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author | Hind Montassif <hind.montassif@gmail.com> | 2021-05-10 10:27:57 +0200 |
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committer | Hind Montassif <hind.montassif@gmail.com> | 2021-05-10 10:27:57 +0200 |
commit | 303b014508f849d8cb8a4369430068f54fa74c46 (patch) | |
tree | 7a840544a5f6d89ac66bc752405d764d455c474c /src/python/gudhi/datasets/generators/_points.cc | |
parent | a1497289e6808d247f3b2be69b97dc9053e2b4d1 (diff) |
Add __init__.py files at every module level
Standardize functions to match the existing ones in sphere
Diffstat (limited to 'src/python/gudhi/datasets/generators/_points.cc')
-rw-r--r-- | src/python/gudhi/datasets/generators/_points.cc | 66 |
1 files changed, 66 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..561fd6d8 --- /dev/null +++ b/src/python/gudhi/datasets/generators/_points.cc @@ -0,0 +1,66 @@ +/* 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_torus(size_t n_samples, int dim, bool uniform) { + + 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, uniform); + } + + 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("torus", &generate_points_on_torus, + py::arg("n_samples"), py::arg("dim"), py::arg("uniform") = false, + R"pbdoc( + Generate random i.i.d. points on a d-torus in R^2d + + :param n_samples: The number of points to be generated. + :type n_samples: integer + :param dim: The dimension. + :type dim: integer + :param uniform: A flag to define if the points generation is uniform (i.e generated as a grid). + :type uniform: bool + :rtype: numpy array of float + :returns: the generated points on a torus. + )pbdoc"); +} |