GUDHI 3.7.1 release

We are pleased to announce the release 3.7.1 of the GUDHI library.

This minor post-release is a bug fix version for python representation module.

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).

The list of bugs that were solved since GUDHI-3.7.0 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.

GUDHI 3.7.1rc1 release

We are pleased to announce the release 3.7.1 of the GUDHI library.

This minor post-release is a bug fix version for python representation module.

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).

The list of bugs that were solved since GUDHI-3.7.0 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.

GUDHI 3.7.0 release

We are pleased to announce the release 3.7.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers new functions to initialize a Simplex tree. Universal wheel for OSx pip package and python 3.11 are now available.

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 3.6.0:

• Simplex tree

  • New functions to initialize from a matrix or insert batches of simplices of the same dimension.

• Rips complex

  • Construction now rejects positional arguments, you need to specify points=X.

• Installation

  • C++17 is the new minimal standard to compile the library. This implies Visual Studio minimal version is now 2017.
  • OSx ARM pip package is now available thanks to a universal wheel
  • Python 3.11 pip package

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.6.0 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

• @albert-github
• @hschreiber
• @mglisse
• @VincentRouvreau
• @wreise

GUDHI 3.7.0rc1 release

We are pleased to announce the release 3.7.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers new functions to initialize a Simplex tree. Universal wheel for OSx pip package and python 3.11 are now available.

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 3.6.0:

• Simplex tree

  • New functions to initialize from a matrix or insert batches of simplices of the same dimension.

• Rips complex

  • Construction now rejects positional arguments, you need to specify points=X.

• Installation

  • C++17 is the new minimal standard to compile the library. This implies Visual Studio minimal version is now 2017.
  • OSx ARM pip package is now available thanks to a universal wheel
  • Python 3.11 pip package

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.6.0 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

• @albert-github
• @hschreiber
• @mglisse
• @VincentRouvreau
• @wreise

GUDHI 3.6.0 release

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,
and weighted version for alpha complex in any dimension D.

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).
For further information, please visit the GUDHI web site.

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
  • lower-star persistence on simplex trees
  • Rips complex

• Cubical complex

  • Cubical complex persistence scikit-learn like interface

• Datasets

  • datasets.remote.fetch_bunny and datasets.remote.fetch_spiral_2d allows to fetch datasets from GUDHI-data

• Alpha complex

  • 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 instead.

• Edge collapse

  • rewriting of the module to improve performance

• Čech complex

  • rewriting of the module to improve performance

• Representations

  • A more flexible Betti curve class capable of computing exact curves

• C++ documentation

  • upgrade and improve performance with new doxygen features

• Simplex tree

  • __deepcopy__, copy and copy constructors for python module
  • expansion_with_blockers python interface

• Installation

  • Boost ≥ 1.66.0 is now required (was ≥ 1.56.0).
  • Python >= 3.5 and cython >= 0.27 are now required.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.5.0 is available on GitHub.

Contributors

• @albert-github
• @gspr
• @Hind-M
• @MathieuCarriere
• @mglisse
• @Soriano-Trigueros
• @VincentRouvreau

GUDHI 3.6.0rc2 release

We are pleased to announce the release 3.6.0rc2 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,
and weighted version for alpha complex in any dimension D.

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).
For further information, please visit the GUDHI web site.

GUDHI 3.6.0rc2 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
  • lower-star persistence on simplex trees
  • Rips complex

• Cubical complex

  • Cubical complex persistence scikit-learn like interface

• Datasets

  • datasets.remote.fetch_bunny and datasets.remote.fetch_spiral_2d allows to fetch datasets from GUDHI-data

• Alpha complex

  • 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 instead.

• Edge collapse

  • rewriting of the module to improve performance

• Čech complex

  • rewriting of the module to improve performance

• Representations

  • A more flexible Betti curve class capable of computing exact curves

• C++ documentation

  • upgrade and improve performance with new doxygen features

• Simplex tree

  • __deepcopy__, copy and copy constructors for python module
  • expansion_with_blockers python interface

• Installation

  • Boost ≥ 1.66.0 is now required (was ≥ 1.56.0).
  • Python >= 3.5 and cython >= 0.27 are now required.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.5.0 is available on GitHub.

Contributors

• @albert-github
• @gspr
• @Hind-M
• @MathieuCarriere
• @mglisse
• @Soriano-Trigueros
• @VincentRouvreau

GUDHI 3.6.0rc1 release

We are pleased to announce the release 3.6.0.rc1 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,
and weighted version for alpha complex in any dimension D.

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).
For further information, please visit the GUDHI web site.

GUDHI 3.6.0rc1 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
  • lower-star persistence on simplex trees
  • Rips complex

• Cubical complex

  • Cubical complex persistence scikit-learn like interface

• Datasets

  • datasets.remote.fetch_bunny and datasets.remote.fetch_spiral_2d allows to fetch datasets from GUDHI-data

• Alpha complex

  • 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 instead.

• Edge collapse

  • rewriting of the module to improve performance

• Čech complex

  • rewriting of the module to improve performance

• Representations

  • A more flexible Betti curve class capable of computing exact curves

• C++ documentation

  • upgrade and improve performance with new doxygen features

• Simplex tree

  • __deepcopy__, copy and copy constructors for python module
  • expansion_with_blockers python interface

• Installation

  • Boost ≥ 1.66.0 is now required (was ≥ 1.56.0).
  • Python >= 3.5 and cython >= 0.27 are now required.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.5.0 is available on GitHub.

Contributors

• @albert-github
• @gspr
• @Hind-M
• @MathieuCarriere
• @mglisse
• @Soriano-Trigueros
• @VincentRouvreau

GUDHI 3.5.0 release

We are pleased to announce the release 3.5.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers Coxeter triangulations and points generators.
The support for python 3.10 is available.

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 3.4.1:

• Coxeter triangulation

  • constructs a piecewise-linear approximation of an m-dimensional smooth manifold embedded in R^d using an ambient triangulation.

• Datasets generators

  • the python module points enables the generation of points on a sphere or a flat torus.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.4.1 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.

GUDHI 3.5.0rc2 release

We are pleased to announce the release 3.5.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers Coxeter triangulations and points generators.
The support for python 3.10 is available.

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 3.4.1:

• Coxeter triangulation

  • constructs a piecewise-linear approximation of an m-dimensional smooth manifold embedded in R^d using an ambient triangulation.

• Datasets generators

  • the python module points enables the generation of points on a sphere or a flat torus.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.4.1 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.

GUDHI 3.5.0rc1 release

We are pleased to announce the release 3.5.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers Coxeter triangulations and points generators.
The support for python 3.10 is available.

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 3.4.1:

• Coxeter triangulation

  • constructs a piecewise-linear approximation of an m-dimensional smooth manifold embedded in R^d using an ambient triangulation.

• Datasets generators

  • the python module points enables the generation of points on a sphere or a flat torus.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.4.1 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.