes all pairwise distances. + */ + template <typename Distance> + void compute_pairwise_distances(Distance ref_distance) { + double d; + std::vector<double> zeros(n); + for (int i = 0; i < n; i++) distances.push_back(zeros); + std::string distance = point_cloud_name; + distance.append("_dist"); + std::ifstream input(distance.c_str(), std::ios::out | std::ios::binary); + + if (input.good()) { + if (verbose) std::cout << "Reading distances..." << std::endl; + for (int i = 0; i < n; i++) { + for (int j = i; j < n; j++) { + input.read((char*)&d, 8); + distances[i][j] = d; + distances[j][i] = d; + } + } + input.close(); + } else { + if (verbose) std::cout << "Computing distances..." << std::endl; + input.close(); + std::ofstream output(distance, std::ios::out | std::ios::binary); + for (int i = 0; i < n; i++) { + int state = (int)floor(100 * (i * 1.0 + 1) / n) % 10; + if (state == 0 && verbose) std::cout << "\r" << state << "%" << std::flush; + for (int j = i; j < n; j++) { + double dis = ref_distance(point_cloud[i], point_cloud[j]); + distances[i][j] = dis; + distances[j][i] = dis; + output.write((char*)&dis, 8); + } + } + output.close(); + if (verbose) std::cout << std::endl; + } + } + + public: // Automatic tuning of Rips complex. + /** \brief Creates a graph G from a Rips complex whose threshold value is automatically tuned with subsampling---see + * \cite Carriere17c. + * + * @param[in] distance distance between data points. + * @param[in] N number of subsampling iteration (the default reasonable value is 100, but there is no guarantee on + * how to choose it). + * @result delta threshold used for computing the Rips complex. + * + */ + template <typename Distance> + double set_graph_from_automatic_rips(Distance distance, int N = 100) { + int m = floor(n / std::exp((1 + rate_power) * std::log(std::log(n) / std::log(rate_constant)))); + m = std::min(m, n - 1); + std::vector<int> samples(m); + double delta = 0; + + if (verbose) std::cout << n << " points in R^" << data_dimension << std::endl; + if (verbose) std::cout << "Subsampling " << m << " points" << std::endl; + + if (distances.size() == 0) compute_pairwise_distances(distance); + + // #pragma omp parallel for + for (int i = 0; i < N; i++) { + SampleWithoutReplacement(n, m, samples); + double hausdorff_dist = 0; + for (int j = 0; j < n; j++) { + double mj = distances[j][samples[0]]; + for (int k = 1; k < m; k++) mj = std::min(mj, distances[j][samples[k]]); + hausdorff_dist = std::max(hausdorff_dist, mj); + } + delta += hausdorff_dist / N; + } + + if (verbose) std::cout << "delta = " << delta << std::endl; + Rips_complex rips_complex_from_points(point_cloud, delta, distance); + rips_complex_from_points.create_complex(st, 1); + fill_adjacency_matrix_from_st(); + + return delta; + } + + // ******************************************************************************************************************* + // Functions. + // ******************************************************************************************************************* + + public: // Set function from file. + /** \brief Creates the function f from a file containing the function values. + * + * @param[in] func_file_name name of the input function file. + * + */ + void set_function_from_file(const std::string& func_file_name) { + int vertex_id = 0; + std::ifstream input(func_file_name); + std::string line; + double f; + while (std::getline(input, line)) { + std::stringstream stream(line); + stream >> f; + func.emplace(vertex_id, f); + vertex_id++; + } + functional_cover = true; + cover_name = func_file_name; + } + + public: // Set function from kth coordinate + /** \brief Creates the function f from the k-th coordinate of the point cloud P. + * + * @param[in] k coordinate to use (start at 0). + * + */ + void set_function_from_coordinate(int k) { + for (int i = 0; i < n; i++) func.emplace(i, point_cloud[i][k]); + char coordinate[100]; + sprintf(coordinate, "coordinate %d", k); + functional_cover = true; + cover_name = coordinate; + } + + public: // Set function from vector. + /** \brief Creates the function f from a vector stored in memory. + * + * @param[in] function input vector of values. + * + */ + template <class InputRange> + void set_function_from_range(InputRange const& function) { + functional_cover = true; + int index = 0; + for (auto v : function) { + func.emplace(index, v); + index++; + } + } + + // ******************************************************************************************************************* + // Covers. + // ******************************************************************************************************************* + + public: // Automatic tuning of resolution. + /** \brief Computes the optimal length of intervals + * (i.e. the smallest interval length avoiding discretization artifacts---see \cite Carriere17c) for a functional + * cover. + * + * @result reso interval length used to compute the cover. + * + */ + double set_automatic_resolution() { + if (!functional_cover) { + std::cout << "Cover needs to come from the preimages of a function." << std::endl; + return 0; + } + if (type != "Nerve" && type != "GIC") { + std::cout << "Type of complex needs to be specified." << std::endl; + return 0; + } + + double reso = 0; + + if (type == "GIC") { + for (auto simplex : st.complex_simplex_range()) { + if (st.dimension(simplex) == 1) { + std::vector<int> vertices; + for (auto vertex : st.simplex_vertex_range(simplex)) vertices.push_back(vertex); + reso = std::max(reso, std::abs(func[vertices[0]] - func[vertices[1]])); + } + } + if (verbose) std::cout << "resolution = " << reso << std::endl; + resolution_double = reso; + } + + if (type == "Nerve") { + for (auto simplex : st.complex_simplex_range()) { + if (st.dimension(simplex) == 1) { + std::vector<int> vertices; + for (auto vertex : st.simplex_vertex_range(simplex)) vertices.push_back(vertex); + reso = std::max(reso, (std::abs(func[vertices[0]] - func[vertices[1]])) / gain); + } + } + if (verbose) std::cout << "resolution = " << reso << std::endl; + resolution_double = reso; + } + + return reso; + } + + public: + /** \brief Sets a length of intervals from a value stored in memory. + * + * @param[in] reso length of intervals. + * + */ + void set_resolution_with_interval_length(double reso) { resolution_double = reso; } + /** \brief Sets a number of intervals from a value stored in memory. + * + * @param[in] reso number of intervals. + * + */ + void set_resolution_with_interval_number(int reso) { resolution_int = reso; } + /** \brief Sets a gain from a value stored in memory (default value 0.3). + * + * @param[in] g gain. + * + */ + void set_gain(double g = 0.3) { gain = g; } + + public: // Set cover with preimages of function. + /** \brief Creates a cover C from the preimages of the function f. + * + */ + void set_cover_from_function() { + if (resolution_double == -1 && resolution_int == -1) { + std::cout << "Number and/or length of intervals not specified" << std::endl; + return; + } + if (gain == -1) { + std::cout << "Gain not specified" << std::endl; + return; + } + + // Read function values and compute min and max + std::map<int, double>::iterator it; + double maxf, minf; + minf = std::numeric_limits<float>::max(); + maxf = std::numeric_limits<float>::min(); + for (it = func.begin(); it != func.end(); it++) { + minf = std::min(minf, it->second); + maxf = std::max(maxf, it->second); + } + int n = func.size(); + if (verbose) std::cout << "Min function value = " << minf << " and Max function value = " << maxf << std::endl; + + // Compute cover of im(f) + std::vector<std::pair<double, double> > intervals; + int res; + + if (resolution_double == -1) { // Case we use an integer for the number of intervals. + double incr = (maxf - minf) / resolution_int; + double x = minf; + double alpha = (incr * gain) / (2 - 2 * gain); + double y = minf + incr + alpha; + std::pair<double, double> interm(x, y); + intervals.push_back(interm); + for (int i = 1; i < resolution_int - 1; i++) { + x = minf + i * incr - alpha; + y = minf + (i + 1) * incr + alpha; + std::pair<double, double> inter(x, y); + intervals.push_back(inter); + } + x = minf + (resolution_int - 1) * incr - alpha; + y = maxf; + std::pair<double, double> interM(x, y); + intervals.push_back(interM); + res = intervals.size(); + if (verbose) { + for (int i = 0; i < res; i++) + std::cout << "Interval " << i << " = [" << intervals[i].first << ", " << intervals[i].second << "]" + << std::endl; + } + } else { + if (resolution_int == -1) { // Case we use a double for the length of the intervals. + double x = minf; + double y = x + resolution_double; + while (y <= maxf && maxf - (y - gain * resolution_double) >= resolution_double) { + std::pair<double, double> inter(x, y); + intervals.push_back(inter); + x = y - gain * resolution_double; + y = x + resolution_double; + } + std::pair<double, double> interM(x, maxf); + intervals.push_back(interM); + res = intervals.size(); + if (verbose) { + for (int i = 0; i < res; i++) + std::cout << "Interval " << i << " = [" << intervals[i].first << ", " << intervals[i].second << "]" + << std::endl; + } + } else { // Case we use an integer and a double for the length of the intervals. + double x = minf; + double y = x + resolution_double; + int count = 0; + while (count < resolution_int && y <= maxf && maxf - (y - gain * resolution_double) >= resolution_double) { + std::pair<double, double> inter(x, y); + intervals.push_back(inter); + count++; + x = y - gain * resolution_double; + y = x + resolution_double; + } + res = intervals.size(); + if (verbose) { + for (int i = 0; i < res; i++) + std::cout << "Interval " << i << " = [" << intervals[i].first << ", " << intervals[i].second << "]" + << std::endl; + } + } + } + + // Sort points according to function values + std::vector<int> points(n); + for (int i = 0; i < n; i++) points[i] = i; + std::sort(points.begin(), points.end(), Less(this->func)); + int id = 0; + int pos = 0; + + for (int i = 0; i < res; i++) { + // Find points in the preimage + std::map<int, std::vector<int> > prop; + std::pair<double, double> inter1 = intervals[i]; + int tmp = pos; + + if (i != res - 1) { + if (i != 0) { + std::pair<double, double> inter3 = intervals[i - 1]; + while (func[points[tmp]] < inter3.second && tmp != n) { + prop[points[tmp]] = adjacency_matrix[points[tmp]]; + tmp++; + } + } + + std::pair<double, double> inter2 = intervals[i + 1]; + while (func[points[tmp]] < inter2.first && tmp != n) { + prop[points[tmp]] = adjacency_matrix[points[tmp]]; + tmp++; + } + + pos = tmp; + while (func[points[tmp]] < inter1.second && tmp != n) { + prop[points[tmp]] = adjacency_matrix[points[tmp]]; + tmp++; + } + + } else { + std::pair<double, double> inter3 = intervals[i - 1]; + while (func[points[tmp]] < inter3.second && tmp != n) { + prop[points[tmp]] = adjacency_matrix[points[tmp]]; + tmp++; + } + + while (tmp != n) { + prop[points[tmp]] = adjacency_matrix[points[tmp]]; + tmp++; + } + } + + // Compute the connected components with DFS + std::map<int, bool> visit; + if (verbose) std::cout << "Preimage of interval " << i << std::endl; + for (std::map<int, std::vector<int> >::iterator it = prop.begin(); it != prop.end(); it++) + visit[it->first] = false; + if (!(prop.empty())) { + for (std::map<int, std::vector<int> >::iterator it = prop.begin(); it != prop.end(); it++) { + if (!(visit[it->first])) { + std::vector<int> cc; + cc.clear(); + dfs(prop, it->first, cc, visit); + int cci = cc.size(); + if (verbose) std::cout << "one CC with " << cci << " points, "; + double average_col = 0; + for (int j = 0; j < cci; j++) { + cover[cc[j]].push_back(id); + cover_back[id].push_back(cc[j]); + average_col += func_color[cc[j]] / cci; + } + cover_fct[id] = i; + cover_color[id] = std::pair<int, double>(cci, average_col); + id++; + } + } + } + if (verbose) std::cout << std::endl; + } + + maximal_dim = id - 1; + } + + public: // Set cover from file. + /** \brief Creates the cover C from a file containing the cover elements of each point (the order has to be the same + * as in the input file!). + * + * @param[in] cover_file_name name of the input cover file. + * + */ + void set_cover_from_file(const std::string& cover_file_name) { + int vertex_id = 0; + Cover_t cov; + std::vector<Cover_t> cov_elts, cov_number; + std::ifstream input(cover_file_name); + std::string line; + while (std::getline(input, line)) { + cov_elts.clear(); + std::stringstream stream(line); + while (stream >> cov) { + cov_elts.push_back(cov); + cov_number.push_back(cov); + cover_fct[cov] = cov; + cover_color[cov].second += func_color[vertex_id]; + cover_color[cov].first++; + cover_back[cov].push_back(vertex_id); + } + cover[vertex_id] = cov_elts; + vertex_id++; + } + std::vector<Cover_t>::iterator it; + std::sort(cov_number.begin(), cov_number.end()); + it = std::unique(cov_number.begin(), cov_number.end()); + cov_number.resize(std::distance(cov_number.begin(), it)); + maximal_dim = cov_number.size() - 1; + for (int i = 0; i <= maximal_dim; i++) cover_color[i].second /= cover_color[i].first; + cover_name = cover_file_name; + } + + public: // Set cover from Voronoi + /** \brief Creates the cover C from the Voronoï cells of a subsampling of the point cloud. + * + * @param[in] distance distance between the points. + * @param[in] m number of points in the subsample. + * + */ + template <typename Distance> + void set_cover_from_Voronoi(Distance distance, int m = 100) { + voronoi_subsamples.resize(m); + SampleWithoutReplacement(n, m, voronoi_subsamples); + if (distances.size() == 0) compute_pairwise_distances(distance); + std::vector<double> mindist(n); + for (int j = 0; j < n; j++) mindist[j] = std::numeric_limits<double>::max(); + + // Compute the geodesic distances to subsamples with Dijkstra + for (int i = 0; i < m; i++) { + if (verbose) std::cout << "Computing geodesic distances to seed " << i << "..." << std::endl; + int seed = voronoi_subsamples[i]; + std::vector<double> dist(n); + std::vector<int> process(n); + for (int j = 0; j < n; j++) { + dist[j] = std::numeric_limits<double>::max(); + process[j] = j; + } + dist[seed] = 0; + int curr_size = process.size(); + int min_point, min_index; + double min_dist; + std::vector<int> neighbors; + int num_neighbors; + + while (curr_size > 0) { + min_dist = std::numeric_limits<double>::max(); + min_index = -1; + min_point = -1; + for (int j = 0; j < curr_size; j++) { + if (dist[process[j]] < min_dist) { + min_point = process[j]; + min_dist = dist[process[j]]; + min_index = j; + } + } + assert(min_index != -1); + process.erase(process.begin() + min_index); + assert(min_point != -1); + neighbors = adjacency_matrix[min_point]; + num_neighbors = neighbors.size(); + for (int j = 0; j < num_neighbors; j++) { + double d = dist[min_point] + distances[min_point][neighbors[j]]; + dist[neighbors[j]] = std::min(dist[neighbors[j]], d); + } + curr_size = process.size(); + } + + for (int j = 0; j < n; j++) + if (mindist[j] > dist[j]) { + mindist[j] = dist[j]; + if (cover[j].size() == 0) + cover[j].push_back(i); + else + cover[j][0] = i; + } + } + + for (int i = 0; i < n; i++) { + cover_back[cover[i][0]].push_back(i); + cover_color[cover[i][0]].second += func_color[i]; + cover_color[cover[i][0]].first++; + } + for (int i = 0; i < m; i++) cover_color[i].second /= cover_color[i].first; + maximal_dim = m - 1; + cover_name = "Voronoi"; + } + + public: // return subset of data corresponding to a node + /** \brief Returns the data subset corresponding to a specific node of the created complex. + * + * @param[in] c ID of the node. + * @result cover_back(c) vector of IDs of data points. + * + */ + const std::vector<int>& subpopulation(Cover_t c) { return cover_back[c]; } + + // ******************************************************************************************************************* + // Visualization. + // ******************************************************************************************************************* + + public: // Set color from file. + /** \brief Computes the function used to color the nodes of the simplicial complex from a file containing the function + * values. + * + * @param[in] color_file_name name of the input color file. + * + */ + void set_color_from_file(const std::string& color_file_name) { + int vertex_id = 0; + std::ifstream input(color_file_name); + std::string line; + double f; + while (std::getline(input, line)) { + std::stringstream stream(line); + stream >> f; + func_color.emplace(vertex_id, f); + vertex_id++; + } + color_name = color_file_name; + } + + public: // Set color from kth coordinate + /** \brief Computes the function used to color the nodes of the simplicial complex from the k-th coordinate. + * + * @param[in] k coordinate to use (start at 0). + * + */ + void set_color_from_coordinate(int k = 0) { + for (int i = 0; i < n; i++) func_color.emplace(i, point_cloud[i][k]); + color_name = "coordinate "; + color_name.append(std::to_string(k)); + } + + public: // Set color from vector. + /** \brief Computes the function used to color the nodes of the simplicial complex from a vector stored in memory. + * + * @param[in] color input vector of values. + * + */ + void set_color_from_vector(std::vector<double> color) { + for (unsigned int i = 0; i < color.size(); i++) func_color.emplace(i, color[i]); + } + + public: // Create a .dot file that can be compiled with neato to produce a .pdf file. + /** \brief Creates a .dot file called SC.dot for neato (part of the graphviz package) once the simplicial complex is + * computed to get a visualization + * of its 1-skeleton in a .pdf file. + */ + void plot_DOT() { + char mapp[11] = "SC.dot"; + std::ofstream graphic(mapp); + graphic << "graph GIC {" << std::endl; + double maxv, minv; + maxv = std::numeric_limits<double>::min(); + minv = std::numeric_limits<double>::max(); + for (std::map<Cover_t, std::pair<int, double> >::iterator iit = cover_color.begin(); iit != cover_color.end(); + iit++) { + maxv = std::max(maxv, iit->second.second); + minv = std::min(minv, iit->second.second); + } + int k = 0; + std::vector<int> nodes; + nodes.clear(); + for (std::map<Cover_t, std::pair<int, double> >::iterator iit = cover_color.begin(); iit != cover_color.end(); + iit++) { + if (iit->second.first > mask) { + nodes.push_back(iit->first); + graphic << iit->first << "[shape=circle fontcolor=black color=black label=\"" << iit->first << ":" + << iit->second.first << "\" style=filled fillcolor=\"" + << (1 - (maxv - iit->second.second) / (maxv - minv)) * 0.6 << ", 1, 1\"]" << std::endl; + k++; + } + } + int ke = 0; + int num_simplices = simplices.size(); + for (int i = 0; i < num_simplices; i++) + if (simplices[i].size() == 2) { + if (cover_color[simplices[i][0]].first > mask && cover_color[simplices[i][1]].first > mask) { + graphic << " " << simplices[i][0] << " -- " << simplices[i][1] << " [weight=15];" << std::endl; + ke++; + } + } + graphic << "}"; + graphic.close(); + std::cout << "SC.dot generated. It can be visualized with e.g. neato." << std::endl; + } + + public: // Create a .txt file that can be compiled with KeplerMapper. + /** \brief Creates a .txt file called SC.txt describing the 1-skeleton, which can then be plotted with e.g. + * KeplerMapper. + */ + void write_info() { + int num_simplices = simplices.size(); + int num_edges = 0; + char mapp[11] = "SC.txt"; + std::ofstream graphic(mapp); + for (int i = 0; i < num_simplices; i++) + if (simplices[i].size() == 2) + if (cover_color[simplices[i][0]].first > mask && cover_color[simplices[i][1]].first > mask) num_edges++; + + graphic << point_cloud_name << std::endl; + graphic << cover_name << std::endl; + graphic << color_name << std::endl; + graphic << resolution_double << " " << gain << std::endl; + graphic << cover_color.size() << " " << num_edges << std::endl; + + for (std::map<Cover_t, std::pair<int, double> >::iterator iit = cover_color.begin(); iit != cover_color.end(); + iit++) + graphic << iit->first << " " << iit->second.second << " " << iit->second.first << std::endl; + + for (int i = 0; i < num_simplices; i++) + if (simplices[i].size() == 2) + if (cover_color[simplices[i][0]].first > mask && cover_color[simplices[i][1]].first > mask) + graphic << simplices[i][0] << " " << simplices[i][1] << std::endl; + graphic.close(); + std::cout << "SC.txt generated. It can be visualized with e.g. python KeplerMapperVisuFromTxtFile.py and firefox." + << std::endl; + } + + public: // Create a .off file that can be visualized (e.g. with Geomview). + /** \brief Creates a .off file called SC.off for 3D visualization, which contains the 2-skeleton of the GIC. + * This function assumes that the cover has been computed with Voronoi. If data points are in 1D or 2D, + * the remaining coordinates of the points embedded in 3D are set to 0. + */ + void plot_OFF() { + assert(cover_name == "Voronoi"); + char gic[11] = "SC.off"; + std::ofstream graphic(gic); + graphic << "OFF" << std::endl; + int m = voronoi_subsamples.size(); + int numedges = 0; + int numfaces = 0; + std::vector<std::vector<int> > edges, faces; + int numsimplices = simplices.size(); + for (int i = 0; i < numsimplices; i++) { + if (simplices[i].size() == 2) { + numedges++; + edges.push_back(simplices[i]); + } + if (simplices[i].size() == 3) { + numfaces++; + faces.push_back(simplices[i]); + } + } + graphic << m << " " << numedges + numfaces << std::endl; + for (int i = 0; i < m; i++) { + if (data_dimension <= 3) { + for (int j = 0; j < data_dimension; j++) graphic << point_cloud[voronoi_subsamples[i]][j] << " "; + for (int j = data_dimension; j < 3; j++) graphic << 0 << " "; + graphic << std::endl; + } else { + for (int j = 0; j < 3; j++) graphic << point_cloud[voronoi_subsamples[i]][j] << " "; + } + } + for (int i = 0; i < numedges; i++) graphic << 2 << " " << edges[i][0] << " " << edges[i][1] << std::endl; + for (int i = 0; i < numfaces; i++) + graphic << 3 << " " << faces[i][0] << " " << faces[i][1] << " " << faces[i][2] << std::endl; + graphic.close(); + std::cout << "SC.off generated. It can be visualized with e.g. geomview." << std::endl; + } + + // ******************************************************************************************************************* + // ******************************************************************************************************************* + + public: + /** \brief Creates the simplicial complex. + * + * @param[in] complex SimplicialComplexForRips to be created. + * + */ + template <typename SimplicialComplexForRips> + void create_complex(SimplicialComplexForRips& complex) { + unsigned int dimension = 0; + for (auto const& simplex : simplices) { + complex.insert_simplex_and_subfaces(simplex); + if (dimension < simplex.size() - 1) dimension = simplex.size() - 1; + } + complex.set_dimension(dimension); + } + + public: + /** \brief Computes the simplices of the simplicial complex. + */ + void find_simplices() { + if (type != "Nerve" && type != "GIC") { + std::cout << "Type of complex needs to be specified." << std::endl; + return; + } + + if (type == "Nerve") { + for (std::map<int, std::vector<Cover_t> >::iterator it = cover.begin(); it != cover.end(); it++) + simplices.push_back(it->second); + std::vector<std::vector<Cover_t> >::iterator it; + std::sort(simplices.begin(), simplices.end()); + it = std::unique(simplices.begin(), simplices.end()); + simplices.resize(std::distance(simplices.begin(), it)); + } + + if (type == "GIC") { + if (functional_cover) { + // Computes the simplices in the GIC by looking at all the edges of the graph and adding the + // corresponding edges in the GIC if the images of the endpoints belong to consecutive intervals. + + if (gain >= 0.5) + throw std::invalid_argument( + "the output of this function is correct ONLY if the cover is minimal, i.e. the gain is less than 0.5."); + + int v1, v2; + + // Loop on all points. + for (std::map<int, std::vector<Cover_t> >::iterator it = cover.begin(); it != cover.end(); it++) { + int vid = it->first; + std::vector<int> neighbors = adjacency_matrix[vid]; + int num_neighb = neighbors.size(); + + // Find cover of current point (vid). + if (cover[vid].size() == 2) + v1 = std::min(cover[vid][0], cover[vid][1]); + else + v1 = cover[vid][0]; + std::vector<int> node(1); + node[0] = v1; + simplices.push_back(node); + + // Loop on neighbors. + for (int i = 0; i < num_neighb; i++) { + int neighb = neighbors[i]; + + // Find cover of neighbor (neighb). + if (cover[neighb].size() == 2) + v2 = std::max(cover[neighb][0], cover[neighb][1]); + else + v2 = cover[neighb][0]; + + // If neighbor is in next interval, add edge. + if (cover_fct[v2] == cover_fct[v1] + 1) { + std::vector<int> edge(2); + edge[0] = v1; + edge[1] = v2; + simplices.push_back(edge); + } + } + } + std::vector<std::vector<Cover_t> >::iterator it; + std::sort(simplices.begin(), simplices.end()); + it = std::unique(simplices.begin(), simplices.end()); + simplices.resize(std::distance(simplices.begin(), it)); + + } else { + // Find IDs of edges to remove + std::vector<int> simplex_to_remove; + int simplex_id = 0; + for (auto simplex : st.complex_simplex_range()) { + if (st.dimension(simplex) == 1) { + std::vector<std::vector<Cover_t> > comp; + for (auto vertex : st.simplex_vertex_range(simplex)) comp.push_back(cover[vertex]); + if (comp[0].size() == 1 && comp[0] == comp[1]) simplex_to_remove.push_back(simplex_id); + } + simplex_id++; + } + + // Remove edges + if (simplex_to_remove.size() > 1) { + int current_id = 1; + auto simplex = st.complex_simplex_range().begin(); + int num_rem = 0; + for (int i = 0; i < simplex_id - 1; i++) { + int j = i + 1; + auto simplex_tmp = simplex; + simplex_tmp++; + if (j == simplex_to_remove[current_id]) { + st.remove_maximal_simplex(*simplex_tmp); + current_id++; + num_rem++; + } else { + simplex++; + } + } + simplex = st.complex_simplex_range().begin(); + for (int i = 0; i < simplex_to_remove[0]; i++) simplex++; + st.remove_maximal_simplex(*simplex); + } + + // Build the Simplex Tree corresponding to the graph + st.expansion(maximal_dim); + + // Find simplices of GIC + simplices.clear(); + for (auto simplex : st.complex_simplex_range()) { + if (!st.has_children(simplex)) { + std::vector<Cover_t> simplx; + for (auto vertex : st.simplex_vertex_range(simplex)) { + unsigned int sz = cover[vertex].size(); + for (unsigned int i = 0; i < sz; i++) { + simplx.push_back(cover[vertex][i]); + } + } + + std::sort(simplx.begin(), simplx.end()); + std::vector<Cover_t>::iterator it = std::unique(simplx.begin(), simplx.end()); + simplx.resize(std::distance(simplx.begin(), it)); + simplices.push_back(simplx); + } + } + std::vector<std::vector<Cover_t> >::iterator it; + std::sort(simplices.begin(), simplices.end()); + it = std::unique(simplices.begin(), simplices.end()); + simplices.resize(std::distance(simplices.begin(), it)); + } + } + } +}; + +} // namespace cover_complex + +} // namespace Gudhi + +#endif // GIC_H_ diff --git a/src/Nerve_GIC/test/CMakeLists.txt b/src/Nerve_GIC/test/CMakeLists.txt new file mode 100644 index 00000000..03fe47ca --- /dev/null +++ b/src/Nerve_GIC/test/CMakeLists.txt @@ -0,0 +1,14 @@ +cmake_minimum_required(VERSION 2.6) +project(Graph_induced_complex_tests) + +include(GUDHI_test_coverage) + +add_executable ( Nerve_GIC_test_unit test_GIC.cpp ) +target_link_libraries(Nerve_GIC_test_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY}) +if (TBB_FOUND) + target_link_libraries(Nerve_GIC_test_unit ${TBB_LIBRARIES}) +endif() + +file(COPY data DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) + +gudhi_add_coverage_test(Nerve_GIC_test_unit) diff --git a/src/Nerve_GIC/test/data/cloud b/src/Nerve_GIC/test/data/cloud new file mode 100644 index 00000000..4a0c170d --- /dev/null +++ b/src/Nerve_GIC/test/data/cloud @@ -0,0 +1,6 @@ +nOFF +3 +3 0 0 +0 0 0 +2 1 0 +4 0 0
\ No newline at end of file diff --git a/src/Nerve_GIC/test/data/cover b/src/Nerve_GIC/test/data/cover new file mode 100644 index 00000000..5f5fbe75 --- /dev/null +++ b/src/Nerve_GIC/test/data/cover @@ -0,0 +1,3 @@ +1 +2 +3
\ No newline at end of file diff --git a/src/Nerve_GIC/test/data/graph b/src/Nerve_GIC/test/data/graph new file mode 100644 index 00000000..37142800 --- /dev/null +++ b/src/Nerve_GIC/test/data/graph @@ -0,0 +1,3 @@ +0 1 +0 2 +1 2
\ No newline at end of file diff --git a/src/Nerve_GIC/test/test_GIC.cpp b/src/Nerve_GIC/test/test_GIC.cpp new file mode 100644 index 00000000..a8b1e7f7 --- /dev/null +++ b/src/Nerve_GIC/test/test_GIC.cpp @@ -0,0 +1,90 @@ +/* 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): Mathieu Carrière + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#define BOOST_TEST_DYN_LINK +#define BOOST_TEST_MODULE "graph_induced_complex" + +#include <boost/test/unit_test.hpp> +#include <cmath> // float comparison +#include <limits> +#include <string> +#include <vector> +#include <algorithm> // std::max +#include <gudhi/GIC.h> +#include <gudhi/distance_functions.h> +#include <gudhi/reader_utils.h> + +BOOST_AUTO_TEST_CASE(check_nerve) { + using Point = std::vector<float>; + Gudhi::cover_complex::Cover_complex<Point> N; + N.set_type("Nerve"); + std::string cloud_file_name("data/cloud"); + N.read_point_cloud(cloud_file_name); + std::string graph_file_name("data/graph"); + N.set_graph_from_file(graph_file_name); + std::string cover_file_name("data/cover"); + N.set_cover_from_file(cover_file_name); + N.find_simplices(); + Gudhi::Simplex_tree<> stree; + N.create_complex(stree); + + BOOST_CHECK(stree.num_vertices() == 3); + BOOST_CHECK((stree.num_simplices() - stree.num_vertices()) == 0); + BOOST_CHECK(stree.dimension() == 0); +} + +BOOST_AUTO_TEST_CASE(check_GIC) { + using Point = std::vector<float>; + Gudhi::cover_complex::Cover_complex<Point> GIC; + GIC.set_type("GIC"); + std::string cloud_file_name("data/cloud"); + GIC.read_point_cloud(cloud_file_name); + std::string graph_file_name("data/graph"); + GIC.set_graph_from_file(graph_file_name); + std::string cover_file_name("data/cover"); + GIC.set_cover_from_file(cover_file_name); + GIC.find_simplices(); + Gudhi::Simplex_tree<> stree; + GIC.create_complex(stree); + + BOOST_CHECK(stree.num_vertices() == 3); + BOOST_CHECK((stree.num_simplices() - stree.num_vertices()) == 4); + BOOST_CHECK(stree.dimension() == 2); +} + +BOOST_AUTO_TEST_CASE(check_voronoiGIC) { + using Point = std::vector<float>; + Gudhi::cover_complex::Cover_complex<Point> GIC; + GIC.set_type("GIC"); + std::string cloud_file_name("data/cloud"); + GIC.read_point_cloud(cloud_file_name); + std::string graph_file_name("data/graph"); + GIC.set_graph_from_file(graph_file_name); + GIC.set_cover_from_Voronoi(Gudhi::Euclidean_distance(), 2); + GIC.find_simplices(); + Gudhi::Simplex_tree<> stree; + GIC.create_complex(stree); + + BOOST_CHECK(stree.num_vertices() == 2); + BOOST_CHECK((stree.num_simplices() - stree.num_vertices()) == 1); + BOOST_CHECK(stree.dimension() == 1); +} diff --git a/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h b/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h index e17e5926..62bbbfc5 100644 --- a/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h +++ b/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h @@ -143,8 +143,8 @@ namespace persistent_cohomology { We provide several example files: run these examples with -h for details on their use, and read the README file. -\li <a href="_persistent_cohomology_2rips_persistence_8cpp-example.html"> -Persistent_cohomology/rips_persistence.cpp</a> computes the Rips complex of a point cloud and outputs its persistence +\li <a href="_rips_complex_2rips_persistence_8cpp-example.html"> +Rips_complex/rips_persistence.cpp</a> computes the Rips complex of a point cloud and outputs its persistence diagram. \code $> ./rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 3 \endcode \code The complex contains 177838 simplices @@ -158,12 +158,12 @@ diagram. Persistent_cohomology/rips_multifield_persistence.cpp</a> computes the Rips complex of a point cloud and outputs its persistence diagram with a family of field coefficients. -\li <a href="_persistent_cohomology_2rips_distance_matrix_persistence_8cpp-example.html"> -Persistent_cohomology/rips_distance_matrix_persistence.cpp</a> computes the Rips complex of a distance matrix and +\li <a href="_rips_complex_2rips_distance_matrix_persistence_8cpp-example.html"> +Rips_complex/rips_distance_matrix_persistence.cpp</a> computes the Rips complex of a distance matrix and outputs its persistence diagram. -\li <a href="_persistent_cohomology_2alpha_complex_3d_persistence_8cpp-example.html"> -Persistent_cohomology/alpha_complex_3d_persistence.cpp</a> computes the persistent homology with +\li <a href="_alpha_complex_2alpha_complex_3d_persistence_8cpp-example.html"> +Alpha_complex/alpha_complex_3d_persistence.cpp</a> computes the persistent homology with \f$\mathbb{Z}/2\mathbb{Z}\f$ coefficients of the alpha complex on points sampling from an OFF file. \code $> ./alpha_complex_3d_persistence ../../data/points/tore3D_300.off 2 0.45 \endcode \code Simplex_tree dim: 3 @@ -172,8 +172,8 @@ Persistent_cohomology/alpha_complex_3d_persistence.cpp</a> computes the persiste 2 1 0.0934117 1.00003 2 2 0.56444 1.03938 \endcode -\li <a href="_persistent_cohomology_2exact_alpha_complex_3d_persistence_8cpp-example.html"> -Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with +\li <a href="_alpha_complex_2exact_alpha_complex_3d_persistence_8cpp-example.html"> +Alpha_complex/exact_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with \f$\mathbb{Z}/2\mathbb{Z}\f$ coefficients of the alpha complex on points sampling from an OFF file. Here, as CGAL computes the exact values, it is slower, but it is necessary when points are on a grid for instance. @@ -182,20 +182,20 @@ for instance. 2 0 0 inf 2 2 0.0002 0.2028 \endcode -\li <a href="_persistent_cohomology_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> -Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with +\li <a href="_alpha_complex_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> +Alpha_complex/weighted_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with \f$\mathbb{Z}/2\mathbb{Z}\f$ coefficients of the weighted alpha complex on points sampling from an OFF file and a weights file. \code $> ./weighted_alpha_complex_3d_persistence ../../data/points/tore3D_300.off ../../data/points/tore3D_300.weights 2 0.45 \endcode \code Simplex_tree dim: 3 -2 -0 0 inf -2 1 0.0682162 1.0001 -2 1 0.0934117 1.00003 -2 2 0.56444 1.03938 \endcode +2 0 -1 inf +2 1 -0.931784 0.000103311 +2 1 -0.906588 2.60165e-05 +2 2 -0.43556 0.0393798 \endcode -\li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html"> -Persistent_cohomology/alpha_complex_persistence.cpp</a> computes the persistent homology with +\li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html"> +Alpha_complex/alpha_complex_persistence.cpp</a> computes the persistent homology with \f$\mathbb{Z}/p\mathbb{Z}\f$ coefficients of the alpha complex on points sampling from an OFF file. \code $> ./alpha_complex_persistence -r 32 -p 2 -m 0.45 ../../data/points/tore3D_300.off \endcode \code Alpha complex is of dimension 3 - 9273 simplices - 300 vertices. @@ -205,10 +205,11 @@ Simplex_tree dim: 3 2 1 0.0934117 1.00003 2 2 0.56444 1.03938 \endcode -\li <a href="_persistent_cohomology_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> -Persistent_cohomology/periodic_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with +\li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> +Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with \f$\mathbb{Z}/2\mathbb{Z}\f$ coefficients of the periodic alpha complex on points sampling from an OFF file. -\code $> ./periodic_alpha_complex_3d_persistence ../../data/points/grid_10_10_10_in_0_1.off 3 1.0 \endcode +\code $> ./periodic_alpha_complex_3d_persistence ../../data/points/grid_10_10_10_in_0_1.off +../../data/points/iso_cuboid_3_in_0_1.txt 3 1.0 \endcode \code Periodic Delaunay computed. Simplex_tree dim: 3 3 0 0 inf diff --git a/src/Persistent_cohomology/example/CMakeLists.txt b/src/Persistent_cohomology/example/CMakeLists.txt index f47de4c3..18e2913b 100644 --- a/src/Persistent_cohomology/example/CMakeLists.txt +++ b/src/Persistent_cohomology/example/CMakeLists.txt @@ -5,12 +5,6 @@ add_executable(plain_homology plain_homology.cpp) add_executable(persistence_from_simple_simplex_tree persistence_from_simple_simplex_tree.cpp) -add_executable(rips_distance_matrix_persistence rips_distance_matrix_persistence.cpp) -target_link_libraries(rips_distance_matrix_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) - -add_executable(rips_persistence rips_persistence.cpp) -target_link_libraries(rips_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) - add_executable(rips_persistence_step_by_step rips_persistence_step_by_step.cpp) target_link_libraries(rips_persistence_step_by_step ${Boost_PROGRAM_OPTIONS_LIBRARY}) @@ -23,8 +17,6 @@ target_link_libraries(persistence_from_file ${Boost_PROGRAM_OPTIONS_LIBRARY}) if (TBB_FOUND) target_link_libraries(plain_homology ${TBB_LIBRARIES}) target_link_libraries(persistence_from_simple_simplex_tree ${TBB_LIBRARIES}) - target_link_libraries(rips_distance_matrix_persistence ${TBB_LIBRARIES}) - target_link_libraries(rips_persistence ${TBB_LIBRARIES}) target_link_libraries(rips_persistence_step_by_step ${TBB_LIBRARIES}) target_link_libraries(rips_persistence_via_boundary_matrix ${TBB_LIBRARIES}) target_link_libraries(persistence_from_file ${TBB_LIBRARIES}) @@ -33,10 +25,6 @@ endif() add_test(NAME Persistent_cohomology_example_plain_homology COMMAND $<TARGET_FILE:plain_homology>) add_test(NAME Persistent_cohomology_example_from_simple_simplex_tree COMMAND $<TARGET_FILE:persistence_from_simple_simplex_tree> "1" "0") -add_test(NAME Persistent_cohomology_example_from_rips_distance_matrix COMMAND $<TARGET_FILE:rips_distance_matrix_persistence> - "${CMAKE_SOURCE_DIR}/data/distance_matrix/full_square_distance_matrix.csv" "-r" "1.0" "-d" "3" "-p" "3" "-m" "0") -add_test(NAME Persistent_cohomology_example_from_rips_on_tore_3D COMMAND $<TARGET_FILE:rips_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-r" "0.25" "-m" "0.5" "-d" "3" "-p" "3") add_test(NAME Persistent_cohomology_example_from_rips_step_by_step_on_tore_3D COMMAND $<TARGET_FILE:rips_persistence_step_by_step> "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-r" "0.25" "-m" "0.5" "-d" "3" "-p" "3") add_test(NAME Persistent_cohomology_example_via_boundary_matrix COMMAND $<TARGET_FILE:rips_persistence_via_boundary_matrix> @@ -48,8 +36,6 @@ add_test(NAME Persistent_cohomology_example_from_file_3_3_100 COMMAND $<TARGET_F install(TARGETS plain_homology DESTINATION bin) install(TARGETS persistence_from_simple_simplex_tree DESTINATION bin) -install(TARGETS rips_distance_matrix_persistence DESTINATION bin) -install(TARGETS rips_persistence DESTINATION bin) install(TARGETS rips_persistence_step_by_step DESTINATION bin) install(TARGETS rips_persistence_via_boundary_matrix DESTINATION bin) install(TARGETS persistence_from_file DESTINATION bin) @@ -69,53 +55,15 @@ if(GMP_FOUND) endif(GMP_FOUND) if(CGAL_FOUND) - add_executable(alpha_complex_3d_persistence alpha_complex_3d_persistence.cpp) - target_link_libraries(alpha_complex_3d_persistence ${CGAL_LIBRARY}) - add_executable(exact_alpha_complex_3d_persistence exact_alpha_complex_3d_persistence.cpp) - target_link_libraries(exact_alpha_complex_3d_persistence ${CGAL_LIBRARY}) - add_executable(weighted_alpha_complex_3d_persistence weighted_alpha_complex_3d_persistence.cpp) - target_link_libraries(weighted_alpha_complex_3d_persistence ${CGAL_LIBRARY}) - - if (TBB_FOUND) - target_link_libraries(alpha_complex_3d_persistence ${TBB_LIBRARIES}) - target_link_libraries(exact_alpha_complex_3d_persistence ${TBB_LIBRARIES}) - target_link_libraries(weighted_alpha_complex_3d_persistence ${TBB_LIBRARIES}) - endif(TBB_FOUND) - add_test(NAME Persistent_cohomology_example_alpha_complex_3d COMMAND $<TARGET_FILE:alpha_complex_3d_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "2" "0.45") - add_test(NAME Persistent_cohomology_example_exact_alpha_complex_3d COMMAND $<TARGET_FILE:exact_alpha_complex_3d_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "2" "0.45") - add_test(NAME Persistent_cohomology_example_weighted_alpha_complex_3d COMMAND $<TARGET_FILE:weighted_alpha_complex_3d_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.weights" "2" "0.45") - - install(TARGETS alpha_complex_3d_persistence DESTINATION bin) - install(TARGETS exact_alpha_complex_3d_persistence DESTINATION bin) - install(TARGETS weighted_alpha_complex_3d_persistence DESTINATION bin) - if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) - add_executable (alpha_complex_persistence alpha_complex_persistence.cpp) - target_link_libraries(alpha_complex_persistence - ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) - - add_executable(periodic_alpha_complex_3d_persistence periodic_alpha_complex_3d_persistence.cpp) - target_link_libraries(periodic_alpha_complex_3d_persistence ${CGAL_LIBRARY}) - add_executable(custom_persistence_sort custom_persistence_sort.cpp) target_link_libraries(custom_persistence_sort ${CGAL_LIBRARY}) if (TBB_FOUND) - target_link_libraries(alpha_complex_persistence ${TBB_LIBRARIES}) - target_link_libraries(periodic_alpha_complex_3d_persistence ${TBB_LIBRARIES}) target_link_libraries(custom_persistence_sort ${TBB_LIBRARIES}) endif(TBB_FOUND) - add_test(NAME Persistent_cohomology_example_alpha_complex COMMAND $<TARGET_FILE:alpha_complex_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "-p" "2" "-m" "0.45") - add_test(NAME Persistent_cohomology_example_periodic_alpha_complex_3d COMMAND $<TARGET_FILE:periodic_alpha_complex_3d_persistence> - "${CMAKE_SOURCE_DIR}/data/points/grid_10_10_10_in_0_1.off" "${CMAKE_SOURCE_DIR}/data/points/iso_cuboid_3_in_0_1.txt" "2" "0") add_test(NAME Persistent_cohomology_example_custom_persistence_sort COMMAND $<TARGET_FILE:custom_persistence_sort>) - install(TARGETS alpha_complex_persistence DESTINATION bin) - install(TARGETS periodic_alpha_complex_3d_persistence DESTINATION bin) install(TARGETS custom_persistence_sort DESTINATION bin) endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) diff --git a/src/Persistent_cohomology/example/README b/src/Persistent_cohomology/example/README index 794b94ae..f39d9584 100644 --- a/src/Persistent_cohomology/example/README +++ b/src/Persistent_cohomology/example/README @@ -1,43 +1,14 @@ -To build the example, run in a Terminal: +To build the examples, run in a Terminal: -cd /path-to-example/ +cd /path-to-examples/ cmake . make *********************************************************************************************************************** Example of use of RIPS: -Computation of the persistent homology with Z/2Z coefficients of the Rips complex on points -sampling a Klein bottle: - -./rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 2 - -output: -2 0 0 inf -2 1 0.0983494 inf -2 1 0.104347 inf -2 2 0.138335 inf - - -Every line is of this format: p1*...*pr dim b d -where - p1*...*pr is the product of prime numbers pi such that the homology feature exists in homology with Z/piZ coefficients. - dim is the dimension of the homological feature, - b and d are respectively the birth and death of the feature and - - - -with Z/3Z coefficients: - -./rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 3 - -output: -3 0 0 inf -3 1 0.0983494 inf -3 1 0.104347 inf -3 2 0.138335 inf - -and the computation with Z/2Z and Z/3Z coefficients simultaneously: +Computation of the persistent homology with Z/2Z and Z/3Z coefficients simultaneously of the Rips complex +on points sampling a 3D torus: ./rips_multifield_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.12 -d 3 -p 2 -q 3 @@ -53,7 +24,13 @@ output: 6 0 0 0.12047 6 0 0 0.120414 -and finally the computation with all Z/pZ for 2 <= p <= 71 (20 first prime numbers): +Every line is of this format: p1*...*pr dim b d +where + p1*...*pr is the product of prime numbers pi such that the homology feature exists in homology with Z/piZ coefficients. + dim is the dimension of the homological feature, + b and d are respectively the birth and death of the feature and + +and the computation with all Z/pZ for 2 <= p <= 71 (20 first prime numbers): ./rips_multifield_persistence ../../data/points/Kl.off -r 0.25 -m 0.5 -d 3 -p 2 -q 71 @@ -70,82 +47,6 @@ output: 557940830126698960967415390 0 0 0.120414 *********************************************************************************************************************** -Example of use of ALPHA: - -For a more verbose mode, please run cmake with option "DEBUG_TRACES=TRUE" and recompile the programs. - -1) 3D special case ------------------- -Computation of the persistent homology with Z/2Z coefficients of the alpha complex on points -sampling a torus 3D: - -./alpha_complex_3d_persistence ../../data/points/tore3D_300.off 2 0.45 - -output: -Simplex_tree dim: 3 -2 0 0 inf -2 1 0.0682162 1.0001 -2 1 0.0934117 1.00003 -2 2 0.56444 1.03938 - -Here we retrieve expected Betti numbers on a tore 3D: -Betti numbers[0] = 1 -Betti numbers[1] = 2 -Betti numbers[2] = 1 - -N.B.: - alpha_complex_3d_persistence accepts only OFF files in 3D dimension. - - filtration values are alpha square values - -2) d-Dimension case -------------------- -Computation of the persistent homology with Z/2Z coefficients of the alpha complex on points -sampling a torus 3D: - -./alpha_complex_persistence -r 32 -p 2 -m 0.45 ../../data/points/tore3D_300.off - -output: -Alpha complex is of dimension 3 - 9273 simplices - 300 vertices. -Simplex_tree dim: 3 -2 0 0 inf -2 1 0.0682162 1.0001 -2 1 0.0934117 1.00003 -2 2 0.56444 1.03938 - -Here we retrieve expected Betti numbers on a tore 3D: -Betti numbers[0] = 1 -Betti numbers[1] = 2 -Betti numbers[2] = 1 - -N.B.: - alpha_complex_persistence accepts OFF files in d-Dimension. - - filtration values are alpha square values - -3) 3D periodic special case ---------------------------- -./periodic_alpha_complex_3d_persistence ../../data/points/grid_10_10_10_in_0_1.off ../../data/points/iso_cuboid_3_in_0_1.txt 3 1.0 - -output: -Periodic Delaunay computed. -Simplex_tree dim: 3 -3 0 0 inf -3 1 0.0025 inf -3 1 0.0025 inf -3 1 0.0025 inf -3 2 0.005 inf -3 2 0.005 inf -3 2 0.005 inf -3 3 0.0075 inf - -Here we retrieve expected Betti numbers on a tore 3D: -Betti numbers[0] = 1 -Betti numbers[1] = 3 -Betti numbers[2] = 3 -Betti numbers[3] = 1 - -N.B.: - periodic_alpha_complex_3d_persistence accepts only OFF files in 3D dimension. In this example, the periodic cube -is hard coded to { x = [0,1]; y = [0,1]; z = [0,1] } - - filtration values are alpha square values - -*********************************************************************************************************************** Example of use of PLAIN HOMOLOGY: This example computes the plain homology of the following simplicial complex without filtration values: diff --git a/src/Persistent_cohomology/example/persistence_from_file.cpp b/src/Persistent_cohomology/example/persistence_from_file.cpp index 67235467..eafa3fd5 100644 --- a/src/Persistent_cohomology/example/persistence_from_file.cpp +++ b/src/Persistent_cohomology/example/persistence_from_file.cpp @@ -61,8 +61,7 @@ int main(int argc, char * argv[]) { simplex_tree_stream >> simplex_tree; std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices" << std::endl; - std::cout << " - dimension " << simplex_tree.dimension() << " - filtration " << simplex_tree.filtration() - << std::endl; + std::cout << " - dimension " << simplex_tree.dimension() << std::endl; /* std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; diff --git a/src/Persistent_cohomology/example/persistence_from_simple_simplex_tree.cpp b/src/Persistent_cohomology/example/persistence_from_simple_simplex_tree.cpp index 7ca9410a..8ef479d4 100644 --- a/src/Persistent_cohomology/example/persistence_from_simple_simplex_tree.cpp +++ b/src/Persistent_cohomology/example/persistence_from_simple_simplex_tree.cpp @@ -142,12 +142,10 @@ int main(int argc, char * const argv[]) { /* An edge [11,6] */ /* An edge [10,12,2] */ - st.set_dimension(2); - st.set_filtration(0.4); std::cout << "The complex contains " << st.num_simplices() << " simplices - " << st.num_vertices() << " vertices " << std::endl; - std::cout << " - dimension " << st.dimension() << " - filtration " << st.filtration() << std::endl; + std::cout << " - dimension " << st.dimension() << std::endl; std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; std::cout << "**************************************************************" << std::endl; diff --git a/src/Persistent_cohomology/example/plain_homology.cpp b/src/Persistent_cohomology/example/plain_homology.cpp index 50f692f2..a5ae09c8 100644 --- a/src/Persistent_cohomology/example/plain_homology.cpp +++ b/src/Persistent_cohomology/example/plain_homology.cpp @@ -64,8 +64,6 @@ int main() { st.insert_simplex_and_subfaces(edge03); st.insert_simplex(edge13); st.insert_simplex(vertex4); - // FIXME: Remove this line - st.set_dimension(2); // Sort the simplices in the order of the filtration st.initialize_filtration(); diff --git a/src/Persistent_cohomology/example/rips_persistence_step_by_step.cpp b/src/Persistent_cohomology/example/rips_persistence_step_by_step.cpp index 554eeba6..c1de0ef8 100644 --- a/src/Persistent_cohomology/example/rips_persistence_step_by_step.cpp +++ b/src/Persistent_cohomology/example/rips_persistence_step_by_step.cpp @@ -45,14 +45,7 @@ using Simplex_tree = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; using Vertex_handle = Simplex_tree::Vertex_handle; using Filtration_value = Simplex_tree::Filtration_value; -using Graph_t = boost::adjacency_list < boost::vecS, boost::vecS, boost::undirectedS -, boost::property < vertex_filtration_t, Filtration_value > -, boost::property < edge_filtration_t, Filtration_value > ->; -using Edge_t = std::pair< Vertex_handle, Vertex_handle >; - -template< typename InputPointRange, typename Distance > -Graph_t compute_proximity_graph(InputPointRange &points, Filtration_value threshold, Distance distance); +using Proximity_graph = Gudhi::Proximity_graph<Simplex_tree>; using Field_Zp = Gudhi::persistent_cohomology::Field_Zp; using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Field_Zp >; @@ -81,8 +74,9 @@ int main(int argc, char * argv[]) { Points_off_reader off_reader(off_file_points); // Compute the proximity graph of the points - Graph_t prox_graph = compute_proximity_graph(off_reader.get_point_cloud(), threshold - , Gudhi::Euclidean_distance()); + Proximity_graph prox_graph = Gudhi::compute_proximity_graph<Simplex_tree>(off_reader.get_point_cloud(), + threshold, + Gudhi::Euclidean_distance()); // Construct the Rips complex in a Simplex Tree Simplex_tree st; @@ -170,48 +164,3 @@ void program_options(int argc, char * argv[] std::abort(); } } - -/** Output the proximity graph of the points. - * - * If points contains n elements, the proximity graph is the graph - * with n vertices, and an edge [u,v] iff the distance function between - * points u and v is smaller than threshold. - * - * The type PointCloud furnishes .begin() and .end() methods, that return - * iterators with value_type Point. - */ -template< typename InputPointRange, typename Distance > -Graph_t compute_proximity_graph(InputPointRange &points, Filtration_value threshold, Distance distance) { - std::vector< Edge_t > edges; - std::vector< Filtration_value > edges_fil; - - Vertex_handle idx_u, idx_v; - Filtration_value fil; - idx_u = 0; - for (auto it_u = points.begin(); it_u != points.end(); ++it_u) { - idx_v = idx_u + 1; - for (auto it_v = it_u + 1; it_v != points.end(); ++it_v, ++idx_v) { - fil = distance(*it_u, *it_v); - if (fil <= threshold) { - edges.emplace_back(idx_u, idx_v); - edges_fil.push_back(fil); - } - } - ++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 - - auto vertex_prop = boost::get(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) { - boost::put(vertex_prop, *vi, 0.); - } - - return skel_graph; -} diff --git a/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h b/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h index 672fda48..e0a147b3 100644 --- a/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h +++ b/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h @@ -591,10 +591,17 @@ class Persistent_cohomology { std::ofstream diagram_out(diagram_name.c_str()); cmp_intervals_by_length cmp(cpx_); std::sort(std::begin(persistent_pairs_), std::end(persistent_pairs_), cmp); + bool has_infinity = std::numeric_limits<Filtration_value>::has_infinity; for (auto pair : persistent_pairs_) { - diagram_out << cpx_->dimension(get<0>(pair)) << " " - << cpx_->filtration(get<0>(pair)) << " " - << cpx_->filtration(get<1>(pair)) << std::endl; + // Special case on windows, inf is "1.#INF" + if (has_infinity && cpx_->filtration(get<1>(pair)) == std::numeric_limits<Filtration_value>::infinity()) { + diagram_out << cpx_->dimension(get<0>(pair)) << " " + << cpx_->filtration(get<0>(pair)) << " inf" << std::endl; + } else { + diagram_out << cpx_->dimension(get<0>(pair)) << " " + << cpx_->filtration(get<0>(pair)) << " " + << cpx_->filtration(get<1>(pair)) << std::endl; + } } } diff --git a/src/Persistent_cohomology/test/betti_numbers_unit_test.cpp b/src/Persistent_cohomology/test/betti_numbers_unit_test.cpp index da418034..0a08d200 100644 --- a/src/Persistent_cohomology/test/betti_numbers_unit_test.cpp +++ b/src/Persistent_cohomology/test/betti_numbers_unit_test.cpp @@ -62,8 +62,6 @@ BOOST_AUTO_TEST_CASE( plain_homology_betti_numbers ) st.insert_simplex_and_subfaces(edge04); st.insert_simplex(edge14); st.insert_simplex(vertex5); - // FIXME: Remove this line - st.set_dimension(3); // Sort the simplices in the order of the filtration st.initialize_filtration(); @@ -170,8 +168,6 @@ BOOST_AUTO_TEST_CASE( betti_numbers ) st.insert_simplex_and_subfaces(edge04, 2.0); st.insert_simplex(edge14, 2.0); st.insert_simplex(vertex5, 1.0); - // FIXME: Remove this line - st.set_dimension(3); // Sort the simplices in the order of the filtration st.initialize_filtration(); diff --git a/src/Persistent_cohomology/test/persistent_cohomology_unit_test.cpp b/src/Persistent_cohomology/test/persistent_cohomology_unit_test.cpp index f8174020..a1c106d5 100644 --- a/src/Persistent_cohomology/test/persistent_cohomology_unit_test.cpp +++ b/src/Persistent_cohomology/test/persistent_cohomology_unit_test.cpp @@ -31,12 +31,11 @@ std::string test_rips_persistence(int coefficient, int min_persistence) { // Display the Simplex_tree std::cout << "The complex contains " << st.num_simplices() << " simplices" << " - dimension= " << st.dimension() - << " - filtration= " << st.filtration() << std::endl; + << std::endl; // Check BOOST_CHECK(st.num_simplices() == 98); BOOST_CHECK(st.dimension() == 3); - BOOST_CHECK(st.filtration() == 1.89); // Sort the simplices in the order of the filtration st.initialize_filtration(); @@ -197,8 +196,6 @@ BOOST_AUTO_TEST_CASE( persistence_constructor_exception ) // To make number of simplices = 255 const short simplex_0[] = {0, 1, 2, 3, 4, 5, 6, 7}; st.insert_simplex_and_subfaces(simplex_0); - // FIXME: Remove this line - st.set_dimension(8); // Sort the simplices in the order of the filtration st.initialize_filtration(); diff --git a/src/Persistent_cohomology/test/persistent_cohomology_unit_test_multi_field.cpp b/src/Persistent_cohomology/test/persistent_cohomology_unit_test_multi_field.cpp index 3537cfa4..9e767943 100644 --- a/src/Persistent_cohomology/test/persistent_cohomology_unit_test_multi_field.cpp +++ b/src/Persistent_cohomology/test/persistent_cohomology_unit_test_multi_field.cpp @@ -31,12 +31,11 @@ std::string test_rips_persistence(int min_coefficient, int max_coefficient, doub // Display the Simplex_tree std::cout << "The complex contains " << st.num_simplices() << " simplices" << " - dimension= " << st.dimension() - << " - filtration= " << st.filtration() << std::endl; + << std::endl; // Check BOOST_CHECK(st.num_simplices() == 58); BOOST_CHECK(st.dimension() == 3); - BOOST_CHECK(st.filtration() == 0.4); // Sort the simplices in the order of the filtration st.initialize_filtration(); diff --git a/src/Rips_complex/example/example_rips_complex_from_csv_distance_matrix_file.cpp b/src/Rips_complex/example/example_rips_complex_from_csv_distance_matrix_file.cpp index 7ae8126f..9e182f1e 100644 --- a/src/Rips_complex/example/example_rips_complex_from_csv_distance_matrix_file.cpp +++ b/src/Rips_complex/example/example_rips_complex_from_csv_distance_matrix_file.cpp @@ -32,7 +32,7 @@ int main(int argc, char **argv) { // Init of a Rips complex from a distance matrix in a csv file // Default separator is ';' // ---------------------------------------------------------------------------- - Distance_matrix distances = read_lower_triangular_matrix_from_csv_file<Filtration_value>(csv_file_name); + Distance_matrix distances = Gudhi::read_lower_triangular_matrix_from_csv_file<Filtration_value>(csv_file_name); Rips_complex rips_complex_from_file(distances, threshold); std::streambuf* streambufffer; diff --git a/src/Rips_complex/test/test_rips_complex.cpp b/src/Rips_complex/test/test_rips_complex.cpp index fc2179f2..fc83f5f7 100644 --- a/src/Rips_complex/test/test_rips_complex.cpp +++ b/src/Rips_complex/test/test_rips_complex.cpp @@ -244,7 +244,7 @@ BOOST_AUTO_TEST_CASE(Rips_doc_csv_file) { std::cout << "========== CSV FILE NAME = " << csv_file_name << " - Rips threshold=" << rips_threshold << "==========" << std::endl; - Distance_matrix distances = read_lower_triangular_matrix_from_csv_file<Filtration_value>(csv_file_name); + Distance_matrix distances = Gudhi::read_lower_triangular_matrix_from_csv_file<Filtration_value>(csv_file_name); Rips_complex rips_complex_from_file(distances, rips_threshold); const int DIMENSION_1 = 1; diff --git a/src/Rips_complex/utilities/CMakeLists.txt b/src/Rips_complex/utilities/CMakeLists.txt new file mode 100644 index 00000000..baa571fa --- /dev/null +++ b/src/Rips_complex/utilities/CMakeLists.txt @@ -0,0 +1,21 @@ +cmake_minimum_required(VERSION 2.6) +project(Rips_complex_utilities) + +add_executable(rips_distance_matrix_persistence rips_distance_matrix_persistence.cpp) +target_link_libraries(rips_distance_matrix_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) + +add_executable(rips_persistence rips_persistence.cpp) +target_link_libraries(rips_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) + +if (TBB_FOUND) + target_link_libraries(rips_distance_matrix_persistence ${TBB_LIBRARIES}) + target_link_libraries(rips_persistence ${TBB_LIBRARIES}) +endif() + +add_test(NAME Rips_complex_utility_from_rips_distance_matrix COMMAND $<TARGET_FILE:rips_distance_matrix_persistence> + "${CMAKE_SOURCE_DIR}/data/distance_matrix/full_square_distance_matrix.csv" "-r" "1.0" "-d" "3" "-p" "3" "-m" "0") +add_test(NAME Rips_complex_utility_from_rips_on_tore_3D COMMAND $<TARGET_FILE:rips_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-r" "0.25" "-m" "0.5" "-d" "3" "-p" "3") + +install(TARGETS rips_distance_matrix_persistence DESTINATION bin) +install(TARGETS rips_persistence DESTINATION bin) diff --git a/src/Rips_complex/utilities/README b/src/Rips_complex/utilities/README new file mode 100644 index 00000000..4d20c806 --- /dev/null +++ b/src/Rips_complex/utilities/README @@ -0,0 +1,74 @@ +# Rips_complex # + +## `rips_persistence` ## +This program computes the persistent homology with coefficient field *Z/pZ* of a Rips complex defined on a set of input points. The output diagram contains one bar per line, written with the convention: + +`p dim birth death` + +where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number). + +**Usage** +`rips_persistence [options] <OFF input file>` + +**Allowed options** + +* `-h [ --help ]` Produce help message +* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output. +* `-r [ --max-edge-length ]` (default = inf) Maximal length of an edge for the Rips complex construction. +* `-d [ --cpx-dimension ]` (default = 1) Maximal dimension of the Rips complex we want to compute. +* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology. +* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals. + +Beware: this program may use a lot of RAM and take a lot of time if `max-edge-length` is set to a large value. + +**Example 1 with Z/2Z coefficients** +`rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 2` + +outputs: +``` +2 0 0 inf +2 1 0.0983494 inf +2 1 0.104347 inf +2 2 0.138335 inf +``` + +**Example 2 with Z/3Z coefficients** + +rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 3 + +outputs: +``` +3 0 0 inf +3 1 0.0983494 inf +3 1 0.104347 inf +3 2 0.138335 inf +``` + + + + +## `rips_distance_matrix_persistence` ## +Same as `rips_persistence` but taking a distance matrix as input.
+
+**Usage** +`rips_persistence [options] <CSV input file>`
+where
+`<CSV input file>` is the path to the file containing a distance matrix. Can be square or lower triangular matrix. Separator is ';'. + +**Example** +`rips_distance_matrix_persistence data/distance_matrix/full_square_distance_matrix.csv -r 15 -d 3 -p 3 -m 0` + +outputs: +``` +The complex contains 46 simplices + and has dimension 3 +3 0 0 inf +3 0 0 8.94427 +3 0 0 7.28011 +3 0 0 6.08276 +3 0 0 5.83095 +3 0 0 5.38516 +3 0 0 5 +3 1 11 12.0416 +3 1 6.32456 6.7082 +``` diff --git a/src/Persistent_cohomology/example/rips_distance_matrix_persistence.cpp b/src/Rips_complex/utilities/rips_distance_matrix_persistence.cpp index 8517e7f6..d38808c7 100644 --- a/src/Persistent_cohomology/example/rips_distance_matrix_persistence.cpp +++ b/src/Rips_complex/utilities/rips_distance_matrix_persistence.cpp @@ -57,7 +57,7 @@ int main(int argc, char * argv[]) { program_options(argc, argv, csv_matrix_file, filediag, threshold, dim_max, p, min_persistence); - Distance_matrix distances = read_lower_triangular_matrix_from_csv_file<Filtration_value>(csv_matrix_file); + Distance_matrix distances = Gudhi::read_lower_triangular_matrix_from_csv_file<Filtration_value>(csv_matrix_file); Rips_complex rips_complex_from_file(distances, threshold); // Construct the Rips complex in a Simplex Tree diff --git a/src/Persistent_cohomology/example/rips_persistence.cpp b/src/Rips_complex/utilities/rips_persistence.cpp index d504798b..d504798b 100644 --- a/src/Persistent_cohomology/example/rips_persistence.cpp +++ b/src/Rips_complex/utilities/rips_persistence.cpp diff --git a/src/Simplex_tree/doc/Intro_simplex_tree.h b/src/Simplex_tree/doc/Intro_simplex_tree.h index f5b72ff6..769491d9 100644 --- a/src/Simplex_tree/doc/Intro_simplex_tree.h +++ b/src/Simplex_tree/doc/Intro_simplex_tree.h @@ -67,10 +67,13 @@ Information of the Simplex Tree: Number of vertices = 10 Number of simplices = 98 \endcode * * \li <a href="_simplex_tree_2example_alpha_shapes_3_simplex_tree_from_off_file_8cpp-example.html"> - * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a> - Simplex tree is computed and displayed from a 3D alpha - * complex (Requires CGAL, GMP and GMPXX to be installed) - * + * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a> - Simplex tree is computed and displayed + * from a 3D alpha complex (Requires CGAL, GMP and GMPXX to be installed). * + * \li <a href="_simplex_tree_2graph_expansion_with_blocker_8cpp-example.html"> + * Simplex_tree/graph_expansion_with_blocker.cpp</a> - Simple simplex tree construction from a one-skeleton graph with + * a simple blocker expansion method. + * * \subsection filteredcomplexeshassecomplex Hasse complex * The second one is the Hasse_complex. The Hasse complex is a data structure representing explicitly all co-dimension * 1 incidence relations in a complex. It is consequently faster when accessing the boundary of a simplex, but is less diff --git a/src/Simplex_tree/example/CMakeLists.txt b/src/Simplex_tree/example/CMakeLists.txt index e22cc92c..b33b2d05 100644 --- a/src/Simplex_tree/example/CMakeLists.txt +++ b/src/Simplex_tree/example/CMakeLists.txt @@ -35,4 +35,21 @@ if(GMP_FOUND AND CGAL_FOUND) install(TARGETS Simplex_tree_example_alpha_shapes_3_from_off DESTINATION bin) + add_executable ( Simplex_tree_example_cech_complex_cgal_mini_sphere_3d cech_complex_cgal_mini_sphere_3d.cpp ) + target_link_libraries(Simplex_tree_example_cech_complex_cgal_mini_sphere_3d ${Boost_PROGRAM_OPTIONS_LIBRARY} ${CGAL_LIBRARY}) + if (TBB_FOUND) + target_link_libraries(Simplex_tree_example_cech_complex_cgal_mini_sphere_3d ${TBB_LIBRARIES}) + endif() + add_test(NAME Simplex_tree_example_cech_complex_cgal_mini_sphere_3d COMMAND $<TARGET_FILE:Simplex_tree_example_cech_complex_cgal_mini_sphere_3d> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" -r 0.3 -d 3) + + install(TARGETS Simplex_tree_example_alpha_shapes_3_from_off DESTINATION bin) endif() + +add_executable ( Simplex_tree_example_graph_expansion_with_blocker graph_expansion_with_blocker.cpp ) +if (TBB_FOUND) + target_link_libraries(Simplex_tree_example_graph_expansion_with_blocker ${TBB_LIBRARIES}) +endif() +add_test(NAME Simplex_tree_example_graph_expansion_with_blocker COMMAND $<TARGET_FILE:Simplex_tree_example_graph_expansion_with_blocker>) + +install(TARGETS Simplex_tree_example_graph_expansion_with_blocker DESTINATION bin) 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 new file mode 100644 index 00000000..217e251f --- /dev/null +++ b/src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp @@ -0,0 +1,234 @@ +/* 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 + * + * Copyright (C) 2014 INRIA Sophia Antipolis-Méditerranée (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/graph_simplicial_complex.h> +#include <gudhi/distance_functions.h> +#include <gudhi/Simplex_tree.h> +#include <gudhi/Points_off_io.h> + +#include <CGAL/Epick_d.h> +#include <CGAL/Min_sphere_of_spheres_d.h> +#include <CGAL/Min_sphere_of_points_d_traits_d.h> + +#include <boost/program_options.hpp> + +#include <string> +#include <vector> +#include <limits> // infinity +#include <utility> // for pair +#include <map> + +// ------------------------------------------------------------------------------- +// cech_complex_cgal_mini_sphere_3d is an example of each step that is required to +// build a Cech over a Simplex_tree. Please refer to cech_persistence to see +// how to do the same thing with the Cech_complex wrapper for less detailed +// steps. +// ------------------------------------------------------------------------------- + +// Types definition +using Simplex_tree = Gudhi::Simplex_tree<>; +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 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 Min_sphere = CGAL::Min_sphere_of_spheres_d<Traits>; + +using Points_off_reader = Gudhi::Points_off_reader<Point>; + +class Cech_blocker { + public: + bool operator()(Simplex_handle sh) { + std::vector<Point> points; +#if DEBUG_TRACES + std::cout << "Cech_blocker on ["; +#endif // DEBUG_TRACES + for (auto vertex : simplex_tree_.simplex_vertex_range(sh)) { + points.push_back(point_cloud_[vertex]); +#if DEBUG_TRACES + std::cout << vertex << ", "; +#endif // DEBUG_TRACES + } + Min_sphere ms(points.begin(),points.end()); + Filtration_value radius = ms.radius(); +#if DEBUG_TRACES + std::cout << "] - radius = " << radius << " - returns " << (radius > threshold_) << std::endl; +#endif // DEBUG_TRACES + simplex_tree_.assign_filtration(sh, radius); + 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) { } + 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); + +void program_options(int argc, char * argv[] + , std::string & off_file_points + , Filtration_value & threshold + , int & dim_max); + +int main(int argc, char * argv[]) { + std::string off_file_points; + Filtration_value threshold; + int dim_max; + + program_options(argc, argv, off_file_points, threshold, dim_max); + + // Extract the points from the file filepoints + Points_off_reader off_reader(off_file_points); + + // 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]); + // Construct the Rips complex in a Simplex Tree + Simplex_tree st; + // insert the proximity graph in the simplex tree + st.insert_graph(prox_graph); + // expand the graph until dimension dim_max + st.expansion_with_blockers(dim_max, Cech_blocker(st, threshold, off_reader.get_point_cloud())); + + std::cout << "The complex contains " << st.num_simplices() << " simplices \n"; + std::cout << " and has dimension " << st.dimension() << " \n"; + + // Sort the simplices in the order of the filtration + st.initialize_filtration(); + +#if DEBUG_TRACES + std::cout << "********************************************************************\n"; + // Display the Simplex_tree - Can not be done in the middle of 2 inserts + 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) << "] "; + for (auto vertex : st.simplex_vertex_range(f_simplex)) { + std::cout << static_cast<int>(vertex) << " "; + } + std::cout << std::endl; + } +#endif // DEBUG_TRACES + return 0; +} + +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"); + + 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."); + + po::positional_options_description pos; + pos.add("input-file", 1); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map 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")) { + std::cout << std::endl; + std::cout << "Construct a Cech complex defined on a set of input points.\n \n"; + + std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} + +/** Output the proximity graph of the points. + * + * If points contains n elements, the proximity graph is the graph + * with n vertices, and an edge [u,v] iff the distance function between + * points u and v is smaller than threshold. + * + * 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; + + Kernel k; + Vertex_handle idx_u, idx_v; + Filtration_value fil; + idx_u = 0; + for (auto it_u = points.begin(); it_u != points.end(); ++it_u) { + idx_v = idx_u + 1; + for (auto it_v = it_u + 1; it_v != points.end(); ++it_v, ++idx_v) { + fil = k.squared_distance_d_object()(*it_u, *it_v); + // For Cech Complex, threshold is a radius (distance /2) + fil = std::sqrt(fil) / 2.; + if (fil <= threshold) { + edges.emplace_back(idx_u, idx_v); + edges_fil.push_back(fil); + } + } + ++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 + + 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) { + boost::put(vertex_prop, *vi, 0.); + } + + return skel_graph; +} diff --git a/src/Simplex_tree/example/example_alpha_shapes_3_simplex_tree_from_off_file.cpp b/src/Simplex_tree/example/example_alpha_shapes_3_simplex_tree_from_off_file.cpp index ff2eebcb..d8289ba9 100644 --- a/src/Simplex_tree/example/example_alpha_shapes_3_simplex_tree_from_off_file.cpp +++ b/src/Simplex_tree/example/example_alpha_shapes_3_simplex_tree_from_off_file.cpp @@ -28,6 +28,8 @@ #include <CGAL/Exact_predicates_inexact_constructions_kernel.h> #include <CGAL/Delaunay_triangulation_3.h> #include <CGAL/Alpha_shape_3.h> +#include <CGAL/Alpha_shape_vertex_base_3.h> +#include <CGAL/Alpha_shape_cell_base_3.h> #include <CGAL/iterator.h> #include <fstream> diff --git a/src/Simplex_tree/example/graph_expansion_with_blocker.cpp b/src/Simplex_tree/example/graph_expansion_with_blocker.cpp new file mode 100644 index 00000000..86bfb8cb --- /dev/null +++ b/src/Simplex_tree/example/graph_expansion_with_blocker.cpp @@ -0,0 +1,79 @@ +/* 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): Vincent Rouvreau + * + * Copyright (C) 2014 + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Simplex_tree.h> + +#include <iostream> + +using Simplex_tree = Gudhi::Simplex_tree<>; +using Simplex_handle = Simplex_tree::Simplex_handle; + +int main(int argc, char * const argv[]) { + + // Construct the Simplex Tree with a 1-skeleton graph example + Simplex_tree simplexTree; + + simplexTree.insert_simplex({0, 1}, 0.); + simplexTree.insert_simplex({0, 2}, 1.); + simplexTree.insert_simplex({0, 3}, 2.); + simplexTree.insert_simplex({1, 2}, 3.); + simplexTree.insert_simplex({1, 3}, 4.); + simplexTree.insert_simplex({2, 3}, 5.); + simplexTree.insert_simplex({2, 4}, 6.); + simplexTree.insert_simplex({3, 6}, 7.); + simplexTree.insert_simplex({4, 5}, 8.); + simplexTree.insert_simplex({4, 6}, 9.); + simplexTree.insert_simplex({5, 6}, 10.); + simplexTree.insert_simplex({6}, 10.); + + simplexTree.expansion_with_blockers(3, [&](Simplex_handle sh){ + bool result = false; + std::cout << "Blocker on ["; + // User can loop on the vertices from the given simplex_handle i.e. + for (auto vertex : simplexTree.simplex_vertex_range(sh)) { + // We block the expansion, if the vertex '6' is in the given list of vertices + if (vertex == 6) + result = true; + std::cout << vertex << ", "; + } + std::cout << "] ( " << simplexTree.filtration(sh); + // User can re-assign a new filtration value directly in the blocker (default is the maximal value of boudaries) + simplexTree.assign_filtration(sh, simplexTree.filtration(sh) + 1.); + + std::cout << " + 1. ) = " << result << std::endl; + + return result; + }); + + std::cout << "********************************************************************\n"; + std::cout << "* The complex contains " << simplexTree.num_simplices() << " simplices"; + std::cout << " - dimension " << simplexTree.dimension() << "\n"; + std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + for (auto f_simplex : simplexTree.filtration_simplex_range()) { + std::cout << " " << "[" << simplexTree.filtration(f_simplex) << "] "; + for (auto vertex : simplexTree.simplex_vertex_range(f_simplex)) + std::cout << "(" << vertex << ")"; + std::cout << std::endl; + } + + return 0; +} diff --git a/src/Simplex_tree/example/mini_simplex_tree.cpp b/src/Simplex_tree/example/mini_simplex_tree.cpp index ad99df23..19e45361 100644 --- a/src/Simplex_tree/example/mini_simplex_tree.cpp +++ b/src/Simplex_tree/example/mini_simplex_tree.cpp @@ -52,8 +52,6 @@ int main() { auto edge03 = {0, 3}; st.insert_simplex_and_subfaces(triangle012); st.insert_simplex_and_subfaces(edge03); - // FIXME: Remove this line - st.set_dimension(2); auto edge02 = {0, 2}; ST::Simplex_handle e = st.find(edge02); diff --git a/src/Simplex_tree/example/simple_simplex_tree.cpp b/src/Simplex_tree/example/simple_simplex_tree.cpp index 60f9a35e..b6b65b88 100644 --- a/src/Simplex_tree/example/simple_simplex_tree.cpp +++ b/src/Simplex_tree/example/simple_simplex_tree.cpp @@ -185,19 +185,16 @@ int main(int argc, char * const argv[]) { } // ++ GENERAL VARIABLE SET - simplexTree.set_filtration(FOURTH_FILTRATION_VALUE); // Max filtration value - simplexTree.set_dimension(2); // Max dimension = 2 -> (2,1,0) std::cout << "********************************************************************\n"; // Display the Simplex_tree - Can not be done in the middle of 2 inserts std::cout << "* The complex contains " << simplexTree.num_simplices() << " simplices\n"; - std::cout << " - dimension " << simplexTree.dimension() << " - filtration " << simplexTree.filtration() << "\n"; + std::cout << " - dimension " << simplexTree.dimension() << "\n"; std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : simplexTree.filtration_simplex_range()) { std::cout << " " << "[" << simplexTree.filtration(f_simplex) << "] "; - for (auto vertex : simplexTree.simplex_vertex_range(f_simplex)) { - std::cout << static_cast<int>(vertex) << " "; - } + for (auto vertex : simplexTree.simplex_vertex_range(f_simplex)) + std::cout << "(" << vertex << ")"; std::cout << std::endl; } // [0.1] 0 @@ -250,5 +247,34 @@ int main(int argc, char * const argv[]) { std::cout << "***+ YES IT IS!\n"; else std::cout << "***- NO IT ISN'T\n"; + + simplexFound = simplexTree.find({ 0, 1 }); + std::cout << "**************IS THE SIMPLEX {0,1} IN THE SIMPLEX TREE ?\n"; + if (simplexFound != simplexTree.null_simplex()) + std::cout << "***+ YES IT IS!\n"; + else + std::cout << "***- NO IT ISN'T\n"; + + std::cout << "**************COFACES OF {0,1} IN CODIMENSION 1 ARE\n"; + for (auto& simplex : simplexTree.cofaces_simplex_range(simplexTree.find({0,1}), 1)) { + for (auto vertex : simplexTree.simplex_vertex_range(simplex)) + std::cout << "(" << vertex << ")"; + std::cout << std::endl; + } + + std::cout << "**************STARS OF {0,1} ARE\n"; + for (auto& simplex : simplexTree.star_simplex_range(simplexTree.find({0,1}))) { + for (auto vertex : simplexTree.simplex_vertex_range(simplex)) + std::cout << "(" << vertex << ")"; + std::cout << std::endl; + } + + std::cout << "**************BOUNDARIES OF {0,1,2} ARE\n"; + for (auto& simplex : simplexTree.boundary_simplex_range(simplexTree.find({0,1,2}))) { + for (auto vertex : simplexTree.simplex_vertex_range(simplex)) + std::cout << "(" << vertex << ")"; + std::cout << std::endl; + } + return 0; } diff --git a/src/Simplex_tree/include/gudhi/Simplex_tree.h b/src/Simplex_tree/include/gudhi/Simplex_tree.h index 317bce23..cb6ab309 100644 --- a/src/Simplex_tree/include/gudhi/Simplex_tree.h +++ b/src/Simplex_tree/include/gudhi/Simplex_tree.h @@ -289,7 +289,6 @@ class Simplex_tree { /** \brief Constructs an empty simplex tree. */ Simplex_tree() : null_vertex_(-1), - threshold_(0), root_(nullptr, null_vertex_), filtration_vect_(), dimension_(-1) { } @@ -297,7 +296,6 @@ class Simplex_tree { /** \brief User-defined copy constructor reproduces the whole tree structure. */ Simplex_tree(const Simplex_tree& simplex_source) : null_vertex_(simplex_source.null_vertex_), - threshold_(simplex_source.threshold_), root_(nullptr, null_vertex_ , simplex_source.root_.members_), filtration_vect_(), dimension_(simplex_source.dimension_) { @@ -323,12 +321,10 @@ class Simplex_tree { /** \brief User-defined move constructor moves the whole tree structure. */ Simplex_tree(Simplex_tree && old) : null_vertex_(std::move(old.null_vertex_)), - threshold_(std::move(old.threshold_)), root_(std::move(old.root_)), filtration_vect_(std::move(old.filtration_vect_)), dimension_(std::move(old.dimension_)) { old.dimension_ = -1; - old.threshold_ = 0; old.root_ = Siblings(nullptr, null_vertex_); } @@ -356,7 +352,6 @@ class Simplex_tree { /** \brief Checks if two simplex trees are equal. */ bool operator==(Simplex_tree& st2) { if ((null_vertex_ != st2.null_vertex_) || - (threshold_ != st2.threshold_) || (dimension_ != st2.dimension_)) return false; return rec_equal(&root_, &st2.root_); @@ -396,25 +391,25 @@ class Simplex_tree { return sh->second.key(); } - /** \brief Returns the simplex associated to a key. + /** \brief Returns the simplex that has index idx in the filtration. * * The filtration must be initialized. * \pre SimplexTreeOptions::store_key */ - Simplex_handle simplex(Simplex_key key) const { - return filtration_vect_[key]; + Simplex_handle simplex(Simplex_key idx) const { + return filtration_vect_[idx]; } /** \brief Returns the filtration value of a simplex. * - * Called on the null_simplex, returns INFINITY. + * Called on the null_simplex, it returns infinity. * If SimplexTreeOptions::store_filtration is false, returns 0. */ static Filtration_value filtration(Simplex_handle sh) { if (sh != null_simplex()) { return sh->second.filtration(); } else { - return INFINITY; + return std::numeric_limits<Filtration_value>::infinity(); } } @@ -427,11 +422,6 @@ class Simplex_tree { sh->second.assign_filtration(fv); } - /** \brief Returns an upper bound of the filtration values of the simplices. */ - Filtration_value filtration() const { - return threshold_; - } - /** \brief Returns a Simplex_handle different from all Simplex_handles * associated to the simplices in the simplicial complex. * @@ -492,7 +482,17 @@ class Simplex_tree { } /** \brief Returns an upper bound on the dimension of the simplicial complex. */ - int dimension() const { + int upper_bound_dimension() const { + return dimension_; + } + + /** \brief Returns the dimension of the simplicial complex. + \details This function is not constant time because it can recompute dimension if required (can be triggered by + `remove_maximal_simplex()` or `prune_above_filtration()`). + */ + int dimension() { + if (dimension_to_be_lowered_) + lower_upper_bound_dimension(); return dimension_; } @@ -601,7 +601,11 @@ class Simplex_tree { // if filtration value unchanged return std::pair<Simplex_handle, bool>(null_simplex(), false); } - // otherwise the insertion has succeeded + // otherwise the insertion has succeeded - size is a size_type + if (static_cast<int>(simplex.size()) - 1 > dimension_) { + // Update dimension if needed + dimension_ = static_cast<int>(simplex.size()) - 1; + } return res_insert; } @@ -757,13 +761,12 @@ class Simplex_tree { return &root_; } - /** Set an upper bound for the filtration values. */ - void set_filtration(Filtration_value fil) { - threshold_ = fil; - } - - /** Set a dimension for the simplicial complex. */ + /** \brief Set a dimension for the simplicial complex. + * \details This function must be used with caution because it disables dimension recomputation when required + * (this recomputation can be triggered by `remove_maximal_simplex()` or `prune_above_filtration()`). + */ void set_dimension(int dimension) { + dimension_to_be_lowered_ = false; dimension_ = dimension; } @@ -1082,6 +1085,120 @@ class Simplex_tree { } public: + /** \brief Expands a simplex tree containing only a graph. Simplices corresponding to cliques in the graph are added + * incrementally, faces before cofaces, unless the simplex has dimension larger than `max_dim` or `block_simplex` + * returns true for this simplex. + * + * @param[in] max_dim Expansion maximal dimension value. + * @param[in] block_simplex Blocker oracle. Its concept is <CODE>bool block_simplex(Simplex_handle sh)</CODE> + * + * The function identifies a candidate simplex whose faces are all already in the complex, inserts + * it with a filtration value corresponding to the maximum of the filtration values of the faces, then calls + * `block_simplex` on a `Simplex_handle` for this new simplex. If `block_simplex` returns true, the simplex is + * removed, otherwise it is kept. Note that the evaluation of `block_simplex` is a good time to update the + * filtration value of the simplex if you want a customized value. The algorithm then proceeds with the next + * candidate. + * + * @warning several candidates of the same dimension may be inserted simultaneously before calling `block_simplex`, + * so if you examine the complex in `block_simplex`, you may hit a few simplices of the same dimension that have not + * been vetted by `block_simplex` yet, or have already been rejected but not yet removed. + */ + template< typename Blocker > + void expansion_with_blockers(int max_dim, Blocker block_simplex) { + // Loop must be from the end to the beginning, as higher dimension simplex are always on the left part of the tree + for (auto& simplex : boost::adaptors::reverse(root_.members())) { + if (has_children(&simplex)) { + siblings_expansion_with_blockers(simplex.second.children(), max_dim, max_dim - 1, block_simplex); + } + } + } + + private: + /** \brief Recursive expansion with blockers of the simplex tree.*/ + template< typename Blocker > + void siblings_expansion_with_blockers(Siblings* siblings, int max_dim, int k, Blocker block_simplex) { + if (dimension_ < max_dim - k) { + dimension_ = max_dim - k; + } + if (k == 0) + return; + // No need to go deeper + if (siblings->members().size() < 2) + return; + // Reverse loop starting before the last one for 'next' to be the last one + for (auto simplex = siblings->members().rbegin() + 1; simplex != siblings->members().rend(); simplex++) { + std::vector<std::pair<Vertex_handle, Node> > intersection; + for(auto next = siblings->members().rbegin(); next != simplex; next++) { + bool to_be_inserted = true; + Filtration_value filt = simplex->second.filtration(); + // If all the boundaries are present, 'next' needs to be inserted + for (Simplex_handle border : boundary_simplex_range(simplex)) { + Simplex_handle border_child = find_child(border, next->first); + if (border_child == null_simplex()) { + to_be_inserted=false; + break; + } + filt = std::max(filt, filtration(border_child)); + } + if (to_be_inserted) { + intersection.emplace_back(next->first, Node(nullptr, filt)); + } + } + if (intersection.size() != 0) { + // Reverse the order to insert + Siblings * new_sib = new Siblings(siblings, // oncles + simplex->first, // parent + boost::adaptors::reverse(intersection)); // boost::container::ordered_unique_range_t + std::vector<Vertex_handle> blocked_new_sib_vertex_list; + // As all intersections are inserted, we can call the blocker function on all new_sib members + for (auto new_sib_member = new_sib->members().begin(); + new_sib_member != new_sib->members().end(); + new_sib_member++) { + bool blocker_result = block_simplex(new_sib_member); + // new_sib member has been blocked by the blocker function + // add it to the list to be removed - do not perform it while looping on it + if (blocker_result) { + blocked_new_sib_vertex_list.push_back(new_sib_member->first); + } + } + if (blocked_new_sib_vertex_list.size() == new_sib->members().size()) { + // Specific case where all have to be deleted + delete new_sib; + // ensure the children property + simplex->second.assign_children(siblings); + } else { + for (auto& blocked_new_sib_member : blocked_new_sib_vertex_list) { + new_sib->members().erase(blocked_new_sib_member); + } + // ensure recursive call + simplex->second.assign_children(new_sib); + siblings_expansion_with_blockers(new_sib, max_dim, k - 1, block_simplex); + } + } else { + // ensure the children property + simplex->second.assign_children(siblings); + } + } + } + + /* \private Returns the Simplex_handle composed of the vertex list (from the Simplex_handle), plus the given + * Vertex_handle if the Vertex_handle is found in the Simplex_handle children list. + * Returns null_simplex() if it does not exist + */ + Simplex_handle find_child(Simplex_handle sh, Vertex_handle vh) const { + if (!has_children(sh)) + return null_simplex(); + + Simplex_handle child = sh->second.children()->find(vh); + // Specific case of boost::flat_map does not find, returns boost::flat_map::end() + // in simplex tree we want a null_simplex() + if (child == sh->second.children()->members().end()) + return null_simplex(); + + return child; + } + + public: /** \brief Write the hasse diagram of the simplicial complex in os. * * Each row in the file correspond to a simplex. A line is written: @@ -1157,6 +1274,9 @@ class Simplex_tree { * \post Some simplex tree functions require the filtration to be valid. `prune_above_filtration()` * function is not launching `initialize_filtration()` but returns the filtration modification information. If the * complex has changed , please call `initialize_filtration()` to recompute it. + * \post Note that the dimension of the simplicial complex may be lower after calling `prune_above_filtration()` + * than it was before. However, `upper_bound_dimension()` will return the old value, which remains a valid upper + * bound. If you care, you can call `dimension()` to recompute the exact dimension. */ bool prune_above_filtration(Filtration_value filtration) { return rec_prune_above_filtration(root(), filtration); @@ -1168,6 +1288,8 @@ class Simplex_tree { auto last = std::remove_if(list.begin(), list.end(), [=](Dit_value_t& simplex) { if (simplex.second.filtration() <= filt) return false; if (has_children(&simplex)) rec_delete(simplex.second.children()); + // dimension may need to be lowered + dimension_to_be_lowered_ = true; return true; }); @@ -1176,6 +1298,8 @@ class Simplex_tree { // Removing the whole siblings, parent becomes a leaf. sib->oncles()->members()[sib->parent()].assign_children(sib->oncles()); delete sib; + // dimension may need to be lowered + dimension_to_be_lowered_ = true; return true; } else { // Keeping some elements of siblings. Remove the others, and recurse in the remaining ones. @@ -1187,12 +1311,45 @@ class Simplex_tree { return modified; } + private: + /** \brief Deep search simplex tree dimension recompute. + * @return True if the dimension was modified, false otherwise. + * \pre Be sure the simplex tree has not a too low dimension value as the deep search stops when the former dimension + * has been reached (cf. `upper_bound_dimension()` and `set_dimension()` methods). + */ + bool lower_upper_bound_dimension() { + // reset automatic detection to recompute + dimension_to_be_lowered_ = false; + int new_dimension = -1; + // Browse the tree from the left to the right as higher dimension cells are more likely on the left part of the tree + for (Simplex_handle sh : complex_simplex_range()) { +#ifdef DEBUG_TRACES + for (auto vertex : simplex_vertex_range(sh)) { + std::cout << " " << vertex; + } + std::cout << std::endl; +#endif // DEBUG_TRACES + + int sh_dimension = dimension(sh); + if (sh_dimension >= dimension_) + // Stop browsing as soon as the dimension is reached, no need to go furter + return false; + new_dimension = std::max(new_dimension, sh_dimension); + } + dimension_ = new_dimension; + return true; + } + + public: /** \brief Remove a maximal simplex. * @param[in] sh Simplex handle on the maximal simplex to remove. * \pre Please check the simplex has no coface before removing it. * \exception std::invalid_argument In debug mode, if sh has children. * \post Be aware that removing is shifting data in a flat_map (initialize_filtration to be done). + * \post Note that the dimension of the simplicial complex may be lower after calling `remove_maximal_simplex()` + * than it was before. However, `upper_bound_dimension()` will return the old value, which remains a valid upper + * bound. If you care, you can call `dimension()` to recompute the exact dimension. */ void remove_maximal_simplex(Simplex_handle sh) { // Guarantee the simplex has no children @@ -1210,13 +1367,13 @@ class Simplex_tree { // Sibling is emptied : must be deleted, and its parent must point on his own Sibling child->oncles()->members().at(child->parent()).assign_children(child->oncles()); delete child; + // dimension may need to be lowered + dimension_to_be_lowered_ = true; } } private: Vertex_handle null_vertex_; - /** \brief Upper bound on the filtration values of the simplices.*/ - Filtration_value threshold_; /** \brief Total number of simplices in the complex, without the empty simplex.*/ /** \brief Set of simplex tree Nodes representing the vertices.*/ Siblings root_; @@ -1224,6 +1381,7 @@ class Simplex_tree { std::vector<Simplex_handle> filtration_vect_; /** \brief Upper bound on the dimension of the simplicial complex.*/ int dimension_; + bool dimension_to_be_lowered_ = false; }; // Print a Simplex_tree in os. @@ -1244,7 +1402,6 @@ std::istream& operator>>(std::istream & is, Simplex_tree<T...> & st) { typedef Simplex_tree<T...> ST; std::vector<typename ST::Vertex_handle> simplex; typename ST::Filtration_value fil; - typename ST::Filtration_value max_fil = 0; int max_dim = -1; while (read_simplex(is, simplex, fil)) { // read all simplices in the file as a list of vertices @@ -1253,15 +1410,11 @@ std::istream& operator>>(std::istream & is, Simplex_tree<T...> & st) { if (max_dim < dim) { max_dim = dim; } - if (max_fil < fil) { - max_fil = fil; - } // insert every simplex in the simplex tree st.insert_simplex(simplex, fil); simplex.clear(); } st.set_dimension(max_dim); - st.set_filtration(max_fil); return is; } diff --git a/src/Simplex_tree/test/CMakeLists.txt b/src/Simplex_tree/test/CMakeLists.txt index 81999de6..8684ad2a 100644 --- a/src/Simplex_tree/test/CMakeLists.txt +++ b/src/Simplex_tree/test/CMakeLists.txt @@ -3,13 +3,29 @@ project(Simplex_tree_tests) include(GUDHI_test_coverage) +# Do not forget to copy test files in current binary dir +file(COPY "simplex_tree_for_unit_test.txt" DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) + add_executable ( Simplex_tree_test_unit simplex_tree_unit_test.cpp ) target_link_libraries(Simplex_tree_test_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY}) if (TBB_FOUND) target_link_libraries(Simplex_tree_test_unit ${TBB_LIBRARIES}) endif() -# Do not forget to copy test files in current binary dir -file(COPY "simplex_tree_for_unit_test.txt" DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) - gudhi_add_coverage_test(Simplex_tree_test_unit) + +add_executable ( Simplex_tree_remove_test_unit simplex_tree_remove_unit_test.cpp ) +target_link_libraries(Simplex_tree_remove_test_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY}) +if (TBB_FOUND) + target_link_libraries(Simplex_tree_remove_test_unit ${TBB_LIBRARIES}) +endif() + +gudhi_add_coverage_test(Simplex_tree_remove_test_unit) + +add_executable ( Simplex_tree_iostream_operator_test_unit simplex_tree_iostream_operator_unit_test.cpp ) +target_link_libraries(Simplex_tree_iostream_operator_test_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY}) +if (TBB_FOUND) + target_link_libraries(Simplex_tree_iostream_operator_test_unit ${TBB_LIBRARIES}) +endif() + +gudhi_add_coverage_test(Simplex_tree_iostream_operator_test_unit) diff --git a/src/Simplex_tree/test/README b/src/Simplex_tree/test/README index 21c3d871..df2ab89a 100644 --- a/src/Simplex_tree/test/README +++ b/src/Simplex_tree/test/README @@ -9,6 +9,6 @@ make To launch with details: *********************** -./SimplexTreeUT --report_level=detailed --log_level=all +./Simplex_tree_test_unit --report_level=detailed --log_level=all ==> echo $? returns 0 in case of success (non-zero otherwise) diff --git a/src/Simplex_tree/test/simplex_tree_graph_expansion_unit_test.cpp b/src/Simplex_tree/test/simplex_tree_graph_expansion_unit_test.cpp new file mode 100644 index 00000000..19ce3321 --- /dev/null +++ b/src/Simplex_tree/test/simplex_tree_graph_expansion_unit_test.cpp @@ -0,0 +1,235 @@ +#include <iostream> +#include <fstream> +#include <string> +#include <algorithm> +#include <utility> // std::pair, std::make_pair +#include <cmath> // float comparison +#include <limits> +#include <functional> // greater + +#define BOOST_TEST_DYN_LINK +#define BOOST_TEST_MODULE "simplex_tree" +#include <boost/test/unit_test.hpp> +#include <boost/mpl/list.hpp> + +// ^ +// /!\ Nothing else from Simplex_tree shall be included to test includes are well defined. +#include "gudhi/Simplex_tree.h" + +using namespace Gudhi; + +typedef boost::mpl::list<Simplex_tree<>, Simplex_tree<Simplex_tree_options_fast_persistence>> list_of_tested_variants; + + +bool AreAlmostTheSame(float a, float b) { + return std::fabs(a - b) < std::numeric_limits<float>::epsilon(); +} + +BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_expansion_with_blockers_3, typeST, list_of_tested_variants) { + using Simplex_handle = typename typeST::Simplex_handle; + // Construct the Simplex Tree with a 1-skeleton graph example + typeST simplex_tree; + + simplex_tree.insert_simplex({0, 1}, 0.); + simplex_tree.insert_simplex({0, 2}, 1.); + simplex_tree.insert_simplex({0, 3}, 2.); + simplex_tree.insert_simplex({1, 2}, 3.); + simplex_tree.insert_simplex({1, 3}, 4.); + simplex_tree.insert_simplex({2, 3}, 5.); + simplex_tree.insert_simplex({2, 4}, 6.); + simplex_tree.insert_simplex({3, 6}, 7.); + simplex_tree.insert_simplex({4, 5}, 8.); + simplex_tree.insert_simplex({4, 6}, 9.); + simplex_tree.insert_simplex({5, 6}, 10.); + simplex_tree.insert_simplex({6}, 10.); + + simplex_tree.expansion_with_blockers(3, [&](Simplex_handle sh){ + bool result = false; + std::cout << "Blocker on ["; + // User can loop on the vertices from the given simplex_handle i.e. + for (auto vertex : simplex_tree.simplex_vertex_range(sh)) { + // We block the expansion, if the vertex '6' is in the given list of vertices + if (vertex == 6) + result = true; + std::cout << vertex << ", "; + } + std::cout << "] ( " << simplex_tree.filtration(sh); + // User can re-assign a new filtration value directly in the blocker (default is the maximal value of boudaries) + simplex_tree.assign_filtration(sh, simplex_tree.filtration(sh) + 1.); + + std::cout << " + 1. ) = " << result << std::endl; + + return result; + }); + + std::cout << "********************************************************************\n"; + std::cout << "simplex_tree_expansion_with_blockers_3\n"; + std::cout << "********************************************************************\n"; + std::cout << "* The complex contains " << simplex_tree.num_simplices() << " simplices"; + std::cout << " - dimension " << simplex_tree.dimension() << "\n"; + std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + for (auto f_simplex : simplex_tree.filtration_simplex_range()) { + std::cout << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; + for (auto vertex : simplex_tree.simplex_vertex_range(f_simplex)) + std::cout << "(" << vertex << ")"; + std::cout << std::endl; + } + + BOOST_CHECK(simplex_tree.num_simplices() == 23); + BOOST_CHECK(simplex_tree.dimension() == 3); + // {4, 5, 6} shall be blocked + BOOST_CHECK(simplex_tree.find({4, 5, 6}) == simplex_tree.null_simplex()); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,1,2})), 4.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,1,3})), 5.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,2,3})), 6.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({1,2,3})), 6.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,1,2,3})), 7.)); + +} + +BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_expansion_with_blockers_2, typeST, list_of_tested_variants) { + using Simplex_handle = typename typeST::Simplex_handle; + // Construct the Simplex Tree with a 1-skeleton graph example + typeST simplex_tree; + + simplex_tree.insert_simplex({0, 1}, 0.); + simplex_tree.insert_simplex({0, 2}, 1.); + simplex_tree.insert_simplex({0, 3}, 2.); + simplex_tree.insert_simplex({1, 2}, 3.); + simplex_tree.insert_simplex({1, 3}, 4.); + simplex_tree.insert_simplex({2, 3}, 5.); + simplex_tree.insert_simplex({2, 4}, 6.); + simplex_tree.insert_simplex({3, 6}, 7.); + simplex_tree.insert_simplex({4, 5}, 8.); + simplex_tree.insert_simplex({4, 6}, 9.); + simplex_tree.insert_simplex({5, 6}, 10.); + simplex_tree.insert_simplex({6}, 10.); + + simplex_tree.expansion_with_blockers(2, [&](Simplex_handle sh){ + bool result = false; + std::cout << "Blocker on ["; + // User can loop on the vertices from the given simplex_handle i.e. + for (auto vertex : simplex_tree.simplex_vertex_range(sh)) { + // We block the expansion, if the vertex '6' is in the given list of vertices + if (vertex == 6) + result = true; + std::cout << vertex << ", "; + } + std::cout << "] ( " << simplex_tree.filtration(sh); + // User can re-assign a new filtration value directly in the blocker (default is the maximal value of boudaries) + simplex_tree.assign_filtration(sh, simplex_tree.filtration(sh) + 1.); + + std::cout << " + 1. ) = " << result << std::endl; + + return result; + }); + + std::cout << "********************************************************************\n"; + std::cout << "simplex_tree_expansion_with_blockers_2\n"; + std::cout << "********************************************************************\n"; + std::cout << "* The complex contains " << simplex_tree.num_simplices() << " simplices"; + std::cout << " - dimension " << simplex_tree.dimension() << "\n"; + std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + for (auto f_simplex : simplex_tree.filtration_simplex_range()) { + std::cout << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; + for (auto vertex : simplex_tree.simplex_vertex_range(f_simplex)) + std::cout << "(" << vertex << ")"; + std::cout << std::endl; + } + + BOOST_CHECK(simplex_tree.num_simplices() == 22); + BOOST_CHECK(simplex_tree.dimension() == 2); + // {4, 5, 6} shall be blocked + BOOST_CHECK(simplex_tree.find({4, 5, 6}) == simplex_tree.null_simplex()); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,1,2})), 4.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,1,3})), 5.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,2,3})), 6.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({1,2,3})), 6.)); + BOOST_CHECK(simplex_tree.find({0,1,2,3}) == simplex_tree.null_simplex()); +} + +BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_expansion, typeST, list_of_tested_variants) { + // Construct the Simplex Tree with a 1-skeleton graph example + typeST simplex_tree; + + simplex_tree.insert_simplex({0, 1}, 0.); + simplex_tree.insert_simplex({0, 2}, 1.); + simplex_tree.insert_simplex({0, 3}, 2.); + simplex_tree.insert_simplex({1, 2}, 3.); + simplex_tree.insert_simplex({1, 3}, 4.); + simplex_tree.insert_simplex({2, 3}, 5.); + simplex_tree.insert_simplex({2, 4}, 6.); + simplex_tree.insert_simplex({3, 6}, 7.); + simplex_tree.insert_simplex({4, 5}, 8.); + simplex_tree.insert_simplex({4, 6}, 9.); + simplex_tree.insert_simplex({5, 6}, 10.); + simplex_tree.insert_simplex({6}, 10.); + + simplex_tree.expansion(3); + std::cout << "********************************************************************\n"; + std::cout << "simplex_tree_expansion_3\n"; + std::cout << "********************************************************************\n"; + std::cout << "* The complex contains " << simplex_tree.num_simplices() << " simplices"; + std::cout << " - dimension " << simplex_tree.dimension() << "\n"; + std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + for (auto f_simplex : simplex_tree.filtration_simplex_range()) { + std::cout << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; + for (auto vertex : simplex_tree.simplex_vertex_range(f_simplex)) + std::cout << "(" << vertex << ")"; + std::cout << std::endl; + } + + BOOST_CHECK(simplex_tree.num_simplices() == 24); + BOOST_CHECK(simplex_tree.dimension() == 3); + + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({4,5,6})), 10.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,1,2})), 3.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,1,3})), 4.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,2,3})), 5.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({1,2,3})), 5.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,1,2,3})), 5.)); + +} + +BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_expansion_2, typeST, list_of_tested_variants) { + // Construct the Simplex Tree with a 1-skeleton graph example + typeST simplex_tree; + + simplex_tree.insert_simplex({0, 1}, 0.); + simplex_tree.insert_simplex({0, 2}, 1.); + simplex_tree.insert_simplex({0, 3}, 2.); + simplex_tree.insert_simplex({1, 2}, 3.); + simplex_tree.insert_simplex({1, 3}, 4.); + simplex_tree.insert_simplex({2, 3}, 5.); + simplex_tree.insert_simplex({2, 4}, 6.); + simplex_tree.insert_simplex({3, 6}, 7.); + simplex_tree.insert_simplex({4, 5}, 8.); + simplex_tree.insert_simplex({4, 6}, 9.); + simplex_tree.insert_simplex({5, 6}, 10.); + simplex_tree.insert_simplex({6}, 10.); + + simplex_tree.expansion(2); + + std::cout << "********************************************************************\n"; + std::cout << "simplex_tree_expansion_2\n"; + std::cout << "********************************************************************\n"; + std::cout << "* The complex contains " << simplex_tree.num_simplices() << " simplices"; + std::cout << " - dimension " << simplex_tree.dimension() << "\n"; + std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + for (auto f_simplex : simplex_tree.filtration_simplex_range()) { + std::cout << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; + for (auto vertex : simplex_tree.simplex_vertex_range(f_simplex)) + std::cout << "(" << vertex << ")"; + std::cout << std::endl; + } + + BOOST_CHECK(simplex_tree.num_simplices() == 23); + BOOST_CHECK(simplex_tree.dimension() == 2); + + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({4,5,6})), 10.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,1,2})), 3.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,1,3})), 4.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({0,2,3})), 5.)); + BOOST_CHECK(AreAlmostTheSame(simplex_tree.filtration(simplex_tree.find({1,2,3})), 5.)); + BOOST_CHECK(simplex_tree.find({0,1,2,3}) == simplex_tree.null_simplex()); +} diff --git a/src/Simplex_tree/test/simplex_tree_iostream_operator_unit_test.cpp b/src/Simplex_tree/test/simplex_tree_iostream_operator_unit_test.cpp new file mode 100644 index 00000000..ecb9f025 --- /dev/null +++ b/src/Simplex_tree/test/simplex_tree_iostream_operator_unit_test.cpp @@ -0,0 +1,136 @@ +#include <iostream> + +#define BOOST_TEST_DYN_LINK +#define BOOST_TEST_MODULE "simplex_tree_iostream_operator" +#include <boost/test/unit_test.hpp> +#include <boost/mpl/list.hpp> + +// ^ +// /!\ Nothing else from Simplex_tree shall be included to test includes are well defined. +#include "gudhi/Simplex_tree.h" + +using namespace Gudhi; + +struct MyOptions : Simplex_tree_options_full_featured { + // Not doing persistence, so we don't need those + static const bool store_key = false; + static const bool store_filtration = false; + // I have few vertices + typedef short Vertex_handle; +}; + +typedef boost::mpl::list<Simplex_tree<>, + Simplex_tree<Simplex_tree_options_fast_persistence> + > list_of_tested_variants; + +BOOST_AUTO_TEST_CASE_TEMPLATE(iostream_operator, Stree_type, list_of_tested_variants) { + std::cout << "********************************************************************" << std::endl; + std::cout << "SIMPLEX TREE IOSTREAM OPERATOR" << std::endl; + + Stree_type st; + + st.insert_simplex_and_subfaces({0, 1, 6, 7}, 4.0); + st.insert_simplex_and_subfaces({3, 4, 5}, 3.0); + st.insert_simplex_and_subfaces({3, 0}, 2.0); + st.insert_simplex_and_subfaces({2, 1, 0}, 3.0); + + st.initialize_filtration(); + // Display the Simplex_tree + std::cout << "The ORIGINAL complex contains " << st.num_simplices() << " simplices - dimension = " + << st.dimension() << std::endl; + std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; + for (auto f_simplex : st.filtration_simplex_range()) { + std::cout << " " << "[" << st.filtration(f_simplex) << "] "; + for (auto vertex : st.simplex_vertex_range(f_simplex)) { + std::cout << (int) vertex << " "; + } + std::cout << std::endl; + } + + // st: + // 1 6 + // o---o + // /X\7/ + // o---o---o---o + // 2 0 3\X/4 + // o + // 5 + std::string iostream_file("simplex_tree_for_iostream_operator_unit_test.txt"); + std::ofstream simplex_tree_ostream(iostream_file.c_str()); + simplex_tree_ostream << st; + simplex_tree_ostream.close(); + + Stree_type read_st; + std::ifstream simplex_tree_istream(iostream_file.c_str()); + simplex_tree_istream >> read_st; + + // Display the Simplex_tree + std::cout << "The READ complex contains " << read_st.num_simplices() << " simplices - dimension = " + << read_st.dimension() << std::endl; + std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; + for (auto f_simplex : read_st.filtration_simplex_range()) { + std::cout << " " << "[" << read_st.filtration(f_simplex) << "] "; + for (auto vertex : read_st.simplex_vertex_range(f_simplex)) { + std::cout << (int) vertex << " "; + } + std::cout << std::endl; + } + + BOOST_CHECK(st == read_st); +} + + +BOOST_AUTO_TEST_CASE(mini_iostream_operator) { + std::cout << "********************************************************************" << std::endl; + std::cout << "MINI SIMPLEX TREE IOSTREAM OPERATOR" << std::endl; + + Simplex_tree<MyOptions> st; + + st.insert_simplex_and_subfaces({0, 1, 6, 7}); + st.insert_simplex_and_subfaces({3, 4, 5}); + st.insert_simplex_and_subfaces({3, 0}); + st.insert_simplex_and_subfaces({2, 1, 0}); + + st.initialize_filtration(); + // Display the Simplex_tree + std::cout << "The ORIGINAL complex contains " << st.num_simplices() << " simplices - dimension = " + << st.dimension() << std::endl; + for (auto f_simplex : st.filtration_simplex_range()) { + std::cout << " " << "[" << st.filtration(f_simplex) << "] "; + for (auto vertex : st.simplex_vertex_range(f_simplex)) { + std::cout << (int) vertex << " "; + } + std::cout << std::endl; + } + + // st: + // 1 6 + // o---o + // /X\7/ + // o---o---o---o + // 2 0 3\X/4 + // o + // 5 + std::string iostream_file("simplex_tree_for_iostream_operator_unit_test.txt"); + std::ofstream simplex_tree_ostream(iostream_file.c_str()); + simplex_tree_ostream << st; + simplex_tree_ostream.close(); + + Simplex_tree<MyOptions> read_st; + std::ifstream simplex_tree_istream(iostream_file.c_str()); + simplex_tree_istream >> read_st; + + // Display the Simplex_tree + std::cout << "The READ complex contains " << read_st.num_simplices() << " simplices - dimension = " + << read_st.dimension() << std::endl; + std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; + for (auto f_simplex : read_st.filtration_simplex_range()) { + std::cout << " " << "[" << read_st.filtration(f_simplex) << "] "; + for (auto vertex : read_st.simplex_vertex_range(f_simplex)) { + std::cout << (int) vertex << " "; + } + std::cout << std::endl; + } + + BOOST_CHECK(st == read_st); +} diff --git a/src/Simplex_tree/test/simplex_tree_remove_unit_test.cpp b/src/Simplex_tree/test/simplex_tree_remove_unit_test.cpp new file mode 100644 index 00000000..dc37375c --- /dev/null +++ b/src/Simplex_tree/test/simplex_tree_remove_unit_test.cpp @@ -0,0 +1,427 @@ +#include <iostream> + +#define BOOST_TEST_DYN_LINK +#define BOOST_TEST_MODULE "simplex_tree_remove" +#include <boost/test/unit_test.hpp> + +// ^ +// /!\ Nothing else from Simplex_tree shall be included to test includes are well defined. +#include "gudhi/Simplex_tree.h" + +using namespace Gudhi; + +struct MyOptions : Simplex_tree_options_full_featured { + // Not doing persistence, so we don't need those + static const bool store_key = false; + static const bool store_filtration = false; + // I have few vertices + typedef short Vertex_handle; +}; + +using Mini_stree = Simplex_tree<MyOptions>; +using Stree = Simplex_tree<>; + +BOOST_AUTO_TEST_CASE(remove_maximal_simplex) { + std::cout << "********************************************************************" << std::endl; + std::cout << "REMOVE MAXIMAL SIMPLEX" << std::endl; + + Mini_stree st; + + st.insert_simplex_and_subfaces({0, 1, 6, 7}); + st.insert_simplex_and_subfaces({3, 4, 5}); + + // Constructs a copy at this state for further test purpose + Mini_stree st_pruned = st; + + st.insert_simplex_and_subfaces({3, 0}); + st.insert_simplex_and_subfaces({2, 1, 0}); + + // Constructs a copy at this state for further test purpose + Mini_stree st_complete = st; + // st_complete and st: + // 1 6 + // o---o + // /X\7/ + // o---o---o---o + // 2 0 3\X/4 + // o + // 5 + // st_pruned: + // 1 6 + // o---o + // \7/ + // o o---o + // 0 3\X/4 + // o + // 5 + +#ifdef GUDHI_DEBUG + std::cout << "Check exception throw in debug mode" << std::endl; + // throw excpt because sh has children + BOOST_CHECK_THROW (st.remove_maximal_simplex(st.find({0, 1, 6})), std::invalid_argument); + BOOST_CHECK_THROW (st.remove_maximal_simplex(st.find({3})), std::invalid_argument); + BOOST_CHECK(st == st_complete); +#endif + std::cout << "st.remove_maximal_simplex({0, 2})" << std::endl; + st.remove_maximal_simplex(st.find({0, 2})); + std::cout << "st.remove_maximal_simplex({0, 1, 2})" << std::endl; + st.remove_maximal_simplex(st.find({0, 1, 2})); + std::cout << "st.remove_maximal_simplex({1, 2})" << std::endl; + st.remove_maximal_simplex(st.find({1, 2})); + std::cout << "st.remove_maximal_simplex({2})" << std::endl; + st.remove_maximal_simplex(st.find({2})); + std::cout << "st.remove_maximal_simplex({3})" << std::endl; + st.remove_maximal_simplex(st.find({0, 3})); + + BOOST_CHECK(st == st_pruned); + // Remove all, but as the simplex tree is not storing filtration, there is no modification + st.prune_above_filtration(0.0); + BOOST_CHECK(st == st_pruned); + + Mini_stree st_wo_seven; + + st_wo_seven.insert_simplex_and_subfaces({0, 1, 6}); + st_wo_seven.insert_simplex_and_subfaces({3, 4, 5}); + // st_wo_seven: + // 1 6 + // o---o + // \X/ + // o o---o + // 0 3\X/4 + // o + // 5 + + // Remove all 7 to test the both remove_maximal_simplex cases (when _members is empty or not) + std::cout << "st.remove_maximal_simplex({0, 1, 6, 7})" << std::endl; + st.remove_maximal_simplex(st.find({0, 1, 6, 7})); + std::cout << "st.remove_maximal_simplex({0, 1, 7})" << std::endl; + st.remove_maximal_simplex(st.find({0, 1, 7})); + std::cout << "st.remove_maximal_simplex({0, 6, 7})" << std::endl; + st.remove_maximal_simplex(st.find({0, 6, 7})); + std::cout << "st.remove_maximal_simplex({0, 7})" << std::endl; + st.remove_maximal_simplex(st.find({0, 7})); + std::cout << "st.remove_maximal_simplex({1, 6, 7})" << std::endl; + st.remove_maximal_simplex(st.find({1, 6, 7})); + std::cout << "st.remove_maximal_simplex({1, 7})" << std::endl; + st.remove_maximal_simplex(st.find({1, 7})); + std::cout << "st.remove_maximal_simplex({6, 7})" << std::endl; + st.remove_maximal_simplex(st.find({6, 7})); + std::cout << "st.remove_maximal_simplex({7})" << std::endl; + st.remove_maximal_simplex(st.find({7})); + + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + + // Check dimension calls lower_upper_bound_dimension to recompute dimension + BOOST_CHECK(st.dimension() == 2); + BOOST_CHECK(st.upper_bound_dimension() == 2); + + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() + << " | st_wo_seven.upper_bound_dimension()=" << st_wo_seven.upper_bound_dimension() << std::endl; + std::cout << "st.dimension()=" << st.dimension() << " | st_wo_seven.dimension()=" << st_wo_seven.dimension() << std::endl; + BOOST_CHECK(st == st_wo_seven); +} + +BOOST_AUTO_TEST_CASE(auto_dimension_set) { + std::cout << "********************************************************************" << std::endl; + std::cout << "DIMENSION ON REMOVE MAXIMAL SIMPLEX" << std::endl; + + Mini_stree st; + + st.insert_simplex_and_subfaces({0, 1, 2}); + st.insert_simplex_and_subfaces({0, 1, 3}); + st.insert_simplex_and_subfaces({1, 2, 3, 4}); + st.insert_simplex_and_subfaces({1, 2, 3, 5}); + st.insert_simplex_and_subfaces({6, 7, 8, 9}); + st.insert_simplex_and_subfaces({6, 7, 8, 10}); + + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 3); + + std::cout << "st.remove_maximal_simplex({6, 7, 8, 10})" << std::endl; + st.remove_maximal_simplex(st.find({6, 7, 8, 10})); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 3); + + std::cout << "st.remove_maximal_simplex({6, 7, 8, 9})" << std::endl; + st.remove_maximal_simplex(st.find({6, 7, 8, 9})); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 3); + + std::cout << "st.remove_maximal_simplex({1, 2, 3, 4})" << std::endl; + st.remove_maximal_simplex(st.find({1, 2, 3, 4})); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 3); + + std::cout << "st.remove_maximal_simplex({1, 2, 3, 5})" << std::endl; + st.remove_maximal_simplex(st.find({1, 2, 3, 5})); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 2); + std::cout << "st.dimension()=" << st.dimension() << std::endl; + + std::cout << "st.insert_simplex_and_subfaces({1, 2, 3, 5})" << std::endl; + st.insert_simplex_and_subfaces({1, 2, 3, 5}); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 3); + + std::cout << "st.insert_simplex_and_subfaces({1, 2, 3, 4})" << std::endl; + st.insert_simplex_and_subfaces({1, 2, 3, 4}); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 3); + + + std::cout << "st.remove_maximal_simplex({1, 2, 3, 5})" << std::endl; + st.remove_maximal_simplex(st.find({1, 2, 3, 5})); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 3); + + + std::cout << "st.remove_maximal_simplex({1, 2, 3, 4})" << std::endl; + st.remove_maximal_simplex(st.find({1, 2, 3, 4})); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 2); + std::cout << "st.dimension()=" << st.dimension() << std::endl; + + std::cout << "st.insert_simplex_and_subfaces({0, 1, 3, 4})" << std::endl; + st.insert_simplex_and_subfaces({0, 1, 3, 4}); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 3); + + std::cout << "st.remove_maximal_simplex({0, 1, 3, 4})" << std::endl; + st.remove_maximal_simplex(st.find({0, 1, 3, 4})); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 2); + std::cout << "st.dimension()=" << st.dimension() << std::endl; + + std::cout << "st.insert_simplex_and_subfaces({1, 2, 3, 5})" << std::endl; + st.insert_simplex_and_subfaces({1, 2, 3, 5}); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 3); + + std::cout << "st.insert_simplex_and_subfaces({1, 2, 3, 4})" << std::endl; + st.insert_simplex_and_subfaces({1, 2, 3, 4}); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 3); + + + // Check you can override the dimension + // This is a limit test case - shall not happen + st.set_dimension(1); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 1); + // check dimension() and lower_upper_bound_dimension() is not giving the right answer because dimension is too low + BOOST_CHECK(st.dimension() == 1); + + + // Check you can override the dimension + // This is a limit test case - shall not happen + st.set_dimension(6); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 6); + // check dimension() do not launch lower_upper_bound_dimension() + BOOST_CHECK(st.dimension() == 6); + + + // Reset with the correct value + st.set_dimension(3); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + BOOST_CHECK(st.dimension() == 3); + + std::cout << "st.insert_simplex_and_subfaces({0, 1, 2, 3, 4, 5, 6})" << std::endl; + st.insert_simplex_and_subfaces({0, 1, 2, 3, 4, 5, 6}); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 6); + BOOST_CHECK(st.dimension() == 6); + + std::cout << "st.remove_maximal_simplex({0, 1, 2, 3, 4, 5, 6})" << std::endl; + st.remove_maximal_simplex(st.find({0, 1, 2, 3, 4, 5, 6})); + std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 6); + BOOST_CHECK(st.dimension() == 5); + +} + +BOOST_AUTO_TEST_CASE(prune_above_filtration) { + std::cout << "********************************************************************" << std::endl; + std::cout << "PRUNE ABOVE FILTRATION" << std::endl; + + Stree st; + + st.insert_simplex_and_subfaces({0, 1, 6, 7}, 1.0); + st.insert_simplex_and_subfaces({3, 4, 5}, 2.0); + + // Constructs a copy at this state for further test purpose + Stree st_pruned = st; + st_pruned.initialize_filtration(); // reset + + st.insert_simplex_and_subfaces({3, 0}, 3.0); + st.insert_simplex_and_subfaces({2, 1, 0}, 4.0); + + // Constructs a copy at this state for further test purpose + Stree st_complete = st; + // st_complete and st: + // 1 6 + // o---o + // /X\7/ + // o---o---o---o + // 2 0 3\X/4 + // o + // 5 + // st_pruned: + // 1 6 + // o---o + // \7/ + // o o---o + // 0 3\X/4 + // o + // 5 + + bool simplex_is_changed = false; + // Check the no action cases + // greater than initial filtration value + simplex_is_changed = st.prune_above_filtration(10.0); + if (simplex_is_changed) + st.initialize_filtration(); + BOOST_CHECK(st == st_complete); + BOOST_CHECK(!simplex_is_changed); + // equal to initial filtration value + simplex_is_changed = st.prune_above_filtration(6.0); + if (simplex_is_changed) + st.initialize_filtration(); + BOOST_CHECK(st == st_complete); + BOOST_CHECK(!simplex_is_changed); + // lower than initial filtration value, but still greater than the maximum filtration value + simplex_is_changed = st.prune_above_filtration(5.0); + if (simplex_is_changed) + st.initialize_filtration(); + BOOST_CHECK(st == st_complete); + BOOST_CHECK(!simplex_is_changed); + + // Display the Simplex_tree + std::cout << "The complex contains " << st.num_simplices() << " simplices"; + std::cout << " - dimension " << st.dimension() << std::endl; + std::cout << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; + for (auto f_simplex : st.filtration_simplex_range()) { + std::cout << " " << "[" << st.filtration(f_simplex) << "] "; + for (auto vertex : st.simplex_vertex_range(f_simplex)) { + std::cout << (int) vertex << " "; + } + std::cout << std::endl; + } + + // Check the pruned cases + simplex_is_changed = st.prune_above_filtration(2.5); + if (simplex_is_changed) + st.initialize_filtration(); + BOOST_CHECK(st == st_pruned); + BOOST_CHECK(simplex_is_changed); + + // Display the Simplex_tree + std::cout << "The complex pruned at 2.5 contains " << st.num_simplices() << " simplices"; + std::cout << " - dimension " << st.dimension() << std::endl; + + simplex_is_changed = st.prune_above_filtration(2.0); + if (simplex_is_changed) + st.initialize_filtration(); + + std::cout << "The complex pruned at 2.0 contains " << st.num_simplices() << " simplices"; + std::cout << " - dimension " << st.dimension() << std::endl; + + BOOST_CHECK(st == st_pruned); + BOOST_CHECK(!simplex_is_changed); + + Stree st_empty; + simplex_is_changed = st.prune_above_filtration(0.0); + BOOST_CHECK(simplex_is_changed == true); + if (simplex_is_changed) + st.initialize_filtration(); + + // Display the Simplex_tree + std::cout << "The complex pruned at 0.0 contains " << st.num_simplices() << " simplices"; + std::cout << " - upper_bound_dimension " << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == 3); + + BOOST_CHECK(st.dimension() == -1); + std::cout << "upper_bound_dimension=" << st.upper_bound_dimension() << std::endl; + BOOST_CHECK(st.upper_bound_dimension() == -1); + + BOOST_CHECK(st == st_empty); + BOOST_CHECK(simplex_is_changed); + + // Test case to the limit + simplex_is_changed = st.prune_above_filtration(-1.0); + if (simplex_is_changed) + st.initialize_filtration(); + BOOST_CHECK(st == st_empty); + BOOST_CHECK(!simplex_is_changed); +} + +BOOST_AUTO_TEST_CASE(mini_prune_above_filtration) { + std::cout << "********************************************************************" << std::endl; + std::cout << "MINI PRUNE ABOVE FILTRATION" << std::endl; + + Mini_stree st; + + st.insert_simplex_and_subfaces({0, 1, 6, 7}); + st.insert_simplex_and_subfaces({3, 4, 5}); + st.insert_simplex_and_subfaces({3, 0}); + st.insert_simplex_and_subfaces({2, 1, 0}); + + // st: + // 1 6 + // o---o + // /X\7/ + // o---o---o---o + // 2 0 3\X/4 + // o + // 5 + + st.initialize_filtration(); + + // Display the Simplex_tree + std::cout << "The complex contains " << st.num_simplices() << " simplices" << std::endl; + BOOST_CHECK(st.num_simplices() == 27); + + // Test case to the limit - With these options, there is no filtration, which means filtration is 0 + bool simplex_is_changed = st.prune_above_filtration(1.0); + if (simplex_is_changed) + st.initialize_filtration(); + // Display the Simplex_tree + std::cout << "The complex pruned at 1.0 contains " << st.num_simplices() << " simplices" << std::endl; + BOOST_CHECK(!simplex_is_changed); + BOOST_CHECK(st.num_simplices() == 27); + + simplex_is_changed = st.prune_above_filtration(0.0); + if (simplex_is_changed) + st.initialize_filtration(); + // Display the Simplex_tree + std::cout << "The complex pruned at 0.0 contains " << st.num_simplices() << " simplices" << std::endl; + BOOST_CHECK(!simplex_is_changed); + BOOST_CHECK(st.num_simplices() == 27); + + // Test case to the limit + simplex_is_changed = st.prune_above_filtration(-1.0); + if (simplex_is_changed) + st.initialize_filtration(); + // Display the Simplex_tree + std::cout << "The complex pruned at -1.0 contains " << st.num_simplices() << " simplices" << std::endl; + BOOST_CHECK(simplex_is_changed); + BOOST_CHECK(st.num_simplices() == 0); + + // Display the Simplex_tree + std::cout << "The complex contains " << st.num_simplices() << " simplices" << std::endl; + +} diff --git a/src/Simplex_tree/test/simplex_tree_unit_test.cpp b/src/Simplex_tree/test/simplex_tree_unit_test.cpp index b06d7ec9..580d610a 100644 --- a/src/Simplex_tree/test/simplex_tree_unit_test.cpp +++ b/src/Simplex_tree/test/simplex_tree_unit_test.cpp @@ -26,7 +26,6 @@ void test_empty_simplex_tree(typeST& tst) { typedef typename typeST::Vertex_handle Vertex_handle; const Vertex_handle DEFAULT_VERTEX_VALUE = Vertex_handle(- 1); BOOST_CHECK(tst.null_vertex() == DEFAULT_VERTEX_VALUE); - BOOST_CHECK(tst.filtration() == 0.0); BOOST_CHECK(tst.num_vertices() == (size_t) 0); BOOST_CHECK(tst.num_simplices() == (size_t) 0); typename typeST::Siblings* STRoot = tst.root(); @@ -98,12 +97,11 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_from_file, typeST, list_of_tested_var // Display the Simplex_tree std::cout << "The complex contains " << st.num_simplices() << " simplices" << std::endl; - std::cout << " - dimension " << st.dimension() << " - filtration " << st.filtration() << std::endl; + std::cout << " - dimension " << st.dimension() << std::endl; // Check BOOST_CHECK(st.num_simplices() == 143353); BOOST_CHECK(st.dimension() == 3); - BOOST_CHECK(AreAlmostTheSame(st.filtration(), 0.4)); int previous_size = 0; for (auto f_simplex : st.filtration_simplex_range()) { @@ -147,7 +145,6 @@ void test_simplex_tree_insert_returns_true(const typePairSimplexBool& returnValu } // Global variables -double max_fil = 0.0; int dim_max = -1; template<class typeST, class Filtration_value> @@ -158,15 +155,8 @@ void set_and_test_simplex_tree_dim_fil(typeST& simplexTree, int vectorSize, cons std::cout << " set_and_test_simplex_tree_dim_fil - dim_max=" << dim_max << std::endl; } - if (fil > max_fil) { - max_fil = fil; - simplexTree.set_filtration(max_fil); - std::cout << " set_and_test_simplex_tree_dim_fil - max_fil=" << max_fil - << std::endl; - } BOOST_CHECK(simplexTree.dimension() == dim_max); - BOOST_CHECK(AreAlmostTheSame(simplexTree.filtration(), max_fil)); // Another way to count simplices: size_t num_simp = 0; @@ -190,7 +180,6 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var const Filtration_value FOURTH_FILTRATION_VALUE = 0.4; // reset since we run the test several times dim_max = -1; - max_fil = 0.0; // TEST OF INSERTION std::cout << "********************************************************************" << std::endl; @@ -308,9 +297,9 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var // Simplex_handle = boost::container::flat_map< typeST::Vertex_handle, Node >::iterator typename typeST::Simplex_handle shReturned = returnValue.first; BOOST_CHECK(shReturned == typename typeST::Simplex_handle(nullptr)); + std::cout << "st.num_vertices()=" << st.num_vertices() << std::endl; BOOST_CHECK(st.num_vertices() == (size_t) 4); // Not incremented !! BOOST_CHECK(st.dimension() == dim_max); - BOOST_CHECK(AreAlmostTheSame(st.filtration(), max_fil)); // ++ ELEVENTH std::cout << " - INSERT (2,1,0) (already inserted)" << std::endl; @@ -325,7 +314,6 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var BOOST_CHECK(shReturned == typename typeST::Simplex_handle(nullptr)); BOOST_CHECK(st.num_vertices() == (size_t) 4); // Not incremented !! BOOST_CHECK(st.dimension() == dim_max); - BOOST_CHECK(AreAlmostTheSame(st.filtration(), max_fil)); /* Inserted simplex: */ /* 1 */ @@ -365,7 +353,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var // Display the Simplex_tree - Can not be done in the middle of 2 inserts std::cout << "The complex contains " << st.num_simplices() << " simplices" << std::endl; - std::cout << " - dimension " << st.dimension() << " - filtration " << st.filtration() << std::endl; + std::cout << " - dimension " << st.dimension() << std::endl; std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; for (auto f_simplex : st.filtration_simplex_range()) { std::cout << " " << "[" << st.filtration(f_simplex) << "] "; @@ -575,7 +563,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(NSimplexAndSubfaces_tree_insertion, typeST, list_o // Display the Simplex_tree - Can not be done in the middle of 2 inserts std::cout << "The complex contains " << st.num_simplices() << " simplices" << std::endl; - std::cout << " - dimension " << st.dimension() << " - filtration " << st.filtration() << std::endl; + std::cout << " - dimension " << st.dimension() << std::endl; std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; for (auto f_simplex : st.filtration_simplex_range()) { std::cout << " " << "[" << st.filtration(f_simplex) << "] "; @@ -630,9 +618,6 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(coface_on_simplex_tree, typeST, list_of_tested_var /* o */ /* 5 */ - // FIXME - st.set_dimension(3); - std::vector<typename typeST::Vertex_handle> simplex_result; std::vector<typename typeST::Simplex_handle> result; std::cout << "First test - Star of (3):" << std::endl; @@ -729,9 +714,6 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(copy_move_on_simplex_tree, typeST, list_of_tested_ /* o */ /* 5 */ - // FIXME - st.set_dimension(3); - std::cout << "Printing st - address = " << &st << std::endl; // Copy constructor @@ -756,7 +738,6 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(copy_move_on_simplex_tree, typeST, list_of_tested_ typeST st_empty; // Check st has been emptied by the move BOOST_CHECK(st == st_empty); - BOOST_CHECK(st.filtration() == 0); BOOST_CHECK(st.dimension() == -1); BOOST_CHECK(st.num_simplices() == 0); BOOST_CHECK(st.num_vertices() == (size_t)0); @@ -882,271 +863,3 @@ BOOST_AUTO_TEST_CASE(make_filtration_non_decreasing) { BOOST_CHECK(st == st_other_copy); } - -struct MyOptions : Simplex_tree_options_full_featured { - // Not doing persistence, so we don't need those - static const bool store_key = false; - static const bool store_filtration = false; - // I have few vertices - typedef short Vertex_handle; -}; - -BOOST_AUTO_TEST_CASE(remove_maximal_simplex) { - std::cout << "********************************************************************" << std::endl; - std::cout << "REMOVE MAXIMAL SIMPLEX" << std::endl; - - - typedef Simplex_tree<MyOptions> miniST; - miniST st; - - // FIXME - st.set_dimension(3); - - st.insert_simplex_and_subfaces({0, 1, 6, 7}); - st.insert_simplex_and_subfaces({3, 4, 5}); - - // Constructs a copy at this state for further test purpose - miniST st_pruned = st; - - st.insert_simplex_and_subfaces({3, 0}); - st.insert_simplex_and_subfaces({2, 1, 0}); - - // Constructs a copy at this state for further test purpose - miniST st_complete = st; - // st_complete and st: - // 1 6 - // o---o - // /X\7/ - // o---o---o---o - // 2 0 3\X/4 - // o - // 5 - // st_pruned: - // 1 6 - // o---o - // \7/ - // o o---o - // 0 3\X/4 - // o - // 5 - -#ifdef GUDHI_DEBUG - std::cout << "Check exception throw in debug mode" << std::endl; - // throw excpt because sh has children - BOOST_CHECK_THROW (st.remove_maximal_simplex(st.find({0, 1, 6})), std::invalid_argument); - BOOST_CHECK_THROW (st.remove_maximal_simplex(st.find({3})), std::invalid_argument); - BOOST_CHECK(st == st_complete); -#endif - - st.remove_maximal_simplex(st.find({0, 2})); - st.remove_maximal_simplex(st.find({0, 1, 2})); - st.remove_maximal_simplex(st.find({1, 2})); - st.remove_maximal_simplex(st.find({2})); - st.remove_maximal_simplex(st.find({0, 3})); - - BOOST_CHECK(st == st_pruned); - // Remove all, but as the simplex tree is not storing filtration, there is no modification - st.prune_above_filtration(0.0); - BOOST_CHECK(st == st_pruned); - - miniST st_wo_seven; - // FIXME - st_wo_seven.set_dimension(3); - - st_wo_seven.insert_simplex_and_subfaces({0, 1, 6}); - st_wo_seven.insert_simplex_and_subfaces({3, 4, 5}); - // st_wo_seven: - // 1 6 - // o---o - // \X/ - // o o---o - // 0 3\X/4 - // o - // 5 - - // Remove all 7 to test the both remove_maximal_simplex cases (when _members is empty or not) - st.remove_maximal_simplex(st.find({0, 1, 6, 7})); - st.remove_maximal_simplex(st.find({0, 1, 7})); - st.remove_maximal_simplex(st.find({0, 6, 7})); - st.remove_maximal_simplex(st.find({0, 7})); - st.remove_maximal_simplex(st.find({1, 6, 7})); - st.remove_maximal_simplex(st.find({1, 7})); - st.remove_maximal_simplex(st.find({6, 7})); - st.remove_maximal_simplex(st.find({7})); - - BOOST_CHECK(st == st_wo_seven); -} - -BOOST_AUTO_TEST_CASE(prune_above_filtration) { - std::cout << "********************************************************************" << std::endl; - std::cout << "PRUNE ABOVE FILTRATION" << std::endl; - typedef Simplex_tree<> typeST; - typeST st; - - // FIXME - st.set_dimension(3); - - st.insert_simplex_and_subfaces({0, 1, 6, 7}, 1.0); - st.insert_simplex_and_subfaces({3, 4, 5}, 2.0); - - // Constructs a copy at this state for further test purpose - typeST st_pruned = st; - st_pruned.initialize_filtration(); // reset - - st.insert_simplex_and_subfaces({3, 0}, 3.0); - st.insert_simplex_and_subfaces({2, 1, 0}, 4.0); - - // Constructs a copy at this state for further test purpose - typeST st_complete = st; - // st_complete and st: - // 1 6 - // o---o - // /X\7/ - // o---o---o---o - // 2 0 3\X/4 - // o - // 5 - // st_pruned: - // 1 6 - // o---o - // \7/ - // o o---o - // 0 3\X/4 - // o - // 5 - - bool simplex_is_changed = false; - // Check the no action cases - // greater than initial filtration value - simplex_is_changed = st.prune_above_filtration(10.0); - if (simplex_is_changed) - st.initialize_filtration(); - BOOST_CHECK(st == st_complete); - BOOST_CHECK(!simplex_is_changed); - // equal to initial filtration value - simplex_is_changed = st.prune_above_filtration(6.0); - if (simplex_is_changed) - st.initialize_filtration(); - BOOST_CHECK(st == st_complete); - BOOST_CHECK(!simplex_is_changed); - // lower than initial filtration value, but still greater than the maximum filtration value - simplex_is_changed = st.prune_above_filtration(5.0); - if (simplex_is_changed) - st.initialize_filtration(); - BOOST_CHECK(st == st_complete); - BOOST_CHECK(!simplex_is_changed); - - // Display the Simplex_tree - std::cout << "The complex contains " << st.num_simplices() << " simplices"; - std::cout << " - dimension " << st.dimension() << std::endl; - std::cout << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; - for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " << "[" << st.filtration(f_simplex) << "] "; - for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << (int) vertex << " "; - } - std::cout << std::endl; - } - - // Check the pruned cases - simplex_is_changed = st.prune_above_filtration(2.5); - if (simplex_is_changed) - st.initialize_filtration(); - BOOST_CHECK(st == st_pruned); - BOOST_CHECK(simplex_is_changed); - - // Display the Simplex_tree - std::cout << "The complex pruned at 2.5 contains " << st.num_simplices() << " simplices"; - std::cout << " - dimension " << st.dimension() << std::endl; - - simplex_is_changed = st.prune_above_filtration(2.0); - if (simplex_is_changed) - st.initialize_filtration(); - - std::cout << "The complex pruned at 2.0 contains " << st.num_simplices() << " simplices"; - std::cout << " - dimension " << st.dimension() << std::endl; - - BOOST_CHECK(st == st_pruned); - BOOST_CHECK(!simplex_is_changed); - - typeST st_empty; - // FIXME - st_empty.set_dimension(3); - simplex_is_changed = st.prune_above_filtration(0.0); - if (simplex_is_changed) - st.initialize_filtration(); - - // Display the Simplex_tree - std::cout << "The complex pruned at 0.0 contains " << st.num_simplices() << " simplices"; - std::cout << " - dimension " << st.dimension() << std::endl; - - BOOST_CHECK(st == st_empty); - BOOST_CHECK(simplex_is_changed); - - // Test case to the limit - simplex_is_changed = st.prune_above_filtration(-1.0); - if (simplex_is_changed) - st.initialize_filtration(); - BOOST_CHECK(st == st_empty); - BOOST_CHECK(!simplex_is_changed); -} - -BOOST_AUTO_TEST_CASE(mini_prune_above_filtration) { - std::cout << "********************************************************************" << std::endl; - std::cout << "MINI PRUNE ABOVE FILTRATION" << std::endl; - typedef Simplex_tree<MyOptions> typeST; - typeST st; - - // FIXME - st.set_dimension(3); - - st.insert_simplex_and_subfaces({0, 1, 6, 7}); - st.insert_simplex_and_subfaces({3, 4, 5}); - st.insert_simplex_and_subfaces({3, 0}); - st.insert_simplex_and_subfaces({2, 1, 0}); - - // st: - // 1 6 - // o---o - // /X\7/ - // o---o---o---o - // 2 0 3\X/4 - // o - // 5 - - st.initialize_filtration(); - - // Display the Simplex_tree - std::cout << "The complex contains " << st.num_simplices() << " simplices" << std::endl; - BOOST_CHECK(st.num_simplices() == 27); - - // Test case to the limit - With these options, there is no filtration, which means filtration is 0 - bool simplex_is_changed = st.prune_above_filtration(1.0); - if (simplex_is_changed) - st.initialize_filtration(); - // Display the Simplex_tree - std::cout << "The complex pruned at 1.0 contains " << st.num_simplices() << " simplices" << std::endl; - BOOST_CHECK(!simplex_is_changed); - BOOST_CHECK(st.num_simplices() == 27); - - simplex_is_changed = st.prune_above_filtration(0.0); - if (simplex_is_changed) - st.initialize_filtration(); - // Display the Simplex_tree - std::cout << "The complex pruned at 0.0 contains " << st.num_simplices() << " simplices" << std::endl; - BOOST_CHECK(!simplex_is_changed); - BOOST_CHECK(st.num_simplices() == 27); - - // Test case to the limit - simplex_is_changed = st.prune_above_filtration(-1.0); - if (simplex_is_changed) - st.initialize_filtration(); - // Display the Simplex_tree - std::cout << "The complex pruned at -1.0 contains " << st.num_simplices() << " simplices" << std::endl; - BOOST_CHECK(simplex_is_changed); - BOOST_CHECK(st.num_simplices() == 0); - - // Display the Simplex_tree - std::cout << "The complex contains " << st.num_simplices() << " simplices" << std::endl; - -}
\ No newline at end of file diff --git a/src/Spatial_searching/doc/Intro_spatial_searching.h b/src/Spatial_searching/doc/Intro_spatial_searching.h index 9a3c1b65..1ee5e92e 100644 --- a/src/Spatial_searching/doc/Intro_spatial_searching.h +++ b/src/Spatial_searching/doc/Intro_spatial_searching.h @@ -46,7 +46,7 @@ namespace spatial_searching { * * \section spatial_searching_examples Example * - * This example generates 500 random points, then performs radius search, and queries for nearest and farthest points using different methods. + * This example generates 500 random points, then performs all-near-neighbors searches, and queries for nearest and furthest neighbors using different methods. * * \include Spatial_searching/example_spatial_searching.cpp * diff --git a/src/Spatial_searching/example/example_spatial_searching.cpp b/src/Spatial_searching/example/example_spatial_searching.cpp index 9e6a8f32..034ad24a 100644 --- a/src/Spatial_searching/example/example_spatial_searching.cpp +++ b/src/Spatial_searching/example/example_spatial_searching.cpp @@ -24,34 +24,34 @@ int main(void) { // 10-nearest neighbor query std::cout << "10 nearest neighbors from points[20]:\n"; - auto knn_range = points_ds.query_k_nearest_neighbors(points[20], 10, true); + auto knn_range = points_ds.k_nearest_neighbors(points[20], 10, true); for (auto const& nghb : knn_range) std::cout << nghb.first << " (sq. dist. = " << nghb.second << ")\n"; // Incremental nearest neighbor query std::cout << "Incremental nearest neighbors:\n"; - auto inn_range = points_ds.query_incremental_nearest_neighbors(points[45]); + auto inn_range = points_ds.incremental_nearest_neighbors(points[45]); // Get the neighbors in distance order until we hit the first point for (auto ins_iterator = inn_range.begin(); ins_iterator->first != 0; ++ins_iterator) std::cout << ins_iterator->first << " (sq. dist. = " << ins_iterator->second << ")\n"; - // 10-farthest neighbor query - std::cout << "10 farthest neighbors from points[20]:\n"; - auto kfn_range = points_ds.query_k_farthest_neighbors(points[20], 10, true); + // 10-furthest neighbor query + std::cout << "10 furthest neighbors from points[20]:\n"; + auto kfn_range = points_ds.k_furthest_neighbors(points[20], 10, true); for (auto const& nghb : kfn_range) std::cout << nghb.first << " (sq. dist. = " << nghb.second << ")\n"; - // Incremental farthest neighbor query - std::cout << "Incremental farthest neighbors:\n"; - auto ifn_range = points_ds.query_incremental_farthest_neighbors(points[45]); + // Incremental furthest neighbor query + std::cout << "Incremental furthest neighbors:\n"; + auto ifn_range = points_ds.incremental_furthest_neighbors(points[45]); // Get the neighbors in distance reverse order until we hit the first point for (auto ifs_iterator = ifn_range.begin(); ifs_iterator->first != 0; ++ifs_iterator) std::cout << ifs_iterator->first << " (sq. dist. = " << ifs_iterator->second << ")\n"; - // Radius search - std::cout << "Radius search:\n"; + // All-near-neighbors search + std::cout << "All-near-neighbors search:\n"; std::vector<std::size_t> rs_result; - points_ds.radius_search(points[45], 0.5, std::back_inserter(rs_result)); + points_ds.all_near_neighbors(points[45], 0.5, std::back_inserter(rs_result)); K k; for (auto const& p_idx : rs_result) std::cout << p_idx << " (sq. dist. = " << k.squared_distance_d_object()(points[p_idx], points[45]) << ")\n"; diff --git a/src/Spatial_searching/include/gudhi/Kd_tree_search.h b/src/Spatial_searching/include/gudhi/Kd_tree_search.h index f13a98f7..ef428002 100644 --- a/src/Spatial_searching/include/gudhi/Kd_tree_search.h +++ b/src/Spatial_searching/include/gudhi/Kd_tree_search.h @@ -42,19 +42,19 @@ namespace spatial_searching { /** * \class Kd_tree_search Kd_tree_search.h gudhi/Kd_tree_search.h - * \brief Spatial tree data structure to perform (approximate) nearest and farthest neighbor search. + * \brief Spatial tree data structure to perform (approximate) nearest and furthest neighbor search. * * \ingroup spatial_searching * * \details * The class Kd_tree_search is a tree-based data structure, based on * <a target="_blank" href="http://doc.cgal.org/latest/Spatial_searching/index.html">CGAL dD spatial searching data structures</a>. - * It provides a simplified API to perform (approximate) nearest and farthest neighbor searches. Contrary to CGAL default behavior, the tree + * It provides a simplified API to perform (approximate) nearest and furthest neighbor searches. Contrary to CGAL default behavior, the tree * does not store the points themselves, but stores indices. * - * There are two types of queries: the <i>k-nearest or k-farthest neighbor query</i>, where <i>k</i> is fixed and the <i>k</i> nearest - * or farthest points are computed right away, - * and the <i>incremental nearest or farthest neighbor query</i>, where no number of neighbors is provided during the call, as the + * There are two types of queries: the <i>k-nearest or k-furthest neighbor query</i>, where <i>k</i> is fixed and the <i>k</i> nearest + * or furthest points are computed right away, + * and the <i>incremental nearest or furthest neighbor query</i>, where no number of neighbors is provided during the call, as the * neighbors will be computed incrementally when the iterator on the range is incremented. * * \tparam Search_traits must be a model of the <a target="_blank" @@ -96,7 +96,7 @@ class Kd_tree_search { typedef CGAL::Orthogonal_k_neighbor_search<STraits> K_neighbor_search; typedef typename K_neighbor_search::Tree Tree; typedef typename K_neighbor_search::Distance Distance; - /// \brief The range returned by a k-nearest or k-farthest neighbor search. + /// \brief The range returned by a k-nearest or k-furthest neighbor search. /// Its value type is `std::pair<std::size_t, FT>` where `first` is the index /// of a point P and `second` is the squared distance between P and the query point. typedef K_neighbor_search KNS_range; @@ -104,7 +104,7 @@ class Kd_tree_search { typedef CGAL::Orthogonal_incremental_neighbor_search< STraits, Distance, CGAL::Sliding_midpoint<STraits>, Tree> Incremental_neighbor_search; - /// \brief The range returned by an incremental nearest or farthest neighbor search. + /// \brief The range returned by an incremental nearest or furthest neighbor search. /// Its value type is `std::pair<std::size_t, FT>` where `first` is the index /// of a point P and `second` is the squared distance between P and the query point. typedef Incremental_neighbor_search INS_range; @@ -171,7 +171,7 @@ class Kd_tree_search { /// @param[in] sorted Indicates if the computed sequence of k-nearest neighbors needs to be sorted. /// @param[in] eps Approximation factor. /// @return A range (whose `value_type` is `std::size_t`) containing the k-nearest neighbors. - KNS_range query_k_nearest_neighbors( + KNS_range k_nearest_neighbors( Point const& p, unsigned int k, bool sorted = true, @@ -193,11 +193,11 @@ class Kd_tree_search { /// \brief Search incrementally for the nearest neighbors from a query point. /// @param[in] p The query point. /// @param[in] eps Approximation factor. - /// @return A range (whose `value_type` is `std::size_t`) containing the + /// @return A range (whose `value_type` is `std::size_t`) containing the /// neighbors sorted by their distance to p. /// All the neighbors are not computed by this function, but they will be /// computed incrementally when the iterator on the range is incremented. - INS_range query_incremental_nearest_neighbors(Point const& p, FT eps = FT(0)) const { + INS_range incremental_nearest_neighbors(Point const& p, FT eps = FT(0)) const { // Initialize the search structure, and search all N points // Note that we need to pass the Distance explicitly since it needs to // know the property map @@ -211,13 +211,13 @@ class Kd_tree_search { return search; } - /// \brief Search for the k-farthest points from a query point. + /// \brief Search for the k-furthest points from a query point. /// @param[in] p The query point. - /// @param[in] k Number of farthest points to search. - /// @param[in] sorted Indicates if the computed sequence of k-farthest neighbors needs to be sorted. + /// @param[in] k Number of furthest points to search. + /// @param[in] sorted Indicates if the computed sequence of k-furthest neighbors needs to be sorted. /// @param[in] eps Approximation factor. - /// @return A range (whose `value_type` is `std::size_t`) containing the k-farthest neighbors. - KNS_range query_k_farthest_neighbors( + /// @return A range (whose `value_type` is `std::size_t`) containing the k-furthest neighbors. + KNS_range k_furthest_neighbors( Point const& p, unsigned int k, bool sorted = true, @@ -236,14 +236,14 @@ class Kd_tree_search { return search; } - /// \brief Search incrementally for the farthest neighbors from a query point. + /// \brief Search incrementally for the furthest neighbors from a query point. /// @param[in] p The query point. /// @param[in] eps Approximation factor. - /// @return A range (whose `value_type` is `std::size_t`) + /// @return A range (whose `value_type` is `std::size_t`) /// containing the neighbors sorted by their distance to p. /// All the neighbors are not computed by this function, but they will be /// computed incrementally when the iterator on the range is incremented. - INS_range query_incremental_farthest_neighbors(Point const& p, FT eps = FT(0)) const { + INS_range incremental_furthest_neighbors(Point const& p, FT eps = FT(0)) const { // Initialize the search structure, and search all N points // Note that we need to pass the Distance explicitly since it needs to // know the property map @@ -264,12 +264,10 @@ class Kd_tree_search { /// Note: `it` is used this way: `*it++ = each_point`. /// @param[in] eps Approximation factor. template <typename OutputIterator> - void radius_search( - Point const& p, - FT radius, - OutputIterator it, - FT eps = FT(0)) const { - + void all_near_neighbors(Point const& p, + FT radius, + OutputIterator it, + FT eps = FT(0)) const { m_tree.search(it, Fuzzy_sphere(p, radius, eps, m_tree.traits())); } diff --git a/src/Spatial_searching/test/test_Kd_tree_search.cpp b/src/Spatial_searching/test/test_Kd_tree_search.cpp index f79114bc..8a8334c3 100644 --- a/src/Spatial_searching/test/test_Kd_tree_search.cpp +++ b/src/Spatial_searching/test/test_Kd_tree_search.cpp @@ -48,12 +48,12 @@ BOOST_AUTO_TEST_CASE(test_Kd_tree_search) { Points_ds points_ds(points); - // Test query_k_nearest_neighbors + // Test k_nearest_neighbors std::size_t closest_pt_index = - points_ds.query_k_nearest_neighbors(points[10], 1, false).begin()->first; + points_ds.k_nearest_neighbors(points[10], 1, false).begin()->first; BOOST_CHECK(closest_pt_index == 10); - auto kns_range = points_ds.query_k_nearest_neighbors(points[20], 10, true); + auto kns_range = points_ds.k_nearest_neighbors(points[20], 10, true); std::vector<std::size_t> knn_result; FT last_dist = -1.; @@ -63,12 +63,12 @@ BOOST_AUTO_TEST_CASE(test_Kd_tree_search) { last_dist = nghb.second; } - // Test query_incremental_nearest_neighbors + // Test incremental_nearest_neighbors closest_pt_index = - points_ds.query_incremental_nearest_neighbors(points[10]).begin()->first; + points_ds.incremental_nearest_neighbors(points[10]).begin()->first; BOOST_CHECK(closest_pt_index == 10); - auto inn_range = points_ds.query_incremental_nearest_neighbors(points[20]); + auto inn_range = points_ds.incremental_nearest_neighbors(points[20]); std::vector<std::size_t> inn_result; last_dist = -1.; @@ -83,8 +83,8 @@ BOOST_AUTO_TEST_CASE(test_Kd_tree_search) { // Same result for KNN and INN? BOOST_CHECK(knn_result == inn_result); - // Test query_k_farthest_neighbors - auto kfn_range = points_ds.query_k_farthest_neighbors(points[20], 10, true); + // Test k_furthest_neighbors + auto kfn_range = points_ds.k_furthest_neighbors(points[20], 10, true); std::vector<std::size_t> kfn_result; last_dist = kfn_range.begin()->second; @@ -94,8 +94,8 @@ BOOST_AUTO_TEST_CASE(test_Kd_tree_search) { last_dist = nghb.second; } - // Test query_k_farthest_neighbors - auto ifn_range = points_ds.query_incremental_farthest_neighbors(points[20]); + // Test k_furthest_neighbors + auto ifn_range = points_ds.incremental_furthest_neighbors(points[20]); std::vector<std::size_t> ifn_result; last_dist = ifn_range.begin()->second; @@ -110,10 +110,10 @@ BOOST_AUTO_TEST_CASE(test_Kd_tree_search) { // Same result for KFN and IFN? BOOST_CHECK(kfn_result == ifn_result); - // Test radius search + // Test all_near_neighbors Point rs_q(rd.get_double(-1., 1), rd.get_double(-1., 1), rd.get_double(-1., 1), rd.get_double(-1., 1)); std::vector<std::size_t> rs_result; - points_ds.radius_search(rs_q, 0.5, std::back_inserter(rs_result)); + points_ds.all_near_neighbors(rs_q, 0.5, std::back_inserter(rs_result)); K k; for (auto const& p_idx : rs_result) BOOST_CHECK(k.squared_distance_d_object()(points[p_idx], rs_q) <= 0.5); diff --git a/src/Subsampling/include/gudhi/sparsify_point_set.h b/src/Subsampling/include/gudhi/sparsify_point_set.h index 507f8c79..7d3b97fb 100644 --- a/src/Subsampling/include/gudhi/sparsify_point_set.h +++ b/src/Subsampling/include/gudhi/sparsify_point_set.h @@ -83,7 +83,7 @@ sparsify_point_set( *output_it++ = *it_pt; - auto ins_range = points_ds.query_incremental_nearest_neighbors(*it_pt); + auto ins_range = points_ds.incremental_nearest_neighbors(*it_pt); // If another point Q is closer that min_squared_dist, mark Q to be dropped for (auto const& neighbor : ins_range) { diff --git a/src/Tangential_complex/include/gudhi/Tangential_complex.h b/src/Tangential_complex/include/gudhi/Tangential_complex.h index 9fa7c825..6f061922 100644 --- a/src/Tangential_complex/include/gudhi/Tangential_complex.h +++ b/src/Tangential_complex/include/gudhi/Tangential_complex.h @@ -155,7 +155,7 @@ class Tangential_complex { >::type Triangulation; typedef typename Triangulation::Geom_traits Tr_traits; typedef typename Triangulation::Weighted_point Tr_point; - typedef typename Triangulation::Bare_point Tr_bare_point; + typedef typename Tr_traits::Base::Point_d Tr_bare_point; typedef typename Triangulation::Vertex_handle Tr_vertex_handle; typedef typename Triangulation::Full_cell_handle Tr_full_cell_handle; typedef typename Tr_traits::Vector_d Tr_vector; @@ -1093,8 +1093,8 @@ class Tangential_complex { std::size_t num_inserted_points = 1; #endif // const int NUM_NEIGHBORS = 150; - // KNS_range ins_range = m_points_ds.query_k_nearest_neighbors(center_pt, NUM_NEIGHBORS); - INS_range ins_range = m_points_ds.query_incremental_nearest_neighbors(center_pt); + // KNS_range ins_range = m_points_ds.k_nearest_neighbors(center_pt, NUM_NEIGHBORS); + INS_range ins_range = m_points_ds.incremental_nearest_neighbors(center_pt); // While building the local triangulation, we keep the radius // of the sphere "star sphere" centered at "center_vertex" @@ -1203,7 +1203,7 @@ class Tangential_complex { Point center_point = compute_perturbed_point(i); // Among updated point, what is the closer from our center point? std::size_t closest_pt_index = - updated_pts_ds.query_k_nearest_neighbors(center_point, 1, false).begin()->first; + updated_pts_ds.k_nearest_neighbors(center_point, 1, false).begin()->first; typename K::Construct_weighted_point_d k_constr_wp = m_k.construct_weighted_point_d_object(); @@ -1315,11 +1315,10 @@ class Tangential_complex { m_k.compute_coordinate_d_object(); #ifdef GUDHI_TC_USE_ANOTHER_POINT_SET_FOR_TANGENT_SPACE_ESTIM - KNS_range kns_range = m_points_ds_for_tse.query_k_nearest_neighbors( - p, num_pts_for_pca, false); + KNS_range kns_range = m_points_ds_for_tse.k_nearest_neighbors(p, num_pts_for_pca, false); const Points &points_for_pca = m_points_for_tse; #else - KNS_range kns_range = m_points_ds.query_k_nearest_neighbors(p, num_pts_for_pca, false); + KNS_range kns_range = m_points_ds.k_nearest_neighbors(p, num_pts_for_pca, false); const Points &points_for_pca = m_points; #endif @@ -1413,11 +1412,10 @@ class Tangential_complex { const Point &p = m_points[*it_index]; #ifdef GUDHI_TC_USE_ANOTHER_POINT_SET_FOR_TANGENT_SPACE_ESTIM - KNS_range kns_range = m_points_ds_for_tse.query_k_nearest_neighbors( - p, num_pts_for_pca, false); + KNS_range kns_range = m_points_ds_for_tse.k_nearest_neighbors(p, num_pts_for_pca, false); const Points &points_for_pca = m_points_for_tse; #else - KNS_range kns_range = m_points_ds.query_k_nearest_neighbors(p, num_pts_for_pca, false); + KNS_range kns_range = m_points_ds.k_nearest_neighbors(p, num_pts_for_pca, false); const Points &points_for_pca = m_points; #endif diff --git a/src/Witness_complex/doc/Witness_complex_doc.h b/src/Witness_complex/doc/Witness_complex_doc.h index 171a185f..5d5c0735 100644 --- a/src/Witness_complex/doc/Witness_complex_doc.h +++ b/src/Witness_complex/doc/Witness_complex_doc.h @@ -90,8 +90,9 @@ int main(int argc, char * const argv[]) { Gudhi::Points_off_reader<Point_d> off_reader(file_name); point_vector = Point_vector(off_reader.get_point_cloud()); - // Choose landmarks - Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); + // Choose landmarks (one can choose either of the two methods below) + // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); + Gudhi::subsampling::choose_n_farthest_points(K(), point_vector, nbL, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); // Compute witness complex Witness_complex witness_complex(landmarks, @@ -107,7 +108,14 @@ int main(int argc, char * const argv[]) { Here is an example of constructing a strong witness complex filtration and computing persistence on it: - \include Witness_complex/example_strong_witness_persistence.cpp + \include Witness_complex/strong_witness_persistence.cpp + + \section witnessexample3 Example3: Computing relaxed witness complex persistence from a distance matrix + + In this example we compute the relaxed witness complex persistence from a given matrix of closest landmarks to each witness. + Each landmark is given as the couple (index, distance). + + \include Witness_complex/example_nearest_landmark_table.cpp \copyright GNU General Public License v3. diff --git a/src/Witness_complex/example/CMakeLists.txt b/src/Witness_complex/example/CMakeLists.txt index cbc53902..a8231392 100644 --- a/src/Witness_complex/example/CMakeLists.txt +++ b/src/Witness_complex/example/CMakeLists.txt @@ -13,39 +13,23 @@ install(TARGETS Witness_complex_example_nearest_landmark_table DESTINATION bin) # CGAL and Eigen3 are required for Euclidean version of Witness if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) add_executable( Witness_complex_example_off example_witness_complex_off.cpp ) - add_executable( Witness_complex_example_strong_off example_strong_witness_complex_off.cpp ) add_executable ( Witness_complex_example_sphere example_witness_complex_sphere.cpp ) - - add_executable ( Witness_complex_example_witness_persistence example_witness_complex_persistence.cpp ) - target_link_libraries(Witness_complex_example_witness_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) - - add_executable ( Witness_complex_example_strong_witness_persistence example_strong_witness_persistence.cpp ) - target_link_libraries(Witness_complex_example_strong_witness_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) - - if (TBB_FOUND) - target_link_libraries(Witness_complex_example_witness_persistence ${TBB_LIBRARIES}) - target_link_libraries(Witness_complex_example_strong_witness_persistence ${TBB_LIBRARIES}) - endif() + + add_executable( Witness_complex_example_strong_off example_strong_witness_complex_off.cpp ) + target_link_libraries(Witness_complex_example_strong_off) add_test(NAME Witness_complex_example_off_test_torus COMMAND $<TARGET_FILE:Witness_complex_example_off> "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "20" "1.0" "3") + add_test(NAME Witness_complex_example_test_sphere_10 + COMMAND $<TARGET_FILE:Witness_complex_example_sphere> "10") add_test(NAME Witness_complex_example_strong_off_test_torus COMMAND $<TARGET_FILE:Witness_complex_example_strong_off> "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "20" "1.0" "3") - add_test(NAME Witness_complex_example_test_sphere_10 - COMMAND $<TARGET_FILE:Witness_complex_example_sphere> "10") - add_test(NAME Witness_complex_example_test_torus_persistence - COMMAND $<TARGET_FILE:Witness_complex_example_witness_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-l" "20" "-a" "0.5") - add_test(NAME Witness_complex_example_strong_test_torus_persistence - COMMAND $<TARGET_FILE:Witness_complex_example_strong_witness_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-l" "20" "-a" "0.5") - + install(TARGETS Witness_complex_example_off DESTINATION bin) - install(TARGETS Witness_complex_example_strong_off DESTINATION bin) install(TARGETS Witness_complex_example_sphere DESTINATION bin) - install(TARGETS Witness_complex_example_witness_persistence DESTINATION bin) - install(TARGETS Witness_complex_example_strong_witness_persistence DESTINATION bin) + install(TARGETS Witness_complex_example_strong_off DESTINATION bin) + endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) diff --git a/src/Witness_complex/example/example_strong_witness_complex_off.cpp b/src/Witness_complex/example/example_strong_witness_complex_off.cpp index 0ee9ee90..bc069654 100644 --- a/src/Witness_complex/example/example_strong_witness_complex_off.cpp +++ b/src/Witness_complex/example/example_strong_witness_complex_off.cpp @@ -23,6 +23,7 @@ #include <gudhi/Simplex_tree.h> #include <gudhi/Euclidean_strong_witness_complex.h> #include <gudhi/pick_n_random_points.h> +#include <gudhi/choose_n_farthest_points.h> #include <gudhi/Points_off_io.h> #include <CGAL/Epick_d.h> @@ -63,9 +64,10 @@ int main(int argc, char * const argv[]) { std::cout << "Successfully read " << point_vector.size() << " points.\n"; std::cout << "Ambient dimension is " << point_vector[0].dimension() << ".\n"; - // Choose landmarks - Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); - + // Choose landmarks (decomment one of the following two lines) + // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); + Gudhi::subsampling::choose_n_farthest_points(K(), point_vector, nbL, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); + // Compute witness complex start = clock(); Witness_complex witness_complex(landmarks, diff --git a/src/Witness_complex/example/example_witness_complex_off.cpp b/src/Witness_complex/example/example_witness_complex_off.cpp index b36dac0d..be11c955 100644 --- a/src/Witness_complex/example/example_witness_complex_off.cpp +++ b/src/Witness_complex/example/example_witness_complex_off.cpp @@ -4,6 +4,7 @@ #include <gudhi/Simplex_tree.h> #include <gudhi/Euclidean_witness_complex.h> #include <gudhi/pick_n_random_points.h> +#include <gudhi/choose_n_farthest_points.h> #include <gudhi/Points_off_io.h> #include <CGAL/Epick_d.h> @@ -44,8 +45,9 @@ int main(int argc, char * const argv[]) { std::cout << "Successfully read " << point_vector.size() << " points.\n"; std::cout << "Ambient dimension is " << point_vector[0].dimension() << ".\n"; - // Choose landmarks - Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); + // Choose landmarks (decomment one of the following two lines) + // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); + Gudhi::subsampling::choose_n_farthest_points(K(), point_vector, nbL, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); // Compute witness complex start = clock(); diff --git a/src/Witness_complex/example/example_witness_complex_sphere.cpp b/src/Witness_complex/example/example_witness_complex_sphere.cpp index 124fd99b..a66da3f9 100644 --- a/src/Witness_complex/example/example_witness_complex_sphere.cpp +++ b/src/Witness_complex/example/example_witness_complex_sphere.cpp @@ -25,6 +25,7 @@ #include <gudhi/Simplex_tree.h> #include <gudhi/Euclidean_witness_complex.h> #include <gudhi/pick_n_random_points.h> +#include <gudhi/choose_n_farthest_points.h> #include <gudhi/reader_utils.h> #include <CGAL/Epick_d.h> @@ -75,7 +76,8 @@ int main(int argc, char * const argv[]) { // Choose landmarks start = clock(); - Gudhi::subsampling::pick_n_random_points(point_vector, number_of_landmarks, std::back_inserter(landmarks)); + // Gudhi::subsampling::pick_n_random_points(point_vector, number_of_landmarks, std::back_inserter(landmarks)); + Gudhi::subsampling::choose_n_farthest_points(K(), point_vector, number_of_landmarks, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); // Compute witness complex Witness_complex witness_complex(landmarks, diff --git a/src/Witness_complex/include/gudhi/Euclidean_strong_witness_complex.h b/src/Witness_complex/include/gudhi/Euclidean_strong_witness_complex.h index fb669ef8..4f3cef4f 100644 --- a/src/Witness_complex/include/gudhi/Euclidean_strong_witness_complex.h +++ b/src/Witness_complex/include/gudhi/Euclidean_strong_witness_complex.h @@ -84,7 +84,7 @@ class Euclidean_strong_witness_complex : landmarks_(std::begin(landmarks), std::end(landmarks)), landmark_tree_(landmarks_) { nearest_landmark_table_.reserve(boost::size(witnesses)); for (auto w : witnesses) - nearest_landmark_table_.push_back(landmark_tree_.query_incremental_nearest_neighbors(w)); + nearest_landmark_table_.push_back(landmark_tree_.incremental_nearest_neighbors(w)); } /** \brief Returns the point corresponding to the given vertex. diff --git a/src/Witness_complex/include/gudhi/Euclidean_witness_complex.h b/src/Witness_complex/include/gudhi/Euclidean_witness_complex.h index 6afe9a5d..ff8bb139 100644 --- a/src/Witness_complex/include/gudhi/Euclidean_witness_complex.h +++ b/src/Witness_complex/include/gudhi/Euclidean_witness_complex.h @@ -86,7 +86,7 @@ class Euclidean_witness_complex : landmarks_(std::begin(landmarks), std::end(landmarks)), landmark_tree_(landmarks) { nearest_landmark_table_.reserve(boost::size(witnesses)); for (auto w : witnesses) - nearest_landmark_table_.push_back(landmark_tree_.query_incremental_nearest_neighbors(w)); + nearest_landmark_table_.push_back(landmark_tree_.incremental_nearest_neighbors(w)); } /** \brief Returns the point corresponding to the given vertex. diff --git a/src/Witness_complex/include/gudhi/Strong_witness_complex.h b/src/Witness_complex/include/gudhi/Strong_witness_complex.h index 6f4bcf60..b3d00b11 100644 --- a/src/Witness_complex/include/gudhi/Strong_witness_complex.h +++ b/src/Witness_complex/include/gudhi/Strong_witness_complex.h @@ -34,7 +34,8 @@ namespace Gudhi { namespace witness_complex { -/* \private + /** + * \private * \class Strong_witness_complex * \brief Constructs strong witness complex for a given table of nearest landmarks with respect to witnesses. * \ingroup witness_complex @@ -127,10 +128,11 @@ class Strong_witness_complex { if ((Landmark_id)simplex.size() - 1 > complex_dim) complex_dim = simplex.size() - 1; } - complex.set_dimension(complex_dim); return true; } + //@} + private: /* \brief Adds recursively all the faces of a certain dimension dim-1 witnessed by the same witness. * Iterator is needed to know until how far we can take landmarks to form simplexes. @@ -171,7 +173,6 @@ class Strong_witness_complex { simplex.pop_back(); } } - //@} }; } // namespace witness_complex diff --git a/src/Witness_complex/include/gudhi/Witness_complex.h b/src/Witness_complex/include/gudhi/Witness_complex.h index bcfe8484..53c38520 100644 --- a/src/Witness_complex/include/gudhi/Witness_complex.h +++ b/src/Witness_complex/include/gudhi/Witness_complex.h @@ -130,7 +130,6 @@ class Witness_complex { } k++; } - complex.set_dimension(k-1); return true; } diff --git a/src/Witness_complex/test/test_euclidean_simple_witness_complex.cpp b/src/Witness_complex/test/test_euclidean_simple_witness_complex.cpp index 62fd1157..4f718203 100644 --- a/src/Witness_complex/test/test_euclidean_simple_witness_complex.cpp +++ b/src/Witness_complex/test/test_euclidean_simple_witness_complex.cpp @@ -75,7 +75,7 @@ BOOST_AUTO_TEST_CASE(simple_witness_complex) { Kd_tree landmark_tree(landmarks); Nearest_landmark_table nearest_landmark_table; for (auto w: witnesses) - nearest_landmark_table.push_back(landmark_tree.query_incremental_nearest_neighbors(w)); + nearest_landmark_table.push_back(landmark_tree.incremental_nearest_neighbors(w)); // Weak witness complex: Euclidean version EuclideanWitnessComplex eucl_witness_complex(landmarks, diff --git a/src/Witness_complex/utilities/CMakeLists.txt b/src/Witness_complex/utilities/CMakeLists.txt new file mode 100644 index 00000000..125a41ff --- /dev/null +++ b/src/Witness_complex/utilities/CMakeLists.txt @@ -0,0 +1,28 @@ +cmake_minimum_required(VERSION 2.6) +project(Witness_complex_utilities) + +# CGAL and Eigen3 are required for Euclidean version of Witness +if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) + + add_executable ( Witness_complex_strong_witness_persistence strong_witness_persistence.cpp ) + target_link_libraries(Witness_complex_strong_witness_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) + + add_executable ( Witness_complex_weak_witness_persistence weak_witness_persistence.cpp ) + target_link_libraries(Witness_complex_weak_witness_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) + + if (TBB_FOUND) + target_link_libraries(Witness_complex_strong_witness_persistence ${TBB_LIBRARIES}) + target_link_libraries(Witness_complex_weak_witness_persistence ${TBB_LIBRARIES}) + endif() + + add_test(NAME Witness_complex_strong_test_torus_persistence + COMMAND $<TARGET_FILE:Witness_complex_strong_witness_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-l" "20" "-a" "0.5") + add_test(NAME Witness_complex_weak_test_torus_persistence + COMMAND $<TARGET_FILE:Witness_complex_weak_witness_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-l" "20" "-a" "0.5") + + install(TARGETS Witness_complex_strong_witness_persistence DESTINATION bin) + install(TARGETS Witness_complex_weak_witness_persistence DESTINATION bin) + +endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) diff --git a/src/Witness_complex/utilities/README b/src/Witness_complex/utilities/README new file mode 100644 index 00000000..1141033e --- /dev/null +++ b/src/Witness_complex/utilities/README @@ -0,0 +1,74 @@ +# Witness_complex #
+
+For more details about the witness complex, please read the [user manual of the package](http://gudhi.gforge.inria.fr/doc/latest/group__witness__complex.html).
+
+## `weak_witness_persistence` ##
+This program computes the persistent homology with coefficient field *Z/pZ* of a Weak witness complex defined on a set of input points. The output diagram contains one bar per line, written with the convention:
+
+`p dim birth death`
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
+
+*Usage*
+`weak_witness_persistence [options] <OFF input file>`
+
+*Allowed options*
+
+* `-h [ --help ]` Produce help message
+* `-l [ --landmarks ]` Number of landmarks to choose from the point cloud.
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. By default, print in std::cout.
+* `-a [ --max-sq-alpha ]` (default = inf) Maximal squared relaxation parameter.
+* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+* `-d [ --cpx-dimension ]` (default = 2147483647) Maximal dimension of the weak witness complex we want to compute.
+
+*Example*
+`weak_witness_persistence data/points/tore3D_1307.off -l 20 -a 0.5 -m 0.006`
+
+outputs:
+```
+Successfully read 1307 points.
+Ambient dimension is 3.
+The complex contains 732 simplices and has dimension 8
+11 0 0 inf
+11 1 0 inf
+11 2 0.0275251 0.0534586
+11 1 0 0.0239952
+```
+
+N.B.: output is random as the 20 landmarks are chosen randomly.
+
+## `strong_witness_persistence` ##
+This program computes the persistent homology with coefficient field *Z/pZ* of a Strong witness complex defined on a set of input points. The output diagram contains one bar per line, written with the convention:
+
+`p dim birth death`
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
+
+*Usage*
+`strong_witness_persistence [options] <OFF input file>`
+
+*Allowed options*
+
+* `-h [ --help ]` Produce help message
+* `-l [ --landmarks ]` Number of landmarks to choose from the point cloud.
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. By default, print in std::cout.
+* `-a [ --max-sq-alpha ]` (default = inf) Maximal squared relaxation parameter.
+* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+* `-d [ --cpx-dimension ]` (default = 2147483647) Maximal dimension of the weak witness complex we want to compute.
+
+*Example*
+`strong_witness_persistence data/points/tore3D_1307.off -l 20 -a 0.5 -m 0.06`
+
+outputs:
+```
+Successfully read 1307 points.
+Ambient dimension is 3.
+The complex contains 1836 simplices and has dimension 8
+11 0 0 inf
+11 1 0.00674748 inf
+11 2 0.0937751 0.235354
+```
+
+N.B.: output is random as the 20 landmarks are chosen randomly.
diff --git a/src/Witness_complex/example/example_strong_witness_persistence.cpp b/src/Witness_complex/utilities/strong_witness_persistence.cpp index f786fe7b..e3e0c1ee 100644 --- a/src/Witness_complex/example/example_strong_witness_persistence.cpp +++ b/src/Witness_complex/utilities/strong_witness_persistence.cpp @@ -25,6 +25,7 @@ #include <gudhi/Persistent_cohomology.h> #include <gudhi/Points_off_io.h> #include <gudhi/pick_n_random_points.h> +#include <gudhi/choose_n_farthest_points.h> #include <boost/program_options.hpp> @@ -76,8 +77,9 @@ int main(int argc, char * argv[]) { std::cout << "Successfully read " << witnesses.size() << " points.\n"; std::cout << "Ambient dimension is " << witnesses[0].dimension() << ".\n"; - // Choose landmarks from witnesses - Gudhi::subsampling::pick_n_random_points(witnesses, nbL, std::back_inserter(landmarks)); + // Choose landmarks (decomment one of the following two lines) + // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); + Gudhi::subsampling::choose_n_farthest_points(K(), witnesses, nbL, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); // Compute witness complex Strong_witness_complex strong_witness_complex(landmarks, diff --git a/src/Witness_complex/example/example_witness_complex_persistence.cpp b/src/Witness_complex/utilities/weak_witness_persistence.cpp index a1146922..a63b0837 100644 --- a/src/Witness_complex/example/example_witness_complex_persistence.cpp +++ b/src/Witness_complex/utilities/weak_witness_persistence.cpp @@ -25,6 +25,7 @@ #include <gudhi/Persistent_cohomology.h> #include <gudhi/Points_off_io.h> #include <gudhi/pick_n_random_points.h> +#include <gudhi/choose_n_farthest_points.h> #include <boost/program_options.hpp> @@ -76,8 +77,9 @@ int main(int argc, char * argv[]) { std::cout << "Successfully read " << witnesses.size() << " points.\n"; std::cout << "Ambient dimension is " << witnesses[0].dimension() << ".\n"; - // Choose landmarks from witnesses - Gudhi::subsampling::pick_n_random_points(witnesses, nbL, std::back_inserter(landmarks)); + // Choose landmarks (decomment one of the following two lines) + // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); + Gudhi::subsampling::choose_n_farthest_points(K(), witnesses, nbL, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); // Compute witness complex Witness_complex witness_complex(landmarks, diff --git a/src/cmake/modules/GUDHI_doxygen_target.cmake b/src/cmake/modules/GUDHI_doxygen_target.cmake index d2cb952d..f3e2d9f5 100644 --- a/src/cmake/modules/GUDHI_doxygen_target.cmake +++ b/src/cmake/modules/GUDHI_doxygen_target.cmake @@ -3,6 +3,11 @@ find_package(Doxygen) if(DOXYGEN_FOUND) # configure_file(${CMAKE_CURRENT_SOURCE_DIR}/Doxyfile.in ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile @ONLY) + #starting from cmake 3.9 the usage of DOXYGEN_EXECUTABLE is deprecated + if(TARGET Doxygen::doxygen) + get_property(DOXYGEN_EXECUTABLE TARGET Doxygen::doxygen PROPERTY IMPORTED_LOCATION) + endif() + add_custom_target(doxygen ${DOXYGEN_EXECUTABLE} ${GUDHI_USER_VERSION_DIR}/Doxyfile WORKING_DIRECTORY ${GUDHI_USER_VERSION_DIR} DEPENDS ${GUDHI_USER_VERSION_DIR}/Doxyfile ${GUDHI_DOXYGEN_DEPENDENCY} diff --git a/src/cmake/modules/GUDHI_third_party_libraries.cmake b/src/cmake/modules/GUDHI_third_party_libraries.cmake index 8f486118..8269c3bf 100644 --- a/src/cmake/modules/GUDHI_third_party_libraries.cmake +++ b/src/cmake/modules/GUDHI_third_party_libraries.cmake @@ -54,10 +54,12 @@ if(CGAL_FOUND) endforeach(CGAL_INCLUDE_DIR ${CGAL_INCLUDE_DIRS}) endif(NOT CGAL_VERSION VERSION_GREATER 4.9.0) - # For dev version - include_directories(BEFORE "src/common/include/gudhi_patches") - # For user version - include_directories(BEFORE "include/gudhi_patches") + if (NOT CGAL_VERSION VERSION_GREATER 4.11.0) + # For dev version + include_directories(BEFORE "src/common/include/gudhi_patches") + # For user version + include_directories(BEFORE "include/gudhi_patches") + endif (NOT CGAL_VERSION VERSION_GREATER 4.11.0) endif() endif() @@ -117,15 +119,13 @@ if(NOT GUDHI_CYTHON_PATH) endif(NOT GUDHI_CYTHON_PATH) if(PYTHONINTERP_FOUND AND CYTHON_FOUND) - # Unitary tests are available through py.test - find_program( PYTEST_PATH py.test ) # Default found version 2 if(PYTHON_VERSION_MAJOR EQUAL 2) # Documentation generation is available through sphinx find_program( SPHINX_PATH sphinx-build ) elseif(PYTHON_VERSION_MAJOR EQUAL 3) # No sphinx-build in Pyton3, just hack it - set(SPHINX_PATH "${CMAKE_SOURCE_DIR}/${GUDHI_CYTHON_PATH}/doc/python3-sphinx-build") + set(SPHINX_PATH "${PYTHON_EXECUTABLE}" "${CMAKE_CURRENT_SOURCE_DIR}/${GUDHI_CYTHON_PATH}/doc/python3-sphinx-build.py") else() message(FATAL_ERROR "ERROR: Try to compile the Cython interface. Python version ${PYTHON_VERSION_STRING} is not valid.") endif(PYTHON_VERSION_MAJOR EQUAL 2) diff --git a/src/cmake/modules/GUDHI_user_version_target.cmake b/src/cmake/modules/GUDHI_user_version_target.cmake index cff64ad2..4abc2574 100644 --- a/src/cmake/modules/GUDHI_user_version_target.cmake +++ b/src/cmake/modules/GUDHI_user_version_target.cmake @@ -48,7 +48,11 @@ if (NOT CMAKE_VERSION VERSION_LESS 2.8.11) copy_directory ${CMAKE_SOURCE_DIR}/src/GudhUI ${GUDHI_USER_VERSION_DIR}/GudhUI) set(GUDHI_DIRECTORIES "doc;example;concept;utilities") - set(GUDHI_INCLUDE_DIRECTORIES "include/gudhi;include/gudhi_patches") + if (NOT CGAL_VERSION VERSION_GREATER 4.11.0) + set(GUDHI_INCLUDE_DIRECTORIES "include/gudhi;include/gudhi_patches") + else () + set(GUDHI_INCLUDE_DIRECTORIES "include/gudhi") + endif () foreach(GUDHI_MODULE ${GUDHI_MODULES_FULL_LIST}) foreach(GUDHI_DIRECTORY ${GUDHI_DIRECTORIES}) diff --git a/src/common/doc/file_formats.h b/src/common/doc/file_formats.h index c145b271..d06b81f5 100644 --- a/src/common/doc/file_formats.h +++ b/src/common/doc/file_formats.h @@ -2,7 +2,7 @@ * (Geometric Understanding in Higher Dimensions) is a generic C++ * library for computational topology. * -* Author(s): Clément Jamin +* Author(s): Clément Jamin * * Copyright (C) 2017 INRIA * @@ -26,7 +26,7 @@ namespace Gudhi { /*! \page fileformats File formats - + \tableofcontents \section FileFormatsPers Persistence Diagram @@ -34,20 +34,89 @@ namespace Gudhi { Such a file, whose extension is usually `.pers`, contains a list of persistence intervals.<br> Lines starting with `#` are ignored (comments).<br> Other lines might contain 2, 3 or 4 values (the number of values on each line must be the same for all lines): - \code{.unparsed} + \verbatim [[field] dimension] birth death - \endcode + \endverbatim Here is a simple sample file: - \code{.unparsed} - # Beautiful persistence diagram + \verbatim + # Persistence diagram example 2 2.7 3.7 2 9.6 14. + # Some comments 3 34.2 34.974 4 3. inf - \endcode + \endverbatim Other sample files can be found in the `data/persistence_diagram` folder. + + Such files can be generated with `Gudhi::persistent_cohomology::Persistent_cohomology::output_diagram()` and read with + `Gudhi::read_persistence_intervals_and_dimension()`, `Gudhi::read_persistence_intervals_grouped_by_dimension()` or + `Gudhi::read_persistence_intervals_in_dimension()`. + + + \section FileFormatsIsoCuboid Iso-cuboid + + Such a file describes an iso-oriented cuboid with diagonal opposite vertices (min_x, min_y, min_z,...) and (max_x, max_y, max_z, ...). The format is:<br> + \verbatim + min_x min_y [min_z ...] + max_x max_y [max_z ...] + \endverbatim + + Here is a simple sample file in the 3D case: + \verbatim + -1. -1. -1. + 1. 1. 1. + \endverbatim + + + \section FileFormatsPerseus Perseus + + This file format is the format used by the Perseus software + (http://www.sas.upenn.edu/~vnanda/perseus/) by Vidit Nanda. + The first line contains a number d begin the dimension of the + bitmap (2 in the example below). Next d lines are the numbers of top dimensional cubes in each dimensions (3 and 3 + in the example below). Next, in lexicographical order, the filtration of top dimensional cubes is given (1 4 6 8 + 20 4 7 6 5 in the example below). + + \image html "exampleBitmap.png" "Example of a input data." + + The input file for the following complex is: + \verbatim + 2 + 3 + 3 + 1 + 4 + 6 + 8 + 20 + 4 + 7 + 6 + 5 + \endverbatim + + To indicate periodic boundary conditions in a + given direction, then number of top dimensional cells in this direction have to be multiplied by -1. For instance: + + \verbatim + 2 + -3 + 3 + 1 + 4 + 6 + 8 + 20 + 4 + 7 + 6 + 5 + \endverbatim + + Indicate that we have imposed periodic boundary conditions in the direction x, but not in the direction y. + */ } // namespace Gudhi diff --git a/src/common/doc/main_page.h b/src/common/doc/main_page.h index e5f135db..108cf6e3 100644 --- a/src/common/doc/main_page.h +++ b/src/common/doc/main_page.h @@ -93,6 +93,24 @@ </td> </tr> </table> + \subsection CoverComplexDataStructure Cover Complexes: Nerves and Graph Induced Complexes + \image html "gicvisu.jpg" "Graph Induced Complex of a point cloud." +<table border="0"> + <tr> + <td width="25%"> + <b>Author:</b> Mathieu Carrière<br> + <b>Introduced in:</b> GUDHI 2.0.1<br> + <b>Copyright:</b> GPL v3<br> + </td> + <td width="75%"> + Nerves and Graph Induced Complexes are cover complexes, i.e. simplicial complexes that provably contain + topological information about the input data. They can be computed with a cover of the + data, that comes i.e. from the preimage of a family of intervals covering the image + of a scalar-valued function defined on the data. <br> + <b>User manual:</b> \ref cover_complex - <b>Reference manual:</b> Gudhi::cover_complex::Cover_complex + </td> + </tr> +</table> \subsection SkeletonBlockerDataStructure Skeleton blocker \image html "ds_representation.png" "Skeleton blocker representation" <table border="0"> @@ -161,7 +179,7 @@ <b>Author:</b> François Godi<br> <b>Introduced in:</b> GUDHI 2.0.0<br> <b>Copyright:</b> GPL v3<br> - <b>Requires:</b> \ref cgal ≥ 4.8.1 and \ref eigen3 + <b>Requires:</b> \ref cgal ≥ 4.8.1 </td> <td width="75%"> Bottleneck distance measures the similarity between two persistence diagrams. @@ -288,30 +306,38 @@ make doxygen * Having CGAL version 4.4.0 or higher installed is recommended. The procedure to install this library according to * your operating system is detailed here http://doc.cgal.org/latest/Manual/installation.html * - * The following examples require the <a target="_blank" href="http://www.cgal.org/">Computational Geometry Algorithms + * The following examples/utilities require the <a target="_blank" href="http://www.cgal.org/">Computational Geometry Algorithms * Library</a> (CGAL \cite cgal:eb-15b) and will not be built if CGAL is not installed: - * \li <a href="_persistent_cohomology_2alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2exact_alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2exact_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/exact_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/weighted_alpha_complex_3d_persistence.cpp</a> * \li <a href="_simplex_tree_2example_alpha_shapes_3_simplex_tree_from_off_file_8cpp-example.html"> * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a> * - * The following example requires CGAL version ≥ 4.6.0: - * \li <a href="_witness_complex_2witness_complex_sphere_8cpp-example.html"> - * Witness_complex/witness_complex_sphere.cpp</a> - * + * The following examples/utilities require CGAL version ≥ 4.6.0: + * \li <a href="_witness_complex_2strong_witness_persistence_8cpp-example.html"> + * Witness_complex/strong_witness_persistence.cpp</a> + * \li <a href="_witness_complex_2weak_witness_persistence_8cpp-example.html"> + * Witness_complex/weak_witness_persistence.cpp</a> + * \li <a href="_witness_complex_2example_strong_witness_complex_off_8cpp-example.html"> + * Witness_complex/example_strong_witness_complex_off.cpp</a> + * \li <a href="_witness_complex_2example_witness_complex_off_8cpp-example.html"> + * Witness_complex/example_witness_complex_off.cpp</a> + * \li <a href="_witness_complex_2example_witness_complex_sphere_8cpp-example.html"> + * Witness_complex/example_witness_complex_sphere.cpp</a> + * * The following example requires CGAL version ≥ 4.7.0: * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html"> * Alpha_complex/Alpha_complex_from_off.cpp</a> * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html"> * Alpha_complex/Alpha_complex_from_points.cpp</a> - * \li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html"> - * Persistent_cohomology/alpha_complex_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/periodic_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html"> + * Alpha_complex/alpha_complex_persistence.cpp</a> + * \li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a> * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html"> * Persistent_cohomology/custom_persistence_sort.cpp</a> * @@ -320,8 +346,8 @@ make doxygen * Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp.cpp</a> * \li <a href="_bottleneck_distance_2bottleneck_basic_example_8cpp-example.html"> * Bottleneck_distance/bottleneck_basic_example.cpp</a> - * \li <a href="_bottleneck_distance_2bottleneck_read_file_example_8cpp-example.html"> - * Bottleneck_distance/bottleneck_read_file_example.cpp</a> + * \li <a href="_bottleneck_distance_2bottleneck_read_file_8cpp-example.html"> + * Bottleneck_distance/bottleneck_distance.cpp</a> * \li <a href="_spatial_searching_2example_spatial_searching_8cpp-example.html"> * Spatial_searching/example_spatial_searching.cpp</a> * \li <a href="_subsampling_2example_choose_n_farthest_points_8cpp-example.html"> @@ -342,19 +368,45 @@ make doxygen * <a target="_blank" href="http://eigen.tuxfamily.org/">Eigen3</a> is a C++ template library for linear algebra: * matrices, vectors, numerical solvers, and related algorithms. * - * The following example requires the <a target="_blank" href="http://eigen.tuxfamily.org/">Eigen3</a> and will not be + * The following examples/utilities require the <a target="_blank" href="http://eigen.tuxfamily.org/">Eigen3</a> and will not be * built if Eigen3 is not installed: * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html"> * Alpha_complex/Alpha_complex_from_off.cpp</a> * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html"> * Alpha_complex/Alpha_complex_from_points.cpp</a> - * \li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html"> - * Persistent_cohomology/alpha_complex_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/periodic_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html"> + * Alpha_complex/alpha_complex_persistence.cpp</a> + * \li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_bottleneck_distance_2alpha_rips_persistence_bottleneck_distance_8cpp-example.html"> + * Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp.cpp</a> * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html"> * Persistent_cohomology/custom_persistence_sort.cpp</a> - * + * \li <a href="_spatial_searching_2example_spatial_searching_8cpp-example.html"> + * Spatial_searching/example_spatial_searching.cpp</a> + * \li <a href="_subsampling_2example_choose_n_farthest_points_8cpp-example.html"> + * Subsampling/example_choose_n_farthest_points.cpp</a> + * \li <a href="_subsampling_2example_custom_kernel_8cpp-example.html"> + * Subsampling/example_custom_kernel.cpp</a> + * \li <a href="_subsampling_2example_pick_n_random_points_8cpp-example.html"> + * Subsampling/example_pick_n_random_points.cpp</a> + * \li <a href="_subsampling_2example_sparsify_point_set_8cpp-example.html"> + * Subsampling/example_sparsify_point_set.cpp</a> + * \li <a href="_tangential_complex_2example_basic_8cpp-example.html"> + * Tangential_complex/example_basic.cpp</a> + * \li <a href="_tangential_complex_2example_with_perturb_8cpp-example.html"> + * Tangential_complex/example_with_perturb.cpp</a> + * \li <a href="_witness_complex_2strong_witness_persistence_8cpp-example.html"> + * Witness_complex/strong_witness_persistence.cpp</a> + * \li <a href="_witness_complex_2weak_witness_persistence_8cpp-example.html"> + * Witness_complex/weak_witness_persistence.cpp</a> + * \li <a href="_witness_complex_2example_strong_witness_complex_off_8cpp-example.html"> + * Witness_complex/example_strong_witness_complex_off.cpp</a> + * \li <a href="_witness_complex_2example_witness_complex_off_8cpp-example.html"> + * Witness_complex/example_witness_complex_off.cpp</a> + * \li <a href="_witness_complex_2example_witness_complex_sphere_8cpp-example.html"> + * Witness_complex/example_witness_complex_sphere.cpp</a> + * * \subsection tbb Threading Building Blocks * <a target="_blank" href="https://www.threadingbuildingblocks.org/">Intel® TBB</a> lets you easily write parallel * C++ programs that take full advantage of multicore performance, that are portable and composable, and that have @@ -362,27 +414,35 @@ make doxygen * * Having Intel® TBB installed is recommended to parallelize and accelerate some GUDHI computations. * - * The following examples are using Intel® TBB if installed: + * The following examples/utilities are using Intel® TBB if installed: * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html"> * Alpha_complex/Alpha_complex_from_off.cpp</a> * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html"> * Alpha_complex/Alpha_complex_from_points.cpp</a> + * \li <a href="_alpha_complex_2alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html"> + * Alpha_complex/alpha_complex_persistence.cpp</a> + * \li <a href="_alpha_complex_2exact_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/exact_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/weighted_alpha_complex_3d_persistence.cpp</a> * \li <a href="_bitmap_cubical_complex_2_bitmap_cubical_complex_8cpp-example.html"> - * Bitmap_cubical_complex/Bitmap_cubical_complex.cpp</a> + * Bitmap_cubical_complex/cubical_complex_persistence.cpp</a> * \li <a href="_bitmap_cubical_complex_2_bitmap_cubical_complex_periodic_boundary_conditions_8cpp-example.html"> - * Bitmap_cubical_complex/Bitmap_cubical_complex_periodic_boundary_conditions.cpp</a> + * Bitmap_cubical_complex/periodic_cubical_complex_persistence.cpp</a> * \li <a href="_bitmap_cubical_complex_2_random_bitmap_cubical_complex_8cpp-example.html"> * Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp</a> - * \li <a href="_persistent_cohomology_2alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html"> - * Persistent_cohomology/alpha_complex_persistence.cpp</a> * \li <a href="_simplex_tree_2simple_simplex_tree_8cpp-example.html"> * Simplex_tree/simple_simplex_tree.cpp</a> * \li <a href="_simplex_tree_2example_alpha_shapes_3_simplex_tree_from_off_file_8cpp-example.html"> * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a> * \li <a href="_simplex_tree_2simplex_tree_from_cliques_of_graph_8cpp-example.html"> * Simplex_tree/simplex_tree_from_cliques_of_graph.cpp</a> + * \li <a href="_simplex_tree_2graph_expansion_with_blocker_8cpp-example.html"> + * Simplex_tree/graph_expansion_with_blocker.cpp</a> * \li <a href="_persistent_cohomology_2alpha_complex_3d_persistence_8cpp-example.html"> * Persistent_cohomology/alpha_complex_3d_persistence.cpp</a> * \li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html"> @@ -395,27 +455,31 @@ make doxygen * Persistent_cohomology/persistence_from_simple_simplex_tree.cpp</a> * \li <a href="_persistent_cohomology_2plain_homology_8cpp-example.html"> * Persistent_cohomology/plain_homology.cpp</a> - * \li <a href="_persistent_cohomology_2rips_distance_matrix_persistence_8cpp-example.html"> - * Persistent_cohomology/rips_distance_matrix_persistence.cpp</a> * \li <a href="_persistent_cohomology_2rips_multifield_persistence_8cpp-example.html"> * Persistent_cohomology/rips_multifield_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2rips_persistence_8cpp-example.html"> - * Persistent_cohomology/rips_persistence.cpp</a> * \li <a href="_persistent_cohomology_2rips_persistence_step_by_step_8cpp-example.html"> * Persistent_cohomology/rips_persistence_step_by_step.cpp</a> * \li <a href="_persistent_cohomology_2exact_alpha_complex_3d_persistence_8cpp-example.html"> * Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp</a> * \li <a href="_persistent_cohomology_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> * Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/periodic_alpha_complex_3d_persistence.cpp</a> * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html"> * Persistent_cohomology/custom_persistence_sort.cpp</a> * \li <a href="_rips_complex_2example_one_skeleton_rips_from_points_8cpp-example.html"> * Rips_complex/example_one_skeleton_rips_from_points.cpp</a> * \li <a href="_rips_complex_2example_rips_complex_from_off_file_8cpp-example.html"> * Rips_complex/example_rips_complex_from_off_file.cpp</a> - * + * \li <a href="_rips_complex_2rips_distance_matrix_persistence_8cpp-example.html"> + * Rips_complex/rips_distance_matrix_persistence.cpp</a> + * \li <a href="_rips_complex_2rips_persistence_8cpp-example.html"> + * Rips_complex/rips_persistence.cpp</a> + * \li <a href="_witness_complex_2strong_witness_persistence_8cpp-example.html"> + * Witness_complex/strong_witness_persistence.cpp</a> + * \li <a href="_witness_complex_2weak_witness_persistence_8cpp-example.html"> + * Witness_complex/weak_witness_persistence.cpp</a> + * \li <a href="_witness_complex_2example_nearest_landmark_table_8cpp-example.html"> + * Witness_complex/example_nearest_landmark_table.cpp</a> + * * \section Contributions Bug reports and contributions * Please help us improving the quality of the GUDHI library. You may report bugs or suggestions to: * \verbatim Contact: gudhi-users@lists.gforge.inria.fr \endverbatim @@ -439,36 +503,37 @@ make doxygen /*! @file Examples * @example Alpha_complex/Alpha_complex_from_off.cpp * @example Alpha_complex/Alpha_complex_from_points.cpp + * @example Alpha_complex/alpha_complex_3d_persistence.cpp + * @example Alpha_complex/alpha_complex_persistence.cpp + * @example Alpha_complex/exact_alpha_complex_3d_persistence.cpp + * @example Alpha_complex/periodic_alpha_complex_3d_persistence.cpp + * @example Alpha_complex/weighted_alpha_complex_3d_persistence.cpp * @example Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp * @example Bottleneck_distance/bottleneck_basic_example.cpp - * @example Bottleneck_distance/bottleneck_read_file_example.cpp - * @example Bitmap_cubical_complex/Bitmap_cubical_complex.cpp - * @example Bitmap_cubical_complex/Bitmap_cubical_complex_periodic_boundary_conditions.cpp + * @example Bottleneck_distance/bottleneck_distance.cpp + * @example Bitmap_cubical_complex/cubical_complex_persistence.cpp + * @example Bitmap_cubical_complex/periodic_cubical_complex_persistence.cpp * @example Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp * @example common/example_CGAL_3D_points_off_reader.cpp * @example common/example_CGAL_points_off_reader.cpp * @example Contraction/Garland_heckbert.cpp * @example Contraction/Rips_contraction.cpp - * @example Persistent_cohomology/alpha_complex_3d_persistence.cpp - * @example Persistent_cohomology/alpha_complex_persistence.cpp * @example Persistent_cohomology/rips_persistence_via_boundary_matrix.cpp - * @example Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp - * @example Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp - * @example Persistent_cohomology/periodic_alpha_complex_3d_persistence.cpp * @example Persistent_cohomology/persistence_from_file.cpp * @example Persistent_cohomology/persistence_from_simple_simplex_tree.cpp * @example Persistent_cohomology/plain_homology.cpp * @example Persistent_cohomology/rips_multifield_persistence.cpp - * @example Persistent_cohomology/rips_distance_matrix_persistence.cpp - * @example Persistent_cohomology/rips_persistence.cpp * @example Persistent_cohomology/custom_persistence_sort.cpp * @example Persistent_cohomology/rips_persistence_step_by_step.cpp * @example Rips_complex/example_one_skeleton_rips_from_points.cpp * @example Rips_complex/example_rips_complex_from_off_file.cpp + * @example Rips_complex/rips_persistence.cpp + * @example Rips_complex/rips_distance_matrix_persistence.cpp * @example Simplex_tree/mini_simplex_tree.cpp * @example Simplex_tree/simple_simplex_tree.cpp * @example Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp * @example Simplex_tree/simplex_tree_from_cliques_of_graph.cpp + * @example Simplex_tree/graph_expansion_with_blocker.cpp * @example Skeleton_blocker/Skeleton_blocker_from_simplices.cpp * @example Skeleton_blocker/Skeleton_blocker_iteration.cpp * @example Skeleton_blocker/Skeleton_blocker_link.cpp @@ -481,9 +546,9 @@ make doxygen * @example Tangential_complex/example_with_perturb.cpp * @example Witness_complex/example_nearest_landmark_table.cpp * @example Witness_complex/example_strong_witness_complex_off.cpp - * @example Witness_complex/example_strong_witness_persistence.cpp * @example Witness_complex/example_witness_complex_off.cpp - * @example Witness_complex/example_witness_complex_persistence.cpp * @example Witness_complex/example_witness_complex_sphere.cpp + * @example Witness_complex/weak_witness_persistence.cpp + * @example Witness_complex/strong_witness_persistence.cpp */
\ No newline at end of file diff --git a/src/common/include/gudhi/graph_simplicial_complex.h b/src/common/include/gudhi/graph_simplicial_complex.h index 5fe7c826..d84421b2 100644 --- a/src/common/include/gudhi/graph_simplicial_complex.h +++ b/src/common/include/gudhi/graph_simplicial_complex.h @@ -28,6 +28,9 @@ #include <utility> // for pair<> #include <vector> #include <map> +#include <tuple> // for std::tie + +namespace Gudhi { /* Edge tag for Boost PropertyGraph. */ struct edge_filtration_t { @@ -39,4 +42,64 @@ struct vertex_filtration_t { typedef boost::vertex_property_tag kind; }; +template <typename SimplicialComplexForProximityGraph> +using Proximity_graph = typename boost::adjacency_list < boost::vecS, boost::vecS, boost::undirectedS +, boost::property < vertex_filtration_t, typename SimplicialComplexForProximityGraph::Filtration_value > +, boost::property < edge_filtration_t, typename SimplicialComplexForProximityGraph::Filtration_value >>; + +/** \brief Computes the proximity graph of the points. + * + * If points contains n elements, the proximity graph is the graph with n vertices, and an edge [u,v] iff the + * distance function between points u and v is smaller than threshold. + * + * \tparam ForwardPointRange furnishes `.begin()` and `.end()` methods. + * + * \tparam Distance furnishes `operator()(const Point& p1, const Point& p2)`, where + * `Point` is a point from the `ForwardPointRange`, and that returns a `Filtration_value`. + */ +template< typename SimplicialComplexForProximityGraph + , typename ForwardPointRange + , typename Distance > +Proximity_graph<SimplicialComplexForProximityGraph> compute_proximity_graph( + const ForwardPointRange& points, + typename SimplicialComplexForProximityGraph::Filtration_value threshold, + Distance distance) { + using Vertex_handle = typename SimplicialComplexForProximityGraph::Vertex_handle; + using Filtration_value = typename SimplicialComplexForProximityGraph::Filtration_value; + + std::vector<std::pair< Vertex_handle, Vertex_handle >> edges; + std::vector< Filtration_value > edges_fil; + std::map< Vertex_handle, Filtration_value > vertices; + + Vertex_handle idx_u, idx_v; + Filtration_value fil; + idx_u = 0; + for (auto it_u = points.begin(); it_u != points.end(); ++it_u) { + idx_v = idx_u + 1; + for (auto it_v = it_u + 1; it_v != points.end(); ++it_v, ++idx_v) { + fil = distance(*it_u, *it_v); + if (fil <= threshold) { + edges.emplace_back(idx_u, idx_v); + edges_fil.push_back(fil); + } + } + ++idx_u; + } + + // Points are labeled from 0 to idx_u-1 + Proximity_graph<SimplicialComplexForProximityGraph> skel_graph(edges.begin(), edges.end(), edges_fil.begin(), idx_u); + + auto vertex_prop = boost::get(vertex_filtration_t(), skel_graph); + + typename boost::graph_traits<Proximity_graph<SimplicialComplexForProximityGraph>>::vertex_iterator vi, vi_end; + for (std::tie(vi, vi_end) = boost::vertices(skel_graph); + vi != vi_end; ++vi) { + boost::put(vertex_prop, *vi, 0.); + } + + return skel_graph; +} + +} // namespace Gudhi + #endif // GRAPH_SIMPLICIAL_COMPLEX_H_ diff --git a/src/common/include/gudhi/reader_utils.h b/src/common/include/gudhi/reader_utils.h index bda93f4f..90be4fc7 100644 --- a/src/common/include/gudhi/reader_utils.h +++ b/src/common/include/gudhi/reader_utils.h @@ -38,6 +38,8 @@ #include <utility> // for pair #include <tuple> // for std::make_tuple +namespace Gudhi { + // Keep this file tag for Doxygen to parse the code, otherwise, functions are not documented. // It is required for global functions and variables. @@ -362,4 +364,6 @@ inline std::vector<std::pair<double, double>> read_persistence_intervals_in_dime return ret; } +} // namespace Gudhi + #endif // READER_UTILS_H_ diff --git a/src/common/test/test_distance_matrix_reader.cpp b/src/common/test/test_distance_matrix_reader.cpp index 95a73bd9..656e6f2e 100644 --- a/src/common/test/test_distance_matrix_reader.cpp +++ b/src/common/test/test_distance_matrix_reader.cpp @@ -36,7 +36,7 @@ BOOST_AUTO_TEST_CASE( lower_triangular_distance_matrix ) { Distance_matrix from_lower_triangular; // Read lower_triangular_distance_matrix.csv file where the separator is a ',' - from_lower_triangular = read_lower_triangular_matrix_from_csv_file<double>("lower_triangular_distance_matrix.csv", + from_lower_triangular = Gudhi::read_lower_triangular_matrix_from_csv_file<double>("lower_triangular_distance_matrix.csv", ','); for (auto& i : from_lower_triangular) { for (auto j : i) { @@ -69,7 +69,7 @@ BOOST_AUTO_TEST_CASE( full_square_distance_matrix ) { Distance_matrix from_full_square; // Read full_square_distance_matrix.csv file where the separator is the default one ';' - from_full_square = read_lower_triangular_matrix_from_csv_file<double>("full_square_distance_matrix.csv"); + from_full_square = Gudhi::read_lower_triangular_matrix_from_csv_file<double>("full_square_distance_matrix.csv"); for (auto& i : from_full_square) { for (auto j : i) { std::cout << j << " "; diff --git a/src/common/test/test_persistence_intervals_reader.cpp b/src/common/test/test_persistence_intervals_reader.cpp index a06fff1e..be299376 100644 --- a/src/common/test/test_persistence_intervals_reader.cpp +++ b/src/common/test/test_persistence_intervals_reader.cpp @@ -27,6 +27,7 @@ #include <utility> // for pair #include <tuple> #include <limits> // for inf +#include <map> #define BOOST_TEST_DYN_LINK #define BOOST_TEST_MODULE "persistence_intervals_reader" @@ -44,7 +45,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_without_dimension ) expected_intervals_by_dimension[-1].push_back(std::make_pair(3., std::numeric_limits<double>::infinity())); Persistence_intervals_by_dimension persistence_intervals_by_dimension = - read_persistence_intervals_grouped_by_dimension("persistence_intervals_without_dimension.pers"); + Gudhi::read_persistence_intervals_grouped_by_dimension("persistence_intervals_without_dimension.pers"); std::cout << "\nread_persistence_intervals_grouped_by_dimension - expected\n"; for (auto map_iter : expected_intervals_by_dimension) { @@ -69,7 +70,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_without_dimension ) expected_intervals_in_dimension.push_back(std::make_pair(3., std::numeric_limits<double>::infinity())); Persistence_intervals persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_without_dimension.pers"); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_without_dimension.pers"); std::cout << "\nread_persistence_intervals_in_dimension - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) @@ -83,22 +84,22 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_without_dimension ) expected_intervals_in_dimension.clear(); persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_without_dimension.pers", 0); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_without_dimension.pers", 0); BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); expected_intervals_in_dimension.clear(); persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_without_dimension.pers", 1); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_without_dimension.pers", 1); BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); expected_intervals_in_dimension.clear(); persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_without_dimension.pers", 2); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_without_dimension.pers", 2); BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); expected_intervals_in_dimension.clear(); persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_without_dimension.pers", 3); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_without_dimension.pers", 3); BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); } @@ -112,7 +113,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_dimension ) expected_intervals_by_dimension[1].push_back(std::make_pair(3., std::numeric_limits<double>::infinity())); Persistence_intervals_by_dimension persistence_intervals_by_dimension = - read_persistence_intervals_grouped_by_dimension("persistence_intervals_with_dimension.pers"); + Gudhi::read_persistence_intervals_grouped_by_dimension("persistence_intervals_with_dimension.pers"); std::cout << "\nread_persistence_intervals_grouped_by_dimension - expected\n"; for (auto map_iter : expected_intervals_by_dimension) { @@ -137,7 +138,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_dimension ) expected_intervals_in_dimension.push_back(std::make_pair(3., std::numeric_limits<double>::infinity())); Persistence_intervals persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers"); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers"); std::cout << "\nread_persistence_intervals_in_dimension - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) @@ -152,7 +153,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_dimension ) expected_intervals_in_dimension.clear(); expected_intervals_in_dimension.push_back(std::make_pair(2.7, 3.7)); persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers", 0); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers", 0); std::cout << "\nread_persistence_intervals_in_dimension 0 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) @@ -168,7 +169,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_dimension ) expected_intervals_in_dimension.push_back(std::make_pair(9.6, 14.)); expected_intervals_in_dimension.push_back(std::make_pair(3., std::numeric_limits<double>::infinity())); persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers", 1); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers", 1); std::cout << "\nread_persistence_intervals_in_dimension 1 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) @@ -182,7 +183,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_dimension ) expected_intervals_in_dimension.clear(); persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers", 2); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers", 2); std::cout << "\nread_persistence_intervals_in_dimension 2 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) @@ -197,7 +198,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_dimension ) expected_intervals_in_dimension.clear(); expected_intervals_in_dimension.push_back(std::make_pair(34.2, 34.974)); persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers", 3); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers", 3); std::cout << "\nread_persistence_intervals_in_dimension 3 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) @@ -221,7 +222,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_field ) expected_intervals_by_dimension[1].push_back(std::make_pair(3., std::numeric_limits<double>::infinity())); Persistence_intervals_by_dimension persistence_intervals_by_dimension = - read_persistence_intervals_grouped_by_dimension("persistence_intervals_with_field.pers"); + Gudhi::read_persistence_intervals_grouped_by_dimension("persistence_intervals_with_field.pers"); std::cout << "\nread_persistence_intervals_grouped_by_dimension - expected\n"; for (auto map_iter : expected_intervals_by_dimension) { @@ -246,7 +247,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_field ) expected_intervals_in_dimension.push_back(std::make_pair(3., std::numeric_limits<double>::infinity())); Persistence_intervals persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers"); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers"); std::cout << "\nread_persistence_intervals_in_dimension - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) @@ -261,7 +262,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_field ) expected_intervals_in_dimension.clear(); expected_intervals_in_dimension.push_back(std::make_pair(2.7, 3.7)); persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers", 0); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers", 0); std::cout << "\nread_persistence_intervals_in_dimension 0 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) @@ -277,7 +278,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_field ) expected_intervals_in_dimension.push_back(std::make_pair(9.6, 14.)); expected_intervals_in_dimension.push_back(std::make_pair(3., std::numeric_limits<double>::infinity())); persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers", 1); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers", 1); std::cout << "\nread_persistence_intervals_in_dimension 1 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) @@ -291,7 +292,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_field ) expected_intervals_in_dimension.clear(); persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers", 2); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers", 2); std::cout << "\nread_persistence_intervals_in_dimension 2 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) @@ -306,7 +307,7 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_field ) expected_intervals_in_dimension.clear(); expected_intervals_in_dimension.push_back(std::make_pair(34.2, 34.974)); persistence_intervals_in_dimension = - read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers", 3); + Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers", 3); std::cout << "\nread_persistence_intervals_in_dimension 3 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) diff --git a/src/common/utilities/README b/src/common/utilities/README index dc841521..18fa8cc4 100644 --- a/src/common/utilities/README +++ b/src/common/utilities/README @@ -1,19 +1,19 @@ -======================= off_file_from_shape_generator ================================== +# Pointset generator # -Example of use : +## `off_file_from_shape_generator` ## -*** on|in sphere|cube|curve|torus|klein generator +Generates a pointset and save it in an OFF file. Command-line is: +`off_file_from_shape_generator on|in sphere|cube|curve|torus|klein <filename> <num_points> <dimension> <parameter1> <parameter2>...` -./off_file_from_shape_generator on sphere onSphere.off 1000 3 15.2 +Warning: "on cube" generator is not available! - => generates a onSphere.off file with 1000 points randomized on a sphere of dimension 3 and radius 15.2 +Examples: -./off_file_from_shape_generator in sphere inSphere.off 100 2 +* Generate an onSphere.off file with 1000 points randomized on a sphere of dimension 3 and radius 15.2: +`off_file_from_shape_generator on sphere onSphere.off 1000 3 15.2` + +* Generate an inSphere.off file with 100 points randomized in a sphere of dimension 2 (circle) and radius 1.0 (default): +`off_file_from_shape_generator in sphere inSphere.off 100 2` - => generates a inSphere.off file with 100 points randomized in a sphere of dimension 2 (circle) and radius 1.0 (default) - -./off_file_from_shape_generator in cube inCube.off 10000 3 5.8 - - => generates a inCube.off file with 10000 points randomized in a cube of dimension 3 and side 5.8 - -!! Warning: hypegenerator on cube is not available !! +* Generates a inCube.off file with 10000 points randomized in a cube of dimension 3 and side 5.8: +`off_file_from_shape_generator in cube inCube.off 10000 3 5.8` diff --git a/src/common/utilities/off_file_from_shape_generator.cpp b/src/common/utilities/off_file_from_shape_generator.cpp index 0f310a13..afcd558c 100644 --- a/src/common/utilities/off_file_from_shape_generator.cpp +++ b/src/common/utilities/off_file_from_shape_generator.cpp @@ -77,7 +77,7 @@ int main(int argc, char **argv) { usage(argv[0]); } - enum class Data_shape { sphere, cube, curve, torus, klein, undefined } ; + enum class Data_shape { sphere, cube, curve, torus, klein, undefined}; Data_shape shape = Data_shape::undefined; if (memcmp(argv[2], "sphere", sizeof("sphere")) == 0) { diff --git a/src/cython/CMakeLists.txt b/src/cython/CMakeLists.txt index 99badffb..afca9d60 100644 --- a/src/cython/CMakeLists.txt +++ b/src/cython/CMakeLists.txt @@ -15,8 +15,18 @@ function( add_gudhi_cython_lib THE_LIB ) endif(EXISTS ${THE_LIB}) endfunction( add_gudhi_cython_lib ) +# THE_TEST is the python test file name (without .py extension) containing tests functions +function( add_gudhi_py_test THE_TEST ) + # use ${PYTHON_EXECUTABLE} -B, otherwise a __pycache__ directory is created in sources by python + # use py.test no cache provider, otherwise a .cache file is created in sources by py.test + add_test(NAME ${THE_TEST}_py_test + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + COMMAND ${PYTHON_EXECUTABLE} -B -m pytest -p no:cacheprovider ${CMAKE_CURRENT_SOURCE_DIR}/test/${THE_TEST}.py) +endfunction( add_gudhi_py_test ) + + if(CYTHON_FOUND) - message("++ ${PYTHON_EXECUTABLE} v.${PYTHON_VERSION_STRING} - Cython is ${CYTHON_EXECUTABLE} - py.test is ${PYTEST_PATH} - Sphinx is ${SPHINX_PATH}") + message("++ ${PYTHON_EXECUTABLE} v.${PYTHON_VERSION_STRING} - Cython is ${CYTHON_EXECUTABLE} - Sphinx is ${SPHINX_PATH}") set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-DBOOST_RESULT_OF_USE_DECLTYPE', ") set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-DBOOST_ALL_NO_LIB', ") @@ -73,76 +83,20 @@ if(CYTHON_FOUND) set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-DCGAL_EIGEN3_ENABLED', ") endif (EIGEN3_FOUND) - # Copy recursively include, cython, example, doc and test repositories before packages finding - # Some tests and doc files are removed in case some packages are not found - file(COPY include DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) - file(COPY cython DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) - file(COPY example DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) - file(COPY test DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) - file(COPY doc DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) - # Developper version for doc images - file(GLOB GUDHI_DEV_DOC_IMAGES "${CMAKE_SOURCE_DIR}/src/*/doc/*.png") - file(COPY ${GUDHI_DEV_DOC_IMAGES} DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/doc/img") - file(GLOB GUDHI_DEV_DOC_IMAGES "${CMAKE_SOURCE_DIR}/src/*/doc/*.svg") - file(COPY ${GUDHI_DEV_DOC_IMAGES} DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/doc/img") - # User version for doc images - file(GLOB GUDHI_USER_DOC_IMAGES "${CMAKE_SOURCE_DIR}/doc/*/*.png") - file(COPY ${GUDHI_USER_DOC_IMAGES} DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/doc/img") - file(GLOB GUDHI_USER_DOC_IMAGES "${CMAKE_SOURCE_DIR}/doc/*/*.svg") - file(COPY ${GUDHI_USER_DOC_IMAGES} DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/doc/img") - # Biblio - file(GLOB GUDHI_BIB_FILES "${CMAKE_SOURCE_DIR}/biblio/*.bib") - file(COPY ${GUDHI_BIB_FILES} DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/doc/") - # Cubical complex perseus doc example - file(GLOB GUDHI_CUBICAL_PERSEUS_FILES "${CMAKE_SOURCE_DIR}/data/bitmap/*cubicalcomplexdoc.txt") - file(COPY ${GUDHI_CUBICAL_PERSEUS_FILES} DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/doc/") - file(COPY "${CMAKE_SOURCE_DIR}/data/points/alphacomplexdoc.off" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/doc/") - file(COPY "${CMAKE_SOURCE_DIR}/data/distance_matrix/full_square_distance_matrix.csv" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/doc/") - # Persistence graphical tools examples - file(COPY "${CMAKE_SOURCE_DIR}/data/bitmap/3d_torus.txt" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/doc/") - file(COPY "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/doc/") - if (NOT CGAL_VERSION VERSION_LESS 4.8.1) - # If CGAL_VERSION >= 4.8.1, include subsampling - set(GUDHI_CYTHON_SUBSAMPLING "include 'cython/subsampling.pyx'") - set(GUDHI_CYTHON_TANGENTIAL_COMPLEX "include 'cython/tangential_complex.pyx'") - set(GUDHI_CYTHON_BOTTLENECK_DISTANCE "include 'cython/bottleneck_distance.pyx'") - else (NOT CGAL_VERSION VERSION_LESS 4.8.1) - # Remove subsampling unitary tests - file(REMOVE ${CMAKE_CURRENT_BINARY_DIR}/test/test_subsampling.py) - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/subsampling_ref.rst") - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/subsampling_sum.rst") - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/subsampling_user.rst") - # Remove tangential complex and bottleneck unitary tests - file(REMOVE ${CMAKE_CURRENT_BINARY_DIR}/test/test_tangential_complex.py) - file(REMOVE ${CMAKE_CURRENT_BINARY_DIR}/test/test_bottleneck_distance.py) - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/bottleneck_distance_ref.rst") - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/bottleneck_distance_sum.rst") - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/bottleneck_distance_user.rst") - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/tangential_complex_ref.rst") - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/tangential_complex_sum.rst") - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/tangential_complex_user.rst") + set(GUDHI_CYTHON_BOTTLENECK_DISTANCE "include '${CMAKE_CURRENT_SOURCE_DIR}/cython/bottleneck_distance.pyx'") endif (NOT CGAL_VERSION VERSION_LESS 4.8.1) - if (NOT CGAL_VERSION VERSION_LESS 4.7.0) - # If CGAL_VERSION >= 4.7.0, include alpha - set(GUDHI_CYTHON_ALPHA_COMPLEX "include 'cython/alpha_complex.pyx'") - else (NOT CGAL_VERSION VERSION_LESS 4.7.0) - # Remove alpha complex unitary tests - file(REMOVE ${CMAKE_CURRENT_BINARY_DIR}/test/test_alpha_complex.py) - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/alpha_complex_ref.rst") - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/alpha_complex_sum.rst") - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/alpha_complex_user.rst") - endif (NOT CGAL_VERSION VERSION_LESS 4.7.0) - if (NOT CGAL_VERSION VERSION_LESS 4.6.0) - # If CGAL_VERSION >= 4.6.0, include euclidean versions of witness complex + if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + set(GUDHI_CYTHON_SUBSAMPLING "include '${CMAKE_CURRENT_SOURCE_DIR}/cython/subsampling.pyx'") + set(GUDHI_CYTHON_TANGENTIAL_COMPLEX "include '${CMAKE_CURRENT_SOURCE_DIR}/cython/tangential_complex.pyx'") + endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) + set(GUDHI_CYTHON_ALPHA_COMPLEX "include '${CMAKE_CURRENT_SOURCE_DIR}/cython/alpha_complex.pyx'") + endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) + if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) set(GUDHI_CYTHON_EUCLIDEAN_WITNESS_COMPLEX - "include 'cython/euclidean_witness_complex.pyx'\ninclude 'cython/euclidean_strong_witness_complex.pyx'\n") - else (NOT CGAL_VERSION VERSION_LESS 4.6.0) - # Remove alpha complex unitary tests - file(REMOVE ${CMAKE_CURRENT_BINARY_DIR}/test/test_euclidean_witness_complex.py) - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/euclidean_witness_complex_ref.rst") - file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/doc/euclidean_strong_witness_complex_ref.rst") - endif (NOT CGAL_VERSION VERSION_LESS 4.6.0) + "include '${CMAKE_CURRENT_SOURCE_DIR}/cython/euclidean_witness_complex.pyx'\ninclude '${CMAKE_CURRENT_SOURCE_DIR}/cython/euclidean_strong_witness_complex.pyx'\n") + endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) if(CGAL_FOUND) # Add CGAL compilation args @@ -193,10 +147,6 @@ if(CYTHON_FOUND) set( GUDHI_CYTHON_RUNTIME_LIBRARY_DIRS "${GUDHI_CYTHON_LIBRARY_DIRS}") endif(UNIX) - # set sphinx-build in make files - configure_file(doc/Makefile.in "${CMAKE_CURRENT_BINARY_DIR}/doc/Makefile" @ONLY) - configure_file(doc/make.bat.in "${CMAKE_CURRENT_BINARY_DIR}/doc/make.bat" @ONLY) - # Generate setup.py file to cythonize Gudhi - This file must be named setup.py by convention configure_file(setup.py.in "${CMAKE_CURRENT_BINARY_DIR}/setup.py" @ONLY) # Generate gudhi.pyx - Gudhi cython file @@ -218,112 +168,147 @@ if(CYTHON_FOUND) PATTERN "*.pyd") # Test examples - if (NOT CGAL_VERSION VERSION_LESS 4.8.1) + if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + # Bottleneck and Alpha add_test(NAME alpha_rips_persistence_bottleneck_distance_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/example/alpha_rips_persistence_bottleneck_distance.py" + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_rips_persistence_bottleneck_distance.py" -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -t 0.15 -d 3) - set_tests_properties(alpha_rips_persistence_bottleneck_distance_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") - - add_test(NAME bottleneck_basic_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/example/bottleneck_basic_example.py") - set_tests_properties(bottleneck_basic_example_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") + # Tangential add_test(NAME tangential_complex_plain_homology_from_off_file_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/example/tangential_complex_plain_homology_from_off_file_example.py" + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/tangential_complex_plain_homology_from_off_file_example.py" --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off) - set_tests_properties(tangential_complex_plain_homology_from_off_file_example_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") + add_gudhi_py_test(test_tangential_complex) + + # Witness complex AND Subsampling add_test(NAME euclidean_strong_witness_complex_diagram_persistence_from_off_file_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py" + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py" --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 1.0 -n 20 -d 2) - set_tests_properties(euclidean_strong_witness_complex_diagram_persistence_from_off_file_example_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") add_test(NAME euclidean_witness_complex_diagram_persistence_from_off_file_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py" + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py" --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 1.0 -n 20 -d 2) - set_tests_properties(euclidean_witness_complex_diagram_persistence_from_off_file_example_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") + + # Subsampling + add_gudhi_py_test(test_subsampling) + + endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + if (NOT CGAL_VERSION VERSION_LESS 4.8.1) + # Bottleneck + add_test(NAME bottleneck_basic_example_py_test + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/bottleneck_basic_example.py") + + add_gudhi_py_test(test_bottleneck_distance) endif (NOT CGAL_VERSION VERSION_LESS 4.8.1) - if (NOT CGAL_VERSION VERSION_LESS 4.7.0) + if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) + # Alpha add_test(NAME alpha_complex_from_points_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/example/alpha_complex_from_points_example.py") - set_tests_properties(alpha_complex_from_points_example_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_complex_from_points_example.py") add_test(NAME alpha_complex_diagram_persistence_from_off_file_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/example/alpha_complex_diagram_persistence_from_off_file_example.py" + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_complex_diagram_persistence_from_off_file_example.py" --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 0.6) - set_tests_properties(alpha_complex_diagram_persistence_from_off_file_example_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") - endif (NOT CGAL_VERSION VERSION_LESS 4.7.0) - if (NOT CGAL_VERSION VERSION_LESS 4.6.0) - endif (NOT CGAL_VERSION VERSION_LESS 4.6.0) + add_gudhi_py_test(test_alpha_complex) + + endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) + + if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) + # Euclidean witness + add_gudhi_py_test(test_euclidean_witness_complex) + endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) + + # Cubical add_test(NAME periodic_cubical_complex_barcode_persistence_from_perseus_file_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py" + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py" --no-barcode -f ${CMAKE_SOURCE_DIR}/data/bitmap/CubicalTwoSphere.txt) - set_tests_properties(periodic_cubical_complex_barcode_persistence_from_perseus_file_example_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") add_test(NAME random_cubical_complex_persistence_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/example/random_cubical_complex_persistence_example.py" + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/random_cubical_complex_persistence_example.py" 10 10 10) - set_tests_properties(random_cubical_complex_persistence_example_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") + add_gudhi_py_test(test_cubical_complex) + + # Rips add_test(NAME rips_complex_diagram_persistence_from_distance_matrix_file_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py" + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py" --no-diagram -f ${CMAKE_SOURCE_DIR}/data/distance_matrix/lower_triangular_distance_matrix.csv -e 12.0 -d 3) - set_tests_properties(rips_complex_diagram_persistence_from_distance_matrix_file_example_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") add_test(NAME rips_complex_diagram_persistence_from_off_file_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/example/rips_complex_diagram_persistence_from_off_file_example.py + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_diagram_persistence_from_off_file_example.py --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -e 0.25 -d 3) - set_tests_properties(rips_complex_diagram_persistence_from_off_file_example_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") add_test(NAME rips_complex_from_points_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/example/rips_complex_from_points_example.py) - set_tests_properties(rips_complex_from_points_example_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_from_points_example.py) + + add_gudhi_py_test(test_rips_complex) + # Simplex tree add_test(NAME simplex_tree_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/example/simplex_tree_example.py) - set_tests_properties(simplex_tree_example_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/simplex_tree_example.py) + add_gudhi_py_test(test_simplex_tree) + + # Witness add_test(NAME witness_complex_from_nearest_landmark_table_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/example/witness_complex_from_nearest_landmark_table.py) - set_tests_properties(witness_complex_from_nearest_landmark_table_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") - - # Unitary tests are available through py.test - if(PYTEST_PATH) - add_test( - NAME gudhi_cython_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} "${PYTEST_PATH}") - set_tests_properties(gudhi_cython_py_test PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}") - endif(PYTEST_PATH) - - # Documentation generation is available through sphinx - if(SPHINX_PATH) - if (UNIX) - add_custom_target(sphinx - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/doc - DEPENDS "${CMAKE_CURRENT_BINARY_DIR}/gudhi.so" - COMMAND make html doctest) - else (UNIX) - add_custom_target(sphinx - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/doc - COMMAND make.bat html doctest) - endif (UNIX) - endif(SPHINX_PATH) + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/witness_complex_from_nearest_landmark_table.py) + + add_gudhi_py_test(test_witness_complex) + + # Reader utils + add_gudhi_py_test(test_reader_utils) + + # Documentation generation is available through sphinx - requires all modules + if(SPHINX_PATH AND NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + set (GUDHI_SPHINX_MESSAGE "Generating API documentation with Sphinx in ${CMAKE_CURRENT_BINARY_DIR}/sphinx/") + # User warning - Sphinx is a static pages generator, and configured to work fine with user_version + # Images and biblio warnings because not found on developper version + if (GUDHI_CYTHON_PATH STREQUAL "src/cython") + set (GUDHI_SPHINX_MESSAGE "${GUDHI_SPHINX_MESSAGE} \n WARNING : Sphinx is configured for user version, you run it on developper version. Images and biblio will miss") + endif() + # sphinx target requires gudhi.so, because conf.py reads gudhi version from it + add_custom_target(sphinx + WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/doc + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${SPHINX_PATH} -b html ${CMAKE_CURRENT_SOURCE_DIR}/doc ${CMAKE_CURRENT_BINARY_DIR}/sphinx + DEPENDS "${CMAKE_CURRENT_BINARY_DIR}/gudhi.so" + COMMENT "${GUDHI_SPHINX_MESSAGE}" VERBATIM) + + add_test(NAME sphinx_py_test + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${SPHINX_PATH} -b doctest ${CMAKE_CURRENT_SOURCE_DIR}/doc ${CMAKE_CURRENT_BINARY_DIR}/doctest) + + endif(SPHINX_PATH AND NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) endif(CYTHON_FOUND) diff --git a/src/cython/cython/periodic_cubical_complex.pyx b/src/cython/cython/periodic_cubical_complex.pyx index 581c7b69..3025f125 100644 --- a/src/cython/cython/periodic_cubical_complex.pyx +++ b/src/cython/cython/periodic_cubical_complex.pyx @@ -33,7 +33,7 @@ __license__ = "GPL v3" cdef extern from "Cubical_complex_interface.h" namespace "Gudhi": cdef cppclass Periodic_cubical_complex_base_interface "Gudhi::Cubical_complex::Cubical_complex_interface<Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double>>": - Periodic_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells) + Periodic_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells, vector[bool] periodic_dimensions) Periodic_cubical_complex_base_interface(string perseus_file) int num_simplices() int dimension() @@ -58,7 +58,7 @@ cdef class PeriodicCubicalComplex: # Fake constructor that does nothing but documenting the constructor def __init__(self, dimensions=None, top_dimensional_cells=None, - perseus_file=''): + periodic_dimensions=None, perseus_file=''): """PeriodicCubicalComplex constructor from dimensions and top_dimensional_cells or from a Perseus-style file name. @@ -66,6 +66,8 @@ cdef class PeriodicCubicalComplex: :type dimensions: list of int :param top_dimensional_cells: A list of cells filtration values. :type top_dimensional_cells: list of double + :param periodic_dimensions: A list of top dimensional cells periodicity value. + :type periodic_dimensions: list of boolean Or @@ -75,10 +77,10 @@ cdef class PeriodicCubicalComplex: # The real cython constructor def __cinit__(self, dimensions=None, top_dimensional_cells=None, - perseus_file=''): - if (dimensions is not None) and (top_dimensional_cells is not None) and (perseus_file is ''): - self.thisptr = new Periodic_cubical_complex_base_interface(dimensions, top_dimensional_cells) - elif (dimensions is None) and (top_dimensional_cells is None) and (perseus_file is not ''): + periodic_dimensions=None, perseus_file=''): + if (dimensions is not None) and (top_dimensional_cells is not None) and (periodic_dimensions is not None) and (perseus_file is ''): + self.thisptr = new Periodic_cubical_complex_base_interface(dimensions, top_dimensional_cells, periodic_dimensions) + elif (dimensions is None) and (top_dimensional_cells is None) and (periodic_dimensions is None) and (perseus_file is not ''): if os.path.isfile(perseus_file): self.thisptr = new Periodic_cubical_complex_base_interface(str.encode(perseus_file)) else: diff --git a/src/cython/cython/persistence_graphical_tools.py b/src/cython/cython/persistence_graphical_tools.py index a984633e..fb837e29 100755 --- a/src/cython/cython/persistence_graphical_tools.py +++ b/src/cython/cython/persistence_graphical_tools.py @@ -1,11 +1,12 @@ import matplotlib.pyplot as plt import numpy as np +import os """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): Vincent Rouvreau + Author(s): Vincent Rouvreau, Bertrand Michel Copyright (C) 2016 INRIA @@ -23,15 +24,17 @@ import numpy as np along with this program. If not, see <http://www.gnu.org/licenses/>. """ -__author__ = "Vincent Rouvreau" +__author__ = "Vincent Rouvreau, Bertrand Michel" __copyright__ = "Copyright (C) 2016 INRIA" __license__ = "GPL v3" -def __min_birth_max_death(persistence): +def __min_birth_max_death(persistence, band_boot=0.): """This function returns (min_birth, max_death) from the persistence. :param persistence: The persistence to plot. :type persistence: list of tuples(dimension, tuple(birth, death)). + :param band_boot: bootstrap band + :type band_boot: float. :returns: (float, float) -- (min_birth, max_death). """ # Look for minimum birth date and maximum death date for plot optimisation @@ -45,6 +48,8 @@ def __min_birth_max_death(persistence): max_death = float(interval[1][0]) if float(interval[1][0]) < min_birth: min_birth = float(interval[1][0]) + if band_boot > 0.: + max_death += band_boot return (min_birth, max_death) """ @@ -59,7 +64,7 @@ def show_palette_values(alpha=0.6): :param alpha: alpha value in [0.0, 1.0] for horizontal bars (default is 0.6). :type alpha: float. - :returns: plot -- An horizontal bar plot of dimensions color. + :returns: plot the dimension palette values. """ colors = [] for color in palette: @@ -70,18 +75,38 @@ def show_palette_values(alpha=0.6): plt.barh(y_pos, y_pos + 1, align='center', alpha=alpha, color=colors) plt.ylabel('Dimension') plt.title('Dimension palette values') + return plt - plt.show() - -def plot_persistence_barcode(persistence, alpha=0.6): +def plot_persistence_barcode(persistence=[], persistence_file='', alpha=0.6, max_barcodes=0): """This function plots the persistence bar code. :param persistence: The persistence to plot. :type persistence: list of tuples(dimension, tuple(birth, death)). + :param persistence_file: A persistence file style name (reset persistence if both are set). + :type persistence_file: string :param alpha: alpha value in [0.0, 1.0] for horizontal bars (default is 0.6). :type alpha: float. + :param max_barcodes: number of maximal barcodes to be displayed + (persistence will be sorted by life time if max_barcodes is set) + :type max_barcodes: int. :returns: plot -- An horizontal bar plot of persistence. """ + if persistence_file is not '': + if os.path.isfile(persistence_file): + # Reset persistence + persistence = [] + diag = read_persistence_intervals_grouped_by_dimension(persistence_file=persistence_file) + for key in diag.keys(): + for persistence_interval in diag[key]: + persistence.append((key, persistence_interval)) + else: + print("file " + persistence_file + " not found.") + return None + + if max_barcodes > 0 and max_barcodes < len(persistence): + # Sort by life time, then takes only the max_plots elements + persistence = sorted(persistence, key=lambda life_time: life_time[1][1]-life_time[1][0], reverse=True)[:max_barcodes] + (min_birth, max_death) = __min_birth_max_death(persistence) ind = 0 delta = ((max_death - min_birth) / 10.0) @@ -106,18 +131,40 @@ def plot_persistence_barcode(persistence, alpha=0.6): plt.title('Persistence barcode') # Ends plot on infinity value and starts a little bit before min_birth plt.axis([axis_start, infinity, 0, ind]) - plt.show() + return plt -def plot_persistence_diagram(persistence, alpha=0.6): - """This function plots the persistence diagram. +def plot_persistence_diagram(persistence=[], persistence_file='', alpha=0.6, band_boot=0., max_plots=0): + """This function plots the persistence diagram with an optional confidence band. :param persistence: The persistence to plot. :type persistence: list of tuples(dimension, tuple(birth, death)). + :param persistence_file: A persistence file style name (reset persistence if both are set). + :type persistence_file: string :param alpha: alpha value in [0.0, 1.0] for points and horizontal infinity line (default is 0.6). :type alpha: float. - :returns: plot -- An diagram plot of persistence. + :param band_boot: bootstrap band (not displayed if :math:`\leq` 0.) + :type band_boot: float. + :param max_plots: number of maximal plots to be displayed + :type max_plots: int. + :returns: plot -- A diagram plot of persistence. """ - (min_birth, max_death) = __min_birth_max_death(persistence) + if persistence_file is not '': + if os.path.isfile(persistence_file): + # Reset persistence + persistence = [] + diag = read_persistence_intervals_grouped_by_dimension(persistence_file=persistence_file) + for key in diag.keys(): + for persistence_interval in diag[key]: + persistence.append((key, persistence_interval)) + else: + print("file " + persistence_file + " not found.") + return None + + if max_plots > 0 and max_plots < len(persistence): + # Sort by life time, then takes only the max_plots elements + persistence = sorted(persistence, key=lambda life_time: life_time[1][1]-life_time[1][0], reverse=True)[:max_plots] + + (min_birth, max_death) = __min_birth_max_death(persistence, band_boot) ind = 0 delta = ((max_death - min_birth) / 10.0) # Replace infinity values with max_death + delta for diagram to be more @@ -131,6 +178,9 @@ def plot_persistence_diagram(persistence, alpha=0.6): plt.plot(x, x, color='k', linewidth=1.0) plt.plot(x, [infinity] * len(x), linewidth=1.0, color='k', alpha=alpha) plt.text(axis_start, infinity, r'$\infty$', color='k', alpha=alpha) + # bootstrap band + if band_boot > 0.: + plt.fill_between(x, x, x+band_boot, alpha=alpha, facecolor='red') # Draw points in loop for interval in reversed(persistence): @@ -149,4 +199,4 @@ def plot_persistence_diagram(persistence, alpha=0.6): plt.ylabel('Death') # Ends plot on infinity value and starts a little bit before min_birth plt.axis([axis_start, infinity, axis_start, infinity + delta]) - plt.show() + return plt diff --git a/src/cython/cython/persistence_representations_intervals.pyx b/src/cython/cython/persistence_representations_intervals.pyx deleted file mode 100644 index c1cc347d..00000000 --- a/src/cython/cython/persistence_representations_intervals.pyx +++ /dev/null @@ -1,318 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libcpp cimport bool -import os - -""" -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): Pawel Dlotko - - Copyright (C) 2017 Swansea University - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Pawel Dlotko" -__copyright__ = "Copyright (C) 2017 Swansea University" -__license__ = "GPL v3" - - -""" -This is a promisse that there will be a class in this file with the following -function signature. Something like C++ predeclaration. -According to Vincent, most of the tutorials in cython suggest to -separate pre-declaration below with the definition of the method. -Hovewer it seems to create problems, that is why we keep them both here. -""" - - - - -cdef extern from "Persistence_intervals_interface.h" namespace "Gudhi::Persistence_representations": - cdef cppclass Persistence_intervals_interface "Gudhi::Persistence_representations::Persistence_intervals_interface": - Persistence_intervals_interface(const char*, unsigned) - Persistence_intervals_interface(const vector[pair[double, double]] intervals) - pair[double, double] get_x_range() const - pair[double, double] get_y_range() const - vector[double] length_of_dominant_intervals(size_t where_to_cut)const - vector[pair[double, double]] dominant_intervals(size_t where_to_cut) const - vector[size_t] histogram_of_lengths(size_t number_of_bins) const - vector[size_t] cumulative_histogram_of_lengths(size_t number_of_bins) const - vector[double] characteristic_function_of_diagram(double x_min, double x_max, size_t number_of_bins)const - vector[double] cumulative_characteristic_function_of_diagram(double x_min, double x_max, size_t number_of_bins)const - vector[pair[double, size_t]] compute_persistent_betti_numbers()const - double project_to_R(int number_of_function) const - size_t number_of_projections_to_R() const - vector[double] vectorize(int number_of_function) const - size_t number_of_vectorize_functions() const - -""" -make sure that here we call the functions from the intermediate .h file, -with dummy names, so that later below we can use the same names of the -functions as in C++ version. -Over here I need to list all the functions that will be used in the file. -So there should be a list of constructors, methors, etc. -to separate the function, use newline. Put there only C++ signature -""" - - -#convention for python class is PersistenceIntervals instead of -#Persistence_intervals for methods it is def num_simplices(self). -cdef class PersistenceIntervals: - """ - Persistence intrvals is a standard representation of persistent homology. This file provide implementation of a number of operations on persistence diagrams. - """ - cdef Persistence_intervals_interface * thisptr - - #do we need a fake constructor here, as in case of bitmaps?? - #We do need it so that we have a doc for python because the - #documentation only read from __init__, it do not read from - #__cinit__, where __ means private memeber - def __init__(self, vector_of_intervals=None, dimension=None, - file_with_intervals=''): - """Persistence interals is a standard representation of - persistent homology. This file provide implementation of a - number of operations on persistence diagrams. - - :param dimensions: A vector of birth-death pairs. - - Or - - :param Gudhi style file togethr with a dimension of birth-death - pairs to consider. - """ - - #The real cython constructor - def __cinit__(self, vector_of_intervals=None, dimension=None, - file_with_intervals=''): - """ - This is a constructor of a class Persistence_intervals. - It either take text file and a positive integer, or a vector - of pairs. In case of file, each line of the input file is - supposed to contain two numbers of a type double - (or convertible to double) representing the birth and the death - of the persistence interval. If the pairs are not sorted so that - birth <= death, then the constructor will sort then that way. - In case of vector of pairs, it simply accept vector of pair of - doubles. - :param vector_of_intervals -- vector of pairs of doubles with - birth-death pairs. None if we construct it from file. - :type vector of pairs of doubles or None - :param dimension -- diension of intervals to be extracted from file - :type nonnegative integer or None - :param file_with_intervals - a path to Gudhi style file with - persistence interfals. - :type string of None. - """ - if (vector_of_intervals is None) and (file_with_intervals is not ''): - if (dimension is not None): - if os.path.isfile(file_with_intervals): - self.thisptr = new Persistence_intervals_interface(file_with_intervals, dimension) - else: - print("file " + file_with_intervals + " not found.") - else: - self.thisptr = new Persistence_intervals_interface(file_with_intervals) - elif (file_with_intervals is '') and (vector_of_intervals is not None): - self.thisptr = new Persistence_intervals_interface(vector_of_intervals) - else: - print("Persistence interals can be constructed from vector of birth-death pairs, vector_of_intervals or a Gudhi-style file.") - - def __dealloc__(self): - """ - destructor - """ - if self.thisptr != NULL: - del self.thisptr - - #from here on this is my try. Do we need to specify the returned type?? - #no, we do not. - def get_x_range(self): - """ - This procedure returns x-range of a given persistence diagram. - """ - if self.thisptr != NULL: - return self.thisptr.get_x_range() - - def get_y_range(self): - """ - This procedure returns y-range of a given persistence diagram. - """ - if self.thisptr != NULL: - return self.thisptr.get_y_range() - - def length_of_dominant_intervals(self, where_to_cut): - """ - Procedure that compute the vector of lengths of the dominant - (i.e. the longest) persistence intervals. The list is - truncated at the parameter of the call where_to_cut - (set by default to 100). - :param where_to_cut -- number of domiannt intervals to be returned. - :type positive integer. - """ - if (self.thisptr != NULL) and (where_to_cut is not None): - return self.thisptr.length_of_dominant_intervals(where_to_cut) - else: - if (self.thisptr != NULL): - return self.thisptr.dominant_intervals(100) - - def dominant_intervals(self, where_to_cut): - """ - Procedure that compute the vector of the dominant (i.e. the longest) - persistence intervals. The parameter of the procedure (set by default - to 100) is the number of dominant intervals returned by the procedure. - :param where_to_cut -- number of lengths of domiannt intervals to - be returned. - :type positive integer. - """ - if (self.thisptr != NULL) and (where_to_cut is not None): - return self.thisptr.dominant_intervals(where_to_cut) - else: - if (self.thisptr != NULL): - return self.thisptr.dominant_intervals(100) - - def histogram_of_lengths(self, number_of_bins): - """ - Procedure to compute a histogram of interval's length. - A histogram is a block plot. The number of blocks is - determined by the first parameter of the function - (set by default to 10). - For the sake of argument let us assume that the length of the - longest interval is 1 and the number of bins is - 10. In this case the i-th block correspond to a range between - i-1/10 and i10. The vale of a block supported at the interval is - the number of persistence intervals of a length between x_0 - and x_1. - :param where_to_cut -- number of bins in the histogram. - :type positive integer. - """ - if (self.thisptr != NULL) and (number_of_bins is not None): - return self.thisptr.histogram_of_lengths(number_of_bins) - else: - if (self.thisptr != NULL): - return self.thisptr.dominant_intervals(100) - - def cumulative_histogram_of_lengths(self, number_of_bins): - """ - Based on a histogram of intervals lengths computed by the - function histogram_of_lengths H the procedure below - computes the cumulative histogram. The i-th position - of the resulting histogram - is the sum of values of H for the positions from 0 to i. - :param where_to_cut -- number of bins in the histogram. - :type positive integer. - """ - if (self.thisptr != NULL) and (number_of_bins is not None): - return self.thisptr.cumulative_histogram_of_lengths(number_of_bins) - else: - if (self.thisptr != NULL): - return self.thisptr.cumulative_histogram_of_lengths(10) - - def characteristic_function_of_diagram(self, x_min, x_max, number_of_bins): - """ - In this procedure we assume that each barcode is a characteristic - function of a hight equal to its length. The persistence diagram - is a sum of such a functions. The procedure below construct a - function being a sum of the characteristic functions of - persistence intervals. The first two parameters are the range in - which the function is to be computed and the last parameter is - the number of bins in the discretization of the interval - [_min,_max] - :param x_min -- Begin of range of function. - :type real number - :param x_max -- End of range of function. - :type real number - :param number_of_bins -- Number of bins in characteristic function. - :type positive integer - """ - if (self.thisptr != NULL) and (x_min is not None) and (x_max is not None) and (number_of_bins is not None): - return self.thisptr.characteristic_function_of_diagram(x_min, x_max , number_of_bins) - else: - if (self.thisptr != NULL) and (x_min is not None) and (x_max is not None): - return self.thisptr.characteristic_function_of_diagram(x_min, x_max, 10) - - def cumulative_characteristic_function_of_diagram(self, x_min, x_max, number_of_bins): - """ - Cumulative version of the function characteristic_function_of_diagram. - :param x_min -- Begin of range of function. - :type real number - :param x_max -- End of range of function. - :type real number - :param number_of_bins -- Number of bins in characteristic function. - :type positive integer - """ - if (self.thisptr != NULL) and (x_min is not None) and (x_max is not None) and (number_of_bins is not None): - return self.thisptr.cumulative_characteristic_function_of_diagram(x_min, x_max, number_of_bins) - else: - if (self.thisptr != NULL) and (x_min is not None) and (x_max is not None): - return self.thisptr.cumulative_characteristic_function_of_diagram(x_min, x_max, 10) - - def compute_persistent_betti_numbers(self): - """ - Compute the function of persistence Betti numbers. The returned - value is a vector of pair. First element of each - pair is a place where persistence Betti numbers change. - Second element of each pair is the value of Persistence Betti - numbers at that point. - """ - if self.thisptr != NULL: - return self.thisptr.compute_persistent_betti_numbers() - - def project_to_R(self, number_of_function): - """ - This is a simple function projecting the persistence intervals - to a real number. The function we use here is a sum - of squared lengths of intervals. It can be naturally interpreted as - sum of step function, where the step hight it equal to the length - of the interval. At the moment this function is not tested, since - it is quite likely to be changed in the future. Given this, when - using it, keep in mind that it - will be most likely changed in the next versions. - :param number of projection - :type positive integer. - """ - if (self.thisptr != NULL) and (number_of_function is not None): - return self.thisptr.project_to_R(number_of_function) - - def number_of_projections_to_R(self): - """ - The function gives the number of possible projections to R. - This function is required by the - Real_valued_topological_data concept. - """ - if self.thisptr != NULL: - return self.thisptr.number_of_projections_to_R() - - def vectorize(self, number_of_function): - """ - Return a family of vectors obtained from the persistence diagram. - The i-th vector consist of the length of i - dominant persistence intervals. - :param number of function to vectorizes - :type positive integer. - """ - if (self.thisptr != NULL) and (number_of_function is not None): - return self.thisptr.vectorize(number_of_function) - - def number_of_vectorize_functions(self): - """ - This function return the number of functions that allows - vectorization of a persistence diagram. It is required - in a concept Vectorized_topological_data - """ - if (self.thisptr != NULL): - return self.thisptr.number_of_vectorize_functions() diff --git a/src/cython/cython/persistence_representations_landscapes.pyx b/src/cython/cython/persistence_representations_landscapes.pyx deleted file mode 100644 index 39b62439..00000000 --- a/src/cython/cython/persistence_representations_landscapes.pyx +++ /dev/null @@ -1,378 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libcpp cimport bool -from cython.operator cimport dereference as deref -import os -import sys - -"""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): Pawel Dlotko - - Copyright (C) 2017 Swansea University - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Pawel Dlotko" -__copyright__ = "Copyright (C) 2017 Swansea University" -__license__ = "GPL v3" - - - -cdef extern from "Persistence_landscape_interface.h" namespace "Gudhi::Persistence_representations": - cdef cppclass Persistence_landscape_interface "Gudhi::Persistence_representations::Persistence_landscape_interface": - Persistence_landscape_interface() - #Persistence_landscape_interface(vector[pair[double, double]], bool, size_t) - #Persistence_landscape_interface(const char*, size_t , size_t) - void load_landscape_from_file(const char*) - void print_to_file(const char*)const - double compute_integral_of_landscape()const - double compute_integral_of_a_level_of_a_landscape(size_t)const - double compute_integral_of_landscape(double)const - double compute_value_at_a_given_point(unsigned , double)const - double compute_maximum()const - double compute_minimum()const - double compute_norm_of_landscape(double) - Persistence_landscape_interface* new_abs_interface() - size_t size()const - double find_max(unsigned)const - double project_to_R(int)const - size_t number_of_projections_to_R()const - vector[double] vectorize(int)const - size_t number_of_vectorize_functions()const - void compute_average(const vector[Persistence_landscape_interface*]&) - void new_compute_average(const vector[Persistence_landscape_interface*]&) - double distance(const Persistence_landscape_interface&, double) - double compute_scalar_product(const Persistence_landscape_interface&)const - pair[double, double] get_y_range(size_t)const - #************** - #static methods - @staticmethod - Persistence_landscape_interface* construct_from_file( const char*, size_t, size_t) - @staticmethod - Persistence_landscape_interface* construct_from_vector_of_pairs( const vector[pair[double, double]], size_t) - #*************** - - - - -#convention for python class is PersistenceIntervals instead of Persistence_intervals -#for methods it is def num_simplices(self). -cdef class PersistenceLandscapes: - - cdef Persistence_landscape_interface* thisptr - - - -#Can we have only one constructor, or can we have more - def __init__(self, vector_of_intervals=None, dimension=None, file_with_intervals='',number_of_levels=sys.maxsize): - """ - This is a class implementing persistence landscapes data structures. - For theoretical description, please consult <i>Statistical topological - data analysis using persistence landscapes</i>\cite bubenik_landscapes_2015, - and for details of algorithms, A persistence landscapes toolbox for - topological statistics</i>\cite bubenik_dlotko_landscapes_2016. - - Persistence landscapes allow vectorization, computations of distances, - computations of projections to Real, computations of averages and - scalar products. Therefore they implement suitable interfaces. It - implements the following concepts: Vectorized_topological_data, - Topological_data_with_distances, Real_valued_topological_data, - Topological_data_with_averages, Topological_data_with_scalar_product - - Note that at the moment, due to rounding errors during the construction - of persistence landscapes, elements which are different by 0.000005 are - considered the same. If the scale in your persistence diagrams is - comparable to this value, please rescale them before use this code. - """ - - - - def __cinit__(self, vector_of_intervals=None, dimension=None, file_with_intervals='',number_of_levels=sys.maxsize): - """ - This is a constructor of a class PersistenceLandscapes. - It either take text file and a positive integer, or a vector - of pairs. The last optional parameter is the nunmer of levels of - the landscapes to be generated. If not set, all the levels will - be generated. In case of file, each line of the input file is, - supposed to contain two numbers of a type double - (or convertible to double) representing the birth and the death - of the persistence interval. If the pairs are not sorted so that - birth <= death, then the constructor will sort then that way. - In case of vector of pairs, it simply accept vector of pair of - doubles. - :param vector_of_intervals -- vector of pairs of doubles with - birth-death pairs. None if we construct it from file. - :type vector of pairs of doubles or None - :param dimension -- diension of intervals to be extracted from file - :type nonnegative integer or None - :param file_with_intervals - a path to Gudhi style file with - persistence interfals. - :type string of None. - :param number_of_levels - number of levels of landscape to be - generated (if not set, all of the are generated). - :type positive integer - """ - if (vector_of_intervals is None) and (file_with_intervals is not ''): - if (dimension is not None): - if os.path.isfile(file_with_intervals): - #self.thisptr = new Persistence_landscape_interface(file_with_intervals, dimension, number_of_levels) - self.thisptr = Persistence_landscape_interface.construct_from_file(file_with_intervals, dimension, number_of_levels) - else: - print("file " + file_with_intervals + " not found.") - else: - #self.thisptr = new Persistence_landscape_interface(file_with_intervals, number_of_levels) - self.thisptr = Persistence_landscape_interface.construct_from_file(file_with_intervals,0, number_of_levels) - elif (file_with_intervals is '') and (vector_of_intervals is not None): - #self.thisptr = new Persistence_landscape_interface(vector_of_intervals, true, number_of_levels) - self.thisptr = Persistence_landscape_interface.construct_from_vector_of_pairs(vector_of_intervals, number_of_levels) - else: - print("Persistence interals can be constructed from vector of birth-death pairs, vector_of_intervals or a Gudhi-style file.") - self.thisptr = new Persistence_landscape_interface() - - def __dealloc__(self): - """ - destructor - """ - if self.thisptr != NULL: - del self.thisptr - - def load_landscape_from_file(self,filename): - """ - This procedure loads a landscape from file. It erase all the data - that was previously stored in this landscape. - :param Name of the file. - :type String - """ - if ( self.thisptr != NULL ) and ( filename is not None ): - self.thisptr.load_landscape_from_file(filename) - - def print_to_file(self,filename) : - """ - The procedure stores a landscape to a file. The file can be later - used by a procedure load_landscape_from_file. - :param Name of the file. - :type String - """ - if ( self.thisptr != NULL ) and ( filename is not None ): - self.thisptr.print_to_file(filename) - - def compute_integral_of_landscape(self): - """ - This function compute integral of the landscape (defined formally as - sum of integrals on R of all landscape functions) - """ - if ( self.thisptr != NULL ): - return self.thisptr.compute_integral_of_landscape() - - def compute_integral_of_a_level_of_a_landscape(self,level): - """ - This function compute integral of the 'level'-level of a landscape. - :param Level of the landscape, n, so that the integral of lambda_n is - computed - :type nonnegative integer. - """ - if ( self.thisptr != NULL ) and ( level is not None ): - return self.thisptr.compute_integral_of_landscape(level) - - def compute_integral_of_landscape(self,p): - """ - This function compute integral of the landscape p-th power of a - landscape (defined formally as sum of integrals on R of p-th powers - of all landscape functions) - :param An positive real p such that the integral of p-th power of - landscape is computed. - :type Real value - """ - if ( self.thisptr != NULL ) and ( p is not None ): - return self.thisptr.compute_integral_of_landscape(p) - - def compute_value_at_a_given_point(self, level, x): - """ - A function that computes the value of a landscape at a given point. - The parameters of the function are: unsigned - level and double x. - The procedure will compute the value of the level-landscape at the - point x. - param: level n of lanscape (positive integer) and real number x. - The value \lambda_n(x) is - type: nonnegative integer - :param A real number x. The value \lambda_n(x) is computed. - :type real - """ - if ( self.thisptr != NULL ) and ( level is not None ) and ( x is not None ): - return self.thisptr.compute_value_at_a_given_point(level,x) - - def compute_maximum( self ): - """ - Computations of maximum (y) value of landscape. - """ - if ( self.thisptr != NULL ): - return self.thisptr.compute_maximum() - - def compute_minimum( self ): - """ - Computations of minimum (y) value of landscape. - """ - if ( self.thisptr != NULL ): - return self.thisptr.compute_minimum() - - def compute_norm_of_landscape(self,i): - """ - Computations of a \f$L^i\f$ norm of landscape, where i is the input parameter. - :type integer. - :param i - """ - if ( self.thisptr != NULL ) and ( i is not None ): - return self.thisptr.compute_norm_of_landscape(i) - - def abs( self ): - """ - Function to compute absolute value of a PL function. The representation - of persistence landscapes allow to store - general PL-function. When computing distance between two landscapes, - we compute difference between - them. In this case, a general PL-function with negative value can - appear as a result. Then in order to compute - distance, we need to take its absolute value. This is the purpose of - this procedure. - """ - if ( self.thisptr != NULL ): - abs_ = PersistenceLandscapes() - abs_.thisptr = self.thisptr.new_abs_interface() - return abs_ - - - def size( self ): - """ - Computes the number of landscape functions. - """ - if ( self.thisptr != NULL ): - return self.thisptr.size() - - def find_max(self, lambda_): - """ - Compute maximal value of lambda-level landscape. - :param level of landscape - :type nonnegative integer - """ - if ( self.thisptr != NULL ) and ( lambda_ is not None ): - return self.thisptr.find_max(lambda_) - - def project_to_R(self, number_of_function): - """ - The number of projections to R is defined to the number of nonzero - landscape functions. I-th projection is an - integral of i-th landscape function over whole R. - This function is required by the Real_valued_topological_data concept. - At the moment this function is not tested, since it is quite likely - to be changed in the future. Given this, when - using it, keep in mind that it - will be most likely changed in the next versions. - :param number of function - :type nonnegative integer - """ - if ( self.thisptr != NULL ) and ( number_of_function is not None ): - return self.thisptr.project_to_R(number_of_function) - - def number_of_projections_to_R(self): - """ - The function gives the number of possible projections to R. This - function is required by the - Real_valued_topological_data concept - """ - if ( self.thisptr != NULL ): - return self.thisptr.number_of_projections_to_R() - - def vectorize(self, number_of_function): - """ - This function produce a vector of doubles based on a landscape. It - is required in a concept - Vectorized_topological_data - :param number of function - :type nonnegative intege - """ - if ( self.thisptr != NULL ) and ( number_of_function is not None ): - return self.thisptr.vectorize(number_of_function) - - def number_of_vectorize_functions(self): - """ - The number of projections to R is defined to the number of nonzero - landscape functions. I-th projection is an - integral of i-th landscape function over whole R. - This function is required by the Real_valued_topological_data concept. - At the moment this function is not tested, since it is quite likely - to be changed in the future. Given this, when - using it, keep in mind that it - will be most likely changed in the next versions - """ - if ( self.thisptr != NULL ): - return self.thisptr.number_of_vectorize_functions() - - def compute_average( self,to_average=[] ): - """ - A function to compute averaged persistence landscape, based on vector - of persistence landscapes. - This function is required by Topological_data_with_averages concept. - :param vector of persistence landscapes to average - :type vectors of references to persistence landscapes - """ - #TODO -- add a check if all objects in the to_average are of the same type. - cdef vector[Persistence_landscape_interface*] cpp_list - if ( self.thisptr != NULL ) and ( to_average is not None ): - for elt in to_average: - cpp_list.push_back((<PersistenceLandscapes>elt).thisptr) - self.thisptr.new_compute_average( cpp_list ) - - - - def distance(self, PersistenceLandscapes second, power): - """ - A function to compute distance between persistence landscape. - The parameter of this function is a Persistence_landscape. - This function is required in Topological_data_with_distances concept. - For max norm distance, set power to std::numeric_limits<double>::max() - :param the landascape to compute distance to - :type PersistenceLandscape - """ - if ( self.thisptr != NULL ) and ( second is not None ) and ( power is not None ): - return self.thisptr.distance( deref(second.thisptr), power) - - def compute_scalar_product(self, PersistenceLandscapes second): - """ - A function to compute scalar product of persistence landscapes. - The parameter of this function is a Persistence_landscape. - This function is required in Topological_data_with_scalar_product concept. - :param the landascape to compute scalar product with - :type PersistenceLandscape - """ - if ( self.thisptr != NULL ) and ( second is not None ): - return self.thisptr.compute_scalar_product( deref(second.thisptr) ) - - def get_y_range(self, level): - """ - This procedure returns y-range of a given level persistence landscape. - If a default value is used, the y-range - of 0th level landscape is given (and this range contains the ranges - of all other landscapes). - :param The level of lrandscape - :type nonnegative integer - """ - if ( self.thisptr != NULL ): - return self.thisptr.get_y_range(level) - diff --git a/src/cython/cython/persistence_representations_landscapes_on_grid.pyx b/src/cython/cython/persistence_representations_landscapes_on_grid.pyx deleted file mode 100644 index f69496cd..00000000 --- a/src/cython/cython/persistence_representations_landscapes_on_grid.pyx +++ /dev/null @@ -1,388 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libcpp cimport bool -from cython.operator cimport dereference as deref -import os -import sys - -"""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): Pawel Dlotko - - Copyright (C) 2017 Swansea University - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Pawel Dlotko" -__copyright__ = "Copyright (C) 2017 Swansea University" -__license__ = "GPL v3" - - - -cdef extern from "Persistence_landscape_on_grid_interface.h" namespace "Gudhi::Persistence_representations": - cdef cppclass Persistence_landscape_on_grid_interface "Gudhi::Persistence_representations::Persistence_landscape_on_grid_interface": - Persistence_landscape_on_grid_interface() - Persistence_landscape_on_grid_interface(vector[pair[double, double]], double grid_min_, double grid_max_, size_t number_of_points_) - Persistence_landscape_on_grid_interface(vector[pair[double, double]], double grid_min_, double grid_max_, size_t number_of_points_, unsigned number_of_levels_of_landscape) - Persistence_landscape_on_grid_interface(const char* filename, double grid_min_, double grid_max_, size_t number_of_points_, - unsigned number_of_levels_of_landscape, unsigned) - Persistence_landscape_on_grid_interface(const char* filename, double grid_min_, double grid_max_, size_t number_of_points_, - unsigned dimension_) - Persistence_landscape_on_grid_interface(const char* filename, size_t number_of_points, unsigned number_of_levels_of_landscape, unsigned dimension) - Persistence_landscape_on_grid_interface(const char* filename, size_t number_of_points, unsigned dimension) - void load_landscape_from_file(const char*) - void print_to_file(const char*)const - double compute_integral_of_landscape()const - double compute_integral_of_a_level_of_a_landscape(size_t)const - double compute_integral_of_landscape(double)const - double compute_value_at_a_given_point(unsigned , double)const - double compute_maximum()const - double compute_minimum()const - double compute_norm_of_landscape(double) - Persistence_landscape_on_grid_interface* new_abs_interface() - size_t size()const - double find_max(unsigned)const - double project_to_R(int)const - size_t number_of_projections_to_R()const - vector[double] vectorize(int)const - size_t number_of_vectorize_functions()const - void compute_average(const vector[Persistence_landscape_on_grid_interface*]&) - void new_compute_average(const vector[Persistence_landscape_on_grid_interface*]&) - double distance(Persistence_landscape_on_grid_interface&, double) - double compute_scalar_product(const Persistence_landscape_on_grid_interface&)const - pair[double, double] get_y_range(size_t)const - - - - -#convention for python class is PersistenceIntervals instead of Persistence_intervals -#for methods it is def num_simplices(self). -cdef class PersistenceLandscapesOnGrid: - - cdef Persistence_landscape_on_grid_interface* thisptr - -#Can we have only one constructor, or can we have more - def __init__(self, grid_min_, grid_max_,number_of_points_, file_with_intervals='', - vector_of_intervals=None, dimension=None,number_of_levels=sys.maxsize): - """ - This is a class implementing persistence landscapes data structures. - For theoretical description, please consult <i>Statistical topological - data analysis using persistence landscapes</i>\cite bubenik_landscapes_2015, - and for details of algorithms, A persistence landscapes toolbox for - topological statistics</i>\cite bubenik_dlotko_landscapes_2016. - - Persistence landscapes allow vectorization, computations of distances, - computations of projections to Real, computations of averages and - scalar products. Therefore they implement suitable interfaces. It - implements the following concepts: Vectorized_topological_data, - Topological_data_with_distances, Real_valued_topological_data, - Topological_data_with_averages, Topological_data_with_scalar_product - - Note that at the moment, due to rounding errors during the construction - of persistence landscapes, elements which are different by 0.000005 are - considered the same. If the scale in your persistence diagrams is - comparable to this value, please rescale them before use this code. - """ - - - - def __cinit__(self, grid_min_, grid_max_,number_of_points_, file_with_intervals='', - vector_of_intervals=None, dimension=None,number_of_levels=sys.maxsize): - """ - This is a constructor of a class PersistenceLandscapes. - It either take text file and a positive integer, or a vector - of pairs. The last optional parameter is the nunmer of levels of - the landscapes to be generated. If not set, all the levels will - be generated. In case of file, each line of the input file is, - supposed to contain two numbers of a type double - (or convertible to double) representing the birth and the death - of the persistence interval. If the pairs are not sorted so that - birth <= death, then the constructor will sort then that way. - In case of vector of pairs, it simply accept vector of pair of - doubles. - :param vector_of_intervals -- vector of pairs of doubles with - birth-death pairs. None if we construct it from file. - :type vector of pairs of doubles or None - :param dimension -- diension of intervals to be extracted from file - :type nonnegative integer or None - :param file_with_intervals - a path to Gudhi style file with - persistence interfals. - :type string of None. - :param grid_min_ - minumum of the grid to be cosntructed. - :type double - :param grid_max_ - maximum of the grid to be cosntructed. - :type double - :param number_of_points_ - number of points in the grid. - :type positive integer - :param number_of_levels - number of levels of landscape to be - generated (if not set, all of the are generated). - :type positive integer - """ - if ( (grid_min_ is None) or ( grid_max_ is None ) or ( number_of_points_ is None ) ): - print "Please provide parameters of the grid in order to construct the persistence landscape on a grid." - else: - if (vector_of_intervals is None) and (file_with_intervals is not ''): - if (dimension is not None): - if os.path.isfile(file_with_intervals): - self.thisptr = new Persistence_landscape_on_grid_interface(file_with_intervals, dimension, grid_min_, grid_max_,number_of_points_, number_of_levels) - else: - print("file " + file_with_intervals + " not found.") - else: - self.thisptr = new Persistence_landscape_on_grid_interface(file_with_intervals,0, grid_min_, grid_max_,number_of_points_, number_of_levels) - else: - if (file_with_intervals is '') and (vector_of_intervals is not None): - self.thisptr = new Persistence_landscape_on_grid_interface(vector_of_intervals, grid_min_, grid_max_, number_of_points_,number_of_levels) - #Persistence_landscape_on_grid_interface(vector_of_intervals, grid_min_, grid_max_,number_of_points_, number_of_levels) - else: - print("Persistence interals can be constructed from vector of birth-death pairs, vector_of_intervals or a Gudhi-style file.") - - -#Persistence_landscape_on_grid_interface(const vector[pair[double, double]], double grid_min_, double grid_max_, size_t number_of_points_, unsigned number_of_levels_of_landscape) - - - - def __dealloc__(self): - """ - destructor - """ - if self.thisptr != NULL: - del self.thisptr - - def load_landscape_from_file(self,filename): - """ - This procedure loads a landscape from file. It erase all the data - that was previously stored in this landscape. - :param Name of the file. - :type String - """ - if ( self.thisptr != NULL ) and ( filename is not None ): - self.thisptr.load_landscape_from_file(filename) - - def print_to_file(self,filename) : - """ - The procedure stores a landscape to a file. The file can be later - used by a procedure load_landscape_from_file. - :param Name of the file. - :type String - """ - if ( self.thisptr != NULL ) and ( filename is not None ): - self.thisptr.print_to_file(filename) - - def compute_integral_of_landscape(self): - """ - This function compute integral of the landscape (defined formally as - sum of integrals on R of all landscape functions) - """ - if ( self.thisptr != NULL ): - return self.thisptr.compute_integral_of_landscape() - - def compute_integral_of_a_level_of_a_landscape(self,level): - """ - This function compute integral of the 'level'-level of a landscape. - :param Level of the landscape, n, so that the integral of lambda_n is - computed - :type nonnegative integer. - """ - if ( self.thisptr != NULL ) and ( level is not None ): - return self.thisptr.compute_integral_of_landscape(level) - - def compute_integral_of_landscape(self,p): - """ - This function compute integral of the landscape p-th power of a - landscape (defined formally as sum of integrals on R of p-th powers - of all landscape functions) - :param An positive real p such that the integral of p-th power of - landscape is computed. - :type Real value - """ - if ( self.thisptr != NULL ) and ( p is not None ): - return self.thisptr.compute_integral_of_landscape(p) - - def compute_value_at_a_given_point(self, level, x): - """ - A function that computes the value of a landscape at a given point. - The parameters of the function are: unsigned - level and double x. - The procedure will compute the value of the level-landscape at the - point x. - param: level n of lanscape (positive integer) and real number x. - The value \lambda_n(x) is - type: nonnegative integer - :param A real number x. The value \lambda_n(x) is computed. - :type real - """ - if ( self.thisptr != NULL ) and ( level is not None ) and ( x is not None ): - return self.thisptr.compute_value_at_a_given_point(level,x) - - def compute_maximum( self ): - """ - Computations of maximum (y) value of landscape. - """ - if ( self.thisptr != NULL ): - return self.thisptr.compute_maximum() - - def compute_minimum( self ): - """ - Computations of minimum (y) value of landscape. - """ - if ( self.thisptr != NULL ): - return self.thisptr.compute_minimum() - - def compute_norm_of_landscape(self,i): - """ - Computations of a \f$L^i\f$ norm of landscape, where i is the input parameter. - :type integer. - :param i - """ - if ( self.thisptr != NULL ) and ( i is not None ): - return self.thisptr.compute_norm_of_landscape(i) - - def abs( self ): - """ - Function to compute absolute value of a PL function. The representation - of persistence landscapes allow to store - general PL-function. When computing distance between two landscapes, - we compute difference between - them. In this case, a general PL-function with negative value can - appear as a result. Then in order to compute - distance, we need to take its absolute value. This is the purpose of - this procedure. - """ - if ( self.thisptr != NULL ): - abs_ = PersistenceLandscapesOnGrid() - abs_.thisptr = self.thisptr.new_abs_interface() - return abs_ - - - def size( self ): - """ - Computes the number of landscape functions. - """ - if ( self.thisptr != NULL ): - return self.thisptr.size() - - def find_max(self, lambda_): - """ - Compute maximal value of lambda-level landscape. - :param level of landscape - :type nonnegative integer - """ - if ( self.thisptr != NULL ) and ( lambda_ is not None ): - return self.thisptr.find_max(lambda_) - - def project_to_R(self, number_of_function): - """ - The number of projections to R is defined to the number of nonzero - landscape functions. I-th projection is an - integral of i-th landscape function over whole R. - This function is required by the Real_valued_topological_data concept. - At the moment this function is not tested, since it is quite likely - to be changed in the future. Given this, when - using it, keep in mind that it - will be most likely changed in the next versions. - :param number of function - :type nonnegative integer - """ - if ( self.thisptr != NULL ) and ( number_of_function is not None ): - return self.thisptr.project_to_R(number_of_function) - - def number_of_projections_to_R(self): - """ - The function gives the number of possible projections to R. This - function is required by the - Real_valued_topological_data concept - """ - if ( self.thisptr != NULL ): - return self.thisptr.number_of_projections_to_R() - - def vectorize(self, number_of_function): - """ - This function produce a vector of doubles based on a landscape. It - is required in a concept - Vectorized_topological_data - :param number of function - :type nonnegative intege - """ - if ( self.thisptr != NULL ) and ( number_of_function is not None ): - return self.thisptr.vectorize(number_of_function) - - def number_of_vectorize_functions(self): - """ - The number of projections to R is defined to the number of nonzero - landscape functions. I-th projection is an - integral of i-th landscape function over whole R. - This function is required by the Real_valued_topological_data concept. - At the moment this function is not tested, since it is quite likely - to be changed in the future. Given this, when - using it, keep in mind that it - will be most likely changed in the next versions - """ - if ( self.thisptr != NULL ): - return self.thisptr.number_of_vectorize_functions() - - def compute_average( self,to_average=[] ): - """ - A function to compute averaged persistence landscape, based on vector - of persistence landscapes. - This function is required by Topological_data_with_averages concept. - :param vector of persistence landscapes to average - :type vectors of references to persistence landscapes - """ - cdef vector[Persistence_landscape_on_grid_interface*] cpp_list - if ( self.thisptr != NULL ) and ( to_average is not None ): - for elt in to_average: - cpp_list.push_back((<PersistenceLandscapesOnGrid>elt).thisptr) - self.thisptr.new_compute_average( cpp_list ) - - - - def distance(self, PersistenceLandscapesOnGrid second, power): - """ - A function to compute distance between persistence landscape. - The parameter of this function is a Persistence_landscape. - This function is required in Topological_data_with_distances concept. - For max norm distance, set power to numeric_limits<double>::max() - :param the landascape to compute distance to - :type PersistenceLandscape - """ - if ( self.thisptr != NULL ) and ( second is not None ) and ( power is not None ): - return self.thisptr.distance(deref(second.thisptr), power) - - def compute_scalar_product(self, PersistenceLandscapesOnGrid second): - """ - A function to compute scalar product of persistence landscapes. - The parameter of this function is a Persistence_landscape. - This function is required in Topological_data_with_scalar_product concept. - :param the landascape to compute scalar product with - :type PersistenceLandscape - """ - if ( self.thisptr != NULL ) and ( second is not None ): - return self.thisptr.compute_scalar_product( deref(second.thisptr) ) - - def get_y_range(self, level): - """ - This procedure returns y-range of a given level persistence landscape. - If a default value is used, the y-range - of 0th level landscape is given (and this range contains the ranges - of all other landscapes). - :param The level of lrandscape - :type nonnegative integer - """ - if ( self.thisptr != NULL ): - return self.thisptr.get_y_range(level) - diff --git a/src/cython/cython/reader_utils.pyx b/src/cython/cython/reader_utils.pyx new file mode 100644 index 00000000..3a17c5a0 --- /dev/null +++ b/src/cython/cython/reader_utils.pyx @@ -0,0 +1,95 @@ +from cython cimport numeric +from libcpp.vector cimport vector +from libcpp.string cimport string +from libcpp.map cimport map +from libcpp.pair cimport pair +import os + +"""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): Vincent Rouvreau + + Copyright (C) 2017 INRIA + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see <http://www.gnu.org/licenses/>. +""" + +__author__ = "Vincent Rouvreau" +__copyright__ = "Copyright (C) 2017 INRIA" +__license__ = "GPL v3" + +cdef extern from "Reader_utils_interface.h" namespace "Gudhi": + vector[vector[double]] read_matrix_from_csv_file(string off_file, char separator) + map[int, vector[pair[double, double]]] read_pers_intervals_grouped_by_dimension(string filename) + vector[pair[double, double]] read_pers_intervals_in_dimension(string filename, int only_this_dim) + +def read_lower_triangular_matrix_from_csv_file(csv_file='', separator=';'): + """Read lower triangular matrix from a CSV style file. + + :param csv_file: A CSV file style name. + :type csv_file: string + :param separator: The value separator in the CSV file. Default value is ';' + :type separator: char + + :returns: The lower triangular matrix. + :rtype: vector[vector[double]] + """ + if csv_file is not '': + if os.path.isfile(csv_file): + return read_matrix_from_csv_file(str.encode(csv_file), ord(separator[0])) + print("file " + csv_file + " not set or not found.") + return [] + +def read_persistence_intervals_grouped_by_dimension(persistence_file=''): + """Reads a file containing persistence intervals. + Each line might contain 2, 3 or 4 values: [[field] dimension] birth death + The return value is an `map[dim, vector[pair[birth, death]]]` + where `dim` is an `int`, `birth` a `double`, and `death` a `double`. + Note: the function does not check that birth <= death. + + :param persistence_file: A persistence file style name. + :type persistence_file: string + + :returns: The persistence pairs grouped by dimension. + :rtype: map[int, vector[pair[double, double]]] + """ + if persistence_file is not '': + if os.path.isfile(persistence_file): + return read_pers_intervals_grouped_by_dimension(str.encode(persistence_file)) + print("file " + persistence_file + " not set or not found.") + return [] + +def read_persistence_intervals_in_dimension(persistence_file='', only_this_dim=-1): + """Reads a file containing persistence intervals. + Each line might contain 2, 3 or 4 values: [[field] dimension] birth death + If `only_this_dim` = -1, dimension is ignored and all lines are returned. + If `only_this_dim` is >= 0, only the lines where dimension = `only_this_dim` + (or where dimension is not specified) are returned. + The return value is an `vector[pair[birth, death]]` + where `birth` a `double`, and `death` a `double`. + Note: the function does not check that birth <= death. + + :param persistence_file: A persistence file style name. + :type persistence_file: string + + :returns: The persistence pairs grouped by dimension. + :rtype: map[int, vector[pair[double, double]]] + """ + if persistence_file is not '': + if os.path.isfile(persistence_file): + return read_pers_intervals_in_dimension(str.encode(persistence_file), only_this_dim) + print("file " + persistence_file + " not set or not found.") + return [] diff --git a/src/cython/cython/simplex_tree.pyx b/src/cython/cython/simplex_tree.pyx index 2acdac3c..8a436619 100644 --- a/src/cython/cython/simplex_tree.pyx +++ b/src/cython/cython/simplex_tree.pyx @@ -2,6 +2,7 @@ from cython cimport numeric from libcpp.vector cimport vector from libcpp.utility cimport pair from libcpp cimport bool +from libcpp.string cimport string """This file is part of the Gudhi Library. The Gudhi library (Geometric Understanding in Higher Dimensions) is a generic C++ @@ -35,14 +36,14 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": cdef cppclass Simplex_tree_interface_full_featured "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>": Simplex_tree() - double filtration() double simplex_filtration(vector[int] simplex) - void set_filtration(double filtration) + void assign_simplex_filtration(vector[int] simplex, double filtration) void initialize_filtration() int num_vertices() int num_simplices() void set_dimension(int dimension) int dimension() + int upper_bound_dimension() bint find_simplex(vector[int] simplex) bint insert_simplex_and_subfaces(vector[int] simplex, double filtration) @@ -51,8 +52,9 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": vector[pair[vector[int], double]] get_star(vector[int] simplex) vector[pair[vector[int], double]] get_cofaces(vector[int] simplex, int dimension) - void remove_maximal_simplex(vector[int] simplex) void expansion(int max_dim) + void remove_maximal_simplex(vector[int] simplex) + bool prune_above_filtration(double filtration) cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi": cdef cppclass Simplex_tree_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_full_featured>>": @@ -61,6 +63,7 @@ cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi": vector[int] betti_numbers() vector[int] persistent_betti_numbers(double from_value, double to_value) vector[pair[double,double]] intervals_in_dimension(int dimension) + void write_output_diagram(string diagram_file_name) # SimplexTree python interface cdef class SimplexTree: @@ -113,13 +116,16 @@ cdef class SimplexTree: """ return self.thisptr.simplex_filtration(simplex) - def set_filtration(self, filtration): - """This function sets the main simplicial complex filtration value. + def assign_filtration(self, simplex, filtration): + """This function assigns the simplicial complex filtration value for a + given N-simplex. - :param filtration: The filtration value. - :type filtration: float. + :param simplex: The N-simplex, represented by a list of vertex. + :type simplex: list of int. + :param filtration: The simplicial complex filtration value. + :type filtration: float """ - self.thisptr.set_filtration(<double> filtration) + self.thisptr.assign_simplex_filtration(simplex, filtration) def initialize_filtration(self): """This function initializes and sorts the simplicial complex @@ -127,9 +133,14 @@ cdef class SimplexTree: .. note:: - This function must be launched before persistence, betti_numbers, - persistent_betti_numbers or get_filtration after inserting or - removing simplices. + This function must be launched before + :func:`persistence()<gudhi.SimplexTree.persistence>`, + :func:`betti_numbers()<gudhi.SimplexTree.betti_numbers>`, + :func:`persistent_betti_numbers()<gudhi.SimplexTree.persistent_betti_numbers>`, + or :func:`get_filtration()<gudhi.SimplexTree.get_filtration>` + after :func:`inserting<gudhi.SimplexTree.insert>` or + :func:`removing<gudhi.SimplexTree.remove_maximal_simplex>` + simplices. """ self.thisptr.initialize_filtration() @@ -156,21 +167,42 @@ cdef class SimplexTree: :returns: the simplicial complex dimension. :rtype: int + + .. note:: + + This function is not constant time because it can recompute + dimension if required (can be triggered by + :func:`remove_maximal_simplex()<gudhi.SimplexTree.remove_maximal_simplex>` + or + :func:`prune_above_filtration()<gudhi.SimplexTree.prune_above_filtration>` + methods). """ return self.thisptr.dimension() - def set_dimension(self, dimension): - """This function sets the dimension of the simplicial complex. + def upper_bound_dimension(self): + """This function returns a valid dimension upper bound of the + simplicial complex. - insert and remove_maximal_simplex functions do not update dimension - value of the `SimplexTree`. + :returns: an upper bound on the dimension of the simplicial complex. + :rtype: int + """ + return self.thisptr.upper_bound_dimension() - `AlphaComplex`, `RipsComplex`, `TangentialComplex` and `WitnessComplex` - automatically sets the correct dimension in their `create_simplex_tree` - functions. + def set_dimension(self, dimension): + """This function sets the dimension of the simplicial complex. :param dimension: The new dimension value. :type dimension: int. + + .. note:: + + This function must be used with caution because it disables + dimension recomputation when required + (this recomputation can be triggered by + :func:`remove_maximal_simplex()<gudhi.SimplexTree.remove_maximal_simplex>` + or + :func:`prune_above_filtration()<gudhi.SimplexTree.prune_above_filtration>` + ). """ self.thisptr.set_dimension(<int>dimension) @@ -294,9 +326,57 @@ cdef class SimplexTree: :param simplex: The N-simplex, represented by a list of vertex. :type simplex: list of int. + + .. note:: + + Be aware that removing is shifting data in a flat_map + (:func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` to be done). + + .. note:: + + The dimension of the simplicial complex may be lower after calling + remove_maximal_simplex than it was before. However, + :func:`upper_bound_dimension()<gudhi.SimplexTree.upper_bound_dimension>` + method will return the old value, which + remains a valid upper bound. If you care, you can call + :func:`dimension()<gudhi.SimplexTree.dimension>` + to recompute the exact dimension. """ self.thisptr.remove_maximal_simplex(simplex) + def prune_above_filtration(self, filtration): + """Prune above filtration value given as parameter. + + :param filtration: Maximum threshold value. + :type filtration: float. + :returns: The filtration modification information. + :rtype: bint + + + .. note:: + + Some simplex tree functions require the filtration to be valid. + prune_above_filtration function is not launching + :func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` + but returns the filtration modification + information. If the complex has changed , please call + :func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` + to recompute it. + + .. note:: + + Note that the dimension of the simplicial complex may be lower + after calling + :func:`prune_above_filtration()<gudhi.SimplexTree.prune_above_filtration>` + than it was before. However, + :func:`upper_bound_dimension()<gudhi.SimplexTree.upper_bound_dimension>` + will return the old value, which remains a + valid upper bound. If you care, you can call + :func:`dimension()<gudhi.SimplexTree.dimension>` + method to recompute the exact dimension. + """ + return self.thisptr.prune_above_filtration(filtration) + def expansion(self, max_dim): """Expands the Simplex_tree containing only its one skeleton until dimension max_dim. @@ -320,7 +400,7 @@ cdef class SimplexTree: """This function returns the persistence of the simplicial complex. :param homology_coeff_field: The homology coefficient field. Must be a - prime number + prime number. Default value is 11. :type homology_coeff_field: int. :param min_persistence: The minimum persistence value to take into account (strictly greater than min_persistence). Default value is @@ -344,8 +424,9 @@ cdef class SimplexTree: :returns: The Betti numbers ([B0, B1, ..., Bn]). :rtype: list of int - :note: betti_numbers function requires persistence function to be - launched first. + :note: betti_numbers function requires + :func:`persistence()<gudhi.SimplexTree.persistence>` + function to be launched first. """ cdef vector[int] bn_result if self.pcohptr != NULL: @@ -369,7 +450,8 @@ cdef class SimplexTree: :returns: The persistent Betti numbers ([B0, B1, ..., Bn]). :rtype: list of int - :note: persistent_betti_numbers function requires persistence + :note: persistent_betti_numbers function requires + :func:`persistence()<gudhi.SimplexTree.persistence>` function to be launched first. """ cdef vector[int] pbn_result @@ -385,12 +467,13 @@ cdef class SimplexTree: complex in a specific dimension. :param dimension: The specific dimension. - :type from_value: int. + :type dimension: int. :returns: The persistence intervals. :rtype: list of pair of float - :note: intervals_in_dim function requires persistence function to be - launched first. + :note: intervals_in_dim function requires + :func:`persistence()<gudhi.SimplexTree.persistence>` + function to be launched first. """ cdef vector[pair[double,double]] intervals_result if self.pcohptr != NULL: @@ -399,3 +482,23 @@ cdef class SimplexTree: print("intervals_in_dim function requires persistence function" " to be launched first.") return intervals_result + + def write_persistence_diagram(self, persistence_file=''): + """This function writes the persistence intervals of the simplicial + complex in a user given file name. + + :param persistence_file: The specific dimension. + :type persistence_file: string. + + :note: intervals_in_dim function requires + :func:`persistence()<gudhi.SimplexTree.persistence>` + function to be launched first. + """ + if self.pcohptr != NULL: + if persistence_file != '': + self.pcohptr.write_output_diagram(str.encode(persistence_file)) + else: + print("persistence_file must be specified") + else: + print("intervals_in_dim function requires persistence function" + " to be launched first.") diff --git a/src/cython/doc/Makefile.in b/src/cython/doc/Makefile.in deleted file mode 100644 index 526350b3..00000000 --- a/src/cython/doc/Makefile.in +++ /dev/null @@ -1,44 +0,0 @@ -# Makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = @SPHINX_PATH@ -PAPER = -BUILDDIR = _build - -# User-friendly check for sphinx-build -ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) -$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) -endif - -# Internal variables. -PAPEROPT_a4 = -D latex_paper_size=a4 -PAPEROPT_letter = -D latex_paper_size=letter -ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . -# the i18n builder cannot share the environment and doctrees with the others -I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . - -.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext - -help: - @echo "Please use \`make <target>' where <target> is one of" - @echo " html to make standalone HTML files" - @echo " doctest to run all doctests embedded in the documentation (if enabled)" - -clean: - rm -f examples.inc - rm -rf $(BUILDDIR)/* - -# GUDHI specific : Examples.inc is generated with generate_examples.py (and deleted on clean) - -html: - ./generate_examples.py - $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." - -doctest: - $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest - @echo "Testing of doctests in the sources finished, look at the " \ - "results in $(BUILDDIR)/doctest/output.txt." diff --git a/src/cython/doc/_templates/layout.html b/src/cython/doc/_templates/layout.html index b11c1236..243f33c6 100644 --- a/src/cython/doc/_templates/layout.html +++ b/src/cython/doc/_templates/layout.html @@ -65,6 +65,7 @@ {#- old style sidebars: using blocks -- should be deprecated #} {%- block sidebartoc %} <h2><a href="index.html">GUDHI</a></h2> +<h2><a href="fileformats.html">File formats</a></h2> <h2><a href="installation.html">GUDHI installation</a></h2> <h2><a href="citation.html">Acknowledging the GUDHI library</a></h2> <h2><a href="genindex.html">Index</a></h2> diff --git a/src/cython/doc/alpha_complex_sum.rst b/src/cython/doc/alpha_complex_sum.rst index a5f6420a..1680a712 100644 --- a/src/cython/doc/alpha_complex_sum.rst +++ b/src/cython/doc/alpha_complex_sum.rst @@ -5,7 +5,7 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+ | .. figure:: | Alpha_complex is a simplicial complex constructed from the finite | -| img/alpha_complex_representation.png | cells of a Delaunay Triangulation. | +| ../../doc/Alpha_complex/alpha_complex_representation.png | cells of a Delaunay Triangulation. | | :alt: Alpha complex representation | | | :figclass: align-center | The filtration value of each simplex is computed as the square of the | | | circumradius of the simplex if the circumsphere is empty (the simplex | diff --git a/src/cython/doc/alpha_complex_user.rst b/src/cython/doc/alpha_complex_user.rst index e8268ef1..db7edd6f 100644 --- a/src/cython/doc/alpha_complex_user.rst +++ b/src/cython/doc/alpha_complex_user.rst @@ -75,7 +75,7 @@ In order to build the alpha complex, first, a Simplex tree is built from the cel (The filtration value is set to NaN, which stands for unknown value): .. figure:: - img/alpha_complex_doc.png + ../../doc/Alpha_complex/alpha_complex_doc.png :figclass: align-center :alt: Simplex tree structure construction example @@ -112,7 +112,7 @@ computes the filtration value of the triangle, and then propagates the filtratio here: .. figure:: - img/alpha_complex_doc_420.png + ../../doc/Alpha_complex/alpha_complex_doc_420.png :figclass: align-center :alt: Filtration value propagation example @@ -142,7 +142,7 @@ Prune above given filtration value ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The simplex tree is pruned from the given maximum alpha squared value (cf. `Simplex_tree::prune_above_filtration()` -int he `C++ version <http://gudhi.gforge.inria.fr/doc/latest/index.html>`_). +in the `C++ version <http://gudhi.gforge.inria.fr/doc/latest/index.html>`_). In the following example, the value is given by the user as argument of the program. @@ -158,7 +158,8 @@ Then, it is asked to display information about the alpha complex: .. testcode:: import gudhi - alpha_complex = gudhi.AlphaComplex(off_file='alphacomplexdoc.off') + alpha_complex = gudhi.AlphaComplex(off_file=gudhi.__root_source_dir__ + \ + '/data/points/alphacomplexdoc.off') simplex_tree = alpha_complex.create_simplex_tree(max_alpha_square=59.0) result_str = 'Alpha complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ repr(simplex_tree.num_simplices()) + ' simplices - ' + \ @@ -200,6 +201,6 @@ the program output is: CGAL citations ============== -.. bibliography:: how_to_cite_cgal.bib +.. bibliography:: ../../biblio/how_to_cite_cgal.bib :filter: docnames :style: unsrt diff --git a/src/cython/doc/bottleneck_distance_sum.rst b/src/cython/doc/bottleneck_distance_sum.rst index 5c475d0d..030fad9e 100644 --- a/src/cython/doc/bottleneck_distance_sum.rst +++ b/src/cython/doc/bottleneck_distance_sum.rst @@ -5,7 +5,7 @@ +-----------------------------------------------------------------+----------------------------------------------------------------------+ | .. figure:: | Bottleneck distance measures the similarity between two persistence | -| img/perturb_pd.png | diagrams. It's the shortest distance b for which there exists a | +| ../../doc/Bottleneck_distance/perturb_pd.png | diagrams. It's the shortest distance b for which there exists a | | :figclass: align-center | perfect matching between the points of the two diagrams (+ all the | | | diagonal points) such that any couple of matched points are at | | Bottleneck distance is the length of | distance at most b. | diff --git a/src/cython/doc/bottleneck_distance_user.rst b/src/cython/doc/bottleneck_distance_user.rst index 0066992f..7692dce2 100644 --- a/src/cython/doc/bottleneck_distance_user.rst +++ b/src/cython/doc/bottleneck_distance_user.rst @@ -25,7 +25,7 @@ This example computes the bottleneck distance from 2 persistence diagrams: message = "Bottleneck distance approximation=" + '%.2f' % gudhi.bottleneck_distance(diag1, diag2, 0.1) print(message) - message = "Bottleneck distance exact value=" + '%.2f' % gudhi.bottleneck_distance(diag1, diag2, 0) + message = "Bottleneck distance value=" + '%.2f' % gudhi.bottleneck_distance(diag1, diag2) print(message) The output is: @@ -33,4 +33,4 @@ The output is: .. testoutput:: Bottleneck distance approximation=0.81 - Bottleneck distance exact value=0.75 + Bottleneck distance value=0.75 diff --git a/src/cython/doc/citation.rst b/src/cython/doc/citation.rst index 6cdfb7cc..f4fdf83b 100644 --- a/src/cython/doc/citation.rst +++ b/src/cython/doc/citation.rst @@ -12,4 +12,4 @@ Manual, as well as for publications directly related to the GUDHI library. GUDHI bibtex ************ -.. literalinclude:: how_to_cite_gudhi.bib +.. literalinclude:: ../../biblio/how_to_cite_gudhi.bib diff --git a/src/cython/doc/conf.py b/src/cython/doc/conf.py index 42bfd59c..19a880d4 100755 --- a/src/cython/doc/conf.py +++ b/src/cython/doc/conf.py @@ -21,7 +21,7 @@ import os #sys.path.insert(0, os.path.abspath('.')) # Path to Gudhi.so from source path -sys.path.insert(0, os.path.abspath('..')) +sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ @@ -58,18 +58,20 @@ source_suffix = '.rst' # The master toctree document. master_doc = 'index' +import gudhi + # General information about the project. -project = u'GUDHI' -copyright = u'2016, GUDHI Editorial Board' +project = gudhi.__name__ +copyright = gudhi.__copyright__ # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. -version = '2.0' +version = gudhi.__version__ # The full version, including alpha/beta/rc tags. -release = '2.0.0' +#release = '2.0.1-rc1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -198,81 +200,3 @@ html_static_path = ['_static'] # Output file base name for HTML help builder. htmlhelp_basename = 'GUDHIdoc' - -# -- Options for LaTeX output --------------------------------------------- - -latex_elements = { -# The paper size ('letterpaper' or 'a4paper'). -#'papersize': 'letterpaper', - -# The font size ('10pt', '11pt' or '12pt'). -#'pointsize': '10pt', - -# Additional stuff for the LaTeX preamble. -#'preamble': '', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - ('index', 'GUDHI.tex', u'GUDHI Documentation', - u'Vincent Rouvreau', 'manual'), -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -#latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -#latex_use_parts = False - -# If true, show page references after internal links. -#latex_show_pagerefs = False - -# If true, show URL addresses after external links. -#latex_show_urls = False - -# Documents to append as an appendix to all manuals. -#latex_appendices = [] - -# If false, no module index is generated. -#latex_domain_indices = True - - -# -- Options for manual page output --------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - ('index', 'gudhi', u'GUDHI Documentation', - [u'Vincent Rouvreau'], 1) -] - -# If true, show URL addresses after external links. -#man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ('index', 'GUDHI', u'GUDHI Documentation', - u'Vincent Rouvreau', 'GUDHI', 'One line description of project.', - 'Miscellaneous'), -] - -# Documents to append as an appendix to all manuals. -#texinfo_appendices = [] - -# If false, no module index is generated. -#texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -#texinfo_show_urls = 'footnote' - -# If true, do not generate a @detailmenu in the "Top" node's menu. -#texinfo_no_detailmenu = False diff --git a/src/cython/doc/cubical_complex_sum.rst b/src/cython/doc/cubical_complex_sum.rst index 3ddf6375..280ad0e0 100644 --- a/src/cython/doc/cubical_complex_sum.rst +++ b/src/cython/doc/cubical_complex_sum.rst @@ -2,14 +2,14 @@ :Author: Pawel Dlotko :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 ================================================================= =================================== =================================== -+-----------------------------------------------------------------+----------------------------------------------------------------------+ -| .. figure:: | The cubical complex is an example of a structured complex useful in | -| img/Cubical_complex_representation.png | computational mathematics (specially rigorous numerics) and image | -| :alt: Cubical complex representation | analysis. | -| :figclass: align-center | | -| | | -| Cubical complex representation | | -+-----------------------------------------------------------------+----------------------------------------------------------------------+ -| :doc:`cubical_complex_user` | * :doc:`cubical_complex_ref` | -| | * :doc:`periodic_cubical_complex_ref` | -+-----------------------------------------------------------------+----------------------------------------------------------------------+ ++--------------------------------------------------------------------------+----------------------------------------------------------------------+ +| .. figure:: | The cubical complex is an example of a structured complex useful in | +| ../../doc/Bitmap_cubical_complex/Cubical_complex_representation.png | computational mathematics (specially rigorous numerics) and image | +| :alt: Cubical complex representation | analysis. | +| :figclass: align-center | | +| | | +| Cubical complex representation | | ++--------------------------------------------------------------------------+----------------------------------------------------------------------+ +| :doc:`cubical_complex_user` | * :doc:`cubical_complex_ref` | +| | * :doc:`periodic_cubical_complex_ref` | ++--------------------------------------------------------------------------+----------------------------------------------------------------------+ diff --git a/src/cython/doc/cubical_complex_user.rst b/src/cython/doc/cubical_complex_user.rst index 344b9554..2bfac62a 100644 --- a/src/cython/doc/cubical_complex_user.rst +++ b/src/cython/doc/cubical_complex_user.rst @@ -59,7 +59,7 @@ directions, allows to determine, dimension, neighborhood, boundary and coboundar :math:`C \in \mathcal{K}`. .. figure:: - img/Cubical_complex_representation.png + ../../doc/Bitmap_cubical_complex/Cubical_complex_representation.png :alt: Cubical complex. :figclass: align-center @@ -87,7 +87,7 @@ in the example below). Next, in lexicographical order, the filtration of top dim 20 4 7 6 5 in the example below). .. figure:: - img/exampleBitmap.png + ../../doc/Bitmap_cubical_complex/exampleBitmap.png :alt: Example of a input data. :figclass: align-center @@ -95,14 +95,15 @@ in the example below). Next, in lexicographical order, the filtration of top dim The input file for the following complex is: -.. literalinclude:: cubicalcomplexdoc.txt +.. literalinclude:: ../../data/bitmap/cubicalcomplexdoc.txt -.. centered:: cubicalcomplexdoc.txt +.. centered:: ../../data/bitmap/cubicalcomplexdoc.txt .. testcode:: import gudhi - cubical_complex = gudhi.CubicalComplex(perseus_file='cubicalcomplexdoc.txt') + cubical_complex = gudhi.CubicalComplex(perseus_file=gudhi.__root_source_dir__ + \ + '/data/bitmap/cubicalcomplexdoc.txt') result_str = 'Cubical complex is of dimension ' + repr(cubical_complex.dimension()) + ' - ' + \ repr(cubical_complex.num_simplices()) + ' simplices.' print(result_str) @@ -127,16 +128,17 @@ complex with periodic boundary conditions. One can also use Perseus style input conditions in a given direction, then number of top dimensional cells in this direction have to be multiplied by -1. For instance: -.. literalinclude:: periodiccubicalcomplexdoc.txt +.. literalinclude:: ../../data/bitmap/periodiccubicalcomplexdoc.txt -.. centered:: periodiccubicalcomplexdoc.txt +.. centered:: ../../data/bitmap/periodiccubicalcomplexdoc.txt Indicate that we have imposed periodic boundary conditions in the direction x, but not in the direction y. .. testcode:: import gudhi - periodic_cc = gudhi.PeriodicCubicalComplex(perseus_file='periodiccubicalcomplexdoc.txt') + periodic_cc = gudhi.PeriodicCubicalComplex(perseus_file=gudhi.__root_source_dir__ + \ + '/data/bitmap/periodiccubicalcomplexdoc.txt') result_str = 'Periodic cubical complex is of dimension ' + repr(periodic_cc.dimension()) + ' - ' + \ repr(periodic_cc.num_simplices()) + ' simplices.' print(result_str) @@ -155,6 +157,6 @@ End user programs are available in cython/example/ folder. Bibliography ============ -.. bibliography:: bibliography.bib +.. bibliography:: ../../bibliography.bib :filter: docnames :style: unsrt diff --git a/src/cython/doc/examples.rst b/src/cython/doc/examples.rst index a89e0596..1e596e18 100644 --- a/src/cython/doc/examples.rst +++ b/src/cython/doc/examples.rst @@ -1,4 +1,21 @@ Examples ######## -.. include:: examples.inc +.. only:: builder_html + + * :download:`rips_complex_from_points_example.py <../example/rips_complex_from_points_example.py>` + * :download:`alpha_complex_from_points_example.py <../example/alpha_complex_from_points_example.py>` + * :download:`simplex_tree_example.py <../example/simplex_tree_example.py>` + * :download:`alpha_rips_persistence_bottleneck_distance.py <../example/alpha_rips_persistence_bottleneck_distance.py>` + * :download:`tangential_complex_plain_homology_from_off_file_example.py <../example/tangential_complex_plain_homology_from_off_file_example.py>` + * :download:`alpha_complex_diagram_persistence_from_off_file_example.py <../example/alpha_complex_diagram_persistence_from_off_file_example.py>` + * :download:`periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py <../example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py>` + * :download:`bottleneck_basic_example.py <../example/bottleneck_basic_example.py>` + * :download:`gudhi_graphical_tools_example.py <../example/gudhi_graphical_tools_example.py>` + * :download:`witness_complex_from_nearest_landmark_table.py <../example/witness_complex_from_nearest_landmark_table.py>` + * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` + * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` + * :download:`rips_complex_diagram_persistence_from_off_file_example.py <../example/rips_complex_diagram_persistence_from_off_file_example.py>` + * :download:`rips_complex_diagram_persistence_from_distance_matrix_file_example.py <../example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py>` + * :download:`rips_persistence_diagram.py <../example/rips_persistence_diagram.py>` + * :download:`random_cubical_complex_persistence_example.py <../example/random_cubical_complex_persistence_example.py>` diff --git a/src/cython/doc/fileformats.rst b/src/cython/doc/fileformats.rst new file mode 100644 index 00000000..156ef4e4 --- /dev/null +++ b/src/cython/doc/fileformats.rst @@ -0,0 +1,33 @@ +File formats +############ + +Persistence Diagram +******************* + +Such a file, whose extension is usually ``.pers``, contains a list of +persistence intervals. + +Lines starting with ``#`` are ignored (comments). + +Other lines might contain 2, 3 or 4 values (the number of values on each line +must be the same for all lines):: + + [[field] dimension] birth death + +Here is a simple sample file:: + + # Persistence diagram example + 2 2.7 3.7 + 2 9.6 14. + # Some comments + 3 34.2 34.974 + 4 3. inf + +Other sample files can be found in the data/persistence_diagram folder. + +Such files can be generated with +:meth:`gudhi.SimplexTree.write_persistence_diagram`, read with +:meth:`gudhi.read_persistence_intervals_grouped_by_dimension`, or +:meth:`gudhi.read_persistence_intervals_in_dimension` and displayed with +:meth:`gudhi.plot_persistence_barcode` or +:meth:`gudhi.plot_persistence_diagram`. diff --git a/src/cython/doc/generate_examples.py b/src/cython/doc/generate_examples.py deleted file mode 100755 index d64d506c..00000000 --- a/src/cython/doc/generate_examples.py +++ /dev/null @@ -1,43 +0,0 @@ -#!/usr/bin/env python - -from os import listdir - -"""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): Vincent Rouvreau - - Copyright (C) 2017 INRIA - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2017 INRIA" -__license__ = "GPL v3" - -""" -generate_examples.py generates examples.inc to be included in examples.rst. -Refer to Makefile and make.bat to see if it is correctly launched. -""" - -output_file = open('examples.inc','w') - -output_file.write('.. only:: builder_html\n\n') - -for file in listdir('../example/'): - output_file.write(" * :download:`" + file + " <../example/" + file + ">`\n") - -output_file.close() diff --git a/src/cython/doc/index.rst b/src/cython/doc/index.rst index f6d10567..3945d72a 100644 --- a/src/cython/doc/index.rst +++ b/src/cython/doc/index.rst @@ -1,8 +1,10 @@ GUDHI Python module documentation ################################# -.. image:: img/Gudhi_banner.png - :align: center +.. figure:: + ../../doc/common/Gudhi_banner.png + :alt: Gudhi banner + :figclass: align-center Introduction ************ @@ -81,6 +83,6 @@ Persistence graphical tools Bibliography ************ -.. bibliography:: bibliography.bib +.. bibliography:: ../../biblio/bibliography.bib :filter: docnames :style: unsrt diff --git a/src/cython/doc/installation.rst b/src/cython/doc/installation.rst index f98a5039..c182f176 100644 --- a/src/cython/doc/installation.rst +++ b/src/cython/doc/installation.rst @@ -68,31 +68,32 @@ The :doc:`Alpha complex </alpha_complex_user>`, C++ library which provides easy access to efficient and reliable geometric algorithms. -Having CGAL version 4.6.0 or higher installed is recommended. The procedure to -install this library according to your operating system is detailed +Having CGAL, the Computational Geometry Algorithms Library, version 4.7.0 or +higher installed is recommended. The procedure to install this library +according to your operating system is detailed `here <http://doc.cgal.org/latest/Manual/installation.html>`_. -The following examples require the Computational Geometry Algorithms Library: - -.. only:: builder_html - - * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` - * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` - -The following example requires CGAL version ≥ 4.7.0: +The following examples requires CGAL version ≥ 4.7.0: .. only:: builder_html * :download:`alpha_complex_diagram_persistence_from_off_file_example.py <../example/alpha_complex_diagram_persistence_from_off_file_example.py>` * :download:`alpha_complex_from_points_example.py <../example/alpha_complex_from_points_example.py>` -The following example requires CGAL version ≥ 4.8.0: +The following examples requires CGAL version ≥ 4.8.0: .. only:: builder_html * :download:`bottleneck_basic_example.py <../example/bottleneck_basic_example.py>` * :download:`tangential_complex_plain_homology_from_off_file_example.py <../example/tangential_complex_plain_homology_from_off_file_example.py>` +The following examples requires CGAL version ≥ 4.8.1: + +.. only:: builder_html + + * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` + * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` + Eigen3 ====== diff --git a/src/cython/doc/make.bat.in b/src/cython/doc/make.bat.in deleted file mode 100644 index ff1a6d56..00000000 --- a/src/cython/doc/make.bat.in +++ /dev/null @@ -1,67 +0,0 @@ -@ECHO OFF
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
- set SPHINXBUILD=@SPHINX_PATH@
-)
-set BUILDDIR=_build
-set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% .
-set I18NSPHINXOPTS=%SPHINXOPTS% .
-if NOT "%PAPER%" == "" (
- set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%
- set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS%
-)
-
-if "%1" == "" goto help
-
-if "%1" == "help" (
- :help
- echo.Please use `make ^<target^>` where ^<target^> is one of
- echo. html to make standalone HTML files
- echo. doctest to run all doctests embedded in the documentation if enabled
- goto end
-)
-
-if "%1" == "clean" (
- del examples.inc
- for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
- del /q /s %BUILDDIR%\*
- goto end
-)
-
-
-%SPHINXBUILD% 2> nul
-if errorlevel 9009 (
- echo.
- echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
- echo.installed, then set the SPHINXBUILD environment variable to point
- echo.to the full path of the 'sphinx-build' executable. Alternatively you
- echo.may add the Sphinx directory to PATH.
- echo.
- echo.If you don't have Sphinx installed, grab it from
- echo.http://sphinx-doc.org/
- exit /b 1
-)
-
-:: GUDHI specific : Examples.inc is generated with generate_examples.py (and deleted on clean)
-
-if "%1" == "html" (
- generate_examples.py
- %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. The HTML pages are in %BUILDDIR%/html.
- goto end
-)
-
-if "%1" == "doctest" (
- %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
- if errorlevel 1 exit /b 1
- echo.
- echo.Testing of doctests in the sources finished, look at the ^
-results in %BUILDDIR%/doctest/output.txt.
- goto end
-)
-
-:end
diff --git a/src/cython/doc/persistence_graphical_tools_user.rst b/src/cython/doc/persistence_graphical_tools_user.rst index cae18323..9033331f 100644 --- a/src/cython/doc/persistence_graphical_tools_user.rst +++ b/src/cython/doc/persistence_graphical_tools_user.rst @@ -14,12 +14,14 @@ This function is useful to show the color palette values of dimension: .. testcode:: import gudhi - gudhi.show_palette_values(alpha=1.0) + plt = gudhi.show_palette_values(alpha=1.0) + plt.show() .. plot:: import gudhi - gudhi.show_palette_values(alpha=1.0) + plt = gudhi.show_palette_values(alpha=1.0) + plt.show() Show persistence as a barcode ----------------------------- @@ -30,17 +32,22 @@ This function can display the persistence result as a barcode: import gudhi - periodic_cc = gudhi.PeriodicCubicalComplex(perseus_file='3d_torus.txt') + periodic_cc = gudhi.PeriodicCubicalComplex(perseus_file=gudhi.__root_source_dir__ + \ + '/data/bitmap/3d_torus.txt') diag = periodic_cc.persistence() - gudhi.plot_persistence_barcode(diag) + plt = gudhi.plot_persistence_barcode(diag) + plt.show() .. plot:: import gudhi - periodic_cc = gudhi.PeriodicCubicalComplex(perseus_file='3d_torus.txt') + periodic_cc = gudhi.PeriodicCubicalComplex(perseus_file=gudhi.__root_source_dir__ + \ + '/data/bitmap/3d_torus.txt') diag = periodic_cc.persistence() - gudhi.plot_persistence_barcode(diag) + print("diag = ", diag) + plt = gudhi.plot_persistence_barcode(diag) + plt.show() Show persistence as a diagram ----------------------------- @@ -51,16 +58,20 @@ This function can display the persistence result as a diagram: import gudhi - rips_complex = gudhi.RipsComplex(off_file='tore3D_300.off', max_edge_length=2.0) + rips_complex = gudhi.RipsComplex(off_file=gudhi.__root_source_dir__ + \ + '/data/points/tore3D_1307.off', max_edge_length=0.2) simplex_tree = rips_complex.create_simplex_tree(max_dimension=3) diag = simplex_tree.persistence() - gudhi.plot_persistence_diagram(diag) + plt = gudhi.plot_persistence_diagram(diag, band_boot=0.13) + plt.show() .. plot:: import gudhi - rips_complex = gudhi.RipsComplex(off_file='tore3D_300.off', max_edge_length=2.0) + rips_complex = gudhi.RipsComplex(off_file=gudhi.__root_source_dir__ + \ + '/data/points/tore3D_1307.off', max_edge_length=0.2) simplex_tree = rips_complex.create_simplex_tree(max_dimension=3) diag = simplex_tree.persistence() - gudhi.plot_persistence_diagram(diag) + plt = gudhi.plot_persistence_diagram(diag, band_boot=0.13) + plt.show() diff --git a/src/cython/doc/persistent_cohomology_sum.rst b/src/cython/doc/persistent_cohomology_sum.rst index d1f79cb4..a26df1dc 100644 --- a/src/cython/doc/persistent_cohomology_sum.rst +++ b/src/cython/doc/persistent_cohomology_sum.rst @@ -4,7 +4,7 @@ +-----------------------------------------------------------------+-----------------------------------------------------------------------+ | .. figure:: | The theory of homology consists in attaching to a topological space | -| img/3DTorus_poch.png | a sequence of (homology) groups, capturing global topological | +| ../../doc/Persistent_cohomology/3DTorus_poch.png | a sequence of (homology) groups, capturing global topological | | :figclass: align-center | features like connected components, holes, cavities, etc. Persistent | | | homology studies the evolution -- birth, life and death -- of these | | Rips Persistent Cohomology on a 3D | features when the topological space is changing. Consequently, the | diff --git a/src/cython/doc/persistent_cohomology_user.rst b/src/cython/doc/persistent_cohomology_user.rst index 72f1a7f7..bf90c163 100644 --- a/src/cython/doc/persistent_cohomology_user.rst +++ b/src/cython/doc/persistent_cohomology_user.rst @@ -109,6 +109,6 @@ We provide several example files: run these examples with -h for details on thei Bibliography ============ -.. bibliography:: bibliography.bib +.. bibliography:: ../../biblio/bibliography.bib :filter: docnames :style: unsrt diff --git a/src/cython/doc/pyplots/barcode_persistence.py b/src/cython/doc/pyplots/barcode_persistence.py index c06ac5a7..de33d506 100755 --- a/src/cython/doc/pyplots/barcode_persistence.py +++ b/src/cython/doc/pyplots/barcode_persistence.py @@ -1,5 +1,7 @@ import gudhi -periodic_cc = gudhi.PeriodicCubicalComplex(perseus_file='../3d_torus.txt') +periodic_cc = gudhi.PeriodicCubicalComplex(perseus_file=gudhi.__root_source_dir__ + \ + '/data/bitmap/3d_torus.txt') diag = periodic_cc.persistence() -gudhi.plot_persistence_barcode(diag) +plt = gudhi.plot_persistence_barcode(diag) +plt.show() diff --git a/src/cython/doc/pyplots/diagram_persistence.py b/src/cython/doc/pyplots/diagram_persistence.py index b4714fe3..c2fbf801 100755 --- a/src/cython/doc/pyplots/diagram_persistence.py +++ b/src/cython/doc/pyplots/diagram_persistence.py @@ -1,5 +1,8 @@ import gudhi -alpha_complex = gudhi.AlphaComplex(off_file='../tore3D_300.off') -diag = alpha_complex.persistence() -gudhi.plot_persistence_diagram(diag) +rips_complex = gudhi.RipsComplex(off_file=gudhi.__root_source_dir__ + \ + '/data/points/tore3D_1307.off', max_edge_length=0.2) +simplex_tree = rips_complex.create_simplex_tree(max_dimension=3) +diag = simplex_tree.persistence() +plt = gudhi.plot_persistence_diagram(diag, band_boot=0.13) +plt.show() diff --git a/src/cython/doc/pyplots/show_palette_values.py b/src/cython/doc/pyplots/show_palette_values.py index e72a55fd..fdf9645f 100755 --- a/src/cython/doc/pyplots/show_palette_values.py +++ b/src/cython/doc/pyplots/show_palette_values.py @@ -1,2 +1,3 @@ import gudhi -gudhi.show_palette_values(alpha=1.0) +plt = gudhi.show_palette_values(alpha=1.0) +plt.show() diff --git a/src/cython/doc/python3-sphinx-build b/src/cython/doc/python3-sphinx-build.py index 44b94169..84d158cf 100755 --- a/src/cython/doc/python3-sphinx-build +++ b/src/cython/doc/python3-sphinx-build.py @@ -1,4 +1,4 @@ -#!/usr/bin/python3 +#!/usr/bin/env python3 """ Emulate sphinx-build for python3 diff --git a/src/cython/doc/reader_utils_ref.rst b/src/cython/doc/reader_utils_ref.rst new file mode 100644 index 00000000..9c1ea6fc --- /dev/null +++ b/src/cython/doc/reader_utils_ref.rst @@ -0,0 +1,11 @@ +============================= +Reader utils reference manual +============================= + +.. autofunction:: gudhi.read_off + +.. autofunction:: gudhi.read_lower_triangular_matrix_from_csv_file + +.. autofunction:: gudhi.read_persistence_intervals_grouped_by_dimension + +.. autofunction:: gudhi.read_persistence_intervals_in_dimension diff --git a/src/cython/doc/rips_complex_sum.rst b/src/cython/doc/rips_complex_sum.rst index 2b65fc19..5616bfa9 100644 --- a/src/cython/doc/rips_complex_sum.rst +++ b/src/cython/doc/rips_complex_sum.rst @@ -4,7 +4,7 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+ | .. figure:: | Rips complex is a simplicial complex constructed from a one skeleton | -| img/rips_complex_representation.png | graph. | +| ../../doc/Rips_complex/rips_complex_representation.png | graph. | | :figclass: align-center | | | | The filtration value of each edge is computed from a user-given | | Rips complex representation | distance function and is inserted until a user-given threshold | diff --git a/src/cython/doc/rips_complex_user.rst b/src/cython/doc/rips_complex_user.rst index f9760976..96ba9944 100644 --- a/src/cython/doc/rips_complex_user.rst +++ b/src/cython/doc/rips_complex_user.rst @@ -26,7 +26,7 @@ structure, and then expands the simplicial complex when required. Vertex name correspond to the index of the point in the given range (aka. the point cloud). .. figure:: - img/rips_complex_representation.png + ../../doc/Rips_complex/rips_complex_representation.png :align: center Rips-complex one skeleton graph representation @@ -101,7 +101,8 @@ Finally, it is asked to display information about the Rips complex. .. testcode:: import gudhi - rips_complex = gudhi.RipsComplex(off_file='alphacomplexdoc.off', max_edge_length=12.0) + rips_complex = gudhi.RipsComplex(off_file=gudhi.__root_source_dir__ + \ + '/data/points/alphacomplexdoc.off', max_edge_length=12.0) simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ repr(simplex_tree.num_simplices()) + ' simplices - ' + \ @@ -205,7 +206,8 @@ Finally, it is asked to display information about the Rips complex. .. testcode:: import gudhi - rips_complex = gudhi.RipsComplex(csv_file='full_square_distance_matrix.csv', max_edge_length=12.0) + rips_complex = gudhi.RipsComplex(csv_file=gudhi.__root_source_dir__ + \ + '/data/distance_matrix/full_square_distance_matrix.csv', max_edge_length=12.0) simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ repr(simplex_tree.num_simplices()) + ' simplices - ' + \ diff --git a/src/cython/doc/simplex_tree_sum.rst b/src/cython/doc/simplex_tree_sum.rst index 3174fb62..fb0e54c1 100644 --- a/src/cython/doc/simplex_tree_sum.rst +++ b/src/cython/doc/simplex_tree_sum.rst @@ -4,7 +4,7 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+ | .. figure:: | The simplex tree is an efficient and flexible data structure for | -| img/Simplex_tree_representation.png | representing general (filtered) simplicial complexes. | +| ../../doc/Simplex_tree/Simplex_tree_representation.png | representing general (filtered) simplicial complexes. | | :alt: Simplex tree representation | | | :figclass: align-center | The data structure is described in | | | :cite:`boissonnatmariasimplextreealgorithmica` | diff --git a/src/cython/doc/tangential_complex_sum.rst b/src/cython/doc/tangential_complex_sum.rst index 2b05bc10..72b4d7ba 100644 --- a/src/cython/doc/tangential_complex_sum.rst +++ b/src/cython/doc/tangential_complex_sum.rst @@ -5,10 +5,10 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+ | .. figure:: | A Tangential Delaunay complex is a simplicial complex designed to | -| img/tc_examples.png | reconstruct a :math:`k`-dimensional manifold embedded in :math:`d`- | +| ../../doc/Tangential_complex/tc_examples.png | reconstruct a :math:`k`-dimensional manifold embedded in :math:`d`- | | :figclass: align-center | dimensional Euclidean space. The input is a point sample coming from | | | an unknown manifold. The running time depends only linearly on the | -| **Tangential complex representation** | extrinsic dimension :math:`d` and exponentially on the intrinsic | +| Tangential complex representation | extrinsic dimension :math:`d` and exponentially on the intrinsic | | | dimension :math:`k`. | +----------------------------------------------------------------+------------------------------------------------------------------------+ | :doc:`tangential_complex_user` | :doc:`tangential_complex_ref` | diff --git a/src/cython/doc/tangential_complex_user.rst b/src/cython/doc/tangential_complex_user.rst index 03f9fea6..efa6d7ce 100644 --- a/src/cython/doc/tangential_complex_user.rst +++ b/src/cython/doc/tangential_complex_user.rst @@ -22,7 +22,7 @@ Let us start with the description of the Tangential complex of a simple example, with :math:`k = 1` and :math:`d = 2`. The input data is 4 points :math:`P` located on a curve embedded in 2D. -.. figure:: img/tc_example_01.png +.. figure:: ../../doc/Tangential_complex/tc_example_01.png :alt: The input :figclass: align-center @@ -31,7 +31,7 @@ example, with :math:`k = 1` and :math:`d = 2`. The input data is 4 points For each point :math:`p`, estimate its tangent subspace :math:`T_p` (e.g. using PCA). -.. figure:: img/tc_example_02.png +.. figure:: ../../doc/Tangential_complex/tc_example_02.png :alt: The estimated normals :figclass: align-center @@ -42,7 +42,7 @@ Let us add the Voronoi diagram of the points in orange. For each point :math:`p`, construct its star in the Delaunay triangulation of :math:`P` restricted to :math:`T_p`. -.. figure:: img/tc_example_03.png +.. figure:: ../../doc/Tangential_complex/tc_example_03.png :alt: The Voronoi diagram :figclass: align-center @@ -62,7 +62,7 @@ simplex is not in the star of all its vertices. Let us take the same example. -.. figure:: img/tc_example_07_before.png +.. figure:: ../../doc/Tangential_complex/tc_example_07_before.png :alt: Before :figclass: align-center @@ -70,7 +70,7 @@ Let us take the same example. Let us slightly move the tangent subspace :math:`T_q` -.. figure:: img/tc_example_07_after.png +.. figure:: ../../doc/Tangential_complex/tc_example_07_after.png :alt: After :figclass: align-center @@ -79,7 +79,7 @@ Let us slightly move the tangent subspace :math:`T_q` Now, the star of :math:`Q` contains :math:`QP`, but the star of :math:`P` does not contain :math:`QP`. We have an inconsistency. -.. figure:: img/tc_example_08.png +.. figure:: ../../doc/Tangential_complex/tc_example_08.png :alt: After :figclass: align-center @@ -122,7 +122,8 @@ This example builds the Tangential complex of point set read in an OFF file. .. testcode:: import gudhi - tc = gudhi.TangentialComplex(off_file='alphacomplexdoc.off') + tc = gudhi.TangentialComplex(off_file=gudhi.__root_source_dir__ + \ + '/data/points/alphacomplexdoc.off') result_str = 'Tangential contains ' + repr(tc.num_simplices()) + \ ' simplices - ' + repr(tc.num_vertices()) + ' vertices.' print(result_str) @@ -190,6 +191,6 @@ The output is: Bibliography ============ -.. bibliography:: bibliography.bib +.. bibliography:: ../../biblio/bibliography.bib :filter: docnames :style: unsrt diff --git a/src/cython/doc/witness_complex_sum.rst b/src/cython/doc/witness_complex_sum.rst index b65522ba..a8a126a0 100644 --- a/src/cython/doc/witness_complex_sum.rst +++ b/src/cython/doc/witness_complex_sum.rst @@ -3,15 +3,17 @@ :Euclidean version requires: CGAL :math:`\geq` 4.6.0 Eigen3 ================================================================= =================================== =================================== -+-----------------------------------------------------------------+----------------------------------------------------------------------+ -| .. image:: | Witness complex :math:`Wit(W,L)` is a simplicial complex defined on | -| img/Witness_complex_representation.png | two sets of points in :math:`\mathbb{R}^D`. | -| | | -| | The data structure is described in | -| | :cite:`boissonnatmariasimplextreealgorithmica`. | -+-----------------------------------------------------------------+----------------------------------------------------------------------+ -| :doc:`witness_complex_user` | * :doc:`witness_complex_ref` | -| | * :doc:`strong_witness_complex_ref` | -| | * :doc:`euclidean_witness_complex_ref` | -| | * :doc:`euclidean_strong_witness_complex_ref` | -+-----------------------------------------------------------------+----------------------------------------------------------------------+ ++-------------------------------------------------------------------+----------------------------------------------------------------------+ +| .. figure:: | Witness complex :math:`Wit(W,L)` is a simplicial complex defined on | +| ../../doc/Witness_complex/Witness_complex_representation.png | two sets of points in :math:`\mathbb{R}^D`. | +| :alt: Witness complex representation | | +| :figclass: align-center | The data structure is described in | +| | :cite:`boissonnatmariasimplextreealgorithmica`. | +| | | +| Witness complex representation | | ++-------------------------------------------------------------------+----------------------------------------------------------------------+ +| :doc:`witness_complex_user` | * :doc:`witness_complex_ref` | +| | * :doc:`strong_witness_complex_ref` | +| | * :doc:`euclidean_witness_complex_ref` | +| | * :doc:`euclidean_strong_witness_complex_ref` | ++-------------------------------------------------------------------+----------------------------------------------------------------------+ diff --git a/src/cython/doc/witness_complex_user.rst b/src/cython/doc/witness_complex_user.rst index aa9cbb2c..29413269 100644 --- a/src/cython/doc/witness_complex_user.rst +++ b/src/cython/doc/witness_complex_user.rst @@ -33,7 +33,7 @@ Both definitions can be relaxed by a real value :math:`\alpha`: which leads to definitions of **weak relaxed witness complex** (or just relaxed witness complex for short) and **strong relaxed witness complex** respectively. -.. figure:: img/swit.svg +.. figure:: ../../doc/Witness_complex/swit.svg :alt: Strongly witnessed simplex :figclass: align-center @@ -126,6 +126,6 @@ Here is an example of constructing a strong witness complex filtration and compu Bibliography ============ -.. bibliography:: bibliography.bib +.. bibliography:: ../../biblio/bibliography.bib :filter: docnames :style: unsrt diff --git a/src/cython/example/alpha_complex_diagram_persistence_from_off_file_example.py b/src/cython/example/alpha_complex_diagram_persistence_from_off_file_example.py index adedc7d2..b4487be4 100755 --- a/src/cython/example/alpha_complex_diagram_persistence_from_off_file_example.py +++ b/src/cython/example/alpha_complex_diagram_persistence_from_off_file_example.py @@ -38,6 +38,7 @@ parser = argparse.ArgumentParser(description='AlphaComplex creation from ' 'points from the given OFF file.') parser.add_argument("-f", "--file", type=str, required=True) parser.add_argument("-a", "--max_alpha_square", type=float, default=0.5) +parser.add_argument("-b", "--band_boot", type=float, default=0.) parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') args = parser.parse_args() @@ -63,7 +64,8 @@ with open(args.file, 'r') as f: print(simplex_tree.betti_numbers()) if args.no_diagram == False: - gudhi.plot_persistence_diagram(diag) + pplot = gudhi.plot_persistence_diagram(diag, band_boot=args.band_boot) + pplot.show() else: print(args.file, "is not a valid OFF file") diff --git a/src/cython/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py b/src/cython/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py index 2371c36c..e3f362dc 100755 --- a/src/cython/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py +++ b/src/cython/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py @@ -40,6 +40,7 @@ parser.add_argument("-f", "--file", type=str, required=True) parser.add_argument("-a", "--max_alpha_square", type=float, required=True) parser.add_argument("-n", "--number_of_landmarks", type=int, required=True) parser.add_argument("-d", "--limit_dimension", type=int, required=True) +parser.add_argument("-b", "--band_boot", type=float, default=0.) parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') args = parser.parse_args() @@ -70,8 +71,8 @@ with open(args.file, 'r') as f: print(simplex_tree.betti_numbers()) if args.no_diagram == False: - gudhi.plot_persistence_diagram(diag) - + pplot = gudhi.plot_persistence_diagram(diag, band_boot=args.band_boot) + pplot.show() else: print(args.file, "is not a valid OFF file") diff --git a/src/cython/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py b/src/cython/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py index 5748aa8a..c236d992 100755 --- a/src/cython/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py +++ b/src/cython/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py @@ -40,6 +40,7 @@ parser.add_argument("-f", "--file", type=str, required=True) parser.add_argument("-a", "--max_alpha_square", type=float, required=True) parser.add_argument("-n", "--number_of_landmarks", type=int, required=True) parser.add_argument("-d", "--limit_dimension", type=int, required=True) +parser.add_argument("-b", "--band_boot", type=float, default=0.) parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') args = parser.parse_args() @@ -70,8 +71,8 @@ with open(args.file, 'r') as f: print(simplex_tree.betti_numbers()) if args.no_diagram == False: - gudhi.plot_persistence_diagram(diag) - + pplot = gudhi.plot_persistence_diagram(diag, band_boot=args.band_boot) + pplot.show() else: print(args.file, "is not a valid OFF file") diff --git a/src/cython/example/gudhi_graphical_tools_example.py b/src/cython/example/gudhi_graphical_tools_example.py index bc3b16ec..ed87806b 100755 --- a/src/cython/example/gudhi_graphical_tools_example.py +++ b/src/cython/example/gudhi_graphical_tools_example.py @@ -44,4 +44,11 @@ gudhi.plot_persistence_barcode(persistence) print("#####################################################################") print("Show diagram persistence example") -gudhi.plot_persistence_diagram(persistence) +pplot = gudhi.plot_persistence_diagram(persistence) +pplot.show() + +print("#####################################################################") +print("Show diagram persistence example with a confidence band") + +pplot = gudhi.plot_persistence_diagram(persistence, band_boot=0.2) +pplot.show() diff --git a/src/cython/example/persistence_representations_diagrams_example.py b/src/cython/example/persistence_representations_diagrams_example.py deleted file mode 100755 index bd7452a0..00000000 --- a/src/cython/example/persistence_representations_diagrams_example.py +++ /dev/null @@ -1,70 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse - -"""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): Pawel Dlotko - - Copyright (C) 2017 Swansea University - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Pawel Dlotko" -__copyright__ = "Copyright (C) 2017 Swansea University" -__license__ = "GPL v3" - -print("#####################################################################") -print("Persistence representations diagrams example") - - -parser = argparse.ArgumentParser(description='Statistics of persistence diagrams from file ', - epilog='Example: ' - 'example/persistence_representations_diagrams_example.py ' - '-f file_with_diagram -d 1') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-d", "--dimension", type=int, default=0) - -args = parser.parse_args() - -print "Here are the parameters of the program: ",args.file," , " ,args.dimension - -p = gudhi.PersistenceIntervals(None,args.dimension,args.file); -min_max_ = p.get_x_range(); -print "Birth-death range : ", min_max_ - -dominant_ten_intervals_length = p.length_of_dominant_intervals(10) -print "Length of ten dominant intervals : ", dominant_ten_intervals_length - -ten_dominant_intervals = p.dominant_intervals(10); -print "Here are the dominant intervals : " , ten_dominant_intervals - -histogram = p.histogram_of_lengths(10); -print "Here is the histogram of barcode's length : ", histogram - -cumulative_histogram = p.cumulative_histogram_of_lengths(10) -print "Cumulative histogram : " ,cumulative_histogram - -char_funct_diag = p.characteristic_function_of_diagram(min_max_[0], min_max_[1],None) -print "Characteristic function of diagram : ",char_funct_diag - -cumul_char_funct_diag = p.cumulative_characteristic_function_of_diagram(min_max_[0], min_max_[1],None) -print "Cumulative characteristic function of diagram : ",cumul_char_funct_diag - -pbns = p.compute_persistent_betti_numbers() -print "Persistence Betti numbers ", pbns diff --git a/src/cython/example/persistence_representations_landscapes_example.py b/src/cython/example/persistence_representations_landscapes_example.py deleted file mode 100755 index 94b68225..00000000 --- a/src/cython/example/persistence_representations_landscapes_example.py +++ /dev/null @@ -1,63 +0,0 @@ -#!/usr/bin/env python - -import gudhi - - -"""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): Pawel Dlotko - - Copyright (C) 2017 Swansea University - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Pawel Dlotko" -__copyright__ = "Copyright (C) 2017 Swansea University" -__license__ = "GPL v3" - -print("#####################################################################") -print("Persistence representations landscapes example") - -persistence1 = [(1,2),(6,8),(0,4),(3,8)] -persistence2 = [(2,9),(1,6),(3,5),(6,10)] - - -#create two persistence landscapes based on persistence1 and persistence2: -l1 = gudhi.PersistenceLandscapes(vector_of_intervals=persistence1, dimension=3) -l2 = gudhi.PersistenceLandscapes(vector_of_intervals=persistence2) - -#This is how to compute integral of landscapes: -print "Integral of the first landscape : ", l1.compute_integral_of_landscape() -print "Integral of the second landscape : ", l2.compute_integral_of_landscape() - -#here are the maxima of the functions: -print "Maximum of l1 : ", l1.compute_maximum() -print "Maximum of l2 : ", l2.compute_maximum() - -#here are the norms of landscapes: -print "L^1 Norm of l1 : ", l1.compute_norm_of_landscape(1.) -print "L^1 Norm of l2 : ", l2.compute_norm_of_landscape(1.) - -#here is the average of landscapes: -average = gudhi.PersistenceLandscapes() -average.compute_average(to_average=[l1, l2]) - -#here is the distance of landscapes: -print "Distance : ", l1.distance(average,1) - -#here is the scalar product of landscapes: -print "Scalar product : ", l1.compute_scalar_product(l2) diff --git a/src/cython/example/persistence_representations_landscapes_on_grid_example.py b/src/cython/example/persistence_representations_landscapes_on_grid_example.py deleted file mode 100755 index 60b0e873..00000000 --- a/src/cython/example/persistence_representations_landscapes_on_grid_example.py +++ /dev/null @@ -1,110 +0,0 @@ -#!/usr/bin/env python - -import gudhi - - -"""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): Pawel Dlotko - - Copyright (C) 2017 Swansea University - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Pawel Dlotko" -__copyright__ = "Copyright (C) 2017 Swansea University" -__license__ = "GPL v3" - -print("#####################################################################") -print("Persistence representations landscapes on a grid example") - -persistence1 = [(1, 2),(6, 8),(0, 4),(3, 8)] -persistence2 = [(2, 9),(1, 6),(3, 5),(6, 10)] - -#create two persistence landscapes based on persistence1 and persistence2: -l1 = PersistenceLandscapeOnGrid(persistence1, 0, 11, 20) -l2 = PersistenceLandscapeOnGrid(persistence2, 0, 11, 20) - -#This is how to compute integral of landscapes: -print "Integral of the first landscape : " , l1.compute_integral_of_landscape() -print "Integral of the second landscape : " , l2.compute_integral_of_landscape() - -#here are the maxima of the functions: -print "Maximum of l1 : " , l1.compute_maximum() -print "Maximum of l2 : " , l2.compute_maximum() - -#here are the norms of landscapes: -print "L^1 Norm of l1 : " , l1.compute_norm_of_landscape(1.) -print "L^1 Norm of l2 : " , l2.compute_norm_of_landscape(1.) - -#here is the average of landscapes: -average = PersistenceLandscapeOnGrid(); -average.compute_average(to_average=[l1, l2]); - - -#here is the distance of landscapes: -print "Distance : " , l1.distance(l2) - -#here is the scalar product of landscapes: -print "Scalar product : " , l1.compute_scalar_product(l2) - - - - - - - - - - - - - - - - - - -persistence1 = [(1,2),(6,8),(0,4),(3,8)] -persistence2 = [(2,9),(1,6),(3,5),(6,10)] - - -#create two persistence landscapes based on persistence1 and persistence2: -l1 = gudhi.PersistenceLandscapes(vector_of_intervals=persistence1, dimension=3) -l2 = gudhi.PersistenceLandscapes(vector_of_intervals=persistence2) - -#This is how to compute integral of landscapes: -print "Integral of the first landscape : ", l1.compute_integral_of_landscape() -print "Integral of the second landscape : ", l2.compute_integral_of_landscape() - -#here are the maxima of the functions: -print "Maximum of l1 : ", l1.compute_maximum() -print "Maximum of l2 : ", l2.compute_maximum() - -#here are the norms of landscapes: -print "L^1 Norm of l1 : ", l1.compute_norm_of_landscape(1.) -print "L^1 Norm of l2 : ", l2.compute_norm_of_landscape(1.) - -#here is the average of landscapes: -average = gudhi.PersistenceLandscapes() -average.compute_average(to_average=[l1, l2]) - -#here is the distance of landscapes: -print "Distance : ", l1.distance(average,1) - -#here is the scalar product of landscapes: -print "Scalar product : ", l1.compute_scalar_product(l2) diff --git a/src/cython/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py b/src/cython/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py index 984dbf1b..3baebd17 100755 --- a/src/cython/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py +++ b/src/cython/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py @@ -39,6 +39,7 @@ parser = argparse.ArgumentParser(description='RipsComplex creation from ' parser.add_argument("-f", "--file", type=str, required=True) parser.add_argument("-e", "--max_edge_length", type=float, default=0.5) parser.add_argument("-d", "--max_dimension", type=int, default=1) +parser.add_argument("-b", "--band_boot", type=float, default=0.) parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') args = parser.parse_args() @@ -61,4 +62,5 @@ print("betti_numbers()=") print(simplex_tree.betti_numbers()) if args.no_diagram == False: - gudhi.plot_persistence_diagram(diag) + pplot = gudhi.plot_persistence_diagram(diag, band_boot=args.band_boot) + pplot.show() diff --git a/src/cython/example/rips_complex_diagram_persistence_from_off_file_example.py b/src/cython/example/rips_complex_diagram_persistence_from_off_file_example.py index 4c21b98e..5951eedf 100755 --- a/src/cython/example/rips_complex_diagram_persistence_from_off_file_example.py +++ b/src/cython/example/rips_complex_diagram_persistence_from_off_file_example.py @@ -39,6 +39,7 @@ parser = argparse.ArgumentParser(description='RipsComplex creation from ' parser.add_argument("-f", "--file", type=str, required=True) parser.add_argument("-e", "--max_edge_length", type=float, default=0.5) parser.add_argument("-d", "--max_dimension", type=int, default=1) +parser.add_argument("-b", "--band_boot", type=float, default=0.) parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') args = parser.parse_args() @@ -64,7 +65,8 @@ with open(args.file, 'r') as f: print(simplex_tree.betti_numbers()) if args.no_diagram == False: - gudhi.plot_persistence_diagram(diag) + pplot = gudhi.plot_persistence_diagram(diag, band_boot=args.band_boot) + pplot.show() else: print(args.file, "is not a valid OFF file") diff --git a/src/cython/example/rips_persistence_diagram.py b/src/cython/example/rips_persistence_diagram.py index 4e5cd2c8..9bfea41c 100755 --- a/src/cython/example/rips_persistence_diagram.py +++ b/src/cython/example/rips_persistence_diagram.py @@ -39,4 +39,5 @@ simplex_tree = rips.create_simplex_tree(max_dimension=1) diag = simplex_tree.persistence(homology_coeff_field=2, min_persistence=0) print("diag=", diag) -gudhi.plot_persistence_diagram(diag) +pplot = gudhi.plot_persistence_diagram(diag) +pplot.show() diff --git a/src/cython/example/simplex_tree_example.py b/src/cython/example/simplex_tree_example.py index 3af20fcf..51a60e73 100755 --- a/src/cython/example/simplex_tree_example.py +++ b/src/cython/example/simplex_tree_example.py @@ -48,11 +48,8 @@ if st.insert([0, 1, 2], filtration=4.0): else: print("Not inserted...") -# FIXME: Remove this line -st.set_dimension(3) print("dimension=", st.dimension()) -st.set_filtration(4.0) st.initialize_filtration() print("filtration=", st.get_filtration()) print("filtration[1, 2]=", st.filtration([1, 2])) diff --git a/src/cython/example/tangential_complex_plain_homology_from_off_file_example.py b/src/cython/example/tangential_complex_plain_homology_from_off_file_example.py index 4845eb47..6145e7f2 100755 --- a/src/cython/example/tangential_complex_plain_homology_from_off_file_example.py +++ b/src/cython/example/tangential_complex_plain_homology_from_off_file_example.py @@ -37,6 +37,7 @@ parser = argparse.ArgumentParser(description='TangentialComplex creation from ' '- Constructs a tangential complex with the ' 'points from the given OFF file') parser.add_argument("-f", "--file", type=str, required=True) +parser.add_argument("-b", "--band_boot", type=float, default=0.) parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') args = parser.parse_args() @@ -59,7 +60,8 @@ with open(args.file, 'r') as f: print(st.betti_numbers()) if args.no_diagram == False: - gudhi.plot_persistence_diagram(diag) + pplot = gudhi.plot_persistence_diagram(diag, band_boot=args.band_boot) + pplot.show() else: print(args.file, "is not a valid OFF file") diff --git a/src/cython/gudhi.pyx.in b/src/cython/gudhi.pyx.in index 53f09c4b..a8dd9f80 100644 --- a/src/cython/gudhi.pyx.in +++ b/src/cython/gudhi.pyx.in @@ -23,18 +23,19 @@ __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 INRIA" __license__ = "GPL v3" +__version__ = "@GUDHI_VERSION@" +# This variable is used by doctest to find files +__root_source_dir__ = "@CMAKE_SOURCE_DIR@" -include "cython/off_reader.pyx" -include "cython/simplex_tree.pyx" -include "cython/rips_complex.pyx" -include "cython/cubical_complex.pyx" -include "cython/periodic_cubical_complex.pyx" -include "cython/persistence_graphical_tools.py" -include "cython/witness_complex.pyx" -include "cython/strong_witness_complex.pyx" -include "cython/persistence_representations_intervals.pyx" -include "cython/persistence_representations_landscapes.pyx" -#include "cython/persistence_representations_landscapes_on_grid.pyx" +include '@CMAKE_CURRENT_SOURCE_DIR@/cython/off_reader.pyx' +include '@CMAKE_CURRENT_SOURCE_DIR@/cython/simplex_tree.pyx' +include '@CMAKE_CURRENT_SOURCE_DIR@/cython/rips_complex.pyx' +include '@CMAKE_CURRENT_SOURCE_DIR@/cython/cubical_complex.pyx' +include '@CMAKE_CURRENT_SOURCE_DIR@/cython/periodic_cubical_complex.pyx' +include '@CMAKE_CURRENT_SOURCE_DIR@/cython/persistence_graphical_tools.py' +include '@CMAKE_CURRENT_SOURCE_DIR@/cython/reader_utils.pyx' +include '@CMAKE_CURRENT_SOURCE_DIR@/cython/witness_complex.pyx' +include '@CMAKE_CURRENT_SOURCE_DIR@/cython/strong_witness_complex.pyx' @GUDHI_CYTHON_ALPHA_COMPLEX@ @GUDHI_CYTHON_EUCLIDEAN_WITNESS_COMPLEX@ @GUDHI_CYTHON_SUBSAMPLING@ diff --git a/src/cython/include/Cubical_complex_interface.h b/src/cython/include/Cubical_complex_interface.h index 7c0148f1..fad92c2c 100644 --- a/src/cython/include/Cubical_complex_interface.h +++ b/src/cython/include/Cubical_complex_interface.h @@ -43,6 +43,12 @@ class Cubical_complex_interface : public Bitmap_cubical_complex<CubicalComplexOp : Bitmap_cubical_complex<CubicalComplexOptions>(dimensions, top_dimensional_cells) { } + Cubical_complex_interface(const std::vector<unsigned>& dimensions, + const std::vector<double>& top_dimensional_cells, + const std::vector<bool>& periodic_dimensions) + : Bitmap_cubical_complex<CubicalComplexOptions>(dimensions, top_dimensional_cells, periodic_dimensions) { + } + Cubical_complex_interface(const std::string& perseus_file) : Bitmap_cubical_complex<CubicalComplexOptions>(perseus_file.c_str()) { } diff --git a/src/cython/include/PSSK_interface.h b/src/cython/include/PSSK_interface.h deleted file mode 100644 index 3b2d336a..00000000 --- a/src/cython/include/PSSK_interface.h +++ /dev/null @@ -1,63 +0,0 @@ -/* 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): Pawel Dlotko - * - * Copyright (C) 2017 Swansea University - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef PSSK_INTERFACE_H_ -#define PSSK_INTERFACE__H_ - - -#include <gudhi/PSSK.h> - - -namespace Gudhi { -namespace Persistence_representations { - -/** -* This is a version of a representation presented in https://arxiv.org/abs/1412.6821 -* In that paper the authors are using the representation just to compute kernel. Over here, we extend the usability by -*far. -* Note that the version presented here is not exact, since we are discretizing the kernel. -* The only difference with respect to the original class is the method of creation. We have full (square) image, and for -*every point (p,q), we add a kernel at (p,q) and the negative kernel -* at (q,p) -**/ - -class PSSK_interface : public PSSK { - public: - PSSK_interface(){} - - PSSK_interface(const std::vector<std::pair<double, double> >& interval, - std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1), size_t number_of_pixels = 1000, - double min_ = -1, double max_ = -1) - : - PSSK(interval,filter,number_of_pixels,min_,max_){} - - PSSK_interface(const char* filename, std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1), - size_t number_of_pixels = 1000, double min_ = -1, double max_ = -1, - unsigned dimension = std::numeric_limits<unsigned>::max()) - :PSSK(filename,filter,number_of_pixels,min_,max_,dimension){} - -}; - -} // namespace Persistence_representations -} // namespace Gudhi - -#endif // PSSK_INTERFACE__H_ diff --git a/src/cython/include/Persistence_heat_maps_interface.h b/src/cython/include/Persistence_heat_maps_interface.h deleted file mode 100644 index fe565313..00000000 --- a/src/cython/include/Persistence_heat_maps_interface.h +++ /dev/null @@ -1,156 +0,0 @@ -/* 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): Pawel Dlotko - * - * Copyright (C) 2016 INRIA (France) - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef PERSISTENCE_HEAT_MAPS_INTERFACE_H_ -#define PERSISTENCE_HEAT_MAPS_INTERFACE_H_ - -// gudhi include -#include <gudhi/read_persistence_from_file.h> -#include <gudhi/common_persistence_representations.h> - -// standard include -#include <vector> -#include <sstream> -#include <iostream> -#include <cmath> -#include <limits> -#include <algorithm> -#include <utility> -#include <string> -#include <functional> - -namespace Gudhi { -namespace Persistence_representations { - -class Persistence_heat_maps_interface : public Persistence_heat_maps { - public: - - Persistence_heat_maps_interface():Persistence_heat_maps(){} - - - Persistence_heat_maps_interface(const std::vector<std::pair<double, double> >& interval, - std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1), - bool erase_below_diagonal = false, size_t number_of_pixels = 1000, - double min_ = std::numeric_limits<double>::max(), - double max_ = std::numeric_limits<double>::max()): - Persistence_heat_maps(interval,filter,erase_below_diagonal,number_of_pixels,min_max_){} - - - Persistence_heat_maps_interface(const char* filename, std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1), - bool erase_below_diagonal = false, size_t number_of_pixels = 1000, - double min_ = std::numeric_limits<double>::max(), - double max_ = std::numeric_limits<double>::max(), - unsigned dimension = std::numeric_limits<unsigned>::max()): - Persistence_heat_maps(filename,filter,erase_below_diagonal,number_of_pixels,min_,max_,dimension){} - - void compute_mean_interface(const std::vector<Persistence_heat_maps*>& maps) - { - this->compute_mean(maps); - } - - void compute_median_interface(const std::vector<Persistence_heat_maps*>& maps) - { - this->compute_median(maps); - } - - void compute_percentage_of_active_interface(const std::vector<Persistence_heat_maps*>& maps, size_t cutoff = 1) - { - this->compute_percentage_of_active(maps,cutoff); - } - - void print_to_file_interface(const char* filename) const - { - this->print_to_file(filename); - } - - void load_from_file_interface(const char* filename) - { - this->load_from_file( filename ); - } - - inline bool check_if_the_same_interface(const Persistence_heat_maps& second) const - { - return this->check_if_the_same( second ); - } - - inline double get_min_interface() const - { - return this->get_min(); - } - - inline double get_max_interface() const - { - return this->get_max(); - } - - std::vector<double> vectorize_interface(int number_of_function) const - { - return this->vectorize(number_of_function); - } - - size_t number_of_vectorize_functions_interface() const - { - return this->number_of_vectorize_functions(); - } - - double project_to_R_interface(int number_of_function) const - { - return this->project_to_R( number_of_function ); - } - - size_t number_of_projections_to_R_interface() const - { - return this->number_of_projections_to_R(); - } - - double distance_interface(const Persistence_heat_maps& second_, double power = 1) const - { - return this->distance( second, power ); - } - - void compute_average_interface(const std::vector<Persistence_heat_maps*>& to_average) - { - this->compute_average( to_average ); - } - - double compute_scalar_product_interface(const Persistence_heat_maps& second_) const - { - return this->compute_scalar_product( second_ ); - } - - std::pair<double, double> get_x_range_interface() const - { - return this->get_x_range(); - } - - std::pair<double, double> get_y_range_interface() const - { - return this->get_y_range(); - } - -}; - - -} // namespace Persistence_representations -} // namespace Gudhi - -#endif // PERSISTENCE_HEAT_MAPS_INTERFACE_H_ diff --git a/src/cython/include/Persistence_intervals_interface.h b/src/cython/include/Persistence_intervals_interface.h deleted file mode 100644 index c3a3dde7..00000000 --- a/src/cython/include/Persistence_intervals_interface.h +++ /dev/null @@ -1,59 +0,0 @@ -/* 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): Pawel Dlotko - * - * Copyright (C) 2017 Swansea University - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_PERSISTENCE_REPRESENTATIONS_INTERVALS_ -#define INCLUDE_PERSISTENCE_REPRESENTATIONS_INTERVALS_ - -#include <gudhi/Persistence_intervals.h> - -#include <iostream> -#include <vector> -#include <string> - -namespace Gudhi { - -//But if we want to have the same names of classes in C++ and cyton side we ned this interface, because othervise we will have a name conflict. And we want to have the same names on the -//C++ and python side for various reasonc (clarity, documentantions etc.). -//If the C++ class we inherid from are template class, we are inherid from concretization, for instance Persistence_intervals<double>. -//Also in this class, we create an interface functions that will be used in the python side. That will allow to have the same name of the functions in the C++ and python side. - -namespace Persistence_representations { - -class Persistence_intervals_interface : public Persistence_intervals -{ - public: - Persistence_intervals_interface(const char* filename, unsigned dimension = std::numeric_limits<unsigned>::max()) - : Persistence_intervals(filename, dimension) { - } - - Persistence_intervals_interface(const std::vector<std::pair<double, double> >& intervals) - : Persistence_intervals(intervals) { - } - -}; - -} // namespace Persistence_representations - -} // namespace Gudhi - -#endif // INCLUDE_PERSISTENCE_REPRESENTATIONS_DIAGRAMS_ - diff --git a/src/cython/include/Persistence_intervals_with_distances_interface.h b/src/cython/include/Persistence_intervals_with_distances_interface.h deleted file mode 100644 index 05caa14a..00000000 --- a/src/cython/include/Persistence_intervals_with_distances_interface.h +++ /dev/null @@ -1,43 +0,0 @@ -/* This file is part of the Gudhi hiLibrary. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Pawel Dlotko - * - * Copyright (C) 2017 Swansea University - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef PERSISTENCE_INTERVALS_WITH_DISTANCES_INTERFACE_H_ -#define PERSISTENCE_INTERVALS_WITH_DISTANCES_INTERFACE_H_ - -#include <gudhi/Persistence_intervals_with_distances.h> - -namespace Gudhi { -namespace Persistence_representations { - -class Persistence_intervals_with_distances_interface : public Persistence_intervals_with_distances { - public: - double distance_interface(const Persistence_intervals_with_distances& second, double power = std::numeric_limits<double>::max(), - double tolerance = 0) const - { - return this->distance( second, power, tolerance ); - } -}; - -} // namespace Persistence_representations -} // namespace Gudhi - -#endif // PERSISTENCE_INTERVALS_WITH_DISTANCES_INTERFACE_H_ diff --git a/src/cython/include/Persistence_landscape_interface.h b/src/cython/include/Persistence_landscape_interface.h deleted file mode 100644 index 339031d4..00000000 --- a/src/cython/include/Persistence_landscape_interface.h +++ /dev/null @@ -1,200 +0,0 @@ -/* 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): Pawel Dlotko - * - * Copyright (C) 2017 Swansea University - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef PERSISTENCE_LANDSCAPE_INTERFACE_H_ -#define PERSISTENCE_LANDSCAPE_INTERFACE_H_ - -#include <gudhi/Persistence_landscape.h> - -namespace Gudhi { -namespace Persistence_representations { - - -class Persistence_landscape_interface : public Persistence_landscape -{ - public: - Persistence_landscape_interface():Persistence_landscape(){} - - Persistence_landscape_interface(const std::vector<std::pair<double, double> >& p, size_t number_of_levels = std::numeric_limits<size_t>::max() ):Persistence_landscape(p,number_of_levels){} - - Persistence_landscape_interface(const char* filename, size_t dimension = std::numeric_limits<unsigned>::max() , size_t number_of_levels = std::numeric_limits<size_t>::max() ):Persistence_landscape(filename,dimension,number_of_levels){} - - //**************** - static Persistence_landscape_interface* construct_from_file( const char* filename, size_t dimension = std::numeric_limits<unsigned>::max() , size_t number_of_levels = std::numeric_limits<size_t>::max() ) - { - Persistence_landscape_interface* result = new Persistence_landscape_interface(filename,dimension,number_of_levels); - return result; - } - static Persistence_landscape_interface* construct_from_vector_of_pairs( const std::vector<std::pair<double, double> >& p, size_t number_of_levels = std::numeric_limits<size_t>::max() ) - { - Persistence_landscape_interface* result = new Persistence_landscape_interface(p,number_of_levels); - return result; - } - - //**************** - - - Persistence_landscape_interface* new_abs_interface() - { - return (Persistence_landscape_interface*)this->new_abs(); - } - - void new_compute_average(const std::vector<Persistence_landscape_interface*>& to_average) - { - std::vector<Persistence_landscape*> to_average_new; - to_average_new.reserve( to_average.size() ); - for ( size_t i = 0 ; i != to_average.size() ; ++i ) - { - to_average_new.push_back( (Persistence_landscape*)to_average[i] ); - } - this->compute_average(to_average_new); - } - -/* - void load_landscape_from_file_interface(const char* filename) - { - this->load_landscape_from_file(filename); - } - - - void print_to_file_interface(const char* filename) const - { - this->print_to_file(filename); - } - - - double compute_integral_of_landscape_interface() const - { - return this->compute_integral_of_landscape(); - } - - - double compute_integral_of_a_level_of_a_landscape_interface(size_t level) const - { - return this->compute_integral_of_a_level_of_a_landscape(level); - } - - - double compute_integral_of_landscape_interface(double p) const - { - return this->compute_integral_of_landscape(p); - } - - - double compute_value_at_a_given_point_interface(unsigned level, double x) const - { - return this->compute_value_at_a_given_point(level,x); - } - - - double compute_maximum_interface() const - { - return this->compute_maximum(); - } - - - double compute_minimum_interface() const - { - return this->compute_minimum(); - } - - - - double compute_norm_of_landscape_interface(double i) - { - return this->compute_norm_of_landscape(i); - } - - - Persistence_landscape abs_interface() - { - return this->abs(); - } - - size_t size_interface() const - { - return this->size(); - } - - double find_max_interface(unsigned lambda) const - { - return this->find_max(); - } - - friend double compute_inner_product_interface(const Persistence_landscape& l1, const Persistence_landscape& l2) - { - return this->compute_inner_product(l1,l2); - } - - double project_to_R_interface(int number_of_function) const - { - return this->project_to_R(number_of_function); - } - - - size_t number_of_projections_to_R_interface() const - { - return this->number_of_projections_to_R(); - } - - - std::vector<double> vectorize_interface(int number_of_function) const - { - return this->vectorize( number_of_function ); - } - - - size_t number_of_vectorize_function_interface() const - { - return this->number_of_vectorize_function(); - } - - - void compute_average_interface(const std::vector<Persistence_landscape*>& to_average) - { - return this->compute_average(to_average); - } - - - double distance_interface(const Persistence_landscape& second, double power = 1) - { - return this->distance( second, power ); - } - - - double compute_scalar_product_interface(const Persistence_landscape& second) const - { - return this->compute_scalar_product( second ); - } - - - std::pair<double, double> get_y_range_interface(size_t level = 0) const - { - return this->get_y_range( level ); - } - */ -}; - -} // namespace Persistence_representations -} // namespace Gudhi - -#endif // PERSISTENCE_LANDSCAPE_INTERFACE_H_ diff --git a/src/cython/include/Persistence_landscape_on_grid_interface.h b/src/cython/include/Persistence_landscape_on_grid_interface.h deleted file mode 100644 index c8ba9f76..00000000 --- a/src/cython/include/Persistence_landscape_on_grid_interface.h +++ /dev/null @@ -1,207 +0,0 @@ -/* 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): Pawel Dlotko - * - * Copyright (C) 2017 Swansea University - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef PERSISTENCE_LANDSCAPE_ON_GRID_INTERFACE_H_ -#define PERSISTENCE_LANDSCAPE_ON_GRID_INTERFACE_H_ - -#include <gudhi/Persistence_landscape_on_grid.h> - -namespace Gudhi { -namespace Persistence_representations { - - -class Persistence_landscape_on_grid_interface : public Persistence_landscape_on_grid -{ - public: - Persistence_landscape_on_grid_interface():Persistence_landscape_on_grid(){} - - Persistence_landscape_on_grid_interface(const std::vector<std::pair<double, double> >& p, double grid_min_, double grid_max_, - size_t number_of_points_):Persistence_landscape_on_grid(p, grid_min_, grid_max_,number_of_points_){} - - - Persistence_landscape_on_grid_interface(const std::vector<std::pair<double, double> >& p, double grid_min_, double grid_max_, - size_t number_of_points_, unsigned number_of_levels_of_landscape): - Persistence_landscape_on_grid(p, grid_min_, grid_max_,number_of_points_, number_of_levels_of_landscape){} - - - Persistence_landscape_on_grid_interface(const char* filename, double grid_min_, double grid_max_, size_t number_of_points_, - unsigned number_of_levels_of_landscape, uint16_t dimension_ = std::numeric_limits<uint16_t>::max()): - Persistence_landscape_on_grid(filename, grid_min_, grid_max_, number_of_points_, number_of_levels_of_landscape, dimension_ ){} - - - Persistence_landscape_on_grid_interface(const char* filename, double grid_min_, double grid_max_, size_t number_of_points_, - uint16_t dimension_ = std::numeric_limits<uint16_t>::max()):Persistence_landscape_on_grid(filename,grid_min_,grid_max_,number_of_points_,dimension_ ){} - - - Persistence_landscape_on_grid_interface(const char* filename, size_t number_of_points, unsigned number_of_levels_of_landscape, uint16_t dimension = std::numeric_limits<uint16_t>::max()): - Persistence_landscape_on_grid(filename,number_of_points,number_of_levels_of_landscape,dimension){} - - - Persistence_landscape_on_grid_interface(const char* filename, size_t number_of_points, uint16_t dimension = std::numeric_limits<uint16_t>::max()): - Persistence_landscape_on_grid(filename,number_of_points,dimension){} - - - Persistence_landscape_on_grid_interface* new_abs_interface() - { - return (Persistence_landscape_on_grid_interface*)this->new_abs(); - } - - void new_compute_average(const std::vector<Persistence_landscape_on_grid_interface*>& to_average) - { - std::vector<Persistence_landscape_on_grid*> to_average_new; - to_average_new.reserve( to_average.size() ); - for ( size_t i = 0 ; i != to_average.size() ; ++i ) - { - to_average_new.push_back( (Persistence_landscape_on_grid*)to_average[i] ); - } - this->compute_average(to_average_new); - } - - -/* - void load_landscape_from_file_interface(const char* filename) - { - this->load_landscape_from_file(filename); - } - - - void print_to_file_interface(const char* filename) const - { - this->print_to_file(filename); - } - - - double compute_integral_of_landscape_interface() const - { - return this->compute_integral_of_landscape(); - } - - - double compute_integral_of_a_level_of_a_landscape_interface(size_t level) const - { - return this->compute_integral_of_a_level_of_a_landscape(level); - } - - - double compute_integral_of_landscape_interface(double p) const - { - return this->compute_integral_of_landscape(p); - } - - - double compute_value_at_a_given_point_interface(unsigned level, double x) const - { - return this->compute_value_at_a_given_point(level,x); - } - - - double compute_maximum_interface() const - { - return this->compute_maximum(); - } - - - double compute_minimum_interface() const - { - return this->compute_minimum(); - } - - - double compute_norm_of_landscape_interface(double i) - { - return this->compute_norm_of_landscape(i); - } - - - Persistence_landscape abs_interface() - { - return this->abs(); - } - - size_t size_interface() const - { - return this->size(); - } - - double find_max_interface(unsigned lambda) const - { - return this->find_max(); - } - - friend double compute_inner_product_interface(const Persistence_landscape& l1, const Persistence_landscape& l2) - { - return this->compute_inner_product(l1,l2); - } - - double project_to_R_interface(int number_of_function) const - { - return this->project_to_R(number_of_function); - } - - - size_t number_of_projections_to_R_interface() const - { - return this->number_of_projections_to_R(); - } - - - std::vector<double> vectorize_interface(int number_of_function) const - { - return this->vectorize( number_of_function ); - } - - - size_t number_of_vectorize_function_interface() const - { - return this->number_of_vectorize_function(); - } - - - void compute_average_interface(const std::vector<Persistence_landscape*>& to_average) - { - return this->compute_average(to_average); - } - - - double distance_interface(const Persistence_landscape& second, double power = 1) - { - return this->distance( second, power ); - } - - - double compute_scalar_product_interface(const Persistence_landscape& second) const - { - return this->compute_scalar_product( second ); - } - - - std::pair<double, double> get_y_range_interface(size_t level = 0) const - { - return this->get_y_range( level ); - } - */ -}; - -} // namespace Persistence_representations -} // namespace Gudhi - -#endif // PERSISTENCE_LANDSCAPE_ON_GRID_INTERFACE_H_ diff --git a/src/cython/include/Persistence_vectors_interface.h b/src/cython/include/Persistence_vectors_interface.h deleted file mode 100644 index 3fc482d7..00000000 --- a/src/cython/include/Persistence_vectors_interface.h +++ /dev/null @@ -1,121 +0,0 @@ -/* 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): Pawel Dlotko - * - * Copyright (C) 2016 INRIA (France) - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef PERSISTENCE_VECTORS_INTERFACE_H_ -#define PERSISTENCE_VECTORS_INTERFACE_H_ - -// gudhi include -#include <gudhi/persistence_vectors.h> -s -namespace Gudhi { -namespace Persistence_representations { - - - -template <typename F> -class Vector_distances_in_diagram_interface : Vector_distances_in_diagram<Euclidean_distance> { - public: - Vector_distances_in_diagram_interface():Vector_distances_in_diagram(){} - - Vector_distances_in_diagram_interface(const std::vector<std::pair<double, double> >& intervals, size_t where_to_cut): - Vector_distances_in_diagram(intervals,where_to_cut){} - - Vector_distances_in_diagram_interface(const char* filename, size_t where_to_cut, - unsigned dimension = std::numeric_limits<unsigned>::max()): - Vector_distances_in_diagram_interface(filename,where_to_cut,dimension){} - - inline double vector_in_position_interface(size_t position) const - { - return this->vector_in_position(position): - } - - inline size_t size_interface() const - { - return this->size(); - } - - void write_to_file_interface(const char* filename) const - { - this->write_to_file( filename ); - } - - void print_to_file_interface(const char* filename) const - { - this->print_to_file(filename); - } - - void load_from_file_interface(const char* filename) - { - this->load_from_file(filename); - } - - double project_to_R_interface(int number_of_function) const - { - return this->project_to_R(number_of_function); - } - - size_t number_of_projections_to_R_interface() const - { - return this->number_of_projections_to_R(); - } - - std::vector<double> vectorize_interface(int number_of_function) const - { - return this->vectorize(number_of_function); - } - - size_t number_of_vectorize_functions_interface() const - { - return this->number_of_vectorize_functions(); - } - - void compute_average_interface(const std::vector<Vector_distances_in_diagram*>& to_average) - { - this->compute_average(to_average); - } - - double distance_interface(const Vector_distances_in_diagram& second, double power = 1) const - { - return this->distance(second,power); - } - - double compute_scalar_product_interface(const Vector_distances_in_diagram& second) const - { - return this->compute_scalar_product(second); - } - - std::pair<double, double> get_x_range_interface() const - { - return this->get_x_range(); - } - - std::pair<double, double> get_y_range_interface() const - { - return this->get_y_range(); - } - -}; - -} // namespace Persistence_representations -} // namespace Gudhi - -#endif // PERSISTENCE_VECTORS_INTERFACE_H_ diff --git a/src/cython/include/Reader_utils_interface.h b/src/cython/include/Reader_utils_interface.h new file mode 100644 index 00000000..8ec34f61 --- /dev/null +++ b/src/cython/include/Reader_utils_interface.h @@ -0,0 +1,56 @@ +/* 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): Vincent Rouvreau + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef INCLUDE_READER_UTILS_INTERFACE_H_ +#define INCLUDE_READER_UTILS_INTERFACE_H_ + +#include <gudhi/reader_utils.h> + +#include <iostream> +#include <vector> +#include <string> +#include <map> +#include <utility> // for pair<> + +namespace Gudhi { + +// Redefine functions with a different name in order the original name can be used in the Python version. +std::vector<std::vector<double>> read_matrix_from_csv_file(const std::string& filename, + const char separator = ';') { + return read_lower_triangular_matrix_from_csv_file<double>(filename, separator); +} + +inline std::map<int, std::vector<std::pair<double, double>>> + read_pers_intervals_grouped_by_dimension(std::string const& filename) { + return read_persistence_intervals_grouped_by_dimension(filename); +} + +inline std::vector<std::pair<double, double>> + read_pers_intervals_in_dimension(std::string const& filename, int only_this_dim = -1) { + return read_persistence_intervals_in_dimension(filename, only_this_dim); +} + + +} // namespace Gudhi + + +#endif // INCLUDE_READER_UTILS_INTERFACE_H_ diff --git a/src/cython/include/Rips_complex_interface.h b/src/cython/include/Rips_complex_interface.h index 6d813f4a..02985727 100644 --- a/src/cython/include/Rips_complex_interface.h +++ b/src/cython/include/Rips_complex_interface.h @@ -66,11 +66,15 @@ class Rips_complex_interface { } else { // Rips construction where values is a distance matrix Distance_matrix distances = - read_lower_triangular_matrix_from_csv_file<Simplex_tree_interface<>::Filtration_value>(file_name); + Gudhi::read_lower_triangular_matrix_from_csv_file<Simplex_tree_interface<>::Filtration_value>(file_name); rips_complex_ = new Rips_complex<Simplex_tree_interface<>::Filtration_value>(distances, threshold); } } + ~Rips_complex_interface() { + delete rips_complex_; + } + void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, int dim_max) { rips_complex_->create_complex(*simplex_tree, dim_max); simplex_tree->initialize_filtration(); diff --git a/src/cython/include/Simplex_tree_interface.h b/src/cython/include/Simplex_tree_interface.h index 09e7e992..54a4f824 100644 --- a/src/cython/include/Simplex_tree_interface.h +++ b/src/cython/include/Simplex_tree_interface.h @@ -52,6 +52,10 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> { return (Base::find(vh) != Base::null_simplex()); } + void assign_simplex_filtration(const Simplex& vh, Filtration_value filtration) { + Base::assign_filtration(Base::find(vh), filtration); + } + bool insert(const Simplex& simplex, Filtration_value filtration = 0) { Insertion_result result = Base::insert_simplex_and_subfaces(simplex, filtration); return (result.second); diff --git a/src/cython/include/Tangential_complex_interface.h b/src/cython/include/Tangential_complex_interface.h index 5e9dc0e4..ecf014b3 100644 --- a/src/cython/include/Tangential_complex_interface.h +++ b/src/cython/include/Tangential_complex_interface.h @@ -106,8 +106,6 @@ class Tangential_complex_interface { void create_simplex_tree(Simplex_tree<>* simplex_tree) { int max_dim = tangential_complex_->create_complex<Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_full_featured>>(*simplex_tree); - // FIXME - simplex_tree->set_dimension(max_dim); simplex_tree->initialize_filtration(); } diff --git a/src/cython/setup.py.in b/src/cython/setup.py.in index c1a1717a..fefa36bb 100644 --- a/src/cython/setup.py.in +++ b/src/cython/setup.py.in @@ -29,7 +29,7 @@ __license__ = "GPL v3" gudhi = Extension( "gudhi", - sources = ['gudhi.pyx',], + sources = ['@CMAKE_CURRENT_BINARY_DIR@/gudhi.pyx',], language = 'c++', extra_compile_args=[@GUDHI_CYTHON_EXTRA_COMPILE_ARGS@], extra_link_args=[@GUDHI_CYTHON_EXTRA_LINK_ARGS@], diff --git a/src/cython/test/test_cubical_complex.py b/src/cython/test/test_cubical_complex.py index 9a365823..0e81554d 100755 --- a/src/cython/test/test_cubical_complex.py +++ b/src/cython/test/test_cubical_complex.py @@ -62,17 +62,17 @@ def test_dimension_or_perseus_file_constructor(): assert cub.__is_defined() == False assert cub.__is_persistence_defined() == False -def test_dimension_constructor(): +def test_dimension_simple_constructor(): cub = CubicalComplex(dimensions=[3, 3], top_dimensional_cells = [1,2,3,4,5,6,7,8,9]) assert cub.__is_defined() == True assert cub.__is_persistence_defined() == False - assert cub.persistence() == [(1, (0.0, 100.0)), (0, (0.0, float('inf')))] + assert cub.persistence() == [(0, (1.0, float('inf')))] assert cub.__is_persistence_defined() == True - assert cub.betti_numbers() == [1, 0] - assert cub.persistent_betti_numbers(0, 1000) == [0, 0] + assert cub.betti_numbers() == [1, 0, 0] + assert cub.persistent_betti_numbers(0, 1000) == [0, 0, 0] -def test_dimension_constructor(): +def test_dimension_file_constructor(): # Create test file test_file = open('CubicalOneSphere.txt', 'w') test_file.write('2\n3\n3\n0\n0\n0\n0\n100\n0\n0\n0\n0\n') diff --git a/src/cython/test/test_persistence_representations_intervals.py b/src/cython/test/test_persistence_representations_intervals.py deleted file mode 100755 index a8f3686e..00000000 --- a/src/cython/test/test_persistence_representations_intervals.py +++ /dev/null @@ -1,87 +0,0 @@ -import gudhi - -"""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): Pawel Dlotko - - Copyright (C) 2017 Swansea University - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Pawel Dlotko" -__copyright__ = "Copyright (C) 2017 Swansea University" -__license__ = "GPL v3" - -epsilon = 0.0000005; - -def test_check_min_max_function(): - p = gudhi.PersistenceIntervals(None,None,"data/file_with_diagram") - min_max_ = p.get_x_range() - assert fabs(min_max_[0] - 0.0290362) <= epsilon - assert fabs(min_max_[1] - 0.994537) <= epsilon - -def test_check_length_of_dominant_intervals(): - p = gudhi.PersistenceIntervals(None,None,"data/file_with_diagram") - dominant_ten_intervals_length = p.length_of_dominant_intervals(10) - dominant_intervals_length_ = [0.862625,0.800893,0.762061,0.756501,0.729367,0.718177,0.708395,0.702844,0.700468,0.622177] - assert dominant_ten_intervals_length == dominant_intervals_length_ - - -def test_check_dominant_intervals(): - p = gudhi.PersistenceIntervals(None,None,"data/file_with_diagram"); - ten_dominant_intervals = p.dominant_intervals(10); - templ = [ (0.114718, 0.977343) , (0.133638, 0.93453) , (0.104599, 0.866659) , (0.149798, 0.906299), (0.247352, 0.976719) , (0.192675, 0.910852) , (0.191836, 0.900231) , (0.284998, 0.987842) , (0.294069, 0.994537), (0.267421, 0.889597)] - assert fabs(ten_dominant_intervals - templ) <= epsilon - - -def test_check_histogram_of_lengths(): - p = gudhi.PersistenceIntervals(None,None,"data/file_with_diagram") - histogram = p.histogram_of_lengths(10); - template_histogram = [10,5,3,4,4,3,6,1,7,1,1] - assert fabs(histogram - template_histogram) <= epsilon - - -def test_check_cumulative_histograms_of_lengths(): - p = gudhi.PersistenceIntervals(None,None,"data/file_with_diagram") - cumulative_histogram = p.cumulative_histogram_of_lengths(10) - template_cumulative_histogram = [10,15,18,22,26,29,35,36,43,44,45] - assert fabs(cumulative_histogram - template_cumulative_histogram) <= epsilon - - -def test_check_characteristic_function_of_diagram(): - p = gudhi.PersistenceIntervals(None,None,"data/file_with_diagram") - min_max_ = p.get_x_range(); - char_funct_diag = p.characteristic_function_of_diagram(min_max_[0], min_max_[1]); - template_char_funct_diag = [0.370665,0.84058,1.24649,1.3664,1.34032,1.31904,1.14076,0.991259,0.800714,0.0676303] - assert fabs(char_funct_diag - template_char_funct_diag) <= 0.0001 - - -def test_check_cumulative_characteristic_function_of_diagram(): - p = gudhi.PersistenceIntervals(None,None,"data/file_with_diagram") - min_max_ = p.get_x_range() - cumul_char_funct_diag = p.cumulative_characteristic_function_of_diagram(min_max_.first, min_max_.second,None); - template_char_funct_diag_cumul = [0.370665,1.21125,2.45774,3.82414,5.16446,6.4835,7.62426,8.61552,9.41623,9.48386] - assert fabs(cumul_char_funct_diag - template_char_funct_diag_cumul) <= 0.0001 - - -def test_check_compute_persistent_betti_numbers(): - p = gudhi.PersistenceIntervals(None,None,"data/file_with_diagram") - pbns = [(0.0290362, 1),(0.0307676, 2),(0.0366312, 3),(0.0544614, 4),(0.0920033, 5),(0.104599, 6),(0.114718, 7),(0.117379, 8),(0.123493, 9),(0.133638, 10)(0.137798, 9),(0.149798, 10),(0.155421, 11),(0.158443, 12)(0.176956, 13),(0.183234, 12),(0.191069, 13),(0.191333, 14),(0.191836, 15),(0.192675, 16),(0.208564, 17),(0.218425, 18),(0.219902, 17),(0.23233, 16),(0.234558, 17),(0.237166, 16),(0.247352, 17),(0.267421, 18),(0.268093, 19),(0.278734, 18),(0.284722, 19),(0.284998, 20),(0.294069, 21),(0.306293, 22),(0.322361, 21),(0.323152, 22),(0.371021, 23),(0.372395, 24),(0.387744, 25),(0.435537, 26),(0.462911, 25),(0.483569, 26),(0.489209, 25),(0.517115, 24),(0.522197, 23),(0.532665, 22),(0.545262, 23),(0.587227, 22),(0.593036, 23),(0.602647, 24),(0.605044, 25),(0.621962, 24),(0.629449, 23),(0.636719, 22),(0.64957, 21),(0.650781, 22),(0.654951, 23),(0.683489, 24),(0.687172, 23),(0.69703, 22),(0.701174, 21),(0.717623, 22),(0.722023, 21),(0.722298, 20),(0.725347, 19),(0.73071, 18),(0.758355, 17),(0.770913, 18),(0.790833, 17),(0.821211, 16),(0.849305, 17),(0.853669, 16),(0.866659, 15),(0.872896, 16),(0.889597, 15),(0.900231, 14),(0.903847, 13),(0.906299, 12),(0.910852, 11),(0.93453, 10),(0.944757, 9),(0.947812, 8),(0.959154, 7),(0.975654, 6),(0.976719, 5),(0.977343, 4),(0.980129, 3),(0.987842, 2),(0.990127, 1),(0.994537, 0)] - pbns_new = p.compute_persistent_betti_numbers(); - assert fabs(pbns - pbns_new) <= epsilon - - diff --git a/src/cython/test/test_persistence_representations_landscapes.py b/src/cython/test/test_persistence_representations_landscapes.py deleted file mode 100755 index f5e6f351..00000000 --- a/src/cython/test/test_persistence_representations_landscapes.py +++ /dev/null @@ -1,139 +0,0 @@ -import gudhi - -""" - 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): Pawel Dlotko - - Copyright (C) 2017 Swansea University - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Pawel Dlotko" -__copyright__ = "Copyright (C) 2017 Swansea University" -__license__ = "GPL v3" - -epsilon = 0.0000005; - -def test_check_construction_of_landscape(): - p = gudhi.Persistence_landscape("data/file_with_diagram",0) - q = gudhi.Persistence_landscape() - q.load_landscape_from_file("data/file_with_landscape_from_file_with_diagram") - assert p == q - - -def test_check_construction_of_landscape_form_gudhi_style_file(): - p = gudhi.Persistence_landscape("data/persistence_file_with_four_entries_per_line", 1) - q = gudhi.Persistence_landscape() - q.load_landscape_from_file("data/persistence_file_with_four_entries_per_line_landscape"); - assert p == q; - -def test_check_computations_of_integrals(): - p = gudhi.Persistence_landscape("data/file_with_diagram",0) - integral = p.compute_integral_of_landscape() - assert fabs(integral - 2.34992) <= 0.00001 - - -def test_check_computations_of_integrals_for_each_level_separatelly(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram"); - p = gudhi.Persistence_landscape(diag) - integrals_for_different_levels = [0.216432,0.204763,0.188793,0.178856,0.163142,0.155015,0.143046,0.133765,0.123531,0.117393,0.111269,0.104283,0.0941308,0.0811208,0.0679001,0.0580801,0.0489647,0.0407936,0.0342599,0.02896,0.0239881,0.0171792,0.0071511,0.00462067,0.00229033,0.000195296] - for lv in range(0, len(integrals_for_different_levels)): - integral = p.compute_integral_of_a_level_of_a_landscape(lv); - assert fabs(integral - integrals_fir_different_levels[lv]) <= 0.00001 - -def test_check_computations_of_integrals_of_powers_of_landscape(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag) - integrals_fir_different_powers = [17.1692,2.34992,0.49857,0.126405,0.0355235] - for power in range(0,5): - integral = p.compute_integral_of_landscape(power) - assert fabs(integral - integrals_fir_different_powers[power]) <= 0.00005 - - -def test_check_computations_of_values_on_different_points(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag); - assert fabs(p.compute_value_at_a_given_point(1, 0.0)) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(1, 0.1) - 0.0692324) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(1, 0.2) - 0.163369) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(1, 0.3) - 0.217115) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(2, 0.0)) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(2, 0.1) - 0.0633688) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(2, 0.2) - 0.122361) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(2, 0.3) - 0.195401) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(3, 0.0)) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(3, 0.1) - 0.0455386) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(3, 0.2) - 0.0954012) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(3, 0.3) - 0.185282) <= 0.00001 - - -def test_check_computations_of_maxima_and_norms(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag) - second = gudhi.Persistence_landscape() - second.load_landscape_from_file("data/file_with_landscape_from_file_with_diagram_1") - sum_ = gudhi.Persistence_landscape() - sum_ = p + second; - assert fabs(p.compute_maximum() - 0.431313) <= 0.00001 - assert fabs(p.compute_norm_of_landscape(1) - 2.34992) <= 0.00001 - assert fabs(p.compute_norm_of_landscape(2) - 0.706095) <= 0.00001 - assert fabs(p.compute_norm_of_landscape(3) - 0.501867) <= 0.00001 - assert fabs(compute_distance_of_landscapes(p, sum_, 1) - 27.9323) <= 0.00005 - assert fabs(compute_distance_of_landscapes(p, sum_, 2) - 2.35199) <= 0.00001 - - - -def test_check_default_parameters_of_distances(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag) - diag1 = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram_1") - q = gudhi.Persistence_landscape(diag1) - dist_numeric_limit_max = p.distance(q, sys.float_info.max); - dist_infinity = p.distance(q, sys.float_info.max); - assert dist_numeric_limit_max == dist_infinity - - -def test_check_computations_of_averages(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag) - diag2 = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram_1") - q = gudhi.Persistence_landscape(diag2) - av = gudhi.Persistence_landscape() - av.compute_average({p, q}) - template_average = Persistence_landscape() - template_averagetemplate_average.load_landscape_from_file("data/average") - assert template_average == av - - -def test_check_computations_of_distances(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag) - diag2 = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram_1") - q = Persistence_landscape(diag2) - assert fabs(p.distance(q) - 25.5824) <= 0.00005 - assert fabs(p.distance(q, 2) - 2.12636) <= 0.00001 - assert fabs(p.distance(q, sys.float_info.max) - 0.359068) <= 0.00001 - - -def test_check_computations_of_scalar_product(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag) - diag2 = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram_1") - q = Persistence_landscape(diag2) - assert fabs(p.compute_scalar_product(q) - 0.754498) <= 0.00001 - diff --git a/src/cython/test/test_persistence_representations_landscapes_on_grid.py b/src/cython/test/test_persistence_representations_landscapes_on_grid.py deleted file mode 100755 index 7a1f2b49..00000000 --- a/src/cython/test/test_persistence_representations_landscapes_on_grid.py +++ /dev/null @@ -1,120 +0,0 @@ -import gudhi - -""" - 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): Pawel Dlotko - - Copyright (C) 2017 Swansea University - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Pawel Dlotko" -__copyright__ = "Copyright (C) 2017 Swansea University" -__license__ = "GPL v3" - -epsilon = 0.0000005; - - - -def test_check_construction_of_landscape: - l = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram_1", 100,sys.maxsize) - l.print_to_file("landscape_from_file_with_diagram_1") - g = gudhi.PersistenceLandscapeOnGrid() - g.load_landscape_from_file("landscape_from_file_with_diagram_1") - assert l == g - - -def test_check_construction_of_landscape_using_only_ten_levels: - number = 10 - l = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram_1", 100, number) - g = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram_1", 100, sys.maxsize) - for level in range(0,number): - v1 = l.vectorize(level) - v2 = g.vectorize(level) - assert v1 == v2 - -def test_check_computations_of_integrals: - p = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram_1", 100, sys.maxsize) - integral = p.compute_integral_of_landscape() - assert fabs(integral - 27.343) <= 0.00005 - -def test_check_computations_of_integrals_for_each_level_separatelly: - p = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram_1", 100, sys.maxsize) - integrals_fir_different_levels = [0.241168,0.239276,0.237882,0.235193,0.230115,0.227626,0.226132.0.223643,0.221651,0.220556,0.21727,0.215976,0.213685,0.211993,0.2102,0.208707,0.207014,0.205122,0.204226,0.202633] - for level in range(0,len(integrals_fir_different_levels)) - integral = p.compute_integral_of_landscape(level); - assert fabs(integral - integrals_fir_different_levels[level]) <= 0.00005 - -def test_check_computations_of_integrals_of_powers_of_landscape: - p = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram_1", 100, sys.maxsize) - integrals_fir_different_powers = [0.241168,0.239276,0.237882,0.235193,0.23011] - for power in range(0:5): - integral = p.compute_integral_of_landscape(power) - assert fabs(integral - integrals_fir_different_powers[power]) <= 0.00001 - -def test_check_computations_of_values_on_different_points: - p = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram_1", 100, sys.maxsize) - results_level_0 = [0.00997867,0.0521921,0.104312,0.156432,0.208552,0.260672,0.312792,0.364912,0.417032,0.429237] - results_level_10 = [7.21433e-05,0.0422135,0.0943335,0.146453,0.198573,0.240715,0.272877,0.324997,0.359232,0.379344] - double x = 0.0012321; - double dx = 0.05212; - for i in range(0,10): - assert almost_equal(p.compute_value_at_a_given_point(0, x), results_level_0[i])); - assert almost_equal(p.compute_value_at_a_given_point(10, x), results_level_10[i])); - x += dx; - -def test_check_computations_of_maxima_and_norms: - p = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram_1", 0., 1., 100) - second = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram_2", 0., 1., 100) - assert fabs(p.compute_maximum() - 0.46) <= 0.00001 - assert fabs(p.compute_norm_of_landscape(1) - 27.3373) <= 0.00001 - assert fabs(p.compute_norm_of_landscape(2) - 1.84143) <= 0.00001 - assert fabs(p.compute_norm_of_landscape(3) - 0.927067) <= 0.00001 - -def test_check_default_parameters_of_distances: - diag = read_persistence_intervals_in_dimension("data/file_with_diagram") - p = gudhi.PersistenceLandscapeOnGrid(diag, 0., 1., 100) - diag1 = read_persistence_intervals_in_dimension("data/file_with_diagram_1") - q = gudhi.PersistenceLandscapeOnGrid(diag1, 0., 1., 100) - dist_numeric_limit_max = p.distance(q, sys.maxsize); - dist_infinity = p.distance(q, sys.maxsize); - assert dist_numeric_limit_max == dist_infinity - -def test_check_computations_of_averages: - p = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram", 0., 1., 100) - q = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram_1", 0., 1., 100) - av = gudhi.PersistenceLandscapeOnGrid() - av.compute_average({&p, &q) - - template_average = gudhi.PersistenceLandscapeOnGrid() - template_average.load_landscape_from_file("data/average_on_a_grid") - assert template_average == av - - -def test_check_computations_of_distances: - p = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram", 0., 1., 10000) - q = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram_1", 0., 1., 10000) - assert fabs(p.distance(q) - 25.5779) <= 0.00005 - assert fabs(p.distance(q, 2) - 2.04891) <= 0.00001 - assert fabs(p.distance(q, sys.maxsize) - 0.359) <= 0.00001 - - -def test_check_computations_of_scalar_product: - p = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram", 0., 1., 10000) - q = gudhi.PersistenceLandscapeOnGrid("data/file_with_diagram_1", 0., 1., 10000) - assert almost_equal(p.compute_scalar_product(q), 0.754367) diff --git a/src/cython/test/test_reader_utils.py b/src/cython/test/test_reader_utils.py new file mode 100755 index 00000000..25591fb3 --- /dev/null +++ b/src/cython/test/test_reader_utils.py @@ -0,0 +1,88 @@ +import gudhi + +"""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): Vincent Rouvreau + + Copyright (C) 2017 INRIA + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see <http://www.gnu.org/licenses/>. +""" + +__author__ = "Vincent Rouvreau" +__copyright__ = "Copyright (C) 2017 INRIA" +__license__ = "GPL v3" + + +def test_non_existing_csv_file(): + # Try to open a non existing file + matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file='pouetpouettralala.toubiloubabdou') + assert matrix == [] + +def test_full_square_distance_matrix_csv_file(): + # Create test file + test_file = open('full_square_distance_matrix.csv', 'w') + test_file.write('0;1;2;3;\n1;0;4;5;\n2;4;0;6;\n3;5;6;0;') + test_file.close() + matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file="full_square_distance_matrix.csv") + assert matrix == [[], [1.0], [2.0, 4.0], [3.0, 5.0, 6.0]] + +def test_lower_triangular_distance_matrix_csv_file(): + # Create test file + test_file = open('lower_triangular_distance_matrix.csv', 'w') + test_file.write('\n1,\n2,3,\n4,5,6,\n7,8,9,10,') + test_file.close() + matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file="lower_triangular_distance_matrix.csv", separator=",") + assert matrix == [[], [1.0], [2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0]] + +def test_non_existing_persistence_file(): + # Try to open a non existing file + persistence = gudhi.read_persistence_intervals_grouped_by_dimension(persistence_file='pouetpouettralala.toubiloubabdou') + assert persistence == [] + persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='pouetpouettralala.toubiloubabdou', only_this_dim=1) + assert persistence == [] + +def test_read_persistence_intervals_without_dimension(): + # Create test file + test_file = open('persistence_intervals_without_dimension.pers', 'w') + test_file.write('# Simple persistence diagram without dimension\n2.7 3.7\n9.6 14.\n34.2 34.974\n3. inf') + test_file.close() + persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_without_dimension.pers') + assert persistence == [(2.7, 3.7), (9.6, 14.), (34.2, 34.974), (3., float('Inf'))] + persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_without_dimension.pers', only_this_dim=0) + assert persistence == [] + persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_without_dimension.pers', only_this_dim=1) + assert persistence == [] + persistence = gudhi.read_persistence_intervals_grouped_by_dimension(persistence_file='persistence_intervals_without_dimension.pers') + assert persistence == {-1: [(2.7, 3.7), (9.6, 14.0), (34.2, 34.974), (3.0, float('Inf'))]} + +def test_read_persistence_intervals_with_dimension(): + # Create test file + test_file = open('persistence_intervals_with_dimension.pers', 'w') + test_file.write('# Simple persistence diagram with dimension\n0 2.7 3.7\n1 9.6 14.\n3 34.2 34.974\n1 3. inf') + test_file.close() + persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_with_dimension.pers') + assert persistence == [(2.7, 3.7), (9.6, 14.), (34.2, 34.974), (3., float('Inf'))] + persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_with_dimension.pers', only_this_dim=0) + assert persistence == [(2.7, 3.7)] + persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_with_dimension.pers', only_this_dim=1) + assert persistence == [(9.6, 14.), (3., float('Inf'))] + persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_with_dimension.pers', only_this_dim=2) + assert persistence == [] + persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_with_dimension.pers', only_this_dim=3) + assert persistence == [(34.2, 34.974)] + persistence = gudhi.read_persistence_intervals_grouped_by_dimension(persistence_file='persistence_intervals_with_dimension.pers') + assert persistence == {0: [(2.7, 3.7)], 1: [(9.6, 14.0), (3.0, float('Inf'))], 3: [(34.2, 34.974)]} diff --git a/src/cython/test/test_simplex_tree.py b/src/cython/test/test_simplex_tree.py index 3ae537e3..6dec5d94 100755 --- a/src/cython/test/test_simplex_tree.py +++ b/src/cython/test/test_simplex_tree.py @@ -34,9 +34,13 @@ def test_insertion(): # insert test assert st.insert([0, 1]) == True + + assert st.dimension() == 1 + assert st.insert([0, 1, 2], filtration=4.0) == True - # FIXME: Remove this line - st.set_dimension(2) + + assert st.dimension() == 2 + assert st.num_simplices() == 7 assert st.num_vertices() == 3 @@ -53,7 +57,6 @@ def test_insertion(): assert st.find([2, 3]) == False # filtration test - st.set_filtration(5.0) st.initialize_filtration() assert st.filtration([0, 1, 2]) == 4.0 assert st.filtration([0, 2]) == 4.0 @@ -87,8 +90,9 @@ def test_insertion(): assert st.find([2]) == True st.initialize_filtration() - assert st.persistence() == [(1, (4.0, float('inf'))), (0, (0.0, float('inf')))] + assert st.persistence(persistence_dim_max = True) == [(1, (4.0, float('inf'))), (0, (0.0, float('inf')))] assert st.__is_persistence_defined() == True + assert st.betti_numbers() == [1, 1] assert st.persistent_betti_numbers(-0.1, 10000.0) == [0, 0] assert st.persistent_betti_numbers(0.0, 10000.0) == [1, 0] @@ -130,3 +134,30 @@ def test_expansion(): ([1, 2], 0.5), ([0, 1, 2], 0.5), ([1, 2, 3], 0.5), ([5], 0.6), ([6], 0.6), ([5, 6], 0.6), ([4], 0.7), ([2, 4], 0.7), ([0, 3], 0.8), ([0, 1, 3], 0.8), ([0, 2, 3], 0.8), ([0, 1, 2, 3], 0.8), ([4, 6], 0.9), ([3, 6], 1.0)] + +def test_automatic_dimension(): + st = SimplexTree() + assert st.__is_defined() == True + assert st.__is_persistence_defined() == False + + # insert test + assert st.insert([0,1,3], filtration=0.5) == True + assert st.insert([0,1,2], filtration=1.) == True + + assert st.num_vertices() == 4 + assert st.num_simplices() == 11 + + assert st.dimension() == 2 + assert st.upper_bound_dimension() == 2 + + assert st.prune_above_filtration(0.6) == True + assert st.dimension() == 2 + assert st.upper_bound_dimension() == 2 + + st.assign_filtration([0, 1, 3], 0.7) + assert st.filtration([0, 1, 3]) == 0.7 + + st.remove_maximal_simplex([0, 1, 3]) + assert st.upper_bound_dimension() == 2 + assert st.dimension() == 1 + assert st.upper_bound_dimension() == 1 |
