The GUDHI library is a generic open source C++ library, with a Python interface, for Topological Data Analysis (TDA) and Higher Dimensional Geometry Understanding.
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.
These are respectively the default options used by the 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_* | :warning: 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.
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.
These are respectively the default options used by the 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_* | :warning: 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.
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.
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.
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.
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.
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.
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.
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.
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.