merge upstream/master into essential parts

12 files changed, 169 insertions, 95 deletions

diff --git a/src/python/doc/_templates/layout.html b/src/python/doc/_templates/layout.html
index a672a281..cd40a51b 100644
--- a/src/python/doc/_templates/layout.html
+++ b/src/python/doc/_templates/layout.html
@@ -175,58 +175,59 @@
         <h1 class="show-for-small-only"><a href="" class="icon-tree"> GUDHI library</a></h1>
       </li>
          <!-- Remove the class "menu-icon" to get rid of menu icon. Take out "Menu" to just have icon alone -->
-        <li class="toggle-topbar menu-icon"><a href="#"><span>Navigation</span></a></li>
+        <li class="toggle-topbar menu-icon"><a href="#"><span>Nav</span></a></li>
       </ul>
       <section class="top-bar-section">
         <ul class="right">
                 <li class="divider"></li>
-                <li><a href="/contact/">Contact</a></li>
+                <li><a  href="/contact/">Contact</a></li>
         </ul>
         <ul class="left">
-                <li><a href="/"> <img src="/assets/img/home.png" alt="&nbsp;&nbsp;GUDHI">&nbsp;&nbsp;GUDHI </a></li>
+                <li><a  href="/"> <img src="/assets/img/home.png" alt="  GUDHI">  GUDHI </a></li>
                 <li class="divider"></li>
                 <li class="has-dropdown">
-                  <a href="#">Project</a>
+                  <a  href="#">Project</a>
                     <ul class="dropdown">
-                        <li><a href="/people/">People</a></li>
-                        <li><a href="/keepintouch/">Keep in touch</a></li>
-                        <li><a href="/partners/">Partners and Funding</a></li>
-                        <li><a href="/relatedprojects/">Related projects</a></li>
-                        <li><a href="/theyaretalkingaboutus/">They are talking about us</a></li>
-                        <li><a href="/inaction/">GUDHI in action</a></li>
+                        <li><a  href="/people/">People</a></li>
+                        <li><a  href="/keepintouch/">Keep in touch</a></li>
+                        <li><a  href="/partners/">Partners and Funding</a></li>
+                        <li><a  href="/relatedprojects/">Related projects</a></li>
+                        <li><a  href="/theyaretalkingaboutus/">They are talking about us</a></li>
+                        <li><a  href="/inaction/">GUDHI in action</a></li>
                     </ul>
                 </li>
                 <li class="divider"></li>
                 <li class="has-dropdown">
-                  <a href="#">Download</a>
+                  <a  href="#">Download</a>
                     <ul class="dropdown">
-                        <li><a href="/licensing/">Licensing</a></li>
-                        <li><a href="https://github.com/GUDHI/gudhi-devel/releases/latest" target="_blank">Get the latest sources</a></li>
-                        <li><a href="/conda/">Conda package</a></li>
-                        <li><a href="/dockerfile/">Dockerfile</a></li>
+                        <li><a  href="/licensing/">Licensing</a></li>
+                        <li><a  href="https://github.com/GUDHI/gudhi-devel/releases/latest" target="_blank">Get the latest sources</a></li>
+                        <li><a  href="/conda/">Conda package</a></li>
+                        <li><a  href="https://pypi.org/project/gudhi/" target="_blank">Pip package</a></li>
+                        <li><a  href="/dockerfile/">Dockerfile</a></li>
                     </ul>
                 </li>
                 <li class="divider"></li>
                 <li class="has-dropdown">
-                  <a href="#">Documentation</a>
+                  <a  href="#">Documentation</a>
                     <ul class="dropdown">
-                        <li><a href="/introduction/">Introduction</a></li>
-                        <li><a href="https://gudhi.inria.fr/doc/latest/installation.html">C++ installation manual</a></li>
-                        <li><a href="https://gudhi.inria.fr/doc/latest/">C++ documentation</a></li>
-                        <li><a href="https://gudhi.inria.fr/python/latest/installation.html">Python installation manual</a></li>
-                        <li><a href="https://gudhi.inria.fr/python/latest/">Python documentation</a></li>
-                        <li><a href="/utils/">Utilities</a></li>
-                        <li><a href="/tutorials/">Tutorials</a></li>
+                        <li><a  href="/introduction/">Introduction</a></li>
+                        <li><a  href="/doc/latest/installation.html">C++ installation manual</a></li>
+                        <li><a  href="/doc/latest/">C++ documentation</a></li>
+                        <li><a  href="/python/latest/installation.html">Python installation manual</a></li>
+                        <li><a  href="/python/latest/">Python documentation</a></li>
+                        <li><a  href="/utils/">Utilities</a></li>
+                        <li><a  href="/tutorials/">Tutorials</a></li>
                     </ul>
                 </li>
                 <li class="divider"></li>
-                <li><a href="/interfaces/">Interfaces</a></li>
+                <li><a  href="/interfaces/">Interfaces</a></li>
                 <li class="divider"></li>
         </ul>
       </section>
     </nav>
-  </div><!-- /#navigation -->  
-  <!-- GUDHI website header BEGIN -->
+  </div><!-- /#navigation -->
+  <!-- GUDHI website header END -->
 
 
 {%- block header %}{% endblock %}
diff --git a/src/python/doc/bottleneck_distance_user.rst b/src/python/doc/bottleneck_distance_user.rst
index 6c6e08d9..7baa76cc 100644
--- a/src/python/doc/bottleneck_distance_user.rst
+++ b/src/python/doc/bottleneck_distance_user.rst
@@ -47,7 +47,7 @@ The following example explains how the distance is computed:
     :figclass: align-center
     
     The point (0, 13) is at distance 6.5 from the diagonal and more
-    specifically from the point (6.5, 6.5)
+    specifically from the point (6.5, 6.5).
 
 
 Basic example
@@ -72,6 +72,6 @@ The output is:
 
 .. testoutput::
 
-    Bottleneck distance approximation = 0.81
+    Bottleneck distance approximation = 0.72
     Bottleneck distance value = 0.75
 
diff --git a/src/python/doc/conf.py b/src/python/doc/conf.py
index 3cc5d1d6..b06baf9c 100755
--- a/src/python/doc/conf.py
+++ b/src/python/doc/conf.py
@@ -44,6 +44,8 @@ extensions = [
     'sphinx_paramlinks',
 ]
 
+bibtex_bibfiles = ['../../biblio/bibliography.bib']
+
 todo_include_todos = True
 # plot option : do not show hyperlinks (Source code, png, hires.png, pdf)
 plot_html_show_source_link = False
diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst
index 3fd4e27a..6a211347 100644
--- a/src/python/doc/cubical_complex_user.rst
+++ b/src/python/doc/cubical_complex_user.rst
@@ -47,8 +47,8 @@ be a set of two elements).
 For further details and theory of cubical complexes, please consult :cite:`kaczynski2004computational` as well as the
 following paper :cite:`peikert2012topological`.
 
-Data structure.
----------------
+Data structure
+--------------
 
 The implementation of Cubical complex provides a representation of complexes that occupy a rectangular region in
 :math:`\mathbb{R}^n`. This extra assumption allows for a memory efficient way of storing cubical complexes in a form
@@ -77,8 +77,8 @@ Knowing the sizes of the bitmap, by a series of modulo operation, we can determi
 present in the product that gives the cube :math:`C`. In a similar way, we can compute boundary and the coboundary of
 each cube. Further details can be found in the literature.
 
-Input Format.
--------------
+Input Format
+------------
 
 In the current implantation, filtration is given at the maximal cubes, and it is then extended by the lower star
 filtration to all cubes. There are a number of constructors that can be used to construct cubical complex by users
@@ -108,8 +108,8 @@ the program output is:
     
     Cubical complex is of dimension 2 - 49 simplices.
 
-Periodic boundary conditions.
------------------------------
+Periodic boundary conditions
+----------------------------
 
 Often one would like to impose periodic boundary conditions to the cubical complex (cf.
 :doc:`periodic_cubical_complex_ref`).
@@ -154,7 +154,13 @@ the program output is:
 
     Periodic cubical complex is of dimension 2 - 42 simplices.
 
-Examples.
----------
+Examples
+--------
 
 End user programs are available in python/example/ folder.
+
+Tutorial
+--------
+
+This `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-cubical-complexes.ipynb>`_
+explains how to represent sublevels sets of functions using cubical complexes.
\ No newline at end of file
diff --git a/src/python/doc/examples.rst b/src/python/doc/examples.rst
index a42227e3..76e5d4c7 100644
--- a/src/python/doc/examples.rst
+++ b/src/python/doc/examples.rst
@@ -7,27 +7,30 @@ Examples
 
 .. only:: builder_html
 
-    * :download:`rips_complex_from_points_example.py <../example/rips_complex_from_points_example.py>`
+    * :download:`alpha_complex_diagram_persistence_from_off_file_example.py <../example/alpha_complex_diagram_persistence_from_off_file_example.py>`
     * :download:`alpha_complex_from_points_example.py <../example/alpha_complex_from_points_example.py>`
-    * :download:`simplex_tree_example.py <../example/simplex_tree_example.py>`
     * :download:`alpha_rips_persistence_bottleneck_distance.py <../example/alpha_rips_persistence_bottleneck_distance.py>`
-    * :download:`tangential_complex_plain_homology_from_off_file_example.py <../example/tangential_complex_plain_homology_from_off_file_example.py>`
-    * :download:`alpha_complex_diagram_persistence_from_off_file_example.py <../example/alpha_complex_diagram_persistence_from_off_file_example.py>`
-    * :download:`periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py <../example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py>`
     * :download:`bottleneck_basic_example.py <../example/bottleneck_basic_example.py>`
-    * :download:`gudhi_graphical_tools_example.py <../example/gudhi_graphical_tools_example.py>`
-    * :download:`plot_simplex_tree_dim012.py <../example/plot_simplex_tree_dim012.py>`
-    * :download:`plot_rips_complex.py <../example/plot_rips_complex.py>`
-    * :download:`plot_alpha_complex.py <../example/plot_alpha_complex.py>`
-    * :download:`witness_complex_from_nearest_landmark_table.py <../example/witness_complex_from_nearest_landmark_table.py>`
+    * :download:`coordinate_graph_induced_complex.py <../example/coordinate_graph_induced_complex.py>`
+    * :download:`diagram_vectorizations_distances_kernels.py <../example/diagram_vectorizations_distances_kernels.py>`
     * :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>`
-    * :download:`rips_complex_diagram_persistence_from_off_file_example.py <../example/rips_complex_diagram_persistence_from_off_file_example.py>`
+    * :download:`functional_graph_induced_complex.py <../example/functional_graph_induced_complex.py>`
+    * :download:`gudhi_graphical_tools_example.py <../example/gudhi_graphical_tools_example.py>`
+    * :download:`nerve_of_a_covering.py <../example/nerve_of_a_covering.py>`
+    * :download:`periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py <../example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py>`
+    * :download:`plot_alpha_complex.py <../example/plot_alpha_complex.py>`
+    * :download:`plot_rips_complex.py <../example/plot_rips_complex.py>`
+    * :download:`plot_simplex_tree_dim012.py <../example/plot_simplex_tree_dim012.py>`
+    * :download:`random_cubical_complex_persistence_example.py <../example/random_cubical_complex_persistence_example.py>`
+    * :download:`rips_complex_diagram_persistence_from_correlation_matrix_file_example.py <../example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py>`
     * :download:`rips_complex_diagram_persistence_from_distance_matrix_file_example.py <../example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py>`
+    * :download:`rips_complex_diagram_persistence_from_off_file_example.py <../example/rips_complex_diagram_persistence_from_off_file_example.py>`
+    * :download:`rips_complex_edge_collapse_example.py <../example/rips_complex_edge_collapse_example.py>`
+    * :download:`rips_complex_from_points_example.py <../example/rips_complex_from_points_example.py>`
     * :download:`rips_persistence_diagram.py <../example/rips_persistence_diagram.py>`
+    * :download:`simplex_tree_example.py <../example/simplex_tree_example.py>`
     * :download:`sparse_rips_persistence_diagram.py <../example/sparse_rips_persistence_diagram.py>`
-    * :download:`random_cubical_complex_persistence_example.py <../example/random_cubical_complex_persistence_example.py>`
-    * :download:`coordinate_graph_induced_complex.py <../example/coordinate_graph_induced_complex.py>`
-    * :download:`functional_graph_induced_complex.py <../example/functional_graph_induced_complex.py>`
+    * :download:`tangential_complex_plain_homology_from_off_file_example.py <../example/tangential_complex_plain_homology_from_off_file_example.py>`
     * :download:`voronoi_graph_induced_complex.py <../example/voronoi_graph_induced_complex.py>`
-    * :download:`nerve_of_a_covering.py <../example/nerve_of_a_covering.py>`
+    * :download:`witness_complex_from_nearest_landmark_table.py <../example/witness_complex_from_nearest_landmark_table.py>`
diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst
index 525ca84e..66efe45a 100644
--- a/src/python/doc/installation.rst
+++ b/src/python/doc/installation.rst
@@ -40,7 +40,7 @@ different, and in particular the `python/` subdirectory is actually `src/python/
 there.
 
 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,
+`CMake <https://www.cmake.org/>`_ :math:`\geq` 3.5  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.
@@ -65,7 +65,7 @@ one can build the GUDHI Python module, by running the following commands in a te
     cd /path-to-gudhi/
     mkdir build
     cd build/
-    cmake ..
+    cmake -DCMAKE_BUILD_TYPE=Release ..
     cd python
     make
 
@@ -323,6 +323,35 @@ The following examples require the `Matplotlib <http://matplotlib.org>`_:
     * :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>`
 
+LaTeX
+~~~~~
+
+If a sufficiently complete LaTeX toolchain is available (including dvipng and ghostscript), the LaTeX option of
+matplotlib is enabled for prettier captions (cf.
+`matplotlib text rendering with LaTeX <https://matplotlib.org/3.3.0/tutorials/text/usetex.html>`_).
+It also requires `type1cm` LaTeX package (not detected by matplotlib).
+
+If you are facing issues with LaTeX rendering, like this one:
+
+.. code-block:: none
+
+    Traceback (most recent call last):
+      File "/usr/lib/python3/dist-packages/matplotlib/texmanager.py", line 302, in _run_checked_subprocess
+        report = subprocess.check_output(command,
+    ...
+    ! LaTeX Error: File `type1cm.sty' not found.
+    ...
+
+This is because the LaTeX package is not installed on your system. On Ubuntu systems you can install texlive-full
+(for all LaTeX packages), or more specific packages like texlive-latex-extra, cm-super.
+
+You can still deactivate LaTeX rendering by saying:
+
+.. code-block:: python
+
+    import gudhi
+    gudhi.persistence_graphical_tools._gudhi_matplotlib_use_tex=False
+
 PyKeOps
 -------
 
@@ -365,6 +394,11 @@ mathematics, science, and engineering.
 :class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package
 `SciPy <http://scipy.org>`_ as a backend if explicitly requested.
 
+TensorFlow
+----------
+
+`TensorFlow <https://www.tensorflow.org>`_ is currently only used in some automatic differentiation tests.
+
 Bug reports and contributions
 *****************************
 
diff --git a/src/python/doc/persistence_graphical_tools_user.rst b/src/python/doc/persistence_graphical_tools_user.rst
index b5a38eb1..d95b9d2b 100644
--- a/src/python/doc/persistence_graphical_tools_user.rst
+++ b/src/python/doc/persistence_graphical_tools_user.rst
@@ -90,3 +90,14 @@ If you want more information on a specific dimension, for instance:
     gudhi.plot_persistence_density(persistence=pers_diag,
         dimension=1, legend=True, axes=axes[1])
     plt.show()
+
+LaTeX support
+-------------
+
+If you are facing issues with `LaTeX <installation.html#latex>`_ rendering, you can still deactivate LaTeX rendering by
+saying:
+
+.. code-block:: python
+
+    import gudhi
+    gudhi.persistence_graphical_tools._gudhi_matplotlib_use_tex=False
diff --git a/src/python/doc/representations.rst b/src/python/doc/representations.rst
index 041e3247..b0477197 100644
--- a/src/python/doc/representations.rst
+++ b/src/python/doc/representations.rst
@@ -12,11 +12,45 @@ This module, originally available at https://github.com/MathieuCarriere/sklearn-
 
 A diagram is represented as a numpy array of shape (n,2), as can be obtained from :func:`~gudhi.SimplexTree.persistence_intervals_in_dimension` for instance. Points at infinity are represented as a numpy array of shape (n,1), storing only the birth time. The classes in this module can handle several persistence diagrams at once. In that case, the diagrams are provided as a list of numpy arrays. Note that it is not necessary for the diagrams to have the same number of points, i.e., for the corresponding arrays to have the same number of rows: all classes can handle arrays with different shapes.
 
-A small example is provided
+Examples
+--------
 
-.. only:: builder_html
+Landscapes
+^^^^^^^^^^
 
-    * :download:`diagram_vectorizations_distances_kernels.py <../example/diagram_vectorizations_distances_kernels.py>`
+This example computes the first two Landscapes associated to a persistence diagram with four points. The landscapes are evaluated on ten samples, leading to two vectors with ten coordinates each, that are eventually concatenated in order to produce a single vector representation.
+
+.. testcode::
+
+    import numpy as np
+    from gudhi.representations import Landscape
+    # A single diagram with 4 points
+    D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])
+    diags = [D]
+    l=Landscape(num_landscapes=2,resolution=10).fit_transform(diags)
+    print(l) 
+
+The output is:
+
+.. testoutput::
+
+    [[1.02851895 2.05703791 2.57129739 1.54277843 0.89995409 1.92847304
+      2.95699199 3.08555686 2.05703791 1.02851895 0.         0.64282435
+      0.         0.         0.51425948 0.         0.         0.
+      0.77138922 1.02851895]]
+
+Various kernels
+^^^^^^^^^^^^^^^
+
+This small example is also provided
+:download:`diagram_vectorizations_distances_kernels.py <../example/diagram_vectorizations_distances_kernels.py>`
+
+Machine Learning and Topological Data Analysis
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+This `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-representations.ipynb>`_ explains how to
+efficiently combine machine learning and topological data analysis with the
+:doc:`representations module<representations>`.
 
 
 Preprocessing
@@ -46,27 +80,3 @@ Metrics
    :members:
    :special-members:
    :show-inheritance:
-
-Basic example
--------------
-
-This example computes the first two Landscapes associated to a persistence diagram with four points. The landscapes are evaluated on ten samples, leading to two vectors with ten coordinates each, that are eventually concatenated in order to produce a single vector representation.
-
-.. testcode::
-
-    import numpy as np
-    from gudhi.representations import Landscape
-    # A single diagram with 4 points
-    D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])
-    diags = [D]
-    l=Landscape(num_landscapes=2,resolution=10).fit_transform(diags)
-    print(l) 
-
-The output is:
-
-.. testoutput::
-
-    [[1.02851895 2.05703791 2.57129739 1.54277843 0.89995409 1.92847304
-      2.95699199 3.08555686 2.05703791 1.02851895 0.         0.64282435
-      0.         0.         0.51425948 0.         0.         0.
-      0.77138922 1.02851895]]
diff --git a/src/python/doc/representations_sum.inc b/src/python/doc/representations_sum.inc
index 323a0920..4298aea9 100644
--- a/src/python/doc/representations_sum.inc
+++ b/src/python/doc/representations_sum.inc
@@ -2,7 +2,7 @@
    :widths: 30 40 30
 
    +------------------------------------------------------------------+----------------------------------------------------------------+-------------------------------------------------------------+
-   | .. figure::                                                      | Vectorizations, distances and kernels that work on persistence | :Author: Mathieu Carrière                                   |
+   | .. figure::                                                      | Vectorizations, distances and kernels that work on persistence | :Author: Mathieu Carrière, Martin Royer                     |
    |      img/sklearn-tda.png                                         | diagrams, compatible with scikit-learn.                        |                                                             |
    |                                                                  |                                                                | :Since: GUDHI 3.1.0                                         |
    |                                                                  |                                                                |                                                             |
diff --git a/src/python/doc/rips_complex_sum.inc b/src/python/doc/rips_complex_sum.inc
index c123ea2a..2cb24990 100644
--- a/src/python/doc/rips_complex_sum.inc
+++ b/src/python/doc/rips_complex_sum.inc
@@ -1,14 +1,14 @@
 .. table::
    :widths: 30 40 30
 
-   +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------+
-   | .. figure::                                                    | The Vietoris-Rips complex is a simplicial complex built as the         | :Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse |
-   |      ../../doc/Rips_complex/rips_complex_representation.png    | clique-complex of a proximity graph.                                   |                                                                      |
-   |      :figclass: align-center                                   |                                                                        | :Since: GUDHI 2.0.0                                                  |
-   |                                                                | We also provide sparse approximations, to speed-up the computation     |                                                                      |
-   |                                                                | of persistent homology, and weighted versions, which are more robust   | :License: MIT                                                        |
-   |                                                                | to outliers.                                                           |                                                                      |
-   |                                                                |                                                                        |                                                                      |
-   +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------+
-   | * :doc:`rips_complex_user`                                     | * :doc:`rips_complex_ref`                                                                                                                     |
-   +----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
+   +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------------------+
+   | .. figure::                                                    | The Vietoris-Rips complex is a simplicial complex built as the         | :Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse, Yuichi Ike |
+   |      ../../doc/Rips_complex/rips_complex_representation.png    | clique-complex of a proximity graph.                                   |                                                                                  |
+   |      :figclass: align-center                                   |                                                                        | :Since: GUDHI 2.0.0                                                              |
+   |                                                                | We also provide sparse approximations, to speed-up the computation     |                                                                                  |
+   |                                                                | of persistent homology, and weighted versions, which are more robust   | :License: MIT                                                                    |
+   |                                                                | to outliers.                                                           |                                                                                  |
+   |                                                                |                                                                        |                                                                                  |
+   +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------------------+
+   | * :doc:`rips_complex_user`                                     | * :doc:`rips_complex_ref`                                                                                                                                 |
+   +----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------+
diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst
index 6048cc4e..27d218d4 100644
--- a/src/python/doc/rips_complex_user.rst
+++ b/src/python/doc/rips_complex_user.rst
@@ -7,9 +7,9 @@ Rips complex user manual
 Definition
 ----------
 
-====================================================================  ================================  ======================
-:Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse  :Since: GUDHI 2.0.0               :License: GPL v3
-====================================================================  ================================  ======================
+================================================================================  ================================  ======================
+:Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse, Yuichi Ike  :Since: GUDHI 2.0.0               :License: GPL v3
+================================================================================  ================================  ======================
 
 +-------------------------------------------+----------------------------------------------------------------------+
 | :doc:`rips_complex_user`                  | :doc:`rips_complex_ref`                                              |
diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst
index d747344b..b3d17495 100644
--- a/src/python/doc/wasserstein_distance_user.rst
+++ b/src/python/doc/wasserstein_distance_user.rst
@@ -191,3 +191,10 @@ The output is:
     [[0.27916667 0.55416667]
      [0.7375     0.7625    ]
      [0.2375     0.2625    ]]
+
+Tutorial
+********
+
+This
+`notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-Barycenters-of-persistence-diagrams.ipynb>`_
+presents the concept of barycenter, or Fréchet mean, of a family of persistence diagrams.
\ No newline at end of file