Merge tag 'tags/gudhi-release-3.3.0' into dfsg/latest

1 files changed, 33 insertions, 35 deletions

diff --git a/.github/next_release.md b/.github/next_release.md
index d3c9ce68..14546066 100644
--- a/.github/next_release.md
+++ b/.github/next_release.md
@@ -1,50 +1,47 @@
-We are pleased to announce the release 3.2.0 of the GUDHI library.
+We are pleased to announce the release 3.3.0 of the GUDHI library.
 
-As a major new feature, the GUDHI library now offers a Python interface to [Hera](https://bitbucket.org/grey_narn/hera/src/master/) to compute the Wasserstein distance.
-[PyBind11](https://github.com/pybind/pybind11) is now required to build the Python module.
+As a major new feature, the GUDHI library now offers a persistence-based clustering algorithm, weighted Rips complex using DTM
+and edge collapse.
 
-We are now using GitHub to develop the GUDHI library, do not hesitate to [fork the GUDHI project on GitHub](https://github.com/GUDHI/gudhi-devel). From a user point of view, we recommend to download GUDHI user version (gudhi.3.2.0.tar.gz).
+The GUDHI library is hosted on GitHub, do not hesitate to [fork the GUDHI project on GitHub](https://github.com/GUDHI/gudhi-devel).
+From a user point of view, we recommend to download GUDHI user version (gudhi.3.3.0.tar.gz).
 
-Below is a list of changes made since GUDHI 3.1.1:
+Below is a list of changes made since GUDHI 3.2.0:
 
-- Point cloud utilities
-     - A new module [Time Delay Embedding](https://gudhi.inria.fr/python/latest/point_cloud.html#time-delay-embedding)
-       to embed time-series data in the R^d according to [Takens' Embedding Theorem](https://en.wikipedia.org/wiki/Takens%27s_theorem)
-       and obtain the coordinates of each point.
-     - A new module [K Nearest Neighbors](https://gudhi.inria.fr/python/latest/point_cloud.html#k-nearest-neighbors)
-       that wraps several implementations for computing the k nearest neighbors in a point set.
-     - A new module [Distance To Measure](https://gudhi.inria.fr/python/latest/point_cloud.html#distance-to-measure)
-       to compute the distance to the empirical measure defined by a point set
+- [DTM density estimator](https://gudhi.inria.fr/python/latest/point_cloud.html#module-gudhi.point_cloud.dtm)
+     - Python implementation of a density estimator based on the distance to the empirical measure defined by a point set.
 
-- [Persistence representations](https://gudhi.inria.fr/python/latest/representations.html)
-     - Interface to Wasserstein distances.
+- [DTM Rips complex](https://gudhi.inria.fr/python/latest/rips_complex_user.html#dtm-rips-complex)
+     - This Python implementation constructs a weighted Rips complex giving larger weights to outliers,
+     which reduces their impact on the persistence diagram
 
-- Rips complex
-     - A new module [Weighted Rips Complex](https://gudhi.inria.fr/python/latest/rips_complex_user.html#weighted-rips-complex)
-       to construct a simplicial complex from a distance matrix and weights on vertices.
+- [Alpha complex](https://gudhi.inria.fr/python/latest/alpha_complex_user.html) - Python interface improvements
+     - 'fast' and 'exact' computations
+     - Delaunay complex construction by not setting filtration values
+     - Use the specific 3d alpha complex automatically to make the computations faster
 
-- [Wassertein distance](https://gudhi.inria.fr/python/latest/wasserstein_distance_user.html)
-     - An [another implementation](https://gudhi.inria.fr/python/latest/wasserstein_distance_user.html#hera)
-       comes from Hera (BSD-3-Clause) which is based on [Geometry Helps to Compare Persistence Diagrams](http://doi.acm.org/10.1145/3064175)
-       by Michael Kerber, Dmitriy Morozov, and Arnur Nigmetov.
-     - `gudhi.wasserstein.wasserstein_distance` has now an option to return the optimal matching that achieves the distance between the two diagrams.
-     - A new module [Barycenters](https://gudhi.inria.fr/python/latest/wasserstein_distance_user.html#barycenters)
-       to estimate the Frechet mean (aka Wasserstein barycenter) between persistence diagrams.
+- [Clustering](https://gudhi.inria.fr/python/latest/clustering.html)
+     - Python implementation of [ToMATo](https://doi.org/10.1145/2535927), a persistence-based clustering algorithm
 
-- [Simplex tree](https://gudhi.inria.fr/python/latest/simplex_tree_ref.html)
-     - Extend filtration method to compute extended persistence
-     - Flag and lower star persistence pairs generators
-     - A new interface to filtration, simplices and skeleton getters to return an iterator
+- [Edge Collapse](https://gudhi.inria.fr/doc/latest/group__edge__collapse.html) of a filtered flag complex
+     - This C++ implementation reduces a filtration of Vietoris-Rips complex from its graph to another smaller
+     flag filtration with the same persistence.
 
-- [Alpha complex](https://gudhi.inria.fr/doc/latest/group__alpha__complex.html)
-     - Improve computations (cache circumcenters computation and point comparison improvement)
+- [Bottleneck distance](https://gudhi.inria.fr/python/latest/bottleneck_distance_user.html)
+     - Python interface to [hera](https://github.com/grey-narn/hera)'s bottleneck distance
 
-- [Persistence graphical tools](https://gudhi.inria.fr/python/latest/persistence_graphical_tools_user.html)
-     - New rendering option proposed (use LaTeX style, add grey block, improved positioning of labels, etc.). 
-     - Can now handle (N x 2) numpy arrays as input 
+- Persistence representations
+     - [Atol](https://gudhi.inria.fr/python/latest/representations.html#gudhi.representations.vector_methods.Atol)
+     is integrated in finite vectorisation methods. This
+     [article](https://www.fujitsu.com/global/about/resources/news/press-releases/2020/0316-01.html) talks about
+     applications using Atol. This module was originally available at
+     [https://github.com/martinroyer/atol](https://github.com/martinroyer/atol)
+     - Python interface change: [Wasserstein metrics](https://gudhi.inria.fr/python/latest/representations.html#gudhi.representations.metrics.WassersteinDistance)
+     is now [hera](https://github.com/grey-narn/hera) by default
 
 - Miscellaneous
-     - The [list of bugs that were solved since GUDHI-3.2.0](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.2.0+is%3Aclosed) is available on GitHub.
+     - The [list of bugs that were solved since GUDHI-3.2.0](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.3.0+is%3Aclosed)
+     is available on GitHub.
 
 All modules are distributed under the terms of the MIT license.
 However, there are still GPL dependencies for many modules. We invite you to check our [license dedicated web page](https://gudhi.inria.fr/licensing/) for further details.
@@ -56,3 +53,4 @@ We provide [bibtex entries](https://gudhi.inria.fr/doc/latest/_citation.html) fo
 Feel free to [contact us](https://gudhi.inria.fr/contact/) in case you have any questions or remarks.
 
 For further information about downloading and installing the library ([C++](https://gudhi.inria.fr/doc/latest/installation.html) or [Python](https://gudhi.inria.fr/python/latest/installation.html)), please visit the [GUDHI web site](https://gudhi.inria.fr/).
+