Merge master and resolve conflicts (mainly src/python/CMakeLists.txt)

7 files changed, 130 insertions, 2 deletions

diff --git a/src/python/doc/alpha_complex_user.rst b/src/python/doc/alpha_complex_user.rst
index 84932c18..94c54053 100644
--- a/src/python/doc/alpha_complex_user.rst
+++ b/src/python/doc/alpha_complex_user.rst
@@ -159,7 +159,10 @@ As the squared radii computed by CGAL are an approximation, it might happen that
 :math:`\alpha^2` values do not quite define a proper filtration (i.e. non-decreasing with
 respect to inclusion).
 We fix that up by calling :func:`~gudhi.SimplexTree.make_filtration_non_decreasing` (cf.
-`C++ version <http://gudhi.gforge.inria.fr/doc/latest/index.html>`_).
+`C++ version <https://gudhi.inria.fr/doc/latest/class_gudhi_1_1_simplex__tree.html>`_).
+
+.. note::
+    This is not the case in `exact` version, this is the reason why it is not called in this case.
 
 Prune above given filtration value
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
diff --git a/src/python/doc/datasets_generators.inc b/src/python/doc/datasets_generators.inc
new file mode 100644
index 00000000..8d169275
--- /dev/null
+++ b/src/python/doc/datasets_generators.inc
@@ -0,0 +1,14 @@
+.. table::
+   :widths: 30 40 30
+
+   +-----------------------------------+--------------------------------------------+--------------------------------------------------------------------------------------+
+   | .. figure::                       | Datasets generators (points).              | :Authors: Hind Montassif                                                             |
+   |     img/sphere_3d.png             |                                            |                                                                                      |
+   |                                   |                                            | :Since: GUDHI 3.5.0                                                                  |
+   |                                   |                                            |                                                                                      |
+   |                                   |                                            | :License: MIT (`LGPL v3 </licensing/>`_)                                             |
+   |                                   |                                            |                                                                                      |
+   |                                   |                                            | :Requires: `CGAL <installation.html#cgal>`_                                          |
+   +-----------------------------------+--------------------------------------------+--------------------------------------------------------------------------------------+
+   | * :doc:`datasets_generators`                                                                                                                                          |
+   +-----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------+
diff --git a/src/python/doc/datasets_generators.rst b/src/python/doc/datasets_generators.rst
new file mode 100644
index 00000000..6f36bce1
--- /dev/null
+++ b/src/python/doc/datasets_generators.rst
@@ -0,0 +1,105 @@
+
+:orphan:
+
+.. To get rid of WARNING: document isn't included in any toctree
+
+===========================
+Datasets generators manual
+===========================
+
+We provide the generation of different customizable datasets to use as inputs for Gudhi complexes and data structures.
+
+
+Points generators
+------------------
+
+The module **points** enables the generation of random points on a sphere, random points on a torus and as a grid.
+
+Points on sphere
+^^^^^^^^^^^^^^^^
+
+The function **sphere** enables the generation of random i.i.d. points uniformly on a (d-1)-sphere in :math:`R^d`.
+The user should provide the number of points to be generated on the sphere :code:`n_samples` and the ambient dimension :code:`ambient_dim`.
+The :code:`radius` of sphere is optional and is equal to **1** by default.
+Only random points generation is currently available.
+
+The generated points are given as an array of shape :math:`(n\_samples, ambient\_dim)`.
+
+Example
+"""""""
+
+.. code-block:: python
+
+   from gudhi.datasets.generators import points
+   from gudhi import AlphaComplex
+
+   # Generate 50 points on a sphere in R^2
+   gen_points = points.sphere(n_samples = 50, ambient_dim = 2, radius = 1, sample = "random")
+
+   # Create an alpha complex from the generated points
+   alpha_complex = AlphaComplex(points = gen_points)
+   
+.. autofunction:: gudhi.datasets.generators.points.sphere
+
+Points on a flat torus
+^^^^^^^^^^^^^^^^
+
+You can also generate points on a torus.
+
+Two functions are available and give the same output: the first one depends on **CGAL** and the second does not and consists of full python code.
+
+On another hand, two sample types are provided: you can either generate i.i.d. points on a d-torus in :math:`R^{2d}` *randomly* or on a *grid*.
+
+First function: **ctorus**
+"""""""""""""""""""""""""""
+
+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.
+In this case, the returned generated points would be an array of shape :math:`(n\_samples, 2*dim)`.
+Otherwise, if set to **'grid'**, the points are generated on a grid and would be given as an array of shape:
+
+.. math::
+
+   ( ⌊n\_samples^{1 \over {dim}}⌋^{dim}, 2*dim )
+
+**Note 1:** The output array first shape is rounded down to the closest perfect :math:`dim^{th}` power.
+
+**Note 2:** This version is recommended when the user wishes to use **'grid'** as sample type, or **'random'** with a relatively small number of samples (~ less than 150).
+
+Example
+"""""""
+.. code-block:: python
+
+   from gudhi.datasets.generators import points
+
+   # Generate 50 points randomly on a torus in R^6
+   gen_points = points.ctorus(n_samples = 50, dim = 3)
+   
+   # Generate 27 points on a torus as a grid in R^6
+   gen_points = points.ctorus(n_samples = 50, dim = 3, sample = 'grid')
+
+.. autofunction:: gudhi.datasets.generators.points.ctorus
+
+Second function: **torus**
+"""""""""""""""""""""""""""
+
+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'**.
+
+**Note:** This version is recommended when the user wishes to use **'random'** as sample type with a great number of samples and a low dimension.
+
+Example
+"""""""
+.. code-block:: python
+
+   from gudhi.datasets.generators import points
+
+   # Generate 50 points randomly on a torus in R^6
+   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, sample = 'grid')
+
+
+.. autofunction:: gudhi.datasets.generators.points.torus
diff --git a/src/python/doc/examples.rst b/src/python/doc/examples.rst
index 76e5d4c7..1442f185 100644
--- a/src/python/doc/examples.rst
+++ b/src/python/doc/examples.rst
@@ -8,6 +8,7 @@ Examples
 .. only:: builder_html
 
     * :download:`alpha_complex_diagram_persistence_from_off_file_example.py <../example/alpha_complex_diagram_persistence_from_off_file_example.py>`
+    * :download:`alpha_complex_from_generated_points_on_sphere_example.py <../example/alpha_complex_from_generated_points_on_sphere_example.py>`
     * :download:`alpha_complex_from_points_example.py <../example/alpha_complex_from_points_example.py>`
     * :download:`alpha_rips_persistence_bottleneck_distance.py <../example/alpha_rips_persistence_bottleneck_distance.py>`
     * :download:`bottleneck_basic_example.py <../example/bottleneck_basic_example.py>`
diff --git a/src/python/doc/img/sphere_3d.png b/src/python/doc/img/sphere_3d.png
new file mode 100644
index 00000000..70f3184f
--- /dev/null
+++ b/src/python/doc/img/sphere_3d.png
diff --git a/src/python/doc/index.rst b/src/python/doc/index.rst
index 040e57a4..2d7921ae 100644
--- a/src/python/doc/index.rst
+++ b/src/python/doc/index.rst
@@ -91,3 +91,8 @@ Clustering
 **********
 
 .. include:: clustering.inc
+
+Datasets generators
+*******************
+
+.. include:: datasets_generators.inc
diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst
index cb7700ce..35c344e3 100644
--- a/src/python/doc/installation.rst
+++ b/src/python/doc/installation.rst
@@ -359,7 +359,7 @@ Python Optimal Transport
 ------------------------
 
 The :doc:`Wasserstein distance </wasserstein_distance_user>`
-module requires `POT <https://pot.readthedocs.io/>`_, a library that provides
+module requires `POT <https://pythonot.github.io/>`_, a library that provides
 several solvers for optimization problems related to Optimal Transport.
 
 PyTorch