gudhi-devel

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.

We are pleased to announce the release 3.4.1.post2 of the GUDHI library.

This minor post-release is a bug fix version to install CGAL for GUDHI windows pip package.

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 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.

We are pleased to announce the release 3.4.1 of the GUDHI library.

This minor release is a bug fix version to make GUDHI compile with CGAL 5.2.

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-3.4.0 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.

We are pleased to announce the release 3.4.1 of the GUDHI library.

This minor release is a bug fix version to make GUDHI compile with CGAL 5.2.

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-3.4.0 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.

We are pleased to announce the release 3.4.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers dD weighted alpha complex, pip and conda packages for Python 3.9.

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.3.4.0.tar.gz).

Below is a list of changes made since GUDHI 3.3.0:

• Alpha complex

  • the C++ weighted version for alpha complex is now available in any dimension D.

• Simplex tree C++ Python

  • A new method to reset the filtrations
  • A new method to get the boundaries of a simplex

• Subsampling

  • The C++ function choose_n_farthest_points() now takes a distance function instead of a kernel as first argument, users can replace k with k.squared_distance_d_object() in each call in their code.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.3.0 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.

We are pleased to announce the release 3.4.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers dD weighted alpha complex, pip and conda packages for Python 3.9.

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.3.4.0rc1.tar.gz).

Below is a list of changes made since GUDHI 3.3.0:

• Alpha complex

  • the C++ weighted version for alpha complex is now available in dimension D.

• Simplex tree C++ Python

  • A new method to reset the filtrations
  • A new method to get the boundaries of a simplex

• Subsampling

  • The C++ function choose_n_farthest_points() now takes a distance function instead of a kernel as first argument, users can replace k with k.squared_distance_d_object() in each call in their code.

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.3.0 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.

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 persistence-based clustering algorithm, weighted Rips complex using DTM
and edge collapse.

The GUDHI library is hosted on GitHub, do not hesitate to fork the GUDHI project on GitHub.
From a user point of view, we recommend to download GUDHI user version (gudhi.3.3.0.tar.gz).

Below is a list of changes made since GUDHI 3.2.0:

• DTM density estimator

  • Python implementation of a density estimator based on the distance to the empirical measure defined by a point set.

• DTM Rips complex

  • This Python implementation constructs a weighted Rips complex giving larger weights to outliers, which reduces their impact on the persistence diagram

• Alpha complex - 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

• Clustering

  • Python implementation of ToMATo, a persistence-based clustering algorithm

• Edge Collapse 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.

• Bottleneck distance

  • Python interface to hera's bottleneck distance

• Persistence representations

  • Atol is integrated in finite vectorisation methods. This article talks about applications using Atol. This module was originally available at https://github.com/martinroyer/atol
  • Python interface change: Wasserstein metrics is now hera by default

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.2.0 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.

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 persistence-based clustering algorithm, weighted Rips complex using DTM
and edge collapse.

The GUDHI library is hosted on GitHub, do not hesitate to fork the GUDHI project on GitHub.
From a user point of view, we recommend to download GUDHI user version (gudhi.3.3.0.tar.gz).

Below is a list of changes made since GUDHI 3.2.0:

• DTM density estimator

  • Python implementation of a density estimator based on the distance to the empirical measure defined by a point set.

• DTM Rips complex

  • This Python implementation constructs a weighted Rips complex giving larger weights to outliers, which reduces their impact on the persistence diagram

• Alpha complex - 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

• Clustering

  • Python implementation of ToMATo, a persistence-based clustering algorithm

• Edge Collapse 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.

• Bottleneck distance

  • Python interface to hera's bottleneck distance

• Persistence representations

  • Atol is integrated in finite vectorisation methods. This article talks about applications using Atol. This module was originally available at https://github.com/martinroyer/atol
  • Python interface change: Wasserstein metrics is now hera by default

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.2.0
    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.

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 persistence-based clustering algorithm, weighted Rips complex using DTM
and edge collapse.

The GUDHI library is hosted on GitHub, do not hesitate to fork the GUDHI project on GitHub.
From a user point of view, we recommend to download GUDHI user version (gudhi.3.3.0.tar.gz).

Below is a list of changes made since GUDHI 3.2.0:

• DTM density estimator

  • Python implementation of a density estimator based on the distance to the empirical measure defined by a point set.

• DTM Rips complex

  • This Python implementation constructs a weighted Rips complex giving larger weights to outliers, which reduces their impact on the persistence diagram

• Alpha complex - 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

• Clustering

  • Python implementation of ToMATo, a persistence-based clustering algorithm

• Edge Collapse 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.

• Bottleneck distance

  • Python interface to hera's bottleneck distance

• Persistence representations

  • Atol is integrated in finite vectorisation methods. This article talks about applications using Atol. This module was originally available at https://github.com/martinroyer/atol
  • Python interface change: Wasserstein metrics is now hera by default

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.2.0
    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.

We are pleased to announce the release 3.2.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers a Python interface to Hera to compute the Wasserstein distance.
PyBind11 is now required to build the Python 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.3.2.0.tar.gz).

Below is a list of changes made since GUDHI 3.1.1:

• Point cloud utilities

  • A new module Time Delay Embedding to embed time-series data in the R^d according to Takens' Embedding Theorem and obtain the coordinates of each point.
  • A new module K Nearest Neighbors that wraps several implementations for computing the k nearest neighbors in a point set.
  • A new module Distance To Measure to compute the distance to the empirical measure defined by a point set

• Persistence representations

  • Interface to Wasserstein distances.

• Rips complex

  • A new module Weighted Rips Complex to construct a simplicial complex from a distance matrix and weights on vertices.

• Wassertein distance

  • An another implementation comes from Hera (BSD-3-Clause) which is based on Geometry Helps to Compare Persistence Diagrams 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 to estimate the Frechet mean (aka Wasserstein barycenter) between persistence diagrams.

• Simplex tree

  • 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

• Alpha complex

  • Improve computations (cache circumcenters computation and point comparison improvement)

• Persistence graphical tools

  • 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

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.2.0 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.

We are pleased to announce the release 3.2.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers a Python interface to Hera to compute the Wasserstein distance.
PyBind11 is now required to build the Python 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.3.2.0.tar.gz).

Below is a list of changes made since GUDHI 3.1.1:

• Point cloud utilities

  • A new module Time Delay Embedding to embed time-series data in the R^d according to Takens' Embedding Theorem and obtain the coordinates of each point.
  • A new module K Nearest Neighbors that wraps several implementations for computing the k nearest neighbors in a point set.
  • A new module Distance To Measure to compute the distance to the empirical measure defined by a point set

• Persistence representations

  • Interface to Wasserstein distances.

• Rips complex

  • A new module Weighted Rips Complex to construct a simplicial complex from a distance matrix and weights on vertices.

• Wassertein distance

  • An another implementation comes from Hera (BSD-3-Clause) which is based on Geometry Helps to Compare Persistence Diagrams 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 to estimate the Frechet mean (aka Wasserstein barycenter) between persistence diagrams.

• Simplex tree

  • 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

• Alpha complex

  • Improve computations (cache circumcenters computation and point comparison improvement)

• Persistence graphical tools

  • 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

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.2.0 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.

We are pleased to announce the release 3.2.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers a Python interface to Hera to compute the Wasserstein distance.
PyBind11 is now required to build the Python 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.3.2.0.tar.gz).

Below is a list of changes made since GUDHI 3.1.1:

• Point cloud utilities

  • A new module Time Delay Embedding to embed time-series data in the R^d according to Takens' Embedding Theorem and obtain the coordinates of each point.
  • A new module K Nearest Neighbors that wraps several implementations for computing the k nearest neighbors in a point set.
  • A new module Distance To Measure to compute the distance to the empirical measure defined by a point set

• Persistence representations

  • Interface to Wasserstein distances.

• Rips complex

  • A new module Weighted Rips Complex to construct a simplicial complex from a distance matrix and weights on vertices.

• Wassertein distance

  • An another implementation comes from Hera (BSD-3-Clause) which is based on Geometry Helps to Compare Persistence Diagrams 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 to estimate the Frechet mean (aka Wasserstein barycenter) between persistence diagrams.

• Simplex tree

  • 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

• Alpha complex

  • Improve computations (cache circumcenters computation and point comparison improvement)

• Persistence graphical tools

  • Use LaTeX style and grey block
  • (N x 2) numpy arrays as input

• Miscellaneous

  • The list of bugs that were solved since GUDHI-3.2.0 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-3.1.1 is a bug-fix release. In particular, it fixes the installation of the Python representation module.

The list of bugs that were solved since gudhi-3.1.0 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-3.1.1 is a bug-fix release. In particular, it fixes the installation of the Python representation module.

The list of bugs that were solved since gudhi-3.1.0 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.

We are pleased to announce the release 3.1.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers 2 new Python modules: Persistence representations and Wasserstein distance.

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.3.1.0.tar.gz).

Below is a list of changes made since Gudhi 3.0.0:

• Persistence representations (new Python module)

  • Vectorizations, distances and kernels that work on persistence diagrams, compatible with scikit-learn. This module was originally available at https://github.com/MathieuCarriere/sklearn-tda and named sklearn_tda.

• Wasserstein distance (new Python module)

  • The q-Wasserstein distance measures the similarity between two persistence diagrams.

• Alpha complex (new C++ interface)

  • Thanks to CGAL 5.0 Epeck_d kernel, an exact computation version of Alpha complex dD is available and the default one (even in Python).

• Persistence graphical tools (new Python interface)

  • Axes as a parameter allows the user to subplot graphics.
  • Use matplotlib default palette (can be user defined).

• Miscellaneous

  • Python read_off function has been renamed read_points_from_off_file as it only reads points from OFF files.
  • See the list of bug fixes.

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.

We are pleased to announce the release 3.1.0 of the GUDHI library.

As a major new feature, the GUDHI library now offers 2 new Python modules: Persistence representations and Wasserstein distance.

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.3.1.0.rc1.tar.gz).

Below is a list of changes made since Gudhi 3.0.0:

• Persistence representations (new Python module)

  • Vectorizations, distances and kernels that work on persistence diagrams, compatible with scikit-learn. This module was originally available at https://github.com/MathieuCarriere/sklearn-tda and named sklearn_tda.

• Wasserstein distance (new Python module)

  • The q-Wasserstein distance measures the similarity between two persistence diagrams.

• Alpha complex (new C++ interface)

  • Thanks to CGAL 5.0 Epeck_d kernel, an exact computation version of Alpha complex dD is available and the default one (even in Python).

• Persistence graphical tools (new Python interface)

  • Axes as a parameter allows the user to subplot graphics.
  • Use matplotlib default palette (can be user defined).

• Miscellaneous

  • Python read_off function has been renamed read_points_from_off_file as it only read points from OFF files.
  • See the list of bug fixes.

All modules are distributed under the terms of the MIT license.
There are still GPL dependencies for many modules, and so for an end-user it doesn't necessarily change much. We invite you to check our license dedicated web page for further details about this change.

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.

We are pleased to announce the release 3.0.0 of the GUDHI library.
As a major new feature, the GUDHI library is now released under a MIT license
in order to ease the external contributions.
There are still GPL dependencies for many modules, and so for an end-user it
doesn't necessarily change much. We invite you to check our
license dedicated web page
for further details about this change.

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.

Below is a list of changes made since Gudhi 2.3.0:

• Persistence graphical tools (new functionnality)

  • Add a persistence density graphical tool

• Rips complex (new Python interface)

  • Sparse Rips complex is now available in Python.

• Alpha complex (new C++ interface)

  • Dedicated Alpha complex for 3d cases. Alpha complex 3d can be standard, weighted, periodic or weighted and periodic.

• Third parties (new dependencies)

  • C++14 is the new standard (C++11 on former versions of GUDHI)
  • boost >= 1.56 is now required (instead of 1.48 on former versions of GUDHI)
  • CGAL >= 4.11 is now required (instead of various requirements on former versions of GUDHI)
  • Eigen >= 3.1.0 is now required (version was not checked)

All modules are distributed under the terms of the MIT license.

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.

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

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

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

We are pleased to announce the release 3.0.0 of the GUDHI library.
As a major new feature, the GUDHI library is now released under a MIT license
in order to ease the external contributions.
There are still GPL dependencies for many modules, and so for an end-user it
doesn't necessarily change much. We invite you to check our
license dedicated web page
for further details about this change.

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.

Below is a list of changes made since Gudhi 2.3.0:

• Persistence graphical tools (new functionnality)

  • Add a persistence density graphical tool

• Rips complex (new Python interface)

  • Sparse Rips complex is now available in Python.

• Alpha complex (new C++ interface)

  • Dedicated Alpha complex for 3d cases. Alpha complex 3d can be standard, weighted, periodic or weighted and periodic.

• Third parties (new dependencies)

  • boost >= 1.56 is now required (instead of 1.48 on former versions of GUDHI)
  • CGAL >= 4.11 is now required (instead of various requirements on former versions of GUDHI)
  • Eigen >= 3.1.0 is now required (version was not checked)

All modules are distributed under the terms of the MIT license.

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.

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

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

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

We are pleased to announce the release 3.0.0 of the GUDHI library.
As a major new feature, the GUDHI library is now released under a MIT license.
We invite you to check our license dedicated web page
for further details about this change.

We are now using GitHub to develop the GUDHI library, do not hesitate to
fork the GUDHI project on GitHub.

Below is a list of changes made since Gudhi 2.3.0:

• Persistence graphical tools (new functionnality)

  • Add a persistence density graphical tool

• Rips complex (new Python interface)

  • Sparse Rips complex is now available in Python.

• Alpha complex (new C++ interface)

  • Dedicated Alpha complex for 3d cases. Alpha complex 3d can be standard, weighted, periodic or weighted and periodic.

• Third parties (new dependencies)

  • boost >= 1.56 is now required (instead of 1.48 on former versions of GUDHI)
  • CGAL >= 4.11 is now required (instead of various requirements on former versions of GUDHI)
  • Eigen >= 3.1.0 is now required (version was not checked)

All modules are distributed under the terms of the MIT license.

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.

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

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

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