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authorVincent Rouvreau <vincent.rouvreau@inria.fr>2022-08-16 12:10:32 +0200
committerVincent Rouvreau <vincent.rouvreau@inria.fr>2022-08-16 12:10:32 +0200
commit879055d6e978ca2f49fa669c375ce3b1e4d84901 (patch)
treedc2958338f0b8fdffb9a499278527700c265f9a4 /.github
parentdfcab6c8d14aab8e75040a6805f57b611c805073 (diff)
For release 3.6.0
Diffstat (limited to '.github')
-rw-r--r--.github/next_release.md28
1 files changed, 14 insertions, 14 deletions
diff --git a/.github/next_release.md b/.github/next_release.md
index 26806de2..72c55999 100644
--- a/.github/next_release.md
+++ b/.github/next_release.md
@@ -1,4 +1,4 @@
-We are pleased to announce the release 3.6.0.rc1 of the GUDHI library.
+We are pleased to announce the release 3.6.0 of the GUDHI library.
As a major new feature, the GUDHI library now offers automatic differentiation for the computation of
persistence diagrams, Cubical complex persistence scikit-learn like interface, datasets fetch methods,
@@ -7,37 +7,37 @@ and weighted version for alpha complex in any dimension D.
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.X.X.tar.gz).
For further information, please visit the [GUDHI web site](https://gudhi.inria.fr/).
-# GUDHI 3.6.0rc2 Release Notes
+# GUDHI 3.6.0 Release Notes
Below is a list of changes made since GUDHI 3.5.0:
- TensorFlow 2 models that can handle automatic differentiation for the computation of persistence diagrams:
- - [Cubical complex](https://gudhi.inria.fr/python/3.6.0rc2/cubical_complex_tflow_itf_ref.html)
- - [lower-star persistence on simplex trees](https://gudhi.inria.fr/python/3.6.0rc2/ls_simplex_tree_tflow_itf_ref.html)
- - [Rips complex](https://gudhi.inria.fr/python/3.6.0rc2/rips_complex_tflow_itf_ref.html)
+ - [Cubical complex](https://gudhi.inria.fr/python/latest/cubical_complex_tflow_itf_ref.html)
+ - [lower-star persistence on simplex trees](https://gudhi.inria.fr/python/latest/ls_simplex_tree_tflow_itf_ref.html)
+ - [Rips complex](https://gudhi.inria.fr/python/latest/rips_complex_tflow_itf_ref.html)
-- [Cubical complex](https://gudhi.inria.fr/python/3.6.0rc2/cubical_complex_sklearn_itf_ref.html)
+- [Cubical complex](https://gudhi.inria.fr/python/latest/cubical_complex_sklearn_itf_ref.html)
- Cubical complex persistence scikit-learn like interface
-- [Datasets](https://gudhi.inria.fr/python/3.6.0rc2/datasets.html)
+- [Datasets](https://gudhi.inria.fr/python/latest/datasets.html)
- `datasets.remote.fetch_bunny` and `datasets.remote.fetch_spiral_2d` allows to fetch datasets from [GUDHI-data](https://github.com/GUDHI/gudhi-data)
-- [Alpha complex](https://gudhi.inria.fr/python/3.6.0rc2/alpha_complex_user.html)
+- [Alpha complex](https://gudhi.inria.fr/python/latest/alpha_complex_user.html)
- python weighted version for alpha complex is now available in any dimension D.
- - `alpha_complex = gudhi.AlphaComplex(off_file='/data/points/tore3D_300.off')` is deprecated, please use [read_points_from_off_file](https://gudhi.inria.fr/python/3.6.0rc2/point_cloud.html#gudhi.read_points_from_off_file) instead.
+ - `alpha_complex = gudhi.AlphaComplex(off_file='/data/points/tore3D_300.off')` is deprecated, please use [read_points_from_off_file](https://gudhi.inria.fr/python/latest/point_cloud.html#gudhi.read_points_from_off_file) instead.
-- [Edge collapse](https://gudhi.inria.fr/doc/3.6.0rc2/group__edge__collapse.html)
+- [Edge collapse](https://gudhi.inria.fr/doc/latest/group__edge__collapse.html)
- rewriting of the module to improve performance
-- [Čech complex](https://gudhi.inria.fr/doc/3.6.0rc2/group__cech__complex.html)
+- [Čech complex](https://gudhi.inria.fr/doc/latest/group__cech__complex.html)
- rewriting of the module to improve performance
-- [Representations](https://gudhi.inria.fr/python/3.6.0rc2/representations.html#gudhi.representations.vector_methods.BettiCurve)
+- [Representations](https://gudhi.inria.fr/python/latest/representations.html#gudhi.representations.vector_methods.BettiCurve)
- A more flexible Betti curve class capable of computing exact curves
-- [C++ documentation](https://gudhi.inria.fr/doc/3.6.0rc2/)
+- [C++ documentation](https://gudhi.inria.fr/doc/latest/)
- upgrade and improve performance with new doxygen features
-- [Simplex tree](https://gudhi.inria.fr/python/3.6.0rc2/simplex_tree_ref.html)
+- [Simplex tree](https://gudhi.inria.fr/python/latest/simplex_tree_ref.html)
- `__deepcopy__`, `copy` and copy constructors for python module
- `expansion_with_blockers` python interface