GUDHI 3.10.1 release

We are pleased to announce the release 3.10.1 of the GUDHI library.

Only bug fixes have been implemented for this minor version.

The list of bugs that were solved 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 for further details.

We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.

We provide bibtex entries for the modules of the User and Reference Manual, as well as for publications directly related to the GUDHI library.

Feel free to contact us in case you have any questions or remarks.

For further information about downloading and installing the library (C++ or Python), please visit the GUDHI web site.

Contributors

• @hschreiber
• @mglisse
• @VincentRouvreau

GUDHI 3.10.0 release

We are pleased to announce the release 3.10.0 of the GUDHI library.

We are now using GitHub to develop the GUDHI library, do not hesitate to fork the GUDHI project on GitHub. From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz).

Below is a list of changes made since GUDHI 3.9.0:

• Persistence matrix

  Matrix API is in a beta version and may change in incompatible ways in the near future.

  • Matrix structure for filtered complexes with multiple functionnalities related to persistence homology, such as
    representative cycles computation or vineyards.

• Rips complex

  • Rips complex persistence scikit-learn like interface

• Čech complex

  • A new utility to compute the Delaunay-Čech filtration on a Delaunay triangulation.

• Installation

  • CGAL ≥ 5.1.0 is now required (was ≥ 4.11.0).
  • Eigen3 ≥ 3.3.0 is now required (was ≥ 3.1.0).

• Maintenance

  • Some bug fix for CGAL ≥ 6.0, NumPy ≥ 2.0, Scikit-learn ≥ 1.4, Matplotlib ≥ 3.6 and TensorFlow ≥ 2.16.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.9.0 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 for further details.

We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.

We provide bibtex entries for the modules of the User and Reference Manual, as well as for publications directly related to the GUDHI library.

Feel free to contact us in case you have any questions or remarks.

For further information about downloading and installing the library (C++ or Python), please visit the GUDHI web site.

Contributors

• @hschreiber
• @MathieuCarriere
• @martinroyer
• @mglisse
• @VincentRouvreau

GUDHI 3.10.0rc2 release

We are pleased to announce the release 3.10.0 of the GUDHI library.

We are now using GitHub to develop the GUDHI library, do not hesitate to fork the GUDHI project on GitHub. From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz).

Below is a list of changes made since GUDHI 3.9.0:

• Persistence matrix

  Matrix API is in a beta version and may change in incompatible ways in the near future.

  • Matrix structure for filtered complexes with multiple functionnalities related to persistence homology, such as
    representative cycles computation or vineyards.

• Rips complex

  • Rips complex persistence scikit-learn like interface

• Čech complex

  • A new utility to compute the Delaunay-Čech filtration on a Delaunay triangulation.

• Installation

  • CGAL ≥ 5.1.0 is now required (was ≥ 4.11.0).
  • Eigen3 ≥ 3.3.0 is now required (was ≥ 3.1.0).

• Maintenance

  • Some bug fix for CGAL ≥ 6.0, NumPy ≥ 2.0, Scikit-learn ≥ 1.4, Matplotlib ≥ 3.6 and TensorFlow ≥ 2.16.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.9.0 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 for further details.

We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.

We provide bibtex entries for the modules of the User and Reference Manual, as well as for publications directly related to the GUDHI library.

Feel free to contact us in case you have any questions or remarks.

For further information about downloading and installing the library (C++ or Python), please visit the GUDHI web site.

Contributors

• @hschreiber
• @MathieuCarriere
• @martinroyer
• @mglisse
• @VincentRouvreau

GUDHI 3.10.0rc1 release

We are pleased to announce the release 3.10.0 of the GUDHI library.

We are now using GitHub to develop the GUDHI library, do not hesitate to fork the GUDHI project on GitHub. From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz).

Below is a list of changes made since GUDHI 3.9.0:

• Persistence matrix

  Matrix API is in a beta version and may change in incompatible ways in the near future.

  • Matrix structure for filtered complexes with multiple functionnalities related to persistence homology, such as
    representative cycles computation or vineyards.

• Rips complex

  • Rips complex persistence scikit-learn like interface

• Čech complex

  • A new utility to compute the Delaunay-Čech filtration on a Delaunay triangulation.

• Installation

  • CGAL ≥ 5.1.0 is now required (was ≥ 4.11.0).
  • Eigen3 ≥ 3.3.0 is now required (was ≥ 3.1.0).

• Maintenance

  • Some bug fix for CGAL ≥ 6.0, NumPy ≥ 2.0, Scikit-learn ≥ 1.4, Matplotlib ≥ 3.6 and TensorFlow ≥ 2.16.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.9.0 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 for further details.

We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.

We provide bibtex entries for the modules of the User and Reference Manual, as well as for publications directly related to the GUDHI library.

Feel free to contact us in case you have any questions or remarks.

For further information about downloading and installing the library (C++ or Python), please visit the GUDHI web site.

Contributors

• @hschreiber
• @MathieuCarriere
• @martinroyer
• @mglisse
• @VincentRouvreau

GUDHI 3.9.0 release

We are pleased to announce the release 3.9.0 of the GUDHI library.

We are now using GitHub to develop the GUDHI library, do not hesitate to fork the GUDHI project on GitHub. From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz).

Below is a list of changes made since GUDHI 3.8.0:

• CubicalPersistence

  • Much faster implementation for the 2d case with input from top-dimensional cells.

• Simplex_tree

  • A helper for_each_simplex that applies a given function object on each simplex
  • A new method num_simplices_by_dimension is now available thanks to this helper.
  • A clear method to empty the data stucture.
  • A new argument ignore_infinite_values for initialize_filtration method to skip infinite values. As a side effect, this change enhances the persistence computation.
  • Simplex_tree_options_full_featured has been renamed Simplex_tree_options_default and Simplex_tree_options_python.
    These are respectively the default options used by the Simplex_tree and by the python interface of the SimplexTree (as before this version).
  • From GUDHI 3.9.0, Simplex_tree_options_full_featured now activates link_nodes_by_label and stable_simplex_handles (making it slower, except for browsing cofaces).

  Simplex_tree_options_*	⚠️ full_featured	default	python	minimal
  store_key	1	1	1	0
  store_filtration	1	1	1	0
  contiguous_vertices	0	0	0	0
  link_nodes_by_label	1	0	0	0
  stable_simplex_handles	1	0	0	0
  Filtration_value	double	double	double

• Simplex_tree options

  • A new option link_nodes_by_label to speed up cofaces and stars access, when set to true.
  • A new option stable_simplex_handles to keep Simplex handles valid even after insertions or removals, when set to true.

• Čech complex

  • A function assign_MEB_filtration that assigns to each simplex a filtration value equal to the squared radius of its minimal enclosing ball (MEB), given a simplicial complex and an embedding of its vertices. Applied on a Delaunay triangulation, it computes the Delaunay-Čech filtration.

• Edge collapse

  • A Python function reduce_graph to simplify a clique filtration (represented as a sparse weighted graph), while preserving its persistent homology.

• Mapper/GIC/Nerve complexes

  • A new method save_to_html to ease the Keppler Mapper visualization

• Installation

  • Boost ≥ 1.71.0 is now required (was ≥ 1.66.0).
  • cython >= 3.0.0 is now supported.
  • Python 3.12 pip package.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.8.0 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 for further details.

We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.

We provide bibtex entries for the modules of the User and Reference Manual, as well as for publications directly related to the GUDHI library.

Feel free to contact us in case you have any questions or remarks.

For further information about downloading and installing the library (C++ or Python), please visit the GUDHI web site.

Contributors

• @DavidLapous
• @hschreiber
• @MathieuCarriere
• @martinroyer
• @mglisse
• @VincentRouvreau

GUDHI 3.9.0rc1 release

We are pleased to announce the release 3.9.0 of the GUDHI library.

We are now using GitHub to develop the GUDHI library, do not hesitate to fork the GUDHI project on GitHub. From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz).

Below is a list of changes made since GUDHI 3.8.0:

• CubicalPersistence

  • Much faster implementation for the 2d case with input from top-dimensional cells.

• Simplex_tree

  • A helper for_each_simplex that applies a given function object on each simplex
  • A new method num_simplices_by_dimension is now available thanks to this helper.
  • A clear method to empty the data stucture.
  • A new argument ignore_infinite_values for initialize_filtration method to skip infinite values. As a side effect, this change enhances the persistence computation.
  • Simplex_tree_options_full_featured has been renamed Simplex_tree_options_default and Simplex_tree_options_python.
    These are respectively the default options used by the Simplex_tree and by the python interface of the SimplexTree (as before this version).
  • From GUDHI 3.9.0, Simplex_tree_options_full_featured now activates link_nodes_by_label and stable_simplex_handles (making it slower, except for browsing cofaces).

  Simplex_tree_options_*	⚠️ full_featured	default	python	minimal
  store_key	1	1	1	0
  store_filtration	1	1	1	0
  contiguous_vertices	0	0	0	0
  link_nodes_by_label	1	0	0	0
  stable_simplex_handles	1	0	0	0
  Filtration_value	double	double	double

• Simplex_tree options

  • A new option link_nodes_by_label to speed up cofaces and stars access, when set to true.
  • A new option stable_simplex_handles to keep Simplex handles valid even after insertions or removals, when set to true.

• Čech complex

  • A function assign_MEB_filtration that assigns to each simplex a filtration value equal to the squared radius of its minimal enclosing ball (MEB), given a simplicial complex and an embedding of its vertices. Applied on a Delaunay triangulation, it computes the Delaunay-Čech filtration.

• Edge collapse

  • A Python function reduce_graph to simplify a clique filtration (represented as a sparse weighted graph), while preserving its persistent homology.

• Mapper/GIC/Nerve complexes

  • A new method save_to_html to ease the Keppler Mapper visualization

• Installation

  • Boost ≥ 1.71.0 is now required (was ≥ 1.66.0).
  • cython >= 3.0.0 is now supported.
  • Python 3.12 pip package.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.8.0 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 for further details.

We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.

We provide bibtex entries for the modules of the User and Reference Manual, as well as for publications directly related to the GUDHI library.

Feel free to contact us in case you have any questions or remarks.

For further information about downloading and installing the library (C++ or Python), please visit the GUDHI web site.

Contributors

• @DavidLapous
• @hschreiber
• @MathieuCarriere
• @martinroyer
• @mglisse
• @VincentRouvreau

GUDHI 3.8.0 release

We are pleased to announce the release 3.8.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers Perslay, a Tensorflow model for the representations module, scikit-learn like interfaces for Cover Complexes, a new function to compute persistence of a function on ℝ and the possibility to build a Cubical Complex as a lower-star filtration from vertices.

We are now using GitHub to develop the GUDHI library, do not hesitate to fork the GUDHI project on GitHub. From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz).

Below is a list of changes made since GUDHI 3.7.1:

• Perslay

  • a TensorFlow layer for persistence diagrams representations.

• Cover Complex

  • New classes to compute Mapper, Graph Induced complex and Nerves with a scikit-learn like interface.

• Persistent cohomology

  • New linear-time compute_persistence_of_function_on_line, also available though CubicalPersistence in Python.

• Cubical complex

  • Add possibility to build a lower-star filtration from vertices instead of top-dimensional cubes.
  • Naming the arguments is now mandatory in CubicalComplex python constructor.
  • Remove newshape mechanism from CubicalPersistence

• Hera version of Wasserstein distance

  • now provides matching in its interface.

• Subsampling

  • New choose_n_farthest_points_metric as a faster alternative of choose_n_farthest_points.

• SimplexTree

  • SimplexTree can now be used with pickle.
  • new prune_above_dimension method.

• Installation

  • CMake 3.8 is the new minimal standard to compile the library.
  • Support for oneAPI TBB (instead of deprecated TBB) to take advantage of multicore performance.
  • pydata-sphinx-theme is the new sphinx theme of the python documentation.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.7.1 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 for further details.

We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.

We provide bibtex entries for the modules of the User and Reference Manual, as well as for publications directly related to the GUDHI library.

Feel free to contact us in case you have any questions or remarks.

For further information about downloading and installing the library (C++ or Python), please visit the GUDHI web site.

Contributors

• @Hind-M
• @MathieuCarriere
• @mglisse
• @wreise
• @VincentRouvreau

GUDHI 3.8.0rc3 release

We are pleased to announce the release 3.8.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers Perslay, a Tensorflow model for the representations module, scikit-learn like interfaces for Cover Complexes, a new function to compute persistence of a function on ℝ and the possibility to build a Cubical Complex as a lower-star filtration from vertices.

We are now using GitHub to develop the GUDHI library, do not hesitate to fork the GUDHI project on GitHub. From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz).

Below is a list of changes made since GUDHI 3.7.1:

• Perslay

  • a TensorFlow layer for persistence diagrams representations.

• Cover Complex

  • New classes to compute Mapper, Graph Induced complex and Nerves with a scikit-learn like interface.

• Persistent cohomology

  • New linear-time compute_persistence_of_function_on_line, also available though CubicalPersistence in Python.

• Cubical complex

  • Add possibility to build a lower-star filtration from vertices instead of top-dimensional cubes.
  • Naming the arguments is now mandatory in CubicalComplex python constructor.
  • Remove newshape mechanism from CubicalPersistence

• Hera version of Wasserstein distance

  • now provides matching in its interface.

• Subsampling

  • New choose_n_farthest_points_metric as a faster alternative of choose_n_farthest_points.

• SimplexTree

  • SimplexTree can now be used with pickle.
  • new prune_above_dimension method.

• Installation

  • CMake 3.8 is the new minimal standard to compile the library.
  • Support for oneAPI TBB (instead of deprecated TBB) to take advantage of multicore performance.
  • pydata-sphinx-theme is the new sphinx theme of the python documentation.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.7.1 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 for further details.

We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.

We provide bibtex entries for the modules of the User and Reference Manual, as well as for publications directly related to the GUDHI library.

Feel free to contact us in case you have any questions or remarks.

For further information about downloading and installing the library (C++ or Python), please visit the GUDHI web site.

Contributors

• @Hind-M
• @MathieuCarriere
• @mglisse
• @wreise
• @VincentRouvreau

GUDHI 3.8.0rc2 release

We are pleased to announce the release 3.8.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers Perslay, a Tensorflow model for the representations module, scikit-learn like interfaces for Cover Complexes, a new function to compute persistence of a function on ℝ and the possibility to build a Cubical Complex as a lower-star filtration from vertices.

We are now using GitHub to develop the GUDHI library, do not hesitate to fork the GUDHI project on GitHub. From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz).

Below is a list of changes made since GUDHI 3.7.1:

• Perslay

  • a TensorFlow layer for persistence diagrams representations.

• Cover Complex

  • New classes to compute Mapper, Graph Induced complex and Nerves with a scikit-learn like interface.

• Persistent cohomology

  • New linear-time compute_persistence_of_function_on_line, also available though CubicalPersistence in Python.

• Cubical complex

  • Add possibility to build a lower-star filtration from vertices instead of top-dimensional cubes.
  • Naming the arguments is now mandatory in CubicalComplex python constructor.
  • Remove newshape mechanism from CubicalPersistence

• Hera version of Wasserstein distance

  • now provides matching in its interface.

• Subsampling

  • New choose_n_farthest_points_metric as a faster alternative of choose_n_farthest_points.

• SimplexTree

  • SimplexTree can now be used with pickle.
  • new prune_above_dimension method.

• Installation

  • CMake 3.8 is the new minimal standard to compile the library.
  • Support for oneAPI TBB (instead of deprecated TBB) to take advantage of multicore performance.
  • pydata-sphinx-theme is the new sphinx theme of the python documentation.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.7.1 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 for further details.

We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.

We provide bibtex entries for the modules of the User and Reference Manual, as well as for publications directly related to the GUDHI library.

Feel free to contact us in case you have any questions or remarks.

For further information about downloading and installing the library (C++ or Python), please visit the GUDHI web site.

Contributors

• @Hind-M
• @MathieuCarriere
• @mglisse
• @wreise
• @VincentRouvreau

GUDHI 3.8.0rc1 release

We are pleased to announce the release 3.8.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers Perslay, a Tensorflow model for the representations module, scikit-learn like interfaces for Cover Complexes, a new function to compute persistence of a function on ℝ and the possibility to build a Cubical Complex as a lower-star filtration from vertices.

We are now using GitHub to develop the GUDHI library, do not hesitate to fork the GUDHI project on GitHub. From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz).

Below is a list of changes made since GUDHI 3.7.1:

• Perslay

  • a TensorFlow layer for persistence diagrams representations.

• Cover Complex

  • New classes to compute Mapper, Graph Induced complex and Nerves with a scikit-learn like interface.

• Persistent cohomology

  • New linear-time compute_persistence_of_function_on_line, also available though CubicalPersistence in Python.

• Cubical complex

  • Add possibility to build a lower-star filtration from vertices instead of top-dimensional cubes.
  • Naming the arguments is now mandatory in CubicalComplex python constructor.
  • Remove newshape mechanism from CubicalPersistence

• Hera version of Wasserstein distance

  • now provides matching in its interface.

• Subsampling

  • New choose_n_farthest_points_metric as a faster alternative of choose_n_farthest_points.

• SimplexTree

  • SimplexTree can now be used with pickle.
  • new prune_above_dimension method.

• Installation

  • CMake 3.8 is the new minimal standard to compile the library.
  • Support for oneAPI TBB (instead of deprecated TBB) to take advantage of multicore performance.
  • pydata-sphinx-theme is the new sphinx theme of the python documentation.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.7.1 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 for further details.

We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.

We provide bibtex entries for the modules of the User and Reference Manual, as well as for publications directly related to the GUDHI library.

Feel free to contact us in case you have any questions or remarks.

For further information about downloading and installing the library (C++ or Python), please visit the GUDHI web site.

Contributors

• @Hind-M
• @MathieuCarriere
• @mglisse
• @wreise
• @VincentRouvreau