From fe75d33d715d038e348b7e48512b14c7488ee4f4 Mon Sep 17 00:00:00 2001 From: Hind-M Date: Tue, 9 Nov 2021 16:08:10 +0100 Subject: Remove sphinx warnings for torus --- src/python/gudhi/datasets/generators/_points.cc | 13 +++---------- src/python/gudhi/datasets/generators/points.py | 5 +---- 2 files changed, 4 insertions(+), 14 deletions(-) (limited to 'src/python/gudhi') diff --git a/src/python/gudhi/datasets/generators/_points.cc b/src/python/gudhi/datasets/generators/_points.cc index 70ce4925..6baed673 100644 --- a/src/python/gudhi/datasets/generators/_points.cc +++ b/src/python/gudhi/datasets/generators/_points.cc @@ -85,9 +85,7 @@ PYBIND11_MODULE(_points, m) { 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 - + 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. @@ -102,19 +100,14 @@ PYBIND11_MODULE(_points, m) { 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 - + 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 - :rtype: numpy array of float. - 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. + :rtype: numpy array of float. 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. :returns: the generated points on a torus. )pbdoc"); } diff --git a/src/python/gudhi/datasets/generators/points.py b/src/python/gudhi/datasets/generators/points.py index cf97777d..481f3f71 100644 --- a/src/python/gudhi/datasets/generators/points.py +++ b/src/python/gudhi/datasets/generators/points.py @@ -41,10 +41,7 @@ def torus(n_samples, dim, sample='random'): :param n_samples: The number of points to be generated. :param dim: The dimension of the torus on which points would be generated in R^2*dim. :param sample: The sample type of the generated points. Can be 'random' or 'grid'. - :returns: numpy array containing 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. + :returns: numpy array containing 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. """ if sample == 'random': # Generate points randomly -- cgit v1.2.3