The GUDHI library is a generic open source C++ library, with a Python interface, for Topological Data Analysis (TDA) and Higher Dimensional Geometry Understanding.
MIT License
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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.
Published by VincentRouvreau 4 months ago
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:
Matrix API is in a beta version and may change in incompatible ways in the near future.
Installation
Maintenance
Miscellaneous
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.
Published by VincentRouvreau 4 months ago
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:
Matrix API is in a beta version and may change in incompatible ways in the near future.
Installation
Maintenance
Miscellaneous
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.
Published by VincentRouvreau 4 months ago
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:
Matrix API is in a beta version and may change in incompatible ways in the near future.
Installation
Maintenance
Miscellaneous
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.
Published by VincentRouvreau 4 months ago
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:
Matrix API is in a beta version and may change in incompatible ways in the near future.
Installation
Maintenance
Miscellaneous
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.
Published by VincentRouvreau 10 months ago
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:
for_each_simplex
that applies a given function object on each simplexnum_simplices_by_dimension
is now available thanks to this helper.clear
method to empty the data stucture.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
.Simplex_tree
and by the python interface of the SimplexTree
(as before this version).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 |
link_nodes_by_label
to speed up cofaces and stars access, when set to true.stable_simplex_handles
to keep Simplex handles valid even after insertions or removals, when set to true.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.reduce_graph
to simplify a clique filtration (represented as a sparse weighted graph), while preserving its persistent homology.save_to_html
to ease the Keppler Mapper visualizationInstallation
Miscellaneous
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.
Published by VincentRouvreau 10 months ago
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:
for_each_simplex
that applies a given function object on each simplexnum_simplices_by_dimension
is now available thanks to this helper.clear
method to empty the data stucture.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
.Simplex_tree
and by the python interface of the SimplexTree
(as before this version).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 |
link_nodes_by_label
to speed up cofaces and stars access, when set to true.stable_simplex_handles
to keep Simplex handles valid even after insertions or removals, when set to true.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.reduce_graph
to simplify a clique filtration (represented as a sparse weighted graph), while preserving its persistent homology.save_to_html
to ease the Keppler Mapper visualizationInstallation
Miscellaneous
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.
Published by VincentRouvreau over 1 year ago
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:
compute_persistence_of_function_on_line
, also available though CubicalPersistence
in Python.newshape
mechanism from CubicalPersistence
Hera version of Wasserstein distance
choose_n_farthest_points_metric
as a faster alternative of choose_n_farthest_points
.SimplexTree
can now be used with pickle
.prune_above_dimension
method.Installation
Miscellaneous
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.
Published by VincentRouvreau over 1 year ago
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:
compute_persistence_of_function_on_line
, also available though CubicalPersistence
in Python.newshape
mechanism from CubicalPersistence
Hera version of Wasserstein distance
choose_n_farthest_points_metric
as a faster alternative of choose_n_farthest_points
.SimplexTree
can now be used with pickle
.prune_above_dimension
method.Installation
Miscellaneous
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.
Published by VincentRouvreau over 1 year ago
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:
compute_persistence_of_function_on_line
, also available though CubicalPersistence
in Python.newshape
mechanism from CubicalPersistence
Hera version of Wasserstein distance
choose_n_farthest_points_metric
as a faster alternative of choose_n_farthest_points
.SimplexTree
can now be used with pickle
.prune_above_dimension
method.Installation
Miscellaneous
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.
Published by VincentRouvreau over 1 year ago
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:
compute_persistence_of_function_on_line
, also available though CubicalPersistence
in Python.newshape
mechanism from CubicalPersistence
Hera version of Wasserstein distance
choose_n_farthest_points_metric
as a faster alternative of choose_n_farthest_points
.SimplexTree
can now be used with pickle
.prune_above_dimension
method.Installation
Miscellaneous
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.
Published by VincentRouvreau almost 2 years ago
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.
Published by VincentRouvreau almost 2 years ago
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.
Published by VincentRouvreau almost 2 years ago
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:
points=X
.Installation
Miscellaneous
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.
Published by VincentRouvreau almost 2 years ago
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:
points=X
.Installation
Miscellaneous
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.
Published by VincentRouvreau about 2 years ago
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.
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:
datasets.remote.fetch_bunny
and datasets.remote.fetch_spiral_2d
allows to fetch datasets from GUDHI-data
alpha_complex = gudhi.AlphaComplex(off_file='/data/points/tore3D_300.off')
is deprecated, please use read_points_from_off_file instead.__deepcopy__
, copy
and copy constructors for python moduleexpansion_with_blockers
python interfaceInstallation
Miscellaneous
Published by VincentRouvreau about 2 years ago
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.
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:
datasets.remote.fetch_bunny
and datasets.remote.fetch_spiral_2d
allows to fetch datasets from GUDHI-data
alpha_complex = gudhi.AlphaComplex(off_file='/data/points/tore3D_300.off')
is deprecated, please use read_points_from_off_file instead.__deepcopy__
, copy
and copy constructors for python moduleexpansion_with_blockers
python interfaceInstallation
Miscellaneous
Published by VincentRouvreau about 2 years ago
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.
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:
datasets.remote.fetch_bunny
and datasets.remote.fetch_spiral_2d
allows to fetch datasets from GUDHI-data
alpha_complex = gudhi.AlphaComplex(off_file='/data/points/tore3D_300.off')
is deprecated, please use read_points_from_off_file instead.__deepcopy__
, copy
and copy constructors for python moduleexpansion_with_blockers
python interfaceInstallation
Miscellaneous
Published by VincentRouvreau almost 3 years ago
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:
points
enables the generation of points on a sphere or a flat torus.Miscellaneous
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.
Published by VincentRouvreau almost 3 years ago
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:
points
enables the generation of points on a sphere or a flat torus.Miscellaneous
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.