1 files changed, 12 insertions, 4 deletions
diff --git a/src/python/gudhi/datasets/generators/_points.cc b/src/python/gudhi/datasets/generators/_points.cc
index 6baed673..82fea25b 100644
--- a/src/python/gudhi/datasets/generators/_points.cc
+++ b/src/python/gudhi/datasets/generators/_points.cc
@@ -85,7 +85,9 @@ 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.
@@ -94,20 +96,26 @@ PYBIND11_MODULE(_points, m) {
           :type radius: float
           :param sample: The sample type. Default and only available value is `"random"`.
           :type sample: string
-          :rtype: numpy array of float
           :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
+          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.
           :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");
 }