diff options
| author | ROUVREAU Vincent <vincent.rouvreau@inria.fr> | 2020-02-12 09:14:27 +0100 |
|---|---|---|
| committer | ROUVREAU Vincent <vincent.rouvreau@inria.fr> | 2020-02-12 09:14:27 +0100 |
| commit | 1dc713cd7ad9574d82cbd9cd6563b76236e9f853 (patch) | |
| tree | 63637f6d0661e4fca99306e26535b5d4e75c1978 | |
| parent | 22c946ecc9594fc496d641b70a19643057295dcf (diff) | |
| parent | ee0f12f1df406c81c6ad860c494eed908021fad9 (diff) | |
Merge branch 'master' into modern_cmake_for_boost
36 files changed, 473 insertions, 265 deletions
diff --git a/.circleci/config.yml b/.circleci/config.yml index 5e45bc14..f4073746 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -45,6 +45,7 @@ jobs: python: docker: - image: gudhi/ci_for_gudhi:latest + parallelism: 4 steps: - checkout - run: @@ -58,10 +59,13 @@ jobs: mkdir build; cd build; cmake -DCMAKE_BUILD_TYPE=Release -DWITH_GUDHI_EXAMPLE=OFF -DWITH_GUDHI_UTILITIES=OFF -DWITH_GUDHI_PYTHON=ON -DPython_ADDITIONAL_VERSIONS=3 ..; - make all; - ctest --output-on-failure; + cd python; + python3 setup.py build_ext -j 4 --inplace; make sphinx; - cp -R python/sphinx /tmp/sphinx; + cp -R sphinx /tmp/sphinx; + python3 setup.py install; + python3 setup.py clean --all; + ctest -j 4 --output-on-failure; - store_artifacts: path: /tmp/sphinx diff --git a/CMakeGUDHIVersion.txt b/CMakeGUDHIVersion.txt index ed19ecfb..0f827b9e 100644 --- a/CMakeGUDHIVersion.txt +++ b/CMakeGUDHIVersion.txt @@ -1,6 +1,6 @@ set (GUDHI_MAJOR_VERSION 3) set (GUDHI_MINOR_VERSION 1) -set (GUDHI_PATCH_VERSION 0) +set (GUDHI_PATCH_VERSION 1) set(GUDHI_VERSION ${GUDHI_MAJOR_VERSION}.${GUDHI_MINOR_VERSION}.${GUDHI_PATCH_VERSION}) message(STATUS "GUDHI version : ${GUDHI_VERSION}") diff --git a/CMakeLists.txt b/CMakeLists.txt index a9f7f989..298e71ca 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -65,5 +65,7 @@ include(GUDHI_user_version_target) # For "make doxygen" - Requires GUDHI_USER_VERSION_DIR to be set - Done in GUDHI_user_version_target for dev version include(GUDHI_doxygen_target) +configure_file(${CMAKE_SOURCE_DIR}/for_dev/for_maintainers/new_gudhi_version_creation.md "${CMAKE_CURRENT_BINARY_DIR}/" @ONLY) + message("++ GUDHI_MODULES list is:\"${GUDHI_MODULES}\"") message("++ GUDHI_MISSING_MODULES list is:\"${GUDHI_MISSING_MODULES}\"") diff --git a/Dockerfile_for_circleci_image b/Dockerfile_for_circleci_image index ff4e6018..ebd2f366 100644 --- a/Dockerfile_for_circleci_image +++ b/Dockerfile_for_circleci_image @@ -49,6 +49,7 @@ RUN apt-get install -y make \ pkg-config RUN pip3 install \ + setuptools \ numpy \ matplotlib \ scipy \ @@ -57,7 +58,8 @@ RUN pip3 install \ scikit-learn \ sphinx \ sphinx-paramlinks \ - sphinxcontrib-bibtex + sphinxcontrib-bibtex \ + tensorflow # apt clean up RUN apt autoremove && rm -rf /var/lib/apt/lists/* diff --git a/Dockerfile_gudhi_installation b/Dockerfile_gudhi_installation index 9fe20730..33864d11 100644 --- a/Dockerfile_gudhi_installation +++ b/Dockerfile_gudhi_installation @@ -50,16 +50,18 @@ RUN pip3 install \ numpy \ matplotlib \ scipy \ - Cython + Cython \ + POT \ + scikit-learn # apt clean up RUN apt autoremove && rm -rf /var/lib/apt/lists/* -RUN curl -LO "https://github.com/GUDHI/gudhi-devel/releases/download/tags%2Fgudhi-release-3.0.0/gudhi.3.0.0.tar.gz" \ -&& tar xf gudhi.3.0.0.tar.gz \ -&& cd gudhi.3.0.0 \ +RUN curl -LO "https://github.com/GUDHI/gudhi-devel/releases/download/tags%2Fgudhi-release-3.1.1/gudhi.3.1.1.tar.gz" \ +&& tar xf gudhi.3.1.1.tar.gz \ +&& cd gudhi.3.1.1 \ && mkdir build && cd build && cmake -DCMAKE_BUILD_TYPE=Release -DWITH_GUDHI_PYTHON=OFF -DPython_ADDITIONAL_VERSIONS=3 .. \ && make all test install \ && cmake -DWITH_GUDHI_PYTHON=ON . \ && cd python \ -&& python3 setup.py install
\ No newline at end of file +&& python3 setup.py install diff --git a/code_conventions.md b/for_dev/code_conventions.md index 5882f78e..5882f78e 100644 --- a/code_conventions.md +++ b/for_dev/code_conventions.md diff --git a/for_dev/for_maintainers/new_gudhi_version_creation.md b/for_dev/for_maintainers/new_gudhi_version_creation.md new file mode 100644 index 00000000..4a40f373 --- /dev/null +++ b/for_dev/for_maintainers/new_gudhi_version_creation.md @@ -0,0 +1,97 @@ +# Create a new GUDHI version + +We will consider that all operations will be performed in a brand new clone of the main project: +```bash +git clone https://github.com/GUDHI/gudhi-devel.git +cd gudhi-devel +``` + +## Version file modification + +**Edit the file CMakeGUDHIVersion.txt**, and increment major, minor, or patch version number, in function of the version new delivery. +```bash +# cf. .gitignore - ignore this if it is a fresh clone version +rm -rf data/points/COIL_database/lucky_cat.off_dist data/points/COIL_database/lucky_cat.off_sc.dot data/points/KleinBottle5D.off_dist data/points/KleinBottle5D.off_sc.dot data/points/human.off_dist data/points/human.off_sc.off data/points/human.off_sc.txt +``` + +Checkin the modifications, build and test the version: +```bash +mkdir build +cd build +cmake -DCGAL_DIR=/your/path/to/CGAL -DWITH_GUDHI_EXAMPLE=ON -DWITH_GUDHI_BENCHMARK=ON -DUSER_VERSION_DIR=gudhi.@GUDHI_VERSION@ -DPython_ADDITIONAL_VERSIONS=3 .. +make user_version +date +"%d-%m-%Y-%T" > gudhi.@GUDHI_VERSION@/timestamp.txt +tar -czvf gudhi.@GUDHI_VERSION@.tar.gz gudhi.@GUDHI_VERSION@ +md5sum gudhi.@GUDHI_VERSION@.tar.gz > md5sum.txt +sha256sum gudhi.@GUDHI_VERSION@.tar.gz > sha256sum.txt +sha512sum gudhi.@GUDHI_VERSION@.tar.gz > sha512sum.txt + +make -j all test +``` + +***[Check there are no error]*** + +## Create the documentation +```bash +mkdir gudhi.doc.@GUDHI_VERSION@ +make doxygen 2>&1 | tee dox.log && grep warning dox.log +``` + +***[Check there are no error and the warnings]*** + +```bash +cp -R gudhi.@GUDHI_VERSION@/doc/html gudhi.doc.@GUDHI_VERSION@/cpp +cd gudhi.@GUDHI_VERSION@ +rm -rf build; mkdir build; cd build; cmake -DCGAL_DIR=/your/path/to/CGAL -DWITH_GUDHI_EXAMPLE=ON -DPython_ADDITIONAL_VERSIONS=3 .. +export LC_ALL=en_US.UTF-8 # cf. bug +make sphinx +``` + +***[Check there are no error]*** + +```bash +cp -R python/sphinx ../../gudhi.doc.@GUDHI_VERSION@/python +cd ../.. +tar -czvf gudhi.doc.@GUDHI_VERSION@.tar.gz gudhi.doc.@GUDHI_VERSION@ + +cd gudhi.@GUDHI_VERSION@/build +make all test +``` + +***[Check there are no error]*** + +## Upload the documentation + +Upload by ftp the content of the directory gudhi.doc.@GUDHI_VERSION@/cpp in a new directory on ForgeLogin@scm.gforge.inria.fr:/home/groups/gudhi/htdocs/doc/@GUDHI_VERSION@ + +Upload by ftp the content of the directory gudhi.doc.@GUDHI_VERSION@/python in a new directory on ForgeLogin@scm.gforge.inria.fr:/home/groups/gudhi/htdocs/python/@GUDHI_VERSION@ + +Through ssh, make the **latest** link to your new version of the documentation: +```bash +ssh ForgeLogin@scm.gforge.inria.fr +cd /home/groups/gudhi/htdocs/doc +rm latest +ln -s @GUDHI_VERSION@ latest +cd /home/groups/gudhi/htdocs/python +rm latest +ln -s @GUDHI_VERSION@ latest +``` + +## Put a version label on files + +* Go on page https://github.com/GUDHI/gudhi-devel/releases/new +* Name the tag: tags/gudhi-release-@GUDHI_VERSION@ +* Name the release GUDHI @GUDHI_VERSION@ +* Write the release note +* Drag'n drop *gudhi.@GUDHI_VERSION@.tar.gz*, *md5sum.txt*, *sha256sum.txt*, *sha512sum.txt* files +* Tick the *This is a pre-release* check button if this is a release candidate (untick if this is an official version) +* Click the *Publish the release* button + +***[Where X, Y and Z corresponds respectively to the major, minor, and patch version number]*** + + +## Mail sending +Send version mail to the following lists : +* gudhi-devel@lists.gforge.inria.fr +* gudhi-users@lists.gforge.inria.fr (not for release candidate) + diff --git a/for_dev/for_maintainers/next_release_template.md b/for_dev/for_maintainers/next_release_template.md new file mode 100644 index 00000000..a2805a55 --- /dev/null +++ b/for_dev/for_maintainers/next_release_template.md @@ -0,0 +1,28 @@ +We are pleased to announce the release 3.X.X of the GUDHI library. + +As a major new feature, the GUDHI library now offers ... + +We are now using GitHub to develop the GUDHI library, do not hesitate to [fork the GUDHI project on GitHub](https://github.com/GUDHI/gudhi-devel). 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.X-1.X-1: + +- [Module](link) + - ... + +- [Module](link) + - ... + +- Miscellaneous + - The [list of bugs that were solved since GUDHI-3.X-1.X-1](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.1.1+is%3Aclosed) 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](https://gudhi.inria.fr/licensing/) 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](https://gudhi.inria.fr/doc/latest/_citation.html) 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](https://gudhi.inria.fr/contact/) in case you have any questions or remarks. + +For further information about downloading and installing the library ([C++](https://gudhi.inria.fr/doc/latest/installation.html) or [Python](https://gudhi.inria.fr/python/latest/installation.html)), please visit the [GUDHI web site](https://gudhi.inria.fr/). + diff --git a/for_dev/next_release.md b/for_dev/next_release.md new file mode 100644 index 00000000..a2805a55 --- /dev/null +++ b/for_dev/next_release.md @@ -0,0 +1,28 @@ +We are pleased to announce the release 3.X.X of the GUDHI library. + +As a major new feature, the GUDHI library now offers ... + +We are now using GitHub to develop the GUDHI library, do not hesitate to [fork the GUDHI project on GitHub](https://github.com/GUDHI/gudhi-devel). 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.X-1.X-1: + +- [Module](link) + - ... + +- [Module](link) + - ... + +- Miscellaneous + - The [list of bugs that were solved since GUDHI-3.X-1.X-1](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.1.1+is%3Aclosed) 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](https://gudhi.inria.fr/licensing/) 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](https://gudhi.inria.fr/doc/latest/_citation.html) 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](https://gudhi.inria.fr/contact/) in case you have any questions or remarks. + +For further information about downloading and installing the library ([C++](https://gudhi.inria.fr/doc/latest/installation.html) or [Python](https://gudhi.inria.fr/python/latest/installation.html)), please visit the [GUDHI web site](https://gudhi.inria.fr/). + diff --git a/next_release.md b/next_release.md deleted file mode 100644 index 08b736b1..00000000 --- a/next_release.md +++ /dev/null @@ -1,36 +0,0 @@ -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](https://github.com/GUDHI/gudhi-devel). 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](https://gudhi.inria.fr/python/3.1.0.rc1/representations.html) (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](https://gudhi.inria.fr/python/3.1.0.rc1/wasserstein_distance_user.html) (new Python module) - - The q-Wasserstein distance measures the similarity between two persistence diagrams. - -- [Alpha complex](https://gudhi.inria.fr/doc/3.1.0.rc1/group__alpha__complex.html) (new C++ interface) - - Thanks to [CGAL 5.0 Epeck_d](https://doc.cgal.org/latest/Kernel_d/structCGAL_1_1Epeck__d.html) kernel, an exact computation version of Alpha complex dD is available and the default one (even in Python). - -- [Persistence graphical tools](https://gudhi.inria.fr/python/3.1.0.rc1/persistence_graphical_tools_user.html) (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](https://github.com/GUDHI/gudhi-devel/issues?utf8=%E2%9C%93&q=is%3Aissue+label%3A3.1.0+). - - -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](https://gudhi.inria.fr/licensing/) 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](https://gudhi.inria.fr/doc/latest/_citation.html) 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](https://gudhi.inria.fr/contact/) in case you have any questions or remarks. - -For further information about downloading and installing the library ([C++](https://gudhi.inria.fr/doc/3.1.0.rc1/installation.html) or [Python](https://gudhi.inria.fr/python/3.1.0.rc1/installation.html)), please visit the [GUDHI web site](https://gudhi.inria.fr/). diff --git a/src/Nerve_GIC/include/gudhi/GIC.h b/src/Nerve_GIC/include/gudhi/GIC.h index a47d6889..2a6d4788 100644 --- a/src/Nerve_GIC/include/gudhi/GIC.h +++ b/src/Nerve_GIC/include/gudhi/GIC.h @@ -14,7 +14,7 @@ #ifdef GUDHI_USE_TBB #include <tbb/parallel_for.h> -#include <tbb/mutex.h> +#include <mutex> #endif #include <gudhi/Debug_utils.h> @@ -459,7 +459,7 @@ class Cover_complex { // This cannot be parallelized if thread_local is not defined // thread_local is not defined for XCode < v.8 #if defined(GUDHI_USE_TBB) && defined(GUDHI_CAN_USE_CXX11_THREAD_LOCAL) - tbb::mutex deltamutex; + std::mutex deltamutex; tbb::parallel_for(0, N, [&](int i){ std::vector<int> samples(m); SampleWithoutReplacement(n, m, samples); @@ -765,7 +765,7 @@ class Cover_complex { #ifdef GUDHI_USE_TBB if (verbose) std::cout << "Computing connected components (parallelized)..." << std::endl; - tbb::mutex covermutex, idmutex; + std::mutex covermutex, idmutex; tbb::parallel_for(0, res, [&](int i){ // Compute connected components Graph G = one_skeleton.create_subgraph(); @@ -895,7 +895,7 @@ class Cover_complex { // Compute the geodesic distances to subsamples with Dijkstra #ifdef GUDHI_USE_TBB if (verbose) std::cout << "Computing geodesic distances (parallelized)..." << std::endl; - tbb::mutex coverMutex; tbb::mutex mindistMutex; + std::mutex coverMutex; std::mutex mindistMutex; tbb::parallel_for(0, m, [&](int i){ int seed = voronoi_subsamples[i]; std::vector<double> dmap(n); diff --git a/src/Simplex_tree/example/CMakeLists.txt b/src/Simplex_tree/example/CMakeLists.txt index 6ba518fa..a0aabee2 100644 --- a/src/Simplex_tree/example/CMakeLists.txt +++ b/src/Simplex_tree/example/CMakeLists.txt @@ -16,6 +16,9 @@ endif() add_test(NAME Simplex_tree_example_simple_simplex_tree COMMAND $<TARGET_FILE:Simplex_tree_example_simple_simplex_tree>) add_executable ( Simplex_tree_example_mini_simplex_tree mini_simplex_tree.cpp ) +if (TBB_FOUND) + target_link_libraries(Simplex_tree_example_mini_simplex_tree ${TBB_LIBRARIES}) +endif() add_test(NAME Simplex_tree_example_mini_simplex_tree COMMAND $<TARGET_FILE:Simplex_tree_example_mini_simplex_tree>) # An example with Simplex-tree using CGAL alpha_shapes_3 diff --git a/src/Tangential_complex/include/gudhi/Tangential_complex.h b/src/Tangential_complex/include/gudhi/Tangential_complex.h index f058fa9f..f007bdd5 100644 --- a/src/Tangential_complex/include/gudhi/Tangential_complex.h +++ b/src/Tangential_complex/include/gudhi/Tangential_complex.h @@ -60,7 +60,7 @@ #ifdef GUDHI_USE_TBB #include <tbb/parallel_for.h> #include <tbb/combinable.h> -#include <tbb/mutex.h> +#include <mutex> #endif // #define GUDHI_TC_EXPORT_NORMALS // Only for 3D surfaces (k=2, d=3) @@ -147,7 +147,7 @@ class Tangential_complex { typedef typename Tr_traits::Vector_d Tr_vector; #if defined(GUDHI_USE_TBB) - typedef tbb::mutex Mutex_for_perturb; + typedef std::mutex Mutex_for_perturb; typedef Vector Translation_for_perturb; typedef std::vector<Atomic_wrapper<FT> > Weights; #else diff --git a/src/Witness_complex/example/CMakeLists.txt b/src/Witness_complex/example/CMakeLists.txt index 5860f3a3..2659798e 100644 --- a/src/Witness_complex/example/CMakeLists.txt +++ b/src/Witness_complex/example/CMakeLists.txt @@ -12,10 +12,18 @@ install(TARGETS Witness_complex_example_nearest_landmark_table DESTINATION bin) # CGAL and Eigen3 are required for Euclidean version of Witness if(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) add_executable( Witness_complex_example_off example_witness_complex_off.cpp ) + if (TBB_FOUND) + target_link_libraries(Witness_complex_example_off ${TBB_LIBRARIES}) + endif() add_executable ( Witness_complex_example_sphere example_witness_complex_sphere.cpp ) + if (TBB_FOUND) + target_link_libraries(Witness_complex_example_sphere ${TBB_LIBRARIES}) + endif() add_executable( Witness_complex_example_strong_off example_strong_witness_complex_off.cpp ) - target_link_libraries(Witness_complex_example_strong_off) + if (TBB_FOUND) + target_link_libraries(Witness_complex_example_strong_off ${TBB_LIBRARIES}) + endif() add_test(NAME Witness_complex_example_off_test_torus COMMAND $<TARGET_FILE:Witness_complex_example_off> diff --git a/src/common/doc/header.html b/src/common/doc/header.html index 9fdb2321..99ab6bb7 100644 --- a/src/common/doc/header.html +++ b/src/common/doc/header.html @@ -56,7 +56,7 @@ $extrastylesheet <a href="#">Download</a> <ul class="dropdown"> <li><a href="/licensing/">Licensing</a></li> - <li><a href="https://gforge.inria.fr/frs/download.php/latestzip/5253/library-latest.zip" target="_blank">Get the latest sources</a></li> + <li><a href="https://github.com/GUDHI/gudhi-devel/releases/latest" target="_blank">Get the latest sources</a></li> <li><a href="/conda/">Conda package</a></li> <li><a href="/dockerfile/">Dockerfile</a></li> </ul> diff --git a/src/common/doc/main_page.md b/src/common/doc/main_page.md index 0b4bfb7a..6ea10b88 100644 --- a/src/common/doc/main_page.md +++ b/src/common/doc/main_page.md @@ -4,8 +4,8 @@ \image html "Gudhi_banner.png" <br><br><br><br> -## Complexes {#Complexes} -### Cubical complex +## Data structures for cell complexes {#Complexes} +### Cubical complexes <table> <tr> @@ -29,246 +29,269 @@ </tr> </table> -### Simplicial complex - -#### Alpha complex +### Simplicial complexes +#### Simplex tree <table> <tr> <td width="35%" rowspan=2> - \image html "alpha_complex_representation.png" + \image html "Simplex_tree_representation.png" </td> <td width="50%"> - Alpha complex is a simplicial complex constructed from the finite cells of a Delaunay Triangulation.<br> - The filtration value of each simplex is computed as the square of the circumradius of the simplex if the - circumsphere is empty (the simplex is then said to be Gabriel), and as the minimum of the filtration - values of the codimension 1 cofaces that make it not Gabriel otherwise. - All simplices that have a filtration value \f$ > \alpha^2 \f$ are removed from the Delaunay complex - when creating the simplicial complex if it is specified.<br> - For performances reasons, it is advised to use \ref cgal ≥ 5.0.0. + The simplex tree is an efficient and flexible + data structure for representing general (filtered) simplicial complexes. The data structure + is described in \cite boissonnatmariasimplextreealgorithmica . </td> <td width="15%"> - <b>Author:</b> Vincent Rouvreau<br> - <b>Introduced in:</b> GUDHI 1.3.0<br> - <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> - <b>Requires:</b> \ref eigen ≥ 3.1.0 and \ref cgal ≥ 4.11.0 + <b>Author:</b> Clément Maria<br> + <b>Introduced in:</b> GUDHI 1.0.0<br> + <b>Copyright:</b> MIT<br> </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref alpha_complex + <b>User manual:</b> \ref simplex_tree </td> </tr> </table> -#### Čech complex +#### Toplex Map <table> - <tr> + <tr> <td width="35%" rowspan=2> - \image html "cech_complex_representation.png" + \image html "map.png" </td> <td width="50%"> - The Čech complex is a simplicial complex constructed from a proximity graph. - The set of all simplices is filtered by the radius of their minimal enclosing ball. + The Toplex map data structure is composed firstly of a raw storage of toplices (the maximal simplices) + and secondly of a map which associate any vertex to a set of pointers toward all toplices + containing this vertex. </td> <td width="15%"> - <b>Author:</b> Vincent Rouvreau<br> - <b>Introduced in:</b> GUDHI 2.2.0<br> - <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> - <b>Includes:</b> [Miniball](https://people.inf.ethz.ch/gaertner/subdir/software/miniball.html)<br> + <b>Author:</b> François Godi<br> + <b>Introduced in:</b> GUDHI 2.1.0<br> + <b>Copyright:</b> MIT<br> </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref cech_complex + <b>User manual:</b> \ref toplex_map </td> </tr> </table> -#### Rips complex +#### Skeleton blocker <table> <tr> <td width="35%" rowspan=2> - \image html "rips_complex_representation.png" + \image html "ds_representation.png" </td> <td width="50%"> - Rips complex is a simplicial complex constructed from a one skeleton graph.<br> - The filtration value of each edge is computed from a user-given distance function and is inserted until a - user-given threshold value.<br> - This complex can be built from a point cloud and a distance function, or from a distance matrix. + The Skeleton-Blocker data-structure proposes a light encoding for simplicial complexes by storing only an *implicit* + representation of its simplices \cite socg_blockers_2011,\cite blockers2012. Intuitively, it just stores the + 1-skeleton of a simplicial complex with a graph and the set of its "missing faces" that is very small in practice. + This data-structure handles all simplicial complexes operations such as simplex enumeration or simplex removal but + operations that are particularly efficient are operations that do not require simplex enumeration such as edge + iteration, link computation or simplex contraction. </td> <td width="15%"> - <b>Author:</b> Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse<br> - <b>Introduced in:</b> GUDHI 2.0.0<br> + <b>Author:</b> David Salinas<br> + <b>Introduced in:</b> GUDHI 1.1.0<br> <b>Copyright:</b> MIT<br> </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref rips_complex + <b>User manual:</b> \ref skbl </td> </tr> </table> -#### Witness complex +#### Basic operation: contraction <table> <tr> <td width="35%" rowspan=2> - \image html "Witness_complex_representation.png" + \image html "sphere_contraction_representation.png" </td> <td width="50%"> - Witness complex \f$ Wit(W,L) \f$ is a simplicial complex defined on two sets of points in \f$\mathbb{R}^D\f$. - The data structure is described in \cite boissonnatmariasimplextreealgorithmica . + The purpose of this package is to offer a user-friendly interface for edge contraction simplification of huge + simplicial complexes. It uses the \ref skbl data-structure whose size remains small during simplification of most + used geometrical complexes of topological data analysis such as the Rips or the Delaunay complexes. In practice, + the size of this data-structure is even much lower than the total number of simplices. </td> <td width="15%"> - <b>Author:</b> Siargey Kachanovich<br> - <b>Introduced in:</b> GUDHI 1.3.0<br> - <b>Copyright:</b> MIT ([GPL v3](../../licensing/) for Euclidean version)<br> - <b>Euclidean version requires:</b> \ref eigen ≥ 3.1.0 and \ref cgal ≥ 4.11.0 + <b>Author:</b> David Salinas<br> + <b>Introduced in:</b> GUDHI 1.1.0<br> + <b>Copyright:</b> MIT [(LGPL v3)](../../licensing/)<br> + <b>Requires:</b> \ref cgal ≥ 4.11.0 </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref witness_complex + <b>User manual:</b> \ref contr </td> </tr> </table> -### Cover Complexes +## Filtrations and reconstructions {#FiltrationsReconstructions} +### Alpha complex + <table> <tr> <td width="35%" rowspan=2> - \image html "gicvisu.jpg" + \image html "alpha_complex_representation.png" </td> <td width="50%"> - Nerves and Graph Induced Complexes are cover complexes, i.e. simplicial complexes that provably contain - topological information about the input data. They can be computed with a cover of the - data, that comes i.e. from the preimage of a family of intervals covering the image - of a scalar-valued function defined on the data. <br> + Alpha complex is a simplicial complex constructed from the finite cells of a Delaunay Triangulation.<br> + The filtration value of each simplex is computed as the square of the circumradius of the simplex if the + circumsphere is empty (the simplex is then said to be Gabriel), and as the minimum of the filtration + values of the codimension 1 cofaces that make it not Gabriel otherwise. + All simplices that have a filtration value \f$ > \alpha^2 \f$ are removed from the Delaunay complex + when creating the simplicial complex if it is specified.<br> + For performances reasons, it is advised to use \ref cgal ≥ 5.0.0. </td> <td width="15%"> - <b>Author:</b> Mathieu Carrière<br> - <b>Introduced in:</b> GUDHI 2.1.0<br> + <b>Author:</b> Vincent Rouvreau<br> + <b>Introduced in:</b> GUDHI 1.3.0<br> <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> - <b>Requires:</b> \ref cgal ≥ 4.11.0 + <b>Requires:</b> \ref eigen ≥ 3.1.0 and \ref cgal ≥ 4.11.0 </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref cover_complex + <b>User manual:</b> \ref alpha_complex </td> </tr> </table> -## Data structures and basic operations {#DataStructuresAndBasicOperations} +### Čech complex -### Data structures +<table> + <tr> + <td width="35%" rowspan=2> + \image html "cech_complex_representation.png" + </td> + <td width="50%"> + The Čech complex is a simplicial complex constructed from a proximity graph. + The set of all simplices is filtered by the radius of their minimal enclosing ball. + </td> + <td width="15%"> + <b>Author:</b> Vincent Rouvreau<br> + <b>Introduced in:</b> GUDHI 2.2.0<br> + <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> + <b>Includes:</b> [Miniball](https://people.inf.ethz.ch/gaertner/subdir/software/miniball.html)<br> + </td> + </tr> + <tr> + <td colspan=2 height="25"> + <b>User manual:</b> \ref cech_complex + </td> + </tr> +</table> + +### Rips complex -#### Simplex tree <table> <tr> <td width="35%" rowspan=2> - \image html "Simplex_tree_representation.png" + \image html "rips_complex_representation.png" </td> <td width="50%"> - The simplex tree is an efficient and flexible - data structure for representing general (filtered) simplicial complexes. The data structure - is described in \cite boissonnatmariasimplextreealgorithmica . + Rips complex is a simplicial complex constructed from a one skeleton graph.<br> + The filtration value of each edge is computed from a user-given distance function and is inserted until a + user-given threshold value.<br> + This complex can be built from a point cloud and a distance function, or from a distance matrix. </td> <td width="15%"> - <b>Author:</b> Clément Maria<br> - <b>Introduced in:</b> GUDHI 1.0.0<br> + <b>Author:</b> Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse<br> + <b>Introduced in:</b> GUDHI 2.0.0<br> <b>Copyright:</b> MIT<br> </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref simplex_tree + <b>User manual:</b> \ref rips_complex </td> </tr> </table> -#### Skeleton blocker +### Witness complex <table> <tr> <td width="35%" rowspan=2> - \image html "ds_representation.png" + \image html "Witness_complex_representation.png" </td> <td width="50%"> - The Skeleton-Blocker data-structure proposes a light encoding for simplicial complexes by storing only an *implicit* - representation of its simplices \cite socg_blockers_2011,\cite blockers2012. Intuitively, it just stores the - 1-skeleton of a simplicial complex with a graph and the set of its "missing faces" that is very small in practice. - This data-structure handles all simplicial complexes operations such as simplex enumeration or simplex removal but - operations that are particularly efficient are operations that do not require simplex enumeration such as edge - iteration, link computation or simplex contraction. + Witness complex \f$ Wit(W,L) \f$ is a simplicial complex defined on two sets of points in \f$\mathbb{R}^D\f$. + The data structure is described in \cite boissonnatmariasimplextreealgorithmica . </td> <td width="15%"> - <b>Author:</b> David Salinas<br> - <b>Introduced in:</b> GUDHI 1.1.0<br> - <b>Copyright:</b> MIT<br> + <b>Author:</b> Siargey Kachanovich<br> + <b>Introduced in:</b> GUDHI 1.3.0<br> + <b>Copyright:</b> MIT ([GPL v3](../../licensing/) for Euclidean version)<br> + <b>Euclidean version requires:</b> \ref eigen ≥ 3.1.0 and \ref cgal ≥ 4.11.0 </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref skbl + <b>User manual:</b> \ref witness_complex </td> </tr> </table> -#### Toplex Map - +### Cover Complexes <table> <tr> <td width="35%" rowspan=2> - \image html "map.png" + \image html "gicvisu.jpg" </td> <td width="50%"> - The Toplex map data structure is composed firstly of a raw storage of toplices (the maximal simplices) - and secondly of a map which associate any vertex to a set of pointers toward all toplices - containing this vertex. + Nerves and Graph Induced Complexes are cover complexes, i.e. simplicial complexes that provably contain + topological information about the input data. They can be computed with a cover of the + data, that comes i.e. from the preimage of a family of intervals covering the image + of a scalar-valued function defined on the data. <br> </td> <td width="15%"> - <b>Author:</b> François Godi<br> + <b>Author:</b> Mathieu Carrière<br> <b>Introduced in:</b> GUDHI 2.1.0<br> - <b>Copyright:</b> MIT<br> + <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> + <b>Requires:</b> \ref cgal ≥ 4.11.0 </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref toplex_map + <b>User manual:</b> \ref cover_complex </td> </tr> </table> -### Basic operations - -#### Contraction +### Tangential complex <table> <tr> <td width="35%" rowspan=2> - \image html "sphere_contraction_representation.png" + \image html "tc_examples.png" </td> <td width="50%"> - The purpose of this package is to offer a user-friendly interface for edge contraction simplification of huge - simplicial complexes. It uses the \ref skbl data-structure whose size remains small during simplification of most - used geometrical complexes of topological data analysis such as the Rips or the Delaunay complexes. In practice, - the size of this data-structure is even much lower than the total number of simplices. + A Tangential Delaunay complex is a <a target="_blank" href="https://en.wikipedia.org/wiki/Simplicial_complex">simplicial complex</a> + designed to reconstruct a \f$ k \f$-dimensional manifold embedded in \f$ d \f$-dimensional Euclidean space. + The input is a point sample coming from an unknown manifold. + The running time depends only linearly on the extrinsic dimension \f$ d \f$ + and exponentially on the intrinsic dimension \f$ k \f$. </td> <td width="15%"> - <b>Author:</b> David Salinas<br> - <b>Introduced in:</b> GUDHI 1.1.0<br> - <b>Copyright:</b> MIT [(LGPL v3)](../../licensing/)<br> - <b>Requires:</b> \ref cgal ≥ 4.11.0 + <b>Author:</b> Clément Jamin<br> + <b>Introduced in:</b> GUDHI 2.0.0<br> + <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> + <b>Requires:</b> \ref eigen ≥ 3.1.0 and \ref cgal ≥ 4.11.0 </td> </tr> <tr> <td colspan=2 height="25"> - <b>User manual:</b> \ref contr + <b>User manual:</b> \ref tangential_complex </td> </tr> </table> @@ -305,36 +328,6 @@ </tr> </table> -## Manifold reconstruction {#ManifoldReconstruction} - -### Tangential complex - -<table> - <tr> - <td width="35%" rowspan=2> - \image html "tc_examples.png" - </td> - <td width="50%"> - A Tangential Delaunay complex is a <a target="_blank" href="https://en.wikipedia.org/wiki/Simplicial_complex">simplicial complex</a> - designed to reconstruct a \f$ k \f$-dimensional manifold embedded in \f$ d \f$-dimensional Euclidean space. - The input is a point sample coming from an unknown manifold. - The running time depends only linearly on the extrinsic dimension \f$ d \f$ - and exponentially on the intrinsic dimension \f$ k \f$. - </td> - <td width="15%"> - <b>Author:</b> Clément Jamin<br> - <b>Introduced in:</b> GUDHI 2.0.0<br> - <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> - <b>Requires:</b> \ref eigen ≥ 3.1.0 and \ref cgal ≥ 4.11.0 - </td> - </tr> - <tr> - <td colspan=2 height="25"> - <b>User manual:</b> \ref tangential_complex - </td> - </tr> -</table> - ## Topological descriptors tools {#TopologicalDescriptorsTools} ### Bottleneck distance @@ -390,3 +383,26 @@ </td> </tr> </table> + +## Point cloud utilities {#PointCloudUtils} + +<table> + <tr> + <td width="35%" rowspan=2> + \f$(x_1,\ldots,x_d)\f$ + </td> + <td width="50%"> + This contains various tools to handle point clouds: spatial searching, subsampling, etc. + </td> + <td width="15%"> + <b>Author:</b> Clément Jamin<br> + <b>Introduced in:</b> GUDHI 1.3.0<br> + <b>Copyright:</b> MIT [(GPL v3)](../../licensing/)<br> + </td> + </tr> + <tr> + <td colspan=2 height="25"> + <b>Manuals:</b> \ref spatial_searching, \ref subsampling + </td> + </tr> +</table> diff --git a/src/python/doc/_templates/layout.html b/src/python/doc/_templates/layout.html index 2f2d9c72..a672a281 100644 --- a/src/python/doc/_templates/layout.html +++ b/src/python/doc/_templates/layout.html @@ -201,7 +201,7 @@ <a href="#">Download</a> <ul class="dropdown"> <li><a href="/licensing/">Licensing</a></li> - <li><a href="https://gforge.inria.fr/frs/download.php/latestzip/5253/library-latest.zip" target="_blank">Get the latest sources</a></li> + <li><a href="https://github.com/GUDHI/gudhi-devel/releases/latest" target="_blank">Get the latest sources</a></li> <li><a href="/conda/">Conda package</a></li> <li><a href="/dockerfile/">Dockerfile</a></li> </ul> diff --git a/src/python/doc/alpha_complex_sum.inc b/src/python/doc/alpha_complex_sum.inc index a1184663..b5af0d27 100644 --- a/src/python/doc/alpha_complex_sum.inc +++ b/src/python/doc/alpha_complex_sum.inc @@ -5,16 +5,13 @@ | .. figure:: | Alpha complex is a simplicial complex constructed from the finite | :Author: Vincent Rouvreau | | ../../doc/Alpha_complex/alpha_complex_representation.png | cells of a Delaunay Triangulation. | | | :alt: Alpha complex representation | | :Introduced in: GUDHI 2.0.0 | - | :figclass: align-center | The filtration value of each simplex is computed as the square of the | | - | | circumradius of the simplex if the circumsphere is empty (the simplex | :Copyright: MIT (`GPL v3 </licensing/>`_) | - | | is then said to be Gabriel), and as the minimum of the filtration | | - | | values of the codimension 1 cofaces that make it not Gabriel | :Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 | - | | otherwise. All simplices that have a filtration value | | - | | :math:`> \alpha^2` are removed from the Delaunay complex | | - | | when creating the simplicial complex if it is specified. | | + | :figclass: align-center | The filtration value of each simplex is computed as the **square** of | | + | | the circumradius of the simplex if the circumsphere is empty (the | :Copyright: MIT (`GPL v3 </licensing/>`_) | + | | simplex is then said to be Gabriel), and as the minimum of the | | + | | filtration values of the codimension 1 cofaces that make it not | :Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 | + | | Gabriel otherwise. | | | | | | - | | This package requires having CGAL version 4.7 or higher (4.8.1 is | | - | | advised for better performance). | | + | | For performances reasons, it is advised to use CGAL ≥ 5.0.0. | | +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ | * :doc:`alpha_complex_user` | * :doc:`alpha_complex_ref` | +----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/reader_utils_ref.rst b/src/python/doc/diagram_readers_ref.rst index b8977a5a..c79daf9c 100644 --- a/src/python/doc/reader_utils_ref.rst +++ b/src/python/doc/diagram_readers_ref.rst @@ -2,13 +2,9 @@ .. To get rid of WARNING: document isn't included in any toctree -============================= -Reader utils reference manual -============================= - -.. autofunction:: gudhi.read_points_from_off_file - -.. autofunction:: gudhi.read_lower_triangular_matrix_from_csv_file +================================ +Diagram readers reference manual +================================ .. autofunction:: gudhi.read_persistence_intervals_grouped_by_dimension diff --git a/src/python/doc/index.rst b/src/python/doc/index.rst index c36a578f..3387a64f 100644 --- a/src/python/doc/index.rst +++ b/src/python/doc/index.rst @@ -6,8 +6,8 @@ GUDHI Python modules documentation :alt: Gudhi banner :figclass: align-center -Complexes -********* +Data structures for cell complexes +********************************** Cubical complexes ================= @@ -17,18 +17,26 @@ Cubical complexes Simplicial complexes ==================== +Simplex tree +------------ + +.. include:: simplex_tree_sum.inc + +Filtrations and reconstructions +******************************* + Alpha complex -------------- +============= .. include:: alpha_complex_sum.inc Rips complex ------------- +============ .. include:: rips_complex_sum.inc Witness complex ---------------- +=============== .. include:: witness_complex_sum.inc @@ -37,16 +45,10 @@ Cover complexes .. include:: nerve_gic_complex_sum.inc -Data structures and basic operations -************************************ - -Data structures -=============== - -Simplex tree ------------- +Tangential complex +================== -.. include:: simplex_tree_sum.inc +.. include:: tangential_complex_sum.inc Topological descriptors computation *********************************** @@ -56,15 +58,6 @@ Persistence cohomology .. include:: persistent_cohomology_sum.inc -Manifold reconstruction -*********************** - -Tangential complex -================== - -.. include:: tangential_complex_sum.inc - - Topological descriptors tools ***************************** @@ -88,6 +81,11 @@ Persistence graphical tools .. include:: persistence_graphical_tools_sum.inc +Point cloud utilities +********************* + +.. include:: point_cloud_sum.inc + Bibliography ************ diff --git a/src/python/doc/point_cloud.rst b/src/python/doc/point_cloud.rst new file mode 100644 index 00000000..d668428a --- /dev/null +++ b/src/python/doc/point_cloud.rst @@ -0,0 +1,22 @@ +:orphan: + +.. To get rid of WARNING: document isn't included in any toctree + +============================ +Point cloud utilities manual +============================ + +File Readers +------------ + +.. autofunction:: gudhi.read_points_from_off_file + +.. autofunction:: gudhi.read_lower_triangular_matrix_from_csv_file + +Subsampling +----------- + +.. automodule:: gudhi.subsampling + :members: + :special-members: + :show-inheritance: diff --git a/src/python/doc/point_cloud_sum.inc b/src/python/doc/point_cloud_sum.inc new file mode 100644 index 00000000..85d52de7 --- /dev/null +++ b/src/python/doc/point_cloud_sum.inc @@ -0,0 +1,15 @@ +.. table:: + :widths: 30 50 20 + + +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ + | | :math:`(x_1, x_2, \ldots, x_d)` | Utilities to process point clouds: read from file, subsample, etc. | :Author: Vincent Rouvreau | + | | :math:`(y_1, y_2, \ldots, y_d)` | | | + | | | :Introduced in: GUDHI 2.0.0 | + | | | | + | | | :Copyright: MIT (`GPL v3 </licensing/>`_) | + | | Parts of this package require CGAL. | | + | | | :Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 | + | | | | + +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ + | * :doc:`point_cloud` | + +----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/example/alpha_complex_from_points_example.py b/src/python/example/alpha_complex_from_points_example.py index a746998c..844d7a82 100755 --- a/src/python/example/alpha_complex_from_points_example.py +++ b/src/python/example/alpha_complex_from_points_example.py @@ -52,4 +52,9 @@ print("star([0])=", simplex_tree.get_star([0])) print("coface([0], 1)=", simplex_tree.get_cofaces([0], 1)) print("point[0]=", alpha_complex.get_point(0)) -print("point[5]=", alpha_complex.get_point(5)) +try: + print("point[5]=", alpha_complex.get_point(5)) +except IndexError: + pass +else: + assert False diff --git a/src/python/gudhi/alpha_complex.pyx b/src/python/gudhi/alpha_complex.pyx index f3ca3dd5..fff3e920 100644 --- a/src/python/gudhi/alpha_complex.pyx +++ b/src/python/gudhi/alpha_complex.pyx @@ -24,11 +24,11 @@ __license__ = "GPL v3" cdef extern from "Alpha_complex_interface.h" namespace "Gudhi": cdef cppclass Alpha_complex_interface "Gudhi::alpha_complex::Alpha_complex_interface": - Alpha_complex_interface(vector[vector[double]] points) + Alpha_complex_interface(vector[vector[double]] points) except + # bool from_file is a workaround for cython to find the correct signature - Alpha_complex_interface(string off_file, bool from_file) - vector[double] get_point(int vertex) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) + Alpha_complex_interface(string off_file, bool from_file) except + + vector[double] get_point(int vertex) except + + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) except + # AlphaComplex python interface cdef class AlphaComplex: @@ -96,8 +96,7 @@ cdef class AlphaComplex: :rtype: list of float :returns: the point. """ - cdef vector[double] point = self.thisptr.get_point(vertex) - return point + return self.thisptr.get_point(vertex) def create_simplex_tree(self, max_alpha_square = float('inf')): """ diff --git a/src/python/gudhi/euclidean_strong_witness_complex.pyx b/src/python/gudhi/euclidean_strong_witness_complex.pyx index 9889f92c..aca6084e 100644 --- a/src/python/gudhi/euclidean_strong_witness_complex.pyx +++ b/src/python/gudhi/euclidean_strong_witness_complex.pyx @@ -22,9 +22,9 @@ __license__ = "GPL v3" cdef extern from "Euclidean_strong_witness_complex_interface.h" namespace "Gudhi": cdef cppclass Euclidean_strong_witness_complex_interface "Gudhi::witness_complex::Euclidean_strong_witness_complex_interface": Euclidean_strong_witness_complex_interface(vector[vector[double]] landmarks, vector[vector[double]] witnesses) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) except + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, - unsigned limit_dimension) + unsigned limit_dimension) except + vector[double] get_point(unsigned vertex) # EuclideanStrongWitnessComplex python interface diff --git a/src/python/gudhi/euclidean_witness_complex.pyx b/src/python/gudhi/euclidean_witness_complex.pyx index e3ce0e82..fb0c2201 100644 --- a/src/python/gudhi/euclidean_witness_complex.pyx +++ b/src/python/gudhi/euclidean_witness_complex.pyx @@ -22,9 +22,9 @@ __license__ = "GPL v3" cdef extern from "Euclidean_witness_complex_interface.h" namespace "Gudhi": cdef cppclass Euclidean_witness_complex_interface "Gudhi::witness_complex::Euclidean_witness_complex_interface": Euclidean_witness_complex_interface(vector[vector[double]] landmarks, vector[vector[double]] witnesses) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) except + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, - unsigned limit_dimension) + unsigned limit_dimension) except + vector[double] get_point(unsigned vertex) # EuclideanWitnessComplex python interface diff --git a/src/python/gudhi/off_reader.pyx b/src/python/gudhi/off_reader.pyx index a0d5bf25..7e6d9d80 100644 --- a/src/python/gudhi/off_reader.pyx +++ b/src/python/gudhi/off_reader.pyx @@ -26,7 +26,7 @@ def read_points_from_off_file(off_file=''): :type off_file: string :returns: The point set. - :rtype: vector[vector[double]] + :rtype: List[List[float]] """ if off_file: if os.path.isfile(off_file): diff --git a/src/python/gudhi/reader_utils.pyx b/src/python/gudhi/reader_utils.pyx index d6033b86..fe1c3a2e 100644 --- a/src/python/gudhi/reader_utils.pyx +++ b/src/python/gudhi/reader_utils.pyx @@ -34,7 +34,7 @@ def read_lower_triangular_matrix_from_csv_file(csv_file='', separator=';'): :type separator: char :returns: The lower triangular matrix. - :rtype: vector[vector[double]] + :rtype: List[List[float]] """ if csv_file: if path.isfile(csv_file): @@ -45,15 +45,15 @@ def read_lower_triangular_matrix_from_csv_file(csv_file='', separator=';'): def read_persistence_intervals_grouped_by_dimension(persistence_file=''): """Reads a file containing persistence intervals. Each line might contain 2, 3 or 4 values: [[field] dimension] birth death - The return value is an `map[dim, vector[pair[birth, death]]]` - where `dim` is an `int`, `birth` a `double`, and `death` a `double`. + The return value is a `dict(dim, list(tuple(birth, death)))` + where `dim` is an `int`, `birth` a `float`, and `death` a `float`. Note: the function does not check that birth <= death. :param persistence_file: A persistence file style name. :type persistence_file: string :returns: The persistence pairs grouped by dimension. - :rtype: map[int, vector[pair[double, double]]] + :rtype: Dict[int, List[Tuple[float, float]]] """ if persistence_file: if path.isfile(persistence_file): diff --git a/src/python/gudhi/rips_complex.pyx b/src/python/gudhi/rips_complex.pyx index 722cdcdc..deb8057a 100644 --- a/src/python/gudhi/rips_complex.pyx +++ b/src/python/gudhi/rips_complex.pyx @@ -28,7 +28,7 @@ cdef extern from "Rips_complex_interface.h" namespace "Gudhi": void init_matrix(vector[vector[double]] values, double threshold) void init_points_sparse(vector[vector[double]] values, double threshold, double sparse) void init_matrix_sparse(vector[vector[double]] values, double threshold, double sparse) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, int dim_max) + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, int dim_max) except + # RipsComplex python interface cdef class RipsComplex: diff --git a/src/python/gudhi/simplex_tree.pxd b/src/python/gudhi/simplex_tree.pxd index 1066d44b..96d14079 100644 --- a/src/python/gudhi/simplex_tree.pxd +++ b/src/python/gudhi/simplex_tree.pxd @@ -39,7 +39,7 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": vector[pair[vector[int], double]] get_star(vector[int] simplex) vector[pair[vector[int], double]] get_cofaces(vector[int] simplex, int dimension) - void expansion(int max_dim) + void expansion(int max_dim) except + void remove_maximal_simplex(vector[int] simplex) bool prune_above_filtration(double filtration) bool make_filtration_non_decreasing() diff --git a/src/python/gudhi/strong_witness_complex.pyx b/src/python/gudhi/strong_witness_complex.pyx index 2c33c3f2..9f89d3ae 100644 --- a/src/python/gudhi/strong_witness_complex.pyx +++ b/src/python/gudhi/strong_witness_complex.pyx @@ -22,9 +22,9 @@ __license__ = "MIT" cdef extern from "Strong_witness_complex_interface.h" namespace "Gudhi": cdef cppclass Strong_witness_complex_interface "Gudhi::witness_complex::Strong_witness_complex_interface": Strong_witness_complex_interface(vector[vector[pair[size_t, double]]] nearest_landmark_table) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) except + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, - unsigned limit_dimension) + unsigned limit_dimension) except + # StrongWitnessComplex python interface cdef class StrongWitnessComplex: diff --git a/src/python/gudhi/subsampling.pyx b/src/python/gudhi/subsampling.pyx index c501d16b..f77c6f75 100644 --- a/src/python/gudhi/subsampling.pyx +++ b/src/python/gudhi/subsampling.pyx @@ -33,13 +33,15 @@ def choose_n_farthest_points(points=None, off_file='', nb_points=0, starting_poi The iteration starts with the landmark `starting point`. :param points: The input point set. - :type points: vector[vector[double]]. + :type points: Iterable[Iterable[float]]. Or :param off_file: An OFF file style name. :type off_file: string + And in both cases + :param nb_points: Number of points of the subsample. :type nb_points: unsigned. :param starting_point: The iteration starts with the landmark `starting \ @@ -47,7 +49,7 @@ def choose_n_farthest_points(points=None, off_file='', nb_points=0, starting_poi index is chosen randomly. :type starting_point: unsigned. :returns: The subsample point set. - :rtype: vector[vector[double]] + :rtype: List[List[float]]. """ if off_file: if os.path.isfile(off_file): @@ -74,17 +76,19 @@ def pick_n_random_points(points=None, off_file='', nb_points=0): """Subsample a point set by picking random vertices. :param points: The input point set. - :type points: vector[vector[double]]. + :type points: Iterable[Iterable[float]]. Or :param off_file: An OFF file style name. :type off_file: string + And in both cases + :param nb_points: Number of points of the subsample. :type nb_points: unsigned. :returns: The subsample point set. - :rtype: vector[vector[double]] + :rtype: List[List[float]] """ if off_file: if os.path.isfile(off_file): @@ -103,18 +107,20 @@ def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0): between any two points is greater than or equal to min_squared_dist. :param points: The input point set. - :type points: vector[vector[double]]. + :type points: Iterable[Iterable[float]]. Or :param off_file: An OFF file style name. :type off_file: string + And in both cases + :param min_squared_dist: Minimum squared distance separating the output \ points. :type min_squared_dist: float. :returns: The subsample point set. - :rtype: vector[vector[double]] + :rtype: List[List[float]] """ if off_file: if os.path.isfile(off_file): diff --git a/src/python/gudhi/witness_complex.pyx b/src/python/gudhi/witness_complex.pyx index b032a5a1..e589d006 100644 --- a/src/python/gudhi/witness_complex.pyx +++ b/src/python/gudhi/witness_complex.pyx @@ -22,9 +22,9 @@ __license__ = "MIT" cdef extern from "Witness_complex_interface.h" namespace "Gudhi": cdef cppclass Witness_complex_interface "Gudhi::witness_complex::Witness_complex_interface": Witness_complex_interface(vector[vector[pair[size_t, double]]] nearest_landmark_table) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) except + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, - unsigned limit_dimension) + unsigned limit_dimension) except + # WitnessComplex python interface cdef class WitnessComplex: diff --git a/src/python/include/Alpha_complex_interface.h b/src/python/include/Alpha_complex_interface.h index e9bbadb0..8614eee3 100644 --- a/src/python/include/Alpha_complex_interface.h +++ b/src/python/include/Alpha_complex_interface.h @@ -50,13 +50,9 @@ class Alpha_complex_interface { std::vector<double> get_point(int vh) { std::vector<double> vd; - try { - Point_d const& ph = alpha_complex_->get_point(vh); - for (auto coord = ph.cartesian_begin(); coord != ph.cartesian_end(); coord++) - vd.push_back(CGAL::to_double(*coord)); - } catch (std::out_of_range const&) { - // std::out_of_range is thrown in case not found. Other exceptions must be re-thrown - } + Point_d const& ph = alpha_complex_->get_point(vh); + for (auto coord = ph.cartesian_begin(); coord != ph.cartesian_end(); coord++) + vd.push_back(CGAL::to_double(*coord)); return vd; } diff --git a/src/python/setup.py.in b/src/python/setup.py.in index 9c2124f4..bd7fb180 100644 --- a/src/python/setup.py.in +++ b/src/python/setup.py.in @@ -8,7 +8,7 @@ - YYYY/MM Author: Description of the modification """ -from setuptools import setup, Extension +from setuptools import setup, Extension, find_packages from Cython.Build import cythonize from numpy import get_include as numpy_get_include import sys @@ -44,7 +44,7 @@ for module in modules: setup( name = 'gudhi', - packages=["gudhi",], + packages=find_packages(), # find_namespace_packages(include=["gudhi*"]) author='GUDHI Editorial Board', author_email='gudhi-contact@lists.gforge.inria.fr', version='@GUDHI_VERSION@', diff --git a/src/python/test/test_alpha_complex.py b/src/python/test/test_alpha_complex.py index 0d9e9e45..3761fe16 100755 --- a/src/python/test/test_alpha_complex.py +++ b/src/python/test/test_alpha_complex.py @@ -65,8 +65,18 @@ def test_infinite_alpha(): assert point_list[1] == alpha_complex.get_point(1) assert point_list[2] == alpha_complex.get_point(2) assert point_list[3] == alpha_complex.get_point(3) - assert alpha_complex.get_point(4) == [] - assert alpha_complex.get_point(125) == [] + try: + alpha_complex.get_point(4) == [] + except IndexError: + pass + else: + assert False + try: + alpha_complex.get_point(125) == [] + except IndexError: + pass + else: + assert False def test_filtered_alpha(): @@ -82,8 +92,18 @@ def test_filtered_alpha(): assert point_list[1] == filtered_alpha.get_point(1) assert point_list[2] == filtered_alpha.get_point(2) assert point_list[3] == filtered_alpha.get_point(3) - assert filtered_alpha.get_point(4) == [] - assert filtered_alpha.get_point(125) == [] + try: + filtered_alpha.get_point(4) == [] + except IndexError: + pass + else: + assert False + try: + filtered_alpha.get_point(125) == [] + except IndexError: + pass + else: + assert False assert simplex_tree.get_filtration() == [ ([0], 0.0), |