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Diffstat (limited to 'src/python/gudhi/datasets/generators/_points.cc')
-rw-r--r-- | src/python/gudhi/datasets/generators/_points.cc | 121 |
1 files changed, 121 insertions, 0 deletions
diff --git a/src/python/gudhi/datasets/generators/_points.cc b/src/python/gudhi/datasets/generators/_points.cc new file mode 100644 index 00000000..82fea25b --- /dev/null +++ b/src/python/gudhi/datasets/generators/_points.cc @@ -0,0 +1,121 @@ +/* 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): Hind Montassif + * + * Copyright (C) 2021 Inria + * + * Modification(s): + * - YYYY/MM Author: Description of the modification + */ + +#include <pybind11/pybind11.h> +#include <pybind11/numpy.h> + +#include <gudhi/random_point_generators.h> +#include <gudhi/Debug_utils.h> + +#include <CGAL/Epick_d.h> + +namespace py = pybind11; + + +typedef CGAL::Epick_d< CGAL::Dynamic_dimension_tag > Kern; + +py::array_t<double> generate_points_on_sphere(size_t n_samples, int ambient_dim, double radius, std::string sample) { + + if (sample != "random") { + throw pybind11::value_error("This sample type is not supported"); + } + + py::array_t<double> points({n_samples, (size_t)ambient_dim}); + + py::buffer_info buf = points.request(); + double *ptr = static_cast<double *>(buf.ptr); + + GUDHI_CHECK(n_samples == buf.shape[0], "Py array first dimension not matching n_samples on sphere"); + GUDHI_CHECK(ambient_dim == buf.shape[1], "Py array second dimension not matching the ambient space dimension"); + + + std::vector<typename Kern::Point_d> points_generated; + + { + py::gil_scoped_release release; + points_generated = Gudhi::generate_points_on_sphere_d<Kern>(n_samples, ambient_dim, radius); + } + + for (size_t i = 0; i < n_samples; i++) + for (int j = 0; j < ambient_dim; j++) + ptr[i*ambient_dim+j] = points_generated[i][j]; + + return points; +} + +py::array_t<double> generate_points_on_torus(size_t n_samples, int dim, std::string sample) { + + if ( (sample != "random") && (sample != "grid")) { + throw pybind11::value_error("This sample type is not supported"); + } + + std::vector<typename Kern::Point_d> points_generated; + + { + py::gil_scoped_release release; + points_generated = Gudhi::generate_points_on_torus_d<Kern>(n_samples, dim, sample); + } + + size_t npoints = points_generated.size(); + + GUDHI_CHECK(2*dim == points_generated[0].size(), "Py array second dimension not matching the double torus dimension"); + + py::array_t<double> points({npoints, (size_t)2*dim}); + + py::buffer_info buf = points.request(); + double *ptr = static_cast<double *>(buf.ptr); + + for (size_t i = 0; i < npoints; i++) + for (int j = 0; j < 2*dim; j++) + ptr[i*(2*dim)+j] = points_generated[i][j]; + + return points; +} + +PYBIND11_MODULE(_points, m) { + m.attr("__license__") = "LGPL v3"; + + m.def("sphere", &generate_points_on_sphere, + py::arg("n_samples"), py::arg("ambient_dim"), + py::arg("radius") = 1., py::arg("sample") = "random", + R"pbdoc( + Generate random i.i.d. points uniformly on a (d-1)-sphere in R^d + + :param n_samples: The number of points to be generated. + :type n_samples: integer + :param ambient_dim: The ambient dimension d. + :type ambient_dim: integer + :param radius: The radius. Default value is `1.`. + :type radius: float + :param sample: The sample type. Default and only available value is `"random"`. + :type sample: string + :returns: the generated points on a sphere. + )pbdoc"); + + m.def("ctorus", &generate_points_on_torus, + py::arg("n_samples"), py::arg("dim"), py::arg("sample") = "random", + R"pbdoc( + Generate random i.i.d. points on a d-torus in R^2d or as a grid + + :param n_samples: The number of points to be generated. + :type n_samples: integer + :param dim: The dimension of the torus on which points would be generated in R^2*dim. + :type dim: integer + :param sample: The sample type. Available values are: `"random"` and `"grid"`. Default value is `"random"`. + :type sample: string + :returns: the generated points on a torus. + + The shape of returned numpy array is: + + If sample is 'random': (n_samples, 2*dim). + + If sample is 'grid': (⌊n_samples**(1./dim)⌋**dim, 2*dim), where shape[0] is rounded down to the closest perfect 'dim'th power. + )pbdoc"); +} |