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-rw-r--r--src/python/doc/_templates/layout.html2
-rw-r--r--src/python/doc/installation.rst5
-rw-r--r--src/python/doc/wasserstein_distance_user.rst17
3 files changed, 17 insertions, 7 deletions
diff --git a/src/python/doc/_templates/layout.html b/src/python/doc/_templates/layout.html
index 2f2d9c72..a672a281 100644
--- a/src/python/doc/_templates/layout.html
+++ b/src/python/doc/_templates/layout.html
@@ -201,7 +201,7 @@
<a href="#">Download</a>
<ul class="dropdown">
<li><a href="/licensing/">Licensing</a></li>
- <li><a href="https://gforge.inria.fr/frs/download.php/latestzip/5253/library-latest.zip" target="_blank">Get the latest sources</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>
</ul>
diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst
index 40f3f44b..d459145b 100644
--- a/src/python/doc/installation.rst
+++ b/src/python/doc/installation.rst
@@ -14,10 +14,11 @@ 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,
-`NumPy <http://numpy.org>`_ and `Cython <https://www.cython.org/>`_ to compile
+`NumPy <http://numpy.org>`_, `Cython <https://www.cython.org/>`_ and
+`pybind11 <https://github.com/pybind/pybind11>`_ to compile
the GUDHI Python module.
It is a multi-platform library and compiles on Linux, Mac OSX and Visual
-Studio 2015.
+Studio 2017.
On `Windows <https://wiki.python.org/moin/WindowsCompilers>`_ , only Python
≥ 3.5 are available because of the required Visual Studio version.
diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst
index 32999a0c..94b454e2 100644
--- a/src/python/doc/wasserstein_distance_user.rst
+++ b/src/python/doc/wasserstein_distance_user.rst
@@ -9,17 +9,26 @@ Definition
.. include:: wasserstein_distance_sum.inc
-This implementation is based on ideas from "Large Scale Computation of Means and Cluster for Persistence Diagrams via Optimal Transport".
+Functions
+---------
+This implementation uses the 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`.
-Function
---------
.. autofunction:: gudhi.wasserstein.wasserstein_distance
+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"
+:cite:`Kerber:2017:GHC:3047249.3064175` by Michael Kerber, Dmitriy
+Morozov, and Arnur Nigmetov.
+
+.. autofunction:: gudhi.hera.wasserstein_distance
Basic example
-------------
-This example computes the 1-Wasserstein distance from 2 persistence diagrams with euclidean ground metric.
+This example computes the 1-Wasserstein distance from 2 persistence diagrams with Euclidean ground metric.
Note that persistence diagrams must be submitted as (n x 2) numpy arrays and must not contain inf values.
.. testcode::