Fix #299

15 files changed, 135 insertions, 107 deletions

diff --git a/src/python/doc/alpha_complex_sum.inc b/src/python/doc/alpha_complex_sum.inc
index 9e6414d0..74331333 100644
--- a/src/python/doc/alpha_complex_sum.inc
+++ b/src/python/doc/alpha_complex_sum.inc
@@ -1,17 +1,17 @@
 .. table::
    :widths: 30 40 30
 
-   +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+
-   | .. figure::                                                    | Alpha complex is a simplicial complex constructed from the finite      | :Author: Vincent Rouvreau                                                                                                   |
-   |      ../../doc/Alpha_complex/alpha_complex_representation.png  | cells of a Delaunay Triangulation.                                     |                                                                                                                             |
-   |      :alt: Alpha complex representation                        |                                                                        | :Since: GUDHI 2.0.0                                                                                                         |
-   |      :figclass: align-center                                   | The filtration value of each simplex is computed as the **square** of  |                                                                                                                             |
-   |                                                                | the circumradius of the simplex if the circumsphere is empty (the      | :License: MIT (`GPL v3 </licensing/>`_)                                                                                     |
-   |                                                                | simplex is then said to be Gabriel), and as the minimum of the         |                                                                                                                             |
-   |                                                                | filtration values of the codimension 1 cofaces that make it not        | :Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 |
-   |                                                                | Gabriel otherwise.                                                     |                                                                                                                             |
-   |                                                                |                                                                        |                                                                                                                             |
-   |                                                                | For performances reasons, it is advised to use CGAL ≥ 5.0.0.           |                                                                                                                             |
-   +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+
-   | * :doc:`alpha_complex_user`                                    | * :doc:`alpha_complex_ref`                                                                                                                                                                           |
-   +----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+   +----------------------------------------------------------------+-------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+
+   | .. figure::                                                    | Alpha complex is a simplicial complex constructed from the finite       | :Author: Vincent Rouvreau                                                                                                   |
+   |      ../../doc/Alpha_complex/alpha_complex_representation.png  | cells of a Delaunay Triangulation.                                      |                                                                                                                             |
+   |      :alt: Alpha complex representation                        |                                                                         | :Since: GUDHI 2.0.0                                                                                                         |
+   |      :figclass: align-center                                   | The filtration value of each simplex is computed as the **square** of   |                                                                                                                             |
+   |                                                                | the circumradius of the simplex if the circumsphere is empty (the       | :License: MIT (`GPL v3 </licensing/>`_)                                                                                     |
+   |                                                                | simplex is then said to be Gabriel), and as the minimum of the          |                                                                                                                             |
+   |                                                                | filtration values of the codimension 1 cofaces that make it not         | :Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 |
+   |                                                                | Gabriel otherwise.                                                      |                                                                                                                             |
+   |                                                                |                                                                         |                                                                                                                             |
+   |                                                                | For performances reasons, it is advised to use CGAL :math:`\geq` 5.0.0. |                                                                                                                             |
+   +----------------------------------------------------------------+-------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+
+   | * :doc:`alpha_complex_user`                                    | * :doc:`alpha_complex_ref`                                                                                                                                                                            |
+   +----------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst
index e4733653..e6e61d75 100644
--- a/src/python/doc/cubical_complex_user.rst
+++ b/src/python/doc/cubical_complex_user.rst
@@ -91,7 +91,7 @@ Currently one input from a text file is used. It uses a format inspired from the
     we allow any filtration values. As a consequence one cannot use ``-1``'s to indicate missing cubes. If you have
     missing cubes in your complex, please set their filtration to :math:`+\infty` (aka. ``inf`` in the file).
 
-The file format is described in details in :ref:`Perseus file format` file format section.
+The file format is described in details in `Perseus file format <fileformats.html#perseus>`__ section.
 
 .. testcode::
 
@@ -120,7 +120,7 @@ conditions are imposed in all directions, then complex :math:`\mathcal{K}` becam
 various constructors from the file Bitmap_cubical_complex_periodic_boundary_conditions_base.h to construct cubical
 complex with periodic boundary conditions.
 
-One can also use Perseus style input files (see :doc:`Perseus <fileformats>`) for the specific periodic case:
+One can also use Perseus style input files (see `Perseus file format <fileformats.html#perseus>`__) for the specific periodic case:
 
 .. testcode::
 
diff --git a/src/python/doc/fileformats.rst b/src/python/doc/fileformats.rst
index 345dfdba..ae1b00f3 100644
--- a/src/python/doc/fileformats.rst
+++ b/src/python/doc/fileformats.rst
@@ -80,8 +80,6 @@ Here is a simple sample file in the 3D case::
     1. 1. 1.
 
 
-.. _Perseus file format:
-
 Perseus
 *******
 
diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst
index 09a843d5..d72e91b5 100644
--- a/src/python/doc/installation.rst
+++ b/src/python/doc/installation.rst
@@ -12,8 +12,8 @@ The easiest way to install the Python version of GUDHI is using
 
 Compiling
 *********
-The library uses c++14 and requires `Boost <https://www.boost.org/>`_ ≥ 1.56.0,
-`CMake <https://www.cmake.org/>`_ ≥ 3.1  to generate makefiles,
+The library uses c++14 and requires `Boost <https://www.boost.org/>`_ :math:`\geq` 1.56.0,
+`CMake <https://www.cmake.org/>`_ :math:`\geq` 3.1  to generate makefiles,
 `NumPy <http://numpy.org>`_, `Cython <https://www.cython.org/>`_ and
 `pybind11 <https://github.com/pybind/pybind11>`_ to compile
 the GUDHI Python module.
@@ -21,7 +21,7 @@ It is a multi-platform library and compiles on Linux, Mac OSX and Visual
 Studio 2017.
 
 On `Windows <https://wiki.python.org/moin/WindowsCompilers>`_ , only Python
-≥ 3.5 are available because of the required Visual Studio version.
+:math:`\geq` 3.5 are available because of the required Visual Studio version.
 
 On other systems, if you have several Python/python installed, the version 2.X
 will be used by default, but you can force it by adding
@@ -30,7 +30,8 @@ will be used by default, but you can force it by adding
 GUDHI Python module compilation
 ===============================
 
-To build the GUDHI Python module, run the following commands in a terminal:
+After making sure that the `Compilation dependencies`_ are properly installed,
+one can build the GUDHI Python module, by running the following commands in a terminal:
 
 .. code-block:: bash
 
@@ -188,8 +189,14 @@ Run the following commands in a terminal:
 Optional third-party library
 ****************************
 
+Compilation dependencies
+========================
+
+These third party dependencies are detected by `CMake <https://www.cmake.org/>`_.
+They have to be installed before performing the `GUDHI Python module compilation`_.
+
 CGAL
-====
+----
 
 Some GUDHI modules (cf. :doc:`modules list </index>`), and few examples
 require `CGAL <https://www.cgal.org/>`_, a C++ library that provides easy
@@ -200,7 +207,7 @@ The procedure to install this library
 according to your operating system is detailed
 `here <http://doc.cgal.org/latest/Manual/installation.html>`_.
 
-The following examples requires CGAL version ≥ 4.11.0:
+The following examples requires CGAL version :math:`\geq` 4.11.0:
 
 .. only:: builder_html
 
@@ -211,23 +218,15 @@ The following examples requires CGAL version ≥ 4.11.0:
     * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>`
     * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>`
 
-EagerPy
-=======
-
-Some Python functions can handle automatic differentiation (possibly only when
-a flag `enable_autodiff=True` is used). In order to reduce code duplication, we
-use `EagerPy <https://eagerpy.jonasrauber.de/>`_ which wraps arrays from
-PyTorch, TensorFlow and JAX in a common interface.
-
 Eigen
-=====
+-----
 
 Some GUDHI modules (cf. :doc:`modules list </index>`), and few examples
 require `Eigen <http://eigen.tuxfamily.org/>`_, a C++ template
 library for linear algebra: matrices, vectors, numerical solvers, and related
 algorithms.
 
-The following examples require `Eigen <http://eigen.tuxfamily.org/>`_ version ≥ 3.1.0:
+The following examples require `Eigen <http://eigen.tuxfamily.org/>`_ version :math:`\geq` 3.1.0:
 
 .. only:: builder_html
 
@@ -237,15 +236,39 @@ The following examples require `Eigen <http://eigen.tuxfamily.org/>`_ version â‰
     * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>`
     * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>`
 
+Threading Building Blocks
+-------------------------
+
+`Intel® TBB <https://www.threadingbuildingblocks.org/>`_ lets you easily write
+parallel C++ programs that take full advantage of multicore performance, that
+are portable and composable, and that have future-proof scalability.
+
+Having Intel® TBB installed is recommended to parallelize and accelerate some
+GUDHI computations.
+
+Run time dependencies
+=====================
+
+These third party dependencies are detected by Python `import` mechanism at run time.
+They can be installed when required.
+
+EagerPy
+-------
+
+Some Python functions can handle automatic differentiation (possibly only when
+a flag `enable_autodiff=True` is used). In order to reduce code duplication, we
+use `EagerPy <https://eagerpy.jonasrauber.de/>`_ which wraps arrays from
+PyTorch, TensorFlow and JAX in a common interface.
+
 Hnswlib
-=======
+-------
 
 :class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package
 `Hnswlib <https://github.com/nmslib/hnswlib>`_ as a backend if explicitly
 requested, to speed-up queries.
 
 Matplotlib
-==========
+----------
 
 The :doc:`persistence graphical tools </persistence_graphical_tools_user>`
 module requires `Matplotlib <http://matplotlib.org>`_, a Python 2D plotting
@@ -267,49 +290,46 @@ The following examples require the `Matplotlib <http://matplotlib.org>`_:
     * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>`
 
 PyKeOps
-=======
+-------
 
 :class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package
 `PyKeOps <https://www.kernel-operations.io/keops/python/>`_ as a backend if
 explicitly requested, to speed-up queries using a GPU.
 
 Python Optimal Transport
-========================
+------------------------
 
 The :doc:`Wasserstein distance </wasserstein_distance_user>`
 module requires `POT <https://pot.readthedocs.io/>`_, a library that provides
 several solvers for optimization problems related to Optimal Transport.
 
 PyTorch
-=======
+-------
 
 `PyTorch <https://pytorch.org/>`_ is currently only used as a dependency of
 `PyKeOps`_, and in some tests.
 
 Scikit-learn
-============
+------------
 
 The :doc:`persistence representations </representations>` module require
 `scikit-learn <https://scikit-learn.org/>`_, a Python-based ecosystem of
 open-source software for machine learning.
 
+:class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package
+`scikit-learn <https://scikit-learn.org/>`_ as a backend if explicitly
+requested.
+
 SciPy
-=====
+-----
 
 The :doc:`persistence graphical tools </persistence_graphical_tools_user>` and
 :doc:`Wasserstein distance </wasserstein_distance_user>` modules require `SciPy
 <http://scipy.org>`_, a Python-based ecosystem of open-source software for
 mathematics, science, and engineering.
 
-Threading Building Blocks
-=========================
-
-`Intel® TBB <https://www.threadingbuildingblocks.org/>`_ lets you easily write
-parallel C++ programs that take full advantage of multicore performance, that
-are portable and composable, and that have future-proof scalability.
-
-Having Intel® TBB installed is recommended to parallelize and accelerate some
-GUDHI computations.
+:class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package
+`SciPy <http://scipy.org>`_ as a backend if explicitly requested.
 
 Bug reports and contributions
 *****************************
diff --git a/src/python/doc/nerve_gic_complex_user.rst b/src/python/doc/nerve_gic_complex_user.rst
index 9101f45d..d5c5438d 100644
--- a/src/python/doc/nerve_gic_complex_user.rst
+++ b/src/python/doc/nerve_gic_complex_user.rst
@@ -13,7 +13,7 @@ Visualizations of the simplicial complexes can be done with either
 neato (from `graphviz <http://www.graphviz.org/>`_),
 `geomview <http://www.geomview.org/>`_,
 `KeplerMapper <https://github.com/MLWave/kepler-mapper>`_.
-Input point clouds are assumed to be OFF files (cf. :doc:`fileformats`).
+Input point clouds are assumed to be OFF files (cf. `OFF file format <fileformats.html#off-file-format>`__).
 
 Covers
 ------
diff --git a/src/python/doc/persistence_graphical_tools_sum.inc b/src/python/doc/persistence_graphical_tools_sum.inc
index b68d3d7e..0f41b420 100644
--- a/src/python/doc/persistence_graphical_tools_sum.inc
+++ b/src/python/doc/persistence_graphical_tools_sum.inc
@@ -1,14 +1,14 @@
 .. table::
    :widths: 30 40 30
 
-   +-----------------------------------------------------------------+-----------------------------------------------------------------------+-----------------------------------------------+
-   | .. figure::                                                     | These graphical tools comes on top of persistence results and allows  | :Author: Vincent Rouvreau, Theo Lacombe       |
-   |      img/graphical_tools_representation.png                     | the user to display easily persistence barcode, diagram or density.   |                                               |
-   |                                                                 |                                                                       | :Since: GUDHI 2.0.0                           |
-   |                                                                 | Note that these functions return the matplotlib axis, allowing        |                                               |
-   |                                                                 | for further modifications (title, aspect, etc.)                       | :License: MIT                                 |
-   |                                                                 |                                                                       |                                               |
-   |                                                                 |                                                                       | :Requires: matplotlib, numpy and scipy        |
-   +-----------------------------------------------------------------+-----------------------------------------------------------------------+-----------------------------------------------+
-   | * :doc:`persistence_graphical_tools_user`                       | * :doc:`persistence_graphical_tools_ref`                                                                              |
-   +-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+
+   +-----------------------------------------------------------------+-----------------------------------------------------------------------+----------------------------------------------------------+
+   | .. figure::                                                     | These graphical tools comes on top of persistence results and allows  | :Author: Vincent Rouvreau, Theo Lacombe                  |
+   |      img/graphical_tools_representation.png                     | the user to display easily persistence barcode, diagram or density.   |                                                          |
+   |                                                                 |                                                                       | :Since: GUDHI 2.0.0                                      |
+   |                                                                 | Note that these functions return the matplotlib axis, allowing        |                                                          |
+   |                                                                 | for further modifications (title, aspect, etc.)                       | :License: MIT                                            |
+   |                                                                 |                                                                       |                                                          |
+   |                                                                 |                                                                       | :Requires: `Matplotlib <installation.html#matplotlib>`__ |
+   +-----------------------------------------------------------------+-----------------------------------------------------------------------+----------------------------------------------------------+
+   | * :doc:`persistence_graphical_tools_user`                       | * :doc:`persistence_graphical_tools_ref`                                                                                         |
+   +-----------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
diff --git a/src/python/doc/persistence_graphical_tools_user.rst b/src/python/doc/persistence_graphical_tools_user.rst
index 91e52703..fce628b1 100644
--- a/src/python/doc/persistence_graphical_tools_user.rst
+++ b/src/python/doc/persistence_graphical_tools_user.rst
@@ -12,9 +12,6 @@ Definition
 Show persistence as a barcode
 -----------------------------
 
-.. note::
-    this function requires matplotlib and numpy to be available
-
 This function can display the persistence result as a barcode:
 
 .. plot::
@@ -36,9 +33,6 @@ This function can display the persistence result as a barcode:
 Show persistence as a diagram
 -----------------------------
 
-.. note::
-    this function requires matplotlib and numpy to be available
-
 This function can display the persistence result as a diagram:
 
 .. plot::
@@ -73,8 +67,7 @@ of shape (N x 2) encoding a persistence diagram (in a given dimension).
 Persistence density
 -------------------
 
-.. note::
-    this function requires matplotlib, numpy and scipy to be available
+:Requires: `SciPy <installation.html#scipy>`__
 
 If you want more information on a specific dimension, for instance:
 
diff --git a/src/python/doc/point_cloud.rst b/src/python/doc/point_cloud.rst
index 192f70db..523a9dfa 100644
--- a/src/python/doc/point_cloud.rst
+++ b/src/python/doc/point_cloud.rst
@@ -16,6 +16,8 @@ File Readers
 Subsampling
 -----------
 
+:Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0
+
 .. automodule:: gudhi.subsampling
    :members:
    :special-members:
diff --git a/src/python/doc/point_cloud_sum.inc b/src/python/doc/point_cloud_sum.inc
index d4761aba..4315cea6 100644
--- a/src/python/doc/point_cloud_sum.inc
+++ b/src/python/doc/point_cloud_sum.inc
@@ -1,15 +1,12 @@
 .. table::
    :widths: 30 40 30
 
-   +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+
-   | | :math:`(x_1, x_2, \ldots, x_d)`                              | Utilities to process point clouds: read from file, subsample,          | :Authors: Vincent Rouvreau, Marc Glisse, Masatoshi Takenouchi                                                               |
-   | | :math:`(y_1, y_2, \ldots, y_d)`                              | find neighbors, embed time series in higher dimension, etc.            |                                                                                                                             |
-   |                                                                |                                                                        | :Since: GUDHI 2.0.0                                                                                                         |
-   |                                                                |                                                                        |                                                                                                                             |
-   |                                                                |                                                                        | :License: MIT (`GPL v3 </licensing/>`_, BSD-3-Clause, Apache-2.0)                                                           |
-   |                                                                | Parts of this package require CGAL.                                    |                                                                                                                             |
-   |                                                                |                                                                        | :Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 |
-   |                                                                |                                                                        |                                                                                                                             |
-   +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+
-   | * :doc:`point_cloud`                                                                                                                                                                                                                                                  |
-   +----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+   +-----------------------------------+---------------------------------------------------------------+-------------------------------------------------------------------+
+   | | :math:`(x_1, x_2, \ldots, x_d)` | Utilities to process point clouds: read from file, subsample, | :Authors: Vincent Rouvreau, Marc Glisse, Masatoshi Takenouchi     |
+   | | :math:`(y_1, y_2, \ldots, y_d)` | find neighbors, embed time series in higher dimension, etc.   |                                                                   |
+   |                                   |                                                               | :Since: GUDHI 2.0.0                                               |
+   |                                   |                                                               |                                                                   |
+   |                                   |                                                               | :License: MIT (`GPL v3 </licensing/>`_, BSD-3-Clause, Apache-2.0) |
+   +-----------------------------------+---------------------------------------------------------------+-------------------------------------------------------------------+
+   | * :doc:`point_cloud`                                                                                                                                                  |
+   +-----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------+
diff --git a/src/python/doc/representations_sum.inc b/src/python/doc/representations_sum.inc
index eac89b9d..cdad4716 100644
--- a/src/python/doc/representations_sum.inc
+++ b/src/python/doc/representations_sum.inc
@@ -1,14 +1,14 @@
 .. table::
    :widths: 30 40 30
 
-   +------------------------------------------------------------------+----------------------------------------------------------------+-----------------------------------------------+
-   | .. figure::                                                      | Vectorizations, distances and kernels that work on persistence | :Author: Mathieu Carrière                     |
-   |      img/sklearn-tda.png                                         | diagrams, compatible with scikit-learn.                        |                                               |
-   |                                                                  |                                                                | :Since: GUDHI 3.1.0                           |
-   |                                                                  |                                                                |                                               |
-   |                                                                  |                                                                | :License: MIT                                 |
-   |                                                                  |                                                                |                                               |
-   |                                                                  |                                                                | :Requires: scikit-learn                       |
-   +------------------------------------------------------------------+----------------------------------------------------------------+-----------------------------------------------+
-   | * :doc:`representations`                                                                                                                                                          |
-   +------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------+
+   +------------------------------------------------------------------+----------------------------------------------------------------+--------------------------------------------------------------+
+   | .. figure::                                                      | Vectorizations, distances and kernels that work on persistence | :Author: Mathieu Carrière                                    |
+   |      img/sklearn-tda.png                                         | diagrams, compatible with scikit-learn.                        |                                                              |
+   |                                                                  |                                                                | :Since: GUDHI 3.1.0                                          |
+   |                                                                  |                                                                |                                                              |
+   |                                                                  |                                                                | :License: MIT                                                |
+   |                                                                  |                                                                |                                                              |
+   |                                                                  |                                                                | :Requires: `Scikit-learn <installation.html#scikit-learn>`__ |
+   +------------------------------------------------------------------+----------------------------------------------------------------+--------------------------------------------------------------+
+   | * :doc:`representations`                                                                                                                                                                         |
+   +------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------+
diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst
index c443bab5..2d2e2ae7 100644
--- a/src/python/doc/wasserstein_distance_user.rst
+++ b/src/python/doc/wasserstein_distance_user.rst
@@ -17,12 +17,21 @@ are measured in norm p, for :math:`1 \leq p \leq \infty`.
 
 Distance Functions
 ------------------
-This first implementation uses the Python Optimal Transport library and is based
-on ideas from "Large Scale Computation of Means and Cluster for Persistence
+
+Optimal Transport
+*****************
+
+:Requires: `Python Optimal Transport <installation.html#python-optimal-transport>`__ (POT) :math:`\geq` 0.5.1 
+
+This first implementation uses the `Python Optimal Transport <installation.html#python-optimal-transport>`__
+library and is based on ideas from "Large Scale Computation of Means and Cluster for Persistence
 Diagrams via Optimal Transport" :cite:`10.5555/3327546.3327645`.
 
 .. autofunction:: gudhi.wasserstein.wasserstein_distance
 
+Hera
+****
+
 This other implementation comes from `Hera
 <https://bitbucket.org/grey_narn/hera/src/master/>`_ (BSD-3-Clause) which is
 based on "Geometry Helps to Compare Persistence Diagrams"
@@ -94,6 +103,8 @@ The output is:
 Barycenters
 -----------
 
+:Requires: `Python Optimal Transport <installation.html#python-optimal-transport>`__ (POT) :math:`\geq` 0.5.1 
+
 A Frechet mean (or barycenter) is a generalization of the arithmetic
 mean in a non linear space such as the one of persistence diagrams.
 Given a set of persistence diagrams :math:`\mu_1 \dots \mu_n`, it is
diff --git a/src/python/gudhi/persistence_graphical_tools.py b/src/python/gudhi/persistence_graphical_tools.py
index cc3db467..e36af304 100644
--- a/src/python/gudhi/persistence_graphical_tools.py
+++ b/src/python/gudhi/persistence_graphical_tools.py
@@ -72,11 +72,11 @@ def plot_persistence_barcode(
     """This function plots the persistence bar code from persistence values list
     , a np.array of shape (N x 2) (representing a diagram 
     in a single homology dimension), 
-    or from a :doc:`persistence file <fileformats>`.
+    or from a `persistence diagram <fileformats.html#persistence-diagram>`__ file.
 
     :param persistence: Persistence intervals values list. Can be grouped by dimension or not.
     :type persistence: an array of (dimension, array of (birth, death)) or an array of (birth, death).
-    :param persistence_file: A :doc:`persistence file <fileformats>` style name
+    :param persistence_file: A `persistence diagram <fileformats.html#persistence-diagram>`__ file style name
         (reset persistence if both are set).
     :type persistence_file: string
     :param alpha: barcode transparency value (0.0 transparent through 1.0
@@ -214,11 +214,11 @@ def plot_persistence_diagram(
 ):
     """This function plots the persistence diagram from persistence values
     list, a np.array of shape (N x 2) representing a diagram in a single
-    homology dimension, or from a :doc:`persistence file <fileformats>`.
+    homology dimension, or from a `persistence diagram <fileformats.html#persistence-diagram>`__ file`.
 
     :param persistence: Persistence intervals values list. Can be grouped by dimension or not.
     :type persistence: an array of (dimension, array of (birth, death)) or an array of (birth, death).
-    :param persistence_file: A :doc:`persistence file <fileformats>` style name
+    :param persistence_file: A `persistence diagram <fileformats.html#persistence-diagram>`__ file style name
         (reset persistence if both are set).
     :type persistence_file: string
     :param alpha: plot transparency value (0.0 transparent through 1.0
@@ -369,17 +369,19 @@ def plot_persistence_density(
     """This function plots the persistence density from persistence
     values list, np.array of shape (N x 2) representing a diagram
     in a single homology dimension,
-    or from a :doc:`persistence file <fileformats>`. Be
-    aware that this function does not distinguish the dimension, it is
+    or from a `persistence diagram <fileformats.html#persistence-diagram>`__ file.
+    Be aware that this function does not distinguish the dimension, it is
     up to you to select the required one. This function also does not handle
     degenerate data set (scipy correlation matrix inversion can fail).
 
+    :Requires: `SciPy <installation.html#scipy>`__
+
     :param persistence: Persistence intervals values list. 
                         Can be grouped by dimension or not.
     :type persistence: an array of (dimension, array of (birth, death)) 
                         or an array of (birth, death).
-    :param persistence_file: A :doc:`persistence file <fileformats>`
-        style name (reset persistence if both are set).
+    :param persistence_file: A `persistence diagram <fileformats.html#persistence-diagram>`__
+        file style name (reset persistence if both are set).
     :type persistence_file: string
     :param nbins: Evaluate a gaussian kde on a regular grid of nbins x
         nbins over data extents (default is 300)
diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py
index 34e80b5d..19363097 100644
--- a/src/python/gudhi/point_cloud/knn.py
+++ b/src/python/gudhi/point_cloud/knn.py
@@ -19,6 +19,10 @@ __license__ = "MIT"
 class KNearestNeighbors:
     """
     Class wrapping several implementations for computing the k nearest neighbors in a point set.
+
+    :Requires: `PyKeOps <installation.html#pykeops>`__, `SciPy <installation.html#scipy>`__,
+        `Scikit-learn <installation.html#scikit-learn>`__, and/or `Hnswlib <installation.html#hnswlib>`__
+        in function of the selected `implementation`.
     """
 
     def __init__(self, k, return_index=True, return_distance=False, metric="euclidean", **kwargs):
diff --git a/src/python/gudhi/point_cloud/timedelay.py b/src/python/gudhi/point_cloud/timedelay.py
index f01df442..5292e752 100644
--- a/src/python/gudhi/point_cloud/timedelay.py
+++ b/src/python/gudhi/point_cloud/timedelay.py
@@ -10,9 +10,8 @@ import numpy as np
 
 
 class TimeDelayEmbedding:
-    """Point cloud transformation class.
-    Embeds time-series data in the R^d according to [Takens' Embedding Theorem]
-    (https://en.wikipedia.org/wiki/Takens%27s_theorem) and obtains the 
+    """Point cloud transformation class. Embeds time-series data in the R^d according to
+    `Takens' Embedding Theorem <https://en.wikipedia.org/wiki/Takens%27s_theorem>`_ and obtains the
     coordinates of each point.
 
     Parameters
diff --git a/src/python/gudhi/representations/metrics.py b/src/python/gudhi/representations/metrics.py
index ce416fb1..0a6dd680 100644
--- a/src/python/gudhi/representations/metrics.py
+++ b/src/python/gudhi/representations/metrics.py
@@ -223,7 +223,9 @@ class SlicedWassersteinDistance(BaseEstimator, TransformerMixin):
 
 class BottleneckDistance(BaseEstimator, TransformerMixin):
     """
-    This is a class for computing the bottleneck distance matrix from a list of persistence diagrams. 
+    This is a class for computing the bottleneck distance matrix from a list of persistence diagrams.
+
+    :Requires: `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0
     """
     def __init__(self, epsilon=None):
         """