/* 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 #include #include #include // Indices are added internally in bottleneck_distance, they are not needed in the input. static auto make_point(double x, double y, py::ssize_t) { return std::pair(x, y); }; // For compatibility with older versions, we want to support e=None. double bottleneck(Dgm d1, Dgm d2, std::optional epsilon) { double e = epsilon.value_or((std::numeric_limits::min)()); // I *think* the call to request() in numpy_to_range_of_pairs has to be before releasing the GIL. auto diag1 = numpy_to_range_of_pairs(d1, make_point); auto diag2 = numpy_to_range_of_pairs(d2, make_point); py::gil_scoped_release release; return Gudhi::persistence_diagram::bottleneck_distance(diag1, diag2, e); } PYBIND11_MODULE(bottleneck, m) { m.attr("__license__") = "GPL v3"; m.def("bottleneck_distance", &bottleneck, py::arg("diagram_1"), py::arg("diagram_2"), py::arg("e") = py::none(), R"pbdoc( Compute the Bottleneck distance between two diagrams. Points at infinity and on the diagonal are supported. :param diagram_1: The first diagram. :type diagram_1: numpy array of shape (m,2) :param diagram_2: The second diagram. :type diagram_2: numpy array of shape (n,2) :param e: If `e` is 0, this uses an expensive algorithm to compute the exact distance. If `e` is not 0, it asks for an additive `e`-approximation, and currently also allows a small multiplicative error (the last 2 or 3 bits of the mantissa may be wrong). This version of the algorithm takes advantage of the limited precision of `double` and is usually a lot faster to compute, whatever the value of `e`. Thus, by default (`e=None`), `e` is the smallest positive double. :type e: float :rtype: float :returns: the bottleneck distance. )pbdoc"); }