Indent properly the docstring

Remove redundant return type

2 files changed, 24 insertions, 10 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");
 }
diff --git a/src/python/gudhi/datasets/generators/points.py b/src/python/gudhi/datasets/generators/points.py
index 481f3f71..9bb2799d 100644
--- a/src/python/gudhi/datasets/generators/points.py
+++ b/src/python/gudhi/datasets/generators/points.py
@@ -19,15 +19,15 @@ def _generate_random_points_on_torus(n_samples, dim):
 
     # Based on angles, construct points of size n_samples*dim on a circle and reshape the result in a n_samples*2*dim array
     array_points = np.column_stack([np.cos(alpha), np.sin(alpha)]).reshape(-1, 2*dim)
-    
+
     return array_points
 
 def _generate_grid_points_on_torus(n_samples, dim):
-    
+
     # Generate points on a dim-torus as a grid
     n_samples_grid = int((n_samples+.5)**(1./dim)) # add .5 to avoid rounding down with numerical approximations
     alpha = np.linspace(0, 2*np.pi, n_samples_grid, endpoint=False)
-    
+
     array_points = np.column_stack([np.cos(alpha), np.sin(alpha)])
     array_points_idx = np.empty([n_samples_grid]*dim + [dim], dtype=int)
     for i, x in enumerate(np.ix_(*([np.arange(n_samples_grid)]*dim))):
@@ -35,13 +35,19 @@ def _generate_grid_points_on_torus(n_samples, dim):
     return array_points[array_points_idx].reshape(-1, 2*dim)
 
 def torus(n_samples, dim, sample='random'):
-    """ 
+    """
     Generate points on a flat dim-torus in R^2dim either randomly or on a grid
-    
+
     :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