diff options
| author | tlacombe <lacombe1993@gmail.com> | 2020-02-14 11:24:17 +0100 |
|---|---|---|
| committer | tlacombe <lacombe1993@gmail.com> | 2020-02-14 11:24:17 +0100 |
| commit | 5ad0b45a5b5387f49fe72843a74e96a346c6d6fe (patch) | |
| tree | 69b389d16485c4b3e5187b14d312843b7e2dec93 /src | |
| parent | 6a6bed7ca21c1ffcf6de9ed09c2a6512ecb66585 (diff) | |
| parent | bed30b19e57669c0b8ad385f1124586ed3499a2d (diff) | |
update from upstream/master and merge CMakeLists
Diffstat (limited to 'src')
76 files changed, 1042 insertions, 785 deletions
diff --git a/src/Alpha_complex/doc/Intro_alpha_complex.h b/src/Alpha_complex/doc/Intro_alpha_complex.h index 3c32a1e6..a8b1a106 100644 --- a/src/Alpha_complex/doc/Intro_alpha_complex.h +++ b/src/Alpha_complex/doc/Intro_alpha_complex.h @@ -31,8 +31,8 @@ namespace alpha_complex { * circumsphere is empty (the simplex is then said to be Gabriel), and as the minimum of the filtration * values of the codimension 1 cofaces that make it not Gabriel otherwise. * - * All simplices that have a filtration value strictly greater than a given alpha squared value are not inserted into - * the complex. + * All simplices that have a filtration value \f$ > \alpha^2 \f$ are removed from the Delaunay complex + * when creating the simplicial complex if it is specified. * * \image html "alpha_complex_representation.png" "Alpha-complex representation" * @@ -46,8 +46,8 @@ namespace alpha_complex { * \cite cgal:s-gkd-19b from CGAL as template parameter. * * \remark - * - When the simplicial complex is constructed with an infinite value of alpha, the complex is a Delaunay - * complex. + * - When an \f$\alpha\f$-complex is constructed with an infinite value of \f$ \alpha^2 \f$, the complex is a Delaunay + * complex (with special filtration values). * - For people only interested in the topology of the \ref alpha_complex (for instance persistence), * \ref alpha_complex is equivalent to the \ref cech_complex and much smaller if you do not bound the radii. * \ref cech_complex can still make sense in higher dimension precisely because you can bound the radii. @@ -135,13 +135,13 @@ namespace alpha_complex { * * \subsubsection nondecreasing Non decreasing filtration values * - * As the squared radii computed by CGAL are an approximation, it might happen that these alpha squared values do not - * quite define a proper filtration (i.e. non-decreasing with respect to inclusion). + * As the squared radii computed by CGAL are an approximation, it might happen that these \f$ \alpha^2 \f$ values do + * not quite define a proper filtration (i.e. non-decreasing with respect to inclusion). * We fix that up by calling `SimplicialComplexForAlpha::make_filtration_non_decreasing()`. * * \subsubsection pruneabove Prune above given filtration value * - * The simplex tree is pruned from the given maximum alpha squared value (cf. + * The simplex tree is pruned from the given maximum \f$ \alpha^2 \f$ value (cf. * `SimplicialComplexForAlpha::prune_above_filtration()`). * In the following example, the value is given by the user as argument of the program. * diff --git a/src/Alpha_complex/doc/alpha_complex_representation.ipe b/src/Alpha_complex/doc/alpha_complex_representation.ipe index e8096b93..40ff1d0f 100644 --- a/src/Alpha_complex/doc/alpha_complex_representation.ipe +++ b/src/Alpha_complex/doc/alpha_complex_representation.ipe @@ -1,7 +1,7 @@ <?xml version="1.0"?> <!DOCTYPE ipe SYSTEM "ipe.dtd"> -<ipe version="70107" creator="Ipe 7.1.10"> -<info created="D:20150603143945" modified="D:20160404172133"/> +<ipe version="70206" creator="Ipe 7.2.7"> +<info created="D:20150603143945" modified="D:20200110100102"/> <ipestyle name="basic"> <symbol name="arrow/arc(spx)"> <path stroke="sym-stroke" fill="sym-stroke" pen="sym-pen"> @@ -305,7 +305,7 @@ h 108.275 743.531 m 166.45 743.531 l </path> -<text matrix="1 0 0 1 142.618 -109.867" transformations="translations" pos="127.397 746.763" stroke="darkgray" type="label" width="6.41" height="4.289" depth="0" valign="baseline">$\alpha$</text> +<text matrix="1 0 0 1 126.618 -109.867" transformations="translations" pos="127.397 746.763" stroke="darkgray" type="label" width="45.707" height="9.041" depth="1.32" valign="baseline" style="math">\alpha = \sqrt{32.0}</text> <use matrix="1 0 0 1 -209.478 12.0238" name="mark/fdisk(sfx)" pos="300 720" size="normal" stroke="black" fill="white"/> <use matrix="1 0 0 1 -210.178 22.1775" name="mark/fdisk(sfx)" pos="280 660" size="normal" stroke="black" fill="white"/> <use matrix="1 0 0 1 -210.178 22.1775" name="mark/fdisk(sfx)" pos="370 690" size="normal" stroke="black" fill="white"/> diff --git a/src/Alpha_complex/doc/alpha_complex_representation.png b/src/Alpha_complex/doc/alpha_complex_representation.png Binary files differindex 7b81cd69..5ebb1e75 100644 --- a/src/Alpha_complex/doc/alpha_complex_representation.png +++ b/src/Alpha_complex/doc/alpha_complex_representation.png diff --git a/src/Alpha_complex/include/gudhi/Alpha_complex.h b/src/Alpha_complex/include/gudhi/Alpha_complex.h index 6b4d8463..f2a05e95 100644 --- a/src/Alpha_complex/include/gudhi/Alpha_complex.h +++ b/src/Alpha_complex/include/gudhi/Alpha_complex.h @@ -121,8 +121,8 @@ class Alpha_complex { // size_type type from CGAL. typedef typename Delaunay_triangulation::size_type size_type; - // Map type to switch from simplex tree vertex handle to CGAL vertex iterator. - typedef typename std::map< std::size_t, CGAL_vertex_iterator > Vector_vertex_iterator; + // Structure to switch from simplex tree vertex handle to CGAL vertex iterator. + typedef typename std::vector< CGAL_vertex_iterator > Vector_vertex_iterator; private: /** \brief Vertex iterator vector to switch from simplex tree vertex handle to CGAL vertex iterator. @@ -191,14 +191,6 @@ class Alpha_complex { return vertex_handle_to_iterator_.at(vertex)->point(); } - /** \brief number_of_vertices returns the number of vertices (same as the number of points). - * - * @return The number of vertices. - */ - std::size_t number_of_vertices() const { - return vertex_handle_to_iterator_.size(); - } - private: template<typename InputPointRange > void init_from_range(const InputPointRange& points) { @@ -238,14 +230,16 @@ class Alpha_complex { hint = pos->full_cell(); } // -------------------------------------------------------------------------------------------- - // double map to retrieve simplex tree vertex handles from CGAL vertex iterator and vice versa + // structure to retrieve CGAL points from vertex handle - one vertex handle per point. + // Needs to be constructed before as vertex handles arrives in no particular order. + vertex_handle_to_iterator_.resize(point_cloud.size()); // Loop on triangulation vertices list for (CGAL_vertex_iterator vit = triangulation_->vertices_begin(); vit != triangulation_->vertices_end(); ++vit) { if (!triangulation_->is_infinite(*vit)) { #ifdef DEBUG_TRACES std::cout << "Vertex insertion - " << vit->data() << " -> " << vit->point() << std::endl; #endif // DEBUG_TRACES - vertex_handle_to_iterator_.emplace(vit->data(), vit); + vertex_handle_to_iterator_[vit->data()] = vit; } } // -------------------------------------------------------------------------------------------- diff --git a/src/Alpha_complex/test/Alpha_complex_unit_test.cpp b/src/Alpha_complex/test/Alpha_complex_unit_test.cpp index 40b3fe09..27b671dd 100644 --- a/src/Alpha_complex/test/Alpha_complex_unit_test.cpp +++ b/src/Alpha_complex/test/Alpha_complex_unit_test.cpp @@ -53,20 +53,12 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_from_OFF_file, TestedKernel, list_of Gudhi::alpha_complex::Alpha_complex<TestedKernel> alpha_complex_from_file(off_file_name); - std::cout << "alpha_complex_from_points.number_of_vertices()=" << alpha_complex_from_file.number_of_vertices() - << std::endl; - BOOST_CHECK(alpha_complex_from_file.number_of_vertices() == 7); - Gudhi::Simplex_tree<> simplex_tree_60; BOOST_CHECK(alpha_complex_from_file.create_complex(simplex_tree_60, max_alpha_square_value)); std::cout << "simplex_tree_60.dimension()=" << simplex_tree_60.dimension() << std::endl; BOOST_CHECK(simplex_tree_60.dimension() == 2); - std::cout << "alpha_complex_from_points.number_of_vertices()=" << alpha_complex_from_file.number_of_vertices() - << std::endl; - BOOST_CHECK(alpha_complex_from_file.number_of_vertices() == 7); - std::cout << "simplex_tree_60.num_vertices()=" << simplex_tree_60.num_vertices() << std::endl; BOOST_CHECK(simplex_tree_60.num_vertices() == 7); @@ -128,10 +120,6 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_from_points) { Gudhi::Simplex_tree<> simplex_tree; BOOST_CHECK(alpha_complex_from_points.create_complex(simplex_tree)); - std::cout << "alpha_complex_from_points.number_of_vertices()=" << alpha_complex_from_points.number_of_vertices() - << std::endl; - BOOST_CHECK(alpha_complex_from_points.number_of_vertices() == points.size()); - // Another way to check num_simplices std::cout << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" << std::endl; int num_simplices = 0; @@ -151,7 +139,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_from_points) { std::cout << "simplex_tree.dimension()=" << simplex_tree.dimension() << std::endl; 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); + BOOST_CHECK(simplex_tree.num_vertices() == points.size()); for (auto f_simplex : simplex_tree.filtration_simplex_range()) { switch (simplex_tree.dimension(f_simplex)) { @@ -261,10 +249,6 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_from_empty_points, TestedKernel, lis Gudhi::Simplex_tree<> simplex_tree; BOOST_CHECK(!alpha_complex_from_points.create_complex(simplex_tree)); - std::cout << "alpha_complex_from_points.number_of_vertices()=" << alpha_complex_from_points.number_of_vertices() - << std::endl; - BOOST_CHECK(alpha_complex_from_points.number_of_vertices() == points.size()); - std::cout << "simplex_tree.num_simplices()=" << simplex_tree.num_simplices() << std::endl; BOOST_CHECK(simplex_tree.num_simplices() == 0); @@ -272,5 +256,45 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_from_empty_points, TestedKernel, lis BOOST_CHECK(simplex_tree.dimension() == -1); std::cout << "simplex_tree.num_vertices()=" << simplex_tree.num_vertices() << std::endl; - BOOST_CHECK(simplex_tree.num_vertices() == 0); + BOOST_CHECK(simplex_tree.num_vertices() == points.size()); +} + +using Inexact_kernel_2 = CGAL::Epick_d< CGAL::Dimension_tag<2> >; +using Exact_kernel_2 = CGAL::Epeck_d< CGAL::Dimension_tag<2> >; +using list_of_kernel_2_variants = boost::mpl::list<Inexact_kernel_2, Exact_kernel_2>; + +BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_with_duplicated_points, TestedKernel, list_of_kernel_2_variants) { + std::cout << "========== Alpha_complex_with_duplicated_points ==========" << std::endl; + + using Point = typename TestedKernel::Point_d; + using Vector_of_points = std::vector<Point>; + + // ---------------------------------------------------------------------------- + // Init of a list of points + // ---------------------------------------------------------------------------- + Vector_of_points points; + points.push_back(Point(1.0, 1.0)); + points.push_back(Point(7.0, 0.0)); + points.push_back(Point(4.0, 6.0)); + points.push_back(Point(9.0, 6.0)); + points.push_back(Point(0.0, 14.0)); + points.push_back(Point(2.0, 19.0)); + points.push_back(Point(9.0, 17.0)); + // duplicated points + points.push_back(Point(1.0, 1.0)); + points.push_back(Point(7.0, 0.0)); + + // ---------------------------------------------------------------------------- + // Init of an alpha complex from the list of points + // ---------------------------------------------------------------------------- + std::cout << "Init" << std::endl; + Gudhi::alpha_complex::Alpha_complex<TestedKernel> alpha_complex_from_points(points); + + Gudhi::Simplex_tree<> simplex_tree; + std::cout << "create_complex" << std::endl; + BOOST_CHECK(alpha_complex_from_points.create_complex(simplex_tree)); + + std::cout << "simplex_tree.num_vertices()=" << simplex_tree.num_vertices() + << std::endl; + BOOST_CHECK(simplex_tree.num_vertices() < points.size()); } diff --git a/src/Bottleneck_distance/include/gudhi/Persistence_graph.h b/src/Bottleneck_distance/include/gudhi/Persistence_graph.h index f791e37c..e1e3522e 100644 --- a/src/Bottleneck_distance/include/gudhi/Persistence_graph.h +++ b/src/Bottleneck_distance/include/gudhi/Persistence_graph.h @@ -25,7 +25,7 @@ namespace Gudhi { namespace persistence_diagram { -/** \internal \brief Structure representing an euclidean bipartite graph containing +/** \internal \brief Structure representing a Euclidean bipartite graph containing * the points from the two persistence diagrams (including the projections). * * \ingroup bottleneck_distance diff --git a/src/Bottleneck_distance/test/bottleneck_unit_test.cpp b/src/Bottleneck_distance/test/bottleneck_unit_test.cpp index 3fc6fc7b..2c520045 100644 --- a/src/Bottleneck_distance/test/bottleneck_unit_test.cpp +++ b/src/Bottleneck_distance/test/bottleneck_unit_test.cpp @@ -15,6 +15,7 @@ #include <random> #include <gudhi/Bottleneck.h> +#include <gudhi/Unitary_tests_utils.h> using namespace Gudhi::persistence_diagram; @@ -59,24 +60,24 @@ BOOST_AUTO_TEST_CASE(persistence_graph) { BOOST_CHECK(g.size() == (n1 + n2)); // BOOST_CHECK((int) d.size() == (n1 + n2)*(n1 + n2) + n1 + n2 + 1); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance(0, 0)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance(0, n1 - 1)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance(0, n1)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance(0, n2 - 1)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance(0, n2)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance(0, (n1 + n2) - 1)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance(n1, 0)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance(n1, n1 - 1)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance(n1, n1)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance(n1, n2 - 1)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance(n1, n2)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance(n1, (n1 + n2) - 1)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance((n1 + n2) - 1, 0)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance((n1 + n2) - 1, n1 - 1)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance((n1 + n2) - 1, n1)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance((n1 + n2) - 1, n2 - 1)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance((n1 + n2) - 1, n2)) > 0); - BOOST_CHECK(std::count(d.begin(), d.end(), g.distance((n1 + n2) - 1, (n1 + n2) - 1)) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance(0, 0))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance(0, n1 - 1))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance(0, n1))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance(0, n2 - 1))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance(0, n2))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance(0, (n1 + n2) - 1))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance(n1, 0))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance(n1, n1 - 1))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance(n1, n1))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance(n1, n2 - 1))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance(n1, n2))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance(n1, (n1 + n2) - 1))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance((n1 + n2) - 1, 0))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance((n1 + n2) - 1, n1 - 1))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance((n1 + n2) - 1, n1))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance((n1 + n2) - 1, n2 - 1))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance((n1 + n2) - 1, n2))) > 0); + BOOST_CHECK(std::count(d.begin(), d.end(), GUDHI_PROTECT_FLOAT(g.distance((n1 + n2) - 1, (n1 + n2) - 1))) > 0); } BOOST_AUTO_TEST_CASE(neighbors_finder) { diff --git a/src/Nerve_GIC/include/gudhi/GIC.h b/src/Nerve_GIC/include/gudhi/GIC.h index b8169c59..2a6d4788 100644 --- a/src/Nerve_GIC/include/gudhi/GIC.h +++ b/src/Nerve_GIC/include/gudhi/GIC.h @@ -14,7 +14,7 @@ #ifdef GUDHI_USE_TBB #include <tbb/parallel_for.h> -#include <tbb/mutex.h> +#include <mutex> #endif #include <gudhi/Debug_utils.h> @@ -459,7 +459,7 @@ class Cover_complex { // This cannot be parallelized if thread_local is not defined // thread_local is not defined for XCode < v.8 #if defined(GUDHI_USE_TBB) && defined(GUDHI_CAN_USE_CXX11_THREAD_LOCAL) - tbb::mutex deltamutex; + std::mutex deltamutex; tbb::parallel_for(0, N, [&](int i){ std::vector<int> samples(m); SampleWithoutReplacement(n, m, samples); @@ -707,7 +707,7 @@ class Cover_complex { // 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(), [=](const int & p1, const int & p2){return (this->func[p1] < this->func[p2]);}); + std::sort(points.begin(), points.end(), [this](int p1, int p2){return (this->func[p1] < this->func[p2]);}); int id = 0; int pos = 0; @@ -765,7 +765,7 @@ class Cover_complex { #ifdef GUDHI_USE_TBB if (verbose) std::cout << "Computing connected components (parallelized)..." << std::endl; - tbb::mutex covermutex, idmutex; + std::mutex covermutex, idmutex; tbb::parallel_for(0, res, [&](int i){ // Compute connected components Graph G = one_skeleton.create_subgraph(); @@ -895,7 +895,7 @@ class Cover_complex { // Compute the geodesic distances to subsamples with Dijkstra #ifdef GUDHI_USE_TBB if (verbose) std::cout << "Computing geodesic distances (parallelized)..." << std::endl; - tbb::mutex coverMutex; tbb::mutex mindistMutex; + std::mutex coverMutex; std::mutex mindistMutex; tbb::parallel_for(0, m, [&](int i){ int seed = voronoi_subsamples[i]; std::vector<double> dmap(n); diff --git a/src/Persistence_representations/include/gudhi/Persistence_intervals.h b/src/Persistence_representations/include/gudhi/Persistence_intervals.h index e2db4572..ea4220ea 100644 --- a/src/Persistence_representations/include/gudhi/Persistence_intervals.h +++ b/src/Persistence_representations/include/gudhi/Persistence_intervals.h @@ -6,6 +6,8 @@ * * Modification(s): * - YYYY/MM Author: Description of the modification + * - 2019/12 Vincent Rouvreau: Fix #118 - Make histogram_of_lengths and cumulative_histogram_of_lengths + * return the exact number_of_bins (was failing on x86) */ #ifndef PERSISTENCE_INTERVALS_H_ @@ -335,6 +337,9 @@ std::vector<size_t> Persistence_intervals::histogram_of_lengths(size_t number_of getchar(); } } + // we want number of bins equals to number_of_bins (some unexpected results on x86) + result[number_of_bins-1]+=result[number_of_bins]; + result.resize(number_of_bins); if (dbg) { for (size_t i = 0; i != result.size(); ++i) std::cerr << result[i] << std::endl; diff --git a/src/Persistence_representations/test/persistence_intervals_test.cpp b/src/Persistence_representations/test/persistence_intervals_test.cpp index 3b7a2049..02ea8edb 100644 --- a/src/Persistence_representations/test/persistence_intervals_test.cpp +++ b/src/Persistence_representations/test/persistence_intervals_test.cpp @@ -6,6 +6,8 @@ * * Modification(s): * - YYYY/MM Author: Description of the modification + * - 2019/12 Vincent Rouvreau: Fix #118 - Make histogram_of_lengths and cumulative_histogram_of_lengths + * return the exact number_of_bins (was failing on x86) */ #define BOOST_TEST_DYN_LINK @@ -32,17 +34,8 @@ BOOST_AUTO_TEST_CASE(check_min_max_function) { BOOST_AUTO_TEST_CASE(check_length_of_dominant_intervals) { Persistence_intervals p("data/file_with_diagram"); std::vector<double> dominant_ten_intervals_length = p.length_of_dominant_intervals(10); - std::vector<double> dominant_intervals_length; - dominant_intervals_length.push_back(0.862625); - dominant_intervals_length.push_back(0.800893); - dominant_intervals_length.push_back(0.762061); - dominant_intervals_length.push_back(0.756501); - dominant_intervals_length.push_back(0.729367); - dominant_intervals_length.push_back(0.718177); - dominant_intervals_length.push_back(0.708395); - dominant_intervals_length.push_back(0.702844); - dominant_intervals_length.push_back(0.700468); - dominant_intervals_length.push_back(0.622177); + std::vector<double> dominant_intervals_length{0.862625, 0.800893, 0.762061, 0.756501, 0.729367, + 0.718177, 0.708395, 0.702844, 0.700468, 0.622177}; for (size_t i = 0; i != dominant_ten_intervals_length.size(); ++i) { GUDHI_TEST_FLOAT_EQUALITY_CHECK(dominant_ten_intervals_length[i], dominant_intervals_length[i], Gudhi::Persistence_representations::epsi); @@ -52,17 +45,11 @@ BOOST_AUTO_TEST_CASE(check_dominant_intervals) { Persistence_intervals p("data/file_with_diagram"); std::vector<std::pair<double, double> > ten_dominant_intervals = p.dominant_intervals(10); - std::vector<std::pair<double, double> > templ; - templ.push_back(std::pair<double, double>(0.114718, 0.977343)); - templ.push_back(std::pair<double, double>(0.133638, 0.93453)); - templ.push_back(std::pair<double, double>(0.104599, 0.866659)); - templ.push_back(std::pair<double, double>(0.149798, 0.906299)); - templ.push_back(std::pair<double, double>(0.247352, 0.976719)); - templ.push_back(std::pair<double, double>(0.192675, 0.910852)); - templ.push_back(std::pair<double, double>(0.191836, 0.900231)); - templ.push_back(std::pair<double, double>(0.284998, 0.987842)); - templ.push_back(std::pair<double, double>(0.294069, 0.994537)); - templ.push_back(std::pair<double, double>(0.267421, 0.889597)); + std::vector<std::pair<double, double> > 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} }; for (size_t i = 0; i != ten_dominant_intervals.size(); ++i) { GUDHI_TEST_FLOAT_EQUALITY_CHECK(ten_dominant_intervals[i].first, templ[i].first, @@ -75,18 +62,7 @@ BOOST_AUTO_TEST_CASE(check_dominant_intervals) { BOOST_AUTO_TEST_CASE(check_histogram_of_lengths) { Persistence_intervals p("data/file_with_diagram"); std::vector<size_t> histogram = p.histogram_of_lengths(10); - std::vector<size_t> template_histogram; - template_histogram.push_back(10); - template_histogram.push_back(5); - template_histogram.push_back(3); - template_histogram.push_back(4); - template_histogram.push_back(4); - template_histogram.push_back(3); - template_histogram.push_back(6); - template_histogram.push_back(1); - template_histogram.push_back(7); - template_histogram.push_back(1); - template_histogram.push_back(1); + std::vector<size_t> template_histogram{10, 5, 3, 4, 4, 3, 6, 1, 7, 2}; for (size_t i = 0; i != histogram.size(); ++i) { BOOST_CHECK(histogram[i] == template_histogram[i]); } @@ -95,18 +71,7 @@ BOOST_AUTO_TEST_CASE(check_histogram_of_lengths) { BOOST_AUTO_TEST_CASE(check_cumulative_histograms_of_lengths) { Persistence_intervals p("data/file_with_diagram"); std::vector<size_t> cumulative_histogram = p.cumulative_histogram_of_lengths(10); - std::vector<size_t> template_cumulative_histogram; - template_cumulative_histogram.push_back(10); - template_cumulative_histogram.push_back(15); - template_cumulative_histogram.push_back(18); - template_cumulative_histogram.push_back(22); - template_cumulative_histogram.push_back(26); - template_cumulative_histogram.push_back(29); - template_cumulative_histogram.push_back(35); - template_cumulative_histogram.push_back(36); - template_cumulative_histogram.push_back(43); - template_cumulative_histogram.push_back(44); - template_cumulative_histogram.push_back(45); + std::vector<size_t> template_cumulative_histogram{10, 15, 18, 22, 26, 29, 35, 36, 43, 45}; for (size_t i = 0; i != cumulative_histogram.size(); ++i) { BOOST_CHECK(cumulative_histogram[i] == template_cumulative_histogram[i]); @@ -116,17 +81,8 @@ BOOST_AUTO_TEST_CASE(check_characteristic_function_of_diagram) { Persistence_intervals p("data/file_with_diagram"); std::pair<double, double> min_max_ = p.get_x_range(); std::vector<double> char_funct_diag = p.characteristic_function_of_diagram(min_max_.first, min_max_.second); - std::vector<double> template_char_funct_diag; - template_char_funct_diag.push_back(0.370665); - template_char_funct_diag.push_back(0.84058); - template_char_funct_diag.push_back(1.24649); - template_char_funct_diag.push_back(1.3664); - template_char_funct_diag.push_back(1.34032); - template_char_funct_diag.push_back(1.31904); - template_char_funct_diag.push_back(1.14076); - template_char_funct_diag.push_back(0.991259); - template_char_funct_diag.push_back(0.800714); - template_char_funct_diag.push_back(0.0676303); + std::vector<double> template_char_funct_diag{0.370665, 0.84058, 1.24649, 1.3664, 1.34032, + 1.31904, 1.14076, 0.991259, 0.800714, 0.0676303}; for (size_t i = 0; i != char_funct_diag.size(); ++i) { GUDHI_TEST_FLOAT_EQUALITY_CHECK(char_funct_diag[i], template_char_funct_diag[i], @@ -139,18 +95,8 @@ BOOST_AUTO_TEST_CASE(check_cumulative_characteristic_function_of_diagram) { std::pair<double, double> min_max_ = p.get_x_range(); std::vector<double> cumul_char_funct_diag = p.cumulative_characteristic_function_of_diagram(min_max_.first, min_max_.second); - std::vector<double> template_char_funct_diag_cumul; - - template_char_funct_diag_cumul.push_back(0.370665); - template_char_funct_diag_cumul.push_back(1.21125); - template_char_funct_diag_cumul.push_back(2.45774); - template_char_funct_diag_cumul.push_back(3.82414); - template_char_funct_diag_cumul.push_back(5.16446); - template_char_funct_diag_cumul.push_back(6.4835); - template_char_funct_diag_cumul.push_back(7.62426); - template_char_funct_diag_cumul.push_back(8.61552); - template_char_funct_diag_cumul.push_back(9.41623); - template_char_funct_diag_cumul.push_back(9.48386); + std::vector<double> 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}; for (size_t i = 0; i != cumul_char_funct_diag.size(); ++i) { GUDHI_TEST_FLOAT_EQUALITY_CHECK(cumul_char_funct_diag[i], template_char_funct_diag_cumul[i], @@ -160,97 +106,29 @@ BOOST_AUTO_TEST_CASE(check_cumulative_characteristic_function_of_diagram) { BOOST_AUTO_TEST_CASE(check_compute_persistent_betti_numbers) { Persistence_intervals p("data/file_with_diagram"); - std::vector<std::pair<double, size_t> > pbns; - pbns.push_back(std::pair<double, size_t>(0.0290362, 1)); - pbns.push_back(std::pair<double, size_t>(0.0307676, 2)); - pbns.push_back(std::pair<double, size_t>(0.0366312, 3)); - pbns.push_back(std::pair<double, size_t>(0.0544614, 4)); - pbns.push_back(std::pair<double, size_t>(0.0920033, 5)); - pbns.push_back(std::pair<double, size_t>(0.104599, 6)); - pbns.push_back(std::pair<double, size_t>(0.114718, 7)); - pbns.push_back(std::pair<double, size_t>(0.117379, 8)); - pbns.push_back(std::pair<double, size_t>(0.123493, 9)); - pbns.push_back(std::pair<double, size_t>(0.133638, 10)); - pbns.push_back(std::pair<double, size_t>(0.137798, 9)); - pbns.push_back(std::pair<double, size_t>(0.149798, 10)); - pbns.push_back(std::pair<double, size_t>(0.155421, 11)); - pbns.push_back(std::pair<double, size_t>(0.158443, 12)); - pbns.push_back(std::pair<double, size_t>(0.176956, 13)); - pbns.push_back(std::pair<double, size_t>(0.183234, 12)); - pbns.push_back(std::pair<double, size_t>(0.191069, 13)); - pbns.push_back(std::pair<double, size_t>(0.191333, 14)); - pbns.push_back(std::pair<double, size_t>(0.191836, 15)); - pbns.push_back(std::pair<double, size_t>(0.192675, 16)); - pbns.push_back(std::pair<double, size_t>(0.208564, 17)); - pbns.push_back(std::pair<double, size_t>(0.218425, 18)); - pbns.push_back(std::pair<double, size_t>(0.219902, 17)); - pbns.push_back(std::pair<double, size_t>(0.23233, 16)); - pbns.push_back(std::pair<double, size_t>(0.234558, 17)); - pbns.push_back(std::pair<double, size_t>(0.237166, 16)); - pbns.push_back(std::pair<double, size_t>(0.247352, 17)); - pbns.push_back(std::pair<double, size_t>(0.267421, 18)); - pbns.push_back(std::pair<double, size_t>(0.268093, 19)); - pbns.push_back(std::pair<double, size_t>(0.278734, 18)); - pbns.push_back(std::pair<double, size_t>(0.284722, 19)); - pbns.push_back(std::pair<double, size_t>(0.284998, 20)); - pbns.push_back(std::pair<double, size_t>(0.294069, 21)); - pbns.push_back(std::pair<double, size_t>(0.306293, 22)); - pbns.push_back(std::pair<double, size_t>(0.322361, 21)); - pbns.push_back(std::pair<double, size_t>(0.323152, 22)); - pbns.push_back(std::pair<double, size_t>(0.371021, 23)); - pbns.push_back(std::pair<double, size_t>(0.372395, 24)); - pbns.push_back(std::pair<double, size_t>(0.387744, 25)); - pbns.push_back(std::pair<double, size_t>(0.435537, 26)); - pbns.push_back(std::pair<double, size_t>(0.462911, 25)); - pbns.push_back(std::pair<double, size_t>(0.483569, 26)); - pbns.push_back(std::pair<double, size_t>(0.489209, 25)); - pbns.push_back(std::pair<double, size_t>(0.517115, 24)); - pbns.push_back(std::pair<double, size_t>(0.522197, 23)); - pbns.push_back(std::pair<double, size_t>(0.532665, 22)); - pbns.push_back(std::pair<double, size_t>(0.545262, 23)); - pbns.push_back(std::pair<double, size_t>(0.587227, 22)); - pbns.push_back(std::pair<double, size_t>(0.593036, 23)); - pbns.push_back(std::pair<double, size_t>(0.602647, 24)); - pbns.push_back(std::pair<double, size_t>(0.605044, 25)); - pbns.push_back(std::pair<double, size_t>(0.621962, 24)); - pbns.push_back(std::pair<double, size_t>(0.629449, 23)); - pbns.push_back(std::pair<double, size_t>(0.636719, 22)); - pbns.push_back(std::pair<double, size_t>(0.64957, 21)); - pbns.push_back(std::pair<double, size_t>(0.650781, 22)); - pbns.push_back(std::pair<double, size_t>(0.654951, 23)); - pbns.push_back(std::pair<double, size_t>(0.683489, 24)); - pbns.push_back(std::pair<double, size_t>(0.687172, 23)); - pbns.push_back(std::pair<double, size_t>(0.69703, 22)); - pbns.push_back(std::pair<double, size_t>(0.701174, 21)); - pbns.push_back(std::pair<double, size_t>(0.717623, 22)); - pbns.push_back(std::pair<double, size_t>(0.722023, 21)); - pbns.push_back(std::pair<double, size_t>(0.722298, 20)); - pbns.push_back(std::pair<double, size_t>(0.725347, 19)); - pbns.push_back(std::pair<double, size_t>(0.73071, 18)); - pbns.push_back(std::pair<double, size_t>(0.758355, 17)); - pbns.push_back(std::pair<double, size_t>(0.770913, 18)); - pbns.push_back(std::pair<double, size_t>(0.790833, 17)); - pbns.push_back(std::pair<double, size_t>(0.821211, 16)); - pbns.push_back(std::pair<double, size_t>(0.849305, 17)); - pbns.push_back(std::pair<double, size_t>(0.853669, 16)); - pbns.push_back(std::pair<double, size_t>(0.866659, 15)); - pbns.push_back(std::pair<double, size_t>(0.872896, 16)); - pbns.push_back(std::pair<double, size_t>(0.889597, 15)); - pbns.push_back(std::pair<double, size_t>(0.900231, 14)); - pbns.push_back(std::pair<double, size_t>(0.903847, 13)); - pbns.push_back(std::pair<double, size_t>(0.906299, 12)); - pbns.push_back(std::pair<double, size_t>(0.910852, 11)); - pbns.push_back(std::pair<double, size_t>(0.93453, 10)); - pbns.push_back(std::pair<double, size_t>(0.944757, 9)); - pbns.push_back(std::pair<double, size_t>(0.947812, 8)); - pbns.push_back(std::pair<double, size_t>(0.959154, 7)); - pbns.push_back(std::pair<double, size_t>(0.975654, 6)); - pbns.push_back(std::pair<double, size_t>(0.976719, 5)); - pbns.push_back(std::pair<double, size_t>(0.977343, 4)); - pbns.push_back(std::pair<double, size_t>(0.980129, 3)); - pbns.push_back(std::pair<double, size_t>(0.987842, 2)); - pbns.push_back(std::pair<double, size_t>(0.990127, 1)); - pbns.push_back(std::pair<double, size_t>(0.994537, 0)); + std::vector<std::pair<double, size_t> > 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} }; std::vector<std::pair<double, size_t> > pbns_new = p.compute_persistent_betti_numbers(); for (size_t i = 0; i != pbns.size(); ++i) { @@ -262,17 +140,8 @@ BOOST_AUTO_TEST_CASE(check_compute_persistent_betti_numbers) { BOOST_AUTO_TEST_CASE(check_k_n_n) { Persistence_intervals p("data/file_with_diagram"); std::vector<double> knn = p.k_n_n(5); - std::vector<double> knn_template; - knn_template.push_back(1.04208); - knn_template.push_back(1.00344); - knn_template.push_back(0.979395); - knn_template.push_back(0.890643); - knn_template.push_back(0.874769); - knn_template.push_back(0.845787); - knn_template.push_back(0.819713); - knn_template.push_back(0.803984); - knn_template.push_back(0.799864); - knn_template.push_back(0.786945); + std::vector<double> knn_template{1.04208, 1.00344, 0.979395, 0.890643, 0.874769, + 0.845787, 0.819713, 0.803984, 0.799864, 0.786945}; for (size_t i = 0; i != knn.size(); ++i) { GUDHI_TEST_FLOAT_EQUALITY_CHECK(knn[i], knn_template[i], Gudhi::Persistence_representations::epsi); diff --git a/src/Simplex_tree/example/CMakeLists.txt b/src/Simplex_tree/example/CMakeLists.txt index f99b164c..8a8cac58 100644 --- a/src/Simplex_tree/example/CMakeLists.txt +++ b/src/Simplex_tree/example/CMakeLists.txt @@ -16,6 +16,9 @@ endif() add_test(NAME Simplex_tree_example_simple_simplex_tree COMMAND $<TARGET_FILE:Simplex_tree_example_simple_simplex_tree>) add_executable ( Simplex_tree_example_mini_simplex_tree mini_simplex_tree.cpp ) +if (TBB_FOUND) + target_link_libraries(Simplex_tree_example_mini_simplex_tree ${TBB_LIBRARIES}) +endif() add_test(NAME Simplex_tree_example_mini_simplex_tree COMMAND $<TARGET_FILE:Simplex_tree_example_mini_simplex_tree>) # An example with Simplex-tree using CGAL alpha_shapes_3 diff --git a/src/Simplex_tree/include/gudhi/Simplex_tree.h b/src/Simplex_tree/include/gudhi/Simplex_tree.h index fafdb01c..76608008 100644 --- a/src/Simplex_tree/include/gudhi/Simplex_tree.h +++ b/src/Simplex_tree/include/gudhi/Simplex_tree.h @@ -1378,7 +1378,7 @@ class Simplex_tree { private: bool rec_prune_above_filtration(Siblings* sib, Filtration_value filt) { auto&& list = sib->members(); - auto last = std::remove_if(list.begin(), list.end(), [=](Dit_value_t& simplex) { + auto last = std::remove_if(list.begin(), list.end(), [this,filt](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 diff --git a/src/Tangential_complex/include/gudhi/Tangential_complex.h b/src/Tangential_complex/include/gudhi/Tangential_complex.h index f058fa9f..f007bdd5 100644 --- a/src/Tangential_complex/include/gudhi/Tangential_complex.h +++ b/src/Tangential_complex/include/gudhi/Tangential_complex.h @@ -60,7 +60,7 @@ #ifdef GUDHI_USE_TBB #include <tbb/parallel_for.h> #include <tbb/combinable.h> -#include <tbb/mutex.h> +#include <mutex> #endif // #define GUDHI_TC_EXPORT_NORMALS // Only for 3D surfaces (k=2, d=3) @@ -147,7 +147,7 @@ class Tangential_complex { typedef typename Tr_traits::Vector_d Tr_vector; #if defined(GUDHI_USE_TBB) - typedef tbb::mutex Mutex_for_perturb; + typedef std::mutex Mutex_for_perturb; typedef Vector Translation_for_perturb; typedef std::vector<Atomic_wrapper<FT> > Weights; #else diff --git a/src/Witness_complex/example/CMakeLists.txt b/src/Witness_complex/example/CMakeLists.txt index 5860f3a3..2659798e 100644 --- a/src/Witness_complex/example/CMakeLists.txt +++ b/src/Witness_complex/example/CMakeLists.txt @@ -12,10 +12,18 @@ 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 4.11.0) add_executable( Witness_complex_example_off example_witness_complex_off.cpp ) + if (TBB_FOUND) + target_link_libraries(Witness_complex_example_off ${TBB_LIBRARIES}) + endif() add_executable ( Witness_complex_example_sphere example_witness_complex_sphere.cpp ) + if (TBB_FOUND) + target_link_libraries(Witness_complex_example_sphere ${TBB_LIBRARIES}) + endif() add_executable( Witness_complex_example_strong_off example_strong_witness_complex_off.cpp ) - target_link_libraries(Witness_complex_example_strong_off) + if (TBB_FOUND) + target_link_libraries(Witness_complex_example_strong_off ${TBB_LIBRARIES}) + endif() add_test(NAME Witness_complex_example_off_test_torus COMMAND $<TARGET_FILE:Witness_complex_example_off> diff --git a/src/cmake/modules/GUDHI_third_party_libraries.cmake b/src/cmake/modules/GUDHI_third_party_libraries.cmake index 24a34150..359d1c12 100644 --- a/src/cmake/modules/GUDHI_third_party_libraries.cmake +++ b/src/cmake/modules/GUDHI_third_party_libraries.cmake @@ -35,6 +35,9 @@ if(CGAL_FOUND) include( ${CGAL_USE_FILE} ) endif() +# For those who dislike bundled dependencies, this indicates where to find a preinstalled Hera. +set(HERA_WASSERSTEIN_INCLUDE_DIR ${CMAKE_SOURCE_DIR}/ext/hera/geom_matching/wasserstein/include CACHE PATH "Directory where one can find Hera's wasserstein.h") + option(WITH_GUDHI_USE_TBB "Build with Intel TBB parallelization" ON) # Find TBB package for parallel sort - not mandatory, just optional. @@ -127,6 +130,7 @@ if( PYTHONINTERP_FOUND ) find_python_module("sphinx") find_python_module("sklearn") find_python_module("ot") + find_python_module("pybind11") endif() if(NOT GUDHI_PYTHON_PATH) diff --git a/src/cmake/modules/GUDHI_user_version_target.cmake b/src/cmake/modules/GUDHI_user_version_target.cmake index 4fa74330..5047252f 100644 --- a/src/cmake/modules/GUDHI_user_version_target.cmake +++ b/src/cmake/modules/GUDHI_user_version_target.cmake @@ -33,8 +33,6 @@ add_custom_command(TARGET user_version PRE_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_BINARY_DIR}/biblio ${GUDHI_USER_VERSION_DIR}/biblio) add_custom_command(TARGET user_version PRE_BUILD COMMAND ${CMAKE_COMMAND} -E - copy ${CMAKE_SOURCE_DIR}/Conventions.txt ${GUDHI_USER_VERSION_DIR}/Conventions.txt) -add_custom_command(TARGET user_version PRE_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_SOURCE_DIR}/README.md ${GUDHI_USER_VERSION_DIR}/README.md) add_custom_command(TARGET user_version PRE_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_SOURCE_DIR}/LICENSE ${GUDHI_USER_VERSION_DIR}/LICENSE) @@ -56,6 +54,9 @@ add_custom_command(TARGET user_version PRE_BUILD COMMAND ${CMAKE_COMMAND} -E add_custom_command(TARGET user_version PRE_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_SOURCE_DIR}/src/GudhUI ${GUDHI_USER_VERSION_DIR}/GudhUI) +add_custom_command(TARGET user_version PRE_BUILD COMMAND ${CMAKE_COMMAND} -E + copy_directory ${CMAKE_SOURCE_DIR}/ext/hera/geom_matching/wasserstein/include ${GUDHI_USER_VERSION_DIR}/ext/hera/geom_matching/wasserstein/include) + set(GUDHI_DIRECTORIES "doc;example;concept;utilities") set(GUDHI_INCLUDE_DIRECTORIES "include/gudhi") @@ -95,4 +96,4 @@ foreach(GUDHI_MODULE ${GUDHI_MODULES_FULL_LIST}) endforeach() endforeach(GUDHI_INCLUDE_DIRECTORY ${GUDHI_INCLUDE_DIRECTORIES}) -endforeach(GUDHI_MODULE ${GUDHI_MODULES_FULL_LIST})
\ No newline at end of file +endforeach(GUDHI_MODULE ${GUDHI_MODULES_FULL_LIST}) diff --git a/src/common/doc/header.html b/src/common/doc/header.html index 9fdb2321..99ab6bb7 100644 --- a/src/common/doc/header.html +++ b/src/common/doc/header.html @@ -56,7 +56,7 @@ $extrastylesheet <a href="#">Download</a> <ul class="dropdown"> <li><a href="/licensing/">Licensing</a></li> - <li><a href="https://gforge.inria.fr/frs/download.php/latestzip/5253/library-latest.zip" target="_blank">Get the latest sources</a></li> + <li><a href="https://github.com/GUDHI/gudhi-devel/releases/latest" target="_blank">Get the latest sources</a></li> <li><a href="/conda/">Conda package</a></li> <li><a href="/dockerfile/">Dockerfile</a></li> </ul> diff --git a/src/common/doc/installation.h b/src/common/doc/installation.h index d70a2efa..ce2c5448 100644 --- a/src/common/doc/installation.h +++ b/src/common/doc/installation.h @@ -33,7 +33,7 @@ make \endverbatim * \subsection testsuites Test suites * To test your build, run the following command in a terminal: * \verbatim make test \endverbatim - * `make test` is using <a href="https://cmake.org/cmake/help/latest/manual/ctest.1.html">Ctest<\a> (CMake test driver + * `make test` is using <a href="https://cmake.org/cmake/help/latest/manual/ctest.1.html">Ctest</a> (CMake test driver * program). If some of the tests are failing, please send us the result of the following command: * \verbatim ctest --output-on-failure \endverbatim * diff --git a/src/common/doc/main_page.md b/src/common/doc/main_page.md index e8d11fdf..6ea10b88 100644 --- a/src/common/doc/main_page.md +++ b/src/common/doc/main_page.md @@ -4,8 +4,8 @@ \image html "Gudhi_banner.png" <br><br><br><br> -## Complexes {#Complexes} -### Cubical complex +## Data structures for cell complexes {#Complexes} +### Cubical complexes <table> <tr> @@ -29,246 +29,269 @@ </tr> </table> -### Simplicial complex - -#### Alpha complex +### Simplicial complexes +#### Simplex tree <table> <tr> <td width="35%" rowspan=2> - \image html "alpha_complex_representation.png" + \image html "Simplex_tree_representation.png" </td> <td width="50%"> - Alpha complex is a simplicial complex constructed from the finite cells of a Delaunay Triangulation.<br> - The filtration value of each simplex is computed as the square of the circumradius of the simplex if the - circumsphere is empty (the simplex is then said to be Gabriel), and as the minimum of the filtration - values of the codimension 1 cofaces that make it not Gabriel otherwise. - All simplices that have a filtration value strictly greater than a given alpha squared value are not inserted into - the complex.<br> - For performances reasons, it is advised to use \ref cgal ≥ 5.0.0. + The simplex tree is an efficient and flexible + data structure for representing general (filtered) simplicial complexes. The data structure + is described in \cite boissonnatmariasimplextreealgorithmica . </td> <td width="15%"> - <b>Author:</b> Vincent Rouvreau<br> - <b>Introduced in:</b> GUDHI 1.3.0<br> - <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> - <b>Requires:</b> \ref eigen ≥ 3.1.0 and \ref cgal ≥ 4.11.0 + <b>Author:</b> Clément Maria<br> + <b>Introduced in:</b> GUDHI 1.0.0<br> + <b>Copyright:</b> MIT<br> </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref alpha_complex + <b>User manual:</b> \ref simplex_tree </td> </tr> </table> -#### ÄŒech complex +#### Toplex Map <table> - <tr> + <tr> <td width="35%" rowspan=2> - \image html "cech_complex_representation.png" + \image html "map.png" </td> <td width="50%"> - The ÄŒech complex is a simplicial complex constructed from a proximity graph. - The set of all simplices is filtered by the radius of their minimal enclosing ball. + The Toplex map data structure is composed firstly of a raw storage of toplices (the maximal simplices) + and secondly of a map which associate any vertex to a set of pointers toward all toplices + containing this vertex. </td> <td width="15%"> - <b>Author:</b> Vincent Rouvreau<br> - <b>Introduced in:</b> GUDHI 2.2.0<br> - <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> - <b>Includes:</b> [Miniball](https://people.inf.ethz.ch/gaertner/subdir/software/miniball.html)<br> + <b>Author:</b> François Godi<br> + <b>Introduced in:</b> GUDHI 2.1.0<br> + <b>Copyright:</b> MIT<br> </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref cech_complex + <b>User manual:</b> \ref toplex_map </td> </tr> </table> -#### Rips complex +#### Skeleton blocker <table> <tr> <td width="35%" rowspan=2> - \image html "rips_complex_representation.png" + \image html "ds_representation.png" </td> <td width="50%"> - Rips complex is a simplicial complex constructed from a one skeleton graph.<br> - The filtration value of each edge is computed from a user-given distance function and is inserted until a - user-given threshold value.<br> - This complex can be built from a point cloud and a distance function, or from a distance matrix. + The Skeleton-Blocker data-structure proposes a light encoding for simplicial complexes by storing only an *implicit* + representation of its simplices \cite socg_blockers_2011,\cite blockers2012. Intuitively, it just stores the + 1-skeleton of a simplicial complex with a graph and the set of its "missing faces" that is very small in practice. + This data-structure handles all simplicial complexes operations such as simplex enumeration or simplex removal but + operations that are particularly efficient are operations that do not require simplex enumeration such as edge + iteration, link computation or simplex contraction. </td> <td width="15%"> - <b>Author:</b> Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse<br> - <b>Introduced in:</b> GUDHI 2.0.0<br> + <b>Author:</b> David Salinas<br> + <b>Introduced in:</b> GUDHI 1.1.0<br> <b>Copyright:</b> MIT<br> </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref rips_complex + <b>User manual:</b> \ref skbl </td> </tr> </table> -#### Witness complex +#### Basic operation: contraction <table> <tr> <td width="35%" rowspan=2> - \image html "Witness_complex_representation.png" + \image html "sphere_contraction_representation.png" </td> <td width="50%"> - Witness complex \f$ Wit(W,L) \f$ is a simplicial complex defined on two sets of points in \f$\mathbb{R}^D\f$. - The data structure is described in \cite boissonnatmariasimplextreealgorithmica . + The purpose of this package is to offer a user-friendly interface for edge contraction simplification of huge + simplicial complexes. It uses the \ref skbl data-structure whose size remains small during simplification of most + used geometrical complexes of topological data analysis such as the Rips or the Delaunay complexes. In practice, + the size of this data-structure is even much lower than the total number of simplices. </td> <td width="15%"> - <b>Author:</b> Siargey Kachanovich<br> - <b>Introduced in:</b> GUDHI 1.3.0<br> - <b>Copyright:</b> MIT ([GPL v3](../../licensing/) for Euclidean version)<br> - <b>Euclidean version requires:</b> \ref eigen ≥ 3.1.0 and \ref cgal ≥ 4.11.0 + <b>Author:</b> David Salinas<br> + <b>Introduced in:</b> GUDHI 1.1.0<br> + <b>Copyright:</b> MIT [(LGPL v3)](../../licensing/)<br> + <b>Requires:</b> \ref cgal ≥ 4.11.0 </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref witness_complex + <b>User manual:</b> \ref contr </td> </tr> </table> -### Cover Complexes +## Filtrations and reconstructions {#FiltrationsReconstructions} +### Alpha complex + <table> <tr> <td width="35%" rowspan=2> - \image html "gicvisu.jpg" + \image html "alpha_complex_representation.png" </td> <td width="50%"> - 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> + Alpha complex is a simplicial complex constructed from the finite cells of a Delaunay Triangulation.<br> + The filtration value of each simplex is computed as the square of the circumradius of the simplex if the + circumsphere is empty (the simplex is then said to be Gabriel), and as the minimum of the filtration + values of the codimension 1 cofaces that make it not Gabriel otherwise. + All simplices that have a filtration value \f$ > \alpha^2 \f$ are removed from the Delaunay complex + when creating the simplicial complex if it is specified.<br> + For performances reasons, it is advised to use \ref cgal ≥ 5.0.0. </td> <td width="15%"> - <b>Author:</b> Mathieu Carrière<br> - <b>Introduced in:</b> GUDHI 2.1.0<br> + <b>Author:</b> Vincent Rouvreau<br> + <b>Introduced in:</b> GUDHI 1.3.0<br> <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> - <b>Requires:</b> \ref cgal ≥ 4.11.0 + <b>Requires:</b> \ref eigen ≥ 3.1.0 and \ref cgal ≥ 4.11.0 </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref cover_complex + <b>User manual:</b> \ref alpha_complex </td> </tr> </table> -## Data structures and basic operations {#DataStructuresAndBasicOperations} +### ÄŒech complex + +<table> + <tr> + <td width="35%" rowspan=2> + \image html "cech_complex_representation.png" + </td> + <td width="50%"> + The ÄŒech complex is a simplicial complex constructed from a proximity graph. + The set of all simplices is filtered by the radius of their minimal enclosing ball. + </td> + <td width="15%"> + <b>Author:</b> Vincent Rouvreau<br> + <b>Introduced in:</b> GUDHI 2.2.0<br> + <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> + <b>Includes:</b> [Miniball](https://people.inf.ethz.ch/gaertner/subdir/software/miniball.html)<br> + </td> + </tr> + <tr> + <td colspan=2 height="25"> + <b>User manual:</b> \ref cech_complex + </td> + </tr> +</table> -### Data structures +### Rips complex -#### Simplex tree <table> <tr> <td width="35%" rowspan=2> - \image html "Simplex_tree_representation.png" + \image html "rips_complex_representation.png" </td> <td width="50%"> - The simplex tree is an efficient and flexible - data structure for representing general (filtered) simplicial complexes. The data structure - is described in \cite boissonnatmariasimplextreealgorithmica . + Rips complex is a simplicial complex constructed from a one skeleton graph.<br> + The filtration value of each edge is computed from a user-given distance function and is inserted until a + user-given threshold value.<br> + This complex can be built from a point cloud and a distance function, or from a distance matrix. </td> <td width="15%"> - <b>Author:</b> Clément Maria<br> - <b>Introduced in:</b> GUDHI 1.0.0<br> + <b>Author:</b> Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse<br> + <b>Introduced in:</b> GUDHI 2.0.0<br> <b>Copyright:</b> MIT<br> </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref simplex_tree + <b>User manual:</b> \ref rips_complex </td> </tr> </table> -#### Skeleton blocker +### Witness complex <table> <tr> <td width="35%" rowspan=2> - \image html "ds_representation.png" + \image html "Witness_complex_representation.png" </td> <td width="50%"> - The Skeleton-Blocker data-structure proposes a light encoding for simplicial complexes by storing only an *implicit* - representation of its simplices \cite socg_blockers_2011,\cite blockers2012. Intuitively, it just stores the - 1-skeleton of a simplicial complex with a graph and the set of its "missing faces" that is very small in practice. - This data-structure handles all simplicial complexes operations such as simplex enumeration or simplex removal but - operations that are particularly efficient are operations that do not require simplex enumeration such as edge - iteration, link computation or simplex contraction. + Witness complex \f$ Wit(W,L) \f$ is a simplicial complex defined on two sets of points in \f$\mathbb{R}^D\f$. + The data structure is described in \cite boissonnatmariasimplextreealgorithmica . </td> <td width="15%"> - <b>Author:</b> David Salinas<br> - <b>Introduced in:</b> GUDHI 1.1.0<br> - <b>Copyright:</b> MIT<br> + <b>Author:</b> Siargey Kachanovich<br> + <b>Introduced in:</b> GUDHI 1.3.0<br> + <b>Copyright:</b> MIT ([GPL v3](../../licensing/) for Euclidean version)<br> + <b>Euclidean version requires:</b> \ref eigen ≥ 3.1.0 and \ref cgal ≥ 4.11.0 </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref skbl + <b>User manual:</b> \ref witness_complex </td> </tr> </table> -#### Toplex Map - +### Cover Complexes <table> <tr> <td width="35%" rowspan=2> - \image html "map.png" + \image html "gicvisu.jpg" </td> <td width="50%"> - The Toplex map data structure is composed firstly of a raw storage of toplices (the maximal simplices) - and secondly of a map which associate any vertex to a set of pointers toward all toplices - containing this vertex. + 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> </td> <td width="15%"> - <b>Author:</b> François Godi<br> + <b>Author:</b> Mathieu Carrière<br> <b>Introduced in:</b> GUDHI 2.1.0<br> - <b>Copyright:</b> MIT<br> + <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> + <b>Requires:</b> \ref cgal ≥ 4.11.0 </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref toplex_map + <b>User manual:</b> \ref cover_complex </td> </tr> </table> -### Basic operations - -#### Contraction +### Tangential complex <table> <tr> <td width="35%" rowspan=2> - \image html "sphere_contraction_representation.png" + \image html "tc_examples.png" </td> <td width="50%"> - The purpose of this package is to offer a user-friendly interface for edge contraction simplification of huge - simplicial complexes. It uses the \ref skbl data-structure whose size remains small during simplification of most - used geometrical complexes of topological data analysis such as the Rips or the Delaunay complexes. In practice, - the size of this data-structure is even much lower than the total number of simplices. + A Tangential Delaunay complex is a <a target="_blank" href="https://en.wikipedia.org/wiki/Simplicial_complex">simplicial complex</a> + designed to reconstruct a \f$ k \f$-dimensional manifold embedded in \f$ d \f$-dimensional Euclidean space. + The input is a point sample coming from an unknown manifold. + The running time depends only linearly on the extrinsic dimension \f$ d \f$ + and exponentially on the intrinsic dimension \f$ k \f$. </td> <td width="15%"> - <b>Author:</b> David Salinas<br> - <b>Introduced in:</b> GUDHI 1.1.0<br> - <b>Copyright:</b> MIT [(LGPL v3)](../../licensing/)<br> - <b>Requires:</b> \ref cgal ≥ 4.11.0 + <b>Author:</b> Clément Jamin<br> + <b>Introduced in:</b> GUDHI 2.0.0<br> + <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> + <b>Requires:</b> \ref eigen ≥ 3.1.0 and \ref cgal ≥ 4.11.0 </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref contr + <b>User manual:</b> \ref tangential_complex </td> </tr> </table> @@ -305,36 +328,6 @@ </tr> </table> -## Manifold reconstruction {#ManifoldReconstruction} - -### Tangential complex - -<table> - <tr> - <td width="35%" rowspan=2> - \image html "tc_examples.png" - </td> - <td width="50%"> - A Tangential Delaunay complex is a <a target="_blank" href="https://en.wikipedia.org/wiki/Simplicial_complex">simplicial complex</a> - designed to reconstruct a \f$ k \f$-dimensional manifold embedded in \f$ d \f$-dimensional Euclidean space. - The input is a point sample coming from an unknown manifold. - The running time depends only linearly on the extrinsic dimension \f$ d \f$ - and exponentially on the intrinsic dimension \f$ k \f$. - </td> - <td width="15%"> - <b>Author:</b> Clément Jamin<br> - <b>Introduced in:</b> GUDHI 2.0.0<br> - <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> - <b>Requires:</b> \ref eigen ≥ 3.1.0 and \ref cgal ≥ 4.11.0 - </td> - </tr> - <tr> - <td colspan=2 height="25"> - <b>User manual:</b> \ref tangential_complex - </td> - </tr> -</table> - ## Topological descriptors tools {#TopologicalDescriptorsTools} ### Bottleneck distance @@ -390,3 +383,26 @@ </td> </tr> </table> + +## Point cloud utilities {#PointCloudUtils} + +<table> + <tr> + <td width="35%" rowspan=2> + \f$(x_1,\ldots,x_d)\f$ + </td> + <td width="50%"> + This contains various tools to handle point clouds: spatial searching, subsampling, etc. + </td> + <td width="15%"> + <b>Author:</b> Clément Jamin<br> + <b>Introduced in:</b> GUDHI 1.3.0<br> + <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> + </td> + </tr> + <tr> + <td colspan=2 height="25"> + <b>Manuals:</b> \ref spatial_searching, \ref subsampling + </td> + </tr> +</table> diff --git a/src/common/include/gudhi/Unitary_tests_utils.h b/src/common/include/gudhi/Unitary_tests_utils.h index 7d039304..9b86460a 100644 --- a/src/common/include/gudhi/Unitary_tests_utils.h +++ b/src/common/include/gudhi/Unitary_tests_utils.h @@ -26,4 +26,15 @@ void GUDHI_TEST_FLOAT_EQUALITY_CHECK(FloatingType a, FloatingType b, BOOST_CHECK(std::fabs(a - b) <= epsilon); } +// That's the usual x86 issue where a+b==a+b can return false (without any NaN) because one of them was stored in +// memory (and thus rounded to 64 bits) while the other is still in a register (80 bits). +template<typename FloatingType > +FloatingType GUDHI_PROTECT_FLOAT(FloatingType value) { + volatile FloatingType protected_value = value; +#ifdef DEBUG_TRACES + std::cout << "GUDHI_PROTECT_FLOAT - " << protected_value << std::endl; +#endif + return protected_value; +} + #endif // UNITARY_TESTS_UTILS_H_ diff --git a/src/python/CMakeLists.txt b/src/python/CMakeLists.txt index 7f9ff38f..8c36f7ee 100644 --- a/src/python/CMakeLists.txt +++ b/src/python/CMakeLists.txt @@ -32,6 +32,10 @@ function( add_gudhi_debug_info DEBUG_INFO ) endfunction( add_gudhi_debug_info ) if(PYTHONINTERP_FOUND) + if(PYBIND11_FOUND) + add_gudhi_debug_info("Pybind11 version ${PYBIND11_VERSION}") + set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'hera', ") + endif() if(CYTHON_FOUND) set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'off_reader', ") set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'simplex_tree', ") @@ -87,6 +91,7 @@ if(PYTHONINTERP_FOUND) endif(MSVC) if(CMAKE_COMPILER_IS_GNUCXX) set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-frounding-math', ") + set(GUDHI_PYBIND11_EXTRA_COMPILE_ARGS "${GUDHI_PYBIND11_EXTRA_COMPILE_ARGS}'-fvisibility=hidden', ") endif(CMAKE_COMPILER_IS_GNUCXX) if (CMAKE_CXX_COMPILER_ID MATCHES Intel) set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-fp-model strict', ") @@ -115,9 +120,9 @@ if(PYTHONINTERP_FOUND) set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'nerve_gic', ") endif () if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) + set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'alpha_complex', ") set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'subsampling', ") set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'tangential_complex', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'alpha_complex', ") set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'euclidean_witness_complex', ") set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'euclidean_strong_witness_complex', ") endif () @@ -163,10 +168,21 @@ if(PYTHONINTERP_FOUND) set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-DCGAL_USE_GMPXX', ") add_GUDHI_PYTHON_lib("${GMPXX_LIBRARIES}") set(GUDHI_PYTHON_LIBRARY_DIRS "${GUDHI_PYTHON_LIBRARY_DIRS}'${GMPXX_LIBRARIES_DIR}', ") - message("** Add gmpxx ${GMPXX_LIBRARIES_DIR}") + message("** Add gmpxx ${GMPXX_LIBRARIES_DIR}") endif(GMPXX_FOUND) endif(GMP_FOUND) - endif(CGAL_FOUND) + if(MPFR_FOUND) + add_gudhi_debug_info("MPFR_LIBRARIES = ${MPFR_LIBRARIES}") + set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-DCGAL_USE_MPFR', ") + add_GUDHI_PYTHON_lib("${MPFR_LIBRARIES}") + # In case CGAL is not header only, all MPFR variables are set except MPFR_LIBRARIES_DIR - Just set it + if(NOT MPFR_LIBRARIES_DIR) + get_filename_component(MPFR_LIBRARIES_DIR ${MPFR_LIBRARIES} PATH) + endif(NOT MPFR_LIBRARIES_DIR) + set(GUDHI_PYTHON_LIBRARY_DIRS "${GUDHI_PYTHON_LIBRARY_DIRS}'${MPFR_LIBRARIES_DIR}', ") + message("** Add mpfr ${MPFR_LIBRARIES}") + endif(MPFR_FOUND) +endif(CGAL_FOUND) # Specific for Mac if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin") @@ -231,7 +247,6 @@ if(PYTHONINTERP_FOUND) 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) - if(MATPLOTLIB_FOUND AND NUMPY_FOUND) # Tangential add_test(NAME tangential_complex_plain_homology_from_off_file_example_py_test @@ -307,7 +322,6 @@ if(PYTHONINTERP_FOUND) WORKING_DIRECTORY ${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") - if(MATPLOTLIB_FOUND AND NUMPY_FOUND) add_test(NAME alpha_complex_diagram_persistence_from_off_file_example_py_test WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} @@ -315,9 +329,7 @@ if(PYTHONINTERP_FOUND) ${PYTHON_EXECUTABLE} "${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) endif() - add_gudhi_py_test(test_alpha_complex) - endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) @@ -385,10 +397,10 @@ if(PYTHONINTERP_FOUND) add_gudhi_py_test(test_reader_utils) # Wasserstein - if(OT_FOUND) + if(OT_FOUND AND PYBIND11_FOUND) add_gudhi_py_test(test_wasserstein_distance) add_gudhi_py_test(test_wasserstein_barycenter) - endif(OT_FOUND) + endif() # Representations if(SKLEARN_FOUND AND MATPLOTLIB_FOUND) @@ -402,32 +414,37 @@ if(PYTHONINTERP_FOUND) if(SCIPY_FOUND) if(SKLEARN_FOUND) if(OT_FOUND) - if(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) - set (GUDHI_SPHINX_MESSAGE "Generating API documentation with Sphinx in ${CMAKE_CURRENT_BINARY_DIR}/sphinx/") - # User warning - Sphinx is a static pages generator, and configured to work fine with user_version - # Images and biblio warnings because not found on developper version - if (GUDHI_PYTHON_PATH STREQUAL "src/python") - set (GUDHI_SPHINX_MESSAGE "${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) + if(PYBIND11_FOUND) + if(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) + set (GUDHI_SPHINX_MESSAGE "Generating API documentation with Sphinx in ${CMAKE_CURRENT_BINARY_DIR}/sphinx/") + # User warning - Sphinx is a static pages generator, and configured to work fine with user_version + # Images and biblio warnings because not found on developper version + if (GUDHI_PYTHON_PATH STREQUAL "src/python") + set (GUDHI_SPHINX_MESSAGE "${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) + 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) - # Set missing or not modules - set(GUDHI_MODULES ${GUDHI_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MODULES") - else(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) - message("++ Python documentation module will not be compiled because it requires a Eigen3 and CGAL version >= 4.11.0") + # Set missing or not modules + set(GUDHI_MODULES ${GUDHI_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MODULES") + else(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) + message("++ Python documentation module will not be compiled because it requires a Eigen3 and CGAL version >= 4.11.0") + set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") + endif(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) + else(PYBIND11_FOUND) + message("++ Python documentation module will not be compiled because pybind11 was not found") set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") - endif(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) + endif(PYBIND11_FOUND) else(OT_FOUND) message("++ Python documentation module will not be compiled because POT was not found") set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") diff --git a/src/python/doc/_templates/layout.html b/src/python/doc/_templates/layout.html index 2f2d9c72..a672a281 100644 --- a/src/python/doc/_templates/layout.html +++ b/src/python/doc/_templates/layout.html @@ -201,7 +201,7 @@ <a href="#">Download</a> <ul class="dropdown"> <li><a href="/licensing/">Licensing</a></li> - <li><a href="https://gforge.inria.fr/frs/download.php/latestzip/5253/library-latest.zip" target="_blank">Get the latest sources</a></li> + <li><a href="https://github.com/GUDHI/gudhi-devel/releases/latest" target="_blank">Get the latest sources</a></li> <li><a href="/conda/">Conda package</a></li> <li><a href="/dockerfile/">Dockerfile</a></li> </ul> diff --git a/src/python/doc/alpha_complex_sum.inc b/src/python/doc/alpha_complex_sum.inc index c5ba9dc7..b5af0d27 100644 --- a/src/python/doc/alpha_complex_sum.inc +++ b/src/python/doc/alpha_complex_sum.inc @@ -5,16 +5,13 @@ | .. figure:: | Alpha complex is a simplicial complex constructed from the finite | :Author: Vincent Rouvreau | | ../../doc/Alpha_complex/alpha_complex_representation.png | cells of a Delaunay Triangulation. | | | :alt: Alpha complex representation | | :Introduced in: GUDHI 2.0.0 | - | :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 | :Copyright: MIT (`GPL v3 </licensing/>`_) | - | | is then said to be Gabriel), and as the minimum of the filtration | | - | | values of the codimension 1 cofaces that make it not Gabriel | :Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 | - | | otherwise. All simplices that have a filtration value strictly | | - | | greater than a given alpha squared value are not inserted into the | | - | | complex. | | + | :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 | :Copyright: MIT (`GPL v3 </licensing/>`_) | + | | simplex is then said to be Gabriel), and as the minimum of the | | + | | filtration values of the codimension 1 cofaces that make it not | :Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 | + | | Gabriel otherwise. | | | | | | - | | This package requires having CGAL version 4.7 or higher (4.8.1 is | | - | | advised for better performance). | | + | | For performances reasons, it is advised to use CGAL ≥ 5.0.0. | | +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ | * :doc:`alpha_complex_user` | * :doc:`alpha_complex_ref` | +----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/alpha_complex_user.rst b/src/python/doc/alpha_complex_user.rst index b7e69e12..60319e84 100644 --- a/src/python/doc/alpha_complex_user.rst +++ b/src/python/doc/alpha_complex_user.rst @@ -16,7 +16,8 @@ Definition Remarks ^^^^^^^ -When an :math:`\alpha`-complex is constructed with an infinite value of :math:`\alpha`, the complex is a Delaunay complex (with special filtration values). +When an :math:`\alpha`-complex is constructed with an infinite value of :math:`\alpha^2`, +the complex is a Delaunay complex (with special filtration values). Example from points ------------------- @@ -137,19 +138,20 @@ sets the filtration value (0 in case of a vertex - propagation will have no effe Non decreasing filtration values ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -As the squared radii computed by CGAL are an approximation, it might happen that these alpha squared values do not -quite define a proper filtration (i.e. non-decreasing with respect to inclusion). +As the squared radii computed by CGAL are an approximation, it might happen that these +:math:`\alpha^2` values do not quite define a proper filtration (i.e. non-decreasing with +respect to inclusion). We fix that up by calling :func:`~gudhi.SimplexTree.make_filtration_non_decreasing` (cf. `C++ version <http://gudhi.gforge.inria.fr/doc/latest/index.html>`_). Prune above given filtration value ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -The simplex tree is pruned from the given maximum alpha squared value (cf. +The simplex tree is pruned from the given maximum :math:`\alpha^2` value (cf. :func:`~gudhi.SimplexTree.prune_above_filtration`). Note that this does not provide any kind of speed-up, since we always first build the full filtered complex, so it is recommended not to use :paramref:`~gudhi.AlphaComplex.create_simplex_tree.max_alpha_square`. -In the following example, a threshold of 59 is used. +In the following example, a threshold of :math:`\alpha^2 = 32.0` is used. Example from OFF file @@ -166,7 +168,7 @@ Then, it is asked to display information about the alpha complex: import gudhi 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) + simplex_tree = alpha_complex.create_simplex_tree(max_alpha_square=32.0) result_str = 'Alpha complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ repr(simplex_tree.num_simplices()) + ' simplices - ' + \ repr(simplex_tree.num_vertices()) + ' vertices.' @@ -179,7 +181,7 @@ the program output is: .. testoutput:: - Alpha complex is of dimension 2 - 23 simplices - 7 vertices. + Alpha complex is of dimension 2 - 20 simplices - 7 vertices. [0] -> 0.00 [1] -> 0.00 [2] -> 0.00 @@ -200,9 +202,6 @@ the program output is: [4, 6] -> 22.74 [4, 5, 6] -> 22.74 [3, 6] -> 30.25 - [2, 6] -> 36.50 - [2, 3, 6] -> 36.50 - [2, 4, 6] -> 37.24 CGAL citations ============== diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst index b13b500e..56cf0170 100644 --- a/src/python/doc/cubical_complex_user.rst +++ b/src/python/doc/cubical_complex_user.rst @@ -142,8 +142,7 @@ Or it can be defined as follows: .. testcode:: from gudhi import PeriodicCubicalComplex as pcc - periodic_cc = pcc(dimensions=[3,3], - top_dimensional_cells= [0, 0, 0, 0, 1, 0, 0, 0, 0], + periodic_cc = pcc(top_dimensional_cells = [[0, 0, 0], [0, 1, 0], [0, 0, 0]], periodic_dimensions=[True, False]) result_str = 'Periodic cubical complex is of dimension ' + repr(periodic_cc.dimension()) + ' - ' + \ repr(periodic_cc.num_simplices()) + ' simplices.' diff --git a/src/python/doc/reader_utils_ref.rst b/src/python/doc/diagram_readers_ref.rst index f3ecebad..c79daf9c 100644 --- a/src/python/doc/reader_utils_ref.rst +++ b/src/python/doc/diagram_readers_ref.rst @@ -2,13 +2,9 @@ .. To get rid of WARNING: document isn't included in any toctree -============================= -Reader utils reference manual -============================= - -.. autofunction:: gudhi.read_off - -.. autofunction:: gudhi.read_lower_triangular_matrix_from_csv_file +================================ +Diagram readers reference manual +================================ .. autofunction:: gudhi.read_persistence_intervals_grouped_by_dimension diff --git a/src/python/doc/index.rst b/src/python/doc/index.rst index c36a578f..3387a64f 100644 --- a/src/python/doc/index.rst +++ b/src/python/doc/index.rst @@ -6,8 +6,8 @@ GUDHI Python modules documentation :alt: Gudhi banner :figclass: align-center -Complexes -********* +Data structures for cell complexes +********************************** Cubical complexes ================= @@ -17,18 +17,26 @@ Cubical complexes Simplicial complexes ==================== +Simplex tree +------------ + +.. include:: simplex_tree_sum.inc + +Filtrations and reconstructions +******************************* + Alpha complex -------------- +============= .. include:: alpha_complex_sum.inc Rips complex ------------- +============ .. include:: rips_complex_sum.inc Witness complex ---------------- +=============== .. include:: witness_complex_sum.inc @@ -37,16 +45,10 @@ Cover complexes .. include:: nerve_gic_complex_sum.inc -Data structures and basic operations -************************************ - -Data structures -=============== - -Simplex tree ------------- +Tangential complex +================== -.. include:: simplex_tree_sum.inc +.. include:: tangential_complex_sum.inc Topological descriptors computation *********************************** @@ -56,15 +58,6 @@ Persistence cohomology .. include:: persistent_cohomology_sum.inc -Manifold reconstruction -*********************** - -Tangential complex -================== - -.. include:: tangential_complex_sum.inc - - Topological descriptors tools ***************************** @@ -88,6 +81,11 @@ Persistence graphical tools .. include:: persistence_graphical_tools_sum.inc +Point cloud utilities +********************* + +.. include:: point_cloud_sum.inc + Bibliography ************ diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst index 50a697c7..d459145b 100644 --- a/src/python/doc/installation.rst +++ b/src/python/doc/installation.rst @@ -14,10 +14,11 @@ Compiling ********* The library uses c++14 and requires `Boost <https://www.boost.org/>`_ ≥ 1.56.0, `CMake <https://www.cmake.org/>`_ ≥ 3.1 to generate makefiles, -`NumPy <http://numpy.org>`_ and `Cython <https://www.cython.org/>`_ to compile +`NumPy <http://numpy.org>`_, `Cython <https://www.cython.org/>`_ and +`pybind11 <https://github.com/pybind/pybind11>`_ to compile the GUDHI Python module. It is a multi-platform library and compiles on Linux, Mac OSX and Visual -Studio 2015. +Studio 2017. On `Windows <https://wiki.python.org/moin/WindowsCompilers>`_ , only Python ≥ 3.5 are available because of the required Visual Studio version. @@ -257,6 +258,13 @@ The :doc:`Wasserstein distance </wasserstein_distance_user>` module requires `POT <https://pot.readthedocs.io/>`_, a library that provides several solvers for optimization problems related to Optimal Transport. +Scikit-learn +============ + +The :doc:`persistence representations </representations>` module require +`scikit-learn <https://scikit-learn.org/>`_, a Python-based ecosystem of +open-source software for machine learning. + SciPy ===== diff --git a/src/python/doc/persistence_graphical_tools_user.rst b/src/python/doc/persistence_graphical_tools_user.rst index f41a926b..80002db6 100644 --- a/src/python/doc/persistence_graphical_tools_user.rst +++ b/src/python/doc/persistence_graphical_tools_user.rst @@ -24,7 +24,7 @@ This function can display the persistence result as a barcode: import gudhi off_file = gudhi.__root_source_dir__ + '/data/points/tore3D_300.off' - point_cloud = gudhi.read_off(off_file=off_file) + point_cloud = gudhi.read_points_from_off_file(off_file=off_file) rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=0.7) simplex_tree = rips_complex.create_simplex_tree(max_dimension=3) diff --git a/src/python/doc/point_cloud.rst b/src/python/doc/point_cloud.rst new file mode 100644 index 00000000..d668428a --- /dev/null +++ b/src/python/doc/point_cloud.rst @@ -0,0 +1,22 @@ +:orphan: + +.. To get rid of WARNING: document isn't included in any toctree + +============================ +Point cloud utilities manual +============================ + +File Readers +------------ + +.. autofunction:: gudhi.read_points_from_off_file + +.. autofunction:: gudhi.read_lower_triangular_matrix_from_csv_file + +Subsampling +----------- + +.. automodule:: gudhi.subsampling + :members: + :special-members: + :show-inheritance: diff --git a/src/python/doc/point_cloud_sum.inc b/src/python/doc/point_cloud_sum.inc new file mode 100644 index 00000000..85d52de7 --- /dev/null +++ b/src/python/doc/point_cloud_sum.inc @@ -0,0 +1,15 @@ +.. table:: + :widths: 30 50 20 + + +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ + | | :math:`(x_1, x_2, \ldots, x_d)` | Utilities to process point clouds: read from file, subsample, etc. | :Author: Vincent Rouvreau | + | | :math:`(y_1, y_2, \ldots, y_d)` | | | + | | | :Introduced in: GUDHI 2.0.0 | + | | | | + | | | :Copyright: MIT (`GPL v3 </licensing/>`_) | + | | Parts of this package require CGAL. | | + | | | :Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 | + | | | | + +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ + | * :doc:`point_cloud` | + +----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst index a8659542..a27573e8 100644 --- a/src/python/doc/rips_complex_user.rst +++ b/src/python/doc/rips_complex_user.rst @@ -136,7 +136,8 @@ Finally, it is asked to display information about the Rips complex. .. testcode:: import gudhi - point_cloud = gudhi.read_off(off_file=gudhi.__root_source_dir__ + '/data/points/alphacomplexdoc.off') + off_file = gudhi.__root_source_dir__ + '/data/points/alphacomplexdoc.off' + point_cloud = gudhi.read_points_from_off_file(off_file = off_file) rips_complex = gudhi.RipsComplex(points=point_cloud, 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()) + ' - ' + \ diff --git a/src/python/doc/wasserstein_distance_sum.inc b/src/python/doc/wasserstein_distance_sum.inc index ffd4d312..a97f428d 100644 --- a/src/python/doc/wasserstein_distance_sum.inc +++ b/src/python/doc/wasserstein_distance_sum.inc @@ -2,12 +2,12 @@ :widths: 30 50 20 +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ - | .. figure:: | The p-Wasserstein distance measures the similarity between two | :Author: Theo Lacombe | + | .. figure:: | The q-Wasserstein distance measures the similarity between two | :Author: Theo Lacombe | | ../../doc/Bottleneck_distance/perturb_pd.png | persistence diagrams. It's the minimum value c that can be achieved | | | :figclass: align-center | by a perfect matching between the points of the two diagrams (+ all | :Introduced in: GUDHI 3.1.0 | | | diagonal points), where the value of a matching is defined as the | | - | Wasserstein distance is the p-th root of the sum of the | p-th root of the sum of all edge lengths to the power p. Edge lengths| :Copyright: MIT | - | edge lengths to the power p. | are measured in norm q, for :math:`1 \leq q \leq \infty`. | | + | Wasserstein distance is the q-th root of the sum of the | q-th root of the sum of all edge lengths to the power q. Edge lengths| :Copyright: MIT | + | edge lengths to the power q. | are measured in norm p, for :math:`1 \leq p \leq \infty`. | | | | | :Requires: Python Optimal Transport (POT) :math:`\geq` 0.5.1 | +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ | * :doc:`wasserstein_distance_user` | | diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst index a049cfb5..94b454e2 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -9,17 +9,26 @@ Definition .. include:: wasserstein_distance_sum.inc -This implementation is based on ideas from "Large Scale Computation of Means and Cluster for Persistence Diagrams via Optimal Transport". +Functions +--------- +This implementation uses the Python Optimal Transport library and is based on +ideas from "Large Scale Computation of Means and Cluster for Persistence +Diagrams via Optimal Transport" :cite:`10.5555/3327546.3327645`. -Function --------- .. autofunction:: gudhi.wasserstein.wasserstein_distance +This other implementation comes from `Hera +<https://bitbucket.org/grey_narn/hera/src/master/>`_ (BSD-3-Clause) which is +based on "Geometry Helps to Compare Persistence Diagrams" +:cite:`Kerber:2017:GHC:3047249.3064175` by Michael Kerber, Dmitriy +Morozov, and Arnur Nigmetov. + +.. autofunction:: gudhi.hera.wasserstein_distance Basic example ------------- -This example computes the 1-Wasserstein distance from 2 persistence diagrams with euclidean ground metric. +This example computes the 1-Wasserstein distance from 2 persistence diagrams with Euclidean ground metric. Note that persistence diagrams must be submitted as (n x 2) numpy arrays and must not contain inf values. .. testcode:: @@ -30,7 +39,7 @@ Note that persistence diagrams must be submitted as (n x 2) numpy arrays and mus diag1 = np.array([[2.7, 3.7],[9.6, 14.],[34.2, 34.974]]) diag2 = np.array([[2.8, 4.45],[9.5, 14.1]]) - message = "Wasserstein distance value = " + '%.2f' % gudhi.wasserstein.wasserstein_distance(diag1, diag2, q=2., p=1.) + message = "Wasserstein distance value = " + '%.2f' % gudhi.wasserstein.wasserstein_distance(diag1, diag2, order=1., internal_p=2.) print(message) The output is: diff --git a/src/python/doc/witness_complex_user.rst b/src/python/doc/witness_complex_user.rst index 45ba5b3b..7087fa98 100644 --- a/src/python/doc/witness_complex_user.rst +++ b/src/python/doc/witness_complex_user.rst @@ -101,7 +101,7 @@ Let's start with a simple example, which reads an off point file and computes a print("#####################################################################") print("EuclideanWitnessComplex creation from points read in a OFF file") - witnesses = gudhi.read_off(off_file=args.file) + witnesses = gudhi.read_points_from_off_file(off_file=args.file) landmarks = gudhi.pick_n_random_points(points=witnesses, nb_points=args.number_of_landmarks) message = "EuclideanWitnessComplex with max_edge_length=" + repr(args.max_alpha_square) + \ diff --git a/src/python/example/alpha_complex_from_points_example.py b/src/python/example/alpha_complex_from_points_example.py index a746998c..844d7a82 100755 --- a/src/python/example/alpha_complex_from_points_example.py +++ b/src/python/example/alpha_complex_from_points_example.py @@ -52,4 +52,9 @@ print("star([0])=", simplex_tree.get_star([0])) print("coface([0], 1)=", simplex_tree.get_cofaces([0], 1)) print("point[0]=", alpha_complex.get_point(0)) -print("point[5]=", alpha_complex.get_point(5)) +try: + print("point[5]=", alpha_complex.get_point(5)) +except IndexError: + pass +else: + assert False diff --git a/src/python/example/alpha_rips_persistence_bottleneck_distance.py b/src/python/example/alpha_rips_persistence_bottleneck_distance.py index 086307ee..d5c33ec8 100755 --- a/src/python/example/alpha_rips_persistence_bottleneck_distance.py +++ b/src/python/example/alpha_rips_persistence_bottleneck_distance.py @@ -35,7 +35,7 @@ args = parser.parse_args() with open(args.file, "r") as f: first_line = f.readline() if (first_line == "OFF\n") or (first_line == "nOFF\n"): - point_cloud = gudhi.read_off(off_file=args.file) + point_cloud = gudhi.read_points_from_off_file(off_file=args.file) print("#####################################################################") print("RipsComplex creation from points read in a OFF file") diff --git a/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py b/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py index 0eedd140..4903667e 100755 --- a/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py +++ b/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py @@ -47,7 +47,7 @@ with open(args.file, "r") as f: print("#####################################################################") print("EuclideanStrongWitnessComplex creation from points read in a OFF file") - witnesses = gudhi.read_off(off_file=args.file) + witnesses = gudhi.read_points_from_off_file(off_file=args.file) landmarks = gudhi.pick_n_random_points( points=witnesses, nb_points=args.number_of_landmarks ) diff --git a/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py b/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py index 1fe55737..339a8577 100755 --- a/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py +++ b/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py @@ -46,7 +46,7 @@ with open(args.file, "r") as f: print("#####################################################################") print("EuclideanWitnessComplex creation from points read in a OFF file") - witnesses = gudhi.read_off(off_file=args.file) + witnesses = gudhi.read_points_from_off_file(off_file=args.file) landmarks = gudhi.pick_n_random_points( points=witnesses, nb_points=args.number_of_landmarks ) diff --git a/src/python/example/plot_rips_complex.py b/src/python/example/plot_rips_complex.py index 1c878db1..214a3c0a 100755 --- a/src/python/example/plot_rips_complex.py +++ b/src/python/example/plot_rips_complex.py @@ -2,7 +2,7 @@ import numpy as np import gudhi -points = np.array(gudhi.read_off('../../data/points/Kl.off')) +points = np.array(gudhi.read_points_from_off_file('../../data/points/Kl.off')) rc = gudhi.RipsComplex(points=points, max_edge_length=.2) st = rc.create_simplex_tree(max_dimension=2) # We are only going to plot the triangles diff --git a/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py b/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py index b9074cf9..c757aca7 100755 --- a/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py +++ b/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py @@ -48,7 +48,7 @@ with open(args.file, "r") as f: message = "RipsComplex with max_edge_length=" + repr(args.max_edge_length) print(message) - point_cloud = gudhi.read_off(off_file=args.file) + point_cloud = gudhi.read_points_from_off_file(off_file=args.file) rips_complex = gudhi.RipsComplex( points=point_cloud, max_edge_length=args.max_edge_length ) diff --git a/src/python/gudhi/__init__.py.in b/src/python/gudhi/__init__.py.in index 0c462b02..79e12fbc 100644 --- a/src/python/gudhi/__init__.py.in +++ b/src/python/gudhi/__init__.py.in @@ -1,5 +1,3 @@ -from importlib import import_module - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau @@ -9,6 +7,9 @@ from importlib import import_module # Modification(s): # - YYYY/MM Author: Description of the modification +from importlib import import_module +from sys import exc_info + __author__ = "GUDHI Editorial Board" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "https://gudhi.inria.fr/licensing/" @@ -17,9 +18,6 @@ __version__ = "@GUDHI_VERSION@" __root_source_dir__ = "@CMAKE_SOURCE_DIR@" __debug_info__ = @GUDHI_PYTHON_DEBUG_INFO@ -from sys import exc_info -from importlib import import_module - __all__ = [@GUDHI_PYTHON_MODULES@ @GUDHI_PYTHON_MODULES_EXTRA@] __available_modules = '' diff --git a/src/python/gudhi/alpha_complex.pyx b/src/python/gudhi/alpha_complex.pyx index 8f2c98d5..fff3e920 100644 --- a/src/python/gudhi/alpha_complex.pyx +++ b/src/python/gudhi/alpha_complex.pyx @@ -1,3 +1,12 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Vincent Rouvreau +# +# Copyright (C) 2016 Inria +# +# Modification(s): +# - YYYY/MM Author: Description of the modification + from cython cimport numeric from libcpp.vector cimport vector from libcpp.utility cimport pair @@ -9,26 +18,17 @@ import os from gudhi.simplex_tree cimport * from gudhi.simplex_tree import SimplexTree -# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. -# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. -# Author(s): Vincent Rouvreau -# -# Copyright (C) 2016 Inria -# -# Modification(s): -# - YYYY/MM Author: Description of the modification - __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "GPL v3" cdef extern from "Alpha_complex_interface.h" namespace "Gudhi": cdef cppclass Alpha_complex_interface "Gudhi::alpha_complex::Alpha_complex_interface": - Alpha_complex_interface(vector[vector[double]] points) + Alpha_complex_interface(vector[vector[double]] points) except + # bool from_file is a workaround for cython to find the correct signature - Alpha_complex_interface(string off_file, bool from_file) - vector[double] get_point(int vertex) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) + Alpha_complex_interface(string off_file, bool from_file) except + + vector[double] get_point(int vertex) except + + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) except + # AlphaComplex python interface cdef class AlphaComplex: @@ -66,10 +66,10 @@ cdef class AlphaComplex: """ # The real cython constructor - def __cinit__(self, points=None, off_file=''): + def __cinit__(self, points = None, off_file = ''): if off_file: if os.path.isfile(off_file): - self.thisptr = new Alpha_complex_interface(str.encode(off_file), True) + self.thisptr = new Alpha_complex_interface(off_file.encode('utf-8'), True) else: print("file " + off_file + " not found.") else: @@ -96,10 +96,9 @@ cdef class AlphaComplex: :rtype: list of float :returns: the point. """ - cdef vector[double] point = self.thisptr.get_point(vertex) - return point + return self.thisptr.get_point(vertex) - def create_simplex_tree(self, max_alpha_square=float('inf')): + def create_simplex_tree(self, max_alpha_square = float('inf')): """ :param max_alpha_square: The maximum alpha square threshold the simplices shall not exceed. Default is set to infinity, and diff --git a/src/python/gudhi/bottleneck.pyx b/src/python/gudhi/bottleneck.pyx index c2361024..af011e88 100644 --- a/src/python/gudhi/bottleneck.pyx +++ b/src/python/gudhi/bottleneck.pyx @@ -1,8 +1,3 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -import os - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau @@ -12,6 +7,11 @@ import os # Modification(s): # - YYYY/MM Author: Description of the modification +from cython cimport numeric +from libcpp.vector cimport vector +from libcpp.utility cimport pair +import os + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "GPL v3" diff --git a/src/python/gudhi/cubical_complex.pyx b/src/python/gudhi/cubical_complex.pyx index 011c407c..cbeda014 100644 --- a/src/python/gudhi/cubical_complex.pyx +++ b/src/python/gudhi/cubical_complex.pyx @@ -1,12 +1,3 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libcpp.string cimport string -from libcpp cimport bool -import os - -from numpy import array as np_array - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau @@ -16,6 +7,15 @@ from numpy import array as np_array # Modification(s): # - YYYY/MM Author: Description of the modification +from cython cimport numeric +from libcpp.vector cimport vector +from libcpp.utility cimport pair +from libcpp.string cimport string +from libcpp cimport bool +import os + +import numpy as np + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" @@ -47,7 +47,7 @@ cdef class CubicalComplex: # Fake constructor that does nothing but documenting the constructor def __init__(self, dimensions=None, top_dimensional_cells=None, - perseus_file=''): + perseus_file=''): """CubicalComplex constructor from dimensions and top_dimensional_cells or from a Perseus-style file name. @@ -58,6 +58,12 @@ cdef class CubicalComplex: Or + :param top_dimensional_cells: A multidimensional array of cells + filtration values. + :type top_dimensional_cells: anything convertible to a numpy ndarray + + Or + :param perseus_file: A Perseus-style file name. :type perseus_file: string """ @@ -65,11 +71,21 @@ cdef class CubicalComplex: # 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 == ''): + if ((dimensions is not None) and (top_dimensional_cells is not None) + and (perseus_file == '')): + self.thisptr = new Bitmap_cubical_complex_base_interface(dimensions, top_dimensional_cells) + elif ((dimensions is None) and (top_dimensional_cells is not None) + and (perseus_file == '')): + top_dimensional_cells = np.array(top_dimensional_cells, + copy = False, + order = 'F') + dimensions = top_dimensional_cells.shape + top_dimensional_cells = top_dimensional_cells.ravel(order='F') self.thisptr = new Bitmap_cubical_complex_base_interface(dimensions, top_dimensional_cells) - elif (dimensions is None) and (top_dimensional_cells is None) and (perseus_file != ''): + elif ((dimensions is None) and (top_dimensional_cells is None) + and (perseus_file != '')): if os.path.isfile(perseus_file): - self.thisptr = new Bitmap_cubical_complex_base_interface(str.encode(perseus_file)) + self.thisptr = new Bitmap_cubical_complex_base_interface(perseus_file.encode('utf-8')) else: print("file " + perseus_file + " not found.") else: @@ -184,4 +200,4 @@ cdef class CubicalComplex: else: print("intervals_in_dim function requires persistence function" " to be launched first.") - return np_array(intervals_result) + return np.array(intervals_result) diff --git a/src/python/gudhi/euclidean_strong_witness_complex.pyx b/src/python/gudhi/euclidean_strong_witness_complex.pyx index e3f451f0..aca6084e 100644 --- a/src/python/gudhi/euclidean_strong_witness_complex.pyx +++ b/src/python/gudhi/euclidean_strong_witness_complex.pyx @@ -1,11 +1,3 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libc.stdint cimport intptr_t - -from gudhi.simplex_tree cimport * -from gudhi.simplex_tree import SimplexTree - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau @@ -15,6 +7,14 @@ from gudhi.simplex_tree import SimplexTree # Modification(s): # - YYYY/MM Author: Description of the modification +from cython cimport numeric +from libcpp.vector cimport vector +from libcpp.utility cimport pair +from libc.stdint cimport intptr_t + +from gudhi.simplex_tree cimport * +from gudhi.simplex_tree import SimplexTree + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "GPL v3" @@ -22,9 +22,9 @@ __license__ = "GPL v3" cdef extern from "Euclidean_strong_witness_complex_interface.h" namespace "Gudhi": cdef cppclass Euclidean_strong_witness_complex_interface "Gudhi::witness_complex::Euclidean_strong_witness_complex_interface": Euclidean_strong_witness_complex_interface(vector[vector[double]] landmarks, vector[vector[double]] witnesses) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) except + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, - unsigned limit_dimension) + unsigned limit_dimension) except + vector[double] get_point(unsigned vertex) # EuclideanStrongWitnessComplex python interface diff --git a/src/python/gudhi/euclidean_witness_complex.pyx b/src/python/gudhi/euclidean_witness_complex.pyx index 84a8ea1a..fb0c2201 100644 --- a/src/python/gudhi/euclidean_witness_complex.pyx +++ b/src/python/gudhi/euclidean_witness_complex.pyx @@ -1,11 +1,3 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libc.stdint cimport intptr_t - -from gudhi.simplex_tree cimport * -from gudhi.simplex_tree import SimplexTree - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau @@ -15,6 +7,14 @@ from gudhi.simplex_tree import SimplexTree # Modification(s): # - YYYY/MM Author: Description of the modification +from cython cimport numeric +from libcpp.vector cimport vector +from libcpp.utility cimport pair +from libc.stdint cimport intptr_t + +from gudhi.simplex_tree cimport * +from gudhi.simplex_tree import SimplexTree + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "GPL v3" @@ -22,9 +22,9 @@ __license__ = "GPL v3" cdef extern from "Euclidean_witness_complex_interface.h" namespace "Gudhi": cdef cppclass Euclidean_witness_complex_interface "Gudhi::witness_complex::Euclidean_witness_complex_interface": Euclidean_witness_complex_interface(vector[vector[double]] landmarks, vector[vector[double]] witnesses) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) except + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, - unsigned limit_dimension) + unsigned limit_dimension) except + vector[double] get_point(unsigned vertex) # EuclideanWitnessComplex python interface diff --git a/src/python/gudhi/hera.cc b/src/python/gudhi/hera.cc new file mode 100644 index 00000000..0d562b4c --- /dev/null +++ b/src/python/gudhi/hera.cc @@ -0,0 +1,71 @@ +/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + * Author(s): Marc Glisse + * + * Copyright (C) 2020 Inria + * + * Modification(s): + * - YYYY/MM Author: Description of the modification + */ + +#include <pybind11/pybind11.h> +#include <pybind11/numpy.h> + +#include <boost/range/iterator_range.hpp> + +#include <wasserstein.h> // Hera + +#include <array> + +namespace py = pybind11; +typedef py::array_t<double, py::array::c_style | py::array::forcecast> Dgm; + +double wasserstein_distance( + Dgm d1, Dgm d2, + double wasserstein_power, double internal_p, + double delta) +{ + py::buffer_info buf1 = d1.request(); + py::buffer_info buf2 = d2.request(); + // shape (n,2) or (0) for empty + if((buf1.ndim!=2 || buf1.shape[1]!=2) && (buf1.ndim!=1 || buf1.shape[0]!=0)) + throw std::runtime_error("Diagram 1 must be an array of size n x 2"); + if((buf2.ndim!=2 || buf2.shape[1]!=2) && (buf2.ndim!=1 || buf2.shape[0]!=0)) + throw std::runtime_error("Diagram 2 must be an array of size n x 2"); + typedef std::array<double, 2> Point; + auto p1 = (Point*)buf1.ptr; + auto p2 = (Point*)buf2.ptr; + auto diag1 = boost::make_iterator_range(p1, p1+buf1.shape[0]); + auto diag2 = boost::make_iterator_range(p2, p2+buf2.shape[0]); + + hera::AuctionParams<double> params; + params.wasserstein_power = wasserstein_power; + // hera encodes infinity as -1... + if(std::isinf(internal_p)) internal_p = hera::get_infinity<double>(); + params.internal_p = internal_p; + params.delta = delta; + // The extra parameters are purposedly not exposed for now. + return hera::wasserstein_dist(diag1, diag2, params); +} + +PYBIND11_MODULE(hera, m) { + m.def("wasserstein_distance", &wasserstein_distance, + py::arg("X"), py::arg("Y"), + py::arg("order") = 1, + py::arg("internal_p") = std::numeric_limits<double>::infinity(), + py::arg("delta") = .01, + R"pbdoc( + Compute the Wasserstein distance between two diagrams. + Points at infinity are supported. + + Parameters: + X (n x 2 numpy array): First diagram + Y (n x 2 numpy array): Second diagram + order (float): Wasserstein exponent W_q + internal_p (float): Internal Minkowski norm L^p in R^2 + delta (float): Relative error 1+delta + + Returns: + float: Approximate Wasserstein distance W_q(X,Y) + )pbdoc"); +} diff --git a/src/python/gudhi/nerve_gic.pyx b/src/python/gudhi/nerve_gic.pyx index acb78564..382e71c5 100644 --- a/src/python/gudhi/nerve_gic.pyx +++ b/src/python/gudhi/nerve_gic.pyx @@ -1,3 +1,12 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Vincent Rouvreau +# +# Copyright (C) 2018 Inria +# +# Modification(s): +# - YYYY/MM Author: Description of the modification + from cython cimport numeric from libcpp.vector cimport vector from libcpp.utility cimport pair @@ -9,15 +18,6 @@ from libc.stdint cimport intptr_t from gudhi.simplex_tree cimport * from gudhi.simplex_tree import SimplexTree -# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. -# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. -# Author(s): Vincent Rouvreau -# -# Copyright (C) 2018 Inria -# -# Modification(s): -# - YYYY/MM Author: Description of the modification - __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2018 Inria" __license__ = "GPL v3" @@ -180,7 +180,7 @@ cdef class CoverComplex: :returns: Read file status. """ if os.path.isfile(off_file): - return self.thisptr.read_point_cloud(str.encode(off_file)) + return self.thisptr.read_point_cloud(off_file.encode('utf-8')) else: print("file " + off_file + " not found.") return False @@ -212,7 +212,7 @@ cdef class CoverComplex: :type color_file_name: string """ if os.path.isfile(color_file_name): - self.thisptr.set_color_from_file(str.encode(color_file_name)) + self.thisptr.set_color_from_file(color_file_name.encode('utf-8')) else: print("file " + color_file_name + " not found.") @@ -233,7 +233,7 @@ cdef class CoverComplex: :type cover_file_name: string """ if os.path.isfile(cover_file_name): - self.thisptr.set_cover_from_file(str.encode(cover_file_name)) + self.thisptr.set_cover_from_file(cover_file_name.encode('utf-8')) else: print("file " + cover_file_name + " not found.") @@ -266,7 +266,7 @@ cdef class CoverComplex: :type func_file_name: string """ if os.path.isfile(func_file_name): - self.thisptr.set_function_from_file(str.encode(func_file_name)) + self.thisptr.set_function_from_file(func_file_name.encode('utf-8')) else: print("file " + func_file_name + " not found.") @@ -307,7 +307,7 @@ cdef class CoverComplex: :type graph_file_name: string """ if os.path.isfile(graph_file_name): - self.thisptr.set_graph_from_file(str.encode(graph_file_name)) + self.thisptr.set_graph_from_file(graph_file_name.encode('utf-8')) else: print("file " + graph_file_name + " not found.") @@ -368,7 +368,7 @@ cdef class CoverComplex: :param type: either "GIC" or "Nerve". :type type: string """ - self.thisptr.set_type(str.encode(type)) + self.thisptr.set_type(type.encode('utf-8')) def set_verbose(self, verbose): """Specifies whether the program should display information or not. diff --git a/src/python/gudhi/off_reader.pyx b/src/python/gudhi/off_reader.pyx index 225e981c..7e6d9d80 100644 --- a/src/python/gudhi/off_reader.pyx +++ b/src/python/gudhi/off_reader.pyx @@ -1,8 +1,3 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.string cimport string -import os - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau @@ -12,6 +7,11 @@ import os # Modification(s): # - YYYY/MM Author: Description of the modification +from cython cimport numeric +from libcpp.vector cimport vector +from libcpp.string cimport string +import os + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" @@ -19,18 +19,18 @@ __license__ = "MIT" cdef extern from "Off_reader_interface.h" namespace "Gudhi": vector[vector[double]] read_points_from_OFF_file(string off_file) -def read_off(off_file=''): +def read_points_from_off_file(off_file=''): """Read points from OFF file. :param off_file: An OFF file style name. :type off_file: string :returns: The point set. - :rtype: vector[vector[double]] + :rtype: List[List[float]] """ if off_file: if os.path.isfile(off_file): - return read_points_from_OFF_file(str.encode(off_file)) + return read_points_from_OFF_file(off_file.encode('utf-8')) else: print("file " + off_file + " not found.") return [] diff --git a/src/python/gudhi/periodic_cubical_complex.pyx b/src/python/gudhi/periodic_cubical_complex.pyx index c89055db..37f76201 100644 --- a/src/python/gudhi/periodic_cubical_complex.pyx +++ b/src/python/gudhi/periodic_cubical_complex.pyx @@ -1,12 +1,3 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libcpp.string cimport string -from libcpp cimport bool -import os - -from numpy import array as np_array - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau @@ -16,6 +7,15 @@ from numpy import array as np_array # Modification(s): # - YYYY/MM Author: Description of the modification +from cython cimport numeric +from libcpp.vector cimport vector +from libcpp.utility cimport pair +from libcpp.string cimport string +from libcpp cimport bool +import os + +import numpy as np + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" @@ -47,7 +47,7 @@ cdef class PeriodicCubicalComplex: # Fake constructor that does nothing but documenting the constructor def __init__(self, dimensions=None, top_dimensional_cells=None, - periodic_dimensions=None, perseus_file=''): + periodic_dimensions=None, perseus_file=''): """PeriodicCubicalComplex constructor from dimensions and top_dimensional_cells or from a Perseus-style file name. @@ -60,6 +60,14 @@ cdef class PeriodicCubicalComplex: Or + :param top_dimensional_cells: A multidimensional array of cells + filtration values. + :type top_dimensional_cells: anything convertible to a numpy ndarray + :param periodic_dimensions: A list of top dimensional cells periodicity value. + :type periodic_dimensions: list of boolean + + Or + :param perseus_file: A Perseus-style file name. :type perseus_file: string """ @@ -67,16 +75,32 @@ cdef class PeriodicCubicalComplex: # The real cython constructor def __cinit__(self, dimensions=None, top_dimensional_cells=None, 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 == ''): - 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 != ''): + if ((dimensions is not None) and (top_dimensional_cells is not None) + and (periodic_dimensions is not None) and (perseus_file == '')): + self.thisptr = new Periodic_cubical_complex_base_interface(dimensions, + top_dimensional_cells, + periodic_dimensions) + elif ((dimensions is None) and (top_dimensional_cells is not None) + and (periodic_dimensions is not None) and (perseus_file == '')): + top_dimensional_cells = np.array(top_dimensional_cells, + copy = False, + order = 'F') + dimensions = top_dimensional_cells.shape + top_dimensional_cells = top_dimensional_cells.ravel(order='F') + 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 != '')): if os.path.isfile(perseus_file): - self.thisptr = new Periodic_cubical_complex_base_interface(str.encode(perseus_file)) + self.thisptr = new Periodic_cubical_complex_base_interface(perseus_file.encode('utf-8')) else: print("file " + perseus_file + " not found.") else: - print("CubicalComplex can be constructed from dimensions and " - "top_dimensional_cells or from a Perseus-style file name.") + print("CubicalComplex can be constructed from dimensions, " + "top_dimensional_cells and periodic_dimensions, or from " + "top_dimensional_cells and periodic_dimensions or from " + "a Perseus-style file name.") def __dealloc__(self): if self.thisptr != NULL: @@ -186,4 +210,4 @@ cdef class PeriodicCubicalComplex: else: print("intervals_in_dim function requires persistence function" " to be launched first.") - return np_array(intervals_result) + return np.array(intervals_result) diff --git a/src/python/gudhi/persistence_graphical_tools.py b/src/python/gudhi/persistence_graphical_tools.py index 7d232c85..246280de 100644 --- a/src/python/gudhi/persistence_graphical_tools.py +++ b/src/python/gudhi/persistence_graphical_tools.py @@ -1,10 +1,3 @@ -from os import path -from math import isfinite -import numpy as np - -from gudhi.reader_utils import read_persistence_intervals_in_dimension -from gudhi.reader_utils import read_persistence_intervals_grouped_by_dimension - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau, Bertrand Michel @@ -14,6 +7,13 @@ from gudhi.reader_utils import read_persistence_intervals_grouped_by_dimension # Modification(s): # - YYYY/MM Author: Description of the modification +from os import path +from math import isfinite +import numpy as np + +from gudhi.reader_utils import read_persistence_intervals_in_dimension +from gudhi.reader_utils import read_persistence_intervals_grouped_by_dimension + __author__ = "Vincent Rouvreau, Bertrand Michel" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" diff --git a/src/python/gudhi/reader_utils.pyx b/src/python/gudhi/reader_utils.pyx index 6994c4f9..fe1c3a2e 100644 --- a/src/python/gudhi/reader_utils.pyx +++ b/src/python/gudhi/reader_utils.pyx @@ -1,12 +1,3 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.string cimport string -from libcpp.map cimport map -from libcpp.pair cimport pair - -from os import path -from numpy import array as np_array - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau @@ -16,6 +7,15 @@ from numpy import array as np_array # Modification(s): # - YYYY/MM Author: Description of the modification +from cython cimport numeric +from libcpp.vector cimport vector +from libcpp.string cimport string +from libcpp.map cimport map +from libcpp.pair cimport pair + +from os import path +from numpy import array as np_array + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2017 Inria" __license__ = "MIT" @@ -34,30 +34,30 @@ def read_lower_triangular_matrix_from_csv_file(csv_file='', separator=';'): :type separator: char :returns: The lower triangular matrix. - :rtype: vector[vector[double]] + :rtype: List[List[float]] """ if csv_file: if path.isfile(csv_file): - return read_matrix_from_csv_file(str.encode(csv_file), ord(separator[0])) + return read_matrix_from_csv_file(csv_file.encode('utf-8'), 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`. + The return value is a `dict(dim, list(tuple(birth, death)))` + where `dim` is an `int`, `birth` a `float`, and `death` a `float`. 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]]] + :rtype: Dict[int, List[Tuple[float, float]]] """ if persistence_file: if path.isfile(persistence_file): - return read_pers_intervals_grouped_by_dimension(str.encode(persistence_file)) + return read_pers_intervals_grouped_by_dimension(persistence_file.encode('utf-8')) print("file " + persistence_file + " not set or not found.") return [] @@ -80,7 +80,7 @@ def read_persistence_intervals_in_dimension(persistence_file='', only_this_dim=- """ if persistence_file: if path.isfile(persistence_file): - return np_array(read_pers_intervals_in_dimension(str.encode( - persistence_file), only_this_dim)) + return np_array(read_pers_intervals_in_dimension(persistence_file.encode( + 'utf-8'), only_this_dim)) print("file " + persistence_file + " not set or not found.") return [] diff --git a/src/python/gudhi/rips_complex.pyx b/src/python/gudhi/rips_complex.pyx index cbbbab0d..deb8057a 100644 --- a/src/python/gudhi/rips_complex.pyx +++ b/src/python/gudhi/rips_complex.pyx @@ -1,3 +1,12 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Vincent Rouvreau +# +# Copyright (C) 2016 Inria +# +# Modification(s): +# - YYYY/MM Author: Description of the modification + from cython cimport numeric from libcpp.vector cimport vector from libcpp.utility cimport pair @@ -8,15 +17,6 @@ from libc.stdint cimport intptr_t from gudhi.simplex_tree cimport * from gudhi.simplex_tree import SimplexTree -# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. -# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. -# Author(s): Vincent Rouvreau -# -# Copyright (C) 2016 Inria -# -# Modification(s): -# - YYYY/MM Author: Description of the modification - __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" @@ -28,7 +28,7 @@ cdef extern from "Rips_complex_interface.h" namespace "Gudhi": void init_matrix(vector[vector[double]] values, double threshold) void init_points_sparse(vector[vector[double]] values, double threshold, double sparse) void init_matrix_sparse(vector[vector[double]] values, double threshold, double sparse) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, int dim_max) + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, int dim_max) except + # RipsComplex python interface cdef class RipsComplex: diff --git a/src/python/gudhi/simplex_tree.pxd b/src/python/gudhi/simplex_tree.pxd index 5f86cfe2..96d14079 100644 --- a/src/python/gudhi/simplex_tree.pxd +++ b/src/python/gudhi/simplex_tree.pxd @@ -1,19 +1,18 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Vincent Rouvreau +# +# Copyright (C) 2016 Inria +# +# Modification(s): +# - YYYY/MM Author: Description of the modification + 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 - https://gudhi.inria.fr/ - which is released under MIT. - See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - Modification(s): - - YYYY/MM Author: Description of the modification -""" - __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" @@ -40,7 +39,7 @@ 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 expansion(int max_dim) + void expansion(int max_dim) except + void remove_maximal_simplex(vector[int] simplex) bool prune_above_filtration(double filtration) bool make_filtration_non_decreasing() diff --git a/src/python/gudhi/simplex_tree.pyx b/src/python/gudhi/simplex_tree.pyx index 4a3cd9bc..b18627c4 100644 --- a/src/python/gudhi/simplex_tree.pyx +++ b/src/python/gudhi/simplex_tree.pyx @@ -1,7 +1,3 @@ -from libc.stdint cimport intptr_t -from numpy import array as np_array -cimport simplex_tree - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau @@ -11,6 +7,10 @@ cimport simplex_tree # Modification(s): # - YYYY/MM Author: Description of the modification +from libc.stdint cimport intptr_t +from numpy import array as np_array +cimport simplex_tree + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" @@ -508,7 +508,7 @@ cdef class SimplexTree: """ if self.pcohptr != NULL: if persistence_file != '': - self.pcohptr.write_output_diagram(str.encode(persistence_file)) + self.pcohptr.write_output_diagram(persistence_file.encode('utf-8')) else: print("persistence_file must be specified") else: diff --git a/src/python/gudhi/strong_witness_complex.pyx b/src/python/gudhi/strong_witness_complex.pyx index 66d49b49..9f89d3ae 100644 --- a/src/python/gudhi/strong_witness_complex.pyx +++ b/src/python/gudhi/strong_witness_complex.pyx @@ -1,11 +1,3 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libc.stdint cimport intptr_t - -from gudhi.simplex_tree cimport * -from gudhi.simplex_tree import SimplexTree - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau @@ -15,6 +7,14 @@ from gudhi.simplex_tree import SimplexTree # Modification(s): # - YYYY/MM Author: Description of the modification +from cython cimport numeric +from libcpp.vector cimport vector +from libcpp.utility cimport pair +from libc.stdint cimport intptr_t + +from gudhi.simplex_tree cimport * +from gudhi.simplex_tree import SimplexTree + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" @@ -22,9 +22,9 @@ __license__ = "MIT" cdef extern from "Strong_witness_complex_interface.h" namespace "Gudhi": cdef cppclass Strong_witness_complex_interface "Gudhi::witness_complex::Strong_witness_complex_interface": Strong_witness_complex_interface(vector[vector[pair[size_t, double]]] nearest_landmark_table) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) except + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, - unsigned limit_dimension) + unsigned limit_dimension) except + # StrongWitnessComplex python interface cdef class StrongWitnessComplex: diff --git a/src/python/gudhi/subsampling.pyx b/src/python/gudhi/subsampling.pyx index e0cd1348..f77c6f75 100644 --- a/src/python/gudhi/subsampling.pyx +++ b/src/python/gudhi/subsampling.pyx @@ -1,9 +1,3 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.string cimport string -from libcpp cimport bool -import os - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau @@ -13,6 +7,12 @@ import os # Modification(s): # - YYYY/MM Author: Description of the modification +from cython cimport numeric +from libcpp.vector cimport vector +from libcpp.string cimport string +from libcpp cimport bool +import os + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "GPL v3" @@ -33,13 +33,15 @@ def choose_n_farthest_points(points=None, off_file='', nb_points=0, starting_poi The iteration starts with the landmark `starting point`. :param points: The input point set. - :type points: vector[vector[double]]. + :type points: Iterable[Iterable[float]]. Or :param off_file: An OFF file style name. :type off_file: string + And in both cases + :param nb_points: Number of points of the subsample. :type nb_points: unsigned. :param starting_point: The iteration starts with the landmark `starting \ @@ -47,15 +49,15 @@ def choose_n_farthest_points(points=None, off_file='', nb_points=0, starting_poi index is chosen randomly. :type starting_point: unsigned. :returns: The subsample point set. - :rtype: vector[vector[double]] + :rtype: List[List[float]]. """ if off_file: if os.path.isfile(off_file): if starting_point == '': - return subsampling_n_farthest_points_from_file(str.encode(off_file), + return subsampling_n_farthest_points_from_file(off_file.encode('utf-8'), nb_points) else: - return subsampling_n_farthest_points_from_file(str.encode(off_file), + return subsampling_n_farthest_points_from_file(off_file.encode('utf-8'), nb_points, starting_point) else: @@ -74,21 +76,23 @@ def pick_n_random_points(points=None, off_file='', nb_points=0): """Subsample a point set by picking random vertices. :param points: The input point set. - :type points: vector[vector[double]]. + :type points: Iterable[Iterable[float]]. Or :param off_file: An OFF file style name. :type off_file: string + And in both cases + :param nb_points: Number of points of the subsample. :type nb_points: unsigned. :returns: The subsample point set. - :rtype: vector[vector[double]] + :rtype: List[List[float]] """ if off_file: if os.path.isfile(off_file): - return subsampling_n_random_points_from_file(str.encode(off_file), + return subsampling_n_random_points_from_file(off_file.encode('utf-8'), nb_points) else: print("file " + off_file + " not found.") @@ -103,22 +107,24 @@ def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0): between any two points is greater than or equal to min_squared_dist. :param points: The input point set. - :type points: vector[vector[double]]. + :type points: Iterable[Iterable[float]]. Or :param off_file: An OFF file style name. :type off_file: string + And in both cases + :param min_squared_dist: Minimum squared distance separating the output \ points. :type min_squared_dist: float. :returns: The subsample point set. - :rtype: vector[vector[double]] + :rtype: List[List[float]] """ if off_file: if os.path.isfile(off_file): - return subsampling_sparsify_points_from_file(str.encode(off_file), + return subsampling_sparsify_points_from_file(off_file.encode('utf-8'), min_squared_dist) else: print("file " + off_file + " not found.") diff --git a/src/python/gudhi/tangential_complex.pyx b/src/python/gudhi/tangential_complex.pyx index f4c8b079..6391488c 100644 --- a/src/python/gudhi/tangential_complex.pyx +++ b/src/python/gudhi/tangential_complex.pyx @@ -1,3 +1,12 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Vincent Rouvreau +# +# Copyright (C) 2016 Inria +# +# Modification(s): +# - YYYY/MM Author: Description of the modification + from cython cimport numeric from libcpp.vector cimport vector from libcpp.utility cimport pair @@ -9,15 +18,6 @@ import os from gudhi.simplex_tree cimport * from gudhi.simplex_tree import SimplexTree -# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. -# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. -# Author(s): Vincent Rouvreau -# -# Copyright (C) 2016 Inria -# -# Modification(s): -# - YYYY/MM Author: Description of the modification - __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "GPL v3" @@ -66,7 +66,7 @@ cdef class TangentialComplex: def __cinit__(self, intrisic_dim, points=None, off_file=''): if off_file: if os.path.isfile(off_file): - self.thisptr = new Tangential_complex_interface(intrisic_dim, str.encode(off_file), True) + self.thisptr = new Tangential_complex_interface(intrisic_dim, off_file.encode('utf-8'), True) else: print("file " + off_file + " not found.") else: diff --git a/src/python/gudhi/wasserstein.py b/src/python/gudhi/wasserstein.py index d8a3104c..13102094 100644 --- a/src/python/gudhi/wasserstein.py +++ b/src/python/gudhi/wasserstein.py @@ -1,10 +1,3 @@ -import numpy as np -import scipy.spatial.distance as sc -try: - import ot -except ImportError: - print("POT (Python Optimal Transport) package is not installed. Try to run $ conda install -c conda-forge pot ; or $ pip install POT") - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Theo Lacombe @@ -14,6 +7,13 @@ except ImportError: # Modification(s): # - YYYY/MM Author: Description of the modification +import numpy as np +import scipy.spatial.distance as sc +try: + import ot +except ImportError: + print("POT (Python Optimal Transport) package is not installed. Try to run $ conda install -c conda-forge pot ; or $ pip install POT") + def _proj_on_diag(X): ''' :param X: (n x 2) array encoding the points of a persistent diagram. @@ -23,26 +23,26 @@ def _proj_on_diag(X): return np.array([Z , Z]).T -def _build_dist_matrix(X, Y, p=2., q=2.): +def _build_dist_matrix(X, Y, order=2., internal_p=2.): ''' :param X: (n x 2) numpy.array encoding the (points of the) first diagram. :param Y: (m x 2) numpy.array encoding the second diagram. - :param q: Ground metric (i.e. norm l_q). - :param p: exponent for the Wasserstein metric. + :param order: exponent for the Wasserstein metric. + :param internal_p: Ground metric (i.e. norm L^p). :returns: (n+1) x (m+1) np.array encoding the cost matrix C. For 1 <= i <= n, 1 <= j <= m, C[i,j] encodes the distance between X[i] and Y[j], while C[i, m+1] (resp. C[n+1, j]) encodes the distance (to the p) between X[i] (resp Y[j]) and its orthogonal proj onto the diagonal. note also that C[n+1, m+1] = 0 (it costs nothing to move from the diagonal to the diagonal). ''' Xdiag = _proj_on_diag(X) Ydiag = _proj_on_diag(Y) - if np.isinf(q): - C = sc.cdist(X,Y, metric='chebyshev')**p - Cxd = np.linalg.norm(X - Xdiag, ord=q, axis=1)**p - Cdy = np.linalg.norm(Y - Ydiag, ord=q, axis=1)**p + if np.isinf(internal_p): + C = sc.cdist(X,Y, metric='chebyshev')**order + Cxd = np.linalg.norm(X - Xdiag, ord=internal_p, axis=1)**order + Cdy = np.linalg.norm(Y - Ydiag, ord=internal_p, axis=1)**order else: - C = sc.cdist(X,Y, metric='minkowski', p=q)**p - Cxd = np.linalg.norm(X - Xdiag, ord=q, axis=1)**p - Cdy = np.linalg.norm(Y - Ydiag, ord=q, axis=1)**p + C = sc.cdist(X,Y, metric='minkowski', p=internal_p)**order + Cxd = np.linalg.norm(X - Xdiag, ord=internal_p, axis=1)**order + Cdy = np.linalg.norm(Y - Ydiag, ord=internal_p, axis=1)**order Cf = np.hstack((C, Cxd[:,None])) Cdy = np.append(Cdy, 0) @@ -51,24 +51,24 @@ def _build_dist_matrix(X, Y, p=2., q=2.): return Cf -def _perstot(X, p, q): +def _perstot(X, order, internal_p): ''' :param X: (n x 2) numpy.array (points of a given diagram). - :param q: Ground metric on the (upper-half) plane (i.e. norm l_q in R^2); Default value is 2 (Euclidean norm). - :param p: exponent for Wasserstein; Default value is 2. + :param order: exponent for Wasserstein. Default value is 2. + :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2); Default value is 2 (Euclidean norm). :returns: float, the total persistence of the diagram (that is, its distance to the empty diagram). ''' Xdiag = _proj_on_diag(X) - return (np.sum(np.linalg.norm(X - Xdiag, ord=q, axis=1)**p))**(1./p) + return (np.sum(np.linalg.norm(X - Xdiag, ord=internal_p, axis=1)**order))**(1./order) -def wasserstein_distance(X, Y, p=2., q=2.): +def wasserstein_distance(X, Y, order=2., internal_p=2.): ''' :param X: (n x 2) numpy.array encoding the (finite points of the) first diagram. Must not contain essential points (i.e. with infinite coordinate). :param Y: (m x 2) numpy.array encoding the second diagram. - :param q: Ground metric on the (upper-half) plane (i.e. norm l_q in R^2); Default value is 2 (euclidean norm). - :param p: exponent for Wasserstein; Default value is 2. - :returns: the p-Wasserstein distance (1 <= p < infinity) with respect to the q-norm as ground metric. + :param order: exponent for Wasserstein; Default value is 2. + :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2); Default value is 2 (Euclidean norm). + :returns: the Wasserstein distance of order q (1 <= q < infinity) between persistence diagrams with respect to the internal_p-norm as ground metric. :rtype: float ''' n = len(X) @@ -79,20 +79,19 @@ def wasserstein_distance(X, Y, p=2., q=2.): if Y.size == 0: return 0. else: - return _perstot(Y, p, q) + return _perstot(Y, order, internal_p) elif Y.size == 0: - return _perstot(X, p, q) + return _perstot(X, order, internal_p) - M = _build_dist_matrix(X, Y, p=p, q=q) + M = _build_dist_matrix(X, Y, order=order, internal_p=internal_p) a = np.full(n+1, 1. / (n + m) ) # weight vector of the input diagram. Uniform here. a[-1] = a[-1] * m # normalized so that we have a probability measure, required by POT b = np.full(m+1, 1. / (n + m) ) # weight vector of the input diagram. Uniform here. b[-1] = b[-1] * n # so that we have a probability measure, required by POT # Comptuation of the otcost using the ot.emd2 library. - # Note: it is the squared Wasserstein distance. + # Note: it is the Wasserstein distance to the power q. # The default numItermax=100000 is not sufficient for some examples with 5000 points, what is a good value? ot_cost = (n+m) * ot.emd2(a, b, M, numItermax=2000000) - return ot_cost ** (1./p) - + return ot_cost ** (1./order) diff --git a/src/python/gudhi/witness_complex.pyx b/src/python/gudhi/witness_complex.pyx index 153fc615..e589d006 100644 --- a/src/python/gudhi/witness_complex.pyx +++ b/src/python/gudhi/witness_complex.pyx @@ -1,11 +1,3 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libc.stdint cimport intptr_t - -from gudhi.simplex_tree cimport * -from gudhi.simplex_tree import SimplexTree - # This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. # See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. # Author(s): Vincent Rouvreau @@ -15,6 +7,14 @@ from gudhi.simplex_tree import SimplexTree # Modification(s): # - YYYY/MM Author: Description of the modification +from cython cimport numeric +from libcpp.vector cimport vector +from libcpp.utility cimport pair +from libc.stdint cimport intptr_t + +from gudhi.simplex_tree cimport * +from gudhi.simplex_tree import SimplexTree + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" @@ -22,9 +22,9 @@ __license__ = "MIT" cdef extern from "Witness_complex_interface.h" namespace "Gudhi": cdef cppclass Witness_complex_interface "Gudhi::witness_complex::Witness_complex_interface": Witness_complex_interface(vector[vector[pair[size_t, double]]] nearest_landmark_table) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) except + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, - unsigned limit_dimension) + unsigned limit_dimension) except + # WitnessComplex python interface cdef class WitnessComplex: diff --git a/src/python/include/Alpha_complex_interface.h b/src/python/include/Alpha_complex_interface.h index 96353cc4..8614eee3 100644 --- a/src/python/include/Alpha_complex_interface.h +++ b/src/python/include/Alpha_complex_interface.h @@ -13,6 +13,7 @@ #include <gudhi/Simplex_tree.h> #include <gudhi/Alpha_complex.h> +#include <CGAL/Epeck_d.h> #include <CGAL/Epick_d.h> #include <boost/range/adaptor/transformed.hpp> @@ -28,7 +29,7 @@ namespace Gudhi { namespace alpha_complex { class Alpha_complex_interface { - using Dynamic_kernel = CGAL::Epick_d< CGAL::Dynamic_dimension_tag >; + using Dynamic_kernel = CGAL::Epeck_d< CGAL::Dynamic_dimension_tag >; using Point_d = Dynamic_kernel::Point_d; public: @@ -49,13 +50,9 @@ class Alpha_complex_interface { std::vector<double> get_point(int vh) { std::vector<double> vd; - try { - Point_d const& ph = alpha_complex_->get_point(vh); - for (auto coord = ph.cartesian_begin(); coord < ph.cartesian_end(); coord++) - vd.push_back(CGAL::to_double(*coord)); - } catch (std::out_of_range const&) { - // std::out_of_range is thrown in case not found. Other exceptions must be re-thrown - } + Point_d const& ph = alpha_complex_->get_point(vh); + for (auto coord = ph.cartesian_begin(); coord != ph.cartesian_end(); coord++) + vd.push_back(CGAL::to_double(*coord)); return vd; } diff --git a/src/python/setup.py.in b/src/python/setup.py.in index 3f1d4424..f968bd59 100644 --- a/src/python/setup.py.in +++ b/src/python/setup.py.in @@ -1,7 +1,3 @@ -from setuptools import setup, Extension -from Cython.Build import cythonize -from numpy import get_include as numpy_get_include - """This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. Author(s): Vincent Rouvreau @@ -12,6 +8,12 @@ from numpy import get_include as numpy_get_include - YYYY/MM Author: Description of the modification """ +from setuptools import setup, Extension, find_packages +from Cython.Build import cythonize +from numpy import get_include as numpy_get_include +import sys +import pybind11 + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" @@ -38,16 +40,29 @@ for module in modules: libraries=libraries, library_dirs=library_dirs, include_dirs=include_dirs, - runtime_library_dirs=runtime_library_dirs,)) + runtime_library_dirs=runtime_library_dirs, + cython_directives = {'language_level': str(sys.version_info[0])},)) + +ext_modules = cythonize(ext_modules) + +ext_modules.append(Extension( + 'gudhi.hera', + sources = [source_dir + 'hera.cc'], + language = 'c++', + include_dirs = include_dirs + + ['@HERA_WASSERSTEIN_INCLUDE_DIR@', + pybind11.get_include(False), pybind11.get_include(True)], + extra_compile_args=extra_compile_args + [@GUDHI_PYBIND11_EXTRA_COMPILE_ARGS@], + )) setup( name = 'gudhi', - packages=["gudhi",], + packages=find_packages(), # find_namespace_packages(include=["gudhi*"]) author='GUDHI Editorial Board', author_email='gudhi-contact@lists.gforge.inria.fr', version='@GUDHI_VERSION@', url='http://gudhi.gforge.inria.fr/', - ext_modules = cythonize(ext_modules), + ext_modules = ext_modules, install_requires = ['cython','numpy >= 1.9',], - setup_requires = ['numpy >= 1.9',], + setup_requires = ['numpy >= 1.9','pybind11',], ) diff --git a/src/python/test/test_alpha_complex.py b/src/python/test/test_alpha_complex.py index 24f8bf53..3761fe16 100755 --- a/src/python/test/test_alpha_complex.py +++ b/src/python/test/test_alpha_complex.py @@ -1,5 +1,3 @@ -from gudhi import AlphaComplex, SimplexTree - """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. Author(s): Vincent Rouvreau @@ -10,6 +8,17 @@ from gudhi import AlphaComplex, SimplexTree - YYYY/MM Author: Description of the modification """ +from gudhi import AlphaComplex, SimplexTree +import math +import numpy as np +import pytest +try: + # python3 + from itertools import zip_longest +except ImportError: + # python2 + from itertools import izip_longest as zip_longest + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" @@ -56,8 +65,18 @@ def test_infinite_alpha(): assert point_list[1] == alpha_complex.get_point(1) assert point_list[2] == alpha_complex.get_point(2) assert point_list[3] == alpha_complex.get_point(3) - assert alpha_complex.get_point(4) == [] - assert alpha_complex.get_point(125) == [] + try: + alpha_complex.get_point(4) == [] + except IndexError: + pass + else: + assert False + try: + alpha_complex.get_point(125) == [] + except IndexError: + pass + else: + assert False def test_filtered_alpha(): @@ -73,8 +92,18 @@ def test_filtered_alpha(): assert point_list[1] == filtered_alpha.get_point(1) assert point_list[2] == filtered_alpha.get_point(2) assert point_list[3] == filtered_alpha.get_point(3) - assert filtered_alpha.get_point(4) == [] - assert filtered_alpha.get_point(125) == [] + try: + filtered_alpha.get_point(4) == [] + except IndexError: + pass + else: + assert False + try: + filtered_alpha.get_point(125) == [] + except IndexError: + pass + else: + assert False assert simplex_tree.get_filtration() == [ ([0], 0.0), @@ -88,3 +117,28 @@ def test_filtered_alpha(): ] assert simplex_tree.get_star([0]) == [([0], 0.0), ([0, 1], 0.25), ([0, 2], 0.25)] assert simplex_tree.get_cofaces([0], 1) == [([0, 1], 0.25), ([0, 2], 0.25)] + +def test_safe_alpha_persistence_comparison(): + #generate periodic signal + time = np.arange(0, 10, 1) + signal = [math.sin(x) for x in time] + delta = math.pi + delayed = [math.sin(x + delta) for x in time] + + #construct embedding + embedding1 = [[signal[i], -signal[i]] for i in range(len(time))] + embedding2 = [[signal[i], delayed[i]] for i in range(len(time))] + + #build alpha complex and simplex tree + alpha_complex1 = AlphaComplex(points=embedding1) + simplex_tree1 = alpha_complex1.create_simplex_tree() + + alpha_complex2 = AlphaComplex(points=embedding2) + simplex_tree2 = alpha_complex2.create_simplex_tree() + + diag1 = simplex_tree1.persistence() + diag2 = simplex_tree2.persistence() + + for (first_p, second_p) in zip_longest(diag1, diag2): + assert first_p[0] == pytest.approx(second_p[0]) + assert first_p[1] == pytest.approx(second_p[1]) diff --git a/src/python/test/test_bottleneck_distance.py b/src/python/test/test_bottleneck_distance.py index f5f019b9..70b2abad 100755 --- a/src/python/test/test_bottleneck_distance.py +++ b/src/python/test/test_bottleneck_distance.py @@ -1,5 +1,3 @@ -import gudhi - """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. Author(s): Vincent Rouvreau @@ -10,6 +8,8 @@ import gudhi - YYYY/MM Author: Description of the modification """ +import gudhi + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" diff --git a/src/python/test/test_cover_complex.py b/src/python/test/test_cover_complex.py index 8cd12272..32bc5a26 100755 --- a/src/python/test/test_cover_complex.py +++ b/src/python/test/test_cover_complex.py @@ -1,5 +1,3 @@ -from gudhi import CoverComplex - """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. Author(s): Vincent Rouvreau @@ -10,6 +8,8 @@ from gudhi import CoverComplex - YYYY/MM Author: Description of the modification """ +from gudhi import CoverComplex + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2018 Inria" __license__ = "MIT" diff --git a/src/python/test/test_cubical_complex.py b/src/python/test/test_cubical_complex.py index 68f54fbe..8c1b2600 100755 --- a/src/python/test/test_cubical_complex.py +++ b/src/python/test/test_cubical_complex.py @@ -1,5 +1,3 @@ -from gudhi import CubicalComplex - """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. Author(s): Vincent Rouvreau @@ -10,6 +8,9 @@ from gudhi import CubicalComplex - YYYY/MM Author: Description of the modification """ +from gudhi import CubicalComplex, PeriodicCubicalComplex +import numpy as np + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" @@ -56,7 +57,7 @@ def test_dimension_or_perseus_file_constructor(): assert cub.__is_persistence_defined() == False -def test_dimension_simple_constructor(): +def simple_constructor(cub): cub = CubicalComplex( dimensions=[3, 3], top_dimensional_cells=[1, 2, 3, 4, 5, 6, 7, 8, 9] ) @@ -67,12 +68,22 @@ def test_dimension_simple_constructor(): assert cub.betti_numbers() == [1, 0, 0] assert cub.persistent_betti_numbers(0, 1000) == [0, 0, 0] - -def test_user_case_simple_constructor(): +def test_simple_constructor_from_top_cells(): cub = CubicalComplex( dimensions=[3, 3], - top_dimensional_cells=[float("inf"), 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], + top_dimensional_cells=[1, 2, 3, 4, 5, 6, 7, 8, 9], ) + simple_constructor(cub) + +def test_simple_constructor_from_numpy_array(): + cub = CubicalComplex( + top_dimensional_cells=np.array([[1, 2, 3], + [4, 5, 6], + [7, 8, 9]]) + ) + simple_constructor(cub) + +def user_case_simple_constructor(cub): assert cub.__is_defined() == True assert cub.__is_persistence_defined() == False assert cub.persistence() == [(1, (0.0, 1.0)), (0, (0.0, float("inf")))] @@ -83,6 +94,20 @@ def test_user_case_simple_constructor(): ) assert other_cub.persistence() == [(1, (0.0, 1.0)), (0, (0.0, float("inf")))] +def test_user_case_simple_constructor_from_top_cells(): + cub = CubicalComplex( + dimensions=[3, 3], + top_dimensional_cells=[float("inf"), 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], + ) + user_case_simple_constructor(cub) + +def test_user_case_simple_constructor_from_numpy_array(): + cub = CubicalComplex( + top_dimensional_cells=np.array([[float("inf"), 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 0.0]]) + ) + user_case_simple_constructor(cub) def test_dimension_file_constructor(): # Create test file @@ -96,3 +121,29 @@ def test_dimension_file_constructor(): assert cub.__is_persistence_defined() == True assert cub.betti_numbers() == [1, 0, 0] assert cub.persistent_betti_numbers(0, 1000) == [1, 0, 0] + +def test_connected_sublevel_sets(): + array_cells = np.array([[3, 3], [2, 2], [4, 4]]) + linear_cells = [3, 3, 2, 2, 4, 4] + dimensions = [2, 3] + periodic_dimensions = [False, False] + # with a numpy array version + cub = CubicalComplex(top_dimensional_cells = array_cells) + assert cub.persistence() == [(0, (2.0, float("inf")))] + assert cub.betti_numbers() == [1, 0, 0] + # with vector of dimensions + cub = CubicalComplex(dimensions = dimensions, + top_dimensional_cells = linear_cells) + assert cub.persistence() == [(0, (2.0, float("inf")))] + assert cub.betti_numbers() == [1, 0, 0] + # periodic with a numpy array version + cub = PeriodicCubicalComplex(top_dimensional_cells = array_cells, + periodic_dimensions = periodic_dimensions) + assert cub.persistence() == [(0, (2.0, float("inf")))] + assert cub.betti_numbers() == [1, 0, 0] + # periodic with vector of dimensions + cub = PeriodicCubicalComplex(dimensions = dimensions, + top_dimensional_cells = linear_cells, + periodic_dimensions = periodic_dimensions) + assert cub.persistence() == [(0, (2.0, float("inf")))] + assert cub.betti_numbers() == [1, 0, 0] diff --git a/src/python/test/test_euclidean_witness_complex.py b/src/python/test/test_euclidean_witness_complex.py index f5eae5fa..c18d2484 100755 --- a/src/python/test/test_euclidean_witness_complex.py +++ b/src/python/test/test_euclidean_witness_complex.py @@ -1,5 +1,3 @@ -import gudhi - """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. Author(s): Vincent Rouvreau @@ -10,6 +8,8 @@ import gudhi - YYYY/MM Author: Description of the modification """ +import gudhi + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" diff --git a/src/python/test/test_reader_utils.py b/src/python/test/test_reader_utils.py index 4c7b32c2..90da6651 100755 --- a/src/python/test/test_reader_utils.py +++ b/src/python/test/test_reader_utils.py @@ -1,6 +1,3 @@ -import gudhi -import numpy as np - """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. Author(s): Vincent Rouvreau @@ -11,6 +8,9 @@ import numpy as np - YYYY/MM Author: Description of the modification """ +import gudhi +import numpy as np + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2017 Inria" __license__ = "MIT" diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py index 4ff65f98..dba7f952 100755 --- a/src/python/test/test_representations.py +++ b/src/python/test/test_representations.py @@ -1,11 +1,12 @@ import os import sys import matplotlib.pyplot as plt -# Disable graphics for testing purposes -plt.show = lambda:None -here = os.path.dirname(os.path.realpath(__file__)) -sys.path.append(here + "/../example") -import diagram_vectorizations_distances_kernels -# pytest is unhappy if there are 0 tests -def test_nothing(): + +def test_representations_examples(): + # Disable graphics for testing purposes + plt.show = lambda:None + here = os.path.dirname(os.path.realpath(__file__)) + sys.path.append(here + "/../example") + import diagram_vectorizations_distances_kernels + return None diff --git a/src/python/test/test_rips_complex.py b/src/python/test/test_rips_complex.py index d55ae22f..b02a68e1 100755 --- a/src/python/test/test_rips_complex.py +++ b/src/python/test/test_rips_complex.py @@ -1,6 +1,3 @@ -from gudhi import RipsComplex -from math import sqrt - """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. Author(s): Vincent Rouvreau @@ -11,6 +8,9 @@ from math import sqrt - YYYY/MM Author: Description of the modification """ +from gudhi import RipsComplex +from math import sqrt + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" diff --git a/src/python/test/test_simplex_tree.py b/src/python/test/test_simplex_tree.py index 8d8971c1..1822c43b 100755 --- a/src/python/test/test_simplex_tree.py +++ b/src/python/test/test_simplex_tree.py @@ -1,5 +1,3 @@ -from gudhi import SimplexTree - """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. Author(s): Vincent Rouvreau @@ -10,6 +8,8 @@ from gudhi import SimplexTree - YYYY/MM Author: Description of the modification """ +from gudhi import SimplexTree + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" diff --git a/src/python/test/test_subsampling.py b/src/python/test/test_subsampling.py index c816e203..fe0985fa 100755 --- a/src/python/test/test_subsampling.py +++ b/src/python/test/test_subsampling.py @@ -1,5 +1,3 @@ -import gudhi - """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. Author(s): Vincent Rouvreau @@ -10,6 +8,8 @@ import gudhi - YYYY/MM Author: Description of the modification """ +import gudhi + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" diff --git a/src/python/test/test_tangential_complex.py b/src/python/test/test_tangential_complex.py index 0f828d8e..e650e99c 100755 --- a/src/python/test/test_tangential_complex.py +++ b/src/python/test/test_tangential_complex.py @@ -1,5 +1,3 @@ -from gudhi import TangentialComplex, SimplexTree - """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. Author(s): Vincent Rouvreau @@ -10,6 +8,8 @@ from gudhi import TangentialComplex, SimplexTree - YYYY/MM Author: Description of the modification """ +from gudhi import TangentialComplex, SimplexTree + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" diff --git a/src/python/test/test_wasserstein_distance.py b/src/python/test/test_wasserstein_distance.py index a6bf9901..6a6b217b 100755 --- a/src/python/test/test_wasserstein_distance.py +++ b/src/python/test/test_wasserstein_distance.py @@ -1,9 +1,6 @@ -from gudhi.wasserstein import wasserstein_distance -import numpy as np - """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. - Author(s): Theo Lacombe + Author(s): Theo Lacombe, Marc Glisse Copyright (C) 2019 Inria @@ -11,38 +8,66 @@ import numpy as np - YYYY/MM Author: Description of the modification """ +from gudhi.wasserstein import wasserstein_distance as pot +from gudhi.hera import wasserstein_distance as hera +import numpy as np +import pytest + __author__ = "Theo Lacombe" __copyright__ = "Copyright (C) 2019 Inria" __license__ = "MIT" - -def test_basic_wasserstein(): +def _basic_wasserstein(wasserstein_distance, delta, test_infinity=True): diag1 = np.array([[2.7, 3.7], [9.6, 14.0], [34.2, 34.974]]) diag2 = np.array([[2.8, 4.45], [9.5, 14.1]]) diag3 = np.array([[0, 2], [4, 6]]) diag4 = np.array([[0, 3], [4, 8]]) - emptydiag = np.array([[]]) + emptydiag = np.array([]) + + # We just need to handle positive numbers here + def approx(x): + return pytest.approx(x, rel=delta) + + assert wasserstein_distance(emptydiag, emptydiag, internal_p=2., order=1.) == 0. + assert wasserstein_distance(emptydiag, emptydiag, internal_p=np.inf, order=1.) == 0. + assert wasserstein_distance(emptydiag, emptydiag, internal_p=np.inf, order=2.) == 0. + assert wasserstein_distance(emptydiag, emptydiag, internal_p=2., order=2.) == 0. + + assert wasserstein_distance(diag3, emptydiag, internal_p=np.inf, order=1.) == approx(2.) + assert wasserstein_distance(diag3, emptydiag, internal_p=1., order=1.) == approx(4.) + + assert wasserstein_distance(diag4, emptydiag, internal_p=1., order=2.) == approx(5.) # thank you Pythagorician triplets + assert wasserstein_distance(diag4, emptydiag, internal_p=np.inf, order=2.) == approx(2.5) + assert wasserstein_distance(diag4, emptydiag, internal_p=2., order=2.) == approx(3.5355339059327378) + + assert wasserstein_distance(diag1, diag2, internal_p=2., order=1.) == approx(1.4453593023967701) + assert wasserstein_distance(diag1, diag2, internal_p=2.35, order=1.74) == approx(0.9772734057168739) + + assert wasserstein_distance(diag1, emptydiag, internal_p=2.35, order=1.7863) == approx(3.141592214572228) - assert wasserstein_distance(emptydiag, emptydiag, q=2., p=1.) == 0. - assert wasserstein_distance(emptydiag, emptydiag, q=np.inf, p=1.) == 0. - assert wasserstein_distance(emptydiag, emptydiag, q=np.inf, p=2.) == 0. - assert wasserstein_distance(emptydiag, emptydiag, q=2., p=2.) == 0. + assert wasserstein_distance(diag3, diag4, internal_p=1., order=1.) == approx(3.) + assert wasserstein_distance(diag3, diag4, internal_p=np.inf, order=1.) == approx(3.) # no diag matching here + assert wasserstein_distance(diag3, diag4, internal_p=np.inf, order=2.) == approx(np.sqrt(5)) + assert wasserstein_distance(diag3, diag4, internal_p=1., order=2.) == approx(np.sqrt(5)) + assert wasserstein_distance(diag3, diag4, internal_p=4.5, order=2.) == approx(np.sqrt(5)) - assert wasserstein_distance(diag3, emptydiag, q=np.inf, p=1.) == 2. - assert wasserstein_distance(diag3, emptydiag, q=1., p=1.) == 4. + if(not test_infinity): + return - assert wasserstein_distance(diag4, emptydiag, q=1., p=2.) == 5. # thank you Pythagorician triplets - assert wasserstein_distance(diag4, emptydiag, q=np.inf, p=2.) == 2.5 - assert wasserstein_distance(diag4, emptydiag, q=2., p=2.) == 3.5355339059327378 + diag5 = np.array([[0, 3], [4, np.inf]]) + diag6 = np.array([[7, 8], [4, 6], [3, np.inf]]) - assert wasserstein_distance(diag1, diag2, q=2., p=1.) == 1.4453593023967701 - assert wasserstein_distance(diag1, diag2, q=2.35, p=1.74) == 0.9772734057168739 + assert wasserstein_distance(diag4, diag5) == np.inf + assert wasserstein_distance(diag5, diag6, order=1, internal_p=np.inf) == approx(4.) - assert wasserstein_distance(diag1, emptydiag, q=2.35, p=1.7863) == 3.141592214572228 +def hera_wrap(delta): + def fun(*kargs,**kwargs): + return hera(*kargs,**kwargs,delta=delta) + return fun - assert wasserstein_distance(diag3, diag4, q=1., p=1.) == 3. - assert wasserstein_distance(diag3, diag4, q=np.inf, p=1.) == 3. # no diag matching here - assert wasserstein_distance(diag3, diag4, q=np.inf, p=2.) == np.sqrt(5) - assert wasserstein_distance(diag3, diag4, q=1., p=2.) == np.sqrt(5) - assert wasserstein_distance(diag3, diag4, q=4.5, p=2.) == np.sqrt(5) +def test_wasserstein_distance_pot(): + _basic_wasserstein(pot, 1e-15, test_infinity=False) +def test_wasserstein_distance_hera(): + _basic_wasserstein(hera_wrap(1e-12), 1e-12) + _basic_wasserstein(hera_wrap(.1), .1) diff --git a/src/python/test/test_witness_complex.py b/src/python/test/test_witness_complex.py index 36ced635..7baf18c9 100755 --- a/src/python/test/test_witness_complex.py +++ b/src/python/test/test_witness_complex.py @@ -1,5 +1,3 @@ -from gudhi import WitnessComplex, StrongWitnessComplex, SimplexTree - """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. Author(s): Vincent Rouvreau @@ -10,6 +8,8 @@ from gudhi import WitnessComplex, StrongWitnessComplex, SimplexTree - YYYY/MM Author: Description of the modification """ +from gudhi import WitnessComplex, StrongWitnessComplex, SimplexTree + __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" |
