Replace 'uniform' flag of torus generation with 'sample' taking two possible values: 'grid'(i.e uniform==True) or 'random' (i.e uniform==False)

2 files changed, 9 insertions, 9 deletions

diff --git a/src/python/doc/datasets_generators.rst b/src/python/doc/datasets_generators.rst
index ef21c9d2..2802eccd 100644
--- a/src/python/doc/datasets_generators.rst
+++ b/src/python/doc/datasets_generators.rst
@@ -52,9 +52,9 @@ First module : **_points**
 """"""""""""""""""""""""""
 
 The user should provide the number of points to be generated on the torus :code:`n_samples`, and the dimension :code:`dim` of the torus on which points would be generated in :math:`R^{2dim}`.
-The flag :code:`uniform` is optional and is set to **False** by default, meaning that the points will be generated randomly.
+The :code:`sample` argument is optional and is set to **'random'** by default.
 In this case, the returned generated points would be an array of shape :math:`(n\_samples, 2*dim)`.
-Otherwise, if set to **True**, the points are generated as a grid and would be given as an array of shape :
+Otherwise, if set to **'grid'**, the points are generated on a grid and would be given as an array of shape :
 
 .. math::
 
@@ -70,7 +70,7 @@ Example
    gen_points = _points.torus(n_samples = 50, dim = 3)
    
    # Generate 27 points on a torus as a grid in R^6
-   gen_points = _points.torus(n_samples = 50, dim = 3, uniform = True)
+   gen_points = _points.torus(n_samples = 50, dim = 3, sample = 'grid')
 
 .. autofunction:: gudhi.datasets.generators._points.torus
 
@@ -79,7 +79,7 @@ Second module : **points**
 
 The user should provide the number of points to be generated on the torus :code:`n_samples` and the dimension :code:`dim` of the torus on which points would be generated in :math:`R^{2dim}`.
 The :code:`sample` argument is optional and is set to **'random'** by default.
-The other allowed value of sample type is **'grid'** and is equivalent to :code:`uniform = True` in the first module.
+The other allowed value of sample type is **'grid'**.
 
 Example
 """""""
diff --git a/src/python/gudhi/datasets/generators/_points.cc b/src/python/gudhi/datasets/generators/_points.cc
index 55b21b2b..6bbdf284 100644
--- a/src/python/gudhi/datasets/generators/_points.cc
+++ b/src/python/gudhi/datasets/generators/_points.cc
@@ -46,13 +46,13 @@ py::array_t<double> generate_points_on_sphere(size_t n_samples, int ambient_dim,
     return points;
 }
 
-py::array_t<double> generate_points_on_torus(size_t n_samples, int dim, bool uniform) {
+py::array_t<double> generate_points_on_torus(size_t n_samples, int dim, std::string sample) {
 
     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);
+        points_generated = Gudhi::generate_points_on_torus_d<Kern>(n_samples, dim, sample);
     }
 
     size_t npoints = points_generated.size();
@@ -93,7 +93,7 @@ PYBIND11_MODULE(_points, m) {
           )pbdoc");
 
     m.def("torus", &generate_points_on_torus,
-          py::arg("n_samples"), py::arg("dim"), py::arg("uniform") = false,
+          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
 
@@ -101,8 +101,8 @@ PYBIND11_MODULE(_points, m) {
           :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 uniform: A flag to define if the points generation is uniform (i.e generated as a grid).
-          :type uniform: bool
+          :param sample: The sample type. Available values are: `"random"` and `"grid"`. Default value is `"random"`.
+          :type sample: string
           :rtype: numpy array of float
           :returns: the generated points on a torus.
           )pbdoc");