diff options
| author | Gard Spreemann <gspr@nonempty.org> | 2020-05-20 08:42:23 +0200 |
|---|---|---|
| committer | Gard Spreemann <gspr@nonempty.org> | 2020-05-20 08:42:23 +0200 |
| commit | 9b3079646ee3f6a494b83e864b3e10b8a93597d0 (patch) | |
| tree | 63ecae8cf0d09b72907805e68f19765c7dd9694a | |
| parent | 81816dae256a9f3c0653b1d21443c3c32da7a974 (diff) | |
| parent | 97e889f34e929f3c2306803b6c37b57926bd1245 (diff) | |
Merge tag 'tags/gudhi-release-3.2.0' into dfsg/latest
319 files changed, 7297 insertions, 3735 deletions
diff --git a/.appveyor.yml b/.appveyor.yml index 4a76ea0a..34f42dea 100644 --- a/.appveyor.yml +++ b/.appveyor.yml @@ -39,6 +39,7 @@ init: install: + - git submodule update --init - vcpkg install tbb:x64-windows boost-disjoint-sets:x64-windows boost-serialization:x64-windows boost-date-time:x64-windows boost-system:x64-windows boost-filesystem:x64-windows boost-units:x64-windows boost-thread:x64-windows boost-program-options:x64-windows eigen3:x64-windows mpfr:x64-windows mpir:x64-windows cgal:x64-windows - SET PATH=c:\Tools\vcpkg\installed\x64-windows\bin;%PATH% - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PYTHON%\Library\bin;%PATH% @@ -48,7 +49,7 @@ install: - pip --version - python -m pip install --upgrade pip - pip install -U setuptools numpy matplotlib scipy Cython pytest - - pip install -U POT + - pip install -U POT pybind11 build_script: - mkdir build diff --git a/.circleci/config.yml b/.circleci/config.yml index f4073746..40ddc08e 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -45,12 +45,13 @@ jobs: python: docker: - image: gudhi/ci_for_gudhi:latest - parallelism: 4 steps: - checkout - run: name: Build and test python module. Generates and tests the python documentation command: | + git submodule init + git submodule update mkdir build; cd build; cmake -DUSER_VERSION_DIR=version ..; @@ -60,12 +61,12 @@ jobs: cd build; cmake -DCMAKE_BUILD_TYPE=Release -DWITH_GUDHI_EXAMPLE=OFF -DWITH_GUDHI_UTILITIES=OFF -DWITH_GUDHI_PYTHON=ON -DPython_ADDITIONAL_VERSIONS=3 ..; cd python; - python3 setup.py build_ext -j 4 --inplace; + python3 setup.py build_ext -j 2 --inplace; make sphinx; cp -R sphinx /tmp/sphinx; python3 setup.py install; python3 setup.py clean --all; - ctest -j 4 --output-on-failure; + ctest -j 2 --output-on-failure; - store_artifacts: path: /tmp/sphinx @@ -78,6 +79,8 @@ jobs: - run: name: Generates the C++ documentation with doxygen command: | + git submodule init + git submodule update mkdir build; cd build; cmake -DCMAKE_BUILD_TYPE=Release -DWITH_GUDHI_EXAMPLE=OFF -DWITH_GUDHI_TEST=OFF -DWITH_GUDHI_UTILITIES=OFF -DWITH_GUDHI_PYTHON=OFF -DUSER_VERSION_DIR=version ..; @@ -97,4 +100,4 @@ workflows: - tests - utils - python - - doxygen
\ No newline at end of file + - doxygen diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md new file mode 100644 index 00000000..a18ff8bd --- /dev/null +++ b/.github/CONTRIBUTING.md @@ -0,0 +1,48 @@ +# Contributing to GUDHI + +First of all, thank you for the time you may take to contribute to GUDHI ! + +# In case you have a question + +Please, check our [contact web page](https://gudhi.inria.fr/contact/). + +# In case you found an issue + +Please, first check [opened issues on GUDHI](https://github.com/GUDHI/gudhi-devel/issues). + +If the problem you are facing is not referenced, do not hesitate to open a [new issue](https://github.com/GUDHI/gudhi-devel/issues/new). + +This place is also a good place if you have some enhancement you want to propose. +There is a label **enhancement** in the [new issue](https://github.com/GUDHI/gudhi-devel/issues/new) page. + +# In case you want to contribute to GUDHI + +## You are not familiar with GitHub ? + +Please take some time to read our [how to use GitHub to contribute to GUDHI](how_to_use_github_to_contribute_to_gudhi.md). + +## Something you want to improve in the documentation + +For C++ documentation, you can find it in the directories: +* *src/common/doc* for the main page and installation instructions +* *src/NAME_OF_THE_MODULE/doc* for the main page of a module +* *src/NAME_OF_THE_MODULE/include/gudhi* for the documentation generated from the code. +We use Doxygen to generate the code and you will be able to verify the result in CircleCI Doxygen target in the artifacts. + +For Python documentation, you can find it in the directories: +* *src/python/doc* for the main page, installation instructionsand for the main pages of the modules +* *src/python/gudhi/NAME_OF_THE_MODULE.pyx* for the documentation generated from the code. +We use Sphinx to generate the code and you will be able to verify the result in CircleCI Sphinx target in the artifacts. + +## Something you want to improve in the code + +We don't ask for any paperwork but we expect you don't submit anything you are not allowed to: +* check that your work contract and your employer allow you to contribute to this open source project. +* insure you do not violate someone's intellectual property. +* ... + +Please, take some time to read our [code conventions](code_conventions.md) + +As a convention, we set a Pull Request as a **Draft Pull Request** when we work on something we want the other contributors to see. + +We click on **Ready for review** to ask for a peer review of the contribution. diff --git a/.github/build-requirements.txt b/.github/build-requirements.txt new file mode 100644 index 00000000..7de60d23 --- /dev/null +++ b/.github/build-requirements.txt @@ -0,0 +1,5 @@ +setuptools +wheel +numpy +Cython +pybind11
\ No newline at end of file diff --git a/code_conventions.md b/.github/code_conventions.md index 5882f78e..9724f722 100644 --- a/code_conventions.md +++ b/.github/code_conventions.md @@ -21,6 +21,27 @@ * The name of the "project" should be in this form: `Package_[tests|examples|…]`. E.g. `project(Simplex_tree_examples)`. * The name if each "target" (first parameter of add_executable) should be in this form: `Package_{name of the cpp file without extension}`. E.g `add_executable(Subsampling_test_sparsify_point_set test_sparsify_point_set.cpp)`. +### Code style +We are using [google c++ style guide](https://google.github.io/styleguide/cppguide.html) recommendations with 120 characters per line of code. +[clang-format](https://clang.llvm.org/docs/ClangFormat.html) can be used to format automatically your code: +```bash +cd src # there is a .clang-format file with these specifications +clang-format -style=file -i Simplex_tree/include/gudhi/Simplex_tree.h # -i means in place, your file will be modified +``` + +### Template +Please use the file [following template](copyright_template.h). + ## Python -In progress...
\ No newline at end of file +In progress... + +### Code style +We are using [PEP8 Python style guide](https://www.python.org/dev/peps/pep-0008/) recommendations with 120 characters per line of code. +[black](https://black.readthedocs.io/en/stable/) can be used to format automatically your code: +```bash +black -l 120 src/python/example/bottleneck_basic_example.py +``` + +### Template +Please use the file [following template](copyright_template.py). diff --git a/.github/copyright_template.h b/.github/copyright_template.h new file mode 100644 index 00000000..30034f1b --- /dev/null +++ b/.github/copyright_template.h @@ -0,0 +1,14 @@ +/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + * Author(s): [AUTHOR NAME] + * + * Copyright (C) [YEAR] [COPYRIGHT] + * + * Modification(s): + * - YYYY/MM Author: Description of the modification + */ + +#ifndef [FILE_NAME]_H_ +#define [FILE_NAME]_H_ + +#endif // [FILE_NAME]_H_
\ No newline at end of file diff --git a/.github/copyright_template.py b/.github/copyright_template.py new file mode 100644 index 00000000..667f985d --- /dev/null +++ b/.github/copyright_template.py @@ -0,0 +1,8 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): [AUTHOR NAME] +# +# Copyright (C) [YEAR] [COPYRIGHT] +# +# Modification(s): +# - YYYY/MM Author: Description of the modification diff --git a/.github/for_maintainers/new_gudhi_version_creation.md b/.github/for_maintainers/new_gudhi_version_creation.md new file mode 100644 index 00000000..8674222b --- /dev/null +++ b/.github/for_maintainers/new_gudhi_version_creation.md @@ -0,0 +1,95 @@ +# 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 +git submodule update --init +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 + +## 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/.github/for_maintainers/next_release_template.md b/.github/for_maintainers/next_release_template.md new file mode 100644 index 00000000..a2805a55 --- /dev/null +++ b/.github/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/.github/how_to_use_github_to_contribute_to_gudhi.md b/.github/how_to_use_github_to_contribute_to_gudhi.md new file mode 100644 index 00000000..747ca39b --- /dev/null +++ b/.github/how_to_use_github_to_contribute_to_gudhi.md @@ -0,0 +1,125 @@ +# How to use github to contribute to gudhi + +Similar information is available in many places: +* https://jarv.is/notes/how-to-pull-request-fork-github/ (this one is using `upstream/master` when creating a new branch) +* https://help.github.com/en/github/getting-started-with-github/fork-a-repo +* https://blog.scottlowe.org/2015/01/27/using-fork-branch-git-workflow/ +* https://gist.github.com/Chaser324/ce0505fbed06b947d962 +* etc + +## Get a github account +I assume the account is called **LOGIN**, please replace as appropriate below. Log in to github.com using this account. + +## Fork GUDHI/gudhi-devel project +Go to https://github.com/GUDHI/gudhi-devel and click on **fork** (top right). +Feel free to also click on the star next to it to show you like the project! +You can see your fork at https://github.com/LOGIN/gudhi-devel + +## Create a local clone on your computer +```bash +git clone https://github.com/LOGIN/gudhi-devel.git +``` + +This creates a directory gudhi-devel, which you are free to move around or rename. For the following, change to that directory: +```bash +cd gudhi-devel +``` + +When you clone the repository, you also need to download the *submodules*. + +## Submodules +Hera, used for Wasserstein distance, is available on an external git repository. To download it: +```bash +git submodule update --init +``` + +## Configuring a remote for a fork +```bash +git remote add upstream https://github.com/GUDHI/gudhi-devel.git +``` + +because you want to see the real gudhi, not just your clone. +(It is perfectly possible to do things in the reverse order, clone from GUDHI and add the one in LOGIN as extra remote, but the names of the remotes may not match the rest of this document. You can change the name of a remote with `git remote rename oldname newname`) + +## Optional remotes +Optional, if you are interested in one of the old branches +```bash +git remote add oldies https://github.com/GUDHI/branches.git +``` + +Or if you want to spy on someone's work. I assume the someone's account is called **SOMEONE** +```bash +git remote add someone https://github.com/SOMEONE/gudhi-devel.git +``` + +## Stay up-to-date +```bash +git fetch -p --all +``` +This is a command you can run quite regularly. +It tells git to check all that happened on github. +It is safe, it will not mess with your files. + +**Reminder:** If the version of a submodule has changed, or if a submodule was added, you may need to: +```bash +git submodule sync +git submodule update --init +``` + +## Create a branch, based on the current master +```bash +git checkout -b some-fancy-name --no-track upstream/master +``` +Your local branch `master` and the one on your github clone are useless and often outdated, but for technical reasons there has to exist at least one branch at all times, it might as well be that one. upstream/master is the real deal, that's what you want to base your new branch on. + +## The real coding is here! +Edit files, test, etc. + +## Commit your changes (locally) +The basic command is just `git commit`, but it will do nothing by default. +You need `git add my_new_file` for every new file you want to commit. +And usually you'll want to use `git commit -a` so that all files that git already knows about and that have been modified get committed. + +## Push your changes (remotely) +```bash +git push -u origin some-fancy-name +``` +This puts a copy of your branch on your online clone of gudhi-devel. +Because of `-u`, it will remember where you like to push this branch, and next time you can just use `git push`. + +## Play again! +Possibly iterate a few times, add more commits and push them. + +## Your pull request is ready +Do not forget to update `.github/next_release.md` to announce your development in the next release note. + +Get your web browser to https://github.com/LOGIN/gudhi-devel, click on the button that says **Branch: some-name** (below the number of commits, above the list of files) and select the branch you are so proud of. +Click on **New pull request** next to it. + +## Follow the instructions ;-) +Note that if your branch is not quite ready, you can make a **draft pull request** (see the arrow next to the confirmation button), and later you will have access to a button to say that the branch is ready for reviews now. +Draft pull requests can be a way to advertise that you are working on something, and possibly ask others for comments or help. + +## Code review +Make sure you follow the discussion on your pull request, answer questions, take comments into account. +You can keep pushing new commits on your branch to your fork of gudhi-devel, the pull request will automatically notice the new commits there. +There is no need to create a new pull request. +Once the branch is under review, fixing issues is good, but please refrain from adding extra features, that just makes the reviewers' job harder and thus slower. +You may want to look at https://github.com/settings/notifications (and other settings nearby) if you don't receive emails when people comment on your pull request. +Some bold reviewer might make changes to your branch. You will then need `git pull` for your local branch to reflect those. + +## Your work is merged! +Once your pull request has been closed (your branch merged), you can remove your branch, both locally and also the branch on your github fork: +```bash +git checkout master # or any other branch, but you cannot remove the branch you are currently in +git branch -d some-fancy-name # local branch delete +git push origin --delete some-fancy-name # remote branch delete +``` +If you add @VincentRouvreau or @mglisse as collaborator (https://github.com/LOGIN/gudhi-devel/settings/collaboration), they may remove the branch on your clone at the same time as they merge the branch, so you only have the local one to remove (or keep if you are nostalgic). + +## Keep in touch +Create a new branch and keep contributing! + +Do not try to reuse an old branch that has already been merged. +Make sure you run the fetch command just before creating any new branch, so you don't base it on some outdated version of master. +You can also work on several branches at the same time, using `git checkout some-fancy-name` and `git checkout name-of-other-branch` to switch between them (commit before switching or things may get complicated). diff --git a/.github/next_release.md b/.github/next_release.md new file mode 100644 index 00000000..d3c9ce68 --- /dev/null +++ b/.github/next_release.md @@ -0,0 +1,58 @@ +We are pleased to announce the release 3.2.0 of the GUDHI library. + +As a major new feature, the GUDHI library now offers a Python interface to [Hera](https://bitbucket.org/grey_narn/hera/src/master/) to compute the Wasserstein distance. +[PyBind11](https://github.com/pybind/pybind11) is now required to build the Python module. + +We are now using GitHub to develop the GUDHI library, do not hesitate to [fork the GUDHI project on GitHub](https://github.com/GUDHI/gudhi-devel). From a user point of view, we recommend to download GUDHI user version (gudhi.3.2.0.tar.gz). + +Below is a list of changes made since GUDHI 3.1.1: + +- Point cloud utilities + - A new module [Time Delay Embedding](https://gudhi.inria.fr/python/latest/point_cloud.html#time-delay-embedding) + to embed time-series data in the R^d according to [Takens' Embedding Theorem](https://en.wikipedia.org/wiki/Takens%27s_theorem) + and obtain the coordinates of each point. + - A new module [K Nearest Neighbors](https://gudhi.inria.fr/python/latest/point_cloud.html#k-nearest-neighbors) + that wraps several implementations for computing the k nearest neighbors in a point set. + - A new module [Distance To Measure](https://gudhi.inria.fr/python/latest/point_cloud.html#distance-to-measure) + to compute the distance to the empirical measure defined by a point set + +- [Persistence representations](https://gudhi.inria.fr/python/latest/representations.html) + - Interface to Wasserstein distances. + +- Rips complex + - A new module [Weighted Rips Complex](https://gudhi.inria.fr/python/latest/rips_complex_user.html#weighted-rips-complex) + to construct a simplicial complex from a distance matrix and weights on vertices. + +- [Wassertein distance](https://gudhi.inria.fr/python/latest/wasserstein_distance_user.html) + - An [another implementation](https://gudhi.inria.fr/python/latest/wasserstein_distance_user.html#hera) + comes from Hera (BSD-3-Clause) which is based on [Geometry Helps to Compare Persistence Diagrams](http://doi.acm.org/10.1145/3064175) + by Michael Kerber, Dmitriy Morozov, and Arnur Nigmetov. + - `gudhi.wasserstein.wasserstein_distance` has now an option to return the optimal matching that achieves the distance between the two diagrams. + - A new module [Barycenters](https://gudhi.inria.fr/python/latest/wasserstein_distance_user.html#barycenters) + to estimate the Frechet mean (aka Wasserstein barycenter) between persistence diagrams. + +- [Simplex tree](https://gudhi.inria.fr/python/latest/simplex_tree_ref.html) + - Extend filtration method to compute extended persistence + - Flag and lower star persistence pairs generators + - A new interface to filtration, simplices and skeleton getters to return an iterator + +- [Alpha complex](https://gudhi.inria.fr/doc/latest/group__alpha__complex.html) + - Improve computations (cache circumcenters computation and point comparison improvement) + +- [Persistence graphical tools](https://gudhi.inria.fr/python/latest/persistence_graphical_tools_user.html) + - New rendering option proposed (use LaTeX style, add grey block, improved positioning of labels, etc.). + - Can now handle (N x 2) numpy arrays as input + +- Miscellaneous + - The [list of bugs that were solved since GUDHI-3.2.0](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.2.0+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/.github/test-requirements.txt b/.github/test-requirements.txt new file mode 100644 index 00000000..fb1df134 --- /dev/null +++ b/.github/test-requirements.txt @@ -0,0 +1,13 @@ +pytest +sphinx +sphinxcontrib-bibtex +sphinx-paramlinks +matplotlib +scipy +scikit-learn +POT +tensorflow +torch +pykeops +hnswlib +eagerpy diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 00000000..f70c570d --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "ext/hera"] + path = ext/hera + url = https://github.com/grey-narn/hera.git diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 60d32ef8..00000000 --- a/.travis.yml +++ /dev/null @@ -1,78 +0,0 @@ -language: cpp - -sudo: required - -git: - depth: 3 - -os: osx -osx_image: xcode10.2 -compiler: clang - -matrix: - include: - - env: - # 1. Only examples and associated tests - - CMAKE_EXAMPLE='ON' CMAKE_TEST='OFF' CMAKE_UTILITIES='OFF' CMAKE_PYTHON='OFF' MAKE_TARGET='all' CTEST_COMMAND='ctest --output-on-failure' - - env: - # 2. Only unitary tests - - CMAKE_EXAMPLE='OFF' CMAKE_TEST='ON' CMAKE_UTILITIES='OFF' CMAKE_PYTHON='OFF' MAKE_TARGET='all' CTEST_COMMAND='ctest --output-on-failure' - - env: - # 3. Only utilities and associated tests - - CMAKE_EXAMPLE='OFF' CMAKE_TEST='OFF' CMAKE_UTILITIES='ON' CMAKE_PYTHON='OFF' MAKE_TARGET='all' CTEST_COMMAND='ctest --output-on-failure' - - env: - # 4. Only doxygen documentation - - CMAKE_EXAMPLE='OFF' CMAKE_TEST='OFF' CMAKE_UTILITIES='OFF' CMAKE_PYTHON='OFF' MAKE_TARGET='doxygen' CTEST_COMMAND='echo No tests for doxygen target' - - env: - # 5. Only Python, associated tests and sphinx documentation - # $ which python3 => /usr/local/bin/python3 - # cmake => -- Found PythonInterp: /usr/local/bin/python3 (found version "3.7.5") - # In python3-sphinx-build.py, print(sys.executable) => /usr/local/opt/python/bin/python3.7 ??? - # should be : MAKE_TARGET='all sphinx' CTEST_COMMAND='ctest --output-on-failure' - - CMAKE_EXAMPLE='OFF' CMAKE_TEST='OFF' CMAKE_UTILITIES='OFF' CMAKE_PYTHON='ON' MAKE_TARGET='all' CTEST_COMMAND='ctest --output-on-failure -E sphinx' - -cache: - directories: - - $HOME/.cache/pip - - $HOME/Library/Caches/Homebrew - -before_install: - - brew update && brew unlink python@2 && brew upgrade python - -addons: - homebrew: - packages: - - python3 - - cmake - - graphviz - - doxygen - - boost - - eigen - - gmp - - mpfr - - tbb - - cgal - -before_cache: - - rm -f $HOME/.cache/pip/log/debug.log - - brew cleanup - -# When installing through libcgal-dev apt, CMake Error at CGAL Exports.cmake The imported target "CGAL::CGAL Qt5" references the file -install: - - python3 -m pip install --upgrade pip setuptools wheel - - python3 -m pip install --user pytest Cython sphinx sphinxcontrib-bibtex sphinx-paramlinks matplotlib numpy scipy scikit-learn - - python3 -m pip install --user POT - -script: - - rm -rf build - - mkdir -p build - - cd build - - cmake -DCMAKE_BUILD_TYPE=Release -DWITH_GUDHI_EXAMPLE=${CMAKE_EXAMPLE} -DWITH_GUDHI_TEST=${CMAKE_TEST} -DWITH_GUDHI_UTILITIES=${CMAKE_UTILITIES} -DWITH_GUDHI_PYTHON=${CMAKE_PYTHON} -DUSER_VERSION_DIR=version -DPython_ADDITIONAL_VERSIONS=3 .. - - make ${MAKE_TARGET} - - ${CTEST_COMMAND} - - cd .. - -notifications: - email: - on_success: change # default: always - on_failure: always # default: always diff --git a/CMakeGUDHIVersion.txt b/CMakeGUDHIVersion.txt index 0f827b9e..ac89fa4d 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 1) +set (GUDHI_MINOR_VERSION 2) +set (GUDHI_PATCH_VERSION 0) set(GUDHI_VERSION ${GUDHI_MAJOR_VERSION}.${GUDHI_MINOR_VERSION}.${GUDHI_PATCH_VERSION}) message(STATUS "GUDHI version : ${GUDHI_VERSION}") diff --git a/CMakeLists.txt b/CMakeLists.txt index 5dcc6803..0b5f5144 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -65,5 +65,7 @@ include(GUDHI_user_version_target) # For "make doxygen" - Requires GUDHI_USER_VERSION_DIR to be set - Done in GUDHI_user_version_target for dev version include(GUDHI_doxygen_target) +configure_file(${CMAKE_SOURCE_DIR}/.github/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 b7d0dcca..c2e8a8f5 100644 --- a/Dockerfile_for_circleci_image +++ b/Dockerfile_for_circleci_image @@ -30,7 +30,7 @@ RUN apt-get install -y make \ cmake \ graphviz \ perl \ - texlive-bibtex-extra \ + texlive-full \ biber \ doxygen \ libboost-all-dev \ @@ -42,23 +42,17 @@ RUN apt-get install -y make \ locales \ python3 \ python3-pip \ - python3-pytest \ python3-tk \ - python3-pybind11 \ + python3-grpcio \ libfreetype6-dev \ - pkg-config + pkg-config \ + curl -RUN pip3 install \ - setuptools \ - numpy \ - matplotlib \ - scipy \ - Cython \ - POT \ - scikit-learn \ - sphinx \ - sphinx-paramlinks \ - sphinxcontrib-bibtex +ADD .github/build-requirements.txt / +ADD .github/test-requirements.txt / + +RUN pip3 install -r build-requirements.txt +RUN pip3 --no-cache-dir install -r test-requirements.txt # apt clean up RUN apt autoremove && rm -rf /var/lib/apt/lists/* diff --git a/Dockerfile_gudhi_installation b/Dockerfile_gudhi_installation index d5d86338..f9e8813b 100644 --- a/Dockerfile_gudhi_installation +++ b/Dockerfile_gudhi_installation @@ -42,6 +42,7 @@ RUN apt-get install -y make \ python3-pip \ python3-pytest \ python3-tk \ + python3-pybind11 \ libfreetype6-dev \ pkg-config \ curl @@ -57,11 +58,11 @@ RUN pip3 install \ # 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-3.1.0/gudhi.3.1.0.tar.gz" \ -&& tar xf gudhi.3.1.0.tar.gz \ -&& cd gudhi.3.1.0 \ +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 @@ -1,7 +1,7 @@ -[](https://travis-ci.org/GUDHI/gudhi-devel) -[](https://circleci.com/gh/GUDHI/gudhi-devel/tree/master) -[](https://ci.appveyor.com/project/GUDHI/gudhi-devel/branch/master) +[](https://dev.azure.com/GUDHI/gudhi-devel/_build/latest?definitionId=1&branchName=master) +[](https://circleci.com/gh/GUDHI/gudhi-devel/tree/master) +[](https://ci.appveyor.com/project/GUDHI/gudhi-devel/branch/master) [](https://anaconda.org/conda-forge/gudhi) [](https://anaconda.org/conda-forge/gudhi) @@ -10,6 +10,15 @@ The GUDHI library is a generic open source C++ library, with a Python interface, for Topological Data Analysis (TDA) and Higher Dimensional Geometry Understanding. The library offers state-of-the-art data structures and algorithms to construct simplicial complexes and compute persistent homology. +# Source code + +We recommend that users get official releases from [the GUDHI website](https://gudhi.inria.fr/). + +For potential contributors, to fully checkout GUDHI, after cloning the git repository, you may also need to checkout its submodules using +```sh +git submodule update --init +``` + # Compilation and installation To install GUDHI, you can follow the [C++ compilation procedure](https://gudhi.inria.fr/doc/latest/installation.html), the [Python compilation procedure](https://gudhi.inria.fr/python/latest/installation.html), use our [conda-forge package](https://gudhi.inria.fr/conda/), or [go with Docker](https://gudhi.inria.fr/dockerfile/). diff --git a/azure-pipelines.yml b/azure-pipelines.yml new file mode 100644 index 00000000..7b5334a7 --- /dev/null +++ b/azure-pipelines.yml @@ -0,0 +1,37 @@ +jobs: + + - job: 'Test' + displayName: "Build and test" + timeoutInMinutes: 0 + cancelTimeoutInMinutes: 60 + pool: + vmImage: macOS-10.14 + variables: + pythonVersion: '3.6' + cmakeBuildType: Release + customInstallation: 'brew update && brew install graphviz doxygen boost eigen gmp mpfr tbb cgal' + + steps: + - bash: echo "##vso[task.prependpath]$CONDA/bin" + displayName: Add conda to PATH + + - bash: sudo conda create --yes --quiet --name gudhi_build_env + displayName: Create Anaconda environment + + - bash: | + source activate gudhi_build_env + sudo conda install --yes --quiet --name gudhi_build_env python=$(pythonVersion) + python -m pip install --user -r .github/build-requirements.txt + python -m pip install --user -r .github/test-requirements.txt + $(customInstallation) + displayName: 'Install build dependencies' + - bash: | + source activate gudhi_build_env + git submodule update --init + mkdir build + cd build + cmake -DCMAKE_BUILD_TYPE:STRING=$(cmakeBuildType) -DWITH_GUDHI_TEST=ON -DWITH_GUDHI_UTILITIES=ON -DWITH_GUDHI_PYTHON=ON -DPython_ADDITIONAL_VERSIONS=3 .. + make -j 4 + make doxygen + ctest -j 4 --output-on-failure -E sphinx # remove sphinx build as it fails + displayName: 'Build, test and documentation generation' diff --git a/biblio/bibliography.bib b/biblio/bibliography.bib index a1b951e0..677ed4df 100644 --- a/biblio/bibliography.bib +++ b/biblio/bibliography.bib @@ -7,11 +7,15 @@ } @article{Carriere17c, - author = {Carri\`ere, Mathieu and Michel, Bertrand and Oudot, Steve}, - title = {{Statistical Analysis and Parameter Selection for Mapper}}, - journal = {CoRR}, - volume = {abs/1706.00204}, - year = {2017} +author = {Carri{\`{e}}re, Mathieu and Michel, Bertrand and Oudot, Steve}, +journal = {Journal of Machine Learning Research}, +pages = {1--39}, +publisher = {JMLR.org}, +title = {{Statistical analysis and parameter selection for Mapper}}, +volume = {19}, +year = {2018}, +url = {http://jmlr.org/papers/v19/17-291.html}, +doi = {10.5555/3291125.3291137} } @inproceedings{Dey13, @@ -20,14 +24,19 @@ booktitle = {Proceedings of the Twenty-ninth Annual Symposium on Computational Geometry}, year = {2013}, pages = {107--116}, + doi = {10.1145/2462356.2462387} } @article{Carriere16, - title={{Structure and Stability of the 1-Dimensional Mapper}}, - author={Carri\`ere, Mathieu and Oudot, Steve}, - journal={CoRR}, - volume= {abs/1511.05823}, - year={2015} +author = {Carri{\`{e}}re, Mathieu and Oudot, Steve}, +journal = {Foundations of Computational Mathematics}, +number = {6}, +pages = {1333--1396}, +publisher = {Springer-Verlag}, +doi = {10.1007/s10208-017-9370-z}, +title = {{Structure and stability of the one-dimensional Mapper}}, +volume = {18}, +year = {2017} } @inproceedings{zigzag_reflection, @@ -36,6 +45,18 @@ year = {2014 $\ \ \ \ \ \ \ \ \ \ \ $ \emph{In Preparation}}, } +@article{Cohen-Steiner2009, +author = {Cohen-Steiner, David and Edelsbrunner, Herbert and Harer, John}, +journal = {Foundations of Computational Mathematics}, +number = {1}, +pages = {79--103}, +publisher = {Springer-Verlag}, +doi = {10.1007/s10208-008-9027-z}, +title = {{Extending persistence using Poincar{\'{e}} and Lefschetz duality}}, +volume = {9}, +year = {2009} +} + @misc{gudhi_stpcoh, author = {Cl\'ement Maria}, title = "\textsc{Gudhi}, Simplex Tree and Persistent Cohomology Packages", @@ -814,6 +835,7 @@ book{hatcher2002algebraic, number = {4}, year = {2010}, pages = {367-405}, + doi = {10.1007/s10208-010-9066-0}, ee = {http://dx.doi.org/10.1007/s10208-010-9066-0}, bibsource = {DBLP, http://dblp.uni-trier.de} } @@ -909,6 +931,7 @@ language={English} booktitle = {Symposium on Computational Geometry}, year = {2014}, pages = {345}, + doi = {10.1145/2582112.2582165}, ee = {http://doi.acm.org/10.1145/2582112.2582165}, bibsource = {DBLP, http://dblp.uni-trier.de} } @@ -1180,3 +1203,79 @@ language={English} booktitle = {In Neural Information Processing Systems}, year = {2007} } +@inproceedings{10.5555/3327546.3327645, +author = {Lacombe, Th\'{e}o and Cuturi, Marco and Oudot, Steve}, +title = {Large Scale Computation of Means and Clusters for Persistence Diagrams Using Optimal Transport}, +year = {2018}, +publisher = {Curran Associates Inc.}, +address = {Red Hook, NY, USA}, +booktitle = {Proceedings of the 32nd International Conference on Neural Information Processing Systems}, +pages = {9792–9802}, +numpages = {11}, +location = {Montr\'{e}al, Canada}, +series = {NIPS’18} +} +@Article{dtm, +author={Chazal, Fr{\'e}d{\'e}ric +and Cohen-Steiner, David +and M{\'e}rigot, Quentin}, +title={Geometric Inference for Probability Measures}, +journal={Foundations of Computational Mathematics}, +year={2011}, +volume={11}, +number={6}, +pages={733-751}, +abstract={Data often comes in the form of a point cloud sampled from an unknown compact subset of Euclidean space. The general goal of geometric inference is then to recover geometric and topological features (e.g., Betti numbers, normals) of this subset from the approximating point cloud data. It appears that the study of distance functions allows one to address many of these questions successfully. However, one of the main limitations of this framework is that it does not cope well with outliers or with background noise. In this paper, we show how to extend the framework of distance functions to overcome this problem. Replacing compact subsets by measures, we introduce a notion of distance function to a probability distribution in Rd. These functions share many properties with classical distance functions, which make them suitable for inference purposes. In particular, by considering appropriate level sets of these distance functions, we show that it is possible to reconstruct offsets of sampled shapes with topological guarantees even in the presence of outliers. Moreover, in settings where empirical measures are considered, these functions can be easily evaluated, making them of particular practical interest.}, +issn={1615-3383}, +doi={10.1007/s10208-011-9098-0}, +url={https://doi.org/10.1007/s10208-011-9098-0} +} +@article{dtmdensity, +author = "Biau, Gérard and Chazal, Frédéric and Cohen-Steiner, David and Devroye, Luc and RodrÃguez, Carlos", +doi = "10.1214/11-EJS606", +fjournal = "Electronic Journal of Statistics", +journal = "Electron. J. Statist.", +pages = "204--237", +publisher = "The Institute of Mathematical Statistics and the Bernoulli Society", +title = "A weighted k-nearest neighbor density estimate for geometric inference", +url = "https://doi.org/10.1214/11-EJS606", +volume = "5", +year = "2011" +} +@article{turner2014frechet, + title={Fr{\'e}chet means for distributions of persistence diagrams}, + author={Turner, Katharine and Mileyko, Yuriy and Mukherjee, Sayan and Harer, John}, + journal={Discrete \& Computational Geometry}, + doi={10.1007/s00454-014-9604-7}, + volume={52}, + number={1}, + pages={44--70}, + year={2014}, + publisher={Springer} +} + +@inproceedings{dtmfiltrations, + author = {Hirokazu Anai and + Fr{\'{e}}d{\'{e}}ric Chazal and + Marc Glisse and + Yuichi Ike and + Hiroya Inakoshi and + Rapha{\"{e}}l Tinarrage and + Yuhei Umeda}, + editor = {Gill Barequet and + Yusu Wang}, + title = {DTM-Based Filtrations}, + booktitle = {35th International Symposium on Computational Geometry, SoCG 2019, + June 18-21, 2019, Portland, Oregon, {USA}}, + series = {LIPIcs}, + volume = {129}, + pages = {58:1--58:15}, + publisher = {Schloss Dagstuhl - Leibniz-Zentrum f{\"{u}}r Informatik}, + year = {2019}, + url = {https://doi.org/10.4230/LIPIcs.SoCG.2019.58}, + doi = {10.4230/LIPIcs.SoCG.2019.58}, + timestamp = {Tue, 11 Feb 2020 15:52:14 +0100}, + biburl = {https://dblp.org/rec/conf/compgeom/AnaiCGIITU19.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} + diff --git a/ext/hera b/ext/hera new file mode 160000 +Subproject 0019cae9dc1e9d11aa03bc59681435ba7f21eea diff --git a/next_release.md b/next_release.md deleted file mode 100644 index 78270d15..00000000 --- a/next_release.md +++ /dev/null @@ -1,14 +0,0 @@ -gudhi-3.1.1 is a bug-fix release. In particular, it fixes the installation of the Python representation module. - -The [list of bugs that were solved since gudhi-3.1.0](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/3.1.1/installation.html) or [Python](https://gudhi.inria.fr/python/3.1.1/installation.html)), please visit the [GUDHI web site](https://gudhi.inria.fr/). diff --git a/src/Alpha_complex/benchmark/Alpha_complex_3d_benchmark.cpp b/src/Alpha_complex/benchmark/Alpha_complex_3d_benchmark.cpp index 99ad94b9..e7d85686 100644 --- a/src/Alpha_complex/benchmark/Alpha_complex_3d_benchmark.cpp +++ b/src/Alpha_complex/benchmark/Alpha_complex_3d_benchmark.cpp @@ -19,7 +19,7 @@ std::ofstream results_csv("results.csv"); template <typename Kernel> void benchmark_points_on_torus_dD(const std::string& msg) { - std::cout << "+ " << msg << std::endl; + std::clog << "+ " << msg << std::endl; results_csv << "\"" << msg << "\";" << std::endl; results_csv << "\"nb_points\";" @@ -29,7 +29,7 @@ void benchmark_points_on_torus_dD(const std::string& msg) { using K = CGAL::Epick_d<CGAL::Dimension_tag<3>>; for (int nb_points = 1000; nb_points <= 125000; nb_points *= 5) { - std::cout << " Alpha complex dD on torus with " << nb_points << " points." << std::endl; + std::clog << " Alpha complex dD on torus with " << nb_points << " points." << std::endl; std::vector<K::Point_d> points_on_torus = Gudhi::generate_points_on_torus_3D<K>(nb_points, 1.0, 0.5); std::vector<typename Kernel::Point_d> points; @@ -41,26 +41,26 @@ void benchmark_points_on_torus_dD(const std::string& msg) { ac_create_clock.begin(); Gudhi::alpha_complex::Alpha_complex<Kernel> alpha_complex_from_points(points); ac_create_clock.end(); - std::cout << ac_create_clock; + std::clog << ac_create_clock; Gudhi::Simplex_tree<> complex; Gudhi::Clock st_create_clock(" benchmark_points_on_torus_dD - complex creation"); st_create_clock.begin(); alpha_complex_from_points.create_complex(complex); st_create_clock.end(); - std::cout << st_create_clock; + std::clog << st_create_clock; results_csv << nb_points << ";" << complex.num_simplices() << ";" << ac_create_clock.num_seconds() << ";" << st_create_clock.num_seconds() << ";" << std::endl; - std::cout << " benchmark_points_on_torus_dD - nb simplices = " << complex.num_simplices() << std::endl; + std::clog << " benchmark_points_on_torus_dD - nb simplices = " << complex.num_simplices() << std::endl; } } template <typename Alpha_complex_3d> void benchmark_points_on_torus_3D(const std::string& msg) { using K = CGAL::Epick_d<CGAL::Dimension_tag<3>>; - std::cout << "+ " << msg << std::endl; + std::clog << "+ " << msg << std::endl; results_csv << "\"" << msg << "\";" << std::endl; results_csv << "\"nb_points\";" @@ -69,7 +69,7 @@ void benchmark_points_on_torus_3D(const std::string& msg) { << "\"complex_creation_time(sec.)\";" << std::endl; for (int nb_points = 1000; nb_points <= 125000; nb_points *= 5) { - std::cout << " Alpha complex 3d on torus with " << nb_points << " points." << std::endl; + std::clog << " Alpha complex 3d on torus with " << nb_points << " points." << std::endl; std::vector<K::Point_d> points_on_torus = Gudhi::generate_points_on_torus_3D<K>(nb_points, 1.0, 0.5); std::vector<typename Alpha_complex_3d::Point_3> points; @@ -81,19 +81,19 @@ void benchmark_points_on_torus_3D(const std::string& msg) { ac_create_clock.begin(); Alpha_complex_3d alpha_complex_from_points(points); ac_create_clock.end(); - std::cout << ac_create_clock; + std::clog << ac_create_clock; Gudhi::Simplex_tree<> complex; Gudhi::Clock st_create_clock(" benchmark_points_on_torus_3D - complex creation"); st_create_clock.begin(); alpha_complex_from_points.create_complex(complex); st_create_clock.end(); - std::cout << st_create_clock; + std::clog << st_create_clock; results_csv << nb_points << ";" << complex.num_simplices() << ";" << ac_create_clock.num_seconds() << ";" << st_create_clock.num_seconds() << ";" << std::endl; - std::cout << " benchmark_points_on_torus_3D - nb simplices = " << complex.num_simplices() << std::endl; + std::clog << " benchmark_points_on_torus_3D - nb simplices = " << complex.num_simplices() << std::endl; } } @@ -101,7 +101,7 @@ template <typename Weighted_alpha_complex_3d> void benchmark_weighted_points_on_torus_3D(const std::string& msg) { using K = CGAL::Epick_d<CGAL::Dimension_tag<3>>; - std::cout << "+ " << msg << std::endl; + std::clog << "+ " << msg << std::endl; results_csv << "\"" << msg << "\";" << std::endl; results_csv << "\"nb_points\";" @@ -112,7 +112,7 @@ void benchmark_weighted_points_on_torus_3D(const std::string& msg) { CGAL::Random random(8); for (int nb_points = 1000; nb_points <= 125000; nb_points *= 5) { - std::cout << " Alpha complex 3d on torus with " << nb_points << " points." << std::endl; + std::clog << " Alpha complex 3d on torus with " << nb_points << " points." << std::endl; std::vector<K::Point_d> points_on_torus = Gudhi::generate_points_on_torus_3D<K>(nb_points, 1.0, 0.5); using Point = typename Weighted_alpha_complex_3d::Bare_point_3; @@ -128,25 +128,25 @@ void benchmark_weighted_points_on_torus_3D(const std::string& msg) { ac_create_clock.begin(); Weighted_alpha_complex_3d alpha_complex_from_points(points); ac_create_clock.end(); - std::cout << ac_create_clock; + std::clog << ac_create_clock; Gudhi::Simplex_tree<> complex; Gudhi::Clock st_create_clock(" benchmark_weighted_points_on_torus_3D - complex creation"); st_create_clock.begin(); alpha_complex_from_points.create_complex(complex); st_create_clock.end(); - std::cout << st_create_clock; + std::clog << st_create_clock; results_csv << nb_points << ";" << complex.num_simplices() << ";" << ac_create_clock.num_seconds() << ";" << st_create_clock.num_seconds() << ";" << std::endl; - std::cout << " benchmark_weighted_points_on_torus_3D - nb simplices = " << complex.num_simplices() << std::endl; + std::clog << " benchmark_weighted_points_on_torus_3D - nb simplices = " << complex.num_simplices() << std::endl; } } template <typename Periodic_alpha_complex_3d> void benchmark_periodic_points(const std::string& msg) { - std::cout << "+ " << msg << std::endl; + std::clog << "+ " << msg << std::endl; results_csv << "\"" << msg << "\";" << std::endl; results_csv << "\"nb_points\";" @@ -157,7 +157,7 @@ void benchmark_periodic_points(const std::string& msg) { CGAL::Random random(8); for (double nb_points = 10.; nb_points <= 40.; nb_points += 10.) { - std::cout << " Periodic alpha complex 3d with " << nb_points * nb_points * nb_points << " points." << std::endl; + std::clog << " Periodic alpha complex 3d with " << nb_points * nb_points * nb_points << " points." << std::endl; using Point = typename Periodic_alpha_complex_3d::Point_3; std::vector<Point> points; @@ -174,25 +174,25 @@ void benchmark_periodic_points(const std::string& msg) { ac_create_clock.begin(); Periodic_alpha_complex_3d alpha_complex_from_points(points, 0., 0., 0., nb_points, nb_points, nb_points); ac_create_clock.end(); - std::cout << ac_create_clock; + std::clog << ac_create_clock; Gudhi::Simplex_tree<> complex; Gudhi::Clock st_create_clock(" benchmark_periodic_points - complex creation"); st_create_clock.begin(); alpha_complex_from_points.create_complex(complex); st_create_clock.end(); - std::cout << st_create_clock; + std::clog << st_create_clock; results_csv << nb_points * nb_points * nb_points << ";" << complex.num_simplices() << ";" << ac_create_clock.num_seconds() << ";" << st_create_clock.num_seconds() << ";" << std::endl; - std::cout << " benchmark_periodic_points - nb simplices = " << complex.num_simplices() << std::endl; + std::clog << " benchmark_periodic_points - nb simplices = " << complex.num_simplices() << std::endl; } } template <typename Weighted_periodic_alpha_complex_3d> void benchmark_weighted_periodic_points(const std::string& msg) { - std::cout << "+ " << msg << std::endl; + std::clog << "+ " << msg << std::endl; results_csv << "\"" << msg << "\";" << std::endl; results_csv << "\"nb_points\";" @@ -203,7 +203,7 @@ void benchmark_weighted_periodic_points(const std::string& msg) { CGAL::Random random(8); for (double nb_points = 10.; nb_points <= 40.; nb_points += 10.) { - std::cout << " Weighted periodic alpha complex 3d with " << nb_points * nb_points * nb_points << " points." + std::clog << " Weighted periodic alpha complex 3d with " << nb_points * nb_points * nb_points << " points." << std::endl; using Point = typename Weighted_periodic_alpha_complex_3d::Bare_point_3; @@ -224,19 +224,19 @@ void benchmark_weighted_periodic_points(const std::string& msg) { ac_create_clock.begin(); Weighted_periodic_alpha_complex_3d alpha_complex_from_points(points, 0., 0., 0., nb_points, nb_points, nb_points); ac_create_clock.end(); - std::cout << ac_create_clock; + std::clog << ac_create_clock; Gudhi::Simplex_tree<> complex; Gudhi::Clock st_create_clock(" benchmark_weighted_periodic_points - complex creation"); st_create_clock.begin(); alpha_complex_from_points.create_complex(complex); st_create_clock.end(); - std::cout << st_create_clock; + std::clog << st_create_clock; results_csv << nb_points * nb_points * nb_points << ";" << complex.num_simplices() << ";" << ac_create_clock.num_seconds() << ";" << st_create_clock.num_seconds() << ";" << std::endl; - std::cout << " benchmark_weighted_periodic_points - nb simplices = " << complex.num_simplices() << std::endl; + std::clog << " benchmark_weighted_periodic_points - nb simplices = " << complex.num_simplices() << std::endl; } } diff --git a/src/Alpha_complex/concept/SimplicialComplexForAlpha.h b/src/Alpha_complex/concept/SimplicialComplexForAlpha.h index 1c6c3b0c..c20c3201 100644 --- a/src/Alpha_complex/concept/SimplicialComplexForAlpha.h +++ b/src/Alpha_complex/concept/SimplicialComplexForAlpha.h @@ -72,6 +72,24 @@ struct SimplicialComplexForAlpha { /** \brief Return type of an insertion of a simplex */ typedef unspecified Insertion_result_type; + + /** \name Map interface + * Conceptually a `std::unordered_map<Simplex_handle,std::size_t>`. + * @{ */ + /** \brief Data stored for each simplex. + * + * Must be an integer type. */ + typedef unspecified Simplex_key; + /** \brief Returns a constant dummy number that is either negative, + * or at least as large as the number of simplices. Suggested value: -1. */ + Simplex_key null_key (); + /** \brief Returns the number stored for a simplex by `assign_key()`. + * + * If `assign_key()` has not been called, it must return `null_key()`. */ + Simplex_key key ( Simplex_handle sh ); + /** \brief Store a number for a simplex, which can later be retrieved with `key()`. */ + void assign_key(Simplex_handle sh, Simplex_key n); + /** @} */ }; } // namespace alpha_complex diff --git a/src/Alpha_complex/doc/Intro_alpha_complex.h b/src/Alpha_complex/doc/Intro_alpha_complex.h index a8b1a106..60da7169 100644 --- a/src/Alpha_complex/doc/Intro_alpha_complex.h +++ b/src/Alpha_complex/doc/Intro_alpha_complex.h @@ -46,16 +46,17 @@ namespace alpha_complex { * \cite cgal:s-gkd-19b from CGAL as template parameter. * * \remark - * - When an \f$\alpha\f$-complex is constructed with an infinite value of \f$ \alpha^2 \f$, the complex is a Delaunay - * complex (with special filtration values). + * - When the simplicial complex is constructed with an infinite value of \f$ \alpha^2 \f$, the complex is a Delaunay + * complex with special filtration values. The Delaunay complex without filtration values is also available by passing + * `default_filtration_value=true` to `Alpha_complex::create_complex`. * - For people only interested in the topology of the \ref alpha_complex (for instance persistence), * \ref alpha_complex is equivalent to the \ref cech_complex and much smaller if you do not bound the radii. * \ref cech_complex can still make sense in higher dimension precisely because you can bound the radii. - * - Using the default `CGAL::Epeck_d` makes the construction safe. If you pass exact=true to create_complex, the - * filtration values are the exact ones converted to the filtration value type of the simplicial complex. This can be - * very slow. If you pass exact=false (the default), the filtration values are only guaranteed to have a small - * multiplicative error compared to the exact value, see <code><a class="el" target="_blank" - * href="https://doc.cgal.org/latest/Number_types/classCGAL_1_1Lazy__exact__nt.html"> + * - Using the default `CGAL::Epeck_d` makes the construction safe. If you pass `exact=true` to + * `Alpha_complex::create_complex`, the filtration values are the exact ones converted to the filtration value type of + * the simplicial complex. This can be very slow. If you pass `exact=false` (the default), the filtration values are + * only guaranteed to have a small multiplicative error compared to the exact value, see <code> + * <a class="el" target="_blank" href="https://doc.cgal.org/latest/Number_types/classCGAL_1_1Lazy__exact__nt.html"> * CGAL::Lazy_exact_nt<NT>::set_relative_precision_of_to_double</a></code> for details. A drawback, when computing * persistence, is that an empty exact interval [10^12,10^12] may become a non-empty approximate interval * [10^12,10^12+10^6]. Using `CGAL::Epick_d` makes the computations slightly faster, and the combinatorics are still diff --git a/src/Alpha_complex/example/Alpha_complex_3d_from_points.cpp b/src/Alpha_complex/example/Alpha_complex_3d_from_points.cpp index 0e359a27..a2c85138 100644 --- a/src/Alpha_complex/example/Alpha_complex_3d_from_points.cpp +++ b/src/Alpha_complex/example/Alpha_complex_3d_from_points.cpp @@ -38,18 +38,18 @@ int main(int argc, char **argv) { // ---------------------------------------------------------------------------- // Display information about the alpha complex // ---------------------------------------------------------------------------- - std::cout << "Alpha complex is of dimension " << simplex.dimension() << " - " << simplex.num_simplices() + std::clog << "Alpha complex is of dimension " << simplex.dimension() << " - " << simplex.num_simplices() << " simplices - " << simplex.num_vertices() << " vertices." << std::endl; - std::cout << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" << std::endl; + std::clog << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" << std::endl; for (auto f_simplex : simplex.filtration_simplex_range()) { - std::cout << " ( "; + std::clog << " ( "; for (auto vertex : simplex.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> " + std::clog << ") -> " << "[" << simplex.filtration(f_simplex) << "] "; - std::cout << std::endl; + std::clog << std::endl; } } return 0; diff --git a/src/Alpha_complex/example/Alpha_complex_from_off.cpp b/src/Alpha_complex/example/Alpha_complex_from_off.cpp index 220a66de..dba1710e 100644 --- a/src/Alpha_complex/example/Alpha_complex_from_off.cpp +++ b/src/Alpha_complex/example/Alpha_complex_from_off.cpp @@ -30,7 +30,7 @@ int main(int argc, char **argv) { ouput_file_stream.open(std::string(argv[3])); streambuffer = ouput_file_stream.rdbuf(); } else { - streambuffer = std::cout.rdbuf(); + streambuffer = std::clog.rdbuf(); } Gudhi::Simplex_tree<> simplex; diff --git a/src/Alpha_complex/example/Alpha_complex_from_points.cpp b/src/Alpha_complex/example/Alpha_complex_from_points.cpp index 6526ca3a..c79535bf 100644 --- a/src/Alpha_complex/example/Alpha_complex_from_points.cpp +++ b/src/Alpha_complex/example/Alpha_complex_from_points.cpp @@ -35,18 +35,18 @@ int main() { // ---------------------------------------------------------------------------- // Display information about the alpha complex // ---------------------------------------------------------------------------- - std::cout << "Alpha complex is of dimension " << simplex.dimension() << + std::clog << "Alpha complex is of dimension " << simplex.dimension() << " - " << simplex.num_simplices() << " simplices - " << simplex.num_vertices() << " vertices." << std::endl; - std::cout << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" << std::endl; + std::clog << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" << std::endl; for (auto f_simplex : simplex.filtration_simplex_range()) { - std::cout << " ( "; + std::clog << " ( "; for (auto vertex : simplex.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> " << "[" << simplex.filtration(f_simplex) << "] "; - std::cout << std::endl; + std::clog << ") -> " << "[" << simplex.filtration(f_simplex) << "] "; + std::clog << std::endl; } } return 0; diff --git a/src/Alpha_complex/example/CMakeLists.txt b/src/Alpha_complex/example/CMakeLists.txt index b0337934..2eecd50c 100644 --- a/src/Alpha_complex/example/CMakeLists.txt +++ b/src/Alpha_complex/example/CMakeLists.txt @@ -32,14 +32,18 @@ if (DIFF_PATH) add_test(Alpha_complex_example_from_off_60_diff_files ${DIFF_PATH} ${CMAKE_CURRENT_BINARY_DIR}/alphaoffreader_result_60.txt ${CMAKE_CURRENT_BINARY_DIR}/alphaoffreader_for_doc_60.txt) + set_tests_properties(Alpha_complex_example_from_off_60_diff_files PROPERTIES DEPENDS Alpha_complex_example_from_off_60) add_test(Alpha_complex_example_from_off_32_diff_files ${DIFF_PATH} ${CMAKE_CURRENT_BINARY_DIR}/alphaoffreader_result_32.txt ${CMAKE_CURRENT_BINARY_DIR}/alphaoffreader_for_doc_32.txt) + set_tests_properties(Alpha_complex_example_from_off_32_diff_files PROPERTIES DEPENDS Alpha_complex_example_from_off_32) add_test(Alpha_complex_example_fast_from_off_60_diff_files ${DIFF_PATH} ${CMAKE_CURRENT_BINARY_DIR}/fastalphaoffreader_result_60.txt ${CMAKE_CURRENT_BINARY_DIR}/alphaoffreader_for_doc_60.txt) + set_tests_properties(Alpha_complex_example_fast_from_off_60_diff_files PROPERTIES DEPENDS Alpha_complex_example_fast_from_off_60) add_test(Alpha_complex_example_fast_from_off_32_diff_files ${DIFF_PATH} ${CMAKE_CURRENT_BINARY_DIR}/fastalphaoffreader_result_32.txt ${CMAKE_CURRENT_BINARY_DIR}/alphaoffreader_for_doc_32.txt) - endif() + set_tests_properties(Alpha_complex_example_fast_from_off_32_diff_files PROPERTIES DEPENDS Alpha_complex_example_fast_from_off_32) +endif() add_executable ( Alpha_complex_example_weighted_3d_from_points Weighted_alpha_complex_3d_from_points.cpp ) target_link_libraries(Alpha_complex_example_weighted_3d_from_points ${CGAL_LIBRARY}) diff --git a/src/Alpha_complex/example/Fast_alpha_complex_from_off.cpp b/src/Alpha_complex/example/Fast_alpha_complex_from_off.cpp index f181005a..64728470 100644 --- a/src/Alpha_complex/example/Fast_alpha_complex_from_off.cpp +++ b/src/Alpha_complex/example/Fast_alpha_complex_from_off.cpp @@ -35,7 +35,7 @@ int main(int argc, char **argv) { ouput_file_stream.open(std::string(argv[3])); streambuffer = ouput_file_stream.rdbuf(); } else { - streambuffer = std::cout.rdbuf(); + streambuffer = std::clog.rdbuf(); } Gudhi::Simplex_tree<> simplex; diff --git a/src/Alpha_complex/example/Weighted_alpha_complex_3d_from_points.cpp b/src/Alpha_complex/example/Weighted_alpha_complex_3d_from_points.cpp index fcf80802..c044194e 100644 --- a/src/Alpha_complex/example/Weighted_alpha_complex_3d_from_points.cpp +++ b/src/Alpha_complex/example/Weighted_alpha_complex_3d_from_points.cpp @@ -34,18 +34,18 @@ int main(int argc, char **argv) { // ---------------------------------------------------------------------------- // Display information about the alpha complex // ---------------------------------------------------------------------------- - std::cout << "Alpha complex is of dimension " << simplex.dimension() << " - " << simplex.num_simplices() + std::clog << "Alpha complex is of dimension " << simplex.dimension() << " - " << simplex.num_simplices() << " simplices - " << simplex.num_vertices() << " vertices." << std::endl; - std::cout << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" << std::endl; + std::clog << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" << std::endl; for (auto f_simplex : simplex.filtration_simplex_range()) { - std::cout << " ( "; + std::clog << " ( "; for (auto vertex : simplex.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> " + std::clog << ") -> " << "[" << simplex.filtration(f_simplex) << "] "; - std::cout << std::endl; + std::clog << std::endl; } } return 0; diff --git a/src/Alpha_complex/include/gudhi/Alpha_complex.h b/src/Alpha_complex/include/gudhi/Alpha_complex.h index f2a05e95..ba91998d 100644 --- a/src/Alpha_complex/include/gudhi/Alpha_complex.h +++ b/src/Alpha_complex/include/gudhi/Alpha_complex.h @@ -112,9 +112,6 @@ class Alpha_complex { typedef typename Kernel::Side_of_bounded_sphere_d Is_Gabriel; typedef typename Kernel::Point_dimension_d Point_Dimension; - // Type required to compute squared radius, or side of bounded sphere on a vector of points. - typedef typename std::vector<Point_d> Vector_of_CGAL_points; - // Vertex_iterator type from CGAL. typedef typename Delaunay_triangulation::Vertex_iterator CGAL_vertex_iterator; @@ -124,6 +121,9 @@ class Alpha_complex { // Structure to switch from simplex tree vertex handle to CGAL vertex iterator. typedef typename std::vector< CGAL_vertex_iterator > Vector_vertex_iterator; + // Numeric type of coordinates in the kernel + typedef typename Kernel::FT FT; + private: /** \brief Vertex iterator vector to switch from simplex tree vertex handle to CGAL vertex iterator. * Vertex handles are inserted sequentially, starting at 0.*/ @@ -132,6 +132,8 @@ class Alpha_complex { Delaunay_triangulation* triangulation_; /** \brief Kernel for triangulation_ functions access.*/ Kernel kernel_; + /** \brief Cache for geometric constructions: circumcenter and squared radius of a simplex.*/ + std::vector<std::pair<Point_d, FT>> cache_, old_cache_; public: /** \brief Alpha_complex constructor from an OFF file name. @@ -237,7 +239,7 @@ class Alpha_complex { for (CGAL_vertex_iterator vit = triangulation_->vertices_begin(); vit != triangulation_->vertices_end(); ++vit) { if (!triangulation_->is_infinite(*vit)) { #ifdef DEBUG_TRACES - std::cout << "Vertex insertion - " << vit->data() << " -> " << vit->point() << std::endl; + std::clog << "Vertex insertion - " << vit->data() << " -> " << vit->point() << std::endl; #endif // DEBUG_TRACES vertex_handle_to_iterator_[vit->data()] = vit; } @@ -246,18 +248,66 @@ class Alpha_complex { } } + /** \brief get_point_ returns the point corresponding to the vertex given as parameter. + * Only for internal use for faster access. + * + * @param[in] vertex Vertex handle of the point to retrieve. + * @return The point found. + */ + const Point_d& get_point_(std::size_t vertex) const { + return vertex_handle_to_iterator_[vertex]->point(); + } + + /// Return a reference to the circumcenter and circumradius, writing them in the cache if necessary. + template<class SimplicialComplexForAlpha> + auto& get_cache(SimplicialComplexForAlpha& cplx, typename SimplicialComplexForAlpha::Simplex_handle s) { + auto k = cplx.key(s); + if(k==cplx.null_key()){ + k = cache_.size(); + cplx.assign_key(s, k); + // Using a transform_range is slower, currently. + thread_local std::vector<Point_d> v; + v.clear(); + for (auto vertex : cplx.simplex_vertex_range(s)) + v.push_back(get_point_(vertex)); + Point_d c = kernel_.construct_circumcenter_d_object()(v.cbegin(), v.cend()); + FT r = kernel_.squared_distance_d_object()(c, v[0]); + cache_.emplace_back(std::move(c), std::move(r)); + } + return cache_[k]; + } + + /// Return the circumradius, either from the old cache or computed, without writing to the cache. + template<class SimplicialComplexForAlpha> + auto radius(SimplicialComplexForAlpha& cplx, typename SimplicialComplexForAlpha::Simplex_handle s) { + auto k = cplx.key(s); + if(k!=cplx.null_key()) + return old_cache_[k].second; + // Using a transform_range is slower, currently. + thread_local std::vector<Point_d> v; + v.clear(); + for (auto vertex : cplx.simplex_vertex_range(s)) + v.push_back(get_point_(vertex)); + return kernel_.compute_squared_radius_d_object()(v.cbegin(), v.cend()); + } + public: /** \brief Inserts all Delaunay triangulation into the simplicial complex. - * It also computes the filtration values accordingly to the \ref createcomplexalgorithm + * It also computes the filtration values accordingly to the \ref createcomplexalgorithm if default_filtration_value + * is not set. * * \tparam SimplicialComplexForAlpha must meet `SimplicialComplexForAlpha` concept. * * @param[in] complex SimplicialComplexForAlpha to be created. * @param[in] max_alpha_square maximum for alpha square value. Default value is +\f$\infty\f$, and there is very - * little point using anything else since it does not save time. + * little point using anything else since it does not save time. Useless if `default_filtration_value` is set to + * `true`. * @param[in] exact Exact filtration values computation. Not exact if `Kernel` is not <a target="_blank" * href="https://doc.cgal.org/latest/Kernel_d/structCGAL_1_1Epeck__d.html">CGAL::Epeck_d</a>. - * + * @param[in] default_filtration_value Set this value to `true` if filtration values are not needed to be computed + * (will be set to `NaN`). + * Default value is `false` (which means compute the filtration values). + * * @return true if creation succeeds, false otherwise. * * @pre Delaunay triangulation must be already constructed with dimension strictly greater than 0. @@ -269,7 +319,8 @@ class Alpha_complex { typename Filtration_value = typename SimplicialComplexForAlpha::Filtration_value> bool create_complex(SimplicialComplexForAlpha& complex, Filtration_value max_alpha_square = std::numeric_limits<Filtration_value>::infinity(), - bool exact = false) { + bool exact = false, + bool default_filtration_value = false) { // From SimplicialComplexForAlpha type required to insert into a simplicial complex (with or without subfaces). typedef typename SimplicialComplexForAlpha::Vertex_handle Vertex_handle; typedef typename SimplicialComplexForAlpha::Simplex_handle Simplex_handle; @@ -296,19 +347,19 @@ class Alpha_complex { ++cit) { Vector_vertex vertexVector; #ifdef DEBUG_TRACES - std::cout << "Simplex_tree insertion "; + std::clog << "Simplex_tree insertion "; #endif // DEBUG_TRACES for (auto vit = cit->vertices_begin(); vit != cit->vertices_end(); ++vit) { if (*vit != nullptr) { #ifdef DEBUG_TRACES - std::cout << " " << (*vit)->data(); + std::clog << " " << (*vit)->data(); #endif // DEBUG_TRACES // Vector of vertex construction for simplex_tree structure vertexVector.push_back((*vit)->data()); } } #ifdef DEBUG_TRACES - std::cout << std::endl; + std::clog << std::endl; #endif // DEBUG_TRACES // Insert each simplex and its subfaces in the simplex tree - filtration is NaN complex.insert_simplex_and_subfaces(vertexVector, std::numeric_limits<double>::quiet_NaN()); @@ -316,62 +367,48 @@ class Alpha_complex { } // -------------------------------------------------------------------------------------------- - // -------------------------------------------------------------------------------------------- - // Will be re-used many times - Vector_of_CGAL_points pointVector; - // ### For i : d -> 0 - for (int decr_dim = triangulation_->maximal_dimension(); decr_dim >= 0; decr_dim--) { - // ### Foreach Sigma of dim i - for (Simplex_handle f_simplex : complex.skeleton_simplex_range(decr_dim)) { - int f_simplex_dim = complex.dimension(f_simplex); - if (decr_dim == f_simplex_dim) { - pointVector.clear(); -#ifdef DEBUG_TRACES - std::cout << "Sigma of dim " << decr_dim << " is"; -#endif // DEBUG_TRACES - for (auto vertex : complex.simplex_vertex_range(f_simplex)) { - pointVector.push_back(get_point(vertex)); -#ifdef DEBUG_TRACES - std::cout << " " << vertex; -#endif // DEBUG_TRACES - } -#ifdef DEBUG_TRACES - std::cout << std::endl; -#endif // DEBUG_TRACES - // ### If filt(Sigma) is NaN : filt(Sigma) = alpha(Sigma) - if (std::isnan(complex.filtration(f_simplex))) { - Filtration_value alpha_complex_filtration = 0.0; - // No need to compute squared_radius on a single point - alpha is 0.0 - if (f_simplex_dim > 0) { - // squared_radius function initialization - Squared_Radius squared_radius = kernel_.compute_squared_radius_d_object(); - - CGAL::NT_converter<typename Geom_traits::FT, Filtration_value> cv; - auto sqrad = squared_radius(pointVector.begin(), pointVector.end()); + if (!default_filtration_value) { + // -------------------------------------------------------------------------------------------- + // ### For i : d -> 0 + for (int decr_dim = triangulation_->maximal_dimension(); decr_dim >= 0; decr_dim--) { + // ### Foreach Sigma of dim i + for (Simplex_handle f_simplex : complex.skeleton_simplex_range(decr_dim)) { + int f_simplex_dim = complex.dimension(f_simplex); + if (decr_dim == f_simplex_dim) { + // ### If filt(Sigma) is NaN : filt(Sigma) = alpha(Sigma) + if (std::isnan(complex.filtration(f_simplex))) { + Filtration_value alpha_complex_filtration = 0.0; + // No need to compute squared_radius on a single point - alpha is 0.0 + if (f_simplex_dim > 0) { + auto const& sqrad = radius(complex, f_simplex); #if CGAL_VERSION_NR >= 1050000000 - if(exact) CGAL::exact(sqrad); + if(exact) CGAL::exact(sqrad); #endif - alpha_complex_filtration = cv(sqrad); - } - complex.assign_filtration(f_simplex, alpha_complex_filtration); + CGAL::NT_converter<FT, Filtration_value> cv; + alpha_complex_filtration = cv(sqrad); + } + complex.assign_filtration(f_simplex, alpha_complex_filtration); #ifdef DEBUG_TRACES - std::cout << "filt(Sigma) is NaN : filt(Sigma) =" << complex.filtration(f_simplex) << std::endl; + std::clog << "filt(Sigma) is NaN : filt(Sigma) =" << complex.filtration(f_simplex) << std::endl; #endif // DEBUG_TRACES + } + // No need to propagate further, unweighted points all have value 0 + if (decr_dim > 1) + propagate_alpha_filtration(complex, f_simplex); } - // No need to propagate further, unweighted points all have value 0 - if (decr_dim > 1) - propagate_alpha_filtration(complex, f_simplex); } + old_cache_ = std::move(cache_); + cache_.clear(); } + // -------------------------------------------------------------------------------------------- + + // -------------------------------------------------------------------------------------------- + // As Alpha value is an approximation, we have to make filtration non decreasing while increasing the dimension + complex.make_filtration_non_decreasing(); + // Remove all simplices that have a filtration value greater than max_alpha_square + complex.prune_above_filtration(max_alpha_square); + // -------------------------------------------------------------------------------------------- } - // -------------------------------------------------------------------------------------------- - - // -------------------------------------------------------------------------------------------- - // As Alpha value is an approximation, we have to make filtration non decreasing while increasing the dimension - complex.make_filtration_non_decreasing(); - // Remove all simplices that have a filtration value greater than max_alpha_square - complex.prune_above_filtration(max_alpha_square); - // -------------------------------------------------------------------------------------------- return true; } @@ -380,20 +417,18 @@ class Alpha_complex { void propagate_alpha_filtration(SimplicialComplexForAlpha& complex, Simplex_handle f_simplex) { // From SimplicialComplexForAlpha type required to assign filtration values. typedef typename SimplicialComplexForAlpha::Filtration_value Filtration_value; -#ifdef DEBUG_TRACES typedef typename SimplicialComplexForAlpha::Vertex_handle Vertex_handle; -#endif // DEBUG_TRACES // ### Foreach Tau face of Sigma for (auto f_boundary : complex.boundary_simplex_range(f_simplex)) { #ifdef DEBUG_TRACES - std::cout << " | --------------------------------------------------\n"; - std::cout << " | Tau "; + std::clog << " | --------------------------------------------------\n"; + std::clog << " | Tau "; for (auto vertex : complex.simplex_vertex_range(f_boundary)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << "is a face of Sigma\n"; - std::cout << " | isnan(complex.filtration(Tau)=" << std::isnan(complex.filtration(f_boundary)) << std::endl; + std::clog << "is a face of Sigma\n"; + std::clog << " | isnan(complex.filtration(Tau)=" << std::isnan(complex.filtration(f_boundary)) << std::endl; #endif // DEBUG_TRACES // ### If filt(Tau) is not NaN if (!std::isnan(complex.filtration(f_boundary))) { @@ -402,37 +437,22 @@ class Alpha_complex { complex.filtration(f_simplex)); complex.assign_filtration(f_boundary, alpha_complex_filtration); #ifdef DEBUG_TRACES - std::cout << " | filt(Tau) = fmin(filt(Tau), filt(Sigma)) = " << complex.filtration(f_boundary) << std::endl; + std::clog << " | filt(Tau) = fmin(filt(Tau), filt(Sigma)) = " << complex.filtration(f_boundary) << std::endl; #endif // DEBUG_TRACES // ### Else } else { - // insert the Tau points in a vector for is_gabriel function - Vector_of_CGAL_points pointVector; -#ifdef DEBUG_TRACES - Vertex_handle vertexForGabriel = Vertex_handle(); -#endif // DEBUG_TRACES - for (auto vertex : complex.simplex_vertex_range(f_boundary)) { - pointVector.push_back(get_point(vertex)); - } - // Retrieve the Sigma point that is not part of Tau - parameter for is_gabriel function - Point_d point_for_gabriel; - for (auto vertex : complex.simplex_vertex_range(f_simplex)) { - point_for_gabriel = get_point(vertex); - if (std::find(pointVector.begin(), pointVector.end(), point_for_gabriel) == pointVector.end()) { -#ifdef DEBUG_TRACES - // vertex is not found in Tau - vertexForGabriel = vertex; -#endif // DEBUG_TRACES - // No need to continue loop - break; - } - } - // is_gabriel function initialization - Is_Gabriel is_gabriel = kernel_.side_of_bounded_sphere_d_object(); - bool is_gab = is_gabriel(pointVector.begin(), pointVector.end(), point_for_gabriel) - != CGAL::ON_BOUNDED_SIDE; + // Find which vertex of f_simplex is missing in f_boundary. We could actually write a variant of boundary_simplex_range that gives pairs (f_boundary, vertex). We rely on the fact that simplex_vertex_range is sorted. + auto longlist = complex.simplex_vertex_range(f_simplex); + auto shortlist = complex.simplex_vertex_range(f_boundary); + auto longiter = std::begin(longlist); + auto shortiter = std::begin(shortlist); + auto enditer = std::end(shortlist); + while(shortiter != enditer && *longiter == *shortiter) { ++longiter; ++shortiter; } + Vertex_handle extra = *longiter; + auto const& cache=get_cache(complex, f_boundary); + bool is_gab = kernel_.squared_distance_d_object()(cache.first, get_point_(extra)) >= cache.second; #ifdef DEBUG_TRACES - std::cout << " | Tau is_gabriel(Sigma)=" << is_gab << " - vertexForGabriel=" << vertexForGabriel << std::endl; + std::clog << " | Tau is_gabriel(Sigma)=" << is_gab << " - vertexForGabriel=" << extra << std::endl; #endif // DEBUG_TRACES // ### If Tau is not Gabriel of Sigma if (false == is_gab) { @@ -440,7 +460,7 @@ class Alpha_complex { Filtration_value alpha_complex_filtration = complex.filtration(f_simplex); complex.assign_filtration(f_boundary, alpha_complex_filtration); #ifdef DEBUG_TRACES - std::cout << " | filt(Tau) = filt(Sigma) = " << complex.filtration(f_boundary) << std::endl; + std::clog << " | filt(Tau) = filt(Sigma) = " << complex.filtration(f_boundary) << std::endl; #endif // DEBUG_TRACES } } diff --git a/src/Alpha_complex/include/gudhi/Alpha_complex_3d.h b/src/Alpha_complex/include/gudhi/Alpha_complex_3d.h index 7f96c94c..c19ebb79 100644 --- a/src/Alpha_complex/include/gudhi/Alpha_complex_3d.h +++ b/src/Alpha_complex/include/gudhi/Alpha_complex_3d.h @@ -61,10 +61,7 @@ namespace Gudhi { namespace alpha_complex { -#ifdef GUDHI_CAN_USE_CXX11_THREAD_LOCAL -thread_local -#endif // GUDHI_CAN_USE_CXX11_THREAD_LOCAL - double RELATIVE_PRECISION_OF_TO_DOUBLE = 0.00001; +thread_local double RELATIVE_PRECISION_OF_TO_DOUBLE = 0.00001; // Value_from_iterator returns the filtration value from an iterator on alpha shapes values // @@ -472,7 +469,7 @@ Weighted_alpha_complex_3d::Weighted_point_3 wp0(Weighted_alpha_complex_3d::Bare_ alpha_shape_3_ptr_->filtration_with_alpha_values(dispatcher); #ifdef DEBUG_TRACES - std::cout << "filtration_with_alpha_values returns : " << objects.size() << " objects" << std::endl; + std::clog << "filtration_with_alpha_values returns : " << objects.size() << " objects" << std::endl; #endif // DEBUG_TRACES using Alpha_value_iterator = typename std::vector<FT>::const_iterator; @@ -484,7 +481,7 @@ Weighted_alpha_complex_3d::Weighted_point_3 wp0(Weighted_alpha_complex_3d::Bare_ if (const Cell_handle* cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { for (auto i = 0; i < 4; i++) { #ifdef DEBUG_TRACES - std::cout << "from cell[" << i << "] - Point coordinates (" << (*cell)->vertex(i)->point() << ")" + std::clog << "from cell[" << i << "] - Point coordinates (" << (*cell)->vertex(i)->point() << ")" << std::endl; #endif // DEBUG_TRACES vertex_list.push_back((*cell)->vertex(i)); @@ -496,7 +493,7 @@ Weighted_alpha_complex_3d::Weighted_point_3 wp0(Weighted_alpha_complex_3d::Bare_ for (auto i = 0; i < 4; i++) { if ((*facet).second != i) { #ifdef DEBUG_TRACES - std::cout << "from facet=[" << i << "] - Point coordinates (" << (*facet).first->vertex(i)->point() << ")" + std::clog << "from facet=[" << i << "] - Point coordinates (" << (*facet).first->vertex(i)->point() << ")" << std::endl; #endif // DEBUG_TRACES vertex_list.push_back((*facet).first->vertex(i)); @@ -508,7 +505,7 @@ Weighted_alpha_complex_3d::Weighted_point_3 wp0(Weighted_alpha_complex_3d::Bare_ } else if (const Edge* edge = CGAL::object_cast<Edge>(&object_iterator)) { for (auto i : {(*edge).second, (*edge).third}) { #ifdef DEBUG_TRACES - std::cout << "from edge[" << i << "] - Point coordinates (" << (*edge).first->vertex(i)->point() << ")" + std::clog << "from edge[" << i << "] - Point coordinates (" << (*edge).first->vertex(i)->point() << ")" << std::endl; #endif // DEBUG_TRACES vertex_list.push_back((*edge).first->vertex(i)); @@ -519,7 +516,7 @@ Weighted_alpha_complex_3d::Weighted_point_3 wp0(Weighted_alpha_complex_3d::Bare_ } else if (const Alpha_vertex_handle* vertex = CGAL::object_cast<Alpha_vertex_handle>(&object_iterator)) { #ifdef DEBUG_TRACES count_vertices++; - std::cout << "from vertex - Point coordinates (" << (*vertex)->point() << ")" << std::endl; + std::clog << "from vertex - Point coordinates (" << (*vertex)->point() << ")" << std::endl; #endif // DEBUG_TRACES vertex_list.push_back((*vertex)); } @@ -531,7 +528,7 @@ Weighted_alpha_complex_3d::Weighted_point_3 wp0(Weighted_alpha_complex_3d::Bare_ // alpha shape not found Complex_vertex_handle vertex = map_cgal_simplex_tree.size(); #ifdef DEBUG_TRACES - std::cout << "Point (" << the_alpha_shape_vertex->point() << ") not found - insert new vertex id " << vertex + std::clog << "Point (" << the_alpha_shape_vertex->point() << ") not found - insert new vertex id " << vertex << std::endl; #endif // DEBUG_TRACES the_simplex.push_back(vertex); @@ -540,7 +537,7 @@ Weighted_alpha_complex_3d::Weighted_point_3 wp0(Weighted_alpha_complex_3d::Bare_ // alpha shape found Complex_vertex_handle vertex = the_map_iterator->second; #ifdef DEBUG_TRACES - std::cout << "Point (" << the_alpha_shape_vertex->point() << ") found as vertex id " << vertex << std::endl; + std::clog << "Point (" << the_alpha_shape_vertex->point() << ") found as vertex id " << vertex << std::endl; #endif // DEBUG_TRACES the_simplex.push_back(vertex); } @@ -549,7 +546,7 @@ Weighted_alpha_complex_3d::Weighted_point_3 wp0(Weighted_alpha_complex_3d::Bare_ Filtration_value filtr = Value_from_iterator<Complexity>::perform(alpha_value_iterator); #ifdef DEBUG_TRACES - std::cout << "filtration = " << filtr << std::endl; + std::clog << "filtration = " << filtr << std::endl; #endif // DEBUG_TRACES complex.insert_simplex(the_simplex, static_cast<Filtration_value>(filtr)); GUDHI_CHECK(alpha_value_iterator != alpha_values.end(), "CGAL provided more simplices than values"); @@ -557,10 +554,10 @@ Weighted_alpha_complex_3d::Weighted_point_3 wp0(Weighted_alpha_complex_3d::Bare_ } #ifdef DEBUG_TRACES - std::cout << "vertices \t" << count_vertices << std::endl; - std::cout << "edges \t\t" << count_edges << std::endl; - std::cout << "facets \t\t" << count_facets << std::endl; - std::cout << "cells \t\t" << count_cells << std::endl; + std::clog << "vertices \t" << count_vertices << std::endl; + std::clog << "edges \t\t" << count_edges << std::endl; + std::clog << "facets \t\t" << count_facets << std::endl; + std::clog << "cells \t\t" << count_cells << std::endl; #endif // DEBUG_TRACES // -------------------------------------------------------------------------------------------- // As Alpha value is an approximation, we have to make filtration non decreasing while increasing the dimension diff --git a/src/Alpha_complex/test/Alpha_complex_3d_unit_test.cpp b/src/Alpha_complex/test/Alpha_complex_3d_unit_test.cpp index cd698a27..a4ecb6ad 100644 --- a/src/Alpha_complex/test/Alpha_complex_3d_unit_test.cpp +++ b/src/Alpha_complex/test/Alpha_complex_3d_unit_test.cpp @@ -54,7 +54,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_3d_from_points) { // ----------------- // Fast version // ----------------- - std::cout << "Fast alpha complex 3d" << std::endl; + std::clog << "Fast alpha complex 3d" << std::endl; std::vector<Fast_alpha_complex_3d::Bare_point_3> points = get_points<Fast_alpha_complex_3d::Bare_point_3>(); Fast_alpha_complex_3d alpha_complex(points); @@ -79,7 +79,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_3d_from_points) { // ----------------- // Exact version // ----------------- - std::cout << "Exact alpha complex 3d" << std::endl; + std::clog << "Exact alpha complex 3d" << std::endl; std::vector<Exact_alpha_complex_3d::Bare_point_3> exact_points = get_points<Exact_alpha_complex_3d::Bare_point_3>(); Exact_alpha_complex_3d exact_alpha_complex(exact_points); @@ -105,13 +105,13 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_3d_from_points) { // --------------------- // Compare both versions // --------------------- - std::cout << "Exact Alpha complex 3d is of dimension " << exact_stree.dimension() << " - Fast is " + std::clog << "Exact Alpha complex 3d is of dimension " << exact_stree.dimension() << " - Fast is " << stree.dimension() << std::endl; BOOST_CHECK(exact_stree.dimension() == stree.dimension()); - std::cout << "Exact Alpha complex 3d num_simplices " << exact_stree.num_simplices() << " - Fast is " + std::clog << "Exact Alpha complex 3d num_simplices " << exact_stree.num_simplices() << " - Fast is " << stree.num_simplices() << std::endl; BOOST_CHECK(exact_stree.num_simplices() == stree.num_simplices()); - std::cout << "Exact Alpha complex 3d num_vertices " << exact_stree.num_vertices() << " - Fast is " + std::clog << "Exact Alpha complex 3d num_vertices " << exact_stree.num_vertices() << " - Fast is " << stree.num_vertices() << std::endl; BOOST_CHECK(exact_stree.num_vertices() == stree.num_vertices()); @@ -119,18 +119,18 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_3d_from_points) { while (sh != stree.filtration_simplex_range().end()) { std::vector<int> simplex; std::vector<int> exact_simplex; - std::cout << "Fast ( "; + std::clog << "Fast ( "; for (auto vertex : stree.simplex_vertex_range(*sh)) { simplex.push_back(vertex); - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> [" << stree.filtration(*sh) << "] "; + std::clog << ") -> [" << stree.filtration(*sh) << "] "; // Find it in the exact structure auto sh_exact = exact_stree.find(simplex); BOOST_CHECK(sh_exact != exact_stree.null_simplex()); - std::cout << " versus [" << exact_stree.filtration(sh_exact) << "] " << std::endl; + std::clog << " versus [" << exact_stree.filtration(sh_exact) << "] " << std::endl; // Exact and non-exact version is not exactly the same due to float comparison GUDHI_TEST_FLOAT_EQUALITY_CHECK(exact_stree.filtration(sh_exact), stree.filtration(*sh)); @@ -139,7 +139,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_3d_from_points) { // ----------------- // Safe version // ----------------- - std::cout << "Safe alpha complex 3d" << std::endl; + std::clog << "Safe alpha complex 3d" << std::endl; std::vector<Safe_alpha_complex_3d::Bare_point_3> safe_points = get_points<Safe_alpha_complex_3d::Bare_point_3>(); Safe_alpha_complex_3d safe_alpha_complex(safe_points); @@ -165,13 +165,13 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_3d_from_points) { // --------------------- // Compare both versions // --------------------- - std::cout << "Safe Alpha complex 3d is of dimension " << safe_stree.dimension() << " - Fast is " + std::clog << "Safe Alpha complex 3d is of dimension " << safe_stree.dimension() << " - Fast is " << stree.dimension() << std::endl; BOOST_CHECK(safe_stree.dimension() == stree.dimension()); - std::cout << "Safe Alpha complex 3d num_simplices " << safe_stree.num_simplices() << " - Fast is " + std::clog << "Safe Alpha complex 3d num_simplices " << safe_stree.num_simplices() << " - Fast is " << stree.num_simplices() << std::endl; BOOST_CHECK(safe_stree.num_simplices() == stree.num_simplices()); - std::cout << "Safe Alpha complex 3d num_vertices " << safe_stree.num_vertices() << " - Fast is " + std::clog << "Safe Alpha complex 3d num_vertices " << safe_stree.num_vertices() << " - Fast is " << stree.num_vertices() << std::endl; BOOST_CHECK(safe_stree.num_vertices() == stree.num_vertices()); @@ -179,18 +179,18 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_3d_from_points) { while (safe_sh != stree.filtration_simplex_range().end()) { std::vector<int> simplex; std::vector<int> exact_simplex; - std::cout << "Fast ( "; + std::clog << "Fast ( "; for (auto vertex : stree.simplex_vertex_range(*safe_sh)) { simplex.push_back(vertex); - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> [" << stree.filtration(*safe_sh) << "] "; + std::clog << ") -> [" << stree.filtration(*safe_sh) << "] "; // Find it in the exact structure auto sh_exact = safe_stree.find(simplex); BOOST_CHECK(sh_exact != safe_stree.null_simplex()); - std::cout << " versus [" << safe_stree.filtration(sh_exact) << "] " << std::endl; + std::clog << " versus [" << safe_stree.filtration(sh_exact) << "] " << std::endl; // Exact and non-exact version is not exactly the same due to float comparison GUDHI_TEST_FLOAT_EQUALITY_CHECK(safe_stree.filtration(sh_exact), stree.filtration(*safe_sh), 1e-15); diff --git a/src/Alpha_complex/test/Alpha_complex_unit_test.cpp b/src/Alpha_complex/test/Alpha_complex_unit_test.cpp index 27b671dd..4b37e4bd 100644 --- a/src/Alpha_complex/test/Alpha_complex_unit_test.cpp +++ b/src/Alpha_complex/test/Alpha_complex_unit_test.cpp @@ -48,7 +48,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_from_OFF_file, TestedKernel, list_of // ---------------------------------------------------------------------------- std::string off_file_name("alphacomplexdoc.off"); double max_alpha_square_value = 60.0; - std::cout << "========== OFF FILE NAME = " << off_file_name << " - alpha²=" << + std::clog << "========== OFF FILE NAME = " << off_file_name << " - alpha²=" << max_alpha_square_value << "==========" << std::endl; Gudhi::alpha_complex::Alpha_complex<TestedKernel> alpha_complex_from_file(off_file_name); @@ -56,29 +56,29 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_from_OFF_file, TestedKernel, list_of Gudhi::Simplex_tree<> simplex_tree_60; BOOST_CHECK(alpha_complex_from_file.create_complex(simplex_tree_60, max_alpha_square_value)); - std::cout << "simplex_tree_60.dimension()=" << simplex_tree_60.dimension() << std::endl; + std::clog << "simplex_tree_60.dimension()=" << simplex_tree_60.dimension() << std::endl; BOOST_CHECK(simplex_tree_60.dimension() == 2); - std::cout << "simplex_tree_60.num_vertices()=" << simplex_tree_60.num_vertices() << std::endl; + std::clog << "simplex_tree_60.num_vertices()=" << simplex_tree_60.num_vertices() << std::endl; BOOST_CHECK(simplex_tree_60.num_vertices() == 7); - std::cout << "simplex_tree_60.num_simplices()=" << simplex_tree_60.num_simplices() << std::endl; + std::clog << "simplex_tree_60.num_simplices()=" << simplex_tree_60.num_simplices() << std::endl; BOOST_CHECK(simplex_tree_60.num_simplices() == 25); max_alpha_square_value = 59.0; - std::cout << "========== OFF FILE NAME = " << off_file_name << " - alpha²=" << + std::clog << "========== OFF FILE NAME = " << off_file_name << " - alpha²=" << max_alpha_square_value << "==========" << std::endl; Gudhi::Simplex_tree<> simplex_tree_59; BOOST_CHECK(alpha_complex_from_file.create_complex(simplex_tree_59, max_alpha_square_value)); - std::cout << "simplex_tree_59.dimension()=" << simplex_tree_59.dimension() << std::endl; + std::clog << "simplex_tree_59.dimension()=" << simplex_tree_59.dimension() << std::endl; BOOST_CHECK(simplex_tree_59.dimension() == 2); - std::cout << "simplex_tree_59.num_vertices()=" << simplex_tree_59.num_vertices() << std::endl; + std::clog << "simplex_tree_59.num_vertices()=" << simplex_tree_59.num_vertices() << std::endl; BOOST_CHECK(simplex_tree_59.num_vertices() == 7); - std::cout << "simplex_tree_59.num_simplices()=" << simplex_tree_59.num_simplices() << std::endl; + std::clog << "simplex_tree_59.num_simplices()=" << simplex_tree_59.num_simplices() << std::endl; BOOST_CHECK(simplex_tree_59.num_simplices() == 23); } @@ -115,30 +115,30 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_from_points) { // ---------------------------------------------------------------------------- Gudhi::alpha_complex::Alpha_complex<Kernel_4> alpha_complex_from_points(points); - std::cout << "========== Alpha_complex_from_points ==========" << std::endl; + std::clog << "========== Alpha_complex_from_points ==========" << std::endl; Gudhi::Simplex_tree<> simplex_tree; BOOST_CHECK(alpha_complex_from_points.create_complex(simplex_tree)); // Another way to check num_simplices - std::cout << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" << std::endl; + std::clog << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" << std::endl; int num_simplices = 0; for (auto f_simplex : simplex_tree.filtration_simplex_range()) { num_simplices++; - std::cout << " ( "; + std::clog << " ( "; for (auto vertex : simplex_tree.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> " << "[" << simplex_tree.filtration(f_simplex) << "] "; - std::cout << std::endl; + std::clog << ") -> " << "[" << simplex_tree.filtration(f_simplex) << "] "; + std::clog << std::endl; } BOOST_CHECK(num_simplices == 15); - std::cout << "simplex_tree.num_simplices()=" << simplex_tree.num_simplices() << std::endl; + std::clog << "simplex_tree.num_simplices()=" << simplex_tree.num_simplices() << std::endl; BOOST_CHECK(simplex_tree.num_simplices() == 15); - std::cout << "simplex_tree.dimension()=" << simplex_tree.dimension() << std::endl; + std::clog << "simplex_tree.dimension()=" << simplex_tree.dimension() << std::endl; BOOST_CHECK(simplex_tree.dimension() == 3); - std::cout << "simplex_tree.num_vertices()=" << simplex_tree.num_vertices() << std::endl; + std::clog << "simplex_tree.num_vertices()=" << simplex_tree.num_vertices() << std::endl; BOOST_CHECK(simplex_tree.num_vertices() == points.size()); for (auto f_simplex : simplex_tree.filtration_simplex_range()) { @@ -162,22 +162,22 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_from_points) { } Point_4 p0 = alpha_complex_from_points.get_point(0); - std::cout << "alpha_complex_from_points.get_point(0)=" << p0 << std::endl; + std::clog << "alpha_complex_from_points.get_point(0)=" << p0 << std::endl; BOOST_CHECK(4 == p0.dimension()); BOOST_CHECK(is_point_in_list(points, p0)); Point_4 p1 = alpha_complex_from_points.get_point(1); - std::cout << "alpha_complex_from_points.get_point(1)=" << p1 << std::endl; + std::clog << "alpha_complex_from_points.get_point(1)=" << p1 << std::endl; BOOST_CHECK(4 == p1.dimension()); BOOST_CHECK(is_point_in_list(points, p1)); Point_4 p2 = alpha_complex_from_points.get_point(2); - std::cout << "alpha_complex_from_points.get_point(2)=" << p2 << std::endl; + std::clog << "alpha_complex_from_points.get_point(2)=" << p2 << std::endl; BOOST_CHECK(4 == p2.dimension()); BOOST_CHECK(is_point_in_list(points, p2)); Point_4 p3 = alpha_complex_from_points.get_point(3); - std::cout << "alpha_complex_from_points.get_point(3)=" << p3 << std::endl; + std::clog << "alpha_complex_from_points.get_point(3)=" << p3 << std::endl; BOOST_CHECK(4 == p3.dimension()); BOOST_CHECK(is_point_in_list(points, p3)); @@ -188,30 +188,27 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_from_points) { // Test after prune_above_filtration bool modified = simplex_tree.prune_above_filtration(0.6); - if (modified) { - simplex_tree.initialize_filtration(); - } BOOST_CHECK(modified); // Another way to check num_simplices - std::cout << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" << std::endl; + std::clog << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" << std::endl; num_simplices = 0; for (auto f_simplex : simplex_tree.filtration_simplex_range()) { num_simplices++; - std::cout << " ( "; + std::clog << " ( "; for (auto vertex : simplex_tree.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> " << "[" << simplex_tree.filtration(f_simplex) << "] "; - std::cout << std::endl; + std::clog << ") -> " << "[" << simplex_tree.filtration(f_simplex) << "] "; + std::clog << std::endl; } BOOST_CHECK(num_simplices == 10); - std::cout << "simplex_tree.num_simplices()=" << simplex_tree.num_simplices() << std::endl; + std::clog << "simplex_tree.num_simplices()=" << simplex_tree.num_simplices() << std::endl; BOOST_CHECK(simplex_tree.num_simplices() == 10); - std::cout << "simplex_tree.dimension()=" << simplex_tree.dimension() << std::endl; + std::clog << "simplex_tree.dimension()=" << simplex_tree.dimension() << std::endl; BOOST_CHECK(simplex_tree.dimension() == 1); - std::cout << "simplex_tree.num_vertices()=" << simplex_tree.num_vertices() << std::endl; + std::clog << "simplex_tree.num_vertices()=" << simplex_tree.num_vertices() << std::endl; BOOST_CHECK(simplex_tree.num_vertices() == 4); for (auto f_simplex : simplex_tree.filtration_simplex_range()) { @@ -231,7 +228,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_from_points) { } BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_from_empty_points, TestedKernel, list_of_kernel_variants) { - std::cout << "========== Alpha_complex_from_empty_points ==========" << std::endl; + std::clog << "========== Alpha_complex_from_empty_points ==========" << std::endl; // ---------------------------------------------------------------------------- // Init of an empty list of points @@ -249,13 +246,13 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_from_empty_points, TestedKernel, lis Gudhi::Simplex_tree<> simplex_tree; BOOST_CHECK(!alpha_complex_from_points.create_complex(simplex_tree)); - std::cout << "simplex_tree.num_simplices()=" << simplex_tree.num_simplices() << std::endl; + std::clog << "simplex_tree.num_simplices()=" << simplex_tree.num_simplices() << std::endl; BOOST_CHECK(simplex_tree.num_simplices() == 0); - std::cout << "simplex_tree.dimension()=" << simplex_tree.dimension() << std::endl; + std::clog << "simplex_tree.dimension()=" << simplex_tree.dimension() << std::endl; BOOST_CHECK(simplex_tree.dimension() == -1); - std::cout << "simplex_tree.num_vertices()=" << simplex_tree.num_vertices() << std::endl; + std::clog << "simplex_tree.num_vertices()=" << simplex_tree.num_vertices() << std::endl; BOOST_CHECK(simplex_tree.num_vertices() == points.size()); } @@ -264,7 +261,7 @@ using Exact_kernel_2 = CGAL::Epeck_d< CGAL::Dimension_tag<2> >; using list_of_kernel_2_variants = boost::mpl::list<Inexact_kernel_2, Exact_kernel_2>; BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_with_duplicated_points, TestedKernel, list_of_kernel_2_variants) { - std::cout << "========== Alpha_complex_with_duplicated_points ==========" << std::endl; + std::clog << "========== Alpha_complex_with_duplicated_points ==========" << std::endl; using Point = typename TestedKernel::Point_d; using Vector_of_points = std::vector<Point>; @@ -287,14 +284,14 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_with_duplicated_points, TestedKernel // ---------------------------------------------------------------------------- // Init of an alpha complex from the list of points // ---------------------------------------------------------------------------- - std::cout << "Init" << std::endl; + std::clog << "Init" << std::endl; Gudhi::alpha_complex::Alpha_complex<TestedKernel> alpha_complex_from_points(points); Gudhi::Simplex_tree<> simplex_tree; - std::cout << "create_complex" << std::endl; + std::clog << "create_complex" << std::endl; BOOST_CHECK(alpha_complex_from_points.create_complex(simplex_tree)); - std::cout << "simplex_tree.num_vertices()=" << simplex_tree.num_vertices() + std::clog << "simplex_tree.num_vertices()=" << simplex_tree.num_vertices() << std::endl; BOOST_CHECK(simplex_tree.num_vertices() < points.size()); } diff --git a/src/Alpha_complex/test/CMakeLists.txt b/src/Alpha_complex/test/CMakeLists.txt index 0476c6d4..fe4b23e4 100644 --- a/src/Alpha_complex/test/CMakeLists.txt +++ b/src/Alpha_complex/test/CMakeLists.txt @@ -8,11 +8,15 @@ if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) add_executable ( Alpha_complex_test_unit Alpha_complex_unit_test.cpp ) target_link_libraries(Alpha_complex_test_unit ${CGAL_LIBRARY}) + add_executable ( Delaunay_complex_test_unit Delaunay_complex_unit_test.cpp ) + target_link_libraries(Delaunay_complex_test_unit ${CGAL_LIBRARY}) if (TBB_FOUND) target_link_libraries(Alpha_complex_test_unit ${TBB_LIBRARIES}) + target_link_libraries(Delaunay_complex_test_unit ${TBB_LIBRARIES}) endif() gudhi_add_boost_test(Alpha_complex_test_unit) + gudhi_add_boost_test(Delaunay_complex_test_unit) add_executable ( Alpha_complex_3d_test_unit Alpha_complex_3d_unit_test.cpp ) target_link_libraries(Alpha_complex_3d_test_unit ${CGAL_LIBRARY}) diff --git a/src/Alpha_complex/test/Delaunay_complex_unit_test.cpp b/src/Alpha_complex/test/Delaunay_complex_unit_test.cpp new file mode 100644 index 00000000..c1cc1fab --- /dev/null +++ b/src/Alpha_complex/test/Delaunay_complex_unit_test.cpp @@ -0,0 +1,68 @@ +/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + * Author(s): Vincent Rouvreau + * + * Copyright (C) 2020 Inria + * + * Modification(s): + * - YYYY/MM Author: Description of the modification + */ + +#define BOOST_TEST_DYN_LINK +#define BOOST_TEST_MODULE "delaunay_complex" +#include <boost/test/unit_test.hpp> +#include <boost/mpl/list.hpp> + +#include <CGAL/Epick_d.h> +#include <CGAL/Epeck_d.h> + +#include <vector> +#include <limits> // NaN +#include <cmath> + +#include <gudhi/Alpha_complex.h> +// to construct a simplex_tree from Delaunay_triangulation +#include <gudhi/graph_simplicial_complex.h> +#include <gudhi/Simplex_tree.h> +#include <gudhi/Unitary_tests_utils.h> +#include <gudhi/random_point_generators.h> + +// Use dynamic_dimension_tag for the user to be able to set dimension +typedef CGAL::Epeck_d< CGAL::Dynamic_dimension_tag > Exact_kernel_d; +// Use static dimension_tag for the user not to be able to set dimension +typedef CGAL::Epeck_d< CGAL::Dimension_tag<5> > Exact_kernel_s; +// Use dynamic_dimension_tag for the user to be able to set dimension +typedef CGAL::Epick_d< CGAL::Dynamic_dimension_tag > Inexact_kernel_d; +// Use static dimension_tag for the user not to be able to set dimension +typedef CGAL::Epick_d< CGAL::Dimension_tag<5> > Inexact_kernel_s; +// The triangulation uses the default instantiation of the TriangulationDataStructure template parameter + +typedef boost::mpl::list<Exact_kernel_d, Exact_kernel_s, Inexact_kernel_d, Inexact_kernel_s> list_of_kernel_variants; + +using Simplex_tree = Gudhi::Simplex_tree<>; +using Simplex_handle = Simplex_tree::Simplex_handle; + +BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_from_OFF_file, TestedKernel, list_of_kernel_variants) { + std::cout << "*****************************************************************************************************"; + using Point = typename TestedKernel::Point_d; + std::vector<Point> points; + // 50 points on a 4-sphere + points = Gudhi::generate_points_on_sphere_d<TestedKernel>(10, 5, 1.); + + Gudhi::alpha_complex::Alpha_complex<TestedKernel> alpha_complex(points); + + // Alpha complex + Simplex_tree stree_from_alpha_complex; + BOOST_CHECK(alpha_complex.create_complex(stree_from_alpha_complex)); + + // Delaunay complex + Simplex_tree stree_from_delaunay_complex; + BOOST_CHECK(alpha_complex.create_complex(stree_from_delaunay_complex, 0., false, true)); + + // Check all the simplices from alpha complex are in the Delaunay complex + for (auto f_simplex : stree_from_alpha_complex.complex_simplex_range()) { + Simplex_handle sh = stree_from_delaunay_complex.find(stree_from_alpha_complex.simplex_vertex_range(f_simplex)); + BOOST_CHECK(std::isnan(stree_from_delaunay_complex.filtration(sh))); + BOOST_CHECK(sh != stree_from_delaunay_complex.null_simplex()); + } +} diff --git a/src/Alpha_complex/test/Periodic_alpha_complex_3d_unit_test.cpp b/src/Alpha_complex/test/Periodic_alpha_complex_3d_unit_test.cpp index 731763fa..9eef920b 100644 --- a/src/Alpha_complex/test/Periodic_alpha_complex_3d_unit_test.cpp +++ b/src/Alpha_complex/test/Periodic_alpha_complex_3d_unit_test.cpp @@ -43,14 +43,14 @@ typedef boost::mpl::list<Fast_periodic_alpha_complex_3d, Safe_periodic_alpha_com periodic_variants_type_list; BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_periodic_throw, Periodic_alpha_complex_3d, periodic_variants_type_list) { - std::cout << "Periodic alpha complex 3d exception throw" << std::endl; + std::clog << "Periodic alpha complex 3d exception throw" << std::endl; using Bare_point_3 = typename Periodic_alpha_complex_3d::Bare_point_3; std::vector<Bare_point_3> p_points; // Not important, this is not what we want to check p_points.push_back(Bare_point_3(0.0, 0.0, 0.0)); - std::cout << "Check exception throw in debug mode" << std::endl; + std::clog << "Check exception throw in debug mode" << std::endl; // Check it throws an exception when the cuboid is not iso BOOST_CHECK_THROW(Periodic_alpha_complex_3d periodic_alpha_complex(p_points, 0., 0., 0., 0.9, 1., 1.), std::invalid_argument); @@ -71,7 +71,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_periodic) { // --------------------- // Fast periodic version // --------------------- - std::cout << "Fast periodic alpha complex 3d" << std::endl; + std::clog << "Fast periodic alpha complex 3d" << std::endl; using Creator = CGAL::Creator_uniform_3<double, Fast_periodic_alpha_complex_3d::Bare_point_3>; CGAL::Random random(7); @@ -106,7 +106,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_periodic) { // ---------------------- // Exact periodic version // ---------------------- - std::cout << "Exact periodic alpha complex 3d" << std::endl; + std::clog << "Exact periodic alpha complex 3d" << std::endl; std::vector<Exact_periodic_alpha_complex_3d::Bare_point_3> e_p_points; @@ -122,13 +122,13 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_periodic) { // --------------------- // Compare both versions // --------------------- - std::cout << "Exact periodic alpha complex 3d is of dimension " << exact_stree.dimension() << " - Non exact is " + std::clog << "Exact periodic alpha complex 3d is of dimension " << exact_stree.dimension() << " - Non exact is " << stree.dimension() << std::endl; BOOST_CHECK(exact_stree.dimension() == stree.dimension()); - std::cout << "Exact periodic alpha complex 3d num_simplices " << exact_stree.num_simplices() << " - Non exact is " + std::clog << "Exact periodic alpha complex 3d num_simplices " << exact_stree.num_simplices() << " - Non exact is " << stree.num_simplices() << std::endl; BOOST_CHECK(exact_stree.num_simplices() == stree.num_simplices()); - std::cout << "Exact periodic alpha complex 3d num_vertices " << exact_stree.num_vertices() << " - Non exact is " + std::clog << "Exact periodic alpha complex 3d num_vertices " << exact_stree.num_vertices() << " - Non exact is " << stree.num_vertices() << std::endl; BOOST_CHECK(exact_stree.num_vertices() == stree.num_vertices()); @@ -155,7 +155,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_periodic) { // ---------------------- // Safe periodic version // ---------------------- - std::cout << "Safe periodic alpha complex 3d" << std::endl; + std::clog << "Safe periodic alpha complex 3d" << std::endl; std::vector<Safe_periodic_alpha_complex_3d::Bare_point_3> s_p_points; diff --git a/src/Alpha_complex/test/Weighted_alpha_complex_3d_unit_test.cpp b/src/Alpha_complex/test/Weighted_alpha_complex_3d_unit_test.cpp index 8035f6e8..6b31bea6 100644 --- a/src/Alpha_complex/test/Weighted_alpha_complex_3d_unit_test.cpp +++ b/src/Alpha_complex/test/Weighted_alpha_complex_3d_unit_test.cpp @@ -55,13 +55,13 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_weighted_throw, Weighted_alpha_compl // weights size is different from w_points size to make weighted Alpha_complex_3d throw in debug mode std::vector<double> weights = {0.01, 0.005, 0.006, 0.01, 0.009, 0.001}; - std::cout << "Check exception throw in debug mode" << std::endl; + std::clog << "Check exception throw in debug mode" << std::endl; BOOST_CHECK_THROW(Weighted_alpha_complex_3d wac(w_points, weights), std::invalid_argument); } #endif BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_weighted, Weighted_alpha_complex_3d, weighted_variants_type_list) { - std::cout << "Weighted alpha complex 3d from points and weights" << std::endl; + std::clog << "Weighted alpha complex 3d from points and weights" << std::endl; using Bare_point_3 = typename Weighted_alpha_complex_3d::Bare_point_3; std::vector<Bare_point_3> w_points; w_points.push_back(Bare_point_3(0.0, 0.0, 0.0)); @@ -78,7 +78,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_weighted, Weighted_alpha_complex_3d, Gudhi::Simplex_tree<> stree; alpha_complex_p_a_w.create_complex(stree); - std::cout << "Weighted alpha complex 3d from weighted points" << std::endl; + std::clog << "Weighted alpha complex 3d from weighted points" << std::endl; using Weighted_point_3 = typename Weighted_alpha_complex_3d::Weighted_point_3; std::vector<Weighted_point_3> weighted_points; @@ -112,13 +112,13 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_weighted, Weighted_alpha_complex_3d, // --------------------- // Compare both versions // --------------------- - std::cout << "Weighted alpha complex 3d is of dimension " << stree_bis.dimension() << " - versus " + std::clog << "Weighted alpha complex 3d is of dimension " << stree_bis.dimension() << " - versus " << stree.dimension() << std::endl; BOOST_CHECK(stree_bis.dimension() == stree.dimension()); - std::cout << "Weighted alpha complex 3d num_simplices " << stree_bis.num_simplices() << " - versus " + std::clog << "Weighted alpha complex 3d num_simplices " << stree_bis.num_simplices() << " - versus " << stree.num_simplices() << std::endl; BOOST_CHECK(stree_bis.num_simplices() == stree.num_simplices()); - std::cout << "Weighted alpha complex 3d num_vertices " << stree_bis.num_vertices() << " - versus " + std::clog << "Weighted alpha complex 3d num_vertices " << stree_bis.num_vertices() << " - versus " << stree.num_vertices() << std::endl; BOOST_CHECK(stree_bis.num_vertices() == stree.num_vertices()); @@ -127,18 +127,18 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_weighted, Weighted_alpha_complex_3d, std::vector<int> simplex; std::vector<int> exact_simplex; #ifdef DEBUG_TRACES - std::cout << " ( "; + std::clog << " ( "; #endif for (auto vertex : stree.simplex_vertex_range(*sh)) { simplex.push_back(vertex); #ifdef DEBUG_TRACES - std::cout << vertex << " "; + std::clog << vertex << " "; #endif } #ifdef DEBUG_TRACES - std::cout << ") -> " + std::clog << ") -> " << "[" << stree.filtration(*sh) << "] "; - std::cout << std::endl; + std::clog << std::endl; #endif // Find it in the exact structure diff --git a/src/Alpha_complex/test/Weighted_periodic_alpha_complex_3d_unit_test.cpp b/src/Alpha_complex/test/Weighted_periodic_alpha_complex_3d_unit_test.cpp index b09e92d5..610b9f3d 100644 --- a/src/Alpha_complex/test/Weighted_periodic_alpha_complex_3d_unit_test.cpp +++ b/src/Alpha_complex/test/Weighted_periodic_alpha_complex_3d_unit_test.cpp @@ -45,7 +45,7 @@ typedef boost::mpl::list<Fast_weighted_periodic_alpha_complex_3d, Exact_weighted #ifdef GUDHI_DEBUG BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_weighted_periodic_throw, Weighted_periodic_alpha_complex_3d, wp_variants_type_list) { - std::cout << "Weighted periodic alpha complex 3d exception throw" << std::endl; + std::clog << "Weighted periodic alpha complex 3d exception throw" << std::endl; using Creator = CGAL::Creator_uniform_3<double, typename Weighted_periodic_alpha_complex_3d::Bare_point_3>; CGAL::Random random(7); @@ -62,7 +62,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_weighted_periodic_throw, Weighted_pe p_weights.push_back(random.get_double(0., 0.01)); } - std::cout << "Cuboid is not iso exception" << std::endl; + std::clog << "Cuboid is not iso exception" << std::endl; // Check it throws an exception when the cuboid is not iso BOOST_CHECK_THROW( Weighted_periodic_alpha_complex_3d wp_alpha_complex(wp_points, p_weights, -1., -1., -1., 0.9, 1., 1.), @@ -83,7 +83,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_weighted_periodic_throw, Weighted_pe Weighted_periodic_alpha_complex_3d wp_alpha_complex(wp_points, p_weights, -1., -1., -1., 1., 1., 1.1), std::invalid_argument); - std::cout << "0 <= point.weight() < 1/64 * domain_size * domain_size exception" << std::endl; + std::clog << "0 <= point.weight() < 1/64 * domain_size * domain_size exception" << std::endl; // Weights must be in range ]0, 1/64 = 0.015625[ double temp = p_weights[25]; p_weights[25] = 1.0; @@ -97,7 +97,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(Alpha_complex_weighted_periodic_throw, Weighted_pe std::invalid_argument); p_weights[14] = temp; - std::cout << "wp_points and p_weights size exception" << std::endl; + std::clog << "wp_points and p_weights size exception" << std::endl; // Weights and points must have the same size // + 1 p_weights.push_back(1e-10); @@ -115,7 +115,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_weighted_periodic) { // --------------------- // Fast weighted periodic version // --------------------- - std::cout << "Fast weighted periodic alpha complex 3d" << std::endl; + std::clog << "Fast weighted periodic alpha complex 3d" << std::endl; using Creator = CGAL::Creator_uniform_3<double, Fast_weighted_periodic_alpha_complex_3d::Bare_point_3>; CGAL::Random random(7); @@ -140,7 +140,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_weighted_periodic) { // ---------------------- // Exact weighted periodic version // ---------------------- - std::cout << "Exact weighted periodic alpha complex 3d" << std::endl; + std::clog << "Exact weighted periodic alpha complex 3d" << std::endl; std::vector<Exact_weighted_periodic_alpha_complex_3d::Bare_point_3> e_p_points; @@ -156,13 +156,13 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_weighted_periodic) { // --------------------- // Compare both versions // --------------------- - std::cout << "Exact weighted periodic alpha complex 3d is of dimension " << exact_stree.dimension() + std::clog << "Exact weighted periodic alpha complex 3d is of dimension " << exact_stree.dimension() << " - Non exact is " << stree.dimension() << std::endl; BOOST_CHECK(exact_stree.dimension() == stree.dimension()); - std::cout << "Exact weighted periodic alpha complex 3d num_simplices " << exact_stree.num_simplices() + std::clog << "Exact weighted periodic alpha complex 3d num_simplices " << exact_stree.num_simplices() << " - Non exact is " << stree.num_simplices() << std::endl; BOOST_CHECK(exact_stree.num_simplices() == stree.num_simplices()); - std::cout << "Exact weighted periodic alpha complex 3d num_vertices " << exact_stree.num_vertices() + std::clog << "Exact weighted periodic alpha complex 3d num_vertices " << exact_stree.num_vertices() << " - Non exact is " << stree.num_vertices() << std::endl; BOOST_CHECK(exact_stree.num_vertices() == stree.num_vertices()); @@ -189,7 +189,7 @@ BOOST_AUTO_TEST_CASE(Alpha_complex_weighted_periodic) { // ---------------------- // Safe weighted periodic version // ---------------------- - std::cout << "Safe weighted periodic alpha complex 3d" << std::endl; + std::clog << "Safe weighted periodic alpha complex 3d" << std::endl; std::vector<Safe_weighted_periodic_alpha_complex_3d::Bare_point_3> s_p_points; diff --git a/src/Alpha_complex/utilities/CMakeLists.txt b/src/Alpha_complex/utilities/CMakeLists.txt index 57b92942..2ffbdde0 100644 --- a/src/Alpha_complex/utilities/CMakeLists.txt +++ b/src/Alpha_complex/utilities/CMakeLists.txt @@ -2,7 +2,7 @@ project(Alpha_complex_utilities) if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) add_executable (alpha_complex_persistence alpha_complex_persistence.cpp) - target_link_libraries(alpha_complex_persistence ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + target_link_libraries(alpha_complex_persistence ${CGAL_LIBRARY} Boost::program_options) if (TBB_FOUND) target_link_libraries(alpha_complex_persistence ${TBB_LIBRARIES}) @@ -16,14 +16,20 @@ if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) if (DIFF_PATH) add_test(Alpha_complex_utilities_diff_exact_alpha_complex ${DIFF_PATH} "exact.pers" "safe.pers") + set_tests_properties(Alpha_complex_utilities_diff_exact_alpha_complex PROPERTIES DEPENDS + "Alpha_complex_utilities_exact_alpha_complex_persistence;Alpha_complex_utilities_safe_alpha_complex_persistence") + add_test(Alpha_complex_utilities_diff_fast_alpha_complex ${DIFF_PATH} "fast.pers" "safe.pers") + set_tests_properties(Alpha_complex_utilities_diff_fast_alpha_complex PROPERTIES DEPENDS + "Alpha_complex_utilities_fast_alpha_complex_persistence;Alpha_complex_utilities_safe_alpha_complex_persistence") + endif() install(TARGETS alpha_complex_persistence DESTINATION bin) add_executable(alpha_complex_3d_persistence alpha_complex_3d_persistence.cpp) - target_link_libraries(alpha_complex_3d_persistence ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + target_link_libraries(alpha_complex_3d_persistence ${CGAL_LIBRARY} Boost::program_options) if (TBB_FOUND) target_link_libraries(alpha_complex_3d_persistence ${TBB_LIBRARIES}) endif(TBB_FOUND) @@ -36,15 +42,19 @@ if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "-p" "2" "-m" "0.45" "-o" "exact_3d.pers" "-e") - add_test(NAME Alpha_complex_utilities_safe_alpha_complex_3d COMMAND $<TARGET_FILE:alpha_complex_3d_persistence> + add_test(NAME Alpha_complex_utilities_fast_alpha_complex_3d COMMAND $<TARGET_FILE:alpha_complex_3d_persistence> "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "-p" "2" "-m" "0.45" "-o" "fast_3d.pers" "-f") if (DIFF_PATH) add_test(Alpha_complex_utilities_diff_exact_alpha_complex_3d ${DIFF_PATH} "exact_3d.pers" "safe_3d.pers") + set_tests_properties(Alpha_complex_utilities_diff_exact_alpha_complex_3d PROPERTIES DEPENDS + "Alpha_complex_utilities_exact_alpha_complex_3d;Alpha_complex_utilities_alpha_complex_3d") add_test(Alpha_complex_utilities_diff_fast_alpha_complex_3d ${DIFF_PATH} "fast_3d.pers" "safe_3d.pers") + set_tests_properties(Alpha_complex_utilities_diff_fast_alpha_complex_3d PROPERTIES DEPENDS + "Alpha_complex_utilities_fast_alpha_complex_3d;Alpha_complex_utilities_alpha_complex_3d") endif() add_test(NAME Alpha_complex_utilities_periodic_alpha_complex_3d_persistence COMMAND $<TARGET_FILE:alpha_complex_3d_persistence> diff --git a/src/Alpha_complex/utilities/alpha_complex_3d_persistence.cpp b/src/Alpha_complex/utilities/alpha_complex_3d_persistence.cpp index 929fc2e8..91899040 100644 --- a/src/Alpha_complex/utilities/alpha_complex_3d_persistence.cpp +++ b/src/Alpha_complex/utilities/alpha_complex_3d_persistence.cpp @@ -222,10 +222,7 @@ int main(int argc, char **argv) { break; } - // Sort the simplices in the order of the filtration - simplex_tree.initialize_filtration(); - - std::cout << "Simplex_tree dim: " << simplex_tree.dimension() << std::endl; + std::clog << "Simplex_tree dim: " << simplex_tree.dimension() << std::endl; // Compute the persistence diagram of the complex Persistent_cohomology pcoh(simplex_tree, true); // initializes the coefficient field for homology @@ -237,7 +234,7 @@ int main(int argc, char **argv) { if (output_file_diag.empty()) { pcoh.output_diagram(); } else { - std::cout << "Result in file: " << output_file_diag << std::endl; + std::clog << "Result in file: " << output_file_diag << std::endl; std::ofstream out(output_file_diag); pcoh.output_diagram(out); out.close(); @@ -266,7 +263,7 @@ void program_options(int argc, char *argv[], std::string &off_file_points, bool "cuboid-file,c", po::value<std::string>(&cuboid_file), "Name of file describing the periodic domain. Format is:\n min_hx min_hy min_hz\n max_hx max_hy max_hz")( "output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout")( + "Name of file in which the persistence diagram is written. Default print in std::clog")( "max-alpha-square-value,r", po::value<Filtration_value>(&alpha_square_max_value) ->default_value(std::numeric_limits<Filtration_value>::infinity()), @@ -288,18 +285,18 @@ void program_options(int argc, char *argv[], std::string &off_file_points, bool po::notify(vm); if (vm.count("help") || !vm.count("input-file") || !vm.count("weight-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of a 3D Alpha complex defined on a set of input points.\n"; - std::cout << "3D Alpha complex can be safe (by default) exact or fast, weighted and/or periodic\n\n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients.\n\n"; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of a 3D Alpha complex defined on a set of input points.\n"; + std::clog << "3D Alpha complex can be safe (by default) exact or fast, weighted and/or periodic\n\n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients.\n\n"; - std::cout << "Usage: " << argv[0] << " [options] input-file weight-file\n\n"; - std::cout << visible << std::endl; + std::clog << "Usage: " << argv[0] << " [options] input-file weight-file\n\n"; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Alpha_complex/utilities/alpha_complex_persistence.cpp b/src/Alpha_complex/utilities/alpha_complex_persistence.cpp index 486347cc..7c898dfd 100644 --- a/src/Alpha_complex/utilities/alpha_complex_persistence.cpp +++ b/src/Alpha_complex/utilities/alpha_complex_persistence.cpp @@ -72,13 +72,10 @@ int main(int argc, char **argv) { // ---------------------------------------------------------------------------- // Display information about the alpha complex // ---------------------------------------------------------------------------- - std::cout << "Simplicial complex is of dimension " << simplex.dimension() << " - " << simplex.num_simplices() + std::clog << "Simplicial complex is of dimension " << simplex.dimension() << " - " << simplex.num_simplices() << " simplices - " << simplex.num_vertices() << " vertices." << std::endl; - // Sort the simplices in the order of the filtration - simplex.initialize_filtration(); - - std::cout << "Simplex_tree dim: " << simplex.dimension() << std::endl; + std::clog << "Simplex_tree dim: " << simplex.dimension() << std::endl; // Compute the persistence diagram of the complex Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Gudhi::persistent_cohomology::Field_Zp> pcoh( simplex); @@ -91,7 +88,7 @@ int main(int argc, char **argv) { if (output_file_diag.empty()) { pcoh.output_diagram(); } else { - std::cout << "Result in file: " << output_file_diag << std::endl; + std::clog << "Result in file: " << output_file_diag << std::endl; std::ofstream out(output_file_diag); pcoh.output_diagram(out); out.close(); @@ -114,7 +111,7 @@ void program_options(int argc, char *argv[], std::string &off_file_points, bool "fast,f", po::bool_switch(&fast), "To activate fast version of Alpha complex (default is false, not available if exact is set)")( "output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout")( + "Name of file in which the persistence diagram is written. Default print in std::clog")( "max-alpha-square-value,r", po::value<Filtration_value>(&alpha_square_max_value) ->default_value(std::numeric_limits<Filtration_value>::infinity()), "Maximal alpha square value for the Alpha complex construction.")( @@ -135,17 +132,17 @@ void program_options(int argc, char *argv[], std::string &off_file_points, bool po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of an Alpha complex defined on a set of input points.\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of an Alpha complex defined on a set of input points.\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Bitmap_cubical_complex/example/Random_bitmap_cubical_complex.cpp b/src/Bitmap_cubical_complex/example/Random_bitmap_cubical_complex.cpp index 46ea8f2e..e5512418 100644 --- a/src/Bitmap_cubical_complex/example/Random_bitmap_cubical_complex.cpp +++ b/src/Bitmap_cubical_complex/example/Random_bitmap_cubical_complex.cpp @@ -21,7 +21,7 @@ int main(int argc, char** argv) { srand(time(0)); - std::cout + std::clog << "This program computes persistent homology, by using bitmap_cubical_complex class, of cubical " << "complexes. The first parameter of the program is the dimension D of the bitmap. The next D parameters are " << "number of top dimensional cubes in each dimension of the bitmap. The program will create random cubical " diff --git a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h index 37514dee..aa255ec2 100644 --- a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h +++ b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h @@ -69,7 +69,7 @@ class Bitmap_cubical_complex : public T { Bitmap_cubical_complex(const char* perseus_style_file) : T(perseus_style_file), key_associated_to_simplex(this->total_number_of_cells + 1) { if (globalDbg) { - std::cerr << "Bitmap_cubical_complex( const char* perseus_style_file )\n"; + std::clog << "Bitmap_cubical_complex( const char* perseus_style_file )\n"; } for (std::size_t i = 0; i != this->total_number_of_cells; ++i) { this->key_associated_to_simplex[i] = i; @@ -137,7 +137,7 @@ class Bitmap_cubical_complex : public T { **/ static Simplex_handle null_simplex() { if (globalDbg) { - std::cerr << "Simplex_handle null_simplex()\n"; + std::clog << "Simplex_handle null_simplex()\n"; } return std::numeric_limits<Simplex_handle>::max(); } @@ -152,7 +152,7 @@ class Bitmap_cubical_complex : public T { **/ inline unsigned dimension(Simplex_handle sh) const { if (globalDbg) { - std::cerr << "unsigned dimension(const Simplex_handle& sh)\n"; + std::clog << "unsigned dimension(const Simplex_handle& sh)\n"; } if (sh != null_simplex()) return this->get_dimension_of_a_cell(sh); return -1; @@ -163,7 +163,7 @@ class Bitmap_cubical_complex : public T { **/ Filtration_value filtration(Simplex_handle sh) { if (globalDbg) { - std::cerr << "Filtration_value filtration(const Simplex_handle& sh)\n"; + std::clog << "Filtration_value filtration(const Simplex_handle& sh)\n"; } // Returns the filtration value of a simplex. if (sh != null_simplex()) return this->data[sh]; @@ -175,7 +175,7 @@ class Bitmap_cubical_complex : public T { **/ static Simplex_key null_key() { if (globalDbg) { - std::cerr << "Simplex_key null_key()\n"; + std::clog << "Simplex_key null_key()\n"; } return std::numeric_limits<Simplex_handle>::max(); } @@ -185,7 +185,7 @@ class Bitmap_cubical_complex : public T { **/ Simplex_key key(Simplex_handle sh) const { if (globalDbg) { - std::cerr << "Simplex_key key(const Simplex_handle& sh)\n"; + std::clog << "Simplex_key key(const Simplex_handle& sh)\n"; } if (sh != null_simplex()) { return this->key_associated_to_simplex[sh]; @@ -198,7 +198,7 @@ class Bitmap_cubical_complex : public T { **/ Simplex_handle simplex(Simplex_key key) { if (globalDbg) { - std::cerr << "Simplex_handle simplex(Simplex_key key)\n"; + std::clog << "Simplex_handle simplex(Simplex_key key)\n"; } if (key != null_key()) { return this->simplex_associated_to_key[key]; @@ -211,7 +211,7 @@ class Bitmap_cubical_complex : public T { **/ void assign_key(Simplex_handle sh, Simplex_key key) { if (globalDbg) { - std::cerr << "void assign_key(Simplex_handle& sh, Simplex_key key)\n"; + std::clog << "void assign_key(Simplex_handle& sh, Simplex_key key)\n"; } if (key == null_key()) return; this->key_associated_to_simplex[sh] = key; @@ -251,7 +251,7 @@ class Bitmap_cubical_complex : public T { Filtration_simplex_iterator operator++() { if (globalDbg) { - std::cerr << "Filtration_simplex_iterator operator++\n"; + std::clog << "Filtration_simplex_iterator operator++\n"; } ++this->position; return (*this); @@ -265,7 +265,7 @@ class Bitmap_cubical_complex : public T { Filtration_simplex_iterator& operator=(const Filtration_simplex_iterator& rhs) { if (globalDbg) { - std::cerr << "Filtration_simplex_iterator operator =\n"; + std::clog << "Filtration_simplex_iterator operator =\n"; } this->b = rhs.b; this->position = rhs.position; @@ -274,21 +274,21 @@ class Bitmap_cubical_complex : public T { bool operator==(const Filtration_simplex_iterator& rhs) const { if (globalDbg) { - std::cerr << "bool operator == ( const Filtration_simplex_iterator& rhs )\n"; + std::clog << "bool operator == ( const Filtration_simplex_iterator& rhs )\n"; } return (this->position == rhs.position); } bool operator!=(const Filtration_simplex_iterator& rhs) const { if (globalDbg) { - std::cerr << "bool operator != ( const Filtration_simplex_iterator& rhs )\n"; + std::clog << "bool operator != ( const Filtration_simplex_iterator& rhs )\n"; } return !(*this == rhs); } Simplex_handle operator*() { if (globalDbg) { - std::cerr << "Simplex_handle operator*()\n"; + std::clog << "Simplex_handle operator*()\n"; } return this->b->simplex_associated_to_key[this->position]; } @@ -314,14 +314,14 @@ class Bitmap_cubical_complex : public T { Filtration_simplex_iterator begin() { if (globalDbg) { - std::cerr << "Filtration_simplex_iterator begin() \n"; + std::clog << "Filtration_simplex_iterator begin() \n"; } return Filtration_simplex_iterator(this->b); } Filtration_simplex_iterator end() { if (globalDbg) { - std::cerr << "Filtration_simplex_iterator end()\n"; + std::clog << "Filtration_simplex_iterator end()\n"; } Filtration_simplex_iterator it(this->b); it.position = this->b->simplex_associated_to_key.size(); @@ -347,7 +347,7 @@ class Bitmap_cubical_complex : public T { **/ Filtration_simplex_range filtration_simplex_range() { if (globalDbg) { - std::cerr << "Filtration_simplex_range filtration_simplex_range()\n"; + std::clog << "Filtration_simplex_range filtration_simplex_range()\n"; } // Returns a range over the simplices of the complex in the order of the filtration return Filtration_simplex_range(this); @@ -370,8 +370,8 @@ class Bitmap_cubical_complex : public T { std::pair<Simplex_handle, Simplex_handle> endpoints(Simplex_handle sh) { std::vector<std::size_t> bdry = this->get_boundary_of_a_cell(sh); if (globalDbg) { - std::cerr << "std::pair<Simplex_handle, Simplex_handle> endpoints( Simplex_handle sh )\n"; - std::cerr << "bdry.size() : " << bdry.size() << "\n"; + std::clog << "std::pair<Simplex_handle, Simplex_handle> endpoints( Simplex_handle sh )\n"; + std::clog << "bdry.size() : " << bdry.size() << "\n"; } // this method returns two first elements from the boundary of sh. if (bdry.size() < 2) @@ -392,7 +392,7 @@ class Bitmap_cubical_complex : public T { public: Skeleton_simplex_iterator(Bitmap_cubical_complex* b, std::size_t d) : b(b), dimension(d) { if (globalDbg) { - std::cerr << "Skeleton_simplex_iterator ( Bitmap_cubical_complex* b , std::size_t d )\n"; + std::clog << "Skeleton_simplex_iterator ( Bitmap_cubical_complex* b , std::size_t d )\n"; } // find the position of the first simplex of a dimension d this->position = 0; @@ -406,7 +406,7 @@ class Bitmap_cubical_complex : public T { Skeleton_simplex_iterator operator++() { if (globalDbg) { - std::cerr << "Skeleton_simplex_iterator operator++()\n"; + std::clog << "Skeleton_simplex_iterator operator++()\n"; } // increment the position as long as you did not get to the next element of the dimension dimension. ++this->position; @@ -425,7 +425,7 @@ class Bitmap_cubical_complex : public T { Skeleton_simplex_iterator& operator=(const Skeleton_simplex_iterator& rhs) { if (globalDbg) { - std::cerr << "Skeleton_simplex_iterator operator =\n"; + std::clog << "Skeleton_simplex_iterator operator =\n"; } this->b = rhs.b; this->position = rhs.position; @@ -435,21 +435,21 @@ class Bitmap_cubical_complex : public T { bool operator==(const Skeleton_simplex_iterator& rhs) const { if (globalDbg) { - std::cerr << "bool operator ==\n"; + std::clog << "bool operator ==\n"; } return (this->position == rhs.position); } bool operator!=(const Skeleton_simplex_iterator& rhs) const { if (globalDbg) { - std::cerr << "bool operator != ( const Skeleton_simplex_iterator& rhs )\n"; + std::clog << "bool operator != ( const Skeleton_simplex_iterator& rhs )\n"; } return !(*this == rhs); } Simplex_handle operator*() { if (globalDbg) { - std::cerr << "Simplex_handle operator*() \n"; + std::clog << "Simplex_handle operator*() \n"; } return this->position; } @@ -476,14 +476,14 @@ class Bitmap_cubical_complex : public T { Skeleton_simplex_iterator begin() { if (globalDbg) { - std::cerr << "Skeleton_simplex_iterator begin()\n"; + std::clog << "Skeleton_simplex_iterator begin()\n"; } return Skeleton_simplex_iterator(this->b, this->dimension); } Skeleton_simplex_iterator end() { if (globalDbg) { - std::cerr << "Skeleton_simplex_iterator end()\n"; + std::clog << "Skeleton_simplex_iterator end()\n"; } Skeleton_simplex_iterator it(this->b, this->dimension); it.position = this->b->data.size(); @@ -500,7 +500,7 @@ class Bitmap_cubical_complex : public T { **/ Skeleton_simplex_range skeleton_simplex_range(unsigned dimension) { if (globalDbg) { - std::cerr << "Skeleton_simplex_range skeleton_simplex_range( unsigned dimension )\n"; + std::clog << "Skeleton_simplex_range skeleton_simplex_range( unsigned dimension )\n"; } return Skeleton_simplex_range(this, dimension); } @@ -515,7 +515,7 @@ class Bitmap_cubical_complex : public T { template <typename T> void Bitmap_cubical_complex<T>::initialize_simplex_associated_to_key() { if (globalDbg) { - std::cerr << "void Bitmap_cubical_complex<T>::initialize_elements_ordered_according_to_filtration() \n"; + std::clog << "void Bitmap_cubical_complex<T>::initialize_elements_ordered_according_to_filtration() \n"; } this->simplex_associated_to_key = std::vector<std::size_t>(this->data.size()); std::iota(std::begin(simplex_associated_to_key), std::end(simplex_associated_to_key), 0); diff --git a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h index 0d6299d2..f8f80ded 100644 --- a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h +++ b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h @@ -13,6 +13,8 @@ #include <gudhi/Bitmap_cubical_complex/counter.h> +#include <boost/config.hpp> + #include <iostream> #include <vector> #include <string> @@ -110,6 +112,16 @@ class Bitmap_cubical_complex_base { virtual inline std::vector<std::size_t> get_coboundary_of_a_cell(std::size_t cell) const; /** + * This function finds a top-dimensional cell that is incident to the input cell and has + * the same filtration value. In case several cells are suitable, an arbitrary one is + * returned. Note that the input parameter can be a cell of any dimension (vertex, edge, etc). + * On the other hand, the output is always indicating the position of + * a top-dimensional cube in the data structure. + * \pre The filtration values are assigned as per `impose_lower_star_filtration()`. + **/ + inline size_t get_top_dimensional_coface_of_a_cell(size_t splx); + + /** * This procedure compute incidence numbers between cubes. For a cube \f$A\f$ of * dimension n and a cube \f$B \subset A\f$ of dimension n-1, an incidence * between \f$A\f$ and \f$B\f$ is the integer with which \f$B\f$ appears in the boundary of \f$A\f$. @@ -142,7 +154,7 @@ class Bitmap_cubical_complex_base { } if (coface_counter[i] != face_counter[i]) { if (number_of_position_in_which_counters_do_not_agree != -1) { - std::cout << "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face.\n"; + std::cerr << "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face.\n"; throw std::logic_error( "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face."); } @@ -197,7 +209,7 @@ class Bitmap_cubical_complex_base { /** * Returns number of all cubes in the data structure. **/ - inline unsigned size() const { return this->data.size(); } + inline std::size_t size() const { return this->data.size(); } /** * Writing to stream operator. By using it we get the values T of cells in order in which they are stored in the @@ -408,7 +420,7 @@ class Bitmap_cubical_complex_base { void print_counter() const { for (std::size_t i = 0; i != this->counter.size(); ++i) { - std::cout << this->counter[i] << " "; + std::clog << this->counter[i] << " "; } } friend class Bitmap_cubical_complex_base; @@ -521,11 +533,11 @@ void Bitmap_cubical_complex_base<T>::put_data_to_bins(std::size_t number_of_bins // now put the data into the appropriate bins: for (std::size_t i = 0; i != this->data.size(); ++i) { if (dbg) { - std::cerr << "Before binning : " << this->data[i] << std::endl; + std::clog << "Before binning : " << this->data[i] << std::endl; } this->data[i] = min_max.first + dx * (this->data[i] - min_max.first) / number_of_bins; if (dbg) { - std::cerr << "After binning : " << this->data[i] << std::endl; + std::clog << "After binning : " << this->data[i] << std::endl; } } } @@ -539,11 +551,11 @@ void Bitmap_cubical_complex_base<T>::put_data_to_bins(T diameter_of_bin) { // now put the data into the appropriate bins: for (std::size_t i = 0; i != this->data.size(); ++i) { if (dbg) { - std::cerr << "Before binning : " << this->data[i] << std::endl; + std::clog << "Before binning : " << this->data[i] << std::endl; } this->data[i] = min_max.first + diameter_of_bin * (this->data[i] - min_max.first) / number_of_bins; if (dbg) { - std::cerr << "After binning : " << this->data[i] << std::endl; + std::clog << "After binning : " << this->data[i] << std::endl; } } } @@ -603,6 +615,19 @@ void Bitmap_cubical_complex_base<T>::setup_bitmap_based_on_top_dimensional_cells } template <typename T> +size_t Bitmap_cubical_complex_base<T>::get_top_dimensional_coface_of_a_cell(size_t splx) { + if (this->get_dimension_of_a_cell(splx) == this->dimension()){return splx;} + else{ + for (auto v : this->get_coboundary_of_a_cell(splx)){ + if(this->get_cell_data(v) == this->get_cell_data(splx)){ + return this->get_top_dimensional_coface_of_a_cell(v); + } + } + } + BOOST_UNREACHABLE_RETURN(-2); +} + +template <typename T> Bitmap_cubical_complex_base<T>::Bitmap_cubical_complex_base(const std::vector<unsigned>& sizes_in_following_directions, const std::vector<T>& top_dimensional_cells) { this->setup_bitmap_based_on_top_dimensional_cells_list(sizes_in_following_directions, top_dimensional_cells); @@ -617,7 +642,7 @@ void Bitmap_cubical_complex_base<T>::read_perseus_style_file(const char* perseus inFiltration >> dimensionOfData; if (dbg) { - std::cerr << "dimensionOfData : " << dimensionOfData << std::endl; + std::clog << "dimensionOfData : " << dimensionOfData << std::endl; } std::vector<unsigned> sizes; @@ -630,7 +655,7 @@ void Bitmap_cubical_complex_base<T>::read_perseus_style_file(const char* perseus sizes.push_back(size_in_this_dimension); dimensions *= size_in_this_dimension; if (dbg) { - std::cerr << "size_in_this_dimension : " << size_in_this_dimension << std::endl; + std::clog << "size_in_this_dimension : " << size_in_this_dimension << std::endl; } } this->set_up_containers(sizes); @@ -651,7 +676,7 @@ void Bitmap_cubical_complex_base<T>::read_perseus_style_file(const char* perseus } if (dbg) { - std::cerr << "Cell of an index : " << it.compute_index_in_bitmap() + std::clog << "Cell of an index : " << it.compute_index_in_bitmap() << " and dimension: " << this->get_dimension_of_a_cell(it.compute_index_in_bitmap()) << " get the value : " << filtrationLevel << std::endl; } @@ -754,20 +779,20 @@ std::vector<std::size_t> Bitmap_cubical_complex_base<T>::get_coboundary_of_a_cel template <typename T> unsigned Bitmap_cubical_complex_base<T>::get_dimension_of_a_cell(std::size_t cell) const { bool dbg = false; - if (dbg) std::cerr << "\n\n\n Computing position o a cell of an index : " << cell << std::endl; + if (dbg) std::clog << "\n\n\n Computing position o a cell of an index : " << cell << std::endl; unsigned dimension = 0; for (std::size_t i = this->multipliers.size(); i != 0; --i) { unsigned position = cell / this->multipliers[i - 1]; if (dbg) { - std::cerr << "i-1 :" << i - 1 << std::endl; - std::cerr << "cell : " << cell << std::endl; - std::cerr << "position : " << position << std::endl; - std::cerr << "multipliers[" << i - 1 << "] = " << this->multipliers[i - 1] << std::endl; + std::clog << "i-1 :" << i - 1 << std::endl; + std::clog << "cell : " << cell << std::endl; + std::clog << "position : " << position << std::endl; + std::clog << "multipliers[" << i - 1 << "] = " << this->multipliers[i - 1] << std::endl; } if (position % 2 == 1) { - if (dbg) std::cerr << "Nonzero length in this direction \n"; + if (dbg) std::clog << "Nonzero length in this direction \n"; dimension++; } cell = cell % this->multipliers[i - 1]; @@ -803,9 +828,9 @@ void Bitmap_cubical_complex_base<T>::impose_lower_star_filtration() { while (indices_to_consider.size()) { if (dbg) { - std::cerr << "indices_to_consider in this iteration \n"; + std::clog << "indices_to_consider in this iteration \n"; for (std::size_t i = 0; i != indices_to_consider.size(); ++i) { - std::cout << indices_to_consider[i] << " "; + std::clog << indices_to_consider[i] << " "; } } std::vector<std::size_t> new_indices_to_consider; @@ -813,14 +838,14 @@ void Bitmap_cubical_complex_base<T>::impose_lower_star_filtration() { std::vector<std::size_t> bd = this->get_boundary_of_a_cell(indices_to_consider[i]); for (std::size_t boundaryIt = 0; boundaryIt != bd.size(); ++boundaryIt) { if (dbg) { - std::cerr << "filtration of a cell : " << bd[boundaryIt] << " is : " << this->data[bd[boundaryIt]] + std::clog << "filtration of a cell : " << bd[boundaryIt] << " is : " << this->data[bd[boundaryIt]] << " while of a cell: " << indices_to_consider[i] << " is: " << this->data[indices_to_consider[i]] << std::endl; } if (this->data[bd[boundaryIt]] > this->data[indices_to_consider[i]]) { this->data[bd[boundaryIt]] = this->data[indices_to_consider[i]]; if (dbg) { - std::cerr << "Setting the value of a cell : " << bd[boundaryIt] + std::clog << "Setting the value of a cell : " << bd[boundaryIt] << " to : " << this->data[indices_to_consider[i]] << std::endl; } } diff --git a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h index edd794fe..18901469 100644 --- a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h +++ b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h @@ -128,7 +128,7 @@ class Bitmap_cubical_complex_periodic_boundary_conditions_base : public Bitmap_c } if (coface_counter[i] != face_counter[i]) { if (number_of_position_in_which_counters_do_not_agree != -1) { - std::cout << "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face.\n"; + std::cerr << "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face.\n"; throw std::logic_error( "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face."); } @@ -237,7 +237,7 @@ Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_comp if (inFiltration.eof()) break; if (dbg) { - std::cerr << "Cell of an index : " << it.compute_index_in_bitmap() + std::clog << "Cell of an index : " << it.compute_index_in_bitmap() << " and dimension: " << this->get_dimension_of_a_cell(it.compute_index_in_bitmap()) << " get the value : " << filtrationLevel << std::endl; } @@ -278,7 +278,7 @@ std::vector<std::size_t> Bitmap_cubical_complex_periodic_boundary_conditions_bas std::size_t cell) const { bool dbg = false; if (dbg) { - std::cerr << "Computations of boundary of a cell : " << cell << std::endl; + std::clog << "Computations of boundary of a cell : " << cell << std::endl; } std::vector<std::size_t> boundary_elements; @@ -292,7 +292,6 @@ std::vector<std::size_t> Bitmap_cubical_complex_periodic_boundary_conditions_bas if (position % 2 == 1) { // if there are no periodic boundary conditions in this direction, we do not have to do anything. if (!directions_in_which_periodic_b_cond_are_to_be_imposed[i - 1]) { - // std::cerr << "A\n"; if (sum_of_dimensions % 2) { boundary_elements.push_back(cell - this->multipliers[i - 1]); boundary_elements.push_back(cell + this->multipliers[i - 1]); @@ -301,12 +300,11 @@ std::vector<std::size_t> Bitmap_cubical_complex_periodic_boundary_conditions_bas boundary_elements.push_back(cell - this->multipliers[i - 1]); } if (dbg) { - std::cerr << cell - this->multipliers[i - 1] << " " << cell + this->multipliers[i - 1] << " "; + std::clog << cell - this->multipliers[i - 1] << " " << cell + this->multipliers[i - 1] << " "; } } else { // in this direction we have to do boundary conditions. Therefore, we need to check if we are not at the end. if (position != 2 * this->sizes[i - 1] - 1) { - // std::cerr << "B\n"; if (sum_of_dimensions % 2) { boundary_elements.push_back(cell - this->multipliers[i - 1]); boundary_elements.push_back(cell + this->multipliers[i - 1]); @@ -315,10 +313,9 @@ std::vector<std::size_t> Bitmap_cubical_complex_periodic_boundary_conditions_bas boundary_elements.push_back(cell - this->multipliers[i - 1]); } if (dbg) { - std::cerr << cell - this->multipliers[i - 1] << " " << cell + this->multipliers[i - 1] << " "; + std::clog << cell - this->multipliers[i - 1] << " " << cell + this->multipliers[i - 1] << " "; } } else { - // std::cerr << "C\n"; if (sum_of_dimensions % 2) { boundary_elements.push_back(cell - this->multipliers[i - 1]); boundary_elements.push_back(cell - (2 * this->sizes[i - 1] - 1) * this->multipliers[i - 1]); @@ -327,7 +324,7 @@ std::vector<std::size_t> Bitmap_cubical_complex_periodic_boundary_conditions_bas boundary_elements.push_back(cell - this->multipliers[i - 1]); } if (dbg) { - std::cerr << cell - this->multipliers[i - 1] << " " + std::clog << cell - this->multipliers[i - 1] << " " << cell - (2 * this->sizes[i - 1] - 1) * this->multipliers[i - 1] << " "; } } diff --git a/src/Bitmap_cubical_complex/test/Bitmap_test.cpp b/src/Bitmap_cubical_complex/test/Bitmap_test.cpp index f18adb36..6f35b6da 100644 --- a/src/Bitmap_cubical_complex/test/Bitmap_test.cpp +++ b/src/Bitmap_cubical_complex/test/Bitmap_test.cpp @@ -1402,12 +1402,12 @@ BOOST_AUTO_TEST_CASE(check_if_boundary_of_boundary_is_zero_periodic_case_2d) { it != ba.all_cells_iterator_end(); ++it) { int i = 1; - // std::cout << "Element : " << *it << std::endl; + // std::clog << "Element : " << *it << std::endl; Bitmap_cubical_complex_periodic_boundary_conditions_base::Boundary_range bdrange = ba.boundary_range(*it); for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd = bdrange.begin(); bd != bdrange.end(); ++bd) { - // std::cout << *bd << " "; + // std::clog << *bd << " "; Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_range second_bdrange = ba.boundary_range(*bd); int j = 1; for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd2 = second_bdrange.begin(); @@ -1441,7 +1441,7 @@ BOOST_AUTO_TEST_CASE(check_if_boundary_of_boundary_is_zero_periodic_case_3d) { std::vector<int> elems_in_boundary(number_of_all_elements, 0); for (Bitmap_cubical_complex_periodic_boundary_conditions::All_cells_iterator it = ba.all_cells_iterator_begin(); it != ba.all_cells_iterator_end(); ++it) { - // std::cout << "Element : " << *it << std::endl; + // std::clog << "Element : " << *it << std::endl; int i = 1; @@ -1449,7 +1449,7 @@ BOOST_AUTO_TEST_CASE(check_if_boundary_of_boundary_is_zero_periodic_case_3d) { for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd = bdrange.begin(); bd != bdrange.end(); ++bd) { Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_range second_bdrange = ba.boundary_range(*bd); - // std::cout << *bd << " "; + // std::clog << *bd << " "; int j = 1; for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd2 = second_bdrange.begin(); bd2 != second_bdrange.end(); ++bd2) { @@ -1551,7 +1551,7 @@ BOOST_AUTO_TEST_CASE(compute_incidence_between_cells_test_periodic_boundary_cond Bitmap_cubical_complex_periodic_boundary_conditions_base::Boundary_range bdrange = ba.boundary_range(*it); for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd = bdrange.begin(); bd != bdrange.end(); ++bd) { - // std::cout << *bd << " "; + // std::clog << *bd << " "; Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_range second_bdrange = ba.boundary_range(*bd); for (Bitmap_cubical_complex_periodic_boundary_conditions::Boundary_iterator bd2 = second_bdrange.begin(); bd2 != second_bdrange.end(); ++bd2) { @@ -1571,11 +1571,11 @@ BOOST_AUTO_TEST_CASE(perseus_file_read) { auto it = increasing.top_dimensional_cells_iterator_begin(); double value = increasing.get_cell_data(*it); - std::cout << "First value of sinusoid.txt is " << value << std::endl; + std::clog << "First value of sinusoid.txt is " << value << std::endl; BOOST_CHECK(value == 10.); // Next value ++it; value = increasing.get_cell_data(*it); - std::cout << "Second value of sinusoid.txt is " << value << std::endl; + std::clog << "Second value of sinusoid.txt is " << value << std::endl; BOOST_CHECK(value == std::numeric_limits<double>::infinity()); } diff --git a/src/Bitmap_cubical_complex/utilities/cubical_complex_persistence.cpp b/src/Bitmap_cubical_complex/utilities/cubical_complex_persistence.cpp index a9792c2d..510861cd 100644 --- a/src/Bitmap_cubical_complex/utilities/cubical_complex_persistence.cpp +++ b/src/Bitmap_cubical_complex/utilities/cubical_complex_persistence.cpp @@ -19,7 +19,7 @@ #include <cstddef> int main(int argc, char** argv) { - std::cout + std::clog << "This program computes persistent homology, by using bitmap_cubical_complex class, of cubical " << "complexes provided in text files in Perseus style (the only numbered in the first line is a dimension D of a" << "bitmap. In the lines I between 2 and D+1 there are numbers of top dimensional cells in the direction I. Let " @@ -62,7 +62,7 @@ int main(int argc, char** argv) { pcoh.output_diagram(out); out.close(); - std::cout << "Result in file: " << output_file_name << "\n"; + std::clog << "Result in file: " << output_file_name << "\n"; return 0; } diff --git a/src/Bitmap_cubical_complex/utilities/periodic_cubical_complex_persistence.cpp b/src/Bitmap_cubical_complex/utilities/periodic_cubical_complex_persistence.cpp index fa97bac0..86816417 100644 --- a/src/Bitmap_cubical_complex/utilities/periodic_cubical_complex_persistence.cpp +++ b/src/Bitmap_cubical_complex/utilities/periodic_cubical_complex_persistence.cpp @@ -20,7 +20,7 @@ #include <string> int main(int argc, char** argv) { - std::cout + std::clog << "This program computes persistent homology, by using " << "Bitmap_cubical_complex_periodic_boundary_conditions class, of cubical complexes provided in text files in " << "Perseus style (the only numbered in the first line is a dimension D of a bitmap. In the lines I between 2 " @@ -64,7 +64,7 @@ int main(int argc, char** argv) { pcoh.output_diagram(out); out.close(); - std::cout << "Result in file: " << output_file_name << "\n"; + std::clog << "Result in file: " << output_file_name << "\n"; return 0; } diff --git a/src/Bottleneck_distance/doc/Intro_bottleneck_distance.h b/src/Bottleneck_distance/doc/Intro_bottleneck_distance.h index bbc952e1..2a988b4b 100644 --- a/src/Bottleneck_distance/doc/Intro_bottleneck_distance.h +++ b/src/Bottleneck_distance/doc/Intro_bottleneck_distance.h @@ -52,7 +52,7 @@ int main() { diag2.emplace_back(0., 13.); double b = Gudhi::persistence_diagram::bottleneck_distance(diag1, diag2); - std::cout << "Bottleneck distance = " << b << std::endl; + std::clog << "Bottleneck distance = " << b << std::endl; } * \endcode * diff --git a/src/Bottleneck_distance/example/CMakeLists.txt b/src/Bottleneck_distance/example/CMakeLists.txt index 3d65963a..9839c59d 100644 --- a/src/Bottleneck_distance/example/CMakeLists.txt +++ b/src/Bottleneck_distance/example/CMakeLists.txt @@ -13,7 +13,7 @@ endif (NOT CGAL_VERSION VERSION_LESS 4.11.0) if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) add_executable (alpha_rips_persistence_bottleneck_distance alpha_rips_persistence_bottleneck_distance.cpp) - target_link_libraries(alpha_rips_persistence_bottleneck_distance ${Boost_PROGRAM_OPTIONS_LIBRARY}) + target_link_libraries(alpha_rips_persistence_bottleneck_distance Boost::program_options) if (TBB_FOUND) target_link_libraries(alpha_rips_persistence_bottleneck_distance ${TBB_LIBRARIES}) diff --git a/src/Bottleneck_distance/example/alpha_rips_persistence_bottleneck_distance.cpp b/src/Bottleneck_distance/example/alpha_rips_persistence_bottleneck_distance.cpp index 6c0dc9bf..ceb9e226 100644 --- a/src/Bottleneck_distance/example/alpha_rips_persistence_bottleneck_distance.cpp +++ b/src/Bottleneck_distance/example/alpha_rips_persistence_bottleneck_distance.cpp @@ -68,12 +68,9 @@ int main(int argc, char * argv[]) { Simplex_tree rips_stree; rips_complex.create_complex(rips_stree, dim_max); - std::cout << "The Rips complex contains " << rips_stree.num_simplices() << " simplices and has dimension " + std::clog << "The Rips complex contains " << rips_stree.num_simplices() << " simplices and has dimension " << rips_stree.dimension() << " \n"; - // Sort the simplices in the order of the filtration - rips_stree.initialize_filtration(); - // Compute the persistence diagram of the complex Persistent_cohomology rips_pcoh(rips_stree); // initializes the coefficient field for homology @@ -89,12 +86,9 @@ int main(int argc, char * argv[]) { Simplex_tree alpha_stree; alpha_complex.create_complex(alpha_stree, threshold * threshold); - std::cout << "The Alpha complex contains " << alpha_stree.num_simplices() << " simplices and has dimension " + std::clog << "The Alpha complex contains " << alpha_stree.num_simplices() << " simplices and has dimension " << alpha_stree.dimension() << " \n"; - // Sort the simplices in the order of the filtration - alpha_stree.initialize_filtration(); - // Compute the persistence diagram of the complex Persistent_cohomology alpha_pcoh(alpha_stree); // initializes the coefficient field for homology @@ -115,12 +109,12 @@ int main(int argc, char * argv[]) { std::transform(alpha_intervals.begin(), alpha_intervals.end(), alpha_intervals.begin(), compute_root_square); double bottleneck_distance = Gudhi::persistence_diagram::bottleneck_distance(rips_intervals, alpha_intervals); - std::cout << "In dimension " << dim << ", bottleneck distance = " << bottleneck_distance << std::endl; + std::clog << "In dimension " << dim << ", bottleneck distance = " << bottleneck_distance << std::endl; if (bottleneck_distance > max_b_distance) max_b_distance = bottleneck_distance; } - std::cout << "================================================================================" << std::endl; - std::cout << "Bottleneck distance is " << max_b_distance << std::endl; + std::clog << "================================================================================" << std::endl; + std::clog << "Bottleneck distance is " << max_b_distance << std::endl; return 0; } @@ -162,17 +156,17 @@ void program_options(int argc, char * argv[] po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of a Rips complex defined on a set of input points.\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of a Rips complex defined on a set of input points.\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Bottleneck_distance/example/bottleneck_basic_example.cpp b/src/Bottleneck_distance/example/bottleneck_basic_example.cpp index 61778a55..e8632a4f 100644 --- a/src/Bottleneck_distance/example/bottleneck_basic_example.cpp +++ b/src/Bottleneck_distance/example/bottleneck_basic_example.cpp @@ -20,9 +20,9 @@ int main() { double b = Gudhi::persistence_diagram::bottleneck_distance(v1, v2); - std::cout << "Bottleneck distance = " << b << std::endl; + std::clog << "Bottleneck distance = " << b << std::endl; b = Gudhi::persistence_diagram::bottleneck_distance(v1, v2, 0.1); - std::cout << "Approx bottleneck distance = " << b << std::endl; + std::clog << "Approx bottleneck distance = " << b << std::endl; } diff --git a/src/Bottleneck_distance/include/gudhi/Persistence_graph.h b/src/Bottleneck_distance/include/gudhi/Persistence_graph.h index f791e37c..e1e3522e 100644 --- a/src/Bottleneck_distance/include/gudhi/Persistence_graph.h +++ b/src/Bottleneck_distance/include/gudhi/Persistence_graph.h @@ -25,7 +25,7 @@ namespace Gudhi { namespace persistence_diagram { -/** \internal \brief Structure representing an euclidean bipartite graph containing +/** \internal \brief Structure representing a Euclidean bipartite graph containing * the points from the two persistence diagrams (including the projections). * * \ingroup bottleneck_distance diff --git a/src/Bottleneck_distance/utilities/bottleneck_distance.cpp b/src/Bottleneck_distance/utilities/bottleneck_distance.cpp index d88a8a0b..01813ba1 100644 --- a/src/Bottleneck_distance/utilities/bottleneck_distance.cpp +++ b/src/Bottleneck_distance/utilities/bottleneck_distance.cpp @@ -18,7 +18,7 @@ int main(int argc, char** argv) { if (argc < 3) { - std::cout << "To run this program please provide as an input two files with persistence diagrams. Each file" << + std::clog << "To run this program please provide as an input two files with persistence diagrams. Each file" << " should contain a birth-death pair per line. Third, optional parameter is an error bound on the bottleneck" << " distance (set by default to the smallest positive double value). If you set the error bound to 0, be" << " aware this version is exact but expensive. The program will now terminate \n"; @@ -32,7 +32,7 @@ int main(int argc, char** argv) { tolerance = atof(argv[3]); } double b = Gudhi::persistence_diagram::bottleneck_distance(diag1, diag2, tolerance); - std::cout << "The distance between the diagrams is : " << b << ". The tolerance is : " << tolerance << std::endl; + std::clog << "The distance between the diagrams is : " << b << ". The tolerance is : " << tolerance << std::endl; return 0; } diff --git a/src/Cech_complex/benchmark/CMakeLists.txt b/src/Cech_complex/benchmark/CMakeLists.txt index b7697764..c04bca53 100644 --- a/src/Cech_complex/benchmark/CMakeLists.txt +++ b/src/Cech_complex/benchmark/CMakeLists.txt @@ -5,7 +5,7 @@ project(Cech_complex_benchmark) file(COPY "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) add_executable(cech_complex_benchmark cech_complex_benchmark.cpp) -target_link_libraries(cech_complex_benchmark ${Boost_SYSTEM_LIBRARY} ${Boost_FILESYSTEM_LIBRARY}) +target_link_libraries(cech_complex_benchmark Boost::filesystem) if (TBB_FOUND) target_link_libraries(cech_complex_benchmark ${TBB_LIBRARIES}) diff --git a/src/Cech_complex/benchmark/cech_complex_benchmark.cpp b/src/Cech_complex/benchmark/cech_complex_benchmark.cpp index d2d71dbf..e489e8a4 100644 --- a/src/Cech_complex/benchmark/cech_complex_benchmark.cpp +++ b/src/Cech_complex/benchmark/cech_complex_benchmark.cpp @@ -68,24 +68,24 @@ int main(int argc, char* argv[]) { Proximity_graph euclidean_prox_graph = Gudhi::compute_proximity_graph<Simplex_tree>( off_reader.get_point_cloud(), threshold, Gudhi::Euclidean_distance()); - std::cout << euclidean_clock << std::endl; + std::clog << euclidean_clock << std::endl; Gudhi::Clock miniball_clock("Minimal_enclosing_ball_radius"); // Compute the proximity graph of the points Proximity_graph miniball_prox_graph = Gudhi::compute_proximity_graph<Simplex_tree>( off_reader.get_point_cloud(), threshold, Minimal_enclosing_ball_radius()); - std::cout << miniball_clock << std::endl; + std::clog << miniball_clock << std::endl; Gudhi::Clock common_miniball_clock("Gudhi::Minimal_enclosing_ball_radius()"); // Compute the proximity graph of the points Proximity_graph common_miniball_prox_graph = Gudhi::compute_proximity_graph<Simplex_tree>( off_reader.get_point_cloud(), threshold, Gudhi::Minimal_enclosing_ball_radius()); - std::cout << common_miniball_clock << std::endl; + std::clog << common_miniball_clock << std::endl; boost::filesystem::path full_path(boost::filesystem::current_path()); - std::cout << "Current path is : " << full_path << std::endl; + std::clog << "Current path is : " << full_path << std::endl; - std::cout << "File name;Radius;Rips time;Cech time; Ratio Rips/Cech time;Rips nb simplices;Cech nb simplices;" + std::clog << "File name;Radius;Rips time;Cech time; Ratio Rips/Cech time;Rips nb simplices;Cech nb simplices;" << std::endl; boost::filesystem::directory_iterator end_itr; // default construction yields past-the-end for (boost::filesystem::directory_iterator itr(boost::filesystem::current_path()); itr != end_itr; ++itr) { @@ -96,8 +96,8 @@ int main(int argc, char* argv[]) { Point p0 = off_reader.get_point_cloud()[0]; for (Filtration_value radius = 0.1; radius < 0.4; radius += 0.1) { - std::cout << itr->path().stem() << ";"; - std::cout << radius << ";"; + std::clog << itr->path().stem() << ";"; + std::clog << radius << ";"; Gudhi::Clock rips_clock("Rips computation"); Rips_complex rips_complex_from_points(off_reader.get_point_cloud(), radius, Gudhi::Minimal_enclosing_ball_radius()); @@ -107,7 +107,7 @@ int main(int argc, char* argv[]) { // Display information about the Rips complex // ------------------------------------------ double rips_sec = rips_clock.num_seconds(); - std::cout << rips_sec << ";"; + std::clog << rips_sec << ";"; Gudhi::Clock cech_clock("Cech computation"); Cech_complex cech_complex_from_points(off_reader.get_point_cloud(), radius); @@ -117,12 +117,12 @@ int main(int argc, char* argv[]) { // Display information about the Cech complex // ------------------------------------------ double cech_sec = cech_clock.num_seconds(); - std::cout << cech_sec << ";"; - std::cout << cech_sec / rips_sec << ";"; + std::clog << cech_sec << ";"; + std::clog << cech_sec / rips_sec << ";"; assert(rips_stree.num_simplices() >= cech_stree.num_simplices()); - std::cout << rips_stree.num_simplices() << ";"; - std::cout << cech_stree.num_simplices() << ";" << std::endl; + std::clog << rips_stree.num_simplices() << ";"; + std::clog << cech_stree.num_simplices() << ";" << std::endl; } } } diff --git a/src/Cech_complex/example/CMakeLists.txt b/src/Cech_complex/example/CMakeLists.txt index ab391215..98757988 100644 --- a/src/Cech_complex/example/CMakeLists.txt +++ b/src/Cech_complex/example/CMakeLists.txt @@ -2,7 +2,7 @@ cmake_minimum_required(VERSION 2.6) project(Cech_complex_examples) add_executable ( Cech_complex_example_step_by_step cech_complex_step_by_step.cpp ) -target_link_libraries(Cech_complex_example_step_by_step ${Boost_PROGRAM_OPTIONS_LIBRARY}) +target_link_libraries(Cech_complex_example_step_by_step Boost::program_options) if (TBB_FOUND) target_link_libraries(Cech_complex_example_step_by_step ${TBB_LIBRARIES}) endif() diff --git a/src/Cech_complex/example/cech_complex_example_from_points.cpp b/src/Cech_complex/example/cech_complex_example_from_points.cpp index 3cc5a4df..1a1f708c 100644 --- a/src/Cech_complex/example/cech_complex_example_from_points.cpp +++ b/src/Cech_complex/example/cech_complex_example_from_points.cpp @@ -37,18 +37,18 @@ int main() { // ---------------------------------------------------------------------------- // Display information about the one skeleton Cech complex // ---------------------------------------------------------------------------- - std::cout << "Cech complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " + std::clog << "Cech complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " << stree.num_vertices() << " vertices." << std::endl; - std::cout << "Iterator on Cech complex simplices in the filtration order, with [filtration value]:" << std::endl; + std::clog << "Iterator on Cech complex simplices in the filtration order, with [filtration value]:" << std::endl; for (auto f_simplex : stree.filtration_simplex_range()) { - std::cout << " ( "; + std::clog << " ( "; for (auto vertex : stree.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> " + std::clog << ") -> " << "[" << stree.filtration(f_simplex) << "] "; - std::cout << std::endl; + std::clog << std::endl; } return 0; } diff --git a/src/Cech_complex/example/cech_complex_step_by_step.cpp b/src/Cech_complex/example/cech_complex_step_by_step.cpp index b3d05697..f59f0293 100644 --- a/src/Cech_complex/example/cech_complex_step_by_step.cpp +++ b/src/Cech_complex/example/cech_complex_step_by_step.cpp @@ -51,12 +51,12 @@ class Cech_blocker { for (auto vertex : simplex_tree_.simplex_vertex_range(sh)) { points.push_back(point_cloud_[vertex]); #ifdef DEBUG_TRACES - std::cout << "#(" << vertex << ")#"; + std::clog << "#(" << vertex << ")#"; #endif // DEBUG_TRACES } Filtration_value radius = Gudhi::Minimal_enclosing_ball_radius()(points); #ifdef DEBUG_TRACES - std::cout << "radius = " << radius << " - " << (radius > max_radius_) << std::endl; + std::clog << "radius = " << radius << " - " << (radius > max_radius_) << std::endl; #endif // DEBUG_TRACES simplex_tree_.assign_filtration(sh, radius); return (radius > max_radius_); @@ -96,23 +96,23 @@ int main(int argc, char* argv[]) { // expand the graph until dimension dim_max st.expansion_with_blockers(dim_max, Cech_blocker(st, max_radius, off_reader.get_point_cloud())); - std::cout << "The complex contains " << st.num_simplices() << " simplices \n"; - std::cout << " and has dimension " << st.dimension() << " \n"; + std::clog << "The complex contains " << st.num_simplices() << " simplices \n"; + std::clog << " and has dimension " << st.dimension() << " \n"; // Sort the simplices in the order of the filtration st.initialize_filtration(); #if DEBUG_TRACES - std::cout << "********************************************************************\n"; - std::cout << "* The complex contains " << st.num_simplices() << " simplices - dimension=" << st.dimension() << "\n"; - std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + std::clog << "********************************************************************\n"; + std::clog << "* The complex contains " << st.num_simplices() << " simplices - dimension=" << st.dimension() << "\n"; + std::clog << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " + std::clog << " " << "[" << st.filtration(f_simplex) << "] "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << static_cast<int>(vertex) << " "; + std::clog << static_cast<int>(vertex) << " "; } - std::cout << std::endl; + std::clog << std::endl; } #endif // DEBUG_TRACES @@ -144,11 +144,11 @@ void program_options(int argc, char* argv[], std::string& off_file_points, Filtr po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Construct a Cech complex defined on a set of input points.\n \n"; + std::clog << std::endl; + std::clog << "Construct a Cech complex defined on a set of input points.\n \n"; - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Cech_complex/include/gudhi/Cech_complex_blocker.h b/src/Cech_complex/include/gudhi/Cech_complex_blocker.h index 068cdde3..31b9aab5 100644 --- a/src/Cech_complex/include/gudhi/Cech_complex_blocker.h +++ b/src/Cech_complex/include/gudhi/Cech_complex_blocker.h @@ -53,12 +53,12 @@ class Cech_blocker { for (auto vertex : sc_ptr_->simplex_vertex_range(sh)) { points.push_back(cc_ptr_->get_point(vertex)); #ifdef DEBUG_TRACES - std::cout << "#(" << vertex << ")#"; + std::clog << "#(" << vertex << ")#"; #endif // DEBUG_TRACES } Filtration_value radius = Gudhi::Minimal_enclosing_ball_radius()(points); #ifdef DEBUG_TRACES - if (radius > cc_ptr_->max_radius()) std::cout << "radius > max_radius => expansion is blocked\n"; + if (radius > cc_ptr_->max_radius()) std::clog << "radius > max_radius => expansion is blocked\n"; #endif // DEBUG_TRACES sc_ptr_->assign_filtration(sh, radius); return (radius > cc_ptr_->max_radius()); diff --git a/src/Cech_complex/test/test_cech_complex.cpp b/src/Cech_complex/test/test_cech_complex.cpp index c6b15d7f..6e00d7b5 100644 --- a/src/Cech_complex/test/test_cech_complex.cpp +++ b/src/Cech_complex/test/test_cech_complex.cpp @@ -58,7 +58,7 @@ BOOST_AUTO_TEST_CASE(Cech_complex_for_documentation) { points.push_back({-0.5, 2.}); // 10 Filtration_value max_radius = 1.0; - std::cout << "========== NUMBER OF POINTS = " << points.size() << " - Cech max_radius = " << max_radius + std::clog << "========== NUMBER OF POINTS = " << points.size() << " - Cech max_radius = " << max_radius << "==========" << std::endl; Cech_complex cech_complex_for_doc(points, max_radius); @@ -72,14 +72,14 @@ BOOST_AUTO_TEST_CASE(Cech_complex_for_documentation) { const int DIMENSION_1 = 1; Simplex_tree st; cech_complex_for_doc.create_complex(st, DIMENSION_1); - std::cout << "st.dimension()=" << st.dimension() << std::endl; + std::clog << "st.dimension()=" << st.dimension() << std::endl; BOOST_CHECK(st.dimension() == DIMENSION_1); const int NUMBER_OF_VERTICES = 11; - std::cout << "st.num_vertices()=" << st.num_vertices() << std::endl; + std::clog << "st.num_vertices()=" << st.num_vertices() << std::endl; BOOST_CHECK(st.num_vertices() == NUMBER_OF_VERTICES); - std::cout << "st.num_simplices()=" << st.num_simplices() << std::endl; + std::clog << "st.num_simplices()=" << st.num_simplices() << std::endl; BOOST_CHECK(st.num_simplices() == 27); // Check filtration values of vertices is 0.0 @@ -91,12 +91,12 @@ BOOST_AUTO_TEST_CASE(Cech_complex_for_documentation) { for (auto f_simplex : st.skeleton_simplex_range(DIMENSION_1)) { if (DIMENSION_1 == st.dimension(f_simplex)) { std::vector<Point> vp; - std::cout << "vertex = ("; + std::clog << "vertex = ("; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << vertex << ","; + std::clog << vertex << ","; vp.push_back(points.at(vertex)); } - std::cout << ") - distance =" << Gudhi::Minimal_enclosing_ball_radius()(vp.at(0), vp.at(1)) + std::clog << ") - distance =" << Gudhi::Minimal_enclosing_ball_radius()(vp.at(0), vp.at(1)) << " - filtration =" << st.filtration(f_simplex) << std::endl; BOOST_CHECK(vp.size() == 2); GUDHI_TEST_FLOAT_EQUALITY_CHECK(st.filtration(f_simplex), @@ -112,13 +112,13 @@ BOOST_AUTO_TEST_CASE(Cech_complex_for_documentation) { Simplex_tree st2; cech_complex_for_doc.create_complex(st2, DIMENSION_2); - std::cout << "st2.dimension()=" << st2.dimension() << std::endl; + std::clog << "st2.dimension()=" << st2.dimension() << std::endl; BOOST_CHECK(st2.dimension() == DIMENSION_2); - std::cout << "st2.num_vertices()=" << st2.num_vertices() << std::endl; + std::clog << "st2.num_vertices()=" << st2.num_vertices() << std::endl; BOOST_CHECK(st2.num_vertices() == NUMBER_OF_VERTICES); - std::cout << "st2.num_simplices()=" << st2.num_simplices() << std::endl; + std::clog << "st2.num_simplices()=" << st2.num_simplices() << std::endl; BOOST_CHECK(st2.num_simplices() == 30); Point_cloud points012; @@ -129,7 +129,7 @@ BOOST_AUTO_TEST_CASE(Cech_complex_for_documentation) { Min_sphere ms012(dimension, points012.begin(), points012.end()); Simplex_tree::Filtration_value f012 = st2.filtration(st2.find({0, 1, 2})); - std::cout << "f012= " << f012 << " | ms012_radius= " << std::sqrt(ms012.squared_radius()) << std::endl; + std::clog << "f012= " << f012 << " | ms012_radius= " << std::sqrt(ms012.squared_radius()) << std::endl; GUDHI_TEST_FLOAT_EQUALITY_CHECK(f012, std::sqrt(ms012.squared_radius())); @@ -140,7 +140,7 @@ BOOST_AUTO_TEST_CASE(Cech_complex_for_documentation) { Min_sphere ms1410(dimension, points1410.begin(), points1410.end()); Simplex_tree::Filtration_value f1410 = st2.filtration(st2.find({1, 4, 10})); - std::cout << "f1410= " << f1410 << " | ms1410_radius= " << std::sqrt(ms1410.squared_radius()) << std::endl; + std::clog << "f1410= " << f1410 << " | ms1410_radius= " << std::sqrt(ms1410.squared_radius()) << std::endl; GUDHI_TEST_FLOAT_EQUALITY_CHECK(f1410, std::sqrt(ms1410.squared_radius())); @@ -151,7 +151,7 @@ BOOST_AUTO_TEST_CASE(Cech_complex_for_documentation) { Min_sphere ms469(dimension, points469.begin(), points469.end()); Simplex_tree::Filtration_value f469 = st2.filtration(st2.find({4, 6, 9})); - std::cout << "f469= " << f469 << " | ms469_radius= " << std::sqrt(ms469.squared_radius()) << std::endl; + std::clog << "f469= " << f469 << " | ms469_radius= " << std::sqrt(ms469.squared_radius()) << std::endl; GUDHI_TEST_FLOAT_EQUALITY_CHECK(f469, std::sqrt(ms469.squared_radius())); @@ -178,35 +178,35 @@ BOOST_AUTO_TEST_CASE(Cech_complex_from_points) { // ---------------------------------------------------------------------------- Cech_complex cech_complex_from_points(points, 2.0); - std::cout << "========== cech_complex_from_points ==========" << std::endl; + std::clog << "========== cech_complex_from_points ==========" << std::endl; Simplex_tree st; const int DIMENSION = 3; cech_complex_from_points.create_complex(st, DIMENSION); // Another way to check num_simplices - std::cout << "Iterator on Cech complex simplices in the filtration order, with [filtration value]:" << std::endl; + std::clog << "Iterator on Cech complex simplices in the filtration order, with [filtration value]:" << std::endl; int num_simplices = 0; for (auto f_simplex : st.filtration_simplex_range()) { num_simplices++; - std::cout << " ( "; + std::clog << " ( "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> " + std::clog << ") -> " << "[" << st.filtration(f_simplex) << "] "; - std::cout << std::endl; + std::clog << std::endl; } BOOST_CHECK(num_simplices == 15); - std::cout << "st.num_simplices()=" << st.num_simplices() << std::endl; + std::clog << "st.num_simplices()=" << st.num_simplices() << std::endl; BOOST_CHECK(st.num_simplices() == 15); - std::cout << "st.dimension()=" << st.dimension() << std::endl; + std::clog << "st.dimension()=" << st.dimension() << std::endl; BOOST_CHECK(st.dimension() == DIMENSION); - std::cout << "st.num_vertices()=" << st.num_vertices() << std::endl; + std::clog << "st.num_vertices()=" << st.num_vertices() << std::endl; BOOST_CHECK(st.num_vertices() == 4); for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << "dimension(" << st.dimension(f_simplex) << ") - f = " << st.filtration(f_simplex) << std::endl; + std::clog << "dimension(" << st.dimension(f_simplex) << ") - f = " << st.filtration(f_simplex) << std::endl; switch (st.dimension(f_simplex)) { case 0: GUDHI_TEST_FLOAT_EQUALITY_CHECK(st.filtration(f_simplex), 0.0); @@ -236,7 +236,7 @@ BOOST_AUTO_TEST_CASE(Cech_create_complex_throw) { // ---------------------------------------------------------------------------- std::string off_file_name("alphacomplexdoc.off"); double max_radius = 12.0; - std::cout << "========== OFF FILE NAME = " << off_file_name << " - Cech max_radius=" << max_radius + std::clog << "========== OFF FILE NAME = " << off_file_name << " - Cech max_radius=" << max_radius << "==========" << std::endl; Gudhi::Points_off_reader<Point> off_reader(off_file_name); @@ -245,7 +245,7 @@ BOOST_AUTO_TEST_CASE(Cech_create_complex_throw) { Simplex_tree stree; std::vector<int> simplex = {0, 1, 2}; stree.insert_simplex_and_subfaces(simplex); - std::cout << "Check exception throw in debug mode" << std::endl; + std::clog << "Check exception throw in debug mode" << std::endl; // throw excpt because stree is not empty BOOST_CHECK_THROW(cech_complex_from_file.create_complex(stree, 1), std::invalid_argument); } diff --git a/src/Cech_complex/utilities/CMakeLists.txt b/src/Cech_complex/utilities/CMakeLists.txt index 30b99729..253d7304 100644 --- a/src/Cech_complex/utilities/CMakeLists.txt +++ b/src/Cech_complex/utilities/CMakeLists.txt @@ -2,7 +2,7 @@ cmake_minimum_required(VERSION 2.6) project(Cech_complex_utilities) add_executable(cech_persistence cech_persistence.cpp) -target_link_libraries(cech_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) +target_link_libraries(cech_persistence Boost::program_options) if (TBB_FOUND) target_link_libraries(cech_persistence ${TBB_LIBRARIES}) diff --git a/src/Cech_complex/utilities/cech_persistence.cpp b/src/Cech_complex/utilities/cech_persistence.cpp index 8cfe018b..daea08e2 100644 --- a/src/Cech_complex/utilities/cech_persistence.cpp +++ b/src/Cech_complex/utilities/cech_persistence.cpp @@ -50,8 +50,8 @@ int main(int argc, char* argv[]) { Simplex_tree simplex_tree; cech_complex_from_file.create_complex(simplex_tree, dim_max); - std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; - std::cout << " and has dimension " << simplex_tree.dimension() << " \n"; + std::clog << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; + std::clog << " and has dimension " << simplex_tree.dimension() << " \n"; // Sort the simplices in the order of the filtration simplex_tree.initialize_filtration(); @@ -85,7 +85,7 @@ void program_options(int argc, char* argv[], std::string& off_file_points, std:: po::options_description visible("Allowed options", 100); visible.add_options()("help,h", "produce help message")( "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout")( + "Name of file in which the persistence diagram is written. Default print in std::clog")( "max-radius,r", po::value<Filtration_value>(&max_radius)->default_value(std::numeric_limits<Filtration_value>::infinity()), "Maximal length of an edge for the Cech complex construction.")( @@ -108,17 +108,17 @@ void program_options(int argc, char* argv[], std::string& off_file_points, std:: po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of a Cech complex defined on a set of input points.\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of a Cech complex defined on a set of input points.\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Contraction/example/CMakeLists.txt b/src/Contraction/example/CMakeLists.txt index f0dc885d..c5d31aca 100644 --- a/src/Contraction/example/CMakeLists.txt +++ b/src/Contraction/example/CMakeLists.txt @@ -4,7 +4,6 @@ if (NOT CGAL_VERSION VERSION_LESS 4.11.0) add_executable(RipsContraction Rips_contraction.cpp) add_executable(GarlandHeckbert Garland_heckbert.cpp) - target_link_libraries(GarlandHeckbert ${Boost_TIMER_LIBRARY}) add_test(NAME Contraction_example_tore3D_0.2 COMMAND $<TARGET_FILE:RipsContraction> "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "0.2") diff --git a/src/Contraction/example/Garland_heckbert.cpp b/src/Contraction/example/Garland_heckbert.cpp index 9c0b5205..489ef5d0 100644 --- a/src/Contraction/example/Garland_heckbert.cpp +++ b/src/Contraction/example/Garland_heckbert.cpp @@ -147,7 +147,7 @@ int main(int argc, char *argv[]) { return EXIT_FAILURE; } - std::cout << "Load complex with " << complex.num_vertices() << " vertices" << std::endl; + std::clog << "Load complex with " << complex.num_vertices() << " vertices" << std::endl; int num_contractions = atoi(argv[3]); @@ -158,10 +158,10 @@ int main(int argc, char *argv[]) { Gudhi::contraction::make_link_valid_contraction<EdgeProfile>(), new GH_visitor(complex)); - std::cout << "Contract " << num_contractions << " edges" << std::endl; + std::clog << "Contract " << num_contractions << " edges" << std::endl; contractor.contract_edges(num_contractions); - std::cout << "Final complex has " << + std::clog << "Final complex has " << complex.num_vertices() << " vertices, " << complex.num_edges() << " edges and " << complex.num_triangles() << " triangles." << std::endl; diff --git a/src/Contraction/example/Rips_contraction.cpp b/src/Contraction/example/Rips_contraction.cpp index b5ce06c1..42dd0910 100644 --- a/src/Contraction/example/Rips_contraction.cpp +++ b/src/Contraction/example/Rips_contraction.cpp @@ -52,13 +52,13 @@ int main(int argc, char *argv[]) { return EXIT_FAILURE; } - std::cout << "Build the Rips complex with " << complex.num_vertices() << " vertices" << std::endl; + std::clog << "Build the Rips complex with " << complex.num_vertices() << " vertices" << std::endl; build_rips(complex, atof(argv[2])); Gudhi::Clock contraction_chrono("Time to simplify and enumerate simplices"); - std::cout << "Initial complex has " << + std::clog << "Initial complex has " << complex.num_vertices() << " vertices and " << complex.num_edges() << " edges" << std::endl; @@ -69,16 +69,16 @@ int main(int argc, char *argv[]) { Gudhi::contraction::make_remove_popable_blockers_visitor<Profile>()); contractor.contract_edges(); - std::cout << "Counting final number of simplices \n"; + std::clog << "Counting final number of simplices \n"; unsigned num_simplices = std::distance(complex.complex_simplex_range().begin(), complex.complex_simplex_range().end()); - std::cout << "Final complex has " << + std::clog << "Final complex has " << complex.num_vertices() << " vertices, " << complex.num_edges() << " edges, " << complex.num_blockers() << " blockers and " << num_simplices << " simplices" << std::endl; - std::cout << contraction_chrono; + std::clog << contraction_chrono; return EXIT_SUCCESS; } diff --git a/src/Contraction/include/gudhi/Edge_contraction.h b/src/Contraction/include/gudhi/Edge_contraction.h index 6058d64b..6c0f4c78 100644 --- a/src/Contraction/include/gudhi/Edge_contraction.h +++ b/src/Contraction/include/gudhi/Edge_contraction.h @@ -164,13 +164,13 @@ int main (int argc, char *argv[]) std::cerr << "Unable to read file:"<<argv[1]<<std::endl; return EXIT_FAILURE; } - std::cout << "Build the Rips complex"<<std::endl; + std::clog << "Build the Rips complex"<<std::endl; build_rips(complex,atof(argv[2])); boost::timer::auto_cpu_timer t; - std::cout << "Initial complex has "<< + std::clog << "Initial complex has "<< complex.num_vertices()<<" vertices and "<< complex.num_edges()<<" edges"<<std::endl; @@ -181,17 +181,17 @@ int main (int argc, char *argv[]) contraction::make_remove_popable_blockers_visitor<Profile>()); contractor.contract_edges(); - std::cout << "Counting final number of simplices \n"; + std::clog << "Counting final number of simplices \n"; unsigned num_simplices = std::distance(complex.star_simplex_range().begin(),complex.star_simplex_range().end()); - std::cout << "Final complex has "<< + std::clog << "Final complex has "<< complex.num_vertices()<<" vertices, "<< complex.num_edges()<<" edges, "<< complex.num_blockers()<<" blockers and "<< num_simplices<<" simplices"<<std::endl; - std::cout << "Time to simplify and enumerate simplices:\n"; + std::clog << "Time to simplify and enumerate simplices:\n"; return EXIT_SUCCESS; } diff --git a/src/Doxyfile.in b/src/Doxyfile.in index ec551882..49e781bd 100644 --- a/src/Doxyfile.in +++ b/src/Doxyfile.in @@ -785,6 +785,7 @@ EXCLUDE = data/ \ GudhUI/ \ cmake/ \ python/ \ + ext/ \ README.md # The EXCLUDE_SYMLINKS tag can be used to select whether or not files or diff --git a/src/GudhUI/model/Model.h b/src/GudhUI/model/Model.h index dd9bdaab..626ef59d 100644 --- a/src/GudhUI/model/Model.h +++ b/src/GudhUI/model/Model.h @@ -60,7 +60,7 @@ class CGAL_geometric_flag_complex_wrapper { void maximal_face(std::vector<int> vertices) { if (!load_only_points_) { - // std::cout << "size:" << vertices.size() << std::endl; + // std::clog << "size:" << vertices.size() << std::endl; for (std::size_t i = 0; i < vertices.size(); ++i) for (std::size_t j = i + 1; j < vertices.size(); ++j) complex_.add_edge_without_blockers(Vertex_handle(vertices[i]), Vertex_handle(vertices[j])); @@ -178,7 +178,7 @@ class Model { void contract_edges(unsigned num_contractions) { Gudhi::Clock c; Edge_contractor<Complex> contractor(complex_, num_contractions); - std::cout << "Time to simplify: " << c.num_seconds() << "s" << std::endl; + std::clog << "Time to simplify: " << c.num_seconds() << "s" << std::endl; } void collapse_vertices(unsigned num_collapses) { @@ -192,14 +192,14 @@ class Model { } void show_graph_stats() { - std::cout << "++++++ Graph stats +++++++" << std::endl; - std::cout << "Num vertices : " << complex_.num_vertices() << std::endl; - std::cout << "Num edges : " << complex_.num_edges() << std::endl; - std::cout << "Num connected components : " << complex_.num_connected_components() << std::endl; - std::cout << "Min/avg/max degree : " << min_degree() << "/" << avg_degree() << "/" << max_degree() << std::endl; - std::cout << "Num connected components : " << complex_.num_connected_components() << std::endl; - std::cout << "Num connected components : " << complex_.num_connected_components() << std::endl; - std::cout << "+++++++++++++++++++++++++" << std::endl; + std::clog << "++++++ Graph stats +++++++" << std::endl; + std::clog << "Num vertices : " << complex_.num_vertices() << std::endl; + std::clog << "Num edges : " << complex_.num_edges() << std::endl; + std::clog << "Num connected components : " << complex_.num_connected_components() << std::endl; + std::clog << "Min/avg/max degree : " << min_degree() << "/" << avg_degree() << "/" << max_degree() << std::endl; + std::clog << "Num connected components : " << complex_.num_connected_components() << std::endl; + std::clog << "Num connected components : " << complex_.num_connected_components() << std::endl; + std::clog << "+++++++++++++++++++++++++" << std::endl; } private: @@ -226,11 +226,11 @@ class Model { public: void show_complex_stats() { - std::cout << "++++++ Mesh stats +++++++" << std::endl; - std::cout << "Num vertices : " << complex_.num_vertices() << std::endl; - std::cout << "Num edges : " << complex_.num_edges() << std::endl; - std::cout << "Num connected components : " << complex_.num_connected_components() << std::endl; - std::cout << "+++++++++++++++++++++++++" << std::endl; + std::clog << "++++++ Mesh stats +++++++" << std::endl; + std::clog << "Num vertices : " << complex_.num_vertices() << std::endl; + std::clog << "Num edges : " << complex_.num_edges() << std::endl; + std::clog << "Num connected components : " << complex_.num_connected_components() << std::endl; + std::clog << "+++++++++++++++++++++++++" << std::endl; } void show_complex_dimension() { @@ -247,18 +247,18 @@ class Model { euler -= 1; } clock.end(); - std::cout << "++++++ Mesh dimension +++++++" << std::endl; - std::cout << "Dimension : " << dimension << std::endl; - std::cout << "Euler characteristic : " << euler << std::endl; - std::cout << "Num simplices : " << num_simplices << std::endl; - std::cout << "Total time: " << clock << std::endl; - std::cout << "Time per simplex: " << clock.num_seconds() / num_simplices << " s" << std::endl; - std::cout << "+++++++++++++++++++++++++" << std::endl; + std::clog << "++++++ Mesh dimension +++++++" << std::endl; + std::clog << "Dimension : " << dimension << std::endl; + std::clog << "Euler characteristic : " << euler << std::endl; + std::clog << "Num simplices : " << num_simplices << std::endl; + std::clog << "Total time: " << clock << std::endl; + std::clog << "Time per simplex: " << clock.num_seconds() / num_simplices << " s" << std::endl; + std::clog << "+++++++++++++++++++++++++" << std::endl; } void show_homology_group() { #ifdef _WIN32 - std::cout << "Works only on linux x64 for the moment\n"; + std::clog << "Works only on linux x64 for the moment\n"; #else Gudhi::Clock clock; run_chomp(); @@ -278,16 +278,16 @@ class Model { else euler -= 1; } - std::cout << "Saw " << num_simplices << " simplices with maximum dimension " << dimension << std::endl; - std::cout << "The euler characteristic is : " << euler << std::endl; + std::clog << "Saw " << num_simplices << " simplices with maximum dimension " << dimension << std::endl; + std::clog << "The euler characteristic is : " << euler << std::endl; } void show_persistence(int p, double threshold, int max_dim, double min_pers) { - Persistence_compute<Complex> persistence(complex_, std::cout, Persistence_params(p, threshold, max_dim, min_pers)); + Persistence_compute<Complex> persistence(complex_, std::clog, Persistence_params(p, threshold, max_dim, min_pers)); } void show_critical_points(double max_distance) { - Critical_points<Complex> critical_points(complex_, std::cout, max_distance); + Critical_points<Complex> critical_points(complex_, std::clog, max_distance); } void show_is_manifold() { @@ -296,12 +296,12 @@ class Model { Is_manifold<Complex> test_manifold(complex_, dim, is_manifold); if (is_manifold) { - std::cout << "The complex is a " << dim << "-manifold\n"; + std::clog << "The complex is a " << dim << "-manifold\n"; } else { if (dim < 4) { - std::cout << "The complex has dimension greater than " << dim << " and is not a manifold\n"; + std::clog << "The complex has dimension greater than " << dim << " and is not a manifold\n"; } else { - std::cout << "The complex has dimension>=4 and may or may not be a manifold\n"; + std::clog << "The complex has dimension>=4 and may or may not be a manifold\n"; } } } @@ -309,10 +309,10 @@ class Model { private: void run_chomp() { save_complex_in_file_for_chomp(); - std::cout << "Call CHOMP library\n"; + std::clog << "Call CHOMP library\n"; int returnValue = system("homsimpl chomp.sim"); if (returnValue != 0) { - std::cout << "homsimpl (from CHOMP) failed. Please check it is installed or available in the PATH." + std::cerr << "homsimpl (from CHOMP) failed. Please check it is installed or available in the PATH." << std::endl; } } diff --git a/src/GudhUI/utils/Bar_code_persistence.h b/src/GudhUI/utils/Bar_code_persistence.h index cd9b009f..b526017a 100644 --- a/src/GudhUI/utils/Bar_code_persistence.h +++ b/src/GudhUI/utils/Bar_code_persistence.h @@ -58,13 +58,13 @@ class Bar_code_persistence { QGraphicsScene * scene = new QGraphicsScene(); view->setScene(scene); double ratio = 600.0 / (max_death - min_birth); - // std::cout << "min_birth=" << min_birth << " - max_death=" << max_death << " - ratio=" << ratio << std::endl; + // std::clog << "min_birth=" << min_birth << " - max_death=" << max_death << " - ratio=" << ratio << std::endl; double height = 0.0, birth = 0.0, death = 0.0; int pers_num = 1; for (auto& persistence : persistence_vector) { height = 5.0 * pers_num; - // std::cout << "[" << pers_num << "] birth=" << persistence.first << " - death=" << persistence.second << std::endl; + // std::clog << "[" << pers_num << "] birth=" << persistence.first << " - death=" << persistence.second << std::endl; if (std::isfinite(persistence.first)) birth = ((persistence.first - min_birth) * ratio) + 50.0; else diff --git a/src/GudhUI/utils/Critical_points.h b/src/GudhUI/utils/Critical_points.h index 32fcf32e..97e58737 100644 --- a/src/GudhUI/utils/Critical_points.h +++ b/src/GudhUI/utils/Critical_points.h @@ -65,7 +65,7 @@ template<typename SkBlComplex> class Critical_points { void anti_collapse_edges(const std::deque<Edge>& edges) { unsigned pos = 0; for (Edge e : edges) { - std::cout << "edge " << pos++ << "/" << edges.size() << "\n"; + std::clog << "edge " << pos++ << "/" << edges.size() << "\n"; auto eh = filled_complex_.add_edge_without_blockers(e.first, e.second); int is_contractible(is_link_reducible(eh)); diff --git a/src/GudhUI/utils/Rips_builder.h b/src/GudhUI/utils/Rips_builder.h index aba1a8e4..0300190c 100644 --- a/src/GudhUI/utils/Rips_builder.h +++ b/src/GudhUI/utils/Rips_builder.h @@ -43,13 +43,13 @@ template<typename SkBlComplex> class Rips_builder { void compute_edges(double alpha) { auto vertices = complex_.vertex_range(); for (auto p = vertices.begin(); p != vertices.end(); ++p) { - std::cout << *p << " "; - std::cout.flush(); + std::clog << *p << " "; + std::clog.flush(); for (auto q = p; ++q != vertices.end(); /**/) if (squared_eucl_distance(complex_.point(*p), complex_.point(*q)) < 4 * alpha * alpha) complex_.add_edge_without_blockers(*p, *q); } - std::cout << std::endl; + std::clog << std::endl; } }; diff --git a/src/GudhUI/view/View_parameter.h b/src/GudhUI/view/View_parameter.h index dfd3aa41..3671f4fb 100644 --- a/src/GudhUI/view/View_parameter.h +++ b/src/GudhUI/view/View_parameter.h @@ -52,13 +52,13 @@ class View_parameter { void change_vertex_mode() { int current_value = vertex_mode; vertex_mode = static_cast<VERTEX_MODE> (++current_value % V_COUNT); - std::cout << "Vertex mode : "; + std::clog << "Vertex mode : "; switch (vertex_mode) { case V_NONE: - std::cout << "empty\n"; + std::clog << "empty\n"; break; case V_SIMPLE: - std::cout << "simple\n"; + std::clog << "simple\n"; break; default: break; diff --git a/src/Hasse_complex/include/gudhi/Hasse_complex.h b/src/Hasse_complex/include/gudhi/Hasse_complex.h index 209fd0b9..8ce8c36f 100644 --- a/src/Hasse_complex/include/gudhi/Hasse_complex.h +++ b/src/Hasse_complex/include/gudhi/Hasse_complex.h @@ -173,9 +173,9 @@ class Hasse_complex { } void display_simplex(Simplex_handle sh) { - std::cout << dimension(sh) << " "; - for (auto sh_b : boundary_simplex_range(sh)) std::cout << sh_b << " "; - std::cout << " " << filtration(sh) << " key=" << key(sh); + std::clog << dimension(sh) << " "; + for (auto sh_b : boundary_simplex_range(sh)) std::clog << sh_b << " "; + std::clog << " " << filtration(sh) << " key=" << key(sh); } void initialize_filtration() { diff --git a/src/Nerve_GIC/example/CoordGIC.cpp b/src/Nerve_GIC/example/CoordGIC.cpp index fd9c224a..f0afdca5 100644 --- a/src/Nerve_GIC/example/CoordGIC.cpp +++ b/src/Nerve_GIC/example/CoordGIC.cpp @@ -40,7 +40,7 @@ int main(int argc, char **argv) { bool check = GIC.read_point_cloud(off_file_name); if (!check) { - std::cout << "Incorrect OFF file." << std::endl; + std::clog << "Incorrect OFF file." << std::endl; } else { GIC.set_type("GIC"); @@ -67,15 +67,15 @@ int main(int argc, char **argv) { // -------------------------------------------- if (verb) { - std::cout << "Coordinate GIC is of dimension " << stree.dimension() << " - " << stree.num_simplices() + std::clog << "Coordinate GIC is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " << stree.num_vertices() << " vertices." << std::endl; - std::cout << "Iterator on coordinate GIC simplices" << std::endl; + std::clog << "Iterator on coordinate GIC simplices" << std::endl; for (auto f_simplex : stree.filtration_simplex_range()) { for (auto vertex : stree.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } } } diff --git a/src/Nerve_GIC/example/FuncGIC.cpp b/src/Nerve_GIC/example/FuncGIC.cpp index 5a323795..518e1826 100644 --- a/src/Nerve_GIC/example/FuncGIC.cpp +++ b/src/Nerve_GIC/example/FuncGIC.cpp @@ -41,7 +41,7 @@ int main(int argc, char **argv) { bool check = GIC.read_point_cloud(off_file_name); if (!check) { - std::cout << "Incorrect OFF file." << std::endl; + std::clog << "Incorrect OFF file." << std::endl; } else { GIC.set_type("GIC"); @@ -65,15 +65,15 @@ int main(int argc, char **argv) { // -------------------------------------------- if (verb) { - std::cout << "Functional GIC is of dimension " << stree.dimension() << " - " << stree.num_simplices() + std::clog << "Functional GIC is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " << stree.num_vertices() << " vertices." << std::endl; - std::cout << "Iterator on functional GIC simplices" << std::endl; + std::clog << "Iterator on functional GIC simplices" << std::endl; for (auto f_simplex : stree.filtration_simplex_range()) { for (auto vertex : stree.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } } } diff --git a/src/Nerve_GIC/include/gudhi/GIC.h b/src/Nerve_GIC/include/gudhi/GIC.h index 2a6d4788..1b1f9323 100644 --- a/src/Nerve_GIC/include/gudhi/GIC.h +++ b/src/Nerve_GIC/include/gudhi/GIC.h @@ -139,19 +139,9 @@ class Cover_complex { for (boost::tie(ei, ei_end) = boost::edges(G); ei != ei_end; ++ei) boost::remove_edge(*ei, G); } - // Thread local is not available on XCode version < V.8 - // If not available, random engine is a class member. -#ifndef GUDHI_CAN_USE_CXX11_THREAD_LOCAL - std::default_random_engine re; -#endif // GUDHI_CAN_USE_CXX11_THREAD_LOCAL - // Find random number in [0,1]. double GetUniform() { - // Thread local is not available on XCode version < V.8 - // If available, random engine is defined for each thread. -#ifdef GUDHI_CAN_USE_CXX11_THREAD_LOCAL thread_local std::default_random_engine re; -#endif // GUDHI_CAN_USE_CXX11_THREAD_LOCAL std::uniform_real_distribution<double> Dist(0, 1); return Dist(re); } @@ -344,7 +334,7 @@ class Cover_complex { if (num_edges(one_skeleton_OFF)) one_skeleton = one_skeleton_OFF; else - std::cout << "No triangulation read in OFF file!" << std::endl; + std::cerr << "No triangulation read in OFF file!" << std::endl; } public: // Set graph from Rips complex. @@ -407,7 +397,7 @@ class Cover_complex { std::ifstream input(distance, std::ios::out | std::ios::binary); if (input.good()) { - if (verbose) std::cout << "Reading distances..." << std::endl; + if (verbose) std::clog << "Reading distances..." << std::endl; for (int i = 0; i < n; i++) { for (int j = i; j < n; j++) { input.read((char*)&d, 8); @@ -417,12 +407,12 @@ class Cover_complex { } input.close(); } else { - if (verbose) std::cout << "Computing distances..." << std::endl; + if (verbose) std::clog << "Computing distances..." << std::endl; input.close(); std::ofstream output(distance, std::ios::out | std::ios::binary); for (int i = 0; i < n; i++) { int state = (int)floor(100 * (i * 1.0 + 1) / n) % 10; - if (state == 0 && verbose) std::cout << "\r" << state << "%" << std::flush; + if (state == 0 && verbose) std::clog << "\r" << state << "%" << std::flush; for (int j = i; j < n; j++) { double dis = ref_distance(point_cloud[i], point_cloud[j]); distances[i][j] = dis; @@ -431,7 +421,7 @@ class Cover_complex { } } output.close(); - if (verbose) std::cout << std::endl; + if (verbose) std::clog << std::endl; } } @@ -451,14 +441,12 @@ class Cover_complex { m = (std::min)(m, n - 1); double delta = 0; - if (verbose) std::cout << n << " points in R^" << data_dimension << std::endl; - if (verbose) std::cout << "Subsampling " << m << " points" << std::endl; + if (verbose) std::clog << n << " points in R^" << data_dimension << std::endl; + if (verbose) std::clog << "Subsampling " << m << " points" << std::endl; if (distances.size() == 0) compute_pairwise_distances(distance); - // 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) + #ifdef GUDHI_USE_TBB std::mutex deltamutex; tbb::parallel_for(0, N, [&](int i){ std::vector<int> samples(m); @@ -487,7 +475,7 @@ class Cover_complex { } #endif - if (verbose) std::cout << "delta = " << delta << std::endl; + if (verbose) std::clog << "delta = " << delta << std::endl; set_graph_from_rips(delta, distance); return delta; } @@ -530,7 +518,7 @@ class Cover_complex { cover_name = "coordinate " + std::to_string(k); } else{ - std::cout << "Only pairwise distances provided---cannot access " << k << "th coordinate; returning null vector instead" << std::endl; + std::cerr << "Only pairwise distances provided---cannot access " << k << "th coordinate; returning null vector instead" << std::endl; for (int i = 0; i < n; i++) func.push_back(0.0); functional_cover = true; cover_name = "null"; @@ -563,11 +551,11 @@ class Cover_complex { */ double set_automatic_resolution() { if (!functional_cover) { - std::cout << "Cover needs to come from the preimages of a function." << std::endl; + std::cerr << "Cover needs to come from the preimages of a function." << std::endl; return 0; } if (type != "Nerve" && type != "GIC") { - std::cout << "Type of complex needs to be specified." << std::endl; + std::cerr << "Type of complex needs to be specified." << std::endl; return 0; } @@ -579,7 +567,7 @@ class Cover_complex { for (boost::tie(ei, ei_end) = boost::edges(one_skeleton); ei != ei_end; ++ei) reso = (std::max)(reso, std::abs(func[index[boost::source(*ei, one_skeleton)]] - func[index[boost::target(*ei, one_skeleton)]])); - if (verbose) std::cout << "resolution = " << reso << std::endl; + if (verbose) std::clog << "resolution = " << reso << std::endl; resolution_double = reso; } @@ -589,7 +577,7 @@ class Cover_complex { reso = (std::max)(reso, std::abs(func[index[boost::source(*ei, one_skeleton)]] - func[index[boost::target(*ei, one_skeleton)]]) / gain); - if (verbose) std::cout << "resolution = " << reso << std::endl; + if (verbose) std::clog << "resolution = " << reso << std::endl; resolution_double = reso; } @@ -622,11 +610,11 @@ class Cover_complex { */ void set_cover_from_function() { if (resolution_double == -1 && resolution_int == -1) { - std::cout << "Number and/or length of intervals not specified" << std::endl; + std::cerr << "Number and/or length of intervals not specified" << std::endl; return; } if (gain == -1) { - std::cout << "Gain not specified" << std::endl; + std::cerr << "Gain not specified" << std::endl; return; } @@ -637,7 +625,7 @@ class Cover_complex { minf = (std::min)(minf, func[i]); maxf = (std::max)(maxf, func[i]); } - if (verbose) std::cout << "Min function value = " << minf << " and Max function value = " << maxf << std::endl; + if (verbose) std::clog << "Min function value = " << minf << " and Max function value = " << maxf << std::endl; // Compute cover of im(f) std::vector<std::pair<double, double> > intervals; @@ -663,7 +651,7 @@ class Cover_complex { res = intervals.size(); if (verbose) { for (int i = 0; i < res; i++) - std::cout << "Interval " << i << " = [" << intervals[i].first << ", " << intervals[i].second << "]" + std::clog << "Interval " << i << " = [" << intervals[i].first << ", " << intervals[i].second << "]" << std::endl; } } else { @@ -681,7 +669,7 @@ class Cover_complex { res = intervals.size(); if (verbose) { for (int i = 0; i < res; i++) - std::cout << "Interval " << i << " = [" << intervals[i].first << ", " << intervals[i].second << "]" + std::clog << "Interval " << i << " = [" << intervals[i].first << ", " << intervals[i].second << "]" << std::endl; } } else { // Case we use an integer and a double for the length of the intervals. @@ -698,7 +686,7 @@ class Cover_complex { res = intervals.size(); if (verbose) { for (int i = 0; i < res; i++) - std::cout << "Interval " << i << " = [" << intervals[i].first << ", " << intervals[i].second << "]" + std::clog << "Interval " << i << " = [" << intervals[i].first << ", " << intervals[i].second << "]" << std::endl; } } @@ -715,7 +703,7 @@ class Cover_complex { std::map<int, std::vector<int> > preimages; std::map<int, double> funcstd; - if (verbose) std::cout << "Computing preimages..." << std::endl; + if (verbose) std::clog << "Computing preimages..." << std::endl; for (int i = 0; i < res; i++) { // Find points in the preimage std::pair<double, double> inter1 = intervals[i]; @@ -764,7 +752,7 @@ class Cover_complex { } #ifdef GUDHI_USE_TBB - if (verbose) std::cout << "Computing connected components (parallelized)..." << std::endl; + if (verbose) std::clog << "Computing connected components (parallelized)..." << std::endl; std::mutex covermutex, idmutex; tbb::parallel_for(0, res, [&](int i){ // Compute connected components @@ -800,7 +788,7 @@ class Cover_complex { idmutex.unlock(); }); #else - if (verbose) std::cout << "Computing connected components..." << std::endl; + if (verbose) std::clog << "Computing connected components..." << std::endl; for (int i = 0; i < res; i++) { // Compute connected components Graph G = one_skeleton.create_subgraph(); @@ -894,7 +882,7 @@ 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; + if (verbose) std::clog << "Computing geodesic distances (parallelized)..." << std::endl; std::mutex coverMutex; std::mutex mindistMutex; tbb::parallel_for(0, m, [&](int i){ int seed = voronoi_subsamples[i]; @@ -916,7 +904,7 @@ class Cover_complex { }); #else for (int i = 0; i < m; i++) { - if (verbose) std::cout << "Computing geodesic distances to seed " << i << "..." << std::endl; + if (verbose) std::clog << "Computing geodesic distances to seed " << i << "..." << std::endl; int seed = voronoi_subsamples[i]; std::vector<double> dmap(n); boost::dijkstra_shortest_paths( @@ -991,7 +979,7 @@ class Cover_complex { color_name.append(std::to_string(k)); } else{ - std::cout << "Only pairwise distances provided---cannot access " << k << "th coordinate; returning null vector instead" << std::endl; + std::cerr << "Only pairwise distances provided---cannot access " << k << "th coordinate; returning null vector instead" << std::endl; for (int i = 0; i < n; i++) func.push_back(0.0); functional_cover = true; cover_name = "null"; @@ -1054,7 +1042,7 @@ class Cover_complex { } graphic << "}"; graphic.close(); - std::cout << mapp << " file generated. It can be visualized with e.g. neato." << std::endl; + std::clog << mapp << " file generated. It can be visualized with e.g. neato." << std::endl; } public: // Create a .txt file that can be compiled with KeplerMapper. @@ -1090,7 +1078,7 @@ class Cover_complex { if (cover_color[simplices[i][0]].first > mask && cover_color[simplices[i][1]].first > mask) graphic << name2id[simplices[i][0]] << " " << name2id[simplices[i][1]] << std::endl; graphic.close(); - std::cout << mapp + std::clog << mapp << " generated. It can be visualized with e.g. python KeplerMapperVisuFromTxtFile.py and firefox." << std::endl; } @@ -1137,7 +1125,7 @@ class Cover_complex { for (int i = 0; i < numfaces; i++) graphic << 3 << " " << faces[i][0] << " " << faces[i][1] << " " << faces[i][2] << std::endl; graphic.close(); - std::cout << mapp << " generated. It can be visualized with e.g. geomview." << std::endl; + std::clog << mapp << " generated. It can be visualized with e.g. geomview." << std::endl; } // ******************************************************************************************************************* @@ -1185,7 +1173,7 @@ class Cover_complex { for (int i = 0; i < max_dim; i++) { std::vector<std::pair<double, double> > bars = pcoh.intervals_in_dimension(i); int num_bars = bars.size(); if(i == 0) num_bars -= 1; - if(verbose) std::cout << num_bars << " interval(s) in dimension " << i << ":" << std::endl; + if(verbose) std::clog << num_bars << " interval(s) in dimension " << i << ":" << std::endl; for (int j = 0; j < num_bars; j++) { double birth = bars[j].first; double death = bars[j].second; @@ -1199,7 +1187,7 @@ class Cover_complex { else death = minf + (2 - death) * (maxf - minf); PD.push_back(std::pair<double, double>(birth, death)); - if (verbose) std::cout << " [" << birth << ", " << death << "]" << std::endl; + if (verbose) std::clog << " [" << birth << ", " << death << "]" << std::endl; } } return PD; @@ -1213,11 +1201,9 @@ class Cover_complex { */ void compute_distribution(unsigned int N = 100) { unsigned int sz = distribution.size(); - if (sz >= N) { - std::cout << "Already done!" << std::endl; - } else { + if (sz < N) { for (unsigned int i = 0; i < N - sz; i++) { - if (verbose) std::cout << "Computing " << i << "th bootstrap, bottleneck distance = "; + if (verbose) std::clog << "Computing " << i << "th bootstrap, bottleneck distance = "; Cover_complex Cboot; Cboot.n = this->n; Cboot.data_dimension = this->data_dimension; Cboot.type = this->type; Cboot.functional_cover = true; @@ -1243,7 +1229,7 @@ class Cover_complex { Cboot.find_simplices(); Cboot.compute_PD(); double db = Gudhi::persistence_diagram::bottleneck_distance(this->PD, Cboot.PD); - if (verbose) std::cout << db << std::endl; + if (verbose) std::clog << db << std::endl; distribution.push_back(db); } @@ -1260,7 +1246,7 @@ class Cover_complex { double compute_distance_from_confidence_level(double alpha) { unsigned int N = distribution.size(); double d = distribution[std::floor(alpha * N)]; - if (verbose) std::cout << "Distance corresponding to confidence " << alpha << " is " << d << std::endl; + if (verbose) std::clog << "Distance corresponding to confidence " << alpha << " is " << d << std::endl; return d; } @@ -1275,7 +1261,7 @@ class Cover_complex { double level = 1; for (unsigned int i = 0; i < N; i++) if (distribution[i] >= d){ level = i * 1.0 / N; break; } - if (verbose) std::cout << "Confidence level of distance " << d << " is " << level << std::endl; + if (verbose) std::clog << "Confidence level of distance " << d << " is " << level << std::endl; return level; } @@ -1288,7 +1274,7 @@ class Cover_complex { double distancemin = (std::numeric_limits<double>::max)(); int N = PD.size(); for (int i = 0; i < N; i++) distancemin = (std::min)(distancemin, 0.5 * std::abs(PD[i].second - PD[i].first)); double p_value = 1 - compute_confidence_level_from_distance(distancemin); - if (verbose) std::cout << "p value = " << p_value << std::endl; + if (verbose) std::clog << "p value = " << p_value << std::endl; return p_value; } @@ -1319,7 +1305,7 @@ class Cover_complex { */ void find_simplices() { if (type != "Nerve" && type != "GIC") { - std::cout << "Type of complex needs to be specified." << std::endl; + std::cerr << "Type of complex needs to be specified." << std::endl; return; } diff --git a/src/Nerve_GIC/utilities/Nerve.cpp b/src/Nerve_GIC/utilities/Nerve.cpp index d34e922c..7b09f89d 100644 --- a/src/Nerve_GIC/utilities/Nerve.cpp +++ b/src/Nerve_GIC/utilities/Nerve.cpp @@ -42,7 +42,7 @@ int main(int argc, char **argv) { bool check = SC.read_point_cloud(off_file_name); if (!check) { - std::cout << "Incorrect OFF file." << std::endl; + std::clog << "Incorrect OFF file." << std::endl; } else { SC.set_type("Nerve"); @@ -67,15 +67,15 @@ int main(int argc, char **argv) { // ---------------------------------------------------------------------------- if (verb) { - std::cout << "Nerve is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " + std::clog << "Nerve is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " << stree.num_vertices() << " vertices." << std::endl; - std::cout << "Iterator on Nerve simplices" << std::endl; + std::clog << "Iterator on Nerve simplices" << std::endl; for (auto f_simplex : stree.filtration_simplex_range()) { for (auto vertex : stree.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } } } diff --git a/src/Nerve_GIC/utilities/VoronoiGIC.cpp b/src/Nerve_GIC/utilities/VoronoiGIC.cpp index 0182c948..117c89fb 100644 --- a/src/Nerve_GIC/utilities/VoronoiGIC.cpp +++ b/src/Nerve_GIC/utilities/VoronoiGIC.cpp @@ -40,7 +40,7 @@ int main(int argc, char **argv) { bool check = GIC.read_point_cloud(off_file_name); if (!check) { - std::cout << "Incorrect OFF file." << std::endl; + std::clog << "Incorrect OFF file." << std::endl; } else { GIC.set_type("GIC"); @@ -61,15 +61,15 @@ int main(int argc, char **argv) { // ---------------------------------------------------------------------------- if (verb) { - std::cout << "Graph induced complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() + std::clog << "Graph induced complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " << stree.num_vertices() << " vertices." << std::endl; - std::cout << "Iterator on graph induced complex simplices" << std::endl; + std::clog << "Iterator on graph induced complex simplices" << std::endl; for (auto f_simplex : stree.filtration_simplex_range()) { for (auto vertex : stree.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } } } diff --git a/src/Persistence_representations/example/persistence_heat_maps.cpp b/src/Persistence_representations/example/persistence_heat_maps.cpp index 1bf3a637..9fd6779a 100644 --- a/src/Persistence_representations/example/persistence_heat_maps.cpp +++ b/src/Persistence_representations/example/persistence_heat_maps.cpp @@ -65,7 +65,7 @@ int main(int argc, char** argv) { median.compute_median(vector_of_maps); // to compute L^1 distance between hm1 and hm2: - std::cout << "The L^1 distance is : " << hm1.distance(hm2, 1) << std::endl; + std::clog << "The L^1 distance is : " << hm1.distance(hm2, 1) << std::endl; // to average of hm1 and hm2: std::vector<Persistence_heat_maps*> to_average; @@ -75,15 +75,15 @@ int main(int argc, char** argv) { av.compute_average(to_average); // to compute scalar product of hm1 and hm2: - std::cout << "Scalar product is : " << hm1.compute_scalar_product(hm2) << std::endl; + std::clog << "Scalar product is : " << hm1.compute_scalar_product(hm2) << std::endl; Persistence_heat_maps hm1k(persistence1, Gaussian_function(1.0)); Persistence_heat_maps hm2k(persistence2, Gaussian_function(1.0)); Persistence_heat_maps hm1i(persistence1, Gaussian_function(1.0), 20, 20, 0, 11, 0, 11); Persistence_heat_maps hm2i(persistence2, Gaussian_function(1.0), 20, 20, 0, 11, 0, 11); - std::cout << "Scalar product computed with exact 2D kernel on grid is : " << hm1i.compute_scalar_product(hm2i) + std::clog << "Scalar product computed with exact 2D kernel on grid is : " << hm1i.compute_scalar_product(hm2i) << std::endl; - std::cout << "Scalar product computed with exact 2D kernel is : " << hm1k.compute_scalar_product(hm2k) << std::endl; + std::clog << "Scalar product computed with exact 2D kernel is : " << hm1k.compute_scalar_product(hm2k) << std::endl; return 0; } diff --git a/src/Persistence_representations/example/persistence_intervals.cpp b/src/Persistence_representations/example/persistence_intervals.cpp index c908581c..748b9ae4 100644 --- a/src/Persistence_representations/example/persistence_intervals.cpp +++ b/src/Persistence_representations/example/persistence_intervals.cpp @@ -18,59 +18,59 @@ using Persistence_intervals = Gudhi::Persistence_representations::Persistence_in int main(int argc, char** argv) { if (argc != 2) { - std::cout << "To run this program, please provide the name of a file with persistence diagram \n"; + std::clog << "To run this program, please provide the name of a file with persistence diagram \n"; return 1; } Persistence_intervals p(argv[1]); std::pair<double, double> min_max_ = p.get_x_range(); - std::cout << "Birth-death range : " << min_max_.first << " " << min_max_.second << std::endl; + std::clog << "Birth-death range : " << min_max_.first << " " << min_max_.second << std::endl; std::vector<double> dominant_ten_intervals_length = p.length_of_dominant_intervals(10); - std::cout << "Length of ten dominant intervals : " << std::endl; + std::clog << "Length of ten dominant intervals : " << std::endl; for (size_t i = 0; i != dominant_ten_intervals_length.size(); ++i) { - std::cout << dominant_ten_intervals_length[i] << std::endl; + std::clog << dominant_ten_intervals_length[i] << std::endl; } std::vector<std::pair<double, double> > ten_dominant_intervals = p.dominant_intervals(10); - std::cout << "Here are the dominant intervals : " << std::endl; + std::clog << "Here are the dominant intervals : " << std::endl; for (size_t i = 0; i != ten_dominant_intervals.size(); ++i) { - std::cout << "( " << ten_dominant_intervals[i].first << "," << ten_dominant_intervals[i].second << std::endl; + std::clog << "( " << ten_dominant_intervals[i].first << "," << ten_dominant_intervals[i].second << std::endl; } std::vector<size_t> histogram = p.histogram_of_lengths(10); - std::cout << "Here is the histogram of barcode's length : " << std::endl; + std::clog << "Here is the histogram of barcode's length : " << std::endl; for (size_t i = 0; i != histogram.size(); ++i) { - std::cout << histogram[i] << " "; + std::clog << histogram[i] << " "; } - std::cout << std::endl; + std::clog << std::endl; std::vector<size_t> cumulative_histogram = p.cumulative_histogram_of_lengths(10); - std::cout << "Cumulative histogram : " << std::endl; + std::clog << "Cumulative histogram : " << std::endl; for (size_t i = 0; i != cumulative_histogram.size(); ++i) { - std::cout << cumulative_histogram[i] << " "; + std::clog << cumulative_histogram[i] << " "; } - std::cout << std::endl; + std::clog << std::endl; std::vector<double> char_funct_diag = p.characteristic_function_of_diagram(min_max_.first, min_max_.second); - std::cout << "Characteristic function of diagram : " << std::endl; + std::clog << "Characteristic function of diagram : " << std::endl; for (size_t i = 0; i != char_funct_diag.size(); ++i) { - std::cout << char_funct_diag[i] << " "; + std::clog << char_funct_diag[i] << " "; } - std::cout << std::endl; + std::clog << std::endl; std::vector<double> cumul_char_funct_diag = p.cumulative_characteristic_function_of_diagram(min_max_.first, min_max_.second); - std::cout << "Cumulative characteristic function of diagram : " << std::endl; + std::clog << "Cumulative characteristic function of diagram : " << std::endl; for (size_t i = 0; i != cumul_char_funct_diag.size(); ++i) { - std::cout << cumul_char_funct_diag[i] << " "; + std::clog << cumul_char_funct_diag[i] << " "; } - std::cout << std::endl; + std::clog << std::endl; - std::cout << "Persistence Betti numbers \n"; + std::clog << "Persistence Betti numbers \n"; std::vector<std::pair<double, size_t> > pbns = p.compute_persistent_betti_numbers(); for (size_t i = 0; i != pbns.size(); ++i) { - std::cout << pbns[i].first << " " << pbns[i].second << std::endl; + std::clog << pbns[i].first << " " << pbns[i].second << std::endl; } return 0; diff --git a/src/Persistence_representations/example/persistence_landscape.cpp b/src/Persistence_representations/example/persistence_landscape.cpp index ff18d105..d39ae0b8 100644 --- a/src/Persistence_representations/example/persistence_landscape.cpp +++ b/src/Persistence_representations/example/persistence_landscape.cpp @@ -37,35 +37,35 @@ int main(int argc, char** argv) { Persistence_landscape l2(persistence2); // This is how to compute integral of landscapes: - std::cout << "Integral of the first landscape : " << l1.compute_integral_of_landscape() << std::endl; - std::cout << "Integral of the second landscape : " << l2.compute_integral_of_landscape() << std::endl; + std::clog << "Integral of the first landscape : " << l1.compute_integral_of_landscape() << std::endl; + std::clog << "Integral of the second landscape : " << l2.compute_integral_of_landscape() << std::endl; // And here how to write landscapes to stream: - std::cout << "l1 : " << l1 << std::endl; - std::cout << "l2 : " << l2 << std::endl; + std::clog << "l1 : " << l1 << std::endl; + std::clog << "l2 : " << l2 << std::endl; // Arithmetic operations on landscapes: Persistence_landscape sum = l1 + l2; - std::cout << "sum : " << sum << std::endl; + std::clog << "sum : " << sum << std::endl; // here are the maxima of the functions: - std::cout << "Maximum of l1 : " << l1.compute_maximum() << std::endl; - std::cout << "Maximum of l2 : " << l2.compute_maximum() << std::endl; + std::clog << "Maximum of l1 : " << l1.compute_maximum() << std::endl; + std::clog << "Maximum of l2 : " << l2.compute_maximum() << std::endl; // here are the norms of landscapes: - std::cout << "L^1 Norm of l1 : " << l1.compute_norm_of_landscape(1.) << std::endl; - std::cout << "L^1 Norm of l2 : " << l2.compute_norm_of_landscape(1.) << std::endl; + std::clog << "L^1 Norm of l1 : " << l1.compute_norm_of_landscape(1.) << std::endl; + std::clog << "L^1 Norm of l2 : " << l2.compute_norm_of_landscape(1.) << std::endl; // here is the average of landscapes: Persistence_landscape average; average.compute_average({&l1, &l2}); - std::cout << "average : " << average << std::endl; + std::clog << "average : " << average << std::endl; // here is the distance of landscapes: - std::cout << "Distance : " << l1.distance(l2) << std::endl; + std::clog << "Distance : " << l1.distance(l2) << std::endl; // here is the scalar product of landscapes: - std::cout << "Scalar product : " << l1.compute_scalar_product(l2) << std::endl; + std::clog << "Scalar product : " << l1.compute_scalar_product(l2) << std::endl; // here is how to create a file which is suitable for visualization via gnuplot: average.plot("average_landscape"); diff --git a/src/Persistence_representations/example/persistence_landscape_on_grid.cpp b/src/Persistence_representations/example/persistence_landscape_on_grid.cpp index 16a58e1d..6d58e167 100644 --- a/src/Persistence_representations/example/persistence_landscape_on_grid.cpp +++ b/src/Persistence_representations/example/persistence_landscape_on_grid.cpp @@ -37,31 +37,31 @@ int main(int argc, char** argv) { Persistence_landscape_on_grid l2(persistence2, 0, 11, 20); // This is how to compute integral of landscapes: - std::cout << "Integral of the first landscape : " << l1.compute_integral_of_landscape() << std::endl; - std::cout << "Integral of the second landscape : " << l2.compute_integral_of_landscape() << std::endl; + std::clog << "Integral of the first landscape : " << l1.compute_integral_of_landscape() << std::endl; + std::clog << "Integral of the second landscape : " << l2.compute_integral_of_landscape() << std::endl; // And here how to write landscapes to stream: - std::cout << "l1 : " << l1 << std::endl; - std::cout << "l2 : " << l2 << std::endl; + std::clog << "l1 : " << l1 << std::endl; + std::clog << "l2 : " << l2 << std::endl; // here are the maxima of the functions: - std::cout << "Maximum of l1 : " << l1.compute_maximum() << std::endl; - std::cout << "Maximum of l2 : " << l2.compute_maximum() << std::endl; + std::clog << "Maximum of l1 : " << l1.compute_maximum() << std::endl; + std::clog << "Maximum of l2 : " << l2.compute_maximum() << std::endl; // here are the norms of landscapes: - std::cout << "L^1 Norm of l1 : " << l1.compute_norm_of_landscape(1.) << std::endl; - std::cout << "L^1 Norm of l2 : " << l2.compute_norm_of_landscape(1.) << std::endl; + std::clog << "L^1 Norm of l1 : " << l1.compute_norm_of_landscape(1.) << std::endl; + std::clog << "L^1 Norm of l2 : " << l2.compute_norm_of_landscape(1.) << std::endl; // here is the average of landscapes: Persistence_landscape_on_grid average; average.compute_average({&l1, &l2}); - std::cout << "average : " << average << std::endl; + std::clog << "average : " << average << std::endl; // here is the distance of landscapes: - std::cout << "Distance : " << l1.distance(l2) << std::endl; + std::clog << "Distance : " << l1.distance(l2) << std::endl; // here is the scalar product of landscapes: - std::cout << "Scalar product : " << l1.compute_scalar_product(l2) << std::endl; + std::clog << "Scalar product : " << l1.compute_scalar_product(l2) << std::endl; // here is how to create a file which is suitable for visualization via gnuplot: average.plot("average_landscape"); diff --git a/src/Persistence_representations/example/persistence_vectors.cpp b/src/Persistence_representations/example/persistence_vectors.cpp index b27e52d2..89e2fb83 100644 --- a/src/Persistence_representations/example/persistence_vectors.cpp +++ b/src/Persistence_representations/example/persistence_vectors.cpp @@ -41,19 +41,19 @@ int main(int argc, char** argv) { Vector_distances_in_diagram v2(persistence2, std::numeric_limits<size_t>::max()); // writing to a stream: - std::cout << "v1 : " << v1 << std::endl; - std::cout << "v2 : " << v2 << std::endl; + std::clog << "v1 : " << v1 << std::endl; + std::clog << "v2 : " << v2 << std::endl; // averages: Vector_distances_in_diagram average; average.compute_average({&v1, &v2}); - std::cout << "Average : " << average << std::endl; + std::clog << "Average : " << average << std::endl; // computations of distances: - std::cout << "l^1 distance : " << v1.distance(v2) << std::endl; + std::clog << "l^1 distance : " << v1.distance(v2) << std::endl; // computations of scalar product: - std::cout << "Scalar product of l1 and l2 : " << v1.compute_scalar_product(v2) << std::endl; + std::clog << "Scalar product of l1 and l2 : " << v1.compute_scalar_product(v2) << std::endl; // create a file with a gnuplot script: v1.plot("plot_of_vector_representation"); diff --git a/src/Persistence_representations/example/sliced_wasserstein.cpp b/src/Persistence_representations/example/sliced_wasserstein.cpp index d5414d00..d4e31ebf 100644 --- a/src/Persistence_representations/example/sliced_wasserstein.cpp +++ b/src/Persistence_representations/example/sliced_wasserstein.cpp @@ -38,10 +38,10 @@ int main(int argc, char** argv) { SW swex1(persistence1, 1, -1); SW swex2(persistence2, 1, -1); - std::cout << "Approx SW kernel: " << sw1.compute_scalar_product(sw2) << std::endl; - std::cout << "Exact SW kernel: " << swex1.compute_scalar_product(swex2) << std::endl; - std::cout << "Distance induced by approx SW kernel: " << sw1.distance(sw2) << std::endl; - std::cout << "Distance induced by exact SW kernel: " << swex1.distance(swex2) << std::endl; + std::clog << "Approx SW kernel: " << sw1.compute_scalar_product(sw2) << std::endl; + std::clog << "Exact SW kernel: " << swex1.compute_scalar_product(swex2) << std::endl; + std::clog << "Distance induced by approx SW kernel: " << sw1.distance(sw2) << std::endl; + std::clog << "Distance induced by exact SW kernel: " << swex1.distance(swex2) << std::endl; return 0; } diff --git a/src/Persistence_representations/include/gudhi/Persistence_heat_maps.h b/src/Persistence_representations/include/gudhi/Persistence_heat_maps.h index b1af3503..fab88489 100644 --- a/src/Persistence_representations/include/gudhi/Persistence_heat_maps.h +++ b/src/Persistence_representations/include/gudhi/Persistence_heat_maps.h @@ -55,9 +55,9 @@ std::vector<std::vector<double> > create_Gaussian_filter(size_t pixel_radius, do } if (dbg) { - std::cerr << "Kernel initialize \n"; - std::cerr << "pixel_radius : " << pixel_radius << std::endl; - std::cerr << "kernel.size() : " << kernel.size() << std::endl; + std::clog << "Kernel initialize \n"; + std::clog << "pixel_radius : " << pixel_radius << std::endl; + std::clog << "kernel.size() : " << kernel.size() << std::endl; getchar(); } @@ -79,12 +79,12 @@ std::vector<std::vector<double> > create_Gaussian_filter(size_t pixel_radius, do } if (dbg) { - std::cerr << "Here is the kernel : \n"; + std::clog << "Here is the kernel : \n"; for (size_t i = 0; i != kernel.size(); ++i) { for (size_t j = 0; j != kernel[i].size(); ++j) { - std::cerr << kernel[i][j] << " "; + std::clog << kernel[i][j] << " "; } - std::cerr << std::endl; + std::clog << std::endl; } } return kernel; @@ -290,16 +290,16 @@ class Persistence_heat_maps { bool dbg = false; if (this->heat_map.size() != second.heat_map.size()) { if (dbg) - std::cerr << "this->heat_map.size() : " << this->heat_map.size() + std::clog << "this->heat_map.size() : " << this->heat_map.size() << " \n second.heat_map.size() : " << second.heat_map.size() << std::endl; return false; } if (this->min_ != second.min_) { - if (dbg) std::cerr << "this->min_ : " << this->min_ << ", second.min_ : " << second.min_ << std::endl; + if (dbg) std::clog << "this->min_ : " << this->min_ << ", second.min_ : " << second.min_ << std::endl; return false; } if (this->max_ != second.max_) { - if (dbg) std::cerr << "this->max_ : " << this->max_ << ", second.max_ : " << second.max_ << std::endl; + if (dbg) std::clog << "this->max_ : " << this->max_ << ", second.max_ : " << second.max_ << std::endl; return false; } // in the other case we may assume that the persistence images are defined on the same domain. @@ -322,15 +322,15 @@ class Persistence_heat_maps { bool operator==(const Persistence_heat_maps& rhs) const { bool dbg = false; if (!this->check_if_the_same(rhs)) { - if (dbg) std::cerr << "The domains are not the same \n"; + if (dbg) std::clog << "The domains are not the same \n"; return false; // in this case, the domains are not the same, so the maps cannot be the same. } for (size_t i = 0; i != this->heat_map.size(); ++i) { for (size_t j = 0; j != this->heat_map[i].size(); ++j) { if (!almost_equal(this->heat_map[i][j], rhs.heat_map[i][j])) { if (dbg) { - std::cerr << "this->heat_map[" << i << "][" << j << "] = " << this->heat_map[i][j] << std::endl; - std::cerr << "rhs.heat_map[" << i << "][" << j << "] = " << rhs.heat_map[i][j] << std::endl; + std::clog << "this->heat_map[" << i << "][" << j << "] = " << this->heat_map[i][j] << std::endl; + std::clog << "rhs.heat_map[" << i << "][" << j << "] = " << rhs.heat_map[i][j] << std::endl; } return false; } @@ -586,14 +586,14 @@ void Persistence_heat_maps<Scalling_of_kernels>::construct(const std::vector<std bool erase_below_diagonal, size_t number_of_pixels, double min_, double max_) { bool dbg = false; - if (dbg) std::cerr << "Entering construct procedure \n"; + if (dbg) std::clog << "Entering construct procedure \n"; Scalling_of_kernels f; this->f = f; - if (dbg) std::cerr << "min and max passed to construct() procedure: " << min_ << " " << max_ << std::endl; + if (dbg) std::clog << "min and max passed to construct() procedure: " << min_ << " " << max_ << std::endl; if (min_ == max_) { - if (dbg) std::cerr << "min and max parameters will be determined based on intervals \n"; + if (dbg) std::clog << "min and max parameters will be determined based on intervals \n"; // in this case, we want the program to set up the min_ and max_ values by itself. min_ = std::numeric_limits<int>::max(); max_ = -std::numeric_limits<int>::max(); @@ -611,9 +611,9 @@ void Persistence_heat_maps<Scalling_of_kernels>::construct(const std::vector<std } if (dbg) { - std::cerr << "min_ : " << min_ << std::endl; - std::cerr << "max_ : " << max_ << std::endl; - std::cerr << "number_of_pixels : " << number_of_pixels << std::endl; + std::clog << "min_ : " << min_ << std::endl; + std::clog << "max_ : " << max_ << std::endl; + std::clog << "number_of_pixels : " << number_of_pixels << std::endl; getchar(); } @@ -628,7 +628,7 @@ void Persistence_heat_maps<Scalling_of_kernels>::construct(const std::vector<std } this->heat_map = heat_map_; - if (dbg) std::cerr << "Done creating of the heat map, now we will fill in the structure \n"; + if (dbg) std::clog << "Done creating of the heat map, now we will fill in the structure \n"; for (size_t pt_nr = 0; pt_nr != intervals_.size(); ++pt_nr) { // compute the value of intervals_[pt_nr] in the grid: @@ -638,9 +638,9 @@ void Persistence_heat_maps<Scalling_of_kernels>::construct(const std::vector<std static_cast<int>((intervals_[pt_nr].second - this->min_) / (this->max_ - this->min_) * number_of_pixels); if (dbg) { - std::cerr << "point : " << intervals_[pt_nr].first << " , " << intervals_[pt_nr].second << std::endl; - std::cerr << "x_grid : " << x_grid << std::endl; - std::cerr << "y_grid : " << y_grid << std::endl; + std::clog << "point : " << intervals_[pt_nr].first << " , " << intervals_[pt_nr].second << std::endl; + std::clog << "x_grid : " << x_grid << std::endl; + std::clog << "y_grid : " << y_grid << std::endl; } // x_grid and y_grid gives a center of the kernel. We want to have its lower left corner. To get this, we need to @@ -650,9 +650,9 @@ void Persistence_heat_maps<Scalling_of_kernels>::construct(const std::vector<std // note that the numbers x_grid and y_grid may be negative. if (dbg) { - std::cerr << "After shift : \n"; - std::cerr << "x_grid : " << x_grid << std::endl; - std::cerr << "y_grid : " << y_grid << std::endl; + std::clog << "After shift : \n"; + std::clog << "x_grid : " << x_grid << std::endl; + std::clog << "y_grid : " << y_grid << std::endl; } double scaling_value = this->f(intervals_[pt_nr]); @@ -663,11 +663,11 @@ void Persistence_heat_maps<Scalling_of_kernels>::construct(const std::vector<std if (((x_grid + i) >= 0) && (x_grid + i < this->heat_map.size()) && ((y_grid + j) >= 0) && (y_grid + j < this->heat_map.size())) { if (dbg) { - std::cerr << y_grid + j << " " << x_grid + i << std::endl; + std::clog << y_grid + j << " " << x_grid + i << std::endl; } this->heat_map[y_grid + j][x_grid + i] += scaling_value * filter[i][j]; if (dbg) { - std::cerr << "Position : (" << x_grid + i << "," << y_grid + j + std::clog << "Position : (" << x_grid + i << "," << y_grid + j << ") got increased by the value : " << filter[i][j] << std::endl; } } @@ -805,7 +805,7 @@ void Persistence_heat_maps<Scalling_of_kernels>::plot(const char* filename) cons out << std::endl; } out.close(); - std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + std::clog << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" << gnuplot_script.str().c_str() << "\'\"" << std::endl; } @@ -842,7 +842,7 @@ void Persistence_heat_maps<Scalling_of_kernels>::load_from_file(const char* file in >> this->min_ >> this->max_; if (dbg) { - std::cerr << "Reading the following values of min and max : " << this->min_ << " , " << this->max_ << std::endl; + std::clog << "Reading the following values of min and max : " << this->min_ << " , " << this->max_ << std::endl; } std::string temp; @@ -859,18 +859,18 @@ void Persistence_heat_maps<Scalling_of_kernels>::load_from_file(const char* file lineSS >> point; line_of_heat_map.push_back(point); if (dbg) { - std::cout << point << " "; + std::clog << point << " "; } } if (dbg) { - std::cout << std::endl; + std::clog << std::endl; getchar(); } if (in.good()) this->heat_map.push_back(line_of_heat_map); } in.close(); - if (dbg) std::cout << "Done \n"; + if (dbg) std::clog << "Done \n"; } // Concretizations of virtual methods: @@ -878,7 +878,7 @@ template <typename Scalling_of_kernels> std::vector<double> Persistence_heat_maps<Scalling_of_kernels>::vectorize(int number_of_function) const { std::vector<double> result; if (!discrete) { - std::cout << "No vectorize method in case of infinite dimensional vectorization" << std::endl; + std::cerr << "No vectorize method in case of infinite dimensional vectorization" << std::endl; return result; } diff --git a/src/Persistence_representations/include/gudhi/Persistence_intervals.h b/src/Persistence_representations/include/gudhi/Persistence_intervals.h index ea4220ea..a6c1d6f0 100644 --- a/src/Persistence_representations/include/gudhi/Persistence_intervals.h +++ b/src/Persistence_representations/include/gudhi/Persistence_intervals.h @@ -185,7 +185,7 @@ class Persistence_intervals { out.close(); - std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + std::clog << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" << gnuplot_script.str().c_str() << "\'\"" << std::endl; } @@ -293,7 +293,7 @@ std::vector<std::pair<double, double> > Persistence_intervals::dominant_interval for (size_t i = 0; i != std::min(where_to_cut, position_length_vector.size()); ++i) { result.push_back(this->intervals[position_length_vector[i].first]); if (dbg) - std::cerr << "Position : " << position_length_vector[i].first << " length : " << position_length_vector[i].second + std::clog << "Position : " << position_length_vector[i].first << " length : " << position_length_vector[i].second << std::endl; } @@ -303,7 +303,7 @@ std::vector<std::pair<double, double> > Persistence_intervals::dominant_interval std::vector<size_t> Persistence_intervals::histogram_of_lengths(size_t number_of_bins) const { bool dbg = false; - if (dbg) std::cerr << "this->intervals.size() : " << this->intervals.size() << std::endl; + if (dbg) std::clog << "this->intervals.size() : " << this->intervals.size() << std::endl; // first find the length of the longest interval: double lengthOfLongest = 0; for (size_t i = 0; i != this->intervals.size(); ++i) { @@ -313,7 +313,7 @@ std::vector<size_t> Persistence_intervals::histogram_of_lengths(size_t number_of } if (dbg) { - std::cerr << "lengthOfLongest : " << lengthOfLongest << std::endl; + std::clog << "lengthOfLongest : " << lengthOfLongest << std::endl; } // this is a container we will use to store the resulting histogram @@ -330,10 +330,10 @@ std::vector<size_t> Persistence_intervals::histogram_of_lengths(size_t number_of ++result[position]; if (dbg) { - std::cerr << "i : " << i << std::endl; - std::cerr << "Interval : [" << this->intervals[i].first << " , " << this->intervals[i].second << " ] \n"; - std::cerr << "relative_length_of_this_interval : " << relative_length_of_this_interval << std::endl; - std::cerr << "position : " << position << std::endl; + std::clog << "i : " << i << std::endl; + std::clog << "Interval : [" << this->intervals[i].first << " , " << this->intervals[i].second << " ] \n"; + std::clog << "relative_length_of_this_interval : " << relative_length_of_this_interval << std::endl; + std::clog << "position : " << position << std::endl; getchar(); } } @@ -342,7 +342,7 @@ std::vector<size_t> Persistence_intervals::histogram_of_lengths(size_t number_of result.resize(number_of_bins); if (dbg) { - for (size_t i = 0; i != result.size(); ++i) std::cerr << result[i] << std::endl; + for (size_t i = 0; i != result.size(); ++i) std::clog << result[i] << std::endl; } return result; } @@ -368,7 +368,7 @@ std::vector<double> Persistence_intervals::characteristic_function_of_diagram(do for (size_t i = 0; i != this->intervals.size(); ++i) { if (dbg) { - std::cerr << "Interval : " << this->intervals[i].first << " , " << this->intervals[i].second << std::endl; + std::clog << "Interval : " << this->intervals[i].first << " , " << this->intervals[i].second << std::endl; } size_t beginIt = 0; @@ -390,8 +390,8 @@ std::vector<double> Persistence_intervals::characteristic_function_of_diagram(do } if (dbg) { - std::cerr << "beginIt : " << beginIt << std::endl; - std::cerr << "endIt : " << endIt << std::endl; + std::clog << "beginIt : " << beginIt << std::endl; + std::clog << "endIt : " << endIt << std::endl; } for (size_t pos = beginIt; pos != endIt; ++pos) { @@ -399,11 +399,11 @@ std::vector<double> Persistence_intervals::characteristic_function_of_diagram(do (this->intervals[i].second - this->intervals[i].first); } if (dbg) { - std::cerr << "Result at this stage \n"; + std::clog << "Result at this stage \n"; for (size_t aa = 0; aa != result.size(); ++aa) { - std::cerr << result[aa] << " "; + std::clog << result[aa] << " "; } - std::cerr << std::endl; + std::clog << std::endl; } } return result; @@ -455,9 +455,9 @@ inline double compute_euclidean_distance(const std::pair<double, double>& f, con std::vector<double> Persistence_intervals::k_n_n(size_t k, size_t where_to_cut) const { bool dbg = false; if (dbg) { - std::cerr << "Here are the intervals : \n"; + std::clog << "Here are the intervals : \n"; for (size_t i = 0; i != this->intervals.size(); ++i) { - std::cerr << "[ " << this->intervals[i].first << " , " << this->intervals[i].second << "] \n"; + std::clog << "[ " << this->intervals[i].first << " , " << this->intervals[i].second << "] \n"; } getchar(); } @@ -486,12 +486,12 @@ std::vector<double> Persistence_intervals::k_n_n(size_t k, size_t where_to_cut) distances_from_diagonal[i] = distanceToDiagonal; if (dbg) { - std::cerr << "Here are the distances form the point : [" << this->intervals[i].first << " , " + std::clog << "Here are the distances form the point : [" << this->intervals[i].first << " , " << this->intervals[i].second << "] in the diagram \n"; for (size_t aa = 0; aa != distancesFromI.size(); ++aa) { - std::cerr << "To : " << i + aa << " : " << distancesFromI[aa] << " "; + std::clog << "To : " << i + aa << " : " << distancesFromI[aa] << " "; } - std::cerr << std::endl; + std::clog << std::endl; getchar(); } @@ -502,18 +502,18 @@ std::vector<double> Persistence_intervals::k_n_n(size_t k, size_t where_to_cut) } } if (dbg) { - std::cerr << "Here is the distance matrix : \n"; + std::clog << "Here is the distance matrix : \n"; for (size_t i = 0; i != distances.size(); ++i) { for (size_t j = 0; j != distances.size(); ++j) { - std::cerr << distances[i][j] << " "; + std::clog << distances[i][j] << " "; } - std::cerr << std::endl; + std::clog << std::endl; } - std::cerr << std::endl << std::endl << "And here are the distances to the diagonal : " << std::endl; + std::clog << std::endl << std::endl << "And here are the distances to the diagonal : " << std::endl; for (size_t i = 0; i != distances_from_diagonal.size(); ++i) { - std::cerr << distances_from_diagonal[i] << " "; + std::clog << distances_from_diagonal[i] << " "; } - std::cerr << std::endl << std::endl; + std::clog << std::endl << std::endl; getchar(); } @@ -526,13 +526,13 @@ std::vector<double> Persistence_intervals::k_n_n(size_t k, size_t where_to_cut) if (k > distancesFromI.size()) { if (dbg) { - std::cerr << "There are not enough neighbors in your set. We set the result to plus infty \n"; + std::clog << "There are not enough neighbors in your set. We set the result to plus infty \n"; } result.push_back(std::numeric_limits<double>::max()); } else { if (distances_from_diagonal[i] > distancesFromI[k]) { if (dbg) { - std::cerr << "The k-th n.n. is on a diagonal. Therefore we set up a distance to diagonal \n"; + std::clog << "The k-th n.n. is on a diagonal. Therefore we set up a distance to diagonal \n"; } result.push_back(distances_from_diagonal[i]); } else { diff --git a/src/Persistence_representations/include/gudhi/Persistence_landscape.h b/src/Persistence_representations/include/gudhi/Persistence_landscape.h index b819ccb6..ce4065b8 100644 --- a/src/Persistence_representations/include/gudhi/Persistence_landscape.h +++ b/src/Persistence_representations/include/gudhi/Persistence_landscape.h @@ -343,7 +343,7 @@ class Persistence_landscape { bool dbg = false; if (dbg) { - std::cerr << "to_average.size() : " << to_average.size() << std::endl; + std::clog << "to_average.size() : " << to_average.size() << std::endl; } std::vector<Persistence_landscape*> nextLevelMerge(to_average.size()); @@ -357,13 +357,13 @@ class Persistence_landscape { while (nextLevelMerge.size() != 1) { if (dbg) { - std::cerr << "nextLevelMerge.size() : " << nextLevelMerge.size() << std::endl; + std::clog << "nextLevelMerge.size() : " << nextLevelMerge.size() << std::endl; } std::vector<Persistence_landscape*> nextNextLevelMerge; nextNextLevelMerge.reserve(to_average.size()); for (size_t i = 0; i < nextLevelMerge.size(); i = i + 2) { if (dbg) { - std::cerr << "i : " << i << std::endl; + std::clog << "i : " << i << std::endl; } Persistence_landscape* l = new Persistence_landscape; if (i + 1 != nextLevelMerge.size()) { @@ -374,7 +374,7 @@ class Persistence_landscape { nextNextLevelMerge.push_back(l); } if (dbg) { - std::cerr << "After this iteration \n"; + std::clog << "After this iteration \n"; getchar(); } @@ -471,25 +471,25 @@ Persistence_landscape::Persistence_landscape(const char* filename, size_t dimens bool operatorEqualDbg = false; bool Persistence_landscape::operator==(const Persistence_landscape& rhs) const { if (this->land.size() != rhs.land.size()) { - if (operatorEqualDbg) std::cerr << "1\n"; + if (operatorEqualDbg) std::clog << "1\n"; return false; } for (size_t level = 0; level != this->land.size(); ++level) { if (this->land[level].size() != rhs.land[level].size()) { - if (operatorEqualDbg) std::cerr << "this->land[level].size() : " << this->land[level].size() << "\n"; - if (operatorEqualDbg) std::cerr << "rhs.land[level].size() : " << rhs.land[level].size() << "\n"; - if (operatorEqualDbg) std::cerr << "2\n"; + if (operatorEqualDbg) std::clog << "this->land[level].size() : " << this->land[level].size() << "\n"; + if (operatorEqualDbg) std::clog << "rhs.land[level].size() : " << rhs.land[level].size() << "\n"; + if (operatorEqualDbg) std::clog << "2\n"; return false; } for (size_t i = 0; i != this->land[level].size(); ++i) { if (!(almost_equal(this->land[level][i].first, rhs.land[level][i].first) && almost_equal(this->land[level][i].second, rhs.land[level][i].second))) { if (operatorEqualDbg) - std::cerr << "this->land[level][i] : " << this->land[level][i].first << " " << this->land[level][i].second + std::clog << "this->land[level][i] : " << this->land[level][i].first << " " << this->land[level][i].second << "\n"; if (operatorEqualDbg) - std::cerr << "rhs.land[level][i] : " << rhs.land[level][i].first << " " << rhs.land[level][i].second << "\n"; - if (operatorEqualDbg) std::cerr << "3\n"; + std::clog << "rhs.land[level][i] : " << rhs.land[level][i].first << " " << rhs.land[level][i].second << "\n"; + if (operatorEqualDbg) std::clog << "3\n"; return false; } } @@ -507,7 +507,7 @@ void Persistence_landscape::construct_persistence_landscape_from_barcode( const std::vector<std::pair<double, double> >& p, size_t number_of_levels) { bool dbg = false; if (dbg) { - std::cerr << "Persistence_landscape::Persistence_landscape( const std::vector< std::pair< double , double > >& p )" + std::clog << "Persistence_landscape::Persistence_landscape( const std::vector< std::pair< double , double > >& p )" << std::endl; } @@ -517,9 +517,9 @@ void Persistence_landscape::construct_persistence_landscape_from_barcode( std::sort(bars.begin(), bars.end(), compare_points_sorting); if (dbg) { - std::cerr << "Bars : \n"; + std::clog << "Bars : \n"; for (size_t i = 0; i != bars.size(); ++i) { - std::cerr << bars[i].first << " " << bars[i].second << "\n"; + std::clog << bars[i].first << " " << bars[i].second << "\n"; } getchar(); } @@ -534,7 +534,7 @@ void Persistence_landscape::construct_persistence_landscape_from_barcode( while (!characteristicPoints.empty()) { if (dbg) { for (size_t i = 0; i != characteristicPoints.size(); ++i) { - std::cout << "(" << characteristicPoints[i].first << " " << characteristicPoints[i].second << ")\n"; + std::clog << "(" << characteristicPoints[i].first << " " << characteristicPoints[i].second << ")\n"; } std::cin.ignore(); } @@ -545,7 +545,7 @@ void Persistence_landscape::construct_persistence_landscape_from_barcode( lambda_n.push_back(characteristicPoints[0]); if (dbg) { - std::cerr << "1 Adding to lambda_n : (" << -std::numeric_limits<int>::max() << " " << 0 << ") , (" + std::clog << "1 Adding to lambda_n : (" << -std::numeric_limits<int>::max() << " " << 0 << ") , (" << minus_length(characteristicPoints[0]) << " " << 0 << ") , (" << characteristicPoints[0].first << " " << characteristicPoints[0].second << ") \n"; } @@ -562,13 +562,13 @@ void Persistence_landscape::construct_persistence_landscape_from_barcode( (birth_plus_deaths(lambda_n[lambda_n.size() - 1]) - minus_length(characteristicPoints[i])) / 2); lambda_n.push_back(point); if (dbg) { - std::cerr << "2 Adding to lambda_n : (" << point.first << " " << point.second << ")\n"; + std::clog << "2 Adding to lambda_n : (" << point.first << " " << point.second << ")\n"; } if (dbg) { - std::cerr << "characteristicPoints[i+p] : " << characteristicPoints[i + p].first << " " + std::clog << "characteristicPoints[i+p] : " << characteristicPoints[i + p].first << " " << characteristicPoints[i + p].second << "\n"; - std::cerr << "point : " << point.first << " " << point.second << "\n"; + std::clog << "point : " << point.first << " " << point.second << "\n"; getchar(); } @@ -577,7 +577,7 @@ void Persistence_landscape::construct_persistence_landscape_from_barcode( (birth_plus_deaths(point) <= birth_plus_deaths(characteristicPoints[i + p]))) { newCharacteristicPoints.push_back(characteristicPoints[i + p]); if (dbg) { - std::cerr << "3.5 Adding to newCharacteristicPoints : (" << characteristicPoints[i + p].first << " " + std::clog << "3.5 Adding to newCharacteristicPoints : (" << characteristicPoints[i + p].first << " " << characteristicPoints[i + p].second << ")\n"; getchar(); } @@ -586,7 +586,7 @@ void Persistence_landscape::construct_persistence_landscape_from_barcode( newCharacteristicPoints.push_back(point); if (dbg) { - std::cerr << "4 Adding to newCharacteristicPoints : (" << point.first << " " << point.second << ")\n"; + std::clog << "4 Adding to newCharacteristicPoints : (" << point.first << " " << point.second << ")\n"; } while ((i + p < characteristicPoints.size()) && @@ -594,15 +594,15 @@ void Persistence_landscape::construct_persistence_landscape_from_barcode( (birth_plus_deaths(point) >= birth_plus_deaths(characteristicPoints[i + p]))) { newCharacteristicPoints.push_back(characteristicPoints[i + p]); if (dbg) { - std::cerr << "characteristicPoints[i+p] : " << characteristicPoints[i + p].first << " " + std::clog << "characteristicPoints[i+p] : " << characteristicPoints[i + p].first << " " << characteristicPoints[i + p].second << "\n"; - std::cerr << "point : " << point.first << " " << point.second << "\n"; - std::cerr << "characteristicPoints[i+p] birth and death : " << minus_length(characteristicPoints[i + p]) + std::clog << "point : " << point.first << " " << point.second << "\n"; + std::clog << "characteristicPoints[i+p] birth and death : " << minus_length(characteristicPoints[i + p]) << " , " << birth_plus_deaths(characteristicPoints[i + p]) << "\n"; - std::cerr << "point birth and death : " << minus_length(point) << " , " << birth_plus_deaths(point) + std::clog << "point birth and death : " << minus_length(point) << " , " << birth_plus_deaths(point) << "\n"; - std::cerr << "3 Adding to newCharacteristicPoints : (" << characteristicPoints[i + p].first << " " + std::clog << "3 Adding to newCharacteristicPoints : (" << characteristicPoints[i + p].first << " " << characteristicPoints[i + p].second << ")\n"; getchar(); } @@ -613,20 +613,20 @@ void Persistence_landscape::construct_persistence_landscape_from_barcode( lambda_n.push_back(std::make_pair(birth_plus_deaths(lambda_n[lambda_n.size() - 1]), 0)); lambda_n.push_back(std::make_pair(minus_length(characteristicPoints[i]), 0)); if (dbg) { - std::cerr << "5 Adding to lambda_n : (" << birth_plus_deaths(lambda_n[lambda_n.size() - 1]) << " " << 0 + std::clog << "5 Adding to lambda_n : (" << birth_plus_deaths(lambda_n[lambda_n.size() - 1]) << " " << 0 << ")\n"; - std::cerr << "5 Adding to lambda_n : (" << minus_length(characteristicPoints[i]) << " " << 0 << ")\n"; + std::clog << "5 Adding to lambda_n : (" << minus_length(characteristicPoints[i]) << " " << 0 << ")\n"; } } lambda_n.push_back(characteristicPoints[i]); if (dbg) { - std::cerr << "6 Adding to lambda_n : (" << characteristicPoints[i].first << " " + std::clog << "6 Adding to lambda_n : (" << characteristicPoints[i].first << " " << characteristicPoints[i].second << ")\n"; } } else { newCharacteristicPoints.push_back(characteristicPoints[i]); if (dbg) { - std::cerr << "7 Adding to newCharacteristicPoints : (" << characteristicPoints[i].first << " " + std::clog << "7 Adding to newCharacteristicPoints : (" << characteristicPoints[i].first << " " << characteristicPoints[i].second << ")\n"; } } @@ -692,7 +692,7 @@ double Persistence_landscape::compute_integral_of_landscape(double p) const { double result = 0; for (size_t i = 0; i != this->land.size(); ++i) { for (size_t nr = 2; nr != this->land[i].size() - 1; ++nr) { - if (dbg) std::cout << "nr : " << nr << "\n"; + if (dbg) std::clog << "nr : " << nr << "\n"; // In this interval, the landscape has a form f(x) = ax+b. We want to compute integral of (ax+b)^p = 1/a * // (ax+b)^{p+1}/(p+1) std::pair<double, double> coef = compute_parameters_of_a_line(this->land[i][nr], this->land[i][nr - 1]); @@ -700,7 +700,7 @@ double Persistence_landscape::compute_integral_of_landscape(double p) const { double b = coef.second; if (dbg) - std::cout << "(" << this->land[i][nr].first << "," << this->land[i][nr].second << ") , " + std::clog << "(" << this->land[i][nr].first << "," << this->land[i][nr].second << ") , " << this->land[i][nr - 1].first << "," << this->land[i][nr].second << ")" << std::endl; if (this->land[i][nr].first == this->land[i][nr - 1].first) continue; if (a != 0) { @@ -710,8 +710,8 @@ double Persistence_landscape::compute_integral_of_landscape(double p) const { result += (this->land[i][nr].first - this->land[i][nr - 1].first) * (pow(this->land[i][nr].second, p)); } if (dbg) { - std::cout << "a : " << a << " , b : " << b << std::endl; - std::cout << "result : " << result << std::endl; + std::clog << "a : " << a << " , b : " << b << std::endl; + std::clog << "result : " << result << std::endl; } } } @@ -730,31 +730,31 @@ double Persistence_landscape::compute_value_at_a_given_point(unsigned level, dou unsigned coordEnd = this->land[level].size() - 2; if (compute_value_at_a_given_pointDbg) { - std::cerr << "Here \n"; - std::cerr << "x : " << x << "\n"; - std::cerr << "this->land[level][coordBegin].first : " << this->land[level][coordBegin].first << "\n"; - std::cerr << "this->land[level][coordEnd].first : " << this->land[level][coordEnd].first << "\n"; + std::clog << "Here \n"; + std::clog << "x : " << x << "\n"; + std::clog << "this->land[level][coordBegin].first : " << this->land[level][coordBegin].first << "\n"; + std::clog << "this->land[level][coordEnd].first : " << this->land[level][coordEnd].first << "\n"; } // in this case x is outside the support of the landscape, therefore the value of the landscape is 0. if (x <= this->land[level][coordBegin].first) return 0; if (x >= this->land[level][coordEnd].first) return 0; - if (compute_value_at_a_given_pointDbg) std::cerr << "Entering to the while loop \n"; + if (compute_value_at_a_given_pointDbg) std::clog << "Entering to the while loop \n"; while (coordBegin + 1 != coordEnd) { if (compute_value_at_a_given_pointDbg) { - std::cerr << "coordBegin : " << coordBegin << "\n"; - std::cerr << "coordEnd : " << coordEnd << "\n"; - std::cerr << "this->land[level][coordBegin].first : " << this->land[level][coordBegin].first << "\n"; - std::cerr << "this->land[level][coordEnd].first : " << this->land[level][coordEnd].first << "\n"; + std::clog << "coordBegin : " << coordBegin << "\n"; + std::clog << "coordEnd : " << coordEnd << "\n"; + std::clog << "this->land[level][coordBegin].first : " << this->land[level][coordBegin].first << "\n"; + std::clog << "this->land[level][coordEnd].first : " << this->land[level][coordEnd].first << "\n"; } unsigned newCord = (unsigned)floor((coordEnd + coordBegin) / 2.0); if (compute_value_at_a_given_pointDbg) { - std::cerr << "newCord : " << newCord << "\n"; - std::cerr << "this->land[level][newCord].first : " << this->land[level][newCord].first << "\n"; + std::clog << "newCord : " << newCord << "\n"; + std::clog << "this->land[level][newCord].first : " << this->land[level][newCord].first << "\n"; std::cin.ignore(); } @@ -767,12 +767,12 @@ double Persistence_landscape::compute_value_at_a_given_point(unsigned level, dou } if (compute_value_at_a_given_pointDbg) { - std::cout << "x : " << x << " is between : " << this->land[level][coordBegin].first << " a " + std::clog << "x : " << x << " is between : " << this->land[level][coordBegin].first << " a " << this->land[level][coordEnd].first << "\n"; - std::cout << "the y coords are : " << this->land[level][coordBegin].second << " a " + std::clog << "the y coords are : " << this->land[level][coordBegin].second << " a " << this->land[level][coordEnd].second << "\n"; - std::cerr << "coordBegin : " << coordBegin << "\n"; - std::cerr << "coordEnd : " << coordEnd << "\n"; + std::clog << "coordBegin : " << coordBegin << "\n"; + std::clog << "coordEnd : " << coordEnd << "\n"; std::cin.ignore(); } return function_value(this->land[level][coordBegin], this->land[level][coordEnd], x); @@ -810,13 +810,13 @@ Persistence_landscape Persistence_landscape::abs() { Persistence_landscape result; for (size_t level = 0; level != this->land.size(); ++level) { if (AbsDbg) { - std::cout << "level: " << level << std::endl; + std::clog << "level: " << level << std::endl; } std::vector<std::pair<double, double> > lambda_n; lambda_n.push_back(std::make_pair(-std::numeric_limits<int>::max(), 0)); for (size_t i = 1; i != this->land[level].size(); ++i) { if (AbsDbg) { - std::cout << "this->land[" << level << "][" << i << "] : " << this->land[level][i].first << " " + std::clog << "this->land[" << level << "][" << i << "] : " << this->land[level][i].first << " " << this->land[level][i].second << std::endl; } // if a line segment between this->land[level][i-1] and this->land[level][i] crosses the x-axis, then we have to @@ -828,15 +828,15 @@ Persistence_landscape Persistence_landscape::abs() { lambda_n.push_back(std::make_pair(zero, 0)); lambda_n.push_back(std::make_pair(this->land[level][i].first, fabs(this->land[level][i].second))); if (AbsDbg) { - std::cout << "Adding pair : (" << zero << ",0)" << std::endl; - std::cout << "In the same step adding pair : (" << this->land[level][i].first << "," + std::clog << "Adding pair : (" << zero << ",0)" << std::endl; + std::clog << "In the same step adding pair : (" << this->land[level][i].first << "," << fabs(this->land[level][i].second) << ") " << std::endl; std::cin.ignore(); } } else { lambda_n.push_back(std::make_pair(this->land[level][i].first, fabs(this->land[level][i].second))); if (AbsDbg) { - std::cout << "Adding pair : (" << this->land[level][i].first << "," << fabs(this->land[level][i].second) + std::clog << "Adding pair : (" << this->land[level][i].first << "," << fabs(this->land[level][i].second) << ") " << std::endl; std::cin.ignore(); } @@ -851,13 +851,13 @@ Persistence_landscape* Persistence_landscape::new_abs() { Persistence_landscape* result = new Persistence_landscape(*this); for (size_t level = 0; level != this->land.size(); ++level) { if (AbsDbg) { - std::cout << "level: " << level << std::endl; + std::clog << "level: " << level << std::endl; } std::vector<std::pair<double, double> > lambda_n; lambda_n.push_back(std::make_pair(-std::numeric_limits<int>::max(), 0)); for (size_t i = 1; i != this->land[level].size(); ++i) { if (AbsDbg) { - std::cout << "this->land[" << level << "][" << i << "] : " << this->land[level][i].first << " " + std::clog << "this->land[" << level << "][" << i << "] : " << this->land[level][i].first << " " << this->land[level][i].second << std::endl; } // if a line segment between this->land[level][i-1] and this->land[level][i] crosses the x-axis, then we have to @@ -869,15 +869,15 @@ Persistence_landscape* Persistence_landscape::new_abs() { lambda_n.push_back(std::make_pair(zero, 0)); lambda_n.push_back(std::make_pair(this->land[level][i].first, fabs(this->land[level][i].second))); if (AbsDbg) { - std::cout << "Adding pair : (" << zero << ",0)" << std::endl; - std::cout << "In the same step adding pair : (" << this->land[level][i].first << "," + std::clog << "Adding pair : (" << zero << ",0)" << std::endl; + std::clog << "In the same step adding pair : (" << this->land[level][i].first << "," << fabs(this->land[level][i].second) << ") " << std::endl; std::cin.ignore(); } } else { lambda_n.push_back(std::make_pair(this->land[level][i].first, fabs(this->land[level][i].second))); if (AbsDbg) { - std::cout << "Adding pair : (" << this->land[level][i].first << "," << fabs(this->land[level][i].second) + std::clog << "Adding pair : (" << this->land[level][i].first << "," << fabs(this->land[level][i].second) << ") " << std::endl; std::cin.ignore(); } @@ -943,11 +943,11 @@ void Persistence_landscape::load_landscape_from_file(const char* filename) { lineSS >> endd; landscapeAtThisLevel.push_back(std::make_pair(beginn, endd)); if (dbg) { - std::cerr << "Reading a point : " << beginn << " , " << endd << std::endl; + std::clog << "Reading a point : " << beginn << " , " << endd << std::endl; } } else { if (dbg) { - std::cout << "IGNORE LINE\n"; + std::clog << "IGNORE LINE\n"; getchar(); } if (!isThisAFirsLine) { @@ -975,7 +975,7 @@ Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscap const Persistence_landscape& land2) { bool operation_on_pair_of_landscapesDBG = false; if (operation_on_pair_of_landscapesDBG) { - std::cout << "operation_on_pair_of_landscapes\n"; + std::clog << "operation_on_pair_of_landscapes\n"; std::cin.ignore(); } Persistence_landscape result; @@ -985,8 +985,8 @@ Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscap if (operation_on_pair_of_landscapesDBG) { for (size_t i = 0; i != std::min(land1.land.size(), land2.land.size()); ++i) { - std::cerr << "land1.land[" << i << "].size() : " << land1.land[i].size() << std::endl; - std::cerr << "land2.land[" << i << "].size() : " << land2.land[i].size() << std::endl; + std::clog << "land1.land[" << i << "].size() : " << land1.land[i].size() << std::endl; + std::clog << "land2.land[" << i << "].size() : " << land2.land[i].size() << std::endl; } getchar(); } @@ -997,20 +997,20 @@ Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscap size_t q = 0; while ((p + 1 < land1.land[i].size()) && (q + 1 < land2.land[i].size())) { if (operation_on_pair_of_landscapesDBG) { - std::cerr << "p : " << p << "\n"; - std::cerr << "q : " << q << "\n"; - std::cerr << "land1.land.size() : " << land1.land.size() << std::endl; - std::cerr << "land2.land.size() : " << land2.land.size() << std::endl; - std::cerr << "land1.land[" << i << "].size() : " << land1.land[i].size() << std::endl; - std::cerr << "land2.land[" << i << "].size() : " << land2.land[i].size() << std::endl; - std::cout << "land1.land[i][p].first : " << land1.land[i][p].first << "\n"; - std::cout << "land2.land[i][q].first : " << land2.land[i][q].first << "\n"; + std::clog << "p : " << p << "\n"; + std::clog << "q : " << q << "\n"; + std::clog << "land1.land.size() : " << land1.land.size() << std::endl; + std::clog << "land2.land.size() : " << land2.land.size() << std::endl; + std::clog << "land1.land[" << i << "].size() : " << land1.land[i].size() << std::endl; + std::clog << "land2.land[" << i << "].size() : " << land2.land[i].size() << std::endl; + std::clog << "land1.land[i][p].first : " << land1.land[i][p].first << "\n"; + std::clog << "land2.land[i][q].first : " << land2.land[i][q].first << "\n"; } if (land1.land[i][p].first < land2.land[i][q].first) { if (operation_on_pair_of_landscapesDBG) { - std::cout << "first \n"; - std::cout << " function_value(land2.land[i][q-1],land2.land[i][q],land1.land[i][p].first) : " + std::clog << "first \n"; + std::clog << " function_value(land2.land[i][q-1],land2.land[i][q],land1.land[i][p].first) : " << function_value(land2.land[i][q - 1], land2.land[i][q], land1.land[i][p].first) << "\n"; } lambda_n.push_back( @@ -1022,12 +1022,12 @@ Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscap } if (land1.land[i][p].first > land2.land[i][q].first) { if (operation_on_pair_of_landscapesDBG) { - std::cout << "Second \n"; - std::cout << "function_value(" << land1.land[i][p - 1].first << " " << land1.land[i][p - 1].second << " ," + std::clog << "Second \n"; + std::clog << "function_value(" << land1.land[i][p - 1].first << " " << land1.land[i][p - 1].second << " ," << land1.land[i][p].first << " " << land1.land[i][p].second << ", " << land2.land[i][q].first << " ) : " << function_value(land1.land[i][p - 1], land1.land[i][p - 1], land2.land[i][q].first) << "\n"; - std::cout << "oper( " << function_value(land1.land[i][p], land1.land[i][p - 1], land2.land[i][q].first) << "," + std::clog << "oper( " << function_value(land1.land[i][p], land1.land[i][p - 1], land2.land[i][q].first) << "," << land2.land[i][q].second << " : " << oper(land2.land[i][q].second, function_value(land1.land[i][p], land1.land[i][p - 1], land2.land[i][q].first)) @@ -1040,19 +1040,19 @@ Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscap continue; } if (land1.land[i][p].first == land2.land[i][q].first) { - if (operation_on_pair_of_landscapesDBG) std::cout << "Third \n"; + if (operation_on_pair_of_landscapesDBG) std::clog << "Third \n"; lambda_n.push_back( std::make_pair(land2.land[i][q].first, oper(land1.land[i][p].second, land2.land[i][q].second))); ++p; ++q; } if (operation_on_pair_of_landscapesDBG) { - std::cout << "Next iteration \n"; + std::clog << "Next iteration \n"; } } while ((p + 1 < land1.land[i].size()) && (q + 1 >= land2.land[i].size())) { if (operation_on_pair_of_landscapesDBG) { - std::cout << "New point : " << land1.land[i][p].first + std::clog << "New point : " << land1.land[i][p].first << " oper(land1.land[i][p].second,0) : " << oper(land1.land[i][p].second, 0) << std::endl; } lambda_n.push_back(std::make_pair(land1.land[i][p].first, oper(land1.land[i][p].second, 0))); @@ -1060,7 +1060,7 @@ Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscap } while ((p + 1 >= land1.land[i].size()) && (q + 1 < land2.land[i].size())) { if (operation_on_pair_of_landscapesDBG) { - std::cout << "New point : " << land2.land[i][q].first + std::clog << "New point : " << land2.land[i][q].first << " oper(0,land2.land[i][q].second) : " << oper(0, land2.land[i][q].second) << std::endl; } lambda_n.push_back(std::make_pair(land2.land[i][q].first, oper(0, land2.land[i][q].second))); @@ -1073,7 +1073,7 @@ Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscap } if (land1.land.size() > std::min(land1.land.size(), land2.land.size())) { if (operation_on_pair_of_landscapesDBG) { - std::cout << "land1.land.size() > std::min( land1.land.size() , land2.land.size() )" << std::endl; + std::clog << "land1.land.size() > std::min( land1.land.size() , land2.land.size() )" << std::endl; } for (size_t i = std::min(land1.land.size(), land2.land.size()); i != std::max(land1.land.size(), land2.land.size()); ++i) { @@ -1088,7 +1088,7 @@ Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscap } if (land2.land.size() > std::min(land1.land.size(), land2.land.size())) { if (operation_on_pair_of_landscapesDBG) { - std::cout << "( land2.land.size() > std::min( land1.land.size() , land2.land.size() ) ) " << std::endl; + std::clog << "( land2.land.size() > std::min( land1.land.size() , land2.land.size() ) ) " << std::endl; } for (size_t i = std::min(land1.land.size(), land2.land.size()); i != std::max(land1.land.size(), land2.land.size()); ++i) { @@ -1102,7 +1102,7 @@ Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscap } } if (operation_on_pair_of_landscapesDBG) { - std::cout << "operation_on_pair_of_landscapes END\n"; + std::clog << "operation_on_pair_of_landscapes END\n"; std::cin.ignore(); } return result; @@ -1110,20 +1110,20 @@ Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscap double compute_maximal_distance_non_symmetric(const Persistence_landscape& pl1, const Persistence_landscape& pl2) { bool dbg = false; - if (dbg) std::cerr << " compute_maximal_distance_non_symmetric \n"; + if (dbg) std::clog << " compute_maximal_distance_non_symmetric \n"; // this distance is not symmetric. It compute ONLY distance between inflection points of pl1 and pl2. double maxDist = 0; size_t minimalNumberOfLevels = std::min(pl1.land.size(), pl2.land.size()); for (size_t level = 0; level != minimalNumberOfLevels; ++level) { if (dbg) { - std::cerr << "Level : " << level << std::endl; - std::cerr << "PL1 : \n"; + std::clog << "Level : " << level << std::endl; + std::clog << "PL1 : \n"; for (size_t i = 0; i != pl1.land[level].size(); ++i) { - std::cerr << "(" << pl1.land[level][i].first << "," << pl1.land[level][i].second << ") \n"; + std::clog << "(" << pl1.land[level][i].first << "," << pl1.land[level][i].second << ") \n"; } - std::cerr << "PL2 : \n"; + std::clog << "PL2 : \n"; for (size_t i = 0; i != pl2.land[level].size(); ++i) { - std::cerr << "(" << pl2.land[level][i].first << "," << pl2.land[level][i].second << ") \n"; + std::clog << "(" << pl2.land[level][i].first << "," << pl2.land[level][i].second << ") \n"; } std::cin.ignore(); } @@ -1143,24 +1143,24 @@ double compute_maximal_distance_non_symmetric(const Persistence_landscape& pl1, if (maxDist <= val) maxDist = val; if (dbg) { - std::cerr << pl1.land[level][i].first << "in [" << pl2.land[level][p2Count].first << "," + std::clog << pl1.land[level][i].first << "in [" << pl2.land[level][p2Count].first << "," << pl2.land[level][p2Count + 1].first << "] \n"; - std::cerr << "pl1[level][i].second : " << pl1.land[level][i].second << std::endl; - std::cerr << "function_value( pl2[level][p2Count] , pl2[level][p2Count+1] , pl1[level][i].first ) : " + std::clog << "pl1[level][i].second : " << pl1.land[level][i].second << std::endl; + std::clog << "function_value( pl2[level][p2Count] , pl2[level][p2Count+1] , pl1[level][i].first ) : " << function_value(pl2.land[level][p2Count], pl2.land[level][p2Count + 1], pl1.land[level][i].first) << std::endl; - std::cerr << "val : " << val << std::endl; + std::clog << "val : " << val << std::endl; std::cin.ignore(); } } } - if (dbg) std::cerr << "minimalNumberOfLevels : " << minimalNumberOfLevels << std::endl; + if (dbg) std::clog << "minimalNumberOfLevels : " << minimalNumberOfLevels << std::endl; if (minimalNumberOfLevels < pl1.land.size()) { for (size_t level = minimalNumberOfLevels; level != pl1.land.size(); ++level) { for (size_t i = 0; i != pl1.land[level].size(); ++i) { - if (dbg) std::cerr << "pl1[level][i].second : " << pl1.land[level][i].second << std::endl; + if (dbg) std::clog << "pl1[level][i].second : " << pl1.land[level][i].second << std::endl; if (maxDist < pl1.land[level][i].second) maxDist = pl1.land[level][i].second; } } @@ -1181,7 +1181,7 @@ double compute_distance_of_landscapes(const Persistence_landscape& first, const lan = lan.abs(); if (dbg) { - std::cerr << "Abs of difference ; " << lan << std::endl; + std::clog << "Abs of difference ; " << lan << std::endl; getchar(); } @@ -1189,17 +1189,17 @@ double compute_distance_of_landscapes(const Persistence_landscape& first, const // \int_{- \infty}^{+\infty}| first-second |^p double result; if (p != 1) { - if (dbg) std::cerr << "Power != 1, compute integral to the power p\n"; + if (dbg) std::clog << "Power != 1, compute integral to the power p\n"; result = lan.compute_integral_of_landscape(p); } else { - if (dbg) std::cerr << "Power = 1, compute integral \n"; + if (dbg) std::clog << "Power = 1, compute integral \n"; result = lan.compute_integral_of_landscape(); } // (\int_{- \infty}^{+\infty}| first-second |^p)^(1/p) return pow(result, 1.0 / p); } else { // p == infty - if (dbg) std::cerr << "Power = infty, compute maximum \n"; + if (dbg) std::clog << "Power = infty, compute maximum \n"; return lan.compute_maximum(); } } @@ -1220,7 +1220,7 @@ double compute_inner_product(const Persistence_landscape& l1, const Persistence_ for (size_t level = 0; level != std::min(l1.size(), l2.size()); ++level) { if (dbg) { - std::cerr << "Computing inner product for a level : " << level << std::endl; + std::clog << "Computing inner product for a level : " << level << std::endl; getchar(); } auto&& l1_land_level = l1.land[level]; @@ -1267,14 +1267,14 @@ double compute_inner_product(const Persistence_landscape& l1, const Persistence_ result += contributionFromThisPart; if (dbg) { - std::cerr << "[l1_land_level[l1It].first,l1_land_level[l1It+1].first] : " << l1_land_level[l1It].first + std::clog << "[l1_land_level[l1It].first,l1_land_level[l1It+1].first] : " << l1_land_level[l1It].first << " , " << l1_land_level[l1It + 1].first << std::endl; - std::cerr << "[l2_land_level[l2It].first,l2_land_level[l2It+1].first] : " << l2_land_level[l2It].first + std::clog << "[l2_land_level[l2It].first,l2_land_level[l2It+1].first] : " << l2_land_level[l2It].first << " , " << l2_land_level[l2It + 1].first << std::endl; - std::cerr << "a : " << a << ", b : " << b << " , c: " << c << ", d : " << d << std::endl; - std::cerr << "x1 : " << x1 << " , x2 : " << x2 << std::endl; - std::cerr << "contributionFromThisPart : " << contributionFromThisPart << std::endl; - std::cerr << "result : " << result << std::endl; + std::clog << "a : " << a << ", b : " << b << " , c: " << c << ", d : " << d << std::endl; + std::clog << "x1 : " << x1 << " , x2 : " << x2 << std::endl; + std::clog << "contributionFromThisPart : " << contributionFromThisPart << std::endl; + std::clog << "result : " << result << std::endl; getchar(); } @@ -1290,11 +1290,11 @@ double compute_inner_product(const Persistence_landscape& l1, const Persistence_ // in this case, we increment both: ++l2It; if (dbg) { - std::cerr << "Incrementing both \n"; + std::clog << "Incrementing both \n"; } } else { if (dbg) { - std::cerr << "Incrementing first \n"; + std::clog << "Incrementing first \n"; } } ++l1It; @@ -1302,7 +1302,7 @@ double compute_inner_product(const Persistence_landscape& l1, const Persistence_ // in this case we increment l2It ++l2It; if (dbg) { - std::cerr << "Incrementing second \n"; + std::clog << "Incrementing second \n"; } } @@ -1361,7 +1361,7 @@ void Persistence_landscape::plot(const char* filename, double xRangeBegin, doubl } out << "EOF" << std::endl; } - std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + std::clog << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" << gnuplot_script.str().c_str() << "\'\"" << std::endl; } diff --git a/src/Persistence_representations/include/gudhi/Persistence_landscape_on_grid.h b/src/Persistence_representations/include/gudhi/Persistence_landscape_on_grid.h index 68bce336..537131da 100644 --- a/src/Persistence_representations/include/gudhi/Persistence_landscape_on_grid.h +++ b/src/Persistence_representations/include/gudhi/Persistence_landscape_on_grid.h @@ -155,9 +155,9 @@ class Persistence_landscape_on_grid { double dx = (this->grid_max - this->grid_min) / static_cast<double>(this->values_of_landscapes.size() - 1); if (dbg) { - std::cerr << "this->grid_max : " << this->grid_max << std::endl; - std::cerr << "this->grid_min : " << this->grid_min << std::endl; - std::cerr << "this->values_of_landscapes.size() : " << this->values_of_landscapes.size() << std::endl; + std::clog << "this->grid_max : " << this->grid_max << std::endl; + std::clog << "this->grid_min : " << this->grid_min << std::endl; + std::clog << "this->values_of_landscapes.size() : " << this->values_of_landscapes.size() << std::endl; getchar(); } @@ -169,14 +169,14 @@ class Persistence_landscape_on_grid { if (this->values_of_landscapes[i].size() > level) current_y = this->values_of_landscapes[i][level]; if (dbg) { - std::cerr << "this->values_of_landscapes[i].size() : " << this->values_of_landscapes[i].size() + std::clog << "this->values_of_landscapes[i].size() : " << this->values_of_landscapes[i].size() << " , level : " << level << std::endl; if (this->values_of_landscapes[i].size() > level) - std::cerr << "this->values_of_landscapes[i][level] : " << this->values_of_landscapes[i][level] << std::endl; - std::cerr << "previous_y : " << previous_y << std::endl; - std::cerr << "current_y : " << current_y << std::endl; - std::cerr << "dx : " << dx << std::endl; - std::cerr << "0.5*dx*( previous_y + current_y ); " << 0.5 * dx * (previous_y + current_y) << std::endl; + std::clog << "this->values_of_landscapes[i][level] : " << this->values_of_landscapes[i][level] << std::endl; + std::clog << "previous_y : " << previous_y << std::endl; + std::clog << "current_y : " << current_y << std::endl; + std::clog << "dx : " << dx << std::endl; + std::clog << "0.5*dx*( previous_y + current_y ); " << 0.5 * dx * (previous_y + current_y) << std::endl; } result += 0.5 * dx * (previous_y + current_y); @@ -213,10 +213,10 @@ class Persistence_landscape_on_grid { if (this->values_of_landscapes[0].size() > level) previous_y = this->values_of_landscapes[0][level]; if (dbg) { - std::cerr << "dx : " << dx << std::endl; - std::cerr << "previous_x : " << previous_x << std::endl; - std::cerr << "previous_y : " << previous_y << std::endl; - std::cerr << "power : " << p << std::endl; + std::clog << "dx : " << dx << std::endl; + std::clog << "previous_x : " << previous_x << std::endl; + std::clog << "previous_y : " << previous_y << std::endl; + std::clog << "power : " << p << std::endl; getchar(); } @@ -225,7 +225,7 @@ class Persistence_landscape_on_grid { double current_y = 0; if (this->values_of_landscapes[i].size() > level) current_y = this->values_of_landscapes[i][level]; - if (dbg) std::cerr << "current_y : " << current_y << std::endl; + if (dbg) std::clog << "current_y : " << current_y << std::endl; if (current_y == previous_y) continue; @@ -235,7 +235,7 @@ class Persistence_landscape_on_grid { double b = coef.second; if (dbg) { - std::cerr << "A line passing through points : (" << previous_x << "," << previous_y << ") and (" << current_x + std::clog << "A line passing through points : (" << previous_x << "," << previous_y << ") and (" << current_x << "," << current_y << ") is : " << a << "x+" << b << std::endl; } @@ -249,14 +249,14 @@ class Persistence_landscape_on_grid { } result += value_to_add; if (dbg) { - std::cerr << "Increasing result by : " << value_to_add << std::endl; - std::cerr << "result : " << result << std::endl; + std::clog << "Increasing result by : " << value_to_add << std::endl; + std::clog << "result : " << result << std::endl; getchar(); } previous_x = current_x; previous_y = current_y; } - if (dbg) std::cerr << "The total result is : " << result << std::endl; + if (dbg) std::clog << "The total result is : " << result << std::endl; return result; } @@ -297,10 +297,10 @@ class Persistence_landscape_on_grid { size_t position = size_t((x - this->grid_min) / dx); if (dbg) { - std::cerr << "This is a procedure compute_value_at_a_given_point \n"; - std::cerr << "level : " << level << std::endl; - std::cerr << "x : " << x << std::endl; - std::cerr << "position : " << position << std::endl; + std::clog << "This is a procedure compute_value_at_a_given_point \n"; + std::clog << "level : " << level << std::endl; + std::clog << "x : " << x << std::endl; + std::clog << "position : " << position << std::endl; } // check if we are not exactly in the grid point: if (almost_equal(position * dx + this->grid_min, x)) { @@ -432,23 +432,23 @@ class Persistence_landscape_on_grid { bool operator==(const Persistence_landscape_on_grid& rhs) const { bool dbg = true; if (this->values_of_landscapes.size() != rhs.values_of_landscapes.size()) { - if (dbg) std::cerr << "values_of_landscapes of incompatible sizes\n"; + if (dbg) std::clog << "values_of_landscapes of incompatible sizes\n"; return false; } if (!almost_equal(this->grid_min, rhs.grid_min)) { - if (dbg) std::cerr << "grid_min not equal\n"; + if (dbg) std::clog << "grid_min not equal\n"; return false; } if (!almost_equal(this->grid_max, rhs.grid_max)) { - if (dbg) std::cerr << "grid_max not equal\n"; + if (dbg) std::clog << "grid_max not equal\n"; return false; } for (size_t i = 0; i != this->values_of_landscapes.size(); ++i) { for (size_t aa = 0; aa != this->values_of_landscapes[i].size(); ++aa) { if (!almost_equal(this->values_of_landscapes[i][aa], rhs.values_of_landscapes[i][aa])) { if (dbg) { - std::cerr << "Problem in the position : " << i << " of values_of_landscapes. \n"; - std::cerr << this->values_of_landscapes[i][aa] << " " << rhs.values_of_landscapes[i][aa] << std::endl; + std::clog << "Problem in the position : " << i << " of values_of_landscapes. \n"; + std::clog << this->values_of_landscapes[i][aa] << " " << rhs.values_of_landscapes[i][aa] << std::endl; } return false; } @@ -615,7 +615,7 @@ class Persistence_landscape_on_grid { double previous_y_l1 = 0; double previous_y_l2 = 0; for (size_t i = 0; i != l1.values_of_landscapes.size(); ++i) { - if (dbg) std::cerr << "i : " << i << std::endl; + if (dbg) std::clog << "i : " << i << std::endl; double current_x = previous_x + dx; double current_y_l1 = 0; @@ -625,11 +625,11 @@ class Persistence_landscape_on_grid { if (l2.values_of_landscapes[i].size() > level) current_y_l2 = l2.values_of_landscapes[i][level]; if (dbg) { - std::cerr << "previous_x : " << previous_x << std::endl; - std::cerr << "previous_y_l1 : " << previous_y_l1 << std::endl; - std::cerr << "current_y_l1 : " << current_y_l1 << std::endl; - std::cerr << "previous_y_l2 : " << previous_y_l2 << std::endl; - std::cerr << "current_y_l2 : " << current_y_l2 << std::endl; + std::clog << "previous_x : " << previous_x << std::endl; + std::clog << "previous_y_l1 : " << previous_y_l1 << std::endl; + std::clog << "current_y_l1 : " << current_y_l1 << std::endl; + std::clog << "previous_y_l2 : " << previous_y_l2 << std::endl; + std::clog << "current_y_l2 : " << current_y_l2 << std::endl; } std::pair<double, double> l1_coords = compute_parameters_of_a_line(std::make_pair(previous_x, previous_y_l1), @@ -646,11 +646,11 @@ class Persistence_landscape_on_grid { double d = l2_coords.second; if (dbg) { - std::cerr << "Here are the formulas for a line: \n"; - std::cerr << "a : " << a << std::endl; - std::cerr << "b : " << b << std::endl; - std::cerr << "c : " << c << std::endl; - std::cerr << "d : " << d << std::endl; + std::clog << "Here are the formulas for a line: \n"; + std::clog << "a : " << a << std::endl; + std::clog << "b : " << b << std::endl; + std::clog << "c : " << c << std::endl; + std::clog << "d : " << d << std::endl; } // now, to compute the inner product in this interval we need to compute the integral of (ax+b)(cx+d) = acx^2 + @@ -663,11 +663,11 @@ class Persistence_landscape_on_grid { (a * d + b * c) / 2 * previous_x * previous_x + b * d * previous_x); if (dbg) { - std::cerr << "Value of the integral on the left end i.e. : " << previous_x << " is : " + std::clog << "Value of the integral on the left end i.e. : " << previous_x << " is : " << a * c / 3 * previous_x * previous_x * previous_x + (a * d + b * c) / 2 * previous_x * previous_x + b * d * previous_x << std::endl; - std::cerr << "Value of the integral on the right end i.e. : " << current_x << " is " + std::clog << "Value of the integral on the right end i.e. : " << current_x << " is " << a * c / 3 * current_x * current_x * current_x + (a * d + b * c) / 2 * current_x * current_x + b * d * current_x << std::endl; @@ -676,8 +676,8 @@ class Persistence_landscape_on_grid { result += added_value; if (dbg) { - std::cerr << "added_value : " << added_value << std::endl; - std::cerr << "result : " << result << std::endl; + std::clog << "added_value : " << added_value << std::endl; + std::clog << "result : " << result << std::endl; getchar(); } @@ -703,8 +703,8 @@ class Persistence_landscape_on_grid { // time: if (dbg) { - std::cerr << "first : " << first << std::endl; - std::cerr << "second : " << second << std::endl; + std::clog << "first : " << first << std::endl; + std::clog << "second : " << second << std::endl; getchar(); } @@ -712,14 +712,14 @@ class Persistence_landscape_on_grid { Persistence_landscape_on_grid lan = first - second; if (dbg) { - std::cerr << "Difference : " << lan << std::endl; + std::clog << "Difference : " << lan << std::endl; } //| first-second |: lan.abs(); if (dbg) { - std::cerr << "Abs : " << lan << std::endl; + std::clog << "Abs : " << lan << std::endl; } if (p < std::numeric_limits<double>::max()) { @@ -727,18 +727,18 @@ class Persistence_landscape_on_grid { double result; if (p != 1) { if (dbg) { - std::cerr << "p : " << p << std::endl; + std::clog << "p : " << p << std::endl; getchar(); } result = lan.compute_integral_of_landscape(p); if (dbg) { - std::cerr << "integral : " << result << std::endl; + std::clog << "integral : " << result << std::endl; getchar(); } } else { result = lan.compute_integral_of_landscape(); if (dbg) { - std::cerr << "integral, without power : " << result << std::endl; + std::clog << "integral, without power : " << result << std::endl; getchar(); } } @@ -820,7 +820,7 @@ class Persistence_landscape_on_grid { this->grid_max = (to_average[0])->grid_max; if (dbg) { - std::cerr << "Computations of average. The data from the current landscape have been cleared. We are ready to do " + std::clog << "Computations of average. The data from the current landscape have been cleared. We are ready to do " "the computations. \n"; } @@ -835,7 +835,7 @@ class Persistence_landscape_on_grid { this->values_of_landscapes[grid_point] = std::vector<double>(maximal_size_of_vector); if (dbg) { - std::cerr << "We are considering the point : " << grid_point + std::clog << "We are considering the point : " << grid_point << " of the grid. In this point, there are at most : " << maximal_size_of_vector << " nonzero landscape functions \n"; } @@ -931,12 +931,12 @@ void Persistence_landscape_on_grid::set_up_values_of_landscapes(const std::vecto size_t number_of_points_, unsigned number_of_levels) { bool dbg = false; if (dbg) { - std::cerr << "Here is the procedure : set_up_values_of_landscapes. The parameters are : grid_min_ : " << grid_min_ + std::clog << "Here is the procedure : set_up_values_of_landscapes. The parameters are : grid_min_ : " << grid_min_ << ", grid_max_ : " << grid_max_ << ", number_of_points_ : " << number_of_points_ << ", number_of_levels: " << number_of_levels << std::endl; - std::cerr << "Here are the intervals at our disposal : \n"; + std::clog << "Here are the intervals at our disposal : \n"; for (size_t i = 0; i != p.size(); ++i) { - std::cerr << p[i].first << " , " << p[i].second << std::endl; + std::clog << p[i].first << " , " << p[i].second << std::endl; } } @@ -976,17 +976,17 @@ void Persistence_landscape_on_grid::set_up_values_of_landscapes(const std::vecto size_t grid_interval_midpoint = (size_t)(0.5 * (grid_interval_begin + grid_interval_end)); if (dbg) { - std::cerr << "Considering an interval : " << p[int_no].first << "," << p[int_no].second << std::endl; + std::clog << "Considering an interval : " << p[int_no].first << "," << p[int_no].second << std::endl; - std::cerr << "grid_interval_begin : " << grid_interval_begin << std::endl; - std::cerr << "grid_interval_end : " << grid_interval_end << std::endl; - std::cerr << "grid_interval_midpoint : " << grid_interval_midpoint << std::endl; + std::clog << "grid_interval_begin : " << grid_interval_begin << std::endl; + std::clog << "grid_interval_end : " << grid_interval_end << std::endl; + std::clog << "grid_interval_midpoint : " << grid_interval_midpoint << std::endl; } double landscape_value = dx; for (size_t i = grid_interval_begin + 1; i < grid_interval_midpoint; ++i) { if (dbg) { - std::cerr << "Adding landscape value (going up) for a point : " << i << " equal : " << landscape_value + std::clog << "Adding landscape value (going up) for a point : " << i << " equal : " << landscape_value << std::endl; } if (number_of_levels != std::numeric_limits<unsigned>::max()) { @@ -1044,7 +1044,7 @@ void Persistence_landscape_on_grid::set_up_values_of_landscapes(const std::vecto } if (dbg) { - std::cerr << "Adding landscape value (going down) for a point : " << i << " equal : " << landscape_value + std::clog << "Adding landscape value (going down) for a point : " << i << " equal : " << landscape_value << std::endl; } } @@ -1246,7 +1246,7 @@ void Persistence_landscape_on_grid::plot(const char* filename, double min_x, dou } out << "EOF" << std::endl; } - std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + std::clog << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" << gnuplot_script.str().c_str() << "\'\"" << std::endl; } diff --git a/src/Persistence_representations/include/gudhi/Persistence_vectors.h b/src/Persistence_representations/include/gudhi/Persistence_vectors.h index 6776f4a3..fab96900 100644 --- a/src/Persistence_representations/include/gudhi/Persistence_vectors.h +++ b/src/Persistence_representations/include/gudhi/Persistence_vectors.h @@ -189,7 +189,7 @@ class Vector_distances_in_diagram { } out << std::endl; out.close(); - std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + std::clog << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" << gnuplot_script.str().c_str() << "\'\"" << std::endl; } @@ -360,9 +360,9 @@ template <typename F> void Vector_distances_in_diagram<F>::compute_sorted_vector_of_distances_via_heap(size_t where_to_cut) { bool dbg = false; if (dbg) { - std::cerr << "Here are the intervals : \n"; + std::clog << "Here are the intervals : \n"; for (size_t i = 0; i != this->intervals.size(); ++i) { - std::cerr << this->intervals[i].first << " , " << this->intervals[i].second << std::endl; + std::clog << this->intervals[i].first << " , " << this->intervals[i].second << std::endl; } } where_to_cut = std::min( @@ -385,14 +385,14 @@ void Vector_distances_in_diagram<F>::compute_sorted_vector_of_distances_via_heap 0.5 * (this->intervals[j].first + this->intervals[j].second))))); if (dbg) { - std::cerr << "Value : " << value << std::endl; - std::cerr << "heap.front() : " << heap.front() << std::endl; + std::clog << "Value : " << value << std::endl; + std::clog << "heap.front() : " << heap.front() << std::endl; getchar(); } if (-value < heap.front()) { if (dbg) { - std::cerr << "Replacing : " << heap.front() << " with : " << -value << std::endl; + std::clog << "Replacing : " << heap.front() << " with : " << -value << std::endl; getchar(); } // remove the first element from the heap @@ -431,11 +431,11 @@ void Vector_distances_in_diagram<F>::compute_sorted_vector_of_distances_via_heap } if (dbg) { - std::cerr << "This is the heap after all the operations :\n"; + std::clog << "This is the heap after all the operations :\n"; for (size_t i = 0; i != heap.size(); ++i) { - std::cout << heap[i] << " "; + std::clog << heap[i] << " "; } - std::cout << std::endl; + std::clog << std::endl; } this->sorted_vector_of_distances = heap; @@ -519,11 +519,11 @@ double Vector_distances_in_diagram<F>::distance(const Vector_distances_in_diagra bool dbg = false; if (dbg) { - std::cerr << "Entering double Vector_distances_in_diagram<F>::distance( const Abs_Topological_data_with_distances* " + std::clog << "Entering double Vector_distances_in_diagram<F>::distance( const Abs_Topological_data_with_distances* " "second , double power ) procedure \n"; - std::cerr << "Power : " << power << std::endl; - std::cerr << "This : " << *this << std::endl; - std::cerr << "second : " << second_ << std::endl; + std::clog << "Power : " << power << std::endl; + std::clog << "This : " << *this << std::endl; + std::clog << "second : " << second_ << std::endl; } double result = 0; @@ -531,7 +531,7 @@ double Vector_distances_in_diagram<F>::distance(const Vector_distances_in_diagra ++i) { if (power == 1) { if (dbg) { - std::cerr << "|" << this->sorted_vector_of_distances[i] << " - " << second_.sorted_vector_of_distances[i] + std::clog << "|" << this->sorted_vector_of_distances[i] << " - " << second_.sorted_vector_of_distances[i] << " | : " << fabs(this->sorted_vector_of_distances[i] - second_.sorted_vector_of_distances[i]) << std::endl; } @@ -545,7 +545,7 @@ double Vector_distances_in_diagram<F>::distance(const Vector_distances_in_diagra result = fabs(this->sorted_vector_of_distances[i] - second_.sorted_vector_of_distances[i]); } if (dbg) { - std::cerr << "| " << this->sorted_vector_of_distances[i] << " - " << second_.sorted_vector_of_distances[i] + std::clog << "| " << this->sorted_vector_of_distances[i] << " - " << second_.sorted_vector_of_distances[i] << " : " << fabs(this->sorted_vector_of_distances[i] - second_.sorted_vector_of_distances[i]) << std::endl; } diff --git a/src/Persistence_representations/include/gudhi/read_persistence_from_file.h b/src/Persistence_representations/include/gudhi/read_persistence_from_file.h index 5c2d2038..a5bc1bca 100644 --- a/src/Persistence_representations/include/gudhi/read_persistence_from_file.h +++ b/src/Persistence_representations/include/gudhi/read_persistence_from_file.h @@ -50,28 +50,28 @@ std::vector<std::pair<double, double> > read_persistence_intervals_in_one_dimens final_barcode.reserve(barcode_initial.size()); if (dbg) { - std::cerr << "Here are the intervals that we read from the file : \n"; + std::clog << "Here are the intervals that we read from the file : \n"; for (size_t i = 0; i != barcode_initial.size(); ++i) { - std::cout << barcode_initial[i].first << " " << barcode_initial[i].second << std::endl; + std::clog << barcode_initial[i].first << " " << barcode_initial[i].second << std::endl; } getchar(); } for (size_t i = 0; i != barcode_initial.size(); ++i) { if (dbg) { - std::cout << "COnsidering interval : " << barcode_initial[i].first << " " << barcode_initial[i].second + std::clog << "Considering interval : " << barcode_initial[i].first << " " << barcode_initial[i].second << std::endl; } if (barcode_initial[i].first > barcode_initial[i].second) { // note that in this case barcode_initial[i].second != std::numeric_limits<double>::infinity() - if (dbg) std::cout << "Swap and enter \n"; + if (dbg) std::clog << "Swap and enter \n"; // swap them to make sure that birth < death final_barcode.push_back(std::pair<double, double>(barcode_initial[i].second, barcode_initial[i].first)); continue; } else { if (barcode_initial[i].second != std::numeric_limits<double>::infinity()) { - if (dbg) std::cout << "Simply enters\n"; + if (dbg) std::clog << "Simply enters\n"; // in this case, due to the previous conditions we know that barcode_initial[i].first < // barcode_initial[i].second, so we put them as they are final_barcode.push_back(std::pair<double, double>(barcode_initial[i].first, barcode_initial[i].second)); @@ -91,11 +91,11 @@ std::vector<std::pair<double, double> > read_persistence_intervals_in_one_dimens } if (dbg) { - std::cerr << "Here are the final bars that we are sending further : \n"; + std::clog << "Here are the final bars that we are sending further : \n"; for (size_t i = 0; i != final_barcode.size(); ++i) { - std::cout << final_barcode[i].first << " " << final_barcode[i].second << std::endl; + std::clog << final_barcode[i].first << " " << final_barcode[i].second << std::endl; } - std::cerr << "final_barcode.size() : " << final_barcode.size() << std::endl; + std::clog << "final_barcode.size() : " << final_barcode.size() << std::endl; getchar(); } diff --git a/src/Persistence_representations/utilities/CMakeLists.txt b/src/Persistence_representations/utilities/CMakeLists.txt index fc51b1d6..85633b7b 100644 --- a/src/Persistence_representations/utilities/CMakeLists.txt +++ b/src/Persistence_representations/utilities/CMakeLists.txt @@ -14,7 +14,7 @@ function(add_persistence_representation_creation_utility creation_utility) install(TARGETS ${creation_utility} DESTINATION bin) endfunction(add_persistence_representation_creation_utility) -function(add_persistence_representation_plot_utility plot_utility tool_extension) +function(add_persistence_representation_plot_utility creation_utility plot_utility tool_extension) add_executable ( ${plot_utility} ${plot_utility}.cpp ) # as the function is called in a subdirectory level, need to '../' to find persistence heat maps files @@ -22,17 +22,21 @@ function(add_persistence_representation_plot_utility plot_utility tool_extension "${CMAKE_CURRENT_BINARY_DIR}/../first.pers${tool_extension}") #add_test(NAME Persistence_representation_utilities_${plot_utility}_second COMMAND $<TARGET_FILE:${plot_utility}> # "${CMAKE_CURRENT_BINARY_DIR}/../second.pers${tool_extension}") + set_tests_properties(Persistence_representation_utilities_${plot_utility}_first PROPERTIES DEPENDS + Persistence_representation_utilities_${creation_utility}) if(GNUPLOT_PATH) add_test(NAME Persistence_representation_utilities_${plot_utility}_first_gnuplot COMMAND ${GNUPLOT_PATH} "-e" "load '${CMAKE_CURRENT_BINARY_DIR}/../first.pers${tool_extension}_GnuplotScript'") #add_test(NAME Persistence_representation_utilities_${plot_utility}_second_gnuplot COMMAND ${GNUPLOT_PATH} # "-e" "load '${CMAKE_CURRENT_BINARY_DIR}/../second.pers${tool_extension}_GnuplotScript'") + set_tests_properties(Persistence_representation_utilities_${plot_utility}_first_gnuplot PROPERTIES DEPENDS + Persistence_representation_utilities_${plot_utility}_first) endif() install(TARGETS ${plot_utility} DESTINATION bin) endfunction(add_persistence_representation_plot_utility) -function(add_persistence_representation_function_utility function_utility tool_extension) +function(add_persistence_representation_function_utility creation_utility function_utility tool_extension) add_executable ( ${function_utility} ${function_utility}.cpp ) # ARGV2 is an optional argument @@ -48,6 +52,8 @@ function(add_persistence_representation_function_utility function_utility tool_e "${CMAKE_CURRENT_BINARY_DIR}/../first.pers${tool_extension}" "${CMAKE_CURRENT_BINARY_DIR}/../second.pers${tool_extension}") endif() + set_tests_properties(Persistence_representation_utilities_${function_utility} PROPERTIES DEPENDS + Persistence_representation_utilities_${creation_utility}) install(TARGETS ${function_utility} DESTINATION bin) endfunction(add_persistence_representation_function_utility) diff --git a/src/Persistence_representations/utilities/persistence_heat_maps/CMakeLists.txt b/src/Persistence_representations/utilities/persistence_heat_maps/CMakeLists.txt index 89ef232f..e4c471c2 100644 --- a/src/Persistence_representations/utilities/persistence_heat_maps/CMakeLists.txt +++ b/src/Persistence_representations/utilities/persistence_heat_maps/CMakeLists.txt @@ -2,13 +2,24 @@ project(Persistence_representations_heat_maps_utilities) add_persistence_representation_creation_utility(create_pssk "10" "-1" "-1" "4" "-1") add_persistence_representation_creation_utility(create_p_h_m_weighted_by_arctan_of_their_persistence "10" "-1" "-1" "4" "-1") + add_persistence_representation_creation_utility(create_p_h_m_weighted_by_distance_from_diagonal "10" "-1" "-1" "4" "-1") +# Tests output the same file +set_tests_properties(Persistence_representation_utilities_create_p_h_m_weighted_by_distance_from_diagonal PROPERTIES DEPENDS + Persistence_representation_utilities_create_p_h_m_weighted_by_arctan_of_their_persistence) + add_persistence_representation_creation_utility(create_p_h_m_weighted_by_squared_diag_distance "10" "-1" "-1" "4" "-1") +# Tests output the same file +set_tests_properties(Persistence_representation_utilities_create_p_h_m_weighted_by_squared_diag_distance PROPERTIES DEPENDS + Persistence_representation_utilities_create_p_h_m_weighted_by_distance_from_diagonal) + # Need to set grid min and max for further average, distance and scalar_product add_persistence_representation_creation_utility(create_persistence_heat_maps "10" "0" "35" "10" "-1") +set_tests_properties(Persistence_representation_utilities_create_persistence_heat_maps PROPERTIES DEPENDS + Persistence_representation_utilities_create_p_h_m_weighted_by_squared_diag_distance) -add_persistence_representation_plot_utility(plot_persistence_heat_map ".mps") +add_persistence_representation_plot_utility(create_persistence_heat_maps plot_persistence_heat_map ".mps") -add_persistence_representation_function_utility(average_persistence_heat_maps ".mps") -add_persistence_representation_function_utility(compute_distance_of_persistence_heat_maps ".mps" "1") -add_persistence_representation_function_utility(compute_scalar_product_of_persistence_heat_maps ".mps") +add_persistence_representation_function_utility(create_persistence_heat_maps average_persistence_heat_maps ".mps") +add_persistence_representation_function_utility(create_persistence_heat_maps compute_distance_of_persistence_heat_maps ".mps" "1") +add_persistence_representation_function_utility(create_persistence_heat_maps compute_scalar_product_of_persistence_heat_maps ".mps") diff --git a/src/Persistence_representations/utilities/persistence_heat_maps/average_persistence_heat_maps.cpp b/src/Persistence_representations/utilities/persistence_heat_maps/average_persistence_heat_maps.cpp index 3d088b58..54b1f77d 100644 --- a/src/Persistence_representations/utilities/persistence_heat_maps/average_persistence_heat_maps.cpp +++ b/src/Persistence_representations/utilities/persistence_heat_maps/average_persistence_heat_maps.cpp @@ -17,12 +17,12 @@ using constant_scaling_function = Gudhi::Persistence_representations::constant_s using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<constant_scaling_function>; int main(int argc, char** argv) { - std::cout << "This program computes average of persistence heat maps stored in files (the files needs to be " + std::clog << "This program computes average of persistence heat maps stored in files (the files needs to be " << "created beforehand).\n" << "The parameters of this programs are names of files with persistence heat maps.\n"; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -46,6 +46,6 @@ int main(int argc, char** argv) { delete maps[i]; } - std::cout << "Average can be found in 'average.mps' file\n"; + std::clog << "Average can be found in 'average.mps' file\n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_heat_maps/compute_distance_of_persistence_heat_maps.cpp b/src/Persistence_representations/utilities/persistence_heat_maps/compute_distance_of_persistence_heat_maps.cpp index 48000bb1..757a97fc 100644 --- a/src/Persistence_representations/utilities/persistence_heat_maps/compute_distance_of_persistence_heat_maps.cpp +++ b/src/Persistence_representations/utilities/persistence_heat_maps/compute_distance_of_persistence_heat_maps.cpp @@ -19,14 +19,14 @@ using constant_scaling_function = Gudhi::Persistence_representations::constant_s using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<constant_scaling_function>; int main(int argc, char** argv) { - std::cout << "This program computes distance of persistence heat maps stored in files (the files needs to be " + std::clog << "This program computes distance of persistence heat maps stored in files (the files needs to be " << "created beforehand).\n" << "The first parameter of a program is an integer p. The program compute L^p distance of the two heat " << "maps. For L^infty distance choose p = -1. \n" << "The remaining parameters of this program are names of files with persistence heat maps.\n"; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -69,14 +69,14 @@ int main(int argc, char** argv) { out.open("distance.mps"); for (size_t i = 0; i != distance.size(); ++i) { for (size_t j = 0; j != distance.size(); ++j) { - std::cout << distance[i][j] << " "; + std::clog << distance[i][j] << " "; out << distance[i][j] << " "; } - std::cout << std::endl; + std::clog << std::endl; out << std::endl; } out.close(); - std::cout << "Distance can be found in 'distance.mps' file\n"; + std::clog << "Distance can be found in 'distance.mps' file\n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_heat_maps/compute_scalar_product_of_persistence_heat_maps.cpp b/src/Persistence_representations/utilities/persistence_heat_maps/compute_scalar_product_of_persistence_heat_maps.cpp index 8a96f1b0..e7f18ce1 100644 --- a/src/Persistence_representations/utilities/persistence_heat_maps/compute_scalar_product_of_persistence_heat_maps.cpp +++ b/src/Persistence_representations/utilities/persistence_heat_maps/compute_scalar_product_of_persistence_heat_maps.cpp @@ -18,12 +18,12 @@ using constant_scaling_function = Gudhi::Persistence_representations::constant_s using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<constant_scaling_function>; int main(int argc, char** argv) { - std::cout << "This program computes scalar product of persistence heat maps stored in a file (the file needs to be " + std::clog << "This program computes scalar product of persistence heat maps stored in a file (the file needs to be " << "created beforehand). \n" << "The parameters of this programs are names of files with persistence heat maps.\n"; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -60,14 +60,14 @@ int main(int argc, char** argv) { out.open("scalar_product.mps"); for (size_t i = 0; i != scalar_product.size(); ++i) { for (size_t j = 0; j != scalar_product.size(); ++j) { - std::cout << scalar_product[i][j] << " "; + std::clog << scalar_product[i][j] << " "; out << scalar_product[i][j] << " "; } - std::cout << std::endl; + std::clog << std::endl; out << std::endl; } out.close(); - std::cout << "Distance can be found in 'scalar_product.mps' file\n"; + std::clog << "Distance can be found in 'scalar_product.mps' file\n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_heat_maps/create_p_h_m_weighted_by_arctan_of_their_persistence.cpp b/src/Persistence_representations/utilities/persistence_heat_maps/create_p_h_m_weighted_by_arctan_of_their_persistence.cpp index f82a39b0..6b38b930 100644 --- a/src/Persistence_representations/utilities/persistence_heat_maps/create_p_h_m_weighted_by_arctan_of_their_persistence.cpp +++ b/src/Persistence_representations/utilities/persistence_heat_maps/create_p_h_m_weighted_by_arctan_of_their_persistence.cpp @@ -20,7 +20,7 @@ using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<arc_tan_of_persistence_of_point>; int main(int argc, char** argv) { - std::cout << "This program creates persistence heat map files (*.mps) of persistence diagrams files (*.pers) " + std::clog << "This program creates persistence heat map files (*.mps) of persistence diagrams files (*.pers) " << "provided as an input.The Gaussian kernels are weighted by the arc tangential of their persistence.\n" << "The first parameter of a program is an integer, a size of a grid.\n" << "The second and third parameters are min and max of the grid. If you want those numbers to be computed " @@ -36,7 +36,7 @@ int main(int argc, char** argv) { << "The remaining parameters are the names of files with persistence diagrams. \n"; if (argc < 7) { - std::cout << "Wrong parameter list, the program will now terminate \n"; + std::clog << "Wrong parameter list, the program will now terminate \n"; return 1; } @@ -58,7 +58,7 @@ int main(int argc, char** argv) { std::vector<std::vector<double> > filter = Gudhi::Persistence_representations::create_Gaussian_filter(stdiv, 1); for (size_t i = 0; i != filenames.size(); ++i) { - std::cout << "Creating a heat map based on a file : " << filenames[i] << std::endl; + std::clog << "Creating a heat map based on a file : " << filenames[i] << std::endl; Persistence_heat_maps l(filenames[i], filter, false, size_of_grid, min_, max_, dimension); std::stringstream ss; diff --git a/src/Persistence_representations/utilities/persistence_heat_maps/create_p_h_m_weighted_by_distance_from_diagonal.cpp b/src/Persistence_representations/utilities/persistence_heat_maps/create_p_h_m_weighted_by_distance_from_diagonal.cpp index 5a657b13..fece2e36 100644 --- a/src/Persistence_representations/utilities/persistence_heat_maps/create_p_h_m_weighted_by_distance_from_diagonal.cpp +++ b/src/Persistence_representations/utilities/persistence_heat_maps/create_p_h_m_weighted_by_distance_from_diagonal.cpp @@ -19,7 +19,7 @@ using distance_from_diagonal_scaling = Gudhi::Persistence_representations::dista using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<distance_from_diagonal_scaling>; int main(int argc, char** argv) { - std::cout << "This program creates persistence heat map files (*.mps) of persistence diagrams files (*.pers) " + std::clog << "This program creates persistence heat map files (*.mps) of persistence diagrams files (*.pers) " << "provided as an input.The Gaussian kernels are weighted by the distance of a center from the " << "diagonal.\n" << "The first parameter of a program is an integer, a size of a grid.\n" @@ -36,7 +36,7 @@ int main(int argc, char** argv) { << "The remaining parameters are the names of files with persistence diagrams. \n"; if (argc < 7) { - std::cout << "Wrong parameter list, the program will now terminate \n"; + std::clog << "Wrong parameter list, the program will now terminate \n"; return 1; } @@ -58,7 +58,7 @@ int main(int argc, char** argv) { std::vector<std::vector<double> > filter = Gudhi::Persistence_representations::create_Gaussian_filter(stdiv, 1); for (size_t i = 0; i != filenames.size(); ++i) { - std::cout << "Creating a heat map based on a file : " << filenames[i] << std::endl; + std::clog << "Creating a heat map based on a file : " << filenames[i] << std::endl; Persistence_heat_maps l(filenames[i], filter, false, size_of_grid, min_, max_, dimension); std::stringstream ss; diff --git a/src/Persistence_representations/utilities/persistence_heat_maps/create_p_h_m_weighted_by_squared_diag_distance.cpp b/src/Persistence_representations/utilities/persistence_heat_maps/create_p_h_m_weighted_by_squared_diag_distance.cpp index 8d67a54d..86e6fc19 100644 --- a/src/Persistence_representations/utilities/persistence_heat_maps/create_p_h_m_weighted_by_squared_diag_distance.cpp +++ b/src/Persistence_representations/utilities/persistence_heat_maps/create_p_h_m_weighted_by_squared_diag_distance.cpp @@ -21,7 +21,7 @@ using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<squared_distance_from_diagonal_scaling>; int main(int argc, char** argv) { - std::cout << "This program creates persistence heat map files (*.mps) of persistence diagrams files (*.pers) " + std::clog << "This program creates persistence heat map files (*.mps) of persistence diagrams files (*.pers) " << "provided as an input.The Gaussian kernels are weighted by the square of distance of a center from the " << "diagonal.\n" << "The first parameter of a program is an integer, a size of a grid.\n" @@ -38,7 +38,7 @@ int main(int argc, char** argv) { << "The remaining parameters are the names of files with persistence diagrams. \n"; if (argc < 7) { - std::cout << "Wrong parameter list, the program will now terminate \n"; + std::clog << "Wrong parameter list, the program will now terminate \n"; return 1; } @@ -60,7 +60,7 @@ int main(int argc, char** argv) { std::vector<std::vector<double> > filter = Gudhi::Persistence_representations::create_Gaussian_filter(stdiv, 1); for (size_t i = 0; i != filenames.size(); ++i) { - std::cout << "Creating a heat map based on a file : " << filenames[i] << std::endl; + std::clog << "Creating a heat map based on a file : " << filenames[i] << std::endl; Persistence_heat_maps l(filenames[i], filter, false, size_of_grid, min_, max_, dimension); std::stringstream ss; diff --git a/src/Persistence_representations/utilities/persistence_heat_maps/create_persistence_heat_maps.cpp b/src/Persistence_representations/utilities/persistence_heat_maps/create_persistence_heat_maps.cpp index 29170c32..ca27f8e3 100644 --- a/src/Persistence_representations/utilities/persistence_heat_maps/create_persistence_heat_maps.cpp +++ b/src/Persistence_representations/utilities/persistence_heat_maps/create_persistence_heat_maps.cpp @@ -19,7 +19,7 @@ using constant_scaling_function = Gudhi::Persistence_representations::constant_s using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<constant_scaling_function>; int main(int argc, char** argv) { - std::cout << "This program creates persistence heat map files (*.mps) of persistence diagrams files (*.pers) " + std::clog << "This program creates persistence heat map files (*.mps) of persistence diagrams files (*.pers) " << "provided as an input.\n" << "The first parameter of a program is an integer, a size of a grid.\n" << "The second and third parameters are min and max of the grid. If you want those numbers to be computed " @@ -35,7 +35,7 @@ int main(int argc, char** argv) { << "The remaining parameters are the names of files with persistence diagrams. \n"; if (argc < 7) { - std::cout << "Wrong parameter list, the program will now terminate \n"; + std::clog << "Wrong parameter list, the program will now terminate \n"; return 1; } size_t size_of_grid = (size_t)atoi(argv[1]); @@ -55,7 +55,7 @@ int main(int argc, char** argv) { std::vector<std::vector<double> > filter = Gudhi::Persistence_representations::create_Gaussian_filter(stdiv, 1); for (size_t i = 0; i != filenames.size(); ++i) { - std::cout << "Creating a heat map based on file : " << filenames[i] << std::endl; + std::clog << "Creating a heat map based on file : " << filenames[i] << std::endl; Persistence_heat_maps l(filenames[i], filter, false, size_of_grid, min_, max_, dimension); std::stringstream ss; diff --git a/src/Persistence_representations/utilities/persistence_heat_maps/create_pssk.cpp b/src/Persistence_representations/utilities/persistence_heat_maps/create_pssk.cpp index 995771b9..d2ebcc7e 100644 --- a/src/Persistence_representations/utilities/persistence_heat_maps/create_pssk.cpp +++ b/src/Persistence_representations/utilities/persistence_heat_maps/create_pssk.cpp @@ -18,7 +18,7 @@ using PSSK = Gudhi::Persistence_representations::PSSK; int main(int argc, char** argv) { - std::cout << "This program creates PSSK files (*.pssk) of persistence diagrams files (*.pers) " + std::clog << "This program creates PSSK files (*.pssk) of persistence diagrams files (*.pers) " << "provided as an input.\n" << "The first parameter of a program is an integer, a size of a grid.\n" << "The second and third parameters are min and max of the grid. If you want those numbers to be computed " @@ -34,7 +34,7 @@ int main(int argc, char** argv) { << "The remaining parameters are the names of files with persistence diagrams. \n"; if (argc < 7) { - std::cout << "Wrong parameter list, the program will now terminate \n"; + std::clog << "Wrong parameter list, the program will now terminate \n"; return 1; } @@ -56,7 +56,7 @@ int main(int argc, char** argv) { std::vector<std::vector<double> > filter = Gudhi::Persistence_representations::create_Gaussian_filter(stdiv, 1); for (size_t i = 0; i != filenames.size(); ++i) { - std::cout << "Creating a PSSK based on a file : " << filenames[i] << std::endl; + std::clog << "Creating a PSSK based on a file : " << filenames[i] << std::endl; PSSK l(filenames[i], filter, size_of_grid, min_, max_, dimension); std::stringstream ss; diff --git a/src/Persistence_representations/utilities/persistence_heat_maps/plot_persistence_heat_map.cpp b/src/Persistence_representations/utilities/persistence_heat_maps/plot_persistence_heat_map.cpp index cf6e07cb..87cc97d1 100644 --- a/src/Persistence_representations/utilities/persistence_heat_maps/plot_persistence_heat_map.cpp +++ b/src/Persistence_representations/utilities/persistence_heat_maps/plot_persistence_heat_map.cpp @@ -17,10 +17,10 @@ using constant_scaling_function = Gudhi::Persistence_representations::constant_s using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<constant_scaling_function>; int main(int argc, char** argv) { - std::cout << "This program creates a gnuplot script from a persistence heat maps stored in a file (the file needs " + std::clog << "This program creates a gnuplot script from a persistence heat maps stored in a file (the file needs " << "to be created beforehand). Please call the code with the name of a single heat maps file \n"; if (argc != 2) { - std::cout << "Wrong parameter list, the program will now terminate \n"; + std::clog << "Wrong parameter list, the program will now terminate \n"; return 1; } Persistence_heat_maps l; diff --git a/src/Persistence_representations/utilities/persistence_intervals/CMakeLists.txt b/src/Persistence_representations/utilities/persistence_intervals/CMakeLists.txt index a025183e..118c1e9b 100644 --- a/src/Persistence_representations/utilities/persistence_intervals/CMakeLists.txt +++ b/src/Persistence_representations/utilities/persistence_intervals/CMakeLists.txt @@ -3,17 +3,16 @@ project(Persistence_representations_intervals_utilities) add_executable ( plot_histogram_of_intervals_lengths plot_histogram_of_intervals_lengths.cpp ) -add_test(NAME plot_histogram_of_intervals_lengths COMMAND $<TARGET_FILE:plot_histogram_of_intervals_lengths> +add_test(NAME Persistence_representation_utilities_plot_histogram_of_intervals_lengths COMMAND $<TARGET_FILE:plot_histogram_of_intervals_lengths> "${CMAKE_CURRENT_BINARY_DIR}/../first.pers" "-1") install(TARGETS plot_histogram_of_intervals_lengths DESTINATION bin) -add_persistence_representation_plot_utility(plot_persistence_intervals "") -add_persistence_representation_plot_utility(plot_persistence_Betti_numbers "") +add_persistence_representation_plot_utility(plot_histogram_of_intervals_lengths plot_persistence_intervals "") +add_persistence_representation_plot_utility(plot_histogram_of_intervals_lengths plot_persistence_Betti_numbers "") add_persistence_representation_creation_utility(compute_birth_death_range_in_persistence_diagram "-1") - add_executable ( compute_number_of_dominant_intervals compute_number_of_dominant_intervals.cpp ) add_test(NAME Persistence_representation_utilities_compute_number_of_dominant_intervals COMMAND $<TARGET_FILE:compute_number_of_dominant_intervals> diff --git a/src/Persistence_representations/utilities/persistence_intervals/compute_birth_death_range_in_persistence_diagram.cpp b/src/Persistence_representations/utilities/persistence_intervals/compute_birth_death_range_in_persistence_diagram.cpp index 519cc47d..72325cad 100644 --- a/src/Persistence_representations/utilities/persistence_intervals/compute_birth_death_range_in_persistence_diagram.cpp +++ b/src/Persistence_representations/utilities/persistence_intervals/compute_birth_death_range_in_persistence_diagram.cpp @@ -18,7 +18,7 @@ using Persistence_intervals = Gudhi::Persistence_representations::Persistence_intervals; int main(int argc, char** argv) { - std::cout << "This program computes the range of birth and death times of persistence pairs in diagrams provided as " + std::clog << "This program computes the range of birth and death times of persistence pairs in diagrams provided as " << "an input.\n" << "The first parameter is the dimension of persistence to be used to create persistence intervals. " << "If your file contains the information about dimension of persistence pairs, please provide here the " @@ -27,7 +27,7 @@ int main(int argc, char** argv) { << "The remaining parameters of the program are the names of files with persistence diagrams.\n"; if (argc < 3) { - std::cout << "Wrong parameter list, the program will now terminate \n"; + std::clog << "Wrong parameter list, the program will now terminate \n"; return 1; } @@ -45,12 +45,12 @@ int main(int argc, char** argv) { double max_ = -std::numeric_limits<double>::max(); for (size_t file_no = 0; file_no != filenames.size(); ++file_no) { - std::cout << "Creating diagram based on a file : " << filenames[file_no] << std::endl; + std::clog << "Creating diagram based on a file : " << filenames[file_no] << std::endl; Persistence_intervals p(filenames[file_no], dimension); std::pair<double, double> min_max_ = p.get_x_range(); if (min_max_.first < min_) min_ = min_max_.first; if (min_max_.second > max_) max_ = min_max_.second; } - std::cout << "Birth-death range : min: " << min_ << ", max: " << max_ << std::endl; + std::clog << "Birth-death range : min: " << min_ << ", max: " << max_ << std::endl; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_intervals/compute_bottleneck_distance.cpp b/src/Persistence_representations/utilities/persistence_intervals/compute_bottleneck_distance.cpp index 6155727a..465bf72e 100644 --- a/src/Persistence_representations/utilities/persistence_intervals/compute_bottleneck_distance.cpp +++ b/src/Persistence_representations/utilities/persistence_intervals/compute_bottleneck_distance.cpp @@ -18,7 +18,7 @@ using Persistence_intervals_with_distances = Gudhi::Persistence_representations::Persistence_intervals_with_distances; int main(int argc, char** argv) { - std::cout << "This program computes the bottleneck distance of persistence pairs in diagrams provided as " + std::clog << "This program computes the bottleneck distance of persistence pairs in diagrams provided as " << "an input.\n" << "The first parameter is the dimension of persistence to be used to create persistence intervals. " << "If your file contains the information about dimension of persistence pairs, please provide here the " @@ -27,7 +27,7 @@ int main(int argc, char** argv) { << "The remaining parameters of the program are the names of files with persistence diagrams.\n"; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -70,14 +70,14 @@ int main(int argc, char** argv) { out.open("distance.itv"); for (size_t i = 0; i != distance.size(); ++i) { for (size_t j = 0; j != distance.size(); ++j) { - std::cout << distance[i][j] << " "; + std::clog << distance[i][j] << " "; out << distance[i][j] << " "; } - std::cout << std::endl; + std::clog << std::endl; out << std::endl; } out.close(); - std::cout << "Distance can be found in 'distance.itv' file\n"; + std::clog << "Distance can be found in 'distance.itv' file\n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_intervals/compute_number_of_dominant_intervals.cpp b/src/Persistence_representations/utilities/persistence_intervals/compute_number_of_dominant_intervals.cpp index dd6e1a5b..ea1fe717 100644 --- a/src/Persistence_representations/utilities/persistence_intervals/compute_number_of_dominant_intervals.cpp +++ b/src/Persistence_representations/utilities/persistence_intervals/compute_number_of_dominant_intervals.cpp @@ -18,10 +18,10 @@ using Persistence_intervals = Gudhi::Persistence_representations::Persistence_intervals; int main(int argc, char** argv) { - std::cout << "This program compute the dominant intervals. A number of intervals to be displayed is a parameter of " + std::clog << "This program compute the dominant intervals. A number of intervals to be displayed is a parameter of " "this program. \n"; if (argc != 4) { - std::cout << "To run this program, please provide the name of a file with persistence diagram, dimension of " + std::clog << "To run this program, please provide the name of a file with persistence diagram, dimension of " "intervals that should be taken into account (if your file contains only persistence pairs in a " "single dimension, set it up to -1) and number of dominant intervals you would like to get \n"; return 1; @@ -33,9 +33,9 @@ int main(int argc, char** argv) { } Persistence_intervals p(argv[1], dimension); std::vector<std::pair<double, double> > dominant_intervals = p.dominant_intervals(atoi(argv[3])); - std::cout << "Here are the dominant intervals : " << std::endl; + std::clog << "Here are the dominant intervals : " << std::endl; for (size_t i = 0; i != dominant_intervals.size(); ++i) { - std::cout << " " << dominant_intervals[i].first << "," << dominant_intervals[i].second << " " << std::endl; + std::clog << " " << dominant_intervals[i].first << "," << dominant_intervals[i].second << " " << std::endl; } return 0; diff --git a/src/Persistence_representations/utilities/persistence_intervals/plot_histogram_of_intervals_lengths.cpp b/src/Persistence_representations/utilities/persistence_intervals/plot_histogram_of_intervals_lengths.cpp index 13d2133f..e5eec3f5 100644 --- a/src/Persistence_representations/utilities/persistence_intervals/plot_histogram_of_intervals_lengths.cpp +++ b/src/Persistence_representations/utilities/persistence_intervals/plot_histogram_of_intervals_lengths.cpp @@ -18,10 +18,10 @@ using Persistence_intervals = Gudhi::Persistence_representations::Persistence_intervals; int main(int argc, char** argv) { - std::cout << "This program computes a histogram of barcode's length. A number of bins in the histogram is a " + std::clog << "This program computes a histogram of barcode's length. A number of bins in the histogram is a " << "parameter of this program. \n"; if ((argc != 3) && (argc != 4)) { - std::cout << "To run this program, please provide the name of a file with persistence diagram and number of " + std::clog << "To run this program, please provide the name of a file with persistence diagram and number of " << "dominant intervals you would like to get. Set a negative number dominant intervals value " << "If your file contains only birth-death pairs.\n" << "The third parameter is the dimension of the persistence that is to be used. If your " @@ -59,7 +59,7 @@ int main(int argc, char** argv) { out << std::endl; out.close(); - std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + std::clog << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" << gnuplot_script.str().c_str() << "\'\"" << std::endl; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_intervals/plot_persistence_Betti_numbers.cpp b/src/Persistence_representations/utilities/persistence_intervals/plot_persistence_Betti_numbers.cpp index 451be77f..27c69e07 100644 --- a/src/Persistence_representations/utilities/persistence_intervals/plot_persistence_Betti_numbers.cpp +++ b/src/Persistence_representations/utilities/persistence_intervals/plot_persistence_Betti_numbers.cpp @@ -19,7 +19,7 @@ using Persistence_intervals = Gudhi::Persistence_representations::Persistence_in int main(int argc, char** argv) { if ((argc != 3) && (argc != 2)) { - std::cout << "This program creates a gnuplot script of Betti numbers from a single persistence diagram file" + std::clog << "This program creates a gnuplot script of Betti numbers from a single persistence diagram file" << "(*.pers).\n" << "To run this program, please provide the name of a file with persistence diagram.\n" << "The second optional parameter of a program is the dimension of the persistence that is to be used. " @@ -68,7 +68,7 @@ int main(int argc, char** argv) { out << std::endl; out.close(); - std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + std::clog << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" << gnuplot_script.str().c_str() << "\'\"" << std::endl; return 0; diff --git a/src/Persistence_representations/utilities/persistence_intervals/plot_persistence_intervals.cpp b/src/Persistence_representations/utilities/persistence_intervals/plot_persistence_intervals.cpp index 09a56869..199a3266 100644 --- a/src/Persistence_representations/utilities/persistence_intervals/plot_persistence_intervals.cpp +++ b/src/Persistence_representations/utilities/persistence_intervals/plot_persistence_intervals.cpp @@ -19,7 +19,7 @@ using Persistence_intervals = Gudhi::Persistence_representations::Persistence_in int main(int argc, char** argv) { if ((argc != 3) && (argc != 2)) { - std::cout << "This program creates a gnuplot script from a single persistence diagram file (*.pers).\n" + std::clog << "This program creates a gnuplot script from a single persistence diagram file (*.pers).\n" << "To run this program, please provide the name of a file with persistence diagram.\n" << "The second optional parameter of a program is the dimension of the persistence that is to be used. " << "If your file contains only birth-death pairs, you can skip this parameter.\n"; diff --git a/src/Persistence_representations/utilities/persistence_landscapes/CMakeLists.txt b/src/Persistence_representations/utilities/persistence_landscapes/CMakeLists.txt index 6b24d032..4df84d36 100644 --- a/src/Persistence_representations/utilities/persistence_landscapes/CMakeLists.txt +++ b/src/Persistence_representations/utilities/persistence_landscapes/CMakeLists.txt @@ -2,8 +2,8 @@ project(Persistence_representations_landscapes_utilities) add_persistence_representation_creation_utility(create_landscapes "-1") -add_persistence_representation_plot_utility(plot_landscapes ".land") +add_persistence_representation_plot_utility(create_landscapes plot_landscapes ".land") -add_persistence_representation_function_utility(average_landscapes ".land") -add_persistence_representation_function_utility(compute_distance_of_landscapes ".land" "1") -add_persistence_representation_function_utility(compute_scalar_product_of_landscapes ".land") +add_persistence_representation_function_utility(create_landscapes average_landscapes ".land") +add_persistence_representation_function_utility(create_landscapes compute_distance_of_landscapes ".land" "1") +add_persistence_representation_function_utility(create_landscapes compute_scalar_product_of_landscapes ".land") diff --git a/src/Persistence_representations/utilities/persistence_landscapes/average_landscapes.cpp b/src/Persistence_representations/utilities/persistence_landscapes/average_landscapes.cpp index 04a0ada4..612e9700 100644 --- a/src/Persistence_representations/utilities/persistence_landscapes/average_landscapes.cpp +++ b/src/Persistence_representations/utilities/persistence_landscapes/average_landscapes.cpp @@ -16,13 +16,13 @@ using Persistence_landscape = Gudhi::Persistence_representations::Persistence_landscape; int main(int argc, char** argv) { - std::cout << "This program computes average of persistence landscapes stored in files (the files needs to be " + std::clog << "This program computes average of persistence landscapes stored in files (the files needs to be " << "created beforehand).\n" << "The parameters of this programs are names of files with persistence landscapes.\n"; std::vector<const char*> filenames; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -46,6 +46,6 @@ int main(int argc, char** argv) { delete lands[i]; } - std::cout << "Average can be found in 'average.land' file\n"; + std::clog << "Average can be found in 'average.land' file\n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_landscapes/compute_distance_of_landscapes.cpp b/src/Persistence_representations/utilities/persistence_landscapes/compute_distance_of_landscapes.cpp index 1093c1aa..2246a37d 100644 --- a/src/Persistence_representations/utilities/persistence_landscapes/compute_distance_of_landscapes.cpp +++ b/src/Persistence_representations/utilities/persistence_landscapes/compute_distance_of_landscapes.cpp @@ -18,14 +18,14 @@ using Persistence_landscape = Gudhi::Persistence_representations::Persistence_landscape; int main(int argc, char** argv) { - std::cout << "This program computes distance of persistence landscapes stored in files (the files needs to be " + std::clog << "This program computes distance of persistence landscapes stored in files (the files needs to be " << "created beforehand).\n" << "The first parameter of a program is an integer p. The program compute L^p distance of the two heat " << "maps. For L^infty distance choose p = -1. \n" << "The remaining parameters of this program are names of files with persistence landscapes.\n"; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -68,14 +68,14 @@ int main(int argc, char** argv) { out.open("distance.land"); for (size_t i = 0; i != distance.size(); ++i) { for (size_t j = 0; j != distance.size(); ++j) { - std::cout << distance[i][j] << " "; + std::clog << distance[i][j] << " "; out << distance[i][j] << " "; } - std::cout << std::endl; + std::clog << std::endl; out << std::endl; } out.close(); - std::cout << "Distance can be found in 'distance.land' file\n"; + std::clog << "Distance can be found in 'distance.land' file\n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_landscapes/compute_scalar_product_of_landscapes.cpp b/src/Persistence_representations/utilities/persistence_landscapes/compute_scalar_product_of_landscapes.cpp index 16b76497..44f50543 100644 --- a/src/Persistence_representations/utilities/persistence_landscapes/compute_scalar_product_of_landscapes.cpp +++ b/src/Persistence_representations/utilities/persistence_landscapes/compute_scalar_product_of_landscapes.cpp @@ -17,12 +17,12 @@ using Persistence_landscape = Gudhi::Persistence_representations::Persistence_landscape; int main(int argc, char** argv) { - std::cout << "This program computes scalar product of persistence landscapes stored in a file (the file needs to be " + std::clog << "This program computes scalar product of persistence landscapes stored in a file (the file needs to be " << "created beforehand). \n" << "The parameters of this programs are names of files with persistence landscapes.\n"; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -59,14 +59,14 @@ int main(int argc, char** argv) { out.open("scalar_product.land"); for (size_t i = 0; i != scalar_product.size(); ++i) { for (size_t j = 0; j != scalar_product.size(); ++j) { - std::cout << scalar_product[i][j] << " "; + std::clog << scalar_product[i][j] << " "; out << scalar_product[i][j] << " "; } - std::cout << std::endl; + std::clog << std::endl; out << std::endl; } out.close(); - std::cout << "Distance can be found in 'scalar_product.land' file\n"; + std::clog << "Distance can be found in 'scalar_product.land' file\n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_landscapes/create_landscapes.cpp b/src/Persistence_representations/utilities/persistence_landscapes/create_landscapes.cpp index 4d772086..fab5c75f 100644 --- a/src/Persistence_representations/utilities/persistence_landscapes/create_landscapes.cpp +++ b/src/Persistence_representations/utilities/persistence_landscapes/create_landscapes.cpp @@ -18,7 +18,7 @@ using Persistence_landscape = Gudhi::Persistence_representations::Persistence_landscape; int main(int argc, char** argv) { - std::cout << "This program creates persistence landscapes files (*.land) of persistence diagrams files (*.pers) " + std::clog << "This program creates persistence landscapes files (*.land) of persistence diagrams files (*.pers) " << "provided as an input.\n" << "The first parameter of this program is a dimension of persistence that will be used in creation of " << "the persistence heat maps." @@ -29,7 +29,7 @@ int main(int argc, char** argv) { << "The remaining parameters are the names of files with persistence diagrams. \n"; if (argc < 3) { - std::cout << "Wrong parameter list, the program will now terminate \n"; + std::clog << "Wrong parameter list, the program will now terminate \n"; return 1; } std::vector<const char*> filenames; @@ -43,7 +43,7 @@ int main(int argc, char** argv) { } for (size_t i = 0; i != filenames.size(); ++i) { - std::cout << "Creating a landscape based on file : " << filenames[i] << std::endl; + std::clog << "Creating a landscape based on file : " << filenames[i] << std::endl; Persistence_landscape l(filenames[i], dimension); std::stringstream ss; ss << filenames[i] << ".land"; diff --git a/src/Persistence_representations/utilities/persistence_landscapes/plot_landscapes.cpp b/src/Persistence_representations/utilities/persistence_landscapes/plot_landscapes.cpp index 1fe03640..da9b9bba 100644 --- a/src/Persistence_representations/utilities/persistence_landscapes/plot_landscapes.cpp +++ b/src/Persistence_representations/utilities/persistence_landscapes/plot_landscapes.cpp @@ -16,10 +16,10 @@ using Persistence_landscape = Gudhi::Persistence_representations::Persistence_landscape; int main(int argc, char** argv) { - std::cout << "This program creates a gnuplot script from a persistence landscape stored in a file (the file needs " + std::clog << "This program creates a gnuplot script from a persistence landscape stored in a file (the file needs " << "to be created beforehand). Please call the code with the name of a single landscape file.\n"; if (argc != 2) { - std::cout << "Wrong parameter list, the program will now terminate \n"; + std::clog << "Wrong parameter list, the program will now terminate \n"; return 1; } diff --git a/src/Persistence_representations/utilities/persistence_landscapes_on_grid/CMakeLists.txt b/src/Persistence_representations/utilities/persistence_landscapes_on_grid/CMakeLists.txt index 36f3196b..8cd965f1 100644 --- a/src/Persistence_representations/utilities/persistence_landscapes_on_grid/CMakeLists.txt +++ b/src/Persistence_representations/utilities/persistence_landscapes_on_grid/CMakeLists.txt @@ -3,8 +3,8 @@ project(Persistence_representations_lanscapes_on_grid_utilities) # Need to set grid min and max for further average, distance and scalar_product add_persistence_representation_creation_utility(create_landscapes_on_grid "100" "0" "35" "-1") -add_persistence_representation_plot_utility(plot_landscapes_on_grid ".g_land") +add_persistence_representation_plot_utility(create_landscapes_on_grid plot_landscapes_on_grid ".g_land") -add_persistence_representation_function_utility(average_landscapes_on_grid ".g_land") -add_persistence_representation_function_utility(compute_distance_of_landscapes_on_grid ".g_land" "1") -add_persistence_representation_function_utility(compute_scalar_product_of_landscapes_on_grid ".g_land") +add_persistence_representation_function_utility(create_landscapes_on_grid average_landscapes_on_grid ".g_land") +add_persistence_representation_function_utility(create_landscapes_on_grid compute_distance_of_landscapes_on_grid ".g_land" "1") +add_persistence_representation_function_utility(create_landscapes_on_grid compute_scalar_product_of_landscapes_on_grid ".g_land") diff --git a/src/Persistence_representations/utilities/persistence_landscapes_on_grid/average_landscapes_on_grid.cpp b/src/Persistence_representations/utilities/persistence_landscapes_on_grid/average_landscapes_on_grid.cpp index f92cde72..39f7a67f 100644 --- a/src/Persistence_representations/utilities/persistence_landscapes_on_grid/average_landscapes_on_grid.cpp +++ b/src/Persistence_representations/utilities/persistence_landscapes_on_grid/average_landscapes_on_grid.cpp @@ -16,12 +16,12 @@ using Persistence_landscape_on_grid = Gudhi::Persistence_representations::Persistence_landscape_on_grid; int main(int argc, char** argv) { - std::cout << "This program computes average of persistence landscapes on grid stored in files (the files needs to " + std::clog << "This program computes average of persistence landscapes on grid stored in files (the files needs to " << "be created beforehand).\n" << "The parameters of this programs are names of files with persistence landscapes on grid.\n"; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -46,6 +46,6 @@ int main(int argc, char** argv) { delete lands[i]; } - std::cout << "Average can be found in 'average.g_land' file\n"; + std::clog << "Average can be found in 'average.g_land' file\n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_landscapes_on_grid/compute_distance_of_landscapes_on_grid.cpp b/src/Persistence_representations/utilities/persistence_landscapes_on_grid/compute_distance_of_landscapes_on_grid.cpp index baec6aeb..01fd09d8 100644 --- a/src/Persistence_representations/utilities/persistence_landscapes_on_grid/compute_distance_of_landscapes_on_grid.cpp +++ b/src/Persistence_representations/utilities/persistence_landscapes_on_grid/compute_distance_of_landscapes_on_grid.cpp @@ -18,14 +18,14 @@ using Persistence_landscape_on_grid = Gudhi::Persistence_representations::Persistence_landscape_on_grid; int main(int argc, char** argv) { - std::cout << "This program computes distance of persistence landscapes on grid stored in files (the files needs to " + std::clog << "This program computes distance of persistence landscapes on grid stored in files (the files needs to " << "be created beforehand).\n" << "The first parameter of a program is an integer p. The program compute L^p distance of the two heat " << "maps. For L^infty distance choose p = -1. \n" << "The remaining parameters of this program are names of files with persistence landscapes on grid.\n"; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -68,14 +68,14 @@ int main(int argc, char** argv) { out.open("distance.g_land"); for (size_t i = 0; i != distance.size(); ++i) { for (size_t j = 0; j != distance.size(); ++j) { - std::cout << distance[i][j] << " "; + std::clog << distance[i][j] << " "; out << distance[i][j] << " "; } - std::cout << std::endl; + std::clog << std::endl; out << std::endl; } out.close(); - std::cout << "Distance can be found in 'distance.g_land' file\n"; + std::clog << "Distance can be found in 'distance.g_land' file\n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_landscapes_on_grid/compute_scalar_product_of_landscapes_on_grid.cpp b/src/Persistence_representations/utilities/persistence_landscapes_on_grid/compute_scalar_product_of_landscapes_on_grid.cpp index e94dacdb..71c2f419 100644 --- a/src/Persistence_representations/utilities/persistence_landscapes_on_grid/compute_scalar_product_of_landscapes_on_grid.cpp +++ b/src/Persistence_representations/utilities/persistence_landscapes_on_grid/compute_scalar_product_of_landscapes_on_grid.cpp @@ -17,13 +17,13 @@ using Persistence_landscape_on_grid = Gudhi::Persistence_representations::Persistence_landscape_on_grid; int main(int argc, char** argv) { - std::cout + std::clog << "This program computes scalar product of persistence landscapes on grid stored in a file (the file needs to " << "be created beforehand). \n" << "The parameters of this programs are names of files with persistence landscapes on grid.\n"; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -60,14 +60,14 @@ int main(int argc, char** argv) { out.open("scalar_product.g_land"); for (size_t i = 0; i != scalar_product.size(); ++i) { for (size_t j = 0; j != scalar_product.size(); ++j) { - std::cout << scalar_product[i][j] << " "; + std::clog << scalar_product[i][j] << " "; out << scalar_product[i][j] << " "; } - std::cout << std::endl; + std::clog << std::endl; out << std::endl; } out.close(); - std::cout << "Distance can be found in 'scalar_product.g_land' file\n"; + std::clog << "Distance can be found in 'scalar_product.g_land' file\n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_landscapes_on_grid/create_landscapes_on_grid.cpp b/src/Persistence_representations/utilities/persistence_landscapes_on_grid/create_landscapes_on_grid.cpp index d510c3df..788313c4 100644 --- a/src/Persistence_representations/utilities/persistence_landscapes_on_grid/create_landscapes_on_grid.cpp +++ b/src/Persistence_representations/utilities/persistence_landscapes_on_grid/create_landscapes_on_grid.cpp @@ -18,7 +18,7 @@ using Persistence_landscape_on_grid = Gudhi::Persistence_representations::Persistence_landscape_on_grid; int main(int argc, char** argv) { - std::cout << "This program creates persistence landscapes on grid files (*.g_land) of persistence diagrams files " + std::clog << "This program creates persistence landscapes on grid files (*.g_land) of persistence diagrams files " << "(*.pers) provided as an input.\n" << "The first parameter of a program is an integer, a size of a grid.\n" << "The second and third parameters are min and max of the grid. If you want those numbers to be computed " @@ -32,7 +32,7 @@ int main(int argc, char** argv) { << "The remaining parameters are the names of files with persistence diagrams. \n"; if (argc < 6) { - std::cout << "Wrong parameter list, the program will now terminate \n"; + std::clog << "Wrong parameter list, the program will now terminate \n"; return 1; } @@ -51,7 +51,7 @@ int main(int argc, char** argv) { } for (size_t i = 0; i != filenames.size(); ++i) { - std::cout << "Creating persistence landscape on a grid based on a file : " << filenames[i] << std::endl; + std::clog << "Creating persistence landscape on a grid based on a file : " << filenames[i] << std::endl; Persistence_landscape_on_grid l; if ((min_ != -1) || (max_ != -1)) { l = Persistence_landscape_on_grid(filenames[i], min_, max_, size_of_grid, dimension); diff --git a/src/Persistence_representations/utilities/persistence_landscapes_on_grid/plot_landscapes_on_grid.cpp b/src/Persistence_representations/utilities/persistence_landscapes_on_grid/plot_landscapes_on_grid.cpp index 4e20f37f..ec6112b5 100644 --- a/src/Persistence_representations/utilities/persistence_landscapes_on_grid/plot_landscapes_on_grid.cpp +++ b/src/Persistence_representations/utilities/persistence_landscapes_on_grid/plot_landscapes_on_grid.cpp @@ -16,11 +16,11 @@ using Persistence_landscape_on_grid = Gudhi::Persistence_representations::Persistence_landscape_on_grid; int main(int argc, char** argv) { - std::cout << "This program creates a gnuplot script from a persistence landscape on grid stored in a file (the file " + std::clog << "This program creates a gnuplot script from a persistence landscape on grid stored in a file (the file " << "needs to be created beforehand). Please call the code with the name of a single landscape on grid file" << ".\n"; if (argc != 2) { - std::cout << "Wrong parameter list, the program will now terminate \n"; + std::clog << "Wrong parameter list, the program will now terminate \n"; return 1; } diff --git a/src/Persistence_representations/utilities/persistence_vectors/CMakeLists.txt b/src/Persistence_representations/utilities/persistence_vectors/CMakeLists.txt index bc982094..5b22ca84 100644 --- a/src/Persistence_representations/utilities/persistence_vectors/CMakeLists.txt +++ b/src/Persistence_representations/utilities/persistence_vectors/CMakeLists.txt @@ -2,8 +2,8 @@ project(Persistence_vectors_utilities) add_persistence_representation_creation_utility(create_persistence_vectors "-1") -add_persistence_representation_plot_utility(plot_persistence_vectors ".vect") +add_persistence_representation_plot_utility(create_persistence_vectors plot_persistence_vectors ".vect") -add_persistence_representation_function_utility(average_persistence_vectors ".vect") -add_persistence_representation_function_utility(compute_distance_of_persistence_vectors ".vect" "1") -add_persistence_representation_function_utility(compute_scalar_product_of_persistence_vectors ".vect") +add_persistence_representation_function_utility(create_persistence_vectors average_persistence_vectors ".vect") +add_persistence_representation_function_utility(create_persistence_vectors compute_distance_of_persistence_vectors ".vect" "1") +add_persistence_representation_function_utility(create_persistence_vectors compute_scalar_product_of_persistence_vectors ".vect") diff --git a/src/Persistence_representations/utilities/persistence_vectors/average_persistence_vectors.cpp b/src/Persistence_representations/utilities/persistence_vectors/average_persistence_vectors.cpp index 89e42f0f..4eb32eb3 100644 --- a/src/Persistence_representations/utilities/persistence_vectors/average_persistence_vectors.cpp +++ b/src/Persistence_representations/utilities/persistence_vectors/average_persistence_vectors.cpp @@ -17,12 +17,12 @@ using Euclidean_distance = Gudhi::Euclidean_distance; using Vector_distances_in_diagram = Gudhi::Persistence_representations::Vector_distances_in_diagram<Euclidean_distance>; int main(int argc, char** argv) { - std::cout << "This program computes average of persistence vectors stored in files (the files needs to " + std::clog << "This program computes average of persistence vectors stored in files (the files needs to " << "be created beforehand).\n" << "The parameters of this programs are names of files with persistence vectors.\n"; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -47,7 +47,7 @@ int main(int argc, char** argv) { delete lands[i]; } - std::cout << "Done \n"; + std::clog << "Done \n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_vectors/compute_distance_of_persistence_vectors.cpp b/src/Persistence_representations/utilities/persistence_vectors/compute_distance_of_persistence_vectors.cpp index 541dd25f..236981a3 100644 --- a/src/Persistence_representations/utilities/persistence_vectors/compute_distance_of_persistence_vectors.cpp +++ b/src/Persistence_representations/utilities/persistence_vectors/compute_distance_of_persistence_vectors.cpp @@ -19,14 +19,14 @@ using Euclidean_distance = Gudhi::Euclidean_distance; using Vector_distances_in_diagram = Gudhi::Persistence_representations::Vector_distances_in_diagram<Euclidean_distance>; int main(int argc, char** argv) { - std::cout << "This program compute distance of persistence vectors stored in a file (the file needs to be created " + std::clog << "This program compute distance of persistence vectors stored in a file (the file needs to be created " "beforehand). \n"; - std::cout << "The first parameter of a program is an integer p. The program compute l^p distance of the vectors. For " + std::clog << "The first parameter of a program is an integer p. The program compute l^p distance of the vectors. For " "l^infty distance choose p = -1. \n"; - std::cout << "The remaining parameters of this programs are names of files with persistence vectors.\n"; + std::clog << "The remaining parameters of this programs are names of files with persistence vectors.\n"; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -69,14 +69,14 @@ int main(int argc, char** argv) { out.open("distance.vect"); for (size_t i = 0; i != distance.size(); ++i) { for (size_t j = 0; j != distance.size(); ++j) { - std::cout << distance[i][j] << " "; + std::clog << distance[i][j] << " "; out << distance[i][j] << " "; } - std::cout << std::endl; + std::clog << std::endl; out << std::endl; } out.close(); - std::cout << "Distance can be found in 'distance.vect' file\n"; + std::clog << "Distance can be found in 'distance.vect' file\n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_vectors/compute_scalar_product_of_persistence_vectors.cpp b/src/Persistence_representations/utilities/persistence_vectors/compute_scalar_product_of_persistence_vectors.cpp index bbc50c98..c6ea0e1c 100644 --- a/src/Persistence_representations/utilities/persistence_vectors/compute_scalar_product_of_persistence_vectors.cpp +++ b/src/Persistence_representations/utilities/persistence_vectors/compute_scalar_product_of_persistence_vectors.cpp @@ -19,12 +19,12 @@ using Euclidean_distance = Gudhi::Euclidean_distance; using Vector_distances_in_diagram = Gudhi::Persistence_representations::Vector_distances_in_diagram<Euclidean_distance>; int main(int argc, char** argv) { - std::cout << "This program computes scalar product of persistence vectors stored in a file (the file needs to " + std::clog << "This program computes scalar product of persistence vectors stored in a file (the file needs to " << "be created beforehand). \n" << "The parameters of this programs are names of files with persistence vectors.\n"; if (argc < 3) { - std::cout << "Wrong number of parameters, the program will now terminate \n"; + std::clog << "Wrong number of parameters, the program will now terminate \n"; return 1; } @@ -61,14 +61,14 @@ int main(int argc, char** argv) { out.open("scalar_product.vect"); for (size_t i = 0; i != scalar_product.size(); ++i) { for (size_t j = 0; j != scalar_product.size(); ++j) { - std::cout << scalar_product[i][j] << " "; + std::clog << scalar_product[i][j] << " "; out << scalar_product[i][j] << " "; } - std::cout << std::endl; + std::clog << std::endl; out << std::endl; } out.close(); - std::cout << "Distance can be found in 'scalar_product.vect' file\n"; + std::clog << "Distance can be found in 'scalar_product.vect' file\n"; return 0; } diff --git a/src/Persistence_representations/utilities/persistence_vectors/create_persistence_vectors.cpp b/src/Persistence_representations/utilities/persistence_vectors/create_persistence_vectors.cpp index f974c3d3..608e04e5 100644 --- a/src/Persistence_representations/utilities/persistence_vectors/create_persistence_vectors.cpp +++ b/src/Persistence_representations/utilities/persistence_vectors/create_persistence_vectors.cpp @@ -19,7 +19,7 @@ using Euclidean_distance = Gudhi::Euclidean_distance; using Vector_distances_in_diagram = Gudhi::Persistence_representations::Vector_distances_in_diagram<Euclidean_distance>; int main(int argc, char** argv) { - std::cout << "This program creates persistence vectors files (*.vect) of persistence diagrams files (*.pers) " + std::clog << "This program creates persistence vectors files (*.vect) of persistence diagrams files (*.pers) " << "provided as an input.\n" << "The first parameter of this program is a dimension of persistence that will be used in creation of " << "the persistence heat maps." @@ -30,11 +30,11 @@ int main(int argc, char** argv) { << "The remaining parameters are the names of files with persistence diagrams. \n"; if (argc < 3) { - std::cout << "Wrong parameter list, the program will now terminate \n"; + std::clog << "Wrong parameter list, the program will now terminate \n"; return 1; } - std::cout << "The remaining parameters are the names of files with persistence diagrams. \n"; + std::clog << "The remaining parameters are the names of files with persistence diagrams. \n"; int dim = atoi(argv[1]); unsigned dimension = std::numeric_limits<unsigned>::max(); if (dim >= 0) { diff --git a/src/Persistence_representations/utilities/persistence_vectors/plot_persistence_vectors.cpp b/src/Persistence_representations/utilities/persistence_vectors/plot_persistence_vectors.cpp index de08fcfe..2decb134 100644 --- a/src/Persistence_representations/utilities/persistence_vectors/plot_persistence_vectors.cpp +++ b/src/Persistence_representations/utilities/persistence_vectors/plot_persistence_vectors.cpp @@ -17,10 +17,10 @@ using Euclidean_distance = Gudhi::Euclidean_distance; using Vector_distances_in_diagram = Gudhi::Persistence_representations::Vector_distances_in_diagram<Euclidean_distance>; int main(int argc, char** argv) { - std::cout << "This program create a Gnuplot script to plot persistence vector. Please call this program with the " + std::clog << "This program create a Gnuplot script to plot persistence vector. Please call this program with the " "name of file with persistence vector. \n"; if (argc != 2) { - std::cout << "Wrong number of parameters, the program will now terminate. \n"; + std::clog << "Wrong number of parameters, the program will now terminate. \n"; return 1; } Vector_distances_in_diagram l; diff --git a/src/Persistent_cohomology/benchmark/CMakeLists.txt b/src/Persistent_cohomology/benchmark/CMakeLists.txt index 2bb3b0c7..f38cc543 100644 --- a/src/Persistent_cohomology/benchmark/CMakeLists.txt +++ b/src/Persistent_cohomology/benchmark/CMakeLists.txt @@ -3,7 +3,7 @@ project(Persistent_cohomology_benchmark) if(GMP_FOUND) if(GMPXX_FOUND) add_executable ( performance_rips_persistence EXCLUDE_FROM_ALL performance_rips_persistence.cpp ) - target_link_libraries(performance_rips_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY} ${GMPXX_LIBRARIES} ${GMP_LIBRARIES}) + target_link_libraries(performance_rips_persistence Boost::program_options ${GMPXX_LIBRARIES} ${GMP_LIBRARIES}) if (TBB_FOUND) target_link_libraries(performance_rips_persistence ${TBB_LIBRARIES}) endif(TBB_FOUND) diff --git a/src/Persistent_cohomology/benchmark/performance_rips_persistence.cpp b/src/Persistent_cohomology/benchmark/performance_rips_persistence.cpp index 45757002..030b072a 100644 --- a/src/Persistent_cohomology/benchmark/performance_rips_persistence.cpp +++ b/src/Persistent_cohomology/benchmark/performance_rips_persistence.cpp @@ -74,7 +74,7 @@ int main(int argc, char * argv[]) { Rips_complex rips_complex_from_file(off_reader.get_point_cloud(), threshold, Gudhi::Euclidean_distance()); end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << "Compute Rips graph in " << elapsed_sec << " ms.\n"; + std::clog << "Compute Rips graph in " << elapsed_sec << " ms.\n"; // Construct the Rips complex in a Simplex Tree Simplex_tree st; @@ -86,16 +86,16 @@ int main(int argc, char * argv[]) { end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << "Compute Rips complex in " << elapsed_sec << " ms.\n"; - std::cout << " - dimension = " << st.dimension() << std::endl; - std::cout << " - number of simplices = " << st.num_simplices() << std::endl; + std::clog << "Compute Rips complex in " << elapsed_sec << " ms.\n"; + std::clog << " - dimension = " << st.dimension() << std::endl; + std::clog << " - number of simplices = " << st.num_simplices() << std::endl; // Sort the simplices in the order of the filtration start = std::chrono::system_clock::now(); st.initialize_filtration(); end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << "Order the simplices of the filtration in " << elapsed_sec << " ms.\n"; + std::clog << "Order the simplices of the filtration in " << elapsed_sec << " ms.\n"; // Copy the keys inside the simplices start = std::chrono::system_clock::now(); @@ -106,22 +106,22 @@ int main(int argc, char * argv[]) { } end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << "Copied the keys inside the simplices in " << elapsed_sec << " ms.\n"; + std::clog << "Copied the keys inside the simplices in " << elapsed_sec << " ms.\n"; // Convert the simplex tree into a hasse diagram start = std::chrono::system_clock::now(); Gudhi::Hasse_complex<> hcpx(st); end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << "Convert the simplex tree into a Hasse diagram in " << elapsed_sec << " ms.\n"; + std::clog << "Convert the simplex tree into a Hasse diagram in " << elapsed_sec << " ms.\n"; - std::cout << "Timings when using a simplex tree: \n"; + std::clog << "Timings when using a simplex tree: \n"; timing_persistence(st, p); timing_persistence(st, q); timing_persistence(st, p, q); - std::cout << "Timings when using a Hasse complex: \n"; + std::clog << "Timings when using a Hasse complex: \n"; timing_persistence(hcpx, p); timing_persistence(hcpx, q); timing_persistence(hcpx, p, q); @@ -130,7 +130,7 @@ int main(int argc, char * argv[]) { } end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << "Running the complex destructors in " << elapsed_sec << " ms.\n"; + std::clog << "Running the complex destructors in " << elapsed_sec << " ms.\n"; return 0; } @@ -145,13 +145,13 @@ timing_persistence(FilteredComplex & cpx Gudhi::persistent_cohomology::Persistent_cohomology< FilteredComplex, Field_Zp > pcoh(cpx); end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << " Initialize pcoh in " << elapsed_sec << " ms.\n"; + std::clog << " Initialize pcoh in " << elapsed_sec << " ms.\n"; // initializes the coefficient field for homology start = std::chrono::system_clock::now(); pcoh.init_coefficients(p); end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << " Initialize the coefficient field in " << elapsed_sec << " ms.\n"; + std::clog << " Initialize the coefficient field in " << elapsed_sec << " ms.\n"; start = std::chrono::system_clock::now(); @@ -159,12 +159,12 @@ timing_persistence(FilteredComplex & cpx end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << " Compute persistent homology in Z/" << p << "Z in " << elapsed_sec << " ms.\n"; + std::clog << " Compute persistent homology in Z/" << p << "Z in " << elapsed_sec << " ms.\n"; start = std::chrono::system_clock::now(); } end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << " Run the persistence destructors in " << elapsed_sec << " ms.\n"; + std::clog << " Run the persistence destructors in " << elapsed_sec << " ms.\n"; } template< typename FilteredComplex> @@ -179,13 +179,13 @@ timing_persistence(FilteredComplex & cpx Gudhi::persistent_cohomology::Persistent_cohomology< FilteredComplex, Multi_field > pcoh(cpx); end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << " Initialize pcoh in " << elapsed_sec << " ms.\n"; + std::clog << " Initialize pcoh in " << elapsed_sec << " ms.\n"; // initializes the coefficient field for homology start = std::chrono::system_clock::now(); pcoh.init_coefficients(p, q); end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << " Initialize the coefficient field in " << elapsed_sec << " ms.\n"; + std::clog << " Initialize the coefficient field in " << elapsed_sec << " ms.\n"; // compute persistent homology, disgarding persistent features of life shorter than min_persistence start = std::chrono::system_clock::now(); @@ -194,11 +194,11 @@ timing_persistence(FilteredComplex & cpx end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << " Compute multi-field persistent homology in all coefficient fields Z/pZ " + std::clog << " Compute multi-field persistent homology in all coefficient fields Z/pZ " << "with p in [" << p << ";" << q << "] in " << elapsed_sec << " ms.\n"; start = std::chrono::system_clock::now(); } end = std::chrono::system_clock::now(); elapsed_sec = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); - std::cout << " Run the persistence destructors in " << elapsed_sec << " ms.\n"; + std::clog << " Run the persistence destructors in " << elapsed_sec << " ms.\n"; } diff --git a/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h b/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h index 46b784d8..b4f9fd2c 100644 --- a/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h +++ b/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h @@ -21,7 +21,7 @@ namespace persistent_cohomology { \author Clément Maria Computation of persistent cohomology using the algorithm of - \cite DBLP:journals/dcg/SilvaMV11 and \cite DBLP:journals/corr/abs-1208-5018 + \cite DBLP:journals/dcg/SilvaMV11 and \cite DBLP:conf/compgeom/DeyFW14 and the Compressed Annotation Matrix implementation of \cite DBLP:conf/esa/BoissonnatDM13 diff --git a/src/Persistent_cohomology/example/CMakeLists.txt b/src/Persistent_cohomology/example/CMakeLists.txt index 94ec13c5..4c08cd68 100644 --- a/src/Persistent_cohomology/example/CMakeLists.txt +++ b/src/Persistent_cohomology/example/CMakeLists.txt @@ -5,13 +5,13 @@ add_executable(plain_homology plain_homology.cpp) add_executable(persistence_from_simple_simplex_tree persistence_from_simple_simplex_tree.cpp) add_executable(rips_persistence_step_by_step rips_persistence_step_by_step.cpp) -target_link_libraries(rips_persistence_step_by_step ${Boost_PROGRAM_OPTIONS_LIBRARY}) +target_link_libraries(rips_persistence_step_by_step Boost::program_options) add_executable(rips_persistence_via_boundary_matrix rips_persistence_via_boundary_matrix.cpp) -target_link_libraries(rips_persistence_via_boundary_matrix ${Boost_PROGRAM_OPTIONS_LIBRARY}) +target_link_libraries(rips_persistence_via_boundary_matrix Boost::program_options) add_executable(persistence_from_file persistence_from_file.cpp) -target_link_libraries(persistence_from_file ${Boost_PROGRAM_OPTIONS_LIBRARY}) +target_link_libraries(persistence_from_file Boost::program_options) if (TBB_FOUND) target_link_libraries(plain_homology ${TBB_LIBRARIES}) @@ -43,7 +43,7 @@ if(GMP_FOUND) if(GMPXX_FOUND) add_executable(rips_multifield_persistence rips_multifield_persistence.cpp ) target_link_libraries(rips_multifield_persistence - ${Boost_PROGRAM_OPTIONS_LIBRARY} ${GMPXX_LIBRARIES} ${GMP_LIBRARIES}) + Boost::program_options ${GMPXX_LIBRARIES} ${GMP_LIBRARIES}) if (TBB_FOUND) target_link_libraries(rips_multifield_persistence ${TBB_LIBRARIES}) endif(TBB_FOUND) diff --git a/src/Persistent_cohomology/example/custom_persistence_sort.cpp b/src/Persistent_cohomology/example/custom_persistence_sort.cpp index be74cf50..410cd987 100644 --- a/src/Persistent_cohomology/example/custom_persistence_sort.cpp +++ b/src/Persistent_cohomology/example/custom_persistence_sort.cpp @@ -70,26 +70,23 @@ struct cmp_intervals_by_dim_then_length { int main(int argc, char **argv) { std::vector<Point> points = random_points(); - std::cout << "Points size=" << points.size() << std::endl; + std::clog << "Points size=" << points.size() << std::endl; // Alpha complex persistence computation from generated points Alpha_complex alpha_complex_from_points(points); - std::cout << "alpha_complex_from_points" << std::endl; + std::clog << "alpha_complex_from_points" << std::endl; Simplex_tree simplex; - std::cout << "simplex" << std::endl; + std::clog << "simplex" << std::endl; if (alpha_complex_from_points.create_complex(simplex, 0.6)) { - std::cout << "simplex" << std::endl; + std::clog << "simplex" << std::endl; // ---------------------------------------------------------------------------- // Display information about the alpha complex // ---------------------------------------------------------------------------- - std::cout << "Simplicial complex is of dimension " << simplex.dimension() << + std::clog << "Simplicial complex is of dimension " << simplex.dimension() << " - " << simplex.num_simplices() << " simplices - " << simplex.num_vertices() << " vertices." << std::endl; - // Sort the simplices in the order of the filtration - simplex.initialize_filtration(); - - std::cout << "Simplex_tree dim: " << simplex.dimension() << std::endl; + std::clog << "Simplex_tree dim: " << simplex.dimension() << std::endl; Persistent_cohomology pcoh(simplex); @@ -102,23 +99,23 @@ int main(int argc, char **argv) { auto persistent_pairs = pcoh.get_persistent_pairs(); std::sort(std::begin(persistent_pairs), std::end(persistent_pairs), cmp); for (auto pair : persistent_pairs) { - std::cout << simplex.dimension(get<0>(pair)) << " " + std::clog << simplex.dimension(get<0>(pair)) << " " << simplex.filtration(get<0>(pair)) << " " << simplex.filtration(get<1>(pair)) << std::endl; } // Persistent Betti numbers - std::cout << "The persistent Betti numbers in interval [0.40, 0.41] are : "; + std::clog << "The persistent Betti numbers in interval [0.40, 0.41] are : "; for (int dim = 0; dim < simplex.dimension(); dim++) - std::cout << "b" << dim << " = " << pcoh.persistent_betti_number(dim, 0.40, 0.41) << " ; "; - std::cout << std::endl; + std::clog << "b" << dim << " = " << pcoh.persistent_betti_number(dim, 0.40, 0.41) << " ; "; + std::clog << std::endl; // Betti numbers std::vector<int> betti_numbers = pcoh.betti_numbers(); - std::cout << "The Betti numbers are : "; + std::clog << "The Betti numbers are : "; for (std::size_t i = 0; i < betti_numbers.size(); i++) - std::cout << "b" << i << " = " << betti_numbers[i] << " ; "; - std::cout << std::endl; + std::clog << "b" << i << " = " << betti_numbers[i] << " ; "; + std::clog << std::endl; } return 0; } diff --git a/src/Persistent_cohomology/example/persistence_from_file.cpp b/src/Persistent_cohomology/example/persistence_from_file.cpp index d169cc63..38c44514 100644 --- a/src/Persistent_cohomology/example/persistence_from_file.cpp +++ b/src/Persistent_cohomology/example/persistence_from_file.cpp @@ -37,9 +37,9 @@ int main(int argc, char * argv[]) { program_options(argc, argv, simplex_tree_file, output_file, p, min_persistence); - std::cout << "Simplex_tree from file=" << simplex_tree_file.c_str() << " - output_file=" << output_file.c_str() + std::clog << "Simplex_tree from file=" << simplex_tree_file.c_str() << " - output_file=" << output_file.c_str() << std::endl; - std::cout << " - p=" << p << " - min_persistence=" << min_persistence << std::endl; + std::clog << " - p=" << p << " - min_persistence=" << min_persistence << std::endl; // Read the list of simplices from a file. Simplex_tree<> simplex_tree; @@ -47,21 +47,18 @@ int main(int argc, char * argv[]) { std::ifstream simplex_tree_stream(simplex_tree_file); simplex_tree_stream >> simplex_tree; - std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices" << std::endl; - std::cout << " - dimension " << simplex_tree.dimension() << std::endl; + std::clog << "The complex contains " << simplex_tree.num_simplices() << " simplices" << std::endl; + std::clog << " - dimension " << simplex_tree.dimension() << std::endl; /* - std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; + std::clog << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; for( auto f_simplex : simplex_tree.filtration_simplex_range() ) - { std::cout << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; + { std::clog << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; for( auto vertex : simplex_tree.simplex_vertex_range(f_simplex) ) - { std::cout << vertex << " "; } - std::cout << std::endl; + { std::clog << vertex << " "; } + std::clog << std::endl; }*/ - // Sort the simplices in the order of the filtration - simplex_tree.initialize_filtration(); - // Compute the persistence diagram of the complex Persistent_cohomology< Simplex_tree<>, Field_Zp > pcoh(simplex_tree); // initializes the coefficient field for homology @@ -96,7 +93,7 @@ void program_options(int argc, char * argv[] visible.add_options() ("help,h", "produce help message") ("output-file,o", po::value<std::string>(&output_file)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout") + "Name of file in which the persistence diagram is written. Default print in std::clog") ("field-charac,p", po::value<int>(&p)->default_value(11), "Characteristic p of the coefficient field Z/pZ for computing homology.") ("min-persistence,m", po::value<Filtration_value>(&min_persistence), @@ -114,17 +111,17 @@ void program_options(int argc, char * argv[] po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of a Rips complex defined on a set of input points.\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of a Rips complex defined on a set of input points.\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Persistent_cohomology/example/persistence_from_simple_simplex_tree.cpp b/src/Persistent_cohomology/example/persistence_from_simple_simplex_tree.cpp index 3c91662f..bffaabdd 100644 --- a/src/Persistent_cohomology/example/persistence_from_simple_simplex_tree.cpp +++ b/src/Persistent_cohomology/example/persistence_from_simple_simplex_tree.cpp @@ -51,62 +51,62 @@ int main(int argc, char * const argv[]) { } // TEST OF INSERTION - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF INSERTION" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF INSERTION" << std::endl; Simplex_tree st; // ++ FIRST - std::cout << " - INSERT (0,1,2)" << std::endl; + std::clog << " - INSERT (0,1,2)" << std::endl; typeVectorVertex SimplexVector = {0, 1, 2}; st.insert_simplex_and_subfaces(SimplexVector, 0.3); // ++ SECOND - std::cout << " - INSERT 3" << std::endl; + std::clog << " - INSERT 3" << std::endl; SimplexVector = {3}; st.insert_simplex_and_subfaces(SimplexVector, 0.1); // ++ THIRD - std::cout << " - INSERT (0,3)" << std::endl; + std::clog << " - INSERT (0,3)" << std::endl; SimplexVector = {0, 3}; st.insert_simplex_and_subfaces(SimplexVector, 0.2); // ++ FOURTH - std::cout << " - INSERT (0,1) (already inserted)" << std::endl; + std::clog << " - INSERT (0,1) (already inserted)" << std::endl; SimplexVector = {0, 1}; st.insert_simplex_and_subfaces(SimplexVector, 0.2); // ++ FIFTH - std::cout << " - INSERT (3,4,5)" << std::endl; + std::clog << " - INSERT (3,4,5)" << std::endl; SimplexVector = {3, 4, 5}; st.insert_simplex_and_subfaces(SimplexVector, 0.3); // ++ SIXTH - std::cout << " - INSERT (0,1,6,7)" << std::endl; + std::clog << " - INSERT (0,1,6,7)" << std::endl; SimplexVector = {0, 1, 6, 7}; st.insert_simplex_and_subfaces(SimplexVector, 0.4); // ++ SEVENTH - std::cout << " - INSERT (4,5,8,9)" << std::endl; + std::clog << " - INSERT (4,5,8,9)" << std::endl; SimplexVector = {4, 5, 8, 9}; st.insert_simplex_and_subfaces(SimplexVector, 0.4); // ++ EIGHTH - std::cout << " - INSERT (9,10,11)" << std::endl; + std::clog << " - INSERT (9,10,11)" << std::endl; SimplexVector = {9, 10, 11}; st.insert_simplex_and_subfaces(SimplexVector, 0.3); // ++ NINETH - std::cout << " - INSERT (2,10,12)" << std::endl; + std::clog << " - INSERT (2,10,12)" << std::endl; SimplexVector = {2, 10, 12}; st.insert_simplex_and_subfaces(SimplexVector, 0.3); // ++ TENTH - std::cout << " - INSERT (11,6)" << std::endl; + std::clog << " - INSERT (11,6)" << std::endl; SimplexVector = {6, 11}; st.insert_simplex_and_subfaces(SimplexVector, 0.2); // ++ ELEVENTH - std::cout << " - INSERT (13,14,15)" << std::endl; + std::clog << " - INSERT (13,14,15)" << std::endl; SimplexVector = {13, 14, 15}; st.insert_simplex_and_subfaces(SimplexVector, 0.25); @@ -131,24 +131,24 @@ int main(int argc, char * const argv[]) { /* An edge [10,12,2] */ - std::cout << "The complex contains " << st.num_simplices() << " simplices - " << st.num_vertices() << " vertices " + std::clog << "The complex contains " << st.num_simplices() << " simplices - " << st.num_vertices() << " vertices " << std::endl; - std::cout << " - dimension " << st.dimension() << std::endl; - std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" + std::clog << " - dimension " << st.dimension() << std::endl; + std::clog << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; - std::cout << "**************************************************************" << std::endl; - std::cout << "strict graph G { " << std::endl; + std::clog << "**************************************************************" << std::endl; + std::clog << "strict graph G { " << std::endl; for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " << "[" << st.filtration(f_simplex) << "] "; + std::clog << " " << "[" << st.filtration(f_simplex) << "] "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << static_cast<int>(vertex) << " -- "; + std::clog << static_cast<int>(vertex) << " -- "; } - std::cout << ";" << std::endl; + std::clog << ";" << std::endl; } - std::cout << "}" << std::endl; - std::cout << "**************************************************************" << std::endl; + std::clog << "}" << std::endl; + std::clog << "**************************************************************" << std::endl; // Compute the persistence diagram of the complex Persistent_cohomology pcoh(st); diff --git a/src/Persistent_cohomology/example/plain_homology.cpp b/src/Persistent_cohomology/example/plain_homology.cpp index 84333e46..236b67de 100644 --- a/src/Persistent_cohomology/example/plain_homology.cpp +++ b/src/Persistent_cohomology/example/plain_homology.cpp @@ -59,9 +59,6 @@ int main() { st.insert_simplex_and_subfaces(edge35); st.insert_simplex(vertex4); - // Sort the simplices in the order of the filtration - st.initialize_filtration(); - // Class for homology computation // By default, since the complex has dimension 1, only 0-dimensional homology would be computed. // Here we also want persistent homology to be computed for the maximal dimension in the complex (persistence_dim_max = true) @@ -83,9 +80,9 @@ int main() { pcoh.output_diagram(); // Print the Betti numbers are b0=2 and b1=2. - std::cout << std::endl; - std::cout << "The Betti numbers are : "; + std::clog << std::endl; + std::clog << "The Betti numbers are : "; for (int i = 0; i < 3; i++) - std::cout << "b" << i << " = " << pcoh.betti_number(i) << " ; "; - std::cout << std::endl; + std::clog << "b" << i << " = " << pcoh.betti_number(i) << " ; "; + std::clog << std::endl; } diff --git a/src/Persistent_cohomology/example/rips_multifield_persistence.cpp b/src/Persistent_cohomology/example/rips_multifield_persistence.cpp index 9eb5ccfc..2edf5bc4 100644 --- a/src/Persistent_cohomology/example/rips_multifield_persistence.cpp +++ b/src/Persistent_cohomology/example/rips_multifield_persistence.cpp @@ -56,11 +56,8 @@ int main(int argc, char * argv[]) { Simplex_tree simplex_tree; rips_complex_from_file.create_complex(simplex_tree, dim_max); - std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; - std::cout << " and has dimension " << simplex_tree.dimension() << " \n"; - - // Sort the simplices in the order of the filtration - simplex_tree.initialize_filtration(); + std::clog << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; + std::clog << " and has dimension " << simplex_tree.dimension() << " \n"; // Compute the persistence diagram of the complex Persistent_cohomology pcoh(simplex_tree); @@ -99,7 +96,7 @@ void program_options(int argc, char * argv[] visible.add_options() ("help,h", "produce help message") ("output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout") + "Name of file in which the persistence diagram is written. Default print in std::clog") ("max-edge-length,r", po::value<Filtration_value>(&threshold)->default_value(0), "Maximal length of an edge for the Rips complex construction.") ("cpx-dimension,d", po::value<int>(&dim_max)->default_value(1), @@ -123,20 +120,20 @@ void program_options(int argc, char * argv[] po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with various coefficient fields \n"; - std::cout << "of a Rips complex defined on a set of input points. The coefficient \n"; - std::cout << "fields are all the Z/rZ for a prime number r contained in the \n"; - std::cout << "specified range [p,q]\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p1*...*pr dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p1*...*pr is the product of prime numbers pi such that the homology \n"; - std::cout << "feature exists in homology with Z/piZ coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with various coefficient fields \n"; + std::clog << "of a Rips complex defined on a set of input points. The coefficient \n"; + std::clog << "fields are all the Z/rZ for a prime number r contained in the \n"; + std::clog << "specified range [p,q]\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p1*...*pr dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p1*...*pr is the product of prime numbers pi such that the homology \n"; + std::clog << "feature exists in homology with Z/piZ coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Persistent_cohomology/example/rips_persistence_step_by_step.cpp b/src/Persistent_cohomology/example/rips_persistence_step_by_step.cpp index 02db05ec..a503d983 100644 --- a/src/Persistent_cohomology/example/rips_persistence_step_by_step.cpp +++ b/src/Persistent_cohomology/example/rips_persistence_step_by_step.cpp @@ -73,11 +73,8 @@ int main(int argc, char * argv[]) { // expand the graph until dimension dim_max st.expansion(dim_max); - std::cout << "The complex contains " << st.num_simplices() << " simplices \n"; - std::cout << " and has dimension " << st.dimension() << " \n"; - - // Sort the simplices in the order of the filtration - st.initialize_filtration(); + std::clog << "The complex contains " << st.num_simplices() << " simplices \n"; + std::clog << " and has dimension " << st.dimension() << " \n"; // Compute the persistence diagram of the complex Persistent_cohomology pcoh(st); @@ -115,7 +112,7 @@ void program_options(int argc, char * argv[] visible.add_options() ("help,h", "produce help message") ("output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout") + "Name of file in which the persistence diagram is written. Default print in std::clog") ("max-edge-length,r", po::value<Filtration_value>(&threshold)->default_value(std::numeric_limits<Filtration_value>::infinity()), "Maximal length of an edge for the Rips complex construction.") @@ -138,17 +135,17 @@ void program_options(int argc, char * argv[] po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of a Rips complex defined on a set of input points.\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of a Rips complex defined on a set of input points.\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Persistent_cohomology/example/rips_persistence_via_boundary_matrix.cpp b/src/Persistent_cohomology/example/rips_persistence_via_boundary_matrix.cpp index 37fa5e93..8c5742aa 100644 --- a/src/Persistent_cohomology/example/rips_persistence_via_boundary_matrix.cpp +++ b/src/Persistent_cohomology/example/rips_persistence_via_boundary_matrix.cpp @@ -17,10 +17,6 @@ #include <boost/program_options.hpp> -#ifdef GUDHI_USE_TBB -#include <tbb/task_scheduler_init.h> -#endif - #include <string> #include <vector> @@ -64,13 +60,8 @@ int main(int argc, char * argv[]) { Simplex_tree& st = *new Simplex_tree; rips_complex_from_file.create_complex(st, dim_max); - std::cout << "The complex contains " << st.num_simplices() << " simplices \n"; - std::cout << " and has dimension " << st.dimension() << " \n"; - -#ifdef GUDHI_USE_TBB - // Unnecessary, but clarifies which operations are parallel. - tbb::task_scheduler_init ts; -#endif + std::clog << "The complex contains " << st.num_simplices() << " simplices \n"; + std::clog << " and has dimension " << st.dimension() << " \n"; // Sort the simplices in the order of the filtration st.initialize_filtration(); @@ -81,10 +72,6 @@ int main(int argc, char * argv[]) { // Convert to a more convenient representation. Gudhi::Hasse_complex<> hcpx(st); -#ifdef GUDHI_USE_TBB - ts.terminate(); -#endif - // Free some space. delete &st; @@ -122,7 +109,7 @@ void program_options(int argc, char * argv[] visible.add_options() ("help,h", "produce help message") ("output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout") + "Name of file in which the persistence diagram is written. Default print in std::clog") ("max-edge-length,r", po::value<Filtration_value>(&threshold)->default_value(0), "Maximal length of an edge for the Rips complex construction.") ("cpx-dimension,d", po::value<int>(&dim_max)->default_value(1), @@ -144,17 +131,17 @@ void program_options(int argc, char * argv[] po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of a Rips complex defined on a set of input points.\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of a Rips complex defined on a set of input points.\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h b/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h index 0f1876d0..d34ee07d 100644 --- a/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h +++ b/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h @@ -288,10 +288,7 @@ class Persistent_cohomology { // with multiplicity. We used to sum the coefficients directly in // annotations_in_boundary by using a map, we now do it later. typedef std::pair<Column *, int> annotation_t; -#ifdef GUDHI_CAN_USE_CXX11_THREAD_LOCAL - thread_local -#endif // GUDHI_CAN_USE_CXX11_THREAD_LOCAL - std::vector<annotation_t> annotations_in_boundary; + thread_local std::vector<annotation_t> annotations_in_boundary; annotations_in_boundary.clear(); int sign = 1 - 2 * (dim_sigma % 2); // \in {-1,1} provides the sign in the // alternate sum in the boundary. @@ -564,35 +561,22 @@ class Persistent_cohomology { void output_diagram(std::ostream& ostream = std::cout) { cmp_intervals_by_length cmp(cpx_); std::sort(std::begin(persistent_pairs_), std::end(persistent_pairs_), cmp); - bool has_infinity = std::numeric_limits<Filtration_value>::has_infinity; for (auto pair : persistent_pairs_) { - // Special case on windows, inf is "1.#INF" (cf. unitary tests and R package TDA) - if (has_infinity && cpx_->filtration(get<1>(pair)) == std::numeric_limits<Filtration_value>::infinity()) { - ostream << get<2>(pair) << " " << cpx_->dimension(get<0>(pair)) << " " - << cpx_->filtration(get<0>(pair)) << " inf " << std::endl; - } else { - ostream << get<2>(pair) << " " << cpx_->dimension(get<0>(pair)) << " " - << cpx_->filtration(get<0>(pair)) << " " - << cpx_->filtration(get<1>(pair)) << " " << std::endl; - } + ostream << get<2>(pair) << " " << cpx_->dimension(get<0>(pair)) << " " + << cpx_->filtration(get<0>(pair)) << " " + << cpx_->filtration(get<1>(pair)) << " " << std::endl; } } void write_output_diagram(std::string diagram_name) { std::ofstream diagram_out(diagram_name.c_str()); + diagram_out.exceptions(diagram_out.failbit); cmp_intervals_by_length cmp(cpx_); std::sort(std::begin(persistent_pairs_), std::end(persistent_pairs_), cmp); - bool has_infinity = std::numeric_limits<Filtration_value>::has_infinity; for (auto pair : persistent_pairs_) { - // Special case on windows, inf is "1.#INF" - if (has_infinity && cpx_->filtration(get<1>(pair)) == std::numeric_limits<Filtration_value>::infinity()) { - diagram_out << cpx_->dimension(get<0>(pair)) << " " - << cpx_->filtration(get<0>(pair)) << " inf" << std::endl; - } else { - diagram_out << cpx_->dimension(get<0>(pair)) << " " - << cpx_->filtration(get<0>(pair)) << " " - << cpx_->filtration(get<1>(pair)) << std::endl; - } + diagram_out << cpx_->dimension(get<0>(pair)) << " " + << cpx_->filtration(get<0>(pair)) << " " + << cpx_->filtration(get<1>(pair)) << std::endl; } } diff --git a/src/Persistent_cohomology/test/betti_numbers_unit_test.cpp b/src/Persistent_cohomology/test/betti_numbers_unit_test.cpp index b9f11607..7a2feeff 100644 --- a/src/Persistent_cohomology/test/betti_numbers_unit_test.cpp +++ b/src/Persistent_cohomology/test/betti_numbers_unit_test.cpp @@ -82,7 +82,7 @@ BOOST_AUTO_TEST_CASE( plain_homology_betti_numbers ) // 2 1 0 inf // means that in Z/2Z-homology, the Betti numbers are b0=2 and b1=1. - std::cout << "BETTI NUMBERS" << std::endl; + std::clog << "BETTI NUMBERS" << std::endl; BOOST_CHECK(pcoh.betti_number(0) == 2); BOOST_CHECK(pcoh.betti_number(1) == 1); @@ -94,7 +94,7 @@ BOOST_AUTO_TEST_CASE( plain_homology_betti_numbers ) BOOST_CHECK(bns[1] == 1); BOOST_CHECK(bns[2] == 0); - std::cout << "GET PERSISTENT PAIRS" << std::endl; + std::clog << "GET PERSISTENT PAIRS" << std::endl; // Custom sort and output persistence cmp_intervals_by_dim_then_length<Mini_simplex_tree> cmp(&st); @@ -118,12 +118,12 @@ BOOST_AUTO_TEST_CASE( plain_homology_betti_numbers ) BOOST_CHECK(st.filtration(get<0>(persistent_pairs[2])) == 0); BOOST_CHECK(get<1>(persistent_pairs[2]) == st.null_simplex()); - std::cout << "INTERVALS IN DIMENSION" << std::endl; + std::clog << "INTERVALS IN DIMENSION" << std::endl; auto intervals_in_dimension_0 = pcoh.intervals_in_dimension(0); - std::cout << "intervals_in_dimension_0.size() = " << intervals_in_dimension_0.size() << std::endl; + std::clog << "intervals_in_dimension_0.size() = " << intervals_in_dimension_0.size() << std::endl; for (std::size_t i = 0; i < intervals_in_dimension_0.size(); i++) - std::cout << "intervals_in_dimension_0[" << i << "] = [" << intervals_in_dimension_0[i].first << "," << + std::clog << "intervals_in_dimension_0[" << i << "] = [" << intervals_in_dimension_0[i].first << "," << intervals_in_dimension_0[i].second << "]" << std::endl; BOOST_CHECK(intervals_in_dimension_0.size() == 2); BOOST_CHECK(intervals_in_dimension_0[0].first == 0); @@ -133,16 +133,16 @@ BOOST_AUTO_TEST_CASE( plain_homology_betti_numbers ) auto intervals_in_dimension_1 = pcoh.intervals_in_dimension(1); - std::cout << "intervals_in_dimension_1.size() = " << intervals_in_dimension_1.size() << std::endl; + std::clog << "intervals_in_dimension_1.size() = " << intervals_in_dimension_1.size() << std::endl; for (std::size_t i = 0; i < intervals_in_dimension_1.size(); i++) - std::cout << "intervals_in_dimension_1[" << i << "] = [" << intervals_in_dimension_1[i].first << "," << + std::clog << "intervals_in_dimension_1[" << i << "] = [" << intervals_in_dimension_1[i].first << "," << intervals_in_dimension_1[i].second << "]" << std::endl; BOOST_CHECK(intervals_in_dimension_1.size() == 1); BOOST_CHECK(intervals_in_dimension_1[0].first == 0); BOOST_CHECK(intervals_in_dimension_1[0].second == std::numeric_limits<Mini_simplex_tree::Filtration_value>::infinity()); auto intervals_in_dimension_2 = pcoh.intervals_in_dimension(2); - std::cout << "intervals_in_dimension_2.size() = " << intervals_in_dimension_2.size() << std::endl; + std::clog << "intervals_in_dimension_2.size() = " << intervals_in_dimension_2.size() << std::endl; BOOST_CHECK(intervals_in_dimension_2.size() == 0); } @@ -259,12 +259,12 @@ BOOST_AUTO_TEST_CASE( betti_numbers ) BOOST_CHECK(st.filtration(get<0>(persistent_pairs[2])) == 1); BOOST_CHECK(get<1>(persistent_pairs[2]) == st.null_simplex()); - std::cout << "INTERVALS IN DIMENSION" << std::endl; + std::clog << "INTERVALS IN DIMENSION" << std::endl; auto intervals_in_dimension_0 = pcoh.intervals_in_dimension(0); - std::cout << "intervals_in_dimension_0.size() = " << intervals_in_dimension_0.size() << std::endl; + std::clog << "intervals_in_dimension_0.size() = " << intervals_in_dimension_0.size() << std::endl; for (std::size_t i = 0; i < intervals_in_dimension_0.size(); i++) - std::cout << "intervals_in_dimension_0[" << i << "] = [" << intervals_in_dimension_0[i].first << "," << + std::clog << "intervals_in_dimension_0[" << i << "] = [" << intervals_in_dimension_0[i].first << "," << intervals_in_dimension_0[i].second << "]" << std::endl; BOOST_CHECK(intervals_in_dimension_0.size() == 2); BOOST_CHECK(intervals_in_dimension_0[0].first == 2); @@ -273,19 +273,19 @@ BOOST_AUTO_TEST_CASE( betti_numbers ) BOOST_CHECK(intervals_in_dimension_0[1].second == std::numeric_limits<Mini_simplex_tree::Filtration_value>::infinity()); auto intervals_in_dimension_1 = pcoh.intervals_in_dimension(1); - std::cout << "intervals_in_dimension_1.size() = " << intervals_in_dimension_1.size() << std::endl; + std::clog << "intervals_in_dimension_1.size() = " << intervals_in_dimension_1.size() << std::endl; for (std::size_t i = 0; i < intervals_in_dimension_1.size(); i++) - std::cout << "intervals_in_dimension_1[" << i << "] = [" << intervals_in_dimension_1[i].first << "," << + std::clog << "intervals_in_dimension_1[" << i << "] = [" << intervals_in_dimension_1[i].first << "," << intervals_in_dimension_1[i].second << "]" << std::endl; BOOST_CHECK(intervals_in_dimension_1.size() == 1); BOOST_CHECK(intervals_in_dimension_1[0].first == 4); BOOST_CHECK(intervals_in_dimension_1[0].second == std::numeric_limits<Mini_simplex_tree::Filtration_value>::infinity()); auto intervals_in_dimension_2 = pcoh.intervals_in_dimension(2); - std::cout << "intervals_in_dimension_2.size() = " << intervals_in_dimension_2.size() << std::endl; + std::clog << "intervals_in_dimension_2.size() = " << intervals_in_dimension_2.size() << std::endl; BOOST_CHECK(intervals_in_dimension_2.size() == 0); - std::cout << "EMPTY COMPLEX" << std::endl; + std::clog << "EMPTY COMPLEX" << std::endl; Simplex_tree empty; empty.initialize_filtration(); St_persistence pcoh_empty(empty, false); diff --git a/src/Persistent_cohomology/test/persistent_cohomology_unit_test.cpp b/src/Persistent_cohomology/test/persistent_cohomology_unit_test.cpp index a1c106d5..fe3f8517 100644 --- a/src/Persistent_cohomology/test/persistent_cohomology_unit_test.cpp +++ b/src/Persistent_cohomology/test/persistent_cohomology_unit_test.cpp @@ -30,7 +30,7 @@ std::string test_rips_persistence(int coefficient, int min_persistence) { simplex_tree_stream.close(); // Display the Simplex_tree - std::cout << "The complex contains " << st.num_simplices() << " simplices" << " - dimension= " << st.dimension() + std::clog << "The complex contains " << st.num_simplices() << " simplices" << " - dimension= " << st.dimension() << std::endl; // Check @@ -76,11 +76,11 @@ void test_rips_persistence_in_dimension(int dimension) { value8.insert(0,std::to_string(dimension)); value9.insert(0,std::to_string(dimension)); - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF RIPS_PERSISTENT_COHOMOLOGY_SINGLE_FIELD DIM=" << dimension << " MIN_PERS=0" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF RIPS_PERSISTENT_COHOMOLOGY_SINGLE_FIELD DIM=" << dimension << " MIN_PERS=0" << std::endl; std::string str_rips_persistence = test_rips_persistence(dimension, 0); - std::cout << str_rips_persistence << std::endl; + std::clog << str_rips_persistence << std::endl; BOOST_CHECK(str_rips_persistence.find(value0) != std::string::npos); // Check found BOOST_CHECK(str_rips_persistence.find(value1) != std::string::npos); // Check found @@ -92,10 +92,10 @@ void test_rips_persistence_in_dimension(int dimension) { BOOST_CHECK(str_rips_persistence.find(value7) != std::string::npos); // Check found BOOST_CHECK(str_rips_persistence.find(value8) != std::string::npos); // Check found BOOST_CHECK(str_rips_persistence.find(value9) != std::string::npos); // Check found - std::cout << "str_rips_persistence=" << str_rips_persistence << std::endl; + std::clog << "str_rips_persistence=" << str_rips_persistence << std::endl; - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF RIPS_PERSISTENT_COHOMOLOGY_SINGLE_FIELD DIM=" << dimension << " MIN_PERS=1" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF RIPS_PERSISTENT_COHOMOLOGY_SINGLE_FIELD DIM=" << dimension << " MIN_PERS=1" << std::endl; str_rips_persistence = test_rips_persistence(dimension, 1); @@ -109,10 +109,10 @@ void test_rips_persistence_in_dimension(int dimension) { BOOST_CHECK(str_rips_persistence.find(value7) != std::string::npos); // Check found BOOST_CHECK(str_rips_persistence.find(value8) != std::string::npos); // Check found BOOST_CHECK(str_rips_persistence.find(value9) != std::string::npos); // Check found - std::cout << "str_rips_persistence=" << str_rips_persistence << std::endl; + std::clog << "str_rips_persistence=" << str_rips_persistence << std::endl; - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF RIPS_PERSISTENT_COHOMOLOGY_SINGLE_FIELD DIM=" << dimension << " MIN_PERS=2" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF RIPS_PERSISTENT_COHOMOLOGY_SINGLE_FIELD DIM=" << dimension << " MIN_PERS=2" << std::endl; str_rips_persistence = test_rips_persistence(dimension, 2); @@ -126,10 +126,10 @@ void test_rips_persistence_in_dimension(int dimension) { BOOST_CHECK(str_rips_persistence.find(value7) == std::string::npos); // Check not found BOOST_CHECK(str_rips_persistence.find(value8) != std::string::npos); // Check found BOOST_CHECK(str_rips_persistence.find(value9) != std::string::npos); // Check found - std::cout << "str_rips_persistence=" << str_rips_persistence << std::endl; + std::clog << "str_rips_persistence=" << str_rips_persistence << std::endl; - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF RIPS_PERSISTENT_COHOMOLOGY_SINGLE_FIELD DIM=" << dimension << " MIN_PERS=Inf" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF RIPS_PERSISTENT_COHOMOLOGY_SINGLE_FIELD DIM=" << dimension << " MIN_PERS=Inf" << std::endl; str_rips_persistence = test_rips_persistence(dimension, (std::numeric_limits<int>::max)()); @@ -143,7 +143,7 @@ void test_rips_persistence_in_dimension(int dimension) { BOOST_CHECK(str_rips_persistence.find(value7) == std::string::npos); // Check not found BOOST_CHECK(str_rips_persistence.find(value8) != std::string::npos); // Check found BOOST_CHECK(str_rips_persistence.find(value9) != std::string::npos); // Check found - std::cout << "str_rips_persistence=" << str_rips_persistence << std::endl; + std::clog << "str_rips_persistence=" << str_rips_persistence << std::endl; } BOOST_AUTO_TEST_CASE( rips_persistent_cohomology_single_field_dim_1 ) diff --git a/src/Persistent_cohomology/test/persistent_cohomology_unit_test_multi_field.cpp b/src/Persistent_cohomology/test/persistent_cohomology_unit_test_multi_field.cpp index 9e767943..3602aa09 100644 --- a/src/Persistent_cohomology/test/persistent_cohomology_unit_test_multi_field.cpp +++ b/src/Persistent_cohomology/test/persistent_cohomology_unit_test_multi_field.cpp @@ -30,7 +30,7 @@ std::string test_rips_persistence(int min_coefficient, int max_coefficient, doub simplex_tree_stream.close(); // Display the Simplex_tree - std::cout << "The complex contains " << st.num_simplices() << " simplices" << " - dimension= " << st.dimension() + std::clog << "The complex contains " << st.num_simplices() << " simplices" << " - dimension= " << st.dimension() << std::endl; // Check @@ -68,11 +68,11 @@ void test_rips_persistence_in_dimension(int min_dimension, int max_dimension) { std::string value6(" 2 0.3 inf"); std::string value7(" 2 0.4 inf"); - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF RIPS_PERSISTENT_COHOMOLOGY_MULTI_FIELD MIN_DIM=" << min_dimension << " MAX_DIM=" << max_dimension << " MIN_PERS=0" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF RIPS_PERSISTENT_COHOMOLOGY_MULTI_FIELD MIN_DIM=" << min_dimension << " MAX_DIM=" << max_dimension << " MIN_PERS=0" << std::endl; std::string str_rips_persistence = test_rips_persistence(min_dimension, max_dimension, 0.0); - std::cout << "str_rips_persistence=" << str_rips_persistence << std::endl; + std::clog << "str_rips_persistence=" << str_rips_persistence << std::endl; BOOST_CHECK(str_rips_persistence.find(value0) != std::string::npos); // Check found BOOST_CHECK(str_rips_persistence.find(value1) != std::string::npos); // Check found diff --git a/src/Rips_complex/example/CMakeLists.txt b/src/Rips_complex/example/CMakeLists.txt index e7772bdb..244a93ec 100644 --- a/src/Rips_complex/example/CMakeLists.txt +++ b/src/Rips_complex/example/CMakeLists.txt @@ -53,15 +53,23 @@ if (DIFF_PATH) add_test(Rips_complex_example_from_off_doc_12_1_diff_files ${DIFF_PATH} ${CMAKE_CURRENT_BINARY_DIR}/ripsoffreader_result_12_1.txt ${CMAKE_CURRENT_BINARY_DIR}/one_skeleton_rips_for_doc.txt) + set_tests_properties(Rips_complex_example_from_off_doc_12_1_diff_files PROPERTIES DEPENDS Rips_complex_example_from_off_doc_12_1) + add_test(Rips_complex_example_from_off_doc_12_3_diff_files ${DIFF_PATH} ${CMAKE_CURRENT_BINARY_DIR}/ripsoffreader_result_12_3.txt ${CMAKE_CURRENT_BINARY_DIR}/full_skeleton_rips_for_doc.txt) + set_tests_properties(Rips_complex_example_from_off_doc_12_3_diff_files PROPERTIES DEPENDS Rips_complex_example_from_off_doc_12_3) + add_test(Rips_complex_example_from_csv_distance_matrix_doc_12_1_diff_files ${DIFF_PATH} ${CMAKE_CURRENT_BINARY_DIR}/ripscsvreader_result_12_1.txt ${CMAKE_CURRENT_BINARY_DIR}/one_skeleton_rips_for_doc.txt) + set_tests_properties(Rips_complex_example_from_csv_distance_matrix_doc_12_1_diff_files PROPERTIES DEPENDS Rips_complex_example_from_csv_distance_matrix_doc_12_1) + add_test(Rips_complex_example_from_csv_distance_matrix_doc_12_3_diff_files ${DIFF_PATH} ${CMAKE_CURRENT_BINARY_DIR}/ripscsvreader_result_12_3.txt ${CMAKE_CURRENT_BINARY_DIR}/full_skeleton_rips_for_doc.txt) + set_tests_properties(Rips_complex_example_from_csv_distance_matrix_doc_12_3_diff_files PROPERTIES DEPENDS Rips_complex_example_from_csv_distance_matrix_doc_12_3) + endif() install(TARGETS Rips_complex_example_from_off DESTINATION bin) diff --git a/src/Rips_complex/example/example_one_skeleton_rips_from_correlation_matrix.cpp b/src/Rips_complex/example/example_one_skeleton_rips_from_correlation_matrix.cpp index 05bacb9f..3d2ba54f 100644 --- a/src/Rips_complex/example/example_one_skeleton_rips_from_correlation_matrix.cpp +++ b/src/Rips_complex/example/example_one_skeleton_rips_from_correlation_matrix.cpp @@ -63,18 +63,18 @@ int main() { // have a reverse filtration (i.e. filtration of boundary of each simplex S // is greater or equal to the filtration of S). // ---------------------------------------------------------------------------- - std::cout << "Rips complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " + std::clog << "Rips complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " << stree.num_vertices() << " vertices." << std::endl; - std::cout << "Iterator on Rips complex simplices in the filtration order, with [filtration value]:" << std::endl; + std::clog << "Iterator on Rips complex simplices in the filtration order, with [filtration value]:" << std::endl; for (auto f_simplex : stree.filtration_simplex_range()) { - std::cout << " ( "; + std::clog << " ( "; for (auto vertex : stree.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> " + std::clog << ") -> " << "[" << stree.filtration(f_simplex) << "] "; - std::cout << std::endl; + std::clog << std::endl; } return 0; diff --git a/src/Rips_complex/example/example_one_skeleton_rips_from_distance_matrix.cpp b/src/Rips_complex/example/example_one_skeleton_rips_from_distance_matrix.cpp index bbc3c755..25f93b03 100644 --- a/src/Rips_complex/example/example_one_skeleton_rips_from_distance_matrix.cpp +++ b/src/Rips_complex/example/example_one_skeleton_rips_from_distance_matrix.cpp @@ -39,19 +39,19 @@ int main() { // ---------------------------------------------------------------------------- // Display information about the one skeleton Rips complex // ---------------------------------------------------------------------------- - std::cout << "Rips complex is of dimension " << stree.dimension() << + std::clog << "Rips complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " << stree.num_vertices() << " vertices." << std::endl; - std::cout << "Iterator on Rips complex simplices in the filtration order, with [filtration value]:" << + std::clog << "Iterator on Rips complex simplices in the filtration order, with [filtration value]:" << std::endl; for (auto f_simplex : stree.filtration_simplex_range()) { - std::cout << " ( "; + std::clog << " ( "; for (auto vertex : stree.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> " << "[" << stree.filtration(f_simplex) << "] "; - std::cout << std::endl; + std::clog << ") -> " << "[" << stree.filtration(f_simplex) << "] "; + std::clog << std::endl; } return 0; diff --git a/src/Rips_complex/example/example_one_skeleton_rips_from_points.cpp b/src/Rips_complex/example/example_one_skeleton_rips_from_points.cpp index a1db8910..d9df245b 100644 --- a/src/Rips_complex/example/example_one_skeleton_rips_from_points.cpp +++ b/src/Rips_complex/example/example_one_skeleton_rips_from_points.cpp @@ -34,19 +34,19 @@ int main() { // ---------------------------------------------------------------------------- // Display information about the one skeleton Rips complex // ---------------------------------------------------------------------------- - std::cout << "Rips complex is of dimension " << stree.dimension() << + std::clog << "Rips complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " << stree.num_vertices() << " vertices." << std::endl; - std::cout << "Iterator on Rips complex simplices in the filtration order, with [filtration value]:" << + std::clog << "Iterator on Rips complex simplices in the filtration order, with [filtration value]:" << std::endl; for (auto f_simplex : stree.filtration_simplex_range()) { - std::cout << " ( "; + std::clog << " ( "; for (auto vertex : stree.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> " << "[" << stree.filtration(f_simplex) << "] "; - std::cout << std::endl; + std::clog << ") -> " << "[" << stree.filtration(f_simplex) << "] "; + std::clog << std::endl; } return 0; } diff --git a/src/Rips_complex/example/example_rips_complex_from_csv_distance_matrix_file.cpp b/src/Rips_complex/example/example_rips_complex_from_csv_distance_matrix_file.cpp index b7040453..c0c57e7b 100644 --- a/src/Rips_complex/example/example_rips_complex_from_csv_distance_matrix_file.cpp +++ b/src/Rips_complex/example/example_rips_complex_from_csv_distance_matrix_file.cpp @@ -42,7 +42,7 @@ int main(int argc, char **argv) { ouput_file_stream.open(std::string(argv[4])); streambuffer = ouput_file_stream.rdbuf(); } else { - streambuffer = std::cout.rdbuf(); + streambuffer = std::clog.rdbuf(); } Simplex_tree stree; diff --git a/src/Rips_complex/example/example_rips_complex_from_off_file.cpp b/src/Rips_complex/example/example_rips_complex_from_off_file.cpp index 36b468a7..9aa7a657 100644 --- a/src/Rips_complex/example/example_rips_complex_from_off_file.cpp +++ b/src/Rips_complex/example/example_rips_complex_from_off_file.cpp @@ -41,7 +41,7 @@ int main(int argc, char **argv) { ouput_file_stream.open(std::string(argv[4])); streambuffer = ouput_file_stream.rdbuf(); } else { - streambuffer = std::cout.rdbuf(); + streambuffer = std::clog.rdbuf(); } Simplex_tree stree; diff --git a/src/Rips_complex/example/example_sparse_rips.cpp b/src/Rips_complex/example/example_sparse_rips.cpp index 1c95b48c..4bd31103 100644 --- a/src/Rips_complex/example/example_sparse_rips.cpp +++ b/src/Rips_complex/example/example_sparse_rips.cpp @@ -25,6 +25,6 @@ int main() { // ---------------------------------------------------------------------------- // Display information about the complex // ---------------------------------------------------------------------------- - std::cout << "Sparse Rips complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() + std::clog << "Sparse Rips complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " << stree.num_vertices() << " vertices." << std::endl; } diff --git a/src/Rips_complex/test/test_rips_complex.cpp b/src/Rips_complex/test/test_rips_complex.cpp index 1225f8df..19dcd283 100644 --- a/src/Rips_complex/test/test_rips_complex.cpp +++ b/src/Rips_complex/test/test_rips_complex.cpp @@ -43,7 +43,7 @@ BOOST_AUTO_TEST_CASE(RIPS_DOC_OFF_file) { // ---------------------------------------------------------------------------- std::string off_file_name("alphacomplexdoc.off"); double rips_threshold = 12.0; - std::cout << "========== OFF FILE NAME = " << off_file_name << " - Rips threshold=" << + std::clog << "========== OFF FILE NAME = " << off_file_name << " - Rips threshold=" << rips_threshold << "==========" << std::endl; Gudhi::Points_off_reader<Point> off_reader(off_file_name); @@ -52,14 +52,14 @@ BOOST_AUTO_TEST_CASE(RIPS_DOC_OFF_file) { const int DIMENSION_1 = 1; Simplex_tree st; rips_complex_from_file.create_complex(st, DIMENSION_1); - std::cout << "st.dimension()=" << st.dimension() << std::endl; + std::clog << "st.dimension()=" << st.dimension() << std::endl; BOOST_CHECK(st.dimension() == DIMENSION_1); const int NUMBER_OF_VERTICES = 7; - std::cout << "st.num_vertices()=" << st.num_vertices() << std::endl; + std::clog << "st.num_vertices()=" << st.num_vertices() << std::endl; BOOST_CHECK(st.num_vertices() == NUMBER_OF_VERTICES); - std::cout << "st.num_simplices()=" << st.num_simplices() << std::endl; + std::clog << "st.num_simplices()=" << st.num_simplices() << std::endl; BOOST_CHECK(st.num_simplices() == 18); // Check filtration values of vertices is 0.0 @@ -71,12 +71,12 @@ BOOST_AUTO_TEST_CASE(RIPS_DOC_OFF_file) { for (auto f_simplex : st.skeleton_simplex_range(DIMENSION_1)) { if (DIMENSION_1 == st.dimension(f_simplex)) { std::vector<Point> vp; - std::cout << "vertex = ("; + std::clog << "vertex = ("; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << vertex << ","; + std::clog << vertex << ","; vp.push_back(off_reader.get_point_cloud().at(vertex)); } - std::cout << ") - distance =" << Gudhi::Euclidean_distance()(vp.at(0), vp.at(1)) << + std::clog << ") - distance =" << Gudhi::Euclidean_distance()(vp.at(0), vp.at(1)) << " - filtration =" << st.filtration(f_simplex) << std::endl; BOOST_CHECK(vp.size() == 2); GUDHI_TEST_FLOAT_EQUALITY_CHECK(st.filtration(f_simplex), Gudhi::Euclidean_distance()(vp.at(0), vp.at(1))); @@ -86,46 +86,46 @@ BOOST_AUTO_TEST_CASE(RIPS_DOC_OFF_file) { const int DIMENSION_2 = 2; Simplex_tree st2; rips_complex_from_file.create_complex(st2, DIMENSION_2); - std::cout << "st2.dimension()=" << st2.dimension() << std::endl; + std::clog << "st2.dimension()=" << st2.dimension() << std::endl; BOOST_CHECK(st2.dimension() == DIMENSION_2); - std::cout << "st2.num_vertices()=" << st2.num_vertices() << std::endl; + std::clog << "st2.num_vertices()=" << st2.num_vertices() << std::endl; BOOST_CHECK(st2.num_vertices() == NUMBER_OF_VERTICES); - std::cout << "st2.num_simplices()=" << st2.num_simplices() << std::endl; + std::clog << "st2.num_simplices()=" << st2.num_simplices() << std::endl; BOOST_CHECK(st2.num_simplices() == 23); Simplex_tree::Filtration_value f01 = st2.filtration(st2.find({0, 1})); Simplex_tree::Filtration_value f02 = st2.filtration(st2.find({0, 2})); Simplex_tree::Filtration_value f12 = st2.filtration(st2.find({1, 2})); Simplex_tree::Filtration_value f012 = st2.filtration(st2.find({0, 1, 2})); - std::cout << "f012= " << f012 << " | f01= " << f01 << " - f02= " << f02 << " - f12= " << f12 << std::endl; + std::clog << "f012= " << f012 << " | f01= " << f01 << " - f02= " << f02 << " - f12= " << f12 << std::endl; GUDHI_TEST_FLOAT_EQUALITY_CHECK(f012, std::max(f01, std::max(f02,f12))); Simplex_tree::Filtration_value f45 = st2.filtration(st2.find({4, 5})); Simplex_tree::Filtration_value f56 = st2.filtration(st2.find({5, 6})); Simplex_tree::Filtration_value f46 = st2.filtration(st2.find({4, 6})); Simplex_tree::Filtration_value f456 = st2.filtration(st2.find({4, 5, 6})); - std::cout << "f456= " << f456 << " | f45= " << f45 << " - f56= " << f56 << " - f46= " << f46 << std::endl; + std::clog << "f456= " << f456 << " | f45= " << f45 << " - f56= " << f56 << " - f46= " << f46 << std::endl; GUDHI_TEST_FLOAT_EQUALITY_CHECK(f456, std::max(f45, std::max(f56,f46))); const int DIMENSION_3 = 3; Simplex_tree st3; rips_complex_from_file.create_complex(st3, DIMENSION_3); - std::cout << "st3.dimension()=" << st3.dimension() << std::endl; + std::clog << "st3.dimension()=" << st3.dimension() << std::endl; BOOST_CHECK(st3.dimension() == DIMENSION_3); - std::cout << "st3.num_vertices()=" << st3.num_vertices() << std::endl; + std::clog << "st3.num_vertices()=" << st3.num_vertices() << std::endl; BOOST_CHECK(st3.num_vertices() == NUMBER_OF_VERTICES); - std::cout << "st3.num_simplices()=" << st3.num_simplices() << std::endl; + std::clog << "st3.num_simplices()=" << st3.num_simplices() << std::endl; BOOST_CHECK(st3.num_simplices() == 24); Simplex_tree::Filtration_value f123 = st3.filtration(st3.find({1, 2, 3})); Simplex_tree::Filtration_value f013 = st3.filtration(st3.find({0, 1, 3})); Simplex_tree::Filtration_value f023 = st3.filtration(st3.find({0, 2, 3})); Simplex_tree::Filtration_value f0123 = st3.filtration(st3.find({0, 1, 2, 3})); - std::cout << "f0123= " << f0123 << " | f012= " << f012 << " - f123= " << f123 << " - f013= " << f013 << + std::clog << "f0123= " << f0123 << " | f012= " << f012 << " - f123= " << f123 << " - f013= " << f013 << " - f023= " << f023 << std::endl; GUDHI_TEST_FLOAT_EQUALITY_CHECK(f0123, std::max(f012, std::max(f123, std::max(f013, f023)))); @@ -176,34 +176,34 @@ BOOST_AUTO_TEST_CASE(Rips_complex_from_points) { // ---------------------------------------------------------------------------- Rips_complex rips_complex_from_points(points, 2.0, Custom_square_euclidean_distance()); - std::cout << "========== Rips_complex_from_points ==========" << std::endl; + std::clog << "========== Rips_complex_from_points ==========" << std::endl; Simplex_tree st; const int DIMENSION = 3; rips_complex_from_points.create_complex(st, DIMENSION); // Another way to check num_simplices - std::cout << "Iterator on Rips complex simplices in the filtration order, with [filtration value]:" << std::endl; + std::clog << "Iterator on Rips complex simplices in the filtration order, with [filtration value]:" << std::endl; int num_simplices = 0; for (auto f_simplex : st.filtration_simplex_range()) { num_simplices++; - std::cout << " ( "; + std::clog << " ( "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> " << "[" << st.filtration(f_simplex) << "] "; - std::cout << std::endl; + std::clog << ") -> " << "[" << st.filtration(f_simplex) << "] "; + std::clog << std::endl; } BOOST_CHECK(num_simplices == 15); - std::cout << "st.num_simplices()=" << st.num_simplices() << std::endl; + std::clog << "st.num_simplices()=" << st.num_simplices() << std::endl; BOOST_CHECK(st.num_simplices() == 15); - std::cout << "st.dimension()=" << st.dimension() << std::endl; + std::clog << "st.dimension()=" << st.dimension() << std::endl; BOOST_CHECK(st.dimension() == DIMENSION); - std::cout << "st.num_vertices()=" << st.num_vertices() << std::endl; + std::clog << "st.num_vertices()=" << st.num_vertices() << std::endl; BOOST_CHECK(st.num_vertices() == 4); for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << "dimension(" << st.dimension(f_simplex) << ") - f = " << st.filtration(f_simplex) << std::endl; + std::clog << "dimension(" << st.dimension(f_simplex) << ") - f = " << st.filtration(f_simplex) << std::endl; switch (st.dimension(f_simplex)) { case 0: GUDHI_TEST_FLOAT_EQUALITY_CHECK(st.filtration(f_simplex), 0.0); @@ -241,34 +241,34 @@ BOOST_AUTO_TEST_CASE(Sparse_rips_complex_from_points) { // .001 is small enough that we get a deterministic result matching the exact Rips Sparse_rips_complex sparse_rips(points, Custom_square_euclidean_distance(), .001); - std::cout << "========== Sparse_rips_complex_from_points ==========" << std::endl; + std::clog << "========== Sparse_rips_complex_from_points ==========" << std::endl; Simplex_tree st; const int DIMENSION = 3; sparse_rips.create_complex(st, DIMENSION); // Another way to check num_simplices - std::cout << "Iterator on Rips complex simplices in the filtration order, with [filtration value]:" << std::endl; + std::clog << "Iterator on Rips complex simplices in the filtration order, with [filtration value]:" << std::endl; int num_simplices = 0; for (auto f_simplex : st.filtration_simplex_range()) { num_simplices++; - std::cout << " ( "; + std::clog << " ( "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << ") -> " << "[" << st.filtration(f_simplex) << "] "; - std::cout << std::endl; + std::clog << ") -> " << "[" << st.filtration(f_simplex) << "] "; + std::clog << std::endl; } BOOST_CHECK(num_simplices == 15); - std::cout << "st.num_simplices()=" << st.num_simplices() << std::endl; + std::clog << "st.num_simplices()=" << st.num_simplices() << std::endl; BOOST_CHECK(st.num_simplices() == 15); - std::cout << "st.dimension()=" << st.dimension() << std::endl; + std::clog << "st.dimension()=" << st.dimension() << std::endl; BOOST_CHECK(st.dimension() == DIMENSION); - std::cout << "st.num_vertices()=" << st.num_vertices() << std::endl; + std::clog << "st.num_vertices()=" << st.num_vertices() << std::endl; BOOST_CHECK(st.num_vertices() == 4); for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << "dimension(" << st.dimension(f_simplex) << ") - f = " << st.filtration(f_simplex) << std::endl; + std::clog << "dimension(" << st.dimension(f_simplex) << ") - f = " << st.filtration(f_simplex) << std::endl; switch (st.dimension(f_simplex)) { case 0: GUDHI_TEST_FLOAT_EQUALITY_CHECK(st.filtration(f_simplex), 0.0); @@ -293,7 +293,7 @@ BOOST_AUTO_TEST_CASE(Rips_doc_csv_file) { // ---------------------------------------------------------------------------- std::string csv_file_name("full_square_distance_matrix.csv"); double rips_threshold = 12.0; - std::cout << "========== CSV FILE NAME = " << csv_file_name << " - Rips threshold=" << + std::clog << "========== CSV FILE NAME = " << csv_file_name << " - Rips threshold=" << rips_threshold << "==========" << std::endl; Distance_matrix distances = Gudhi::read_lower_triangular_matrix_from_csv_file<Filtration_value>(csv_file_name); @@ -302,14 +302,14 @@ BOOST_AUTO_TEST_CASE(Rips_doc_csv_file) { const int DIMENSION_1 = 1; Simplex_tree st; rips_complex_from_file.create_complex(st, DIMENSION_1); - std::cout << "st.dimension()=" << st.dimension() << std::endl; + std::clog << "st.dimension()=" << st.dimension() << std::endl; BOOST_CHECK(st.dimension() == DIMENSION_1); const int NUMBER_OF_VERTICES = 7; - std::cout << "st.num_vertices()=" << st.num_vertices() << std::endl; + std::clog << "st.num_vertices()=" << st.num_vertices() << std::endl; BOOST_CHECK(st.num_vertices() == NUMBER_OF_VERTICES); - std::cout << "st.num_simplices()=" << st.num_simplices() << std::endl; + std::clog << "st.num_simplices()=" << st.num_simplices() << std::endl; BOOST_CHECK(st.num_simplices() == 18); // Check filtration values of vertices is 0.0 @@ -321,12 +321,12 @@ BOOST_AUTO_TEST_CASE(Rips_doc_csv_file) { for (auto f_simplex : st.skeleton_simplex_range(DIMENSION_1)) { if (DIMENSION_1 == st.dimension(f_simplex)) { std::vector<Simplex_tree::Vertex_handle> vvh; - std::cout << "vertex = ("; + std::clog << "vertex = ("; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << vertex << ","; + std::clog << vertex << ","; vvh.push_back(vertex); } - std::cout << ") - filtration =" << st.filtration(f_simplex) << std::endl; + std::clog << ") - filtration =" << st.filtration(f_simplex) << std::endl; BOOST_CHECK(vvh.size() == 2); GUDHI_TEST_FLOAT_EQUALITY_CHECK(st.filtration(f_simplex), distances[vvh.at(0)][vvh.at(1)]); } @@ -335,46 +335,46 @@ BOOST_AUTO_TEST_CASE(Rips_doc_csv_file) { const int DIMENSION_2 = 2; Simplex_tree st2; rips_complex_from_file.create_complex(st2, DIMENSION_2); - std::cout << "st2.dimension()=" << st2.dimension() << std::endl; + std::clog << "st2.dimension()=" << st2.dimension() << std::endl; BOOST_CHECK(st2.dimension() == DIMENSION_2); - std::cout << "st2.num_vertices()=" << st2.num_vertices() << std::endl; + std::clog << "st2.num_vertices()=" << st2.num_vertices() << std::endl; BOOST_CHECK(st2.num_vertices() == NUMBER_OF_VERTICES); - std::cout << "st2.num_simplices()=" << st2.num_simplices() << std::endl; + std::clog << "st2.num_simplices()=" << st2.num_simplices() << std::endl; BOOST_CHECK(st2.num_simplices() == 23); Simplex_tree::Filtration_value f01 = st2.filtration(st2.find({0, 1})); Simplex_tree::Filtration_value f02 = st2.filtration(st2.find({0, 2})); Simplex_tree::Filtration_value f12 = st2.filtration(st2.find({1, 2})); Simplex_tree::Filtration_value f012 = st2.filtration(st2.find({0, 1, 2})); - std::cout << "f012= " << f012 << " | f01= " << f01 << " - f02= " << f02 << " - f12= " << f12 << std::endl; + std::clog << "f012= " << f012 << " | f01= " << f01 << " - f02= " << f02 << " - f12= " << f12 << std::endl; GUDHI_TEST_FLOAT_EQUALITY_CHECK(f012, std::max(f01, std::max(f02,f12))); Simplex_tree::Filtration_value f45 = st2.filtration(st2.find({4, 5})); Simplex_tree::Filtration_value f56 = st2.filtration(st2.find({5, 6})); Simplex_tree::Filtration_value f46 = st2.filtration(st2.find({4, 6})); Simplex_tree::Filtration_value f456 = st2.filtration(st2.find({4, 5, 6})); - std::cout << "f456= " << f456 << " | f45= " << f45 << " - f56= " << f56 << " - f46= " << f46 << std::endl; + std::clog << "f456= " << f456 << " | f45= " << f45 << " - f56= " << f56 << " - f46= " << f46 << std::endl; GUDHI_TEST_FLOAT_EQUALITY_CHECK(f456, std::max(f45, std::max(f56,f46))); const int DIMENSION_3 = 3; Simplex_tree st3; rips_complex_from_file.create_complex(st3, DIMENSION_3); - std::cout << "st3.dimension()=" << st3.dimension() << std::endl; + std::clog << "st3.dimension()=" << st3.dimension() << std::endl; BOOST_CHECK(st3.dimension() == DIMENSION_3); - std::cout << "st3.num_vertices()=" << st3.num_vertices() << std::endl; + std::clog << "st3.num_vertices()=" << st3.num_vertices() << std::endl; BOOST_CHECK(st3.num_vertices() == NUMBER_OF_VERTICES); - std::cout << "st3.num_simplices()=" << st3.num_simplices() << std::endl; + std::clog << "st3.num_simplices()=" << st3.num_simplices() << std::endl; BOOST_CHECK(st3.num_simplices() == 24); Simplex_tree::Filtration_value f123 = st3.filtration(st3.find({1, 2, 3})); Simplex_tree::Filtration_value f013 = st3.filtration(st3.find({0, 1, 3})); Simplex_tree::Filtration_value f023 = st3.filtration(st3.find({0, 2, 3})); Simplex_tree::Filtration_value f0123 = st3.filtration(st3.find({0, 1, 2, 3})); - std::cout << "f0123= " << f0123 << " | f012= " << f012 << " - f123= " << f123 << " - f013= " << f013 << + std::clog << "f0123= " << f0123 << " | f012= " << f012 << " - f123= " << f123 << " - f013= " << f013 << " - f023= " << f023 << std::endl; GUDHI_TEST_FLOAT_EQUALITY_CHECK(f0123, std::max(f012, std::max(f123, std::max(f013, f023)))); @@ -389,7 +389,7 @@ BOOST_AUTO_TEST_CASE(Rips_create_complex_throw) { // ---------------------------------------------------------------------------- std::string off_file_name("alphacomplexdoc.off"); double rips_threshold = 12.0; - std::cout << "========== OFF FILE NAME = " << off_file_name << " - Rips threshold=" << + std::clog << "========== OFF FILE NAME = " << off_file_name << " - Rips threshold=" << rips_threshold << "==========" << std::endl; Gudhi::Points_off_reader<Point> off_reader(off_file_name); @@ -398,7 +398,7 @@ BOOST_AUTO_TEST_CASE(Rips_create_complex_throw) { Simplex_tree stree; std::vector<int> simplex = {0, 1, 2}; stree.insert_simplex_and_subfaces(simplex); - std::cout << "Check exception throw in debug mode" << std::endl; + std::clog << "Check exception throw in debug mode" << std::endl; // throw excpt because stree is not empty BOOST_CHECK_THROW (rips_complex_from_file.create_complex(stree, 1), std::invalid_argument); } diff --git a/src/Rips_complex/utilities/CMakeLists.txt b/src/Rips_complex/utilities/CMakeLists.txt index 4b565628..d2448d7b 100644 --- a/src/Rips_complex/utilities/CMakeLists.txt +++ b/src/Rips_complex/utilities/CMakeLists.txt @@ -1,16 +1,16 @@ project(Rips_complex_utilities) add_executable(rips_distance_matrix_persistence rips_distance_matrix_persistence.cpp) -target_link_libraries(rips_distance_matrix_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) +target_link_libraries(rips_distance_matrix_persistence Boost::program_options) add_executable(rips_persistence rips_persistence.cpp) -target_link_libraries(rips_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) +target_link_libraries(rips_persistence Boost::program_options) add_executable(rips_correlation_matrix_persistence rips_correlation_matrix_persistence.cpp) -target_link_libraries(rips_correlation_matrix_persistence ${Boost_SYSTEM_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) +target_link_libraries(rips_correlation_matrix_persistence Boost::system Boost::program_options) add_executable(sparse_rips_persistence sparse_rips_persistence.cpp) -target_link_libraries(sparse_rips_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) +target_link_libraries(sparse_rips_persistence Boost::program_options) if (TBB_FOUND) target_link_libraries(rips_distance_matrix_persistence ${TBB_LIBRARIES}) diff --git a/src/Rips_complex/utilities/rips_correlation_matrix_persistence.cpp b/src/Rips_complex/utilities/rips_correlation_matrix_persistence.cpp index 585de4a0..b473738e 100644 --- a/src/Rips_complex/utilities/rips_correlation_matrix_persistence.cpp +++ b/src/Rips_complex/utilities/rips_correlation_matrix_persistence.cpp @@ -68,11 +68,8 @@ int main(int argc, char* argv[]) { Simplex_tree simplex_tree; rips_complex_from_file.create_complex(simplex_tree, dim_max); - std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; - std::cout << " and has dimension " << simplex_tree.dimension() << " \n"; - - // Sort the simplices in the order of the filtration - simplex_tree.initialize_filtration(); + std::clog << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; + std::clog << " and has dimension " << simplex_tree.dimension() << " \n"; // Compute the persistence diagram of the complex Persistent_cohomology pcoh(simplex_tree); @@ -121,7 +118,7 @@ void program_options(int argc, char* argv[], std::string& csv_matrix_file, std:: po::options_description visible("Allowed options", 100); visible.add_options()("help,h", "produce help message")( "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout")( + "Name of file in which the persistence diagram is written. Default print in std::clog")( "min-edge-corelation,c", po::value<Filtration_value>(&correlation_min)->default_value(0), "Minimal corelation of an edge for the Rips complex construction.")( "cpx-dimension,d", po::value<int>(&dim_max)->default_value(1), @@ -143,17 +140,17 @@ void program_options(int argc, char* argv[], std::string& csv_matrix_file, std:: po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of a Rips complex defined on a corelation matrix.\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of a Rips complex defined on a corelation matrix.\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Rips_complex/utilities/rips_distance_matrix_persistence.cpp b/src/Rips_complex/utilities/rips_distance_matrix_persistence.cpp index ad429e11..6306755d 100644 --- a/src/Rips_complex/utilities/rips_distance_matrix_persistence.cpp +++ b/src/Rips_complex/utilities/rips_distance_matrix_persistence.cpp @@ -47,11 +47,8 @@ int main(int argc, char* argv[]) { Simplex_tree simplex_tree; rips_complex_from_file.create_complex(simplex_tree, dim_max); - std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; - std::cout << " and has dimension " << simplex_tree.dimension() << " \n"; - - // Sort the simplices in the order of the filtration - simplex_tree.initialize_filtration(); + std::clog << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; + std::clog << " and has dimension " << simplex_tree.dimension() << " \n"; // Compute the persistence diagram of the complex Persistent_cohomology pcoh(simplex_tree); @@ -82,7 +79,7 @@ void program_options(int argc, char* argv[], std::string& csv_matrix_file, std:: po::options_description visible("Allowed options", 100); visible.add_options()("help,h", "produce help message")( "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout")( + "Name of file in which the persistence diagram is written. Default print in std::clog")( "max-edge-length,r", po::value<Filtration_value>(&threshold)->default_value(std::numeric_limits<Filtration_value>::infinity()), "Maximal length of an edge for the Rips complex construction.")( @@ -105,17 +102,17 @@ void program_options(int argc, char* argv[], std::string& csv_matrix_file, std:: po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of a Rips complex defined on a set of distance matrix.\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of a Rips complex defined on a set of distance matrix.\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Rips_complex/utilities/rips_persistence.cpp b/src/Rips_complex/utilities/rips_persistence.cpp index daa7e1db..9d7490b3 100644 --- a/src/Rips_complex/utilities/rips_persistence.cpp +++ b/src/Rips_complex/utilities/rips_persistence.cpp @@ -49,11 +49,8 @@ int main(int argc, char* argv[]) { Simplex_tree simplex_tree; rips_complex_from_file.create_complex(simplex_tree, dim_max); - std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; - std::cout << " and has dimension " << simplex_tree.dimension() << " \n"; - - // Sort the simplices in the order of the filtration - simplex_tree.initialize_filtration(); + std::clog << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; + std::clog << " and has dimension " << simplex_tree.dimension() << " \n"; // Compute the persistence diagram of the complex Persistent_cohomology pcoh(simplex_tree); @@ -84,7 +81,7 @@ void program_options(int argc, char* argv[], std::string& off_file_points, std:: po::options_description visible("Allowed options", 100); visible.add_options()("help,h", "produce help message")( "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout")( + "Name of file in which the persistence diagram is written. Default print in std::clog")( "max-edge-length,r", po::value<Filtration_value>(&threshold)->default_value(std::numeric_limits<Filtration_value>::infinity()), "Maximal length of an edge for the Rips complex construction.")( @@ -107,17 +104,17 @@ void program_options(int argc, char* argv[], std::string& off_file_points, std:: po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of a Rips complex defined on a set of input points.\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of a Rips complex defined on a set of input points.\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Rips_complex/utilities/sparse_rips_persistence.cpp b/src/Rips_complex/utilities/sparse_rips_persistence.cpp index cefd8a67..ac935b41 100644 --- a/src/Rips_complex/utilities/sparse_rips_persistence.cpp +++ b/src/Rips_complex/utilities/sparse_rips_persistence.cpp @@ -51,11 +51,8 @@ int main(int argc, char* argv[]) { Simplex_tree simplex_tree; sparse_rips.create_complex(simplex_tree, dim_max); - std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; - std::cout << " and has dimension " << simplex_tree.dimension() << " \n"; - - // Sort the simplices in the order of the filtration - simplex_tree.initialize_filtration(); + std::clog << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; + std::clog << " and has dimension " << simplex_tree.dimension() << " \n"; // Compute the persistence diagram of the complex Persistent_cohomology pcoh(simplex_tree); @@ -87,7 +84,7 @@ void program_options(int argc, char* argv[], std::string& off_file_points, std:: po::options_description visible("Allowed options", 100); visible.add_options()("help,h", "produce help message")( "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout")( + "Name of file in which the persistence diagram is written. Default print in std::clog")( "max-edge-length,r", po::value<Filtration_value>(&threshold)->default_value(std::numeric_limits<Filtration_value>::infinity()), "Maximal length of an edge for the Rips complex construction.")( @@ -112,17 +109,17 @@ void program_options(int argc, char* argv[], std::string& off_file_points, std:: po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of a sparse 1/(1-epsilon)-approximation of the Rips complex \ndefined on a set of input points.\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of a sparse 1/(1-epsilon)-approximation of the Rips complex \ndefined on a set of input points.\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Simplex_tree/example/CMakeLists.txt b/src/Simplex_tree/example/CMakeLists.txt index 8a8cac58..a0aabee2 100644 --- a/src/Simplex_tree/example/CMakeLists.txt +++ b/src/Simplex_tree/example/CMakeLists.txt @@ -35,7 +35,7 @@ endif() if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) add_executable ( Simplex_tree_example_cech_complex_cgal_mini_sphere_3d cech_complex_cgal_mini_sphere_3d.cpp ) - target_link_libraries(Simplex_tree_example_cech_complex_cgal_mini_sphere_3d ${Boost_PROGRAM_OPTIONS_LIBRARY} ${CGAL_LIBRARY}) + target_link_libraries(Simplex_tree_example_cech_complex_cgal_mini_sphere_3d Boost::program_options ${CGAL_LIBRARY}) if (TBB_FOUND) target_link_libraries(Simplex_tree_example_cech_complex_cgal_mini_sphere_3d ${TBB_LIBRARIES}) endif() diff --git a/src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp b/src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp index d716fb1f..0e7e382b 100644 --- a/src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp +++ b/src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp @@ -55,18 +55,18 @@ class Cech_blocker { bool operator()(Simplex_handle sh) { std::vector<Point> points; #if DEBUG_TRACES - std::cout << "Cech_blocker on ["; + std::clog << "Cech_blocker on ["; #endif // DEBUG_TRACES for (auto vertex : simplex_tree_.simplex_vertex_range(sh)) { points.push_back(point_cloud_[vertex]); #if DEBUG_TRACES - std::cout << vertex << ", "; + std::clog << vertex << ", "; #endif // DEBUG_TRACES } Min_sphere ms(points.begin(), points.end()); Filtration_value radius = ms.radius(); #if DEBUG_TRACES - std::cout << "] - radius = " << radius << " - returns " << (radius > threshold_) << std::endl; + std::clog << "] - radius = " << radius << " - returns " << (radius > threshold_) << std::endl; #endif // DEBUG_TRACES simplex_tree_.assign_filtration(sh, radius); return (radius > threshold_); @@ -106,24 +106,24 @@ int main(int argc, char* argv[]) { // expand the graph until dimension dim_max st.expansion_with_blockers(dim_max, Cech_blocker(st, threshold, off_reader.get_point_cloud())); - std::cout << "The complex contains " << st.num_simplices() << " simplices \n"; - std::cout << " and has dimension " << st.dimension() << " \n"; + std::clog << "The complex contains " << st.num_simplices() << " simplices \n"; + std::clog << " and has dimension " << st.dimension() << " \n"; // Sort the simplices in the order of the filtration st.initialize_filtration(); #if DEBUG_TRACES - std::cout << "********************************************************************\n"; + std::clog << "********************************************************************\n"; // Display the Simplex_tree - Can not be done in the middle of 2 inserts - std::cout << "* The complex contains " << st.num_simplices() << " simplices - dimension=" << st.dimension() << "\n"; - std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + std::clog << "* The complex contains " << st.num_simplices() << " simplices - dimension=" << st.dimension() << "\n"; + std::clog << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " + std::clog << " " << "[" << st.filtration(f_simplex) << "] "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << static_cast<int>(vertex) << " "; + std::clog << static_cast<int>(vertex) << " "; } - std::cout << std::endl; + std::clog << std::endl; } #endif // DEBUG_TRACES return 0; @@ -154,11 +154,11 @@ void program_options(int argc, char* argv[], std::string& off_file_points, Filtr po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Construct a Cech complex defined on a set of input points.\n \n"; + std::clog << std::endl; + std::clog << "Construct a Cech complex defined on a set of input points.\n \n"; - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Simplex_tree/example/example_alpha_shapes_3_simplex_tree_from_off_file.cpp b/src/Simplex_tree/example/example_alpha_shapes_3_simplex_tree_from_off_file.cpp index e455c426..8ee7ab74 100644 --- a/src/Simplex_tree/example/example_alpha_shapes_3_simplex_tree_from_off_file.cpp +++ b/src/Simplex_tree/example/example_alpha_shapes_3_simplex_tree_from_off_file.cpp @@ -63,7 +63,7 @@ Vertex_list from(const Cell_handle& ch) { Vertex_list the_list; for (auto i = 0; i < 4; i++) { #ifdef DEBUG_TRACES - std::cout << "from cell[" << i << "]=" << ch->vertex(i)->point() << std::endl; + std::clog << "from cell[" << i << "]=" << ch->vertex(i)->point() << std::endl; #endif // DEBUG_TRACES the_list.push_back(ch->vertex(i)); } @@ -75,7 +75,7 @@ Vertex_list from(const Facet& fct) { for (auto i = 0; i < 4; i++) { if (fct.second != i) { #ifdef DEBUG_TRACES - std::cout << "from facet=[" << i << "]" << fct.first->vertex(i)->point() << std::endl; + std::clog << "from facet=[" << i << "]" << fct.first->vertex(i)->point() << std::endl; #endif // DEBUG_TRACES the_list.push_back(fct.first->vertex(i)); } @@ -88,7 +88,7 @@ Vertex_list from(const Edge& edg) { for (auto i = 0; i < 4; i++) { if ((edg.second == i) || (edg.third == i)) { #ifdef DEBUG_TRACES - std::cout << "from edge[" << i << "]=" << edg.first->vertex(i)->point() << std::endl; + std::clog << "from edge[" << i << "]=" << edg.first->vertex(i)->point() << std::endl; #endif // DEBUG_TRACES the_list.push_back(edg.first->vertex(i)); } @@ -99,7 +99,7 @@ Vertex_list from(const Edge& edg) { Vertex_list from(const Alpha_shape_3::Vertex_handle& vh) { Vertex_list the_list; #ifdef DEBUG_TRACES - std::cout << "from vertex=" << vh->point() << std::endl; + std::clog << "from vertex=" << vh->point() << std::endl; #endif // DEBUG_TRACES the_list.push_back(vh); return the_list; @@ -128,7 +128,7 @@ int main(int argc, char * const argv[]) { // alpha shape construction from points. CGAL has a strange behavior in REGULARIZED mode. Alpha_shape_3 as(lp.begin(), lp.end(), 0, Alpha_shape_3::GENERAL); #ifdef DEBUG_TRACES - std::cout << "Alpha shape computed in GENERAL mode" << std::endl; + std::clog << "Alpha shape computed in GENERAL mode" << std::endl; #endif // DEBUG_TRACES // filtration with alpha values from alpha shape @@ -140,7 +140,7 @@ int main(int argc, char * const argv[]) { as.filtration_with_alpha_values(disp); #ifdef DEBUG_TRACES - std::cout << "filtration_with_alpha_values returns : " << the_objects.size() << " objects" << std::endl; + std::clog << "filtration_with_alpha_values returns : " << the_objects.size() << " objects" << std::endl; #endif // DEBUG_TRACES Alpha_shape_3::size_type count_vertices = 0; @@ -177,7 +177,7 @@ int main(int argc, char * const argv[]) { // alpha shape not found Simplex_tree_vertex vertex = map_cgal_simplex_tree.size(); #ifdef DEBUG_TRACES - std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] not found - insert_simplex " << vertex << "\n"; + std::clog << "vertex [" << the_alpha_shape_vertex->point() << "] not found - insert_simplex " << vertex << "\n"; #endif // DEBUG_TRACES the_simplex_tree.push_back(vertex); map_cgal_simplex_tree.insert(Alpha_shape_simplex_tree_pair(the_alpha_shape_vertex, vertex)); @@ -185,14 +185,14 @@ int main(int argc, char * const argv[]) { // alpha shape found Simplex_tree_vertex vertex = the_map_iterator->second; #ifdef DEBUG_TRACES - std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] found in " << vertex << std::endl; + std::clog << "vertex [" << the_alpha_shape_vertex->point() << "] found in " << vertex << std::endl; #endif // DEBUG_TRACES the_simplex_tree.push_back(vertex); } } // Construction of the simplex_tree #ifdef DEBUG_TRACES - std::cout << "filtration = " << *the_alpha_value_iterator << std::endl; + std::clog << "filtration = " << *the_alpha_value_iterator << std::endl; #endif // DEBUG_TRACES simplex_tree.insert_simplex(the_simplex_tree, std::sqrt(*the_alpha_value_iterator)); if (the_alpha_value_iterator != the_alpha_values.end()) @@ -201,61 +201,61 @@ int main(int argc, char * const argv[]) { std::cerr << "This shall not happen" << std::endl; } #ifdef DEBUG_TRACES - std::cout << "vertices \t\t" << count_vertices << std::endl; - std::cout << "edges \t\t" << count_edges << std::endl; - std::cout << "facets \t\t" << count_facets << std::endl; - std::cout << "cells \t\t" << count_cells << std::endl; + std::clog << "vertices \t\t" << count_vertices << std::endl; + std::clog << "edges \t\t" << count_edges << std::endl; + std::clog << "facets \t\t" << count_facets << std::endl; + std::clog << "cells \t\t" << count_cells << std::endl; - std::cout << "Information of the Simplex Tree:\n"; - std::cout << " Number of vertices = " << simplex_tree.num_vertices() << " "; - std::cout << " Number of simplices = " << simplex_tree.num_simplices() << std::endl << std::endl; + std::clog << "Information of the Simplex Tree:\n"; + std::clog << " Number of vertices = " << simplex_tree.num_vertices() << " "; + std::clog << " Number of simplices = " << simplex_tree.num_simplices() << std::endl << std::endl; #endif // DEBUG_TRACES #ifdef DEBUG_TRACES - std::cout << "Iterator on vertices: \n"; + std::clog << "Iterator on vertices: \n"; for (auto vertex : simplex_tree.complex_vertex_range()) { - std::cout << vertex << " "; + std::clog << vertex << " "; } #endif // DEBUG_TRACES - std::cout << simplex_tree << std::endl; + std::clog << simplex_tree << std::endl; #ifdef DEBUG_TRACES - std::cout << std::endl << std::endl << "Iterator on simplices:\n"; + std::clog << std::endl << std::endl << "Iterator on simplices:\n"; for (auto simplex : simplex_tree.complex_simplex_range()) { - std::cout << " "; + std::clog << " "; for (auto vertex : simplex_tree.simplex_vertex_range(simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } #endif // DEBUG_TRACES #ifdef DEBUG_TRACES - std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:\n"; + std::clog << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : simplex_tree.filtration_simplex_range()) { - std::cout << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; + std::clog << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; for (auto vertex : simplex_tree.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } #endif // DEBUG_TRACES #ifdef DEBUG_TRACES - std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, and their boundary simplices:\n"; + std::clog << std::endl << std::endl << "Iterator on Simplices in the filtration, and their boundary simplices:\n"; for (auto f_simplex : simplex_tree.filtration_simplex_range()) { - std::cout << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; + std::clog << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; for (auto vertex : simplex_tree.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; for (auto b_simplex : simplex_tree.boundary_simplex_range(f_simplex)) { - std::cout << " " << "[" << simplex_tree.filtration(b_simplex) << "] "; + std::clog << " " << "[" << simplex_tree.filtration(b_simplex) << "] "; for (auto vertex : simplex_tree.simplex_vertex_range(b_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } } #endif // DEBUG_TRACES diff --git a/src/Simplex_tree/example/graph_expansion_with_blocker.cpp b/src/Simplex_tree/example/graph_expansion_with_blocker.cpp index 494f8b1d..df52bf43 100644 --- a/src/Simplex_tree/example/graph_expansion_with_blocker.cpp +++ b/src/Simplex_tree/example/graph_expansion_with_blocker.cpp @@ -34,31 +34,31 @@ int main(int argc, char* const argv[]) { stree.expansion_with_blockers(3, [&](Simplex_handle sh) { bool result = false; - std::cout << "Blocker on ["; + std::clog << "Blocker on ["; // User can loop on the vertices from the given simplex_handle i.e. for (auto vertex : stree.simplex_vertex_range(sh)) { // We block the expansion, if the vertex '6' is in the given list of vertices if (vertex == 6) result = true; - std::cout << vertex << ", "; + std::clog << vertex << ", "; } - std::cout << "] ( " << stree.filtration(sh); + std::clog << "] ( " << stree.filtration(sh); // User can re-assign a new filtration value directly in the blocker (default is the maximal value of boudaries) stree.assign_filtration(sh, stree.filtration(sh) + 1.); - std::cout << " + 1. ) = " << result << std::endl; + std::clog << " + 1. ) = " << result << std::endl; return result; }); - std::cout << "********************************************************************\n"; - std::cout << "* The complex contains " << stree.num_simplices() << " simplices"; - std::cout << " - dimension " << stree.dimension() << "\n"; - std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + std::clog << "********************************************************************\n"; + std::clog << "* The complex contains " << stree.num_simplices() << " simplices"; + std::clog << " - dimension " << stree.dimension() << "\n"; + std::clog << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : stree.filtration_simplex_range()) { - std::cout << " " + std::clog << " " << "[" << stree.filtration(f_simplex) << "] "; - for (auto vertex : stree.simplex_vertex_range(f_simplex)) std::cout << "(" << vertex << ")"; - std::cout << std::endl; + for (auto vertex : stree.simplex_vertex_range(f_simplex)) std::clog << "(" << vertex << ")"; + std::clog << std::endl; } return 0; diff --git a/src/Simplex_tree/example/mini_simplex_tree.cpp b/src/Simplex_tree/example/mini_simplex_tree.cpp index bbc582c7..4043bffd 100644 --- a/src/Simplex_tree/example/mini_simplex_tree.cpp +++ b/src/Simplex_tree/example/mini_simplex_tree.cpp @@ -48,7 +48,7 @@ int main() { for (ST::Simplex_handle t : st.cofaces_simplex_range(e, 1)) { // Only coface is 012 for (ST::Vertex_handle v : st.simplex_vertex_range(t)) // v in { 0, 1, 2 } - std::cout << v; - std::cout << '\n'; + std::clog << v; + std::clog << '\n'; } } diff --git a/src/Simplex_tree/example/simple_simplex_tree.cpp b/src/Simplex_tree/example/simple_simplex_tree.cpp index 4353939f..e8bec596 100644 --- a/src/Simplex_tree/example/simple_simplex_tree.cpp +++ b/src/Simplex_tree/example/simple_simplex_tree.cpp @@ -28,8 +28,8 @@ int main(int argc, char* const argv[]) { const Filtration_value FOURTH_FILTRATION_VALUE = 0.4; // TEST OF INSERTION - std::cout << "********************************************************************" << std::endl; - std::cout << "EXAMPLE OF SIMPLE INSERTION" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "EXAMPLE OF SIMPLE INSERTION" << std::endl; // Construct the Simplex Tree Simplex_tree simplexTree; @@ -41,140 +41,139 @@ int main(int argc, char* const argv[]) { /* 2 0 3 */ // ++ FIRST - std::cout << " * INSERT 0" << std::endl; + std::clog << " * INSERT 0" << std::endl; typeVectorVertex firstSimplexVector = {0}; typePairSimplexBool returnValue = simplexTree.insert_simplex(firstSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); if (returnValue.second == true) { - std::cout << " + 0 INSERTED" << std::endl; + std::clog << " + 0 INSERTED" << std::endl; } else { - std::cout << " - 0 NOT INSERTED" << std::endl; + std::clog << " - 0 NOT INSERTED" << std::endl; } // ++ SECOND - std::cout << " * INSERT 1" << std::endl; + std::clog << " * INSERT 1" << std::endl; typeVectorVertex secondSimplexVector = {1}; returnValue = simplexTree.insert_simplex(secondSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); if (returnValue.second == true) { - std::cout << " + 1 INSERTED" << std::endl; + std::clog << " + 1 INSERTED" << std::endl; } else { - std::cout << " - 1 NOT INSERTED" << std::endl; + std::clog << " - 1 NOT INSERTED" << std::endl; } // ++ THIRD - std::cout << " * INSERT (0,1)" << std::endl; + std::clog << " * INSERT (0,1)" << std::endl; typeVectorVertex thirdSimplexVector = {0, 1}; returnValue = simplexTree.insert_simplex(thirdSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); if (returnValue.second == true) { - std::cout << " + (0,1) INSERTED" << std::endl; + std::clog << " + (0,1) INSERTED" << std::endl; } else { - std::cout << " - (0,1) NOT INSERTED" << std::endl; + std::clog << " - (0,1) NOT INSERTED" << std::endl; } // ++ FOURTH - std::cout << " * INSERT 2" << std::endl; + std::clog << " * INSERT 2" << std::endl; typeVectorVertex fourthSimplexVector = {2}; returnValue = simplexTree.insert_simplex(fourthSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); if (returnValue.second == true) { - std::cout << " + 2 INSERTED" << std::endl; + std::clog << " + 2 INSERTED" << std::endl; } else { - std::cout << " - 2 NOT INSERTED" << std::endl; + std::clog << " - 2 NOT INSERTED" << std::endl; } // ++ FIFTH - std::cout << " * INSERT (2,0)" << std::endl; + std::clog << " * INSERT (2,0)" << std::endl; typeVectorVertex fifthSimplexVector = {2, 0}; returnValue = simplexTree.insert_simplex(fifthSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); if (returnValue.second == true) { - std::cout << " + (2,0) INSERTED" << std::endl; + std::clog << " + (2,0) INSERTED" << std::endl; } else { - std::cout << " - (2,0) NOT INSERTED" << std::endl; + std::clog << " - (2,0) NOT INSERTED" << std::endl; } // ++ SIXTH - std::cout << " * INSERT (2,1)" << std::endl; + std::clog << " * INSERT (2,1)" << std::endl; typeVectorVertex sixthSimplexVector = {2, 1}; returnValue = simplexTree.insert_simplex(sixthSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); if (returnValue.second == true) { - std::cout << " + (2,1) INSERTED" << std::endl; + std::clog << " + (2,1) INSERTED" << std::endl; } else { - std::cout << " - (2,1) NOT INSERTED" << std::endl; + std::clog << " - (2,1) NOT INSERTED" << std::endl; } // ++ SEVENTH - std::cout << " * INSERT (2,1,0)" << std::endl; + std::clog << " * INSERT (2,1,0)" << std::endl; typeVectorVertex seventhSimplexVector = {2, 1, 0}; returnValue = simplexTree.insert_simplex(seventhSimplexVector, Filtration_value(THIRD_FILTRATION_VALUE)); if (returnValue.second == true) { - std::cout << " + (2,1,0) INSERTED" << std::endl; + std::clog << " + (2,1,0) INSERTED" << std::endl; } else { - std::cout << " - (2,1,0) NOT INSERTED" << std::endl; + std::clog << " - (2,1,0) NOT INSERTED" << std::endl; } // ++ EIGHTH - std::cout << " * INSERT 3" << std::endl; + std::clog << " * INSERT 3" << std::endl; typeVectorVertex eighthSimplexVector = {3}; returnValue = simplexTree.insert_simplex(eighthSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); if (returnValue.second == true) { - std::cout << " + 3 INSERTED" << std::endl; + std::clog << " + 3 INSERTED" << std::endl; } else { - std::cout << " - 3 NOT INSERTED" << std::endl; + std::clog << " - 3 NOT INSERTED" << std::endl; } // ++ NINETH - std::cout << " * INSERT (3,0)" << std::endl; + std::clog << " * INSERT (3,0)" << std::endl; typeVectorVertex ninethSimplexVector = {3, 0}; returnValue = simplexTree.insert_simplex(ninethSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); if (returnValue.second == true) { - std::cout << " + (3,0) INSERTED" << std::endl; + std::clog << " + (3,0) INSERTED" << std::endl; } else { - std::cout << " - (3,0) NOT INSERTED" << std::endl; + std::clog << " - (3,0) NOT INSERTED" << std::endl; } // ++ TENTH - std::cout << " * INSERT 0 (already inserted)" << std::endl; + std::clog << " * INSERT 0 (already inserted)" << std::endl; typeVectorVertex tenthSimplexVector = {0}; // With a different filtration value returnValue = simplexTree.insert_simplex(tenthSimplexVector, Filtration_value(FOURTH_FILTRATION_VALUE)); if (returnValue.second == true) { - std::cout << " + 0 INSERTED" << std::endl; + std::clog << " + 0 INSERTED" << std::endl; } else { - std::cout << " - 0 NOT INSERTED" << std::endl; + std::clog << " - 0 NOT INSERTED" << std::endl; } // ++ ELEVENTH - std::cout << " * INSERT (2,1,0) (already inserted)" << std::endl; + std::clog << " * INSERT (2,1,0) (already inserted)" << std::endl; typeVectorVertex eleventhSimplexVector = {2, 1, 0}; returnValue = simplexTree.insert_simplex(eleventhSimplexVector, Filtration_value(FOURTH_FILTRATION_VALUE)); if (returnValue.second == true) { - std::cout << " + (2,1,0) INSERTED" << std::endl; + std::clog << " + (2,1,0) INSERTED" << std::endl; } else { - std::cout << " - (2,1,0) NOT INSERTED" << std::endl; + std::clog << " - (2,1,0) NOT INSERTED" << std::endl; } // ++ GENERAL VARIABLE SET - std::cout << "********************************************************************\n"; - // Display the Simplex_tree - Can not be done in the middle of 2 inserts - std::cout << "* The complex contains " << simplexTree.num_simplices() << " simplices\n"; - std::cout << " - dimension " << simplexTree.dimension() << "\n"; - std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + std::clog << "********************************************************************\n"; + std::clog << "* The complex contains " << simplexTree.num_simplices() << " simplices\n"; + std::clog << " - dimension " << simplexTree.dimension() << "\n"; + std::clog << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : simplexTree.filtration_simplex_range()) { - std::cout << " " + std::clog << " " << "[" << simplexTree.filtration(f_simplex) << "] "; - for (auto vertex : simplexTree.simplex_vertex_range(f_simplex)) std::cout << "(" << vertex << ")"; - std::cout << std::endl; + for (auto vertex : simplexTree.simplex_vertex_range(f_simplex)) std::clog << "(" << vertex << ")"; + std::clog << std::endl; } // [0.1] 0 // [0.1] 1 @@ -190,66 +189,66 @@ int main(int argc, char* const argv[]) { // Find in the simplex_tree // ------------------------------------------------------------------------------------------------------------------ Simplex_tree::Simplex_handle simplexFound = simplexTree.find(secondSimplexVector); - std::cout << "**************IS THE SIMPLEX {1} IN THE SIMPLEX TREE ?\n"; + std::clog << "**************IS THE SIMPLEX {1} IN THE SIMPLEX TREE ?\n"; if (simplexFound != simplexTree.null_simplex()) - std::cout << "***+ YES IT IS!\n"; + std::clog << "***+ YES IT IS!\n"; else - std::cout << "***- NO IT ISN'T\n"; + std::clog << "***- NO IT ISN'T\n"; typeVectorVertex unknownSimplexVector = {15}; simplexFound = simplexTree.find(unknownSimplexVector); - std::cout << "**************IS THE SIMPLEX {15} IN THE SIMPLEX TREE ?\n"; + std::clog << "**************IS THE SIMPLEX {15} IN THE SIMPLEX TREE ?\n"; if (simplexFound != simplexTree.null_simplex()) - std::cout << "***+ YES IT IS!\n"; + std::clog << "***+ YES IT IS!\n"; else - std::cout << "***- NO IT ISN'T\n"; + std::clog << "***- NO IT ISN'T\n"; simplexFound = simplexTree.find(fifthSimplexVector); - std::cout << "**************IS THE SIMPLEX {2,0} IN THE SIMPLEX TREE ?\n"; + std::clog << "**************IS THE SIMPLEX {2,0} IN THE SIMPLEX TREE ?\n"; if (simplexFound != simplexTree.null_simplex()) - std::cout << "***+ YES IT IS!\n"; + std::clog << "***+ YES IT IS!\n"; else - std::cout << "***- NO IT ISN'T\n"; + std::clog << "***- NO IT ISN'T\n"; typeVectorVertex otherSimplexVector = {1, 15}; simplexFound = simplexTree.find(otherSimplexVector); - std::cout << "**************IS THE SIMPLEX {15,1} IN THE SIMPLEX TREE ?\n"; + std::clog << "**************IS THE SIMPLEX {15,1} IN THE SIMPLEX TREE ?\n"; if (simplexFound != simplexTree.null_simplex()) - std::cout << "***+ YES IT IS!\n"; + std::clog << "***+ YES IT IS!\n"; else - std::cout << "***- NO IT ISN'T\n"; + std::clog << "***- NO IT ISN'T\n"; typeVectorVertex invSimplexVector = {1, 2, 0}; simplexFound = simplexTree.find(invSimplexVector); - std::cout << "**************IS THE SIMPLEX {1,2,0} IN THE SIMPLEX TREE ?\n"; + std::clog << "**************IS THE SIMPLEX {1,2,0} IN THE SIMPLEX TREE ?\n"; if (simplexFound != simplexTree.null_simplex()) - std::cout << "***+ YES IT IS!\n"; + std::clog << "***+ YES IT IS!\n"; else - std::cout << "***- NO IT ISN'T\n"; + std::clog << "***- NO IT ISN'T\n"; simplexFound = simplexTree.find({0, 1}); - std::cout << "**************IS THE SIMPLEX {0,1} IN THE SIMPLEX TREE ?\n"; + std::clog << "**************IS THE SIMPLEX {0,1} IN THE SIMPLEX TREE ?\n"; if (simplexFound != simplexTree.null_simplex()) - std::cout << "***+ YES IT IS!\n"; + std::clog << "***+ YES IT IS!\n"; else - std::cout << "***- NO IT ISN'T\n"; + std::clog << "***- NO IT ISN'T\n"; - std::cout << "**************COFACES OF {0,1} IN CODIMENSION 1 ARE\n"; + std::clog << "**************COFACES OF {0,1} IN CODIMENSION 1 ARE\n"; for (auto& simplex : simplexTree.cofaces_simplex_range(simplexTree.find({0, 1}), 1)) { - for (auto vertex : simplexTree.simplex_vertex_range(simplex)) std::cout << "(" << vertex << ")"; - std::cout << std::endl; + for (auto vertex : simplexTree.simplex_vertex_range(simplex)) std::clog << "(" << vertex << ")"; + std::clog << std::endl; } - std::cout << "**************STARS OF {0,1} ARE\n"; + std::clog << "**************STARS OF {0,1} ARE\n"; for (auto& simplex : simplexTree.star_simplex_range(simplexTree.find({0, 1}))) { - for (auto vertex : simplexTree.simplex_vertex_range(simplex)) std::cout << "(" << vertex << ")"; - std::cout << std::endl; + for (auto vertex : simplexTree.simplex_vertex_range(simplex)) std::clog << "(" << vertex << ")"; + std::clog << std::endl; } - std::cout << "**************BOUNDARIES OF {0,1,2} ARE\n"; + std::clog << "**************BOUNDARIES OF {0,1,2} ARE\n"; for (auto& simplex : simplexTree.boundary_simplex_range(simplexTree.find({0, 1, 2}))) { - for (auto vertex : simplexTree.simplex_vertex_range(simplex)) std::cout << "(" << vertex << ")"; - std::cout << std::endl; + for (auto vertex : simplexTree.simplex_vertex_range(simplex)) std::clog << "(" << vertex << ")"; + std::clog << std::endl; } return 0; diff --git a/src/Simplex_tree/example/simplex_tree_from_cliques_of_graph.cpp b/src/Simplex_tree/example/simplex_tree_from_cliques_of_graph.cpp index f6dfa53c..6278efa7 100644 --- a/src/Simplex_tree/example/simplex_tree_from_cliques_of_graph.cpp +++ b/src/Simplex_tree/example/simplex_tree_from_cliques_of_graph.cpp @@ -42,67 +42,67 @@ int main(int argc, char * const argv[]) { // insert the graph in the simplex tree as 1-skeleton st.insert_graph(g); end = clock(); - std::cout << "Insert the 1-skeleton in the simplex tree in " + std::clog << "Insert the 1-skeleton in the simplex tree in " << static_cast<double>(end - start) / CLOCKS_PER_SEC << " s. \n"; start = clock(); // expand the 1-skeleton until dimension max_dim st.expansion(max_dim); end = clock(); - std::cout << "max_dim = " << max_dim << "\n"; - std::cout << "Expand the simplex tree in " + std::clog << "max_dim = " << max_dim << "\n"; + std::clog << "Expand the simplex tree in " << static_cast<double>(end - start) / CLOCKS_PER_SEC << " s. \n"; - std::cout << "Information of the Simplex Tree: " << std::endl; - std::cout << " Number of vertices = " << st.num_vertices() << " "; - std::cout << " Number of simplices = " << st.num_simplices() << std::endl; - std::cout << std::endl << std::endl; + std::clog << "Information of the Simplex Tree: " << std::endl; + std::clog << " Number of vertices = " << st.num_vertices() << " "; + std::clog << " Number of simplices = " << st.num_simplices() << std::endl; + std::clog << std::endl << std::endl; - std::cout << "Iterator on vertices: "; + std::clog << "Iterator on vertices: "; for (auto vertex : st.complex_vertex_range()) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; - std::cout << std::endl << std::endl; + std::clog << std::endl << std::endl; - std::cout << "Iterator on simplices: " << std::endl; + std::clog << "Iterator on simplices: " << std::endl; for (auto simplex : st.complex_simplex_range()) { - std::cout << " "; + std::clog << " "; for (auto vertex : st.simplex_vertex_range(simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } - std::cout << std::endl << std::endl; + std::clog << std::endl << std::endl; - std::cout << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; + std::clog << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " << "[" << st.filtration(f_simplex) << "] "; + std::clog << " " << "[" << st.filtration(f_simplex) << "] "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } - std::cout << std::endl << std::endl; + std::clog << std::endl << std::endl; - std::cout << "Iterator on Simplices in the filtration, and their boundary simplices:" << std::endl; + std::clog << "Iterator on Simplices in the filtration, and their boundary simplices:" << std::endl; for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " << "[" << st.filtration(f_simplex) << "] "; + std::clog << " " << "[" << st.filtration(f_simplex) << "] "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; for (auto b_simplex : st.boundary_simplex_range(f_simplex)) { - std::cout << " " << "[" << st.filtration(b_simplex) << "] "; + std::clog << " " << "[" << st.filtration(b_simplex) << "] "; for (auto vertex : st.simplex_vertex_range(b_simplex)) { - std::cout << vertex << " "; + std::clog << vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } } return 0; diff --git a/src/Simplex_tree/include/gudhi/Simplex_tree.h b/src/Simplex_tree/include/gudhi/Simplex_tree.h index 76608008..889dbd00 100644 --- a/src/Simplex_tree/include/gudhi/Simplex_tree.h +++ b/src/Simplex_tree/include/gudhi/Simplex_tree.h @@ -24,6 +24,7 @@ #include <boost/iterator/transform_iterator.hpp> #include <boost/graph/adjacency_list.hpp> #include <boost/range/adaptor/reversed.hpp> +#include <boost/container/static_vector.hpp> #ifdef GUDHI_USE_TBB #include <tbb/parallel_sort.h> @@ -41,6 +42,20 @@ namespace Gudhi { +/** + * \class Extended_simplex_type Simplex_tree.h gudhi/Simplex_tree.h + * \brief Extended simplex type data structure for representing the type of simplices in an extended filtration. + * + * \details The extended simplex type can be either UP (which means + * that the simplex was present originally, and is thus part of the ascending extended filtration), DOWN (which means + * that the simplex is the cone of an original simplex, and is thus part of the descending extended filtration) or + * EXTRA (which means the simplex is the cone point). + * + * Details may be found in \cite Cohen-Steiner2009 and section 2.2 in \cite Carriere16. + * + */ +enum class Extended_simplex_type {UP, DOWN, EXTRA}; + struct Simplex_tree_options_full_featured; /** @@ -86,6 +101,8 @@ class Simplex_tree { /* \brief Set of nodes sharing a same parent in the simplex tree. */ typedef Simplex_tree_siblings<Simplex_tree, Dictionary> Siblings; + + struct Key_simplex_base_real { Key_simplex_base_real() : key_(-1) {} void assign_key(Simplex_key k) { key_ = k; } @@ -99,6 +116,12 @@ class Simplex_tree { void assign_key(Simplex_key); Simplex_key key() const; }; + struct Extended_filtration_data { + Filtration_value minval; + Filtration_value maxval; + Extended_filtration_data(){} + Extended_filtration_data(Filtration_value vmin, Filtration_value vmax): minval(vmin), maxval(vmax) {} + }; typedef typename std::conditional<Options::store_key, Key_simplex_base_real, Key_simplex_base_dummy>::type Key_simplex_base; @@ -119,7 +142,10 @@ class Simplex_tree { public: /** \brief Handle type to a simplex contained in the simplicial complex represented - * by the simplex tree. */ + * by the simplex tree. + * + * They are essentially pointers into internal vectors, and any insertion or removal + * of a simplex may invalidate any other Simplex_handle in the complex. */ typedef typename Dictionary::iterator Simplex_handle; private: @@ -232,11 +258,9 @@ class Simplex_tree { * * The filtration must be valid. If the filtration has not been initialized yet, the * method initializes it (i.e. order the simplices). If the complex has changed since the last time the filtration - * was initialized, please call `initialize_filtration()` to recompute it. */ + * was initialized, please call `clear_filtration()` or `initialize_filtration()` to recompute it. */ Filtration_simplex_range const& filtration_simplex_range(Indexing_tag = Indexing_tag()) { - if (filtration_vect_.empty()) { - initialize_filtration(); - } + maybe_initialize_filtration(); return filtration_vect_; } @@ -246,8 +270,8 @@ class Simplex_tree { * which is consequenlty * equal to \f$(-1)^{\text{dim} \sigma}\f$ the canonical orientation on the simplex. */ - Simplex_vertex_range simplex_vertex_range(Simplex_handle sh) { - assert(sh != null_simplex()); // Empty simplex + Simplex_vertex_range simplex_vertex_range(Simplex_handle sh) const { + GUDHI_CHECK(sh != null_simplex(), "empty simplex"); return Simplex_vertex_range(Simplex_vertex_iterator(this, sh), Simplex_vertex_iterator(this)); } @@ -286,7 +310,7 @@ class Simplex_tree { /** \brief User-defined copy constructor reproduces the whole tree structure. */ Simplex_tree(const Simplex_tree& complex_source) { #ifdef DEBUG_TRACES - std::cout << "Simplex_tree copy constructor" << std::endl; + std::clog << "Simplex_tree copy constructor" << std::endl; #endif // DEBUG_TRACES copy_from(complex_source); } @@ -296,7 +320,7 @@ class Simplex_tree { */ Simplex_tree(Simplex_tree && complex_source) { #ifdef DEBUG_TRACES - std::cout << "Simplex_tree move constructor" << std::endl; + std::clog << "Simplex_tree move constructor" << std::endl; #endif // DEBUG_TRACES move_from(complex_source); @@ -313,7 +337,7 @@ class Simplex_tree { /** \brief User-defined copy assignment reproduces the whole tree structure. */ Simplex_tree& operator= (const Simplex_tree& complex_source) { #ifdef DEBUG_TRACES - std::cout << "Simplex_tree copy assignment" << std::endl; + std::clog << "Simplex_tree copy assignment" << std::endl; #endif // DEBUG_TRACES // Self-assignment detection if (&complex_source != this) { @@ -330,7 +354,7 @@ class Simplex_tree { */ Simplex_tree& operator=(Simplex_tree&& complex_source) { #ifdef DEBUG_TRACES - std::cout << "Simplex_tree move assignment" << std::endl; + std::clog << "Simplex_tree move assignment" << std::endl; #endif // DEBUG_TRACES // Self-assignment detection if (&complex_source != this) { @@ -450,10 +474,19 @@ class Simplex_tree { return true; } + /** \brief Returns the filtration value of a simplex. + * + * Same as `filtration()`, but does not handle `null_simplex()`. + */ + static Filtration_value filtration_(Simplex_handle sh) { + GUDHI_CHECK (sh != null_simplex(), "null simplex"); + return sh->second.filtration(); + } + public: /** \brief Returns the key associated to a simplex. * - * The filtration must be initialized. + * If no key has been assigned, returns `null_key()`. * \pre SimplexTreeOptions::store_key */ static Simplex_key key(Simplex_handle sh) { @@ -463,7 +496,6 @@ class Simplex_tree { /** \brief Returns the simplex that has index idx in the filtration. * * The filtration must be initialized. - * \pre SimplexTreeOptions::store_key */ Simplex_handle simplex(Simplex_key idx) const { return filtration_vect_[idx]; @@ -499,8 +531,7 @@ class Simplex_tree { return Dictionary_it(nullptr); } - /** \brief Returns a key different for all keys associated to the - * simplices of the simplicial complex. */ + /** \brief Returns a fixed number not in the interval [0, `num_simplices()`). */ static Simplex_key null_key() { return -1; } @@ -755,12 +786,7 @@ class Simplex_tree { if (first == last) return { null_simplex(), true }; // FIXME: false would make more sense to me. - // Copy before sorting - // Thread local is not available on XCode version < V.8 - It will slow down computation -#ifdef GUDHI_CAN_USE_CXX11_THREAD_LOCAL - thread_local -#endif // GUDHI_CAN_USE_CXX11_THREAD_LOCAL - std::vector<Vertex_handle> copy; + thread_local std::vector<Vertex_handle> copy; copy.clear(); copy.insert(copy.end(), first, last); std::sort(copy.begin(), copy.end()); @@ -827,7 +853,7 @@ class Simplex_tree { /** Returns the Siblings containing a simplex.*/ template<class SimplexHandle> - Siblings* self_siblings(SimplexHandle sh) { + static Siblings* self_siblings(SimplexHandle sh) { if (sh->second.children()->parent() == sh->first) return sh->second.children()->oncles(); else @@ -850,15 +876,13 @@ class Simplex_tree { } public: - /** \brief Initializes the filtrations, i.e. sort the - * simplices according to their order in the filtration and initializes all Simplex_keys. + /** \brief Initializes the filtration cache, i.e. sorts the + * simplices according to their order in the filtration. * - * After calling this method, filtration_simplex_range() becomes valid, and each simplex is - * assigned a Simplex_key corresponding to its order in the filtration (from 0 to m-1 for a - * simplicial complex with m simplices). + * It always recomputes the cache, even if one already exists. * - * Will be automatically called when calling filtration_simplex_range() - * if the filtration has never been initialized yet. */ + * Any insertion, deletion or change of filtration value invalidates this cache, + * which can be cleared with clear_filtration(). */ void initialize_filtration() { filtration_vect_.clear(); filtration_vect_.reserve(num_simplices()); @@ -880,6 +904,21 @@ class Simplex_tree { std::stable_sort(filtration_vect_.begin(), filtration_vect_.end(), is_before_in_filtration(this)); #endif } + /** \brief Initializes the filtration cache if it isn't initialized yet. + * + * Automatically called by filtration_simplex_range(). */ + void maybe_initialize_filtration() { + if (filtration_vect_.empty()) { + initialize_filtration(); + } + } + /** \brief Clears the filtration cache produced by initialize_filtration(). + * + * Useful when initialize_filtration() has already been called and we perform an operation + * (say an insertion) that invalidates the cache. */ + void clear_filtration() { + filtration_vect_.clear(); + } private: /** Recursive search of cofaces @@ -1101,6 +1140,7 @@ class Simplex_tree { * 1 when calling the method. */ void expansion(int max_dim) { if (max_dim <= 1) return; + clear_filtration(); // Drop the cache. dimension_ = max_dim; for (Dictionary_it root_it = root_.members_.begin(); root_it != root_.members_.end(); ++root_it) { @@ -1123,10 +1163,7 @@ class Simplex_tree { Dictionary_it next = siblings->members().begin(); ++next; -#ifdef GUDHI_CAN_USE_CXX11_THREAD_LOCAL - thread_local -#endif // GUDHI_CAN_USE_CXX11_THREAD_LOCAL - std::vector<std::pair<Vertex_handle, Node> > inter; + thread_local std::vector<std::pair<Vertex_handle, Node> > inter; for (Dictionary_it s_h = siblings->members().begin(); s_h != siblings->members().end(); ++s_h, ++next) { Simplex_handle root_sh = find_vertex(s_h->first); @@ -1314,9 +1351,8 @@ class Simplex_tree { /** \brief This function ensures that each simplex has a higher filtration value than its faces by increasing the * filtration values. * @return True if any filtration value was modified, false if the filtration was already non-decreasing. - * \post Some simplex tree functions require the filtration to be valid. `make_filtration_non_decreasing()` - * function is not launching `initialize_filtration()` but returns the filtration modification information. If the - * complex has changed , please call `initialize_filtration()` to recompute it. + * + * If a simplex has a `NaN` filtration value, it is considered lower than any other defined filtration value. */ bool make_filtration_non_decreasing() { bool modified = false; @@ -1326,6 +1362,8 @@ class Simplex_tree { modified |= rec_make_filtration_non_decreasing(simplex.second.children()); } } + if(modified) + clear_filtration(); // Drop the cache. return modified; } @@ -1347,7 +1385,9 @@ class Simplex_tree { }); Filtration_value max_filt_border_value = filtration(*max_border); - if (simplex.second.filtration() < max_filt_border_value) { + // Replacing if(f<max) with if(!(f>=max)) would mean that if f is NaN, we replace it with the max of the children. + // That seems more useful than keeping NaN. + if (!(simplex.second.filtration() >= max_filt_border_value)) { // Store the filtration modification information modified = true; simplex.second.assign_filtration(max_filt_border_value); @@ -1363,16 +1403,16 @@ class Simplex_tree { public: /** \brief Prune above filtration value given as parameter. * @param[in] filtration Maximum threshold value. - * @return The filtration modification information. - * \post Some simplex tree functions require the filtration to be valid. `prune_above_filtration()` - * function is not launching `initialize_filtration()` but returns the filtration modification information. If the - * complex has changed , please call `initialize_filtration()` to recompute it. + * @return True if any simplex was removed, false if all simplices already had a value below the threshold. * \post Note that the dimension of the simplicial complex may be lower after calling `prune_above_filtration()` * than it was before. However, `upper_bound_dimension()` will return the old value, which remains a valid upper * bound. If you care, you can call `dimension()` to recompute the exact dimension. */ bool prune_above_filtration(Filtration_value filtration) { - return rec_prune_above_filtration(root(), filtration); + bool modified = rec_prune_above_filtration(root(), filtration); + if(modified) + clear_filtration(); // Drop the cache. + return modified; } private: @@ -1418,9 +1458,9 @@ class Simplex_tree { for (Simplex_handle sh : complex_simplex_range()) { #ifdef DEBUG_TRACES for (auto vertex : simplex_vertex_range(sh)) { - std::cout << " " << vertex; + std::clog << " " << vertex; } - std::cout << std::endl; + std::clog << std::endl; #endif // DEBUG_TRACES int sh_dimension = dimension(sh); @@ -1439,7 +1479,6 @@ class Simplex_tree { * @param[in] sh Simplex handle on the maximal simplex to remove. * \pre Please check the simplex has no coface before removing it. * \exception std::invalid_argument In debug mode, if sh has children. - * \post Be aware that removing is shifting data in a flat_map (initialize_filtration to be done). * \post Note that the dimension of the simplicial complex may be lower after calling `remove_maximal_simplex()` * than it was before. However, `upper_bound_dimension()` will return the old value, which remains a valid upper * bound. If you care, you can call `dimension()` to recompute the exact dimension. @@ -1465,6 +1504,169 @@ class Simplex_tree { } } + /** \brief Retrieve the original filtration value for a given simplex in the Simplex_tree. Since the + * computation of extended persistence requires modifying the filtration values, this function can be used + * to recover the original values. Moreover, computing extended persistence requires adding new simplices + * in the Simplex_tree. Hence, this function also outputs the type of each simplex. It can be either UP (which means + * that the simplex was present originally, and is thus part of the ascending extended filtration), DOWN (which means + * that the simplex is the cone of an original simplex, and is thus part of the descending extended filtration) or + * EXTRA (which means the simplex is the cone point). See the definition of Extended_simplex_type. Note that if the simplex type is DOWN, the original filtration value + * is set to be the original filtration value of the corresponding (not coned) original simplex. + * \pre This function should be called only if `extend_filtration()` has been called first! + * \post The output filtration value is supposed to be the same, but might be a little different, than the + * original filtration value, due to the internal transformation (scaling to [-2,-1]) that is + * performed on the original filtration values during the computation of extended persistence. + * @param[in] f Filtration value of the simplex in the extended (i.e., modified) filtration. + * @param[in] efd Structure containing the minimum and maximum values of the original filtration. This the output of `extend_filtration()`. + * @return A pair containing the original filtration value of the simplex as well as the simplex type. + */ + std::pair<Filtration_value, Extended_simplex_type> decode_extended_filtration(Filtration_value f, const Extended_filtration_data& efd){ + std::pair<Filtration_value, Extended_simplex_type> p; + Filtration_value minval = efd.minval; + Filtration_value maxval = efd.maxval; + if (f >= -2 && f <= -1){ + p.first = minval + (maxval-minval)*(f + 2); p.second = Extended_simplex_type::UP; + } + else if (f >= 1 && f <= 2){ + p.first = minval - (maxval-minval)*(f - 2); p.second = Extended_simplex_type::DOWN; + } + else{ + p.first = std::numeric_limits<Filtration_value>::quiet_NaN(); p.second = Extended_simplex_type::EXTRA; + } + return p; + }; + + /** \brief Extend filtration for computing extended persistence. + * This function only uses the filtration values at the 0-dimensional simplices, + * and computes the extended persistence diagram induced by the lower-star filtration + * computed with these values. + * \post Note that after calling this function, the filtration + * values are actually modified. The function `decode_extended_filtration()` + * retrieves the original values and outputs the extended simplex type. + * \pre Note that this code creates an extra vertex internally, so you should make sure that + * the Simplex tree does not contain a vertex with the largest Vertex_handle. + * @return A data structure containing the maximum and minimum values of the original filtration. + * It is meant to be provided as input to `decode_extended_filtration()` in order to retrieve + * the original filtration values for each simplex. + */ + Extended_filtration_data extend_filtration() { + clear_filtration(); // Drop the cache. + + // Compute maximum and minimum of filtration values + Vertex_handle maxvert = std::numeric_limits<Vertex_handle>::min(); + Filtration_value minval = std::numeric_limits<Filtration_value>::infinity(); + Filtration_value maxval = -std::numeric_limits<Filtration_value>::infinity(); + for (auto sh = root_.members().begin(); sh != root_.members().end(); ++sh){ + Filtration_value f = this->filtration(sh); + minval = std::min(minval, f); + maxval = std::max(maxval, f); + maxvert = std::max(sh->first, maxvert); + } + + GUDHI_CHECK(maxvert < std::numeric_limits<Vertex_handle>::max(), std::invalid_argument("Simplex_tree contains a vertex with the largest Vertex_handle")); + maxvert += 1; + + Simplex_tree st_copy = *this; + + // Add point for coning the simplicial complex + this->insert_simplex({maxvert}, -3); + + // For each simplex + std::vector<Vertex_handle> vr; + for (auto sh_copy : st_copy.complex_simplex_range()){ + + // Locate simplex + vr.clear(); + for (auto vh : st_copy.simplex_vertex_range(sh_copy)){ + vr.push_back(vh); + } + auto sh = this->find(vr); + + // Create cone on simplex + vr.push_back(maxvert); + if (this->dimension(sh) == 0){ + Filtration_value v = this->filtration(sh); + Filtration_value scaled_v = (v-minval)/(maxval-minval); + // Assign ascending value between -2 and -1 to vertex + this->assign_filtration(sh, -2 + scaled_v); + // Assign descending value between 1 and 2 to cone on vertex + this->insert_simplex(vr, 2 - scaled_v); + } + else{ + // Assign value -3 to simplex and cone on simplex + this->assign_filtration(sh, -3); + this->insert_simplex(vr, -3); + } + } + + // Automatically assign good values for simplices + this->make_filtration_non_decreasing(); + + // Return the filtration data + Extended_filtration_data efd(minval, maxval); + return efd; + } + + /** \brief Returns a vertex of `sh` that has the same filtration value as `sh` if it exists, and `null_vertex()` otherwise. + * + * For a lower-star filtration built with `make_filtration_non_decreasing()`, this is a way to invert the process and find out which vertex had its filtration value propagated to `sh`. + * If several vertices have the same filtration value, the one it returns is arbitrary. */ + Vertex_handle vertex_with_same_filtration(Simplex_handle sh) { + auto filt = filtration_(sh); + for(auto v : simplex_vertex_range(sh)) + if(filtration_(find_vertex(v)) == filt) + return v; + return null_vertex(); + } + + /** \brief Returns an edge of `sh` that has the same filtration value as `sh` if it exists, and `null_simplex()` otherwise. + * + * For a flag-complex built with `expansion()`, this is a way to invert the process and find out which edge had its filtration value propagated to `sh`. + * If several edges have the same filtration value, the one it returns is arbitrary. + * + * \pre `sh` must have dimension at least 1. */ + Simplex_handle edge_with_same_filtration(Simplex_handle sh) { + // See issue #251 for potential speed improvements. + auto&& vertices = simplex_vertex_range(sh); // vertices in decreasing order + auto end = std::end(vertices); + auto vi = std::begin(vertices); + GUDHI_CHECK(vi != end, "empty simplex"); + auto v0 = *vi; + ++vi; + GUDHI_CHECK(vi != end, "simplex of dimension 0"); + if(std::next(vi) == end) return sh; // shortcut for dimension 1 + boost::container::static_vector<Vertex_handle, 40> suffix; + suffix.push_back(v0); + auto filt = filtration_(sh); + do + { + Vertex_handle v = *vi; + auto&& children1 = find_vertex(v)->second.children()->members_; + for(auto w : suffix){ + // Can we take advantage of the fact that suffix is ordered? + Simplex_handle s = children1.find(w); + if(filtration_(s) == filt) + return s; + } + suffix.push_back(v); + } + while(++vi != end); + return null_simplex(); + } + + /** \brief Returns a minimal face of `sh` that has the same filtration value as `sh`. + * + * For a filtration built with `make_filtration_non_decreasing()`, this is a way to invert the process and find out which simplex had its filtration value propagated to `sh`. + * If several minimal (for inclusion) simplices have the same filtration value, the one it returns is arbitrary, and it is not guaranteed to be the one with smallest dimension. */ + Simplex_handle minimal_simplex_with_same_filtration(Simplex_handle sh) { + auto filt = filtration_(sh); + // Naive implementation, it can be sped up. + for(auto b : boundary_simplex_range(sh)) + if(filtration_(b) == filt) + return minimal_simplex_with_same_filtration(b); + return sh; // None of its faces has the same filtration. + } + private: Vertex_handle null_vertex_; /** \brief Total number of simplices in the complex, without the empty simplex.*/ diff --git a/src/Simplex_tree/include/gudhi/Simplex_tree/Simplex_tree_iterators.h b/src/Simplex_tree/include/gudhi/Simplex_tree/Simplex_tree_iterators.h index efccf2f2..9007b6bd 100644 --- a/src/Simplex_tree/include/gudhi/Simplex_tree/Simplex_tree_iterators.h +++ b/src/Simplex_tree/include/gudhi/Simplex_tree/Simplex_tree_iterators.h @@ -15,9 +15,7 @@ #include <boost/iterator/iterator_facade.hpp> #include <boost/version.hpp> -#if BOOST_VERSION >= 105600 -# include <boost/container/static_vector.hpp> -#endif +#include <boost/container/static_vector.hpp> #include <vector> @@ -42,13 +40,13 @@ class Simplex_tree_simplex_vertex_iterator : public boost::iterator_facade< typedef typename SimplexTree::Siblings Siblings; typedef typename SimplexTree::Vertex_handle Vertex_handle; - explicit Simplex_tree_simplex_vertex_iterator(SimplexTree * st) + explicit Simplex_tree_simplex_vertex_iterator(SimplexTree const* st) : // any end() iterator sib_(nullptr), v_(st->null_vertex()) { } - Simplex_tree_simplex_vertex_iterator(SimplexTree * st, Simplex_handle sh) + Simplex_tree_simplex_vertex_iterator(SimplexTree const* st, Simplex_handle sh) : sib_(st->self_siblings(sh)), v_(sh->first) { } @@ -166,15 +164,11 @@ class Simplex_tree_boundary_simplex_iterator : public boost::iterator_facade< // Most of the storage should be moved to the range, iterators should be light. Vertex_handle last_; // last vertex of the simplex Vertex_handle next_; // next vertex to push in suffix_ -#if BOOST_VERSION >= 105600 // 40 seems a conservative bound on the dimension of a Simplex_tree for now, // as it would not fit on the biggest hard-drive. boost::container::static_vector<Vertex_handle, 40> suffix_; // static_vector still has some overhead compared to a trivial hand-made // version using std::aligned_storage, or compared to making suffix_ static. -#else - std::vector<Vertex_handle> suffix_; -#endif Siblings * sib_; // where the next search will start from Simplex_handle sh_; // current Simplex_handle in the boundary SimplexTree * st_; // simplex containing the simplicial complex diff --git a/src/Simplex_tree/test/CMakeLists.txt b/src/Simplex_tree/test/CMakeLists.txt index 8b9163f5..cf2b0153 100644 --- a/src/Simplex_tree/test/CMakeLists.txt +++ b/src/Simplex_tree/test/CMakeLists.txt @@ -28,3 +28,9 @@ if (TBB_FOUND) target_link_libraries(Simplex_tree_ctor_and_move_test_unit ${TBB_LIBRARIES}) endif() gudhi_add_boost_test(Simplex_tree_ctor_and_move_test_unit) + +add_executable ( Simplex_tree_make_filtration_non_decreasing_test_unit simplex_tree_make_filtration_non_decreasing_unit_test.cpp ) +if (TBB_FOUND) + target_link_libraries(Simplex_tree_make_filtration_non_decreasing_test_unit ${TBB_LIBRARIES}) +endif() +gudhi_add_boost_test(Simplex_tree_make_filtration_non_decreasing_test_unit) diff --git a/src/Simplex_tree/test/simplex_tree_ctor_and_move_unit_test.cpp b/src/Simplex_tree/test/simplex_tree_ctor_and_move_unit_test.cpp index c0615b12..229ae46f 100644 --- a/src/Simplex_tree/test/simplex_tree_ctor_and_move_unit_test.cpp +++ b/src/Simplex_tree/test/simplex_tree_ctor_and_move_unit_test.cpp @@ -30,16 +30,16 @@ void print_simplex_filtration(Simplex_tree& st, const std::string& msg) { // Required before browsing through filtration values st.initialize_filtration(); - std::cout << "********************************************************************\n"; - std::cout << "* " << msg << "\n"; - std::cout << "* The complex contains " << st.num_simplices() << " simplices"; - std::cout << " - dimension " << st.dimension() << "\n"; - std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + std::clog << "********************************************************************\n"; + std::clog << "* " << msg << "\n"; + std::clog << "* The complex contains " << st.num_simplices() << " simplices"; + std::clog << " - dimension " << st.dimension() << "\n"; + std::clog << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " + std::clog << " " << "[" << st.filtration(f_simplex) << "] "; - for (auto vertex : st.simplex_vertex_range(f_simplex)) std::cout << "(" << vertex << ")"; - std::cout << std::endl; + for (auto vertex : st.simplex_vertex_range(f_simplex)) std::clog << "(" << vertex << ")"; + std::clog << std::endl; } } @@ -70,8 +70,8 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_copy_constructor, Simplex_tree, list_of_te print_simplex_filtration(st, "Default Simplex_tree is initialized"); - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF COPY CONSTRUCTOR" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF COPY CONSTRUCTOR" << std::endl; Simplex_tree st1(st); Simplex_tree st2(st); @@ -82,8 +82,8 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_copy_constructor, Simplex_tree, list_of_te BOOST_CHECK(st == st2); BOOST_CHECK(st1 == st); - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF COPY ASSIGNMENT" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF COPY ASSIGNMENT" << std::endl; Simplex_tree st3; // To check there is no memory leak st3.insert_simplex_and_subfaces({9, 10, 11}, 200.0); @@ -103,8 +103,8 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_copy_constructor, Simplex_tree, list_of_te BOOST_CHECK(st3 == st); - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF MOVE CONSTRUCTOR" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF MOVE CONSTRUCTOR" << std::endl; Simplex_tree st5(std::move(st1)); print_simplex_filtration(st5, "First move constructor from the default Simplex_tree"); print_simplex_filtration(st1, "First moved Simplex_tree shall be empty"); @@ -122,8 +122,8 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_copy_constructor, Simplex_tree, list_of_te BOOST_CHECK(empty_st == st2); BOOST_CHECK(st1 == empty_st); - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF MOVE ASSIGNMENT" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF MOVE ASSIGNMENT" << std::endl; Simplex_tree st7; // To check there is no memory leak diff --git a/src/Simplex_tree/test/simplex_tree_graph_expansion_unit_test.cpp b/src/Simplex_tree/test/simplex_tree_graph_expansion_unit_test.cpp index fab25eb8..881a06ae 100644 --- a/src/Simplex_tree/test/simplex_tree_graph_expansion_unit_test.cpp +++ b/src/Simplex_tree/test/simplex_tree_graph_expansion_unit_test.cpp @@ -55,34 +55,34 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_expansion_with_blockers_3, typeST, li simplex_tree.expansion_with_blockers(3, [&](Simplex_handle sh){ bool result = false; - std::cout << "Blocker on ["; + std::clog << "Blocker on ["; // User can loop on the vertices from the given simplex_handle i.e. for (auto vertex : simplex_tree.simplex_vertex_range(sh)) { // We block the expansion, if the vertex '6' is in the given list of vertices if (vertex == 6) result = true; - std::cout << vertex << ", "; + std::clog << vertex << ", "; } - std::cout << "] ( " << simplex_tree.filtration(sh); + std::clog << "] ( " << simplex_tree.filtration(sh); // User can re-assign a new filtration value directly in the blocker (default is the maximal value of boudaries) simplex_tree.assign_filtration(sh, simplex_tree.filtration(sh) + 1.); - std::cout << " + 1. ) = " << result << std::endl; + std::clog << " + 1. ) = " << result << std::endl; return result; }); - std::cout << "********************************************************************\n"; - std::cout << "simplex_tree_expansion_with_blockers_3\n"; - std::cout << "********************************************************************\n"; - std::cout << "* The complex contains " << simplex_tree.num_simplices() << " simplices"; - std::cout << " - dimension " << simplex_tree.dimension() << "\n"; - std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + std::clog << "********************************************************************\n"; + std::clog << "simplex_tree_expansion_with_blockers_3\n"; + std::clog << "********************************************************************\n"; + std::clog << "* The complex contains " << simplex_tree.num_simplices() << " simplices"; + std::clog << " - dimension " << simplex_tree.dimension() << "\n"; + std::clog << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : simplex_tree.filtration_simplex_range()) { - std::cout << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; + std::clog << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; for (auto vertex : simplex_tree.simplex_vertex_range(f_simplex)) - std::cout << "(" << vertex << ")"; - std::cout << std::endl; + std::clog << "(" << vertex << ")"; + std::clog << std::endl; } BOOST_CHECK(simplex_tree.num_simplices() == 23); @@ -117,34 +117,34 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_expansion_with_blockers_2, typeST, li simplex_tree.expansion_with_blockers(2, [&](Simplex_handle sh){ bool result = false; - std::cout << "Blocker on ["; + std::clog << "Blocker on ["; // User can loop on the vertices from the given simplex_handle i.e. for (auto vertex : simplex_tree.simplex_vertex_range(sh)) { // We block the expansion, if the vertex '6' is in the given list of vertices if (vertex == 6) result = true; - std::cout << vertex << ", "; + std::clog << vertex << ", "; } - std::cout << "] ( " << simplex_tree.filtration(sh); + std::clog << "] ( " << simplex_tree.filtration(sh); // User can re-assign a new filtration value directly in the blocker (default is the maximal value of boudaries) simplex_tree.assign_filtration(sh, simplex_tree.filtration(sh) + 1.); - std::cout << " + 1. ) = " << result << std::endl; + std::clog << " + 1. ) = " << result << std::endl; return result; }); - std::cout << "********************************************************************\n"; - std::cout << "simplex_tree_expansion_with_blockers_2\n"; - std::cout << "********************************************************************\n"; - std::cout << "* The complex contains " << simplex_tree.num_simplices() << " simplices"; - std::cout << " - dimension " << simplex_tree.dimension() << "\n"; - std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + std::clog << "********************************************************************\n"; + std::clog << "simplex_tree_expansion_with_blockers_2\n"; + std::clog << "********************************************************************\n"; + std::clog << "* The complex contains " << simplex_tree.num_simplices() << " simplices"; + std::clog << " - dimension " << simplex_tree.dimension() << "\n"; + std::clog << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : simplex_tree.filtration_simplex_range()) { - std::cout << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; + std::clog << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; for (auto vertex : simplex_tree.simplex_vertex_range(f_simplex)) - std::cout << "(" << vertex << ")"; - std::cout << std::endl; + std::clog << "(" << vertex << ")"; + std::clog << std::endl; } BOOST_CHECK(simplex_tree.num_simplices() == 22); @@ -176,17 +176,17 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_expansion, typeST, list_of_tested_var simplex_tree.insert_simplex({6}, 10.); simplex_tree.expansion(3); - std::cout << "********************************************************************\n"; - std::cout << "simplex_tree_expansion_3\n"; - std::cout << "********************************************************************\n"; - std::cout << "* The complex contains " << simplex_tree.num_simplices() << " simplices"; - std::cout << " - dimension " << simplex_tree.dimension() << "\n"; - std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + std::clog << "********************************************************************\n"; + std::clog << "simplex_tree_expansion_3\n"; + std::clog << "********************************************************************\n"; + std::clog << "* The complex contains " << simplex_tree.num_simplices() << " simplices"; + std::clog << " - dimension " << simplex_tree.dimension() << "\n"; + std::clog << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : simplex_tree.filtration_simplex_range()) { - std::cout << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; + std::clog << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; for (auto vertex : simplex_tree.simplex_vertex_range(f_simplex)) - std::cout << "(" << vertex << ")"; - std::cout << std::endl; + std::clog << "(" << vertex << ")"; + std::clog << std::endl; } BOOST_CHECK(simplex_tree.num_simplices() == 24); @@ -220,17 +220,17 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_expansion_2, typeST, list_of_tested_v simplex_tree.expansion(2); - std::cout << "********************************************************************\n"; - std::cout << "simplex_tree_expansion_2\n"; - std::cout << "********************************************************************\n"; - std::cout << "* The complex contains " << simplex_tree.num_simplices() << " simplices"; - std::cout << " - dimension " << simplex_tree.dimension() << "\n"; - std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + std::clog << "********************************************************************\n"; + std::clog << "simplex_tree_expansion_2\n"; + std::clog << "********************************************************************\n"; + std::clog << "* The complex contains " << simplex_tree.num_simplices() << " simplices"; + std::clog << " - dimension " << simplex_tree.dimension() << "\n"; + std::clog << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : simplex_tree.filtration_simplex_range()) { - std::cout << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; + std::clog << " " << "[" << simplex_tree.filtration(f_simplex) << "] "; for (auto vertex : simplex_tree.simplex_vertex_range(f_simplex)) - std::cout << "(" << vertex << ")"; - std::cout << std::endl; + std::clog << "(" << vertex << ")"; + std::clog << std::endl; } BOOST_CHECK(simplex_tree.num_simplices() == 23); diff --git a/src/Simplex_tree/test/simplex_tree_iostream_operator_unit_test.cpp b/src/Simplex_tree/test/simplex_tree_iostream_operator_unit_test.cpp index 28c29489..20007488 100644 --- a/src/Simplex_tree/test/simplex_tree_iostream_operator_unit_test.cpp +++ b/src/Simplex_tree/test/simplex_tree_iostream_operator_unit_test.cpp @@ -34,8 +34,8 @@ typedef boost::mpl::list<Simplex_tree<>, > list_of_tested_variants; BOOST_AUTO_TEST_CASE_TEMPLATE(iostream_operator, Stree_type, list_of_tested_variants) { - std::cout << "********************************************************************" << std::endl; - std::cout << "SIMPLEX TREE IOSTREAM OPERATOR" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "SIMPLEX TREE IOSTREAM OPERATOR" << std::endl; Stree_type st; @@ -46,15 +46,15 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(iostream_operator, Stree_type, list_of_tested_vari st.initialize_filtration(); // Display the Simplex_tree - std::cout << "The ORIGINAL complex contains " << st.num_simplices() << " simplices - dimension = " + std::clog << "The ORIGINAL complex contains " << st.num_simplices() << " simplices - dimension = " << st.dimension() << std::endl; - std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; + std::clog << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " << "[" << st.filtration(f_simplex) << "] "; + std::clog << " " << "[" << st.filtration(f_simplex) << "] "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << (int) vertex << " "; + std::clog << (int) vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } // st: @@ -75,15 +75,15 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(iostream_operator, Stree_type, list_of_tested_vari simplex_tree_istream >> read_st; // Display the Simplex_tree - std::cout << "The READ complex contains " << read_st.num_simplices() << " simplices - dimension = " + std::clog << "The READ complex contains " << read_st.num_simplices() << " simplices - dimension = " << read_st.dimension() << std::endl; - std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; + std::clog << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; for (auto f_simplex : read_st.filtration_simplex_range()) { - std::cout << " " << "[" << read_st.filtration(f_simplex) << "] "; + std::clog << " " << "[" << read_st.filtration(f_simplex) << "] "; for (auto vertex : read_st.simplex_vertex_range(f_simplex)) { - std::cout << (int) vertex << " "; + std::clog << (int) vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } BOOST_CHECK(st == read_st); @@ -91,8 +91,8 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(iostream_operator, Stree_type, list_of_tested_vari BOOST_AUTO_TEST_CASE(mini_iostream_operator) { - std::cout << "********************************************************************" << std::endl; - std::cout << "MINI SIMPLEX TREE IOSTREAM OPERATOR" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "MINI SIMPLEX TREE IOSTREAM OPERATOR" << std::endl; Simplex_tree<MyOptions> st; @@ -103,14 +103,14 @@ BOOST_AUTO_TEST_CASE(mini_iostream_operator) { st.initialize_filtration(); // Display the Simplex_tree - std::cout << "The ORIGINAL complex contains " << st.num_simplices() << " simplices - dimension = " + std::clog << "The ORIGINAL complex contains " << st.num_simplices() << " simplices - dimension = " << st.dimension() << std::endl; for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " << "[" << st.filtration(f_simplex) << "] "; + std::clog << " " << "[" << st.filtration(f_simplex) << "] "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << (int) vertex << " "; + std::clog << (int) vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } // st: @@ -131,15 +131,15 @@ BOOST_AUTO_TEST_CASE(mini_iostream_operator) { simplex_tree_istream >> read_st; // Display the Simplex_tree - std::cout << "The READ complex contains " << read_st.num_simplices() << " simplices - dimension = " + std::clog << "The READ complex contains " << read_st.num_simplices() << " simplices - dimension = " << read_st.dimension() << std::endl; - std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; + std::clog << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; for (auto f_simplex : read_st.filtration_simplex_range()) { - std::cout << " " << "[" << read_st.filtration(f_simplex) << "] "; + std::clog << " " << "[" << read_st.filtration(f_simplex) << "] "; for (auto vertex : read_st.simplex_vertex_range(f_simplex)) { - std::cout << (int) vertex << " "; + std::clog << (int) vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } BOOST_CHECK(st == read_st); diff --git a/src/Simplex_tree/test/simplex_tree_make_filtration_non_decreasing_unit_test.cpp b/src/Simplex_tree/test/simplex_tree_make_filtration_non_decreasing_unit_test.cpp new file mode 100644 index 00000000..e0e7cadf --- /dev/null +++ b/src/Simplex_tree/test/simplex_tree_make_filtration_non_decreasing_unit_test.cpp @@ -0,0 +1,148 @@ +/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + * Author(s): Vincent Rouvreau + * + * Copyright (C) 2020 Inria + * + * Modification(s): + * - YYYY/MM Author: Description of the modification + */ + +#include <iostream> +#include <limits> // for NaN +#include <cmath> // for isNaN + +#define BOOST_TEST_DYN_LINK +#define BOOST_TEST_MODULE "simplex_tree_make_filtration_non_decreasing" +#include <boost/test/unit_test.hpp> +#include <boost/mpl/list.hpp> + +// ^ +// /!\ Nothing else from Simplex_tree shall be included to test includes are well defined. +#include "gudhi/Simplex_tree.h" + +using namespace Gudhi; + +typedef boost::mpl::list<Simplex_tree<>, Simplex_tree<Simplex_tree_options_fast_persistence>> list_of_tested_variants; + +BOOST_AUTO_TEST_CASE_TEMPLATE(make_filtration_non_decreasing, typeST, list_of_tested_variants) { + typeST st; + + st.insert_simplex_and_subfaces({2, 1, 0}, 2.0); + st.insert_simplex_and_subfaces({3, 0}, 2.0); + st.insert_simplex_and_subfaces({3, 4, 5}, 2.0); + + /* Inserted simplex: */ + /* 1 */ + /* o */ + /* /X\ */ + /* o---o---o---o */ + /* 2 0 3\X/4 */ + /* o */ + /* 5 */ + + std::clog << "Check default insertion ensures the filtration values are non decreasing" << std::endl; + BOOST_CHECK(!st.make_filtration_non_decreasing()); + + // Because of non decreasing property of simplex tree, { 0 } , { 1 } and { 0, 1 } are going to be set from value 2.0 + // to 1.0 + st.insert_simplex_and_subfaces({0, 1, 6, 7}, 1.0); + + // Inserted simplex: + // 1 6 + // o---o + // /X\7/ + // o---o---o---o + // 2 0 3\X/4 + // o + // 5 + + std::clog << "Check default second insertion ensures the filtration values are non decreasing" << std::endl; + BOOST_CHECK(!st.make_filtration_non_decreasing()); + + // Copy original simplex tree + typeST st_copy = st; + + // Modify specific values for st to become like st_copy thanks to make_filtration_non_decreasing + st.assign_filtration(st.find({0,1,6,7}), 0.8); + st.assign_filtration(st.find({0,1,6}), 0.9); + st.assign_filtration(st.find({0,6}), 0.6); + st.assign_filtration(st.find({3,4,5}), 1.2); + st.assign_filtration(st.find({3,4}), 1.1); + st.assign_filtration(st.find({4,5}), 1.99); + + std::clog << "Check the simplex_tree is rolled back in case of decreasing filtration values" << std::endl; + BOOST_CHECK(st.make_filtration_non_decreasing()); + BOOST_CHECK(st == st_copy); + + // Other simplex tree + typeST st_other; + st_other.insert_simplex_and_subfaces({2, 1, 0}, 3.0); // This one is different from st + st_other.insert_simplex_and_subfaces({3, 0}, 2.0); + st_other.insert_simplex_and_subfaces({3, 4, 5}, 2.0); + st_other.insert_simplex_and_subfaces({0, 1, 6, 7}, 1.0); + + // Modify specific values for st to become like st_other thanks to make_filtration_non_decreasing + st.assign_filtration(st.find({2}), 3.0); + // By modifying just the simplex {2} + // {0,1,2}, {1,2} and {0,2} will be modified + + std::clog << "Check the simplex_tree is repaired in case of decreasing filtration values" << std::endl; + BOOST_CHECK(st.make_filtration_non_decreasing()); + BOOST_CHECK(st == st_other); + + // Modify specific values for st still to be non-decreasing + st.assign_filtration(st.find({0,1,2}), 10.0); + st.assign_filtration(st.find({0,2}), 9.0); + st.assign_filtration(st.find({0,1,6,7}), 50.0); + st.assign_filtration(st.find({0,1,6}), 49.0); + st.assign_filtration(st.find({0,1,7}), 48.0); + // Other copy simplex tree + typeST st_other_copy = st; + + std::clog << "Check the simplex_tree is not modified in case of non-decreasing filtration values" << std::endl; + BOOST_CHECK(!st.make_filtration_non_decreasing()); + BOOST_CHECK(st == st_other_copy); + +} + +BOOST_AUTO_TEST_CASE_TEMPLATE(make_filtration_non_decreasing_on_nan_values, typeST, list_of_tested_variants) { + typeST st; + + st.insert_simplex_and_subfaces({2, 1, 0}, std::numeric_limits<double>::quiet_NaN()); + st.insert_simplex_and_subfaces({3, 0}, std::numeric_limits<double>::quiet_NaN()); + st.insert_simplex_and_subfaces({3, 4, 5}, std::numeric_limits<double>::quiet_NaN()); + + /* Inserted simplex: */ + /* 1 */ + /* o */ + /* /X\ */ + /* o---o---o---o */ + /* 2 0 3\X/4 */ + /* o */ + /* 5 */ + + std::clog << "SPECIFIC CASE:" << std::endl; + std::clog << "Insertion with NaN values does not ensure the filtration values are non decreasing" << std::endl; + st.make_filtration_non_decreasing(); + + std::clog << "Check all filtration values are NaN" << std::endl; + for (auto f_simplex : st.complex_simplex_range()) { + BOOST_CHECK(std::isnan(st.filtration(f_simplex))); + } + + st.assign_filtration(st.find({0}), 0.); + st.assign_filtration(st.find({1}), 0.); + st.assign_filtration(st.find({2}), 0.); + st.assign_filtration(st.find({3}), 0.); + st.assign_filtration(st.find({4}), 0.); + st.assign_filtration(st.find({5}), 0.); + + std::clog << "Check make_filtration_non_decreasing is modifying the simplicial complex" << std::endl; + BOOST_CHECK(st.make_filtration_non_decreasing()); + + std::clog << "Check all filtration values are now defined" << std::endl; + for (auto f_simplex : st.complex_simplex_range()) { + BOOST_CHECK(!std::isnan(st.filtration(f_simplex))); + } +} diff --git a/src/Simplex_tree/test/simplex_tree_remove_unit_test.cpp b/src/Simplex_tree/test/simplex_tree_remove_unit_test.cpp index 97347992..36b8b3c6 100644 --- a/src/Simplex_tree/test/simplex_tree_remove_unit_test.cpp +++ b/src/Simplex_tree/test/simplex_tree_remove_unit_test.cpp @@ -32,8 +32,8 @@ using Mini_stree = Simplex_tree<MyOptions>; using Stree = Simplex_tree<>; BOOST_AUTO_TEST_CASE(remove_maximal_simplex) { - std::cout << "********************************************************************" << std::endl; - std::cout << "REMOVE MAXIMAL SIMPLEX" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "REMOVE MAXIMAL SIMPLEX" << std::endl; Mini_stree st; @@ -66,21 +66,21 @@ BOOST_AUTO_TEST_CASE(remove_maximal_simplex) { // 5 #ifdef GUDHI_DEBUG - std::cout << "Check exception throw in debug mode" << std::endl; + std::clog << "Check exception throw in debug mode" << std::endl; // throw excpt because sh has children BOOST_CHECK_THROW (st.remove_maximal_simplex(st.find({0, 1, 6})), std::invalid_argument); BOOST_CHECK_THROW (st.remove_maximal_simplex(st.find({3})), std::invalid_argument); BOOST_CHECK(st == st_complete); #endif - std::cout << "st.remove_maximal_simplex({0, 2})" << std::endl; + std::clog << "st.remove_maximal_simplex({0, 2})" << std::endl; st.remove_maximal_simplex(st.find({0, 2})); - std::cout << "st.remove_maximal_simplex({0, 1, 2})" << std::endl; + std::clog << "st.remove_maximal_simplex({0, 1, 2})" << std::endl; st.remove_maximal_simplex(st.find({0, 1, 2})); - std::cout << "st.remove_maximal_simplex({1, 2})" << std::endl; + std::clog << "st.remove_maximal_simplex({1, 2})" << std::endl; st.remove_maximal_simplex(st.find({1, 2})); - std::cout << "st.remove_maximal_simplex({2})" << std::endl; + std::clog << "st.remove_maximal_simplex({2})" << std::endl; st.remove_maximal_simplex(st.find({2})); - std::cout << "st.remove_maximal_simplex({3})" << std::endl; + std::clog << "st.remove_maximal_simplex({3})" << std::endl; st.remove_maximal_simplex(st.find({0, 3})); BOOST_CHECK(st == st_pruned); @@ -102,39 +102,39 @@ BOOST_AUTO_TEST_CASE(remove_maximal_simplex) { // 5 // Remove all 7 to test the both remove_maximal_simplex cases (when _members is empty or not) - std::cout << "st.remove_maximal_simplex({0, 1, 6, 7})" << std::endl; + std::clog << "st.remove_maximal_simplex({0, 1, 6, 7})" << std::endl; st.remove_maximal_simplex(st.find({0, 1, 6, 7})); - std::cout << "st.remove_maximal_simplex({0, 1, 7})" << std::endl; + std::clog << "st.remove_maximal_simplex({0, 1, 7})" << std::endl; st.remove_maximal_simplex(st.find({0, 1, 7})); - std::cout << "st.remove_maximal_simplex({0, 6, 7})" << std::endl; + std::clog << "st.remove_maximal_simplex({0, 6, 7})" << std::endl; st.remove_maximal_simplex(st.find({0, 6, 7})); - std::cout << "st.remove_maximal_simplex({0, 7})" << std::endl; + std::clog << "st.remove_maximal_simplex({0, 7})" << std::endl; st.remove_maximal_simplex(st.find({0, 7})); - std::cout << "st.remove_maximal_simplex({1, 6, 7})" << std::endl; + std::clog << "st.remove_maximal_simplex({1, 6, 7})" << std::endl; st.remove_maximal_simplex(st.find({1, 6, 7})); - std::cout << "st.remove_maximal_simplex({1, 7})" << std::endl; + std::clog << "st.remove_maximal_simplex({1, 7})" << std::endl; st.remove_maximal_simplex(st.find({1, 7})); - std::cout << "st.remove_maximal_simplex({6, 7})" << std::endl; + std::clog << "st.remove_maximal_simplex({6, 7})" << std::endl; st.remove_maximal_simplex(st.find({6, 7})); - std::cout << "st.remove_maximal_simplex({7})" << std::endl; + std::clog << "st.remove_maximal_simplex({7})" << std::endl; st.remove_maximal_simplex(st.find({7})); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); // Check dimension calls lower_upper_bound_dimension to recompute dimension BOOST_CHECK(st.dimension() == 2); BOOST_CHECK(st.upper_bound_dimension() == 2); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << " | st_wo_seven.upper_bound_dimension()=" << st_wo_seven.upper_bound_dimension() << std::endl; - std::cout << "st.dimension()=" << st.dimension() << " | st_wo_seven.dimension()=" << st_wo_seven.dimension() << std::endl; + std::clog << "st.dimension()=" << st.dimension() << " | st_wo_seven.dimension()=" << st_wo_seven.dimension() << std::endl; BOOST_CHECK(st == st_wo_seven); } BOOST_AUTO_TEST_CASE(auto_dimension_set) { - std::cout << "********************************************************************" << std::endl; - std::cout << "DIMENSION ON REMOVE MAXIMAL SIMPLEX" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "DIMENSION ON REMOVE MAXIMAL SIMPLEX" << std::endl; Mini_stree st; @@ -148,80 +148,80 @@ BOOST_AUTO_TEST_CASE(auto_dimension_set) { BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 3); - std::cout << "st.remove_maximal_simplex({6, 7, 8, 10})" << std::endl; + std::clog << "st.remove_maximal_simplex({6, 7, 8, 10})" << std::endl; st.remove_maximal_simplex(st.find({6, 7, 8, 10})); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 3); - std::cout << "st.remove_maximal_simplex({6, 7, 8, 9})" << std::endl; + std::clog << "st.remove_maximal_simplex({6, 7, 8, 9})" << std::endl; st.remove_maximal_simplex(st.find({6, 7, 8, 9})); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 3); - std::cout << "st.remove_maximal_simplex({1, 2, 3, 4})" << std::endl; + std::clog << "st.remove_maximal_simplex({1, 2, 3, 4})" << std::endl; st.remove_maximal_simplex(st.find({1, 2, 3, 4})); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 3); - std::cout << "st.remove_maximal_simplex({1, 2, 3, 5})" << std::endl; + std::clog << "st.remove_maximal_simplex({1, 2, 3, 5})" << std::endl; st.remove_maximal_simplex(st.find({1, 2, 3, 5})); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 2); - std::cout << "st.dimension()=" << st.dimension() << std::endl; + std::clog << "st.dimension()=" << st.dimension() << std::endl; - std::cout << "st.insert_simplex_and_subfaces({1, 2, 3, 5})" << std::endl; + std::clog << "st.insert_simplex_and_subfaces({1, 2, 3, 5})" << std::endl; st.insert_simplex_and_subfaces({1, 2, 3, 5}); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 3); - std::cout << "st.insert_simplex_and_subfaces({1, 2, 3, 4})" << std::endl; + std::clog << "st.insert_simplex_and_subfaces({1, 2, 3, 4})" << std::endl; st.insert_simplex_and_subfaces({1, 2, 3, 4}); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 3); - std::cout << "st.remove_maximal_simplex({1, 2, 3, 5})" << std::endl; + std::clog << "st.remove_maximal_simplex({1, 2, 3, 5})" << std::endl; st.remove_maximal_simplex(st.find({1, 2, 3, 5})); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 3); - std::cout << "st.remove_maximal_simplex({1, 2, 3, 4})" << std::endl; + std::clog << "st.remove_maximal_simplex({1, 2, 3, 4})" << std::endl; st.remove_maximal_simplex(st.find({1, 2, 3, 4})); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 2); - std::cout << "st.dimension()=" << st.dimension() << std::endl; + std::clog << "st.dimension()=" << st.dimension() << std::endl; - std::cout << "st.insert_simplex_and_subfaces({0, 1, 3, 4})" << std::endl; + std::clog << "st.insert_simplex_and_subfaces({0, 1, 3, 4})" << std::endl; st.insert_simplex_and_subfaces({0, 1, 3, 4}); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 3); - std::cout << "st.remove_maximal_simplex({0, 1, 3, 4})" << std::endl; + std::clog << "st.remove_maximal_simplex({0, 1, 3, 4})" << std::endl; st.remove_maximal_simplex(st.find({0, 1, 3, 4})); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 2); - std::cout << "st.dimension()=" << st.dimension() << std::endl; + std::clog << "st.dimension()=" << st.dimension() << std::endl; - std::cout << "st.insert_simplex_and_subfaces({1, 2, 3, 5})" << std::endl; + std::clog << "st.insert_simplex_and_subfaces({1, 2, 3, 5})" << std::endl; st.insert_simplex_and_subfaces({1, 2, 3, 5}); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 3); - std::cout << "st.insert_simplex_and_subfaces({1, 2, 3, 4})" << std::endl; + std::clog << "st.insert_simplex_and_subfaces({1, 2, 3, 4})" << std::endl; st.insert_simplex_and_subfaces({1, 2, 3, 4}); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 3); @@ -229,7 +229,7 @@ BOOST_AUTO_TEST_CASE(auto_dimension_set) { // Check you can override the dimension // This is a limit test case - shall not happen st.set_dimension(1); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 1); // check dimension() and lower_upper_bound_dimension() is not giving the right answer because dimension is too low BOOST_CHECK(st.dimension() == 1); @@ -238,7 +238,7 @@ BOOST_AUTO_TEST_CASE(auto_dimension_set) { // Check you can override the dimension // This is a limit test case - shall not happen st.set_dimension(6); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 6); // check dimension() do not launch lower_upper_bound_dimension() BOOST_CHECK(st.dimension() == 6); @@ -246,27 +246,27 @@ BOOST_AUTO_TEST_CASE(auto_dimension_set) { // Reset with the correct value st.set_dimension(3); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == 3); - std::cout << "st.insert_simplex_and_subfaces({0, 1, 2, 3, 4, 5, 6})" << std::endl; + std::clog << "st.insert_simplex_and_subfaces({0, 1, 2, 3, 4, 5, 6})" << std::endl; st.insert_simplex_and_subfaces({0, 1, 2, 3, 4, 5, 6}); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 6); BOOST_CHECK(st.dimension() == 6); - std::cout << "st.remove_maximal_simplex({0, 1, 2, 3, 4, 5, 6})" << std::endl; + std::clog << "st.remove_maximal_simplex({0, 1, 2, 3, 4, 5, 6})" << std::endl; st.remove_maximal_simplex(st.find({0, 1, 2, 3, 4, 5, 6})); - std::cout << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; + std::clog << "st.upper_bound_dimension()=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 6); BOOST_CHECK(st.dimension() == 5); } BOOST_AUTO_TEST_CASE(prune_above_filtration) { - std::cout << "********************************************************************" << std::endl; - std::cout << "PRUNE ABOVE FILTRATION" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "PRUNE ABOVE FILTRATION" << std::endl; Stree st; @@ -321,15 +321,15 @@ BOOST_AUTO_TEST_CASE(prune_above_filtration) { BOOST_CHECK(!simplex_is_changed); // Display the Simplex_tree - std::cout << "The complex contains " << st.num_simplices() << " simplices"; - std::cout << " - dimension " << st.dimension() << std::endl; - std::cout << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; + std::clog << "The complex contains " << st.num_simplices() << " simplices"; + std::clog << " - dimension " << st.dimension() << std::endl; + std::clog << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " << "[" << st.filtration(f_simplex) << "] "; + std::clog << " " << "[" << st.filtration(f_simplex) << "] "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << (int) vertex << " "; + std::clog << (int) vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } // Check the pruned cases @@ -340,15 +340,15 @@ BOOST_AUTO_TEST_CASE(prune_above_filtration) { BOOST_CHECK(simplex_is_changed); // Display the Simplex_tree - std::cout << "The complex pruned at 2.5 contains " << st.num_simplices() << " simplices"; - std::cout << " - dimension " << st.dimension() << std::endl; + std::clog << "The complex pruned at 2.5 contains " << st.num_simplices() << " simplices"; + std::clog << " - dimension " << st.dimension() << std::endl; simplex_is_changed = st.prune_above_filtration(2.0); if (simplex_is_changed) st.initialize_filtration(); - std::cout << "The complex pruned at 2.0 contains " << st.num_simplices() << " simplices"; - std::cout << " - dimension " << st.dimension() << std::endl; + std::clog << "The complex pruned at 2.0 contains " << st.num_simplices() << " simplices"; + std::clog << " - dimension " << st.dimension() << std::endl; BOOST_CHECK(st == st_pruned); BOOST_CHECK(!simplex_is_changed); @@ -360,12 +360,12 @@ BOOST_AUTO_TEST_CASE(prune_above_filtration) { st.initialize_filtration(); // Display the Simplex_tree - std::cout << "The complex pruned at 0.0 contains " << st.num_simplices() << " simplices"; - std::cout << " - upper_bound_dimension " << st.upper_bound_dimension() << std::endl; + std::clog << "The complex pruned at 0.0 contains " << st.num_simplices() << " simplices"; + std::clog << " - upper_bound_dimension " << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == 3); BOOST_CHECK(st.dimension() == -1); - std::cout << "upper_bound_dimension=" << st.upper_bound_dimension() << std::endl; + std::clog << "upper_bound_dimension=" << st.upper_bound_dimension() << std::endl; BOOST_CHECK(st.upper_bound_dimension() == -1); BOOST_CHECK(st == st_empty); @@ -380,8 +380,8 @@ BOOST_AUTO_TEST_CASE(prune_above_filtration) { } BOOST_AUTO_TEST_CASE(mini_prune_above_filtration) { - std::cout << "********************************************************************" << std::endl; - std::cout << "MINI PRUNE ABOVE FILTRATION" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "MINI PRUNE ABOVE FILTRATION" << std::endl; Mini_stree st; @@ -402,7 +402,7 @@ BOOST_AUTO_TEST_CASE(mini_prune_above_filtration) { st.initialize_filtration(); // Display the Simplex_tree - std::cout << "The complex contains " << st.num_simplices() << " simplices" << std::endl; + std::clog << "The complex contains " << st.num_simplices() << " simplices" << std::endl; BOOST_CHECK(st.num_simplices() == 27); // Test case to the limit - With these options, there is no filtration, which means filtration is 0 @@ -410,7 +410,7 @@ BOOST_AUTO_TEST_CASE(mini_prune_above_filtration) { if (simplex_is_changed) st.initialize_filtration(); // Display the Simplex_tree - std::cout << "The complex pruned at 1.0 contains " << st.num_simplices() << " simplices" << std::endl; + std::clog << "The complex pruned at 1.0 contains " << st.num_simplices() << " simplices" << std::endl; BOOST_CHECK(!simplex_is_changed); BOOST_CHECK(st.num_simplices() == 27); @@ -418,7 +418,7 @@ BOOST_AUTO_TEST_CASE(mini_prune_above_filtration) { if (simplex_is_changed) st.initialize_filtration(); // Display the Simplex_tree - std::cout << "The complex pruned at 0.0 contains " << st.num_simplices() << " simplices" << std::endl; + std::clog << "The complex pruned at 0.0 contains " << st.num_simplices() << " simplices" << std::endl; BOOST_CHECK(!simplex_is_changed); BOOST_CHECK(st.num_simplices() == 27); @@ -427,11 +427,11 @@ BOOST_AUTO_TEST_CASE(mini_prune_above_filtration) { if (simplex_is_changed) st.initialize_filtration(); // Display the Simplex_tree - std::cout << "The complex pruned at -1.0 contains " << st.num_simplices() << " simplices" << std::endl; + std::clog << "The complex pruned at -1.0 contains " << st.num_simplices() << " simplices" << std::endl; BOOST_CHECK(simplex_is_changed); BOOST_CHECK(st.num_simplices() == 0); // Display the Simplex_tree - std::cout << "The complex contains " << st.num_simplices() << " simplices" << std::endl; + std::clog << "The complex contains " << st.num_simplices() << " simplices" << std::endl; } diff --git a/src/Simplex_tree/test/simplex_tree_unit_test.cpp b/src/Simplex_tree/test/simplex_tree_unit_test.cpp index 58bfa8db..9b5fa8fe 100644 --- a/src/Simplex_tree/test/simplex_tree_unit_test.cpp +++ b/src/Simplex_tree/test/simplex_tree_unit_test.cpp @@ -48,22 +48,22 @@ void test_empty_simplex_tree(typeST& tst) { template<class typeST> void test_iterators_on_empty_simplex_tree(typeST& tst) { - std::cout << "Iterator on vertices: " << std::endl; + std::clog << "Iterator on vertices: " << std::endl; for (auto vertex : tst.complex_vertex_range()) { - std::cout << "vertice:" << vertex << std::endl; + std::clog << "vertice:" << vertex << std::endl; BOOST_CHECK(false); // shall be empty } - std::cout << "Iterator on simplices: " << std::endl; + std::clog << "Iterator on simplices: " << std::endl; for (auto simplex : tst.complex_simplex_range()) { BOOST_CHECK(simplex != simplex); // shall be empty - to remove warning of non-used simplex } - std::cout + std::clog << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; for (auto f_simplex : tst.filtration_simplex_range()) { BOOST_CHECK(false); // shall be empty - std::cout << "test_iterators_on_empty_simplex_tree - filtration=" + std::clog << "test_iterators_on_empty_simplex_tree - filtration=" << tst.filtration(f_simplex) << std::endl; } } @@ -72,15 +72,15 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_when_empty, typeST, list_of_tested_va typedef std::pair<typename typeST::Simplex_handle, bool> typePairSimplexBool; typedef std::vector<typename typeST::Vertex_handle> typeVectorVertex; - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF DEFAULT CONSTRUCTOR" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF DEFAULT CONSTRUCTOR" << std::endl; typeST st; test_empty_simplex_tree(st); test_iterators_on_empty_simplex_tree(st); // TEST OF EMPTY INSERTION - std::cout << "TEST OF EMPTY INSERTION" << std::endl; + std::clog << "TEST OF EMPTY INSERTION" << std::endl; typeVectorVertex simplexVectorEmpty; BOOST_CHECK(simplexVectorEmpty.empty() == true); typePairSimplexBool returnEmptyValue = st.insert_simplex(simplexVectorEmpty, 0.0); @@ -98,8 +98,8 @@ bool AreAlmostTheSame(float a, float b) { BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_from_file, typeST, list_of_tested_variants) { // TEST OF INSERTION - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF SIMPLEX TREE FROM A FILE" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF SIMPLEX TREE FROM A FILE" << std::endl; typeST st; std::string inputFile("simplex_tree_for_unit_test.txt"); @@ -107,8 +107,8 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_from_file, typeST, list_of_tested_var simplex_tree_stream >> st; // Display the Simplex_tree - std::cout << "The complex contains " << st.num_simplices() << " simplices" << std::endl; - std::cout << " - dimension " << st.dimension() << std::endl; + std::clog << "The complex contains " << st.num_simplices() << " simplices" << std::endl; + std::clog << " - dimension " << st.dimension() << std::endl; // Check BOOST_CHECK(st.num_simplices() == 143353); @@ -134,13 +134,13 @@ template<class typeST, class typeSimplex> void test_simplex_tree_contains(typeST& simplexTree, typeSimplex& simplex, int pos) { auto f_simplex = simplexTree.filtration_simplex_range().begin() + pos; - std::cout << "test_simplex_tree_contains - filtration=" << simplexTree.filtration(*f_simplex) << "||" << simplex.second << std::endl; + std::clog << "test_simplex_tree_contains - filtration=" << simplexTree.filtration(*f_simplex) << "||" << simplex.second << std::endl; BOOST_CHECK(AreAlmostTheSame(simplexTree.filtration(*f_simplex), simplex.second)); int simplexIndex = simplex.first.size() - 1; std::sort(simplex.first.begin(), simplex.first.end()); // if the simplex wasn't sorted, the next test could fail for (auto vertex : simplexTree.simplex_vertex_range(*f_simplex)) { - std::cout << "test_simplex_tree_contains - vertex=" << vertex << "||" << simplex.first.at(simplexIndex) << std::endl; + std::clog << "test_simplex_tree_contains - vertex=" << vertex << "||" << simplex.first.at(simplexIndex) << std::endl; BOOST_CHECK(vertex == simplex.first.at(simplexIndex)); BOOST_CHECK(simplexIndex >= 0); simplexIndex--; @@ -163,7 +163,7 @@ void set_and_test_simplex_tree_dim_fil(typeST& simplexTree, int vectorSize, cons if (vectorSize > dim_max + 1) { dim_max = vectorSize - 1; simplexTree.set_dimension(dim_max); - std::cout << " set_and_test_simplex_tree_dim_fil - dim_max=" << dim_max + std::clog << " set_and_test_simplex_tree_dim_fil - dim_max=" << dim_max << std::endl; } @@ -193,12 +193,12 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var dim_max = -1; // TEST OF INSERTION - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF INSERTION" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF INSERTION" << std::endl; typeST st; // ++ FIRST - std::cout << " - INSERT 0" << std::endl; + std::clog << " - INSERT 0" << std::endl; typeVectorVertex firstSimplexVector{0}; BOOST_CHECK(firstSimplexVector.size() == 1); typeSimplex firstSimplex = std::make_pair(firstSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); @@ -209,7 +209,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var BOOST_CHECK(st.num_vertices() == (size_t) 1); // ++ SECOND - std::cout << " - INSERT 1" << std::endl; + std::clog << " - INSERT 1" << std::endl; typeVectorVertex secondSimplexVector{1}; BOOST_CHECK(secondSimplexVector.size() == 1); typeSimplex secondSimplex = std::make_pair(secondSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); @@ -220,7 +220,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var BOOST_CHECK(st.num_vertices() == (size_t) 2); // ++ THIRD - std::cout << " - INSERT (0,1)" << std::endl; + std::clog << " - INSERT (0,1)" << std::endl; typeVectorVertex thirdSimplexVector{0, 1}; BOOST_CHECK(thirdSimplexVector.size() == 2); typeSimplex thirdSimplex = std::make_pair(thirdSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); @@ -231,7 +231,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var BOOST_CHECK(st.num_vertices() == (size_t) 2); // Not incremented !! // ++ FOURTH - std::cout << " - INSERT 2" << std::endl; + std::clog << " - INSERT 2" << std::endl; typeVectorVertex fourthSimplexVector{2}; BOOST_CHECK(fourthSimplexVector.size() == 1); typeSimplex fourthSimplex = std::make_pair(fourthSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); @@ -242,7 +242,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var BOOST_CHECK(st.num_vertices() == (size_t) 3); // ++ FIFTH - std::cout << " - INSERT (2,0)" << std::endl; + std::clog << " - INSERT (2,0)" << std::endl; typeVectorVertex fifthSimplexVector{2, 0}; BOOST_CHECK(fifthSimplexVector.size() == 2); typeSimplex fifthSimplex = std::make_pair(fifthSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); @@ -253,7 +253,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var BOOST_CHECK(st.num_vertices() == (size_t) 3); // Not incremented !! // ++ SIXTH - std::cout << " - INSERT (2,1)" << std::endl; + std::clog << " - INSERT (2,1)" << std::endl; typeVectorVertex sixthSimplexVector{2, 1}; BOOST_CHECK(sixthSimplexVector.size() == 2); typeSimplex sixthSimplex = std::make_pair(sixthSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); @@ -264,7 +264,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var BOOST_CHECK(st.num_vertices() == (size_t) 3); // Not incremented !! // ++ SEVENTH - std::cout << " - INSERT (2,1,0)" << std::endl; + std::clog << " - INSERT (2,1,0)" << std::endl; typeVectorVertex seventhSimplexVector{2, 1, 0}; BOOST_CHECK(seventhSimplexVector.size() == 3); typeSimplex seventhSimplex = std::make_pair(seventhSimplexVector, Filtration_value(THIRD_FILTRATION_VALUE)); @@ -275,7 +275,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var BOOST_CHECK(st.num_vertices() == (size_t) 3); // Not incremented !! // ++ EIGHTH - std::cout << " - INSERT 3" << std::endl; + std::clog << " - INSERT 3" << std::endl; typeVectorVertex eighthSimplexVector{3}; BOOST_CHECK(eighthSimplexVector.size() == 1); typeSimplex eighthSimplex = std::make_pair(eighthSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); @@ -286,7 +286,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var BOOST_CHECK(st.num_vertices() == (size_t) 4); // ++ NINETH - std::cout << " - INSERT (3,0)" << std::endl; + std::clog << " - INSERT (3,0)" << std::endl; typeVectorVertex ninethSimplexVector{3, 0}; BOOST_CHECK(ninethSimplexVector.size() == 2); typeSimplex ninethSimplex = std::make_pair(ninethSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); @@ -297,7 +297,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var BOOST_CHECK(st.num_vertices() == (size_t) 4); // Not incremented !! // ++ TENTH - std::cout << " - INSERT 0 (already inserted)" << std::endl; + std::clog << " - INSERT 0 (already inserted)" << std::endl; typeVectorVertex tenthSimplexVector{0}; BOOST_CHECK(tenthSimplexVector.size() == 1); // With a different filtration value @@ -308,12 +308,12 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var // Simplex_handle = boost::container::flat_map< typeST::Vertex_handle, Node >::iterator typename typeST::Simplex_handle shReturned = returnValue.first; BOOST_CHECK(shReturned == typename typeST::Simplex_handle(nullptr)); - std::cout << "st.num_vertices()=" << st.num_vertices() << std::endl; + std::clog << "st.num_vertices()=" << st.num_vertices() << std::endl; BOOST_CHECK(st.num_vertices() == (size_t) 4); // Not incremented !! BOOST_CHECK(st.dimension() == dim_max); // ++ ELEVENTH - std::cout << " - INSERT (2,1,0) (already inserted)" << std::endl; + std::clog << " - INSERT (2,1,0) (already inserted)" << std::endl; typeVectorVertex eleventhSimplexVector{2, 1, 0}; BOOST_CHECK(eleventhSimplexVector.size() == 3); typeSimplex eleventhSimplex = std::make_pair(eleventhSimplexVector, Filtration_value(FOURTH_FILTRATION_VALUE)); @@ -343,35 +343,35 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insertion, typeST, list_of_tested_var // [0.2] 3 0 // [0.3] 2 1 0 // !! Be careful, simplex are sorted by filtration value on insertion !! - std::cout << "simplex_tree_insertion - first - 0" << std::endl; + std::clog << "simplex_tree_insertion - first - 0" << std::endl; test_simplex_tree_contains(st, firstSimplex, 0); // (0) -> 0 - std::cout << "simplex_tree_insertion - second - 1" << std::endl; + std::clog << "simplex_tree_insertion - second - 1" << std::endl; test_simplex_tree_contains(st, secondSimplex, 1); // (1) -> 1 - std::cout << "simplex_tree_insertion - third - 4" << std::endl; + std::clog << "simplex_tree_insertion - third - 4" << std::endl; test_simplex_tree_contains(st, thirdSimplex, 4); // (0,1) -> 4 - std::cout << "simplex_tree_insertion - fourth - 2" << std::endl; + std::clog << "simplex_tree_insertion - fourth - 2" << std::endl; test_simplex_tree_contains(st, fourthSimplex, 2); // (2) -> 2 - std::cout << "simplex_tree_insertion - fifth - 5" << std::endl; + std::clog << "simplex_tree_insertion - fifth - 5" << std::endl; test_simplex_tree_contains(st, fifthSimplex, 5); // (2,0) -> 5 - std::cout << "simplex_tree_insertion - sixth - 6" << std::endl; + std::clog << "simplex_tree_insertion - sixth - 6" << std::endl; test_simplex_tree_contains(st, sixthSimplex, 6); //(2,1) -> 6 - std::cout << "simplex_tree_insertion - seventh - 8" << std::endl; + std::clog << "simplex_tree_insertion - seventh - 8" << std::endl; test_simplex_tree_contains(st, seventhSimplex, 8); // (2,1,0) -> 8 - std::cout << "simplex_tree_insertion - eighth - 3" << std::endl; + std::clog << "simplex_tree_insertion - eighth - 3" << std::endl; test_simplex_tree_contains(st, eighthSimplex, 3); // (3) -> 3 - std::cout << "simplex_tree_insertion - nineth - 7" << std::endl; + std::clog << "simplex_tree_insertion - nineth - 7" << std::endl; test_simplex_tree_contains(st, ninethSimplex, 7); // (3,0) -> 7 // Display the Simplex_tree - Can not be done in the middle of 2 inserts - std::cout << "The complex contains " << st.num_simplices() << " simplices" << std::endl; - std::cout << " - dimension " << st.dimension() << std::endl; - std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; + std::clog << "The complex contains " << st.num_simplices() << " simplices" << std::endl; + std::clog << " - dimension " << st.dimension() << std::endl; + std::clog << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " << "[" << st.filtration(f_simplex) << "] "; + std::clog << " " << "[" << st.filtration(f_simplex) << "] "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << (int) vertex << " "; + std::clog << (int) vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } } @@ -380,14 +380,14 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(NSimplexAndSubfaces_tree_insertion, typeST, list_o typedef std::pair<typename typeST::Simplex_handle, bool> typePairSimplexBool; typedef std::vector<typename typeST::Vertex_handle> typeVectorVertex; typedef std::pair<typeVectorVertex, typename typeST::Filtration_value> typeSimplex; - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST OF RECURSIVE INSERTION" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST OF RECURSIVE INSERTION" << std::endl; typeST st; typePairSimplexBool returnValue; int position = 0; // ++ FIRST - std::cout << " - INSERT (2,1,0)" << std::endl; + std::clog << " - INSERT (2,1,0)" << std::endl; typeVectorVertex SimplexVector1{2, 1, 0}; BOOST_CHECK(SimplexVector1.size() == 3); returnValue = st.insert_simplex_and_subfaces(SimplexVector1); @@ -400,13 +400,13 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(NSimplexAndSubfaces_tree_insertion, typeST, list_o std::sort(SimplexVector1.begin(), SimplexVector1.end(), std::greater<typename typeST::Vertex_handle>()); for (auto vertex : st.simplex_vertex_range(returnValue.first)) { // Check returned Simplex_handle - std::cout << "vertex = " << vertex << " | vector[" << position << "] = " << SimplexVector1[position] << std::endl; + std::clog << "vertex = " << vertex << " | vector[" << position << "] = " << SimplexVector1[position] << std::endl; BOOST_CHECK(vertex == SimplexVector1[position]); position++; } // ++ SECOND - std::cout << " - INSERT 3" << std::endl; + std::clog << " - INSERT 3" << std::endl; typeVectorVertex SimplexVector2{3}; BOOST_CHECK(SimplexVector2.size() == 1); returnValue = st.insert_simplex_and_subfaces(SimplexVector2); @@ -419,13 +419,13 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(NSimplexAndSubfaces_tree_insertion, typeST, list_o std::sort(SimplexVector2.begin(), SimplexVector2.end(), std::greater<typename typeST::Vertex_handle>()); for (auto vertex : st.simplex_vertex_range(returnValue.first)) { // Check returned Simplex_handle - std::cout << "vertex = " << vertex << " | vector[" << position << "] = " << SimplexVector2[position] << std::endl; + std::clog << "vertex = " << vertex << " | vector[" << position << "] = " << SimplexVector2[position] << std::endl; BOOST_CHECK(vertex == SimplexVector2[position]); position++; } // ++ THIRD - std::cout << " - INSERT (0,3)" << std::endl; + std::clog << " - INSERT (0,3)" << std::endl; typeVectorVertex SimplexVector3{3, 0}; BOOST_CHECK(SimplexVector3.size() == 2); returnValue = st.insert_simplex_and_subfaces(SimplexVector3); @@ -438,13 +438,13 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(NSimplexAndSubfaces_tree_insertion, typeST, list_o std::sort(SimplexVector3.begin(), SimplexVector3.end(), std::greater<typename typeST::Vertex_handle>()); for (auto vertex : st.simplex_vertex_range(returnValue.first)) { // Check returned Simplex_handle - std::cout << "vertex = " << vertex << " | vector[" << position << "] = " << SimplexVector3[position] << std::endl; + std::clog << "vertex = " << vertex << " | vector[" << position << "] = " << SimplexVector3[position] << std::endl; BOOST_CHECK(vertex == SimplexVector3[position]); position++; } // ++ FOURTH - std::cout << " - INSERT (1,0) (already inserted)" << std::endl; + std::clog << " - INSERT (1,0) (already inserted)" << std::endl; typeVectorVertex SimplexVector4{1, 0}; BOOST_CHECK(SimplexVector4.size() == 2); returnValue = st.insert_simplex_and_subfaces(SimplexVector4); @@ -455,7 +455,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(NSimplexAndSubfaces_tree_insertion, typeST, list_o BOOST_CHECK(false == returnValue.second); // ++ FIFTH - std::cout << " - INSERT (3,4,5)" << std::endl; + std::clog << " - INSERT (3,4,5)" << std::endl; typeVectorVertex SimplexVector5{3, 4, 5}; BOOST_CHECK(SimplexVector5.size() == 3); returnValue = st.insert_simplex_and_subfaces(SimplexVector5); @@ -468,13 +468,13 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(NSimplexAndSubfaces_tree_insertion, typeST, list_o std::sort(SimplexVector5.begin(), SimplexVector5.end(), std::greater<typename typeST::Vertex_handle>()); for (auto vertex : st.simplex_vertex_range(returnValue.first)) { // Check returned Simplex_handle - std::cout << "vertex = " << vertex << " | vector[" << position << "] = " << SimplexVector5[position] << std::endl; + std::clog << "vertex = " << vertex << " | vector[" << position << "] = " << SimplexVector5[position] << std::endl; BOOST_CHECK(vertex == SimplexVector5[position]); position++; } // ++ SIXTH - std::cout << " - INSERT (0,1,6,7)" << std::endl; + std::clog << " - INSERT (0,1,6,7)" << std::endl; typeVectorVertex SimplexVector6{0, 1, 6, 7}; BOOST_CHECK(SimplexVector6.size() == 4); returnValue = st.insert_simplex_and_subfaces(SimplexVector6); @@ -487,7 +487,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(NSimplexAndSubfaces_tree_insertion, typeST, list_o std::sort(SimplexVector6.begin(), SimplexVector6.end(), std::greater<typename typeST::Vertex_handle>()); for (auto vertex : st.simplex_vertex_range(returnValue.first)) { // Check returned Simplex_handle - std::cout << "vertex = " << vertex << " | vector[" << position << "] = " << SimplexVector6[position] << std::endl; + std::clog << "vertex = " << vertex << " | vector[" << position << "] = " << SimplexVector6[position] << std::endl; BOOST_CHECK(vertex == SimplexVector6[position]); position++; } @@ -525,63 +525,63 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(NSimplexAndSubfaces_tree_insertion, typeST, list_o // ------------------------------------------------------------------------------------------------------------------ typeVectorVertex simpleSimplexVector{1}; typename typeST::Simplex_handle simplexFound = st.find(simpleSimplexVector); - std::cout << "**************IS THE SIMPLEX {1} IN THE SIMPLEX TREE ?\n"; + std::clog << "**************IS THE SIMPLEX {1} IN THE SIMPLEX TREE ?\n"; if (simplexFound != st.null_simplex()) - std::cout << "***+ YES IT IS!\n"; + std::clog << "***+ YES IT IS!\n"; else - std::cout << "***- NO IT ISN'T\n"; + std::clog << "***- NO IT ISN'T\n"; // Check it is found BOOST_CHECK(simplexFound != st.null_simplex()); typeVectorVertex unknownSimplexVector{15}; simplexFound = st.find(unknownSimplexVector); - std::cout << "**************IS THE SIMPLEX {15} IN THE SIMPLEX TREE ?\n"; + std::clog << "**************IS THE SIMPLEX {15} IN THE SIMPLEX TREE ?\n"; if (simplexFound != st.null_simplex()) - std::cout << "***+ YES IT IS!\n"; + std::clog << "***+ YES IT IS!\n"; else - std::cout << "***- NO IT ISN'T\n"; + std::clog << "***- NO IT ISN'T\n"; // Check it is NOT found BOOST_CHECK(simplexFound == st.null_simplex()); simplexFound = st.find(SimplexVector6); - std::cout << "**************IS THE SIMPLEX {0,1,6,7} IN THE SIMPLEX TREE ?\n"; + std::clog << "**************IS THE SIMPLEX {0,1,6,7} IN THE SIMPLEX TREE ?\n"; if (simplexFound != st.null_simplex()) - std::cout << "***+ YES IT IS!\n"; + std::clog << "***+ YES IT IS!\n"; else - std::cout << "***- NO IT ISN'T\n"; + std::clog << "***- NO IT ISN'T\n"; // Check it is found BOOST_CHECK(simplexFound != st.null_simplex()); typeVectorVertex otherSimplexVector{1, 15}; simplexFound = st.find(otherSimplexVector); - std::cout << "**************IS THE SIMPLEX {15,1} IN THE SIMPLEX TREE ?\n"; + std::clog << "**************IS THE SIMPLEX {15,1} IN THE SIMPLEX TREE ?\n"; if (simplexFound != st.null_simplex()) - std::cout << "***+ YES IT IS!\n"; + std::clog << "***+ YES IT IS!\n"; else - std::cout << "***- NO IT ISN'T\n"; + std::clog << "***- NO IT ISN'T\n"; // Check it is NOT found BOOST_CHECK(simplexFound == st.null_simplex()); typeVectorVertex invSimplexVector{1, 2, 0}; simplexFound = st.find(invSimplexVector); - std::cout << "**************IS THE SIMPLEX {1,2,0} IN THE SIMPLEX TREE ?\n"; + std::clog << "**************IS THE SIMPLEX {1,2,0} IN THE SIMPLEX TREE ?\n"; if (simplexFound != st.null_simplex()) - std::cout << "***+ YES IT IS!\n"; + std::clog << "***+ YES IT IS!\n"; else - std::cout << "***- NO IT ISN'T\n"; + std::clog << "***- NO IT ISN'T\n"; // Check it is found BOOST_CHECK(simplexFound != st.null_simplex()); // Display the Simplex_tree - Can not be done in the middle of 2 inserts - std::cout << "The complex contains " << st.num_simplices() << " simplices" << std::endl; - std::cout << " - dimension " << st.dimension() << std::endl; - std::cout << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; + std::clog << "The complex contains " << st.num_simplices() << " simplices" << std::endl; + std::clog << " - dimension " << st.dimension() << std::endl; + std::clog << std::endl << std::endl << "Iterator on Simplices in the filtration, with [filtration value]:" << std::endl; for (auto f_simplex : st.filtration_simplex_range()) { - std::cout << " " << "[" << st.filtration(f_simplex) << "] "; + std::clog << " " << "[" << st.filtration(f_simplex) << "] "; for (auto vertex : st.simplex_vertex_range(f_simplex)) { - std::cout << (int) vertex << " "; + std::clog << (int) vertex << " "; } - std::cout << std::endl; + std::clog << std::endl; } } @@ -595,17 +595,17 @@ void test_cofaces(typeST& st, const std::vector<Vertex_handle>& expected, int di for (auto simplex = cofaces.begin(); simplex != cofaces.end(); ++simplex) { typename typeST::Simplex_vertex_range rg = st.simplex_vertex_range(*simplex); for (auto vertex = rg.begin(); vertex != rg.end(); ++vertex) { - std::cout << "(" << *vertex << ")"; + std::clog << "(" << *vertex << ")"; } - std::cout << std::endl; + std::clog << std::endl; BOOST_CHECK(std::find(res.begin(), res.end(), *simplex) != res.end()); } } BOOST_AUTO_TEST_CASE_TEMPLATE(coface_on_simplex_tree, typeST, list_of_tested_variants) { typedef std::vector<typename typeST::Vertex_handle> typeVectorVertex; - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST COFACE ALGORITHM" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST COFACE ALGORITHM" << std::endl; typeST st; typeVectorVertex SimplexVector{2, 1, 0}; @@ -631,7 +631,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(coface_on_simplex_tree, typeST, list_of_tested_var std::vector<typename typeST::Vertex_handle> simplex_result; std::vector<typename typeST::Simplex_handle> result; - std::cout << "First test - Star of (3):" << std::endl; + std::clog << "First test - Star of (3):" << std::endl; simplex_result = {3}; result.push_back(st.find(simplex_result)); @@ -656,7 +656,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(coface_on_simplex_tree, typeST, list_of_tested_var vertex.push_back(1); vertex.push_back(7); - std::cout << "Second test - Star of (1,7): " << std::endl; + std::clog << "Second test - Star of (1,7): " << std::endl; simplex_result = {7, 1}; result.push_back(st.find(simplex_result)); @@ -673,7 +673,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(coface_on_simplex_tree, typeST, list_of_tested_var test_cofaces(st, vertex, 0, result); result.clear(); - std::cout << "Third test - 2-dimension Cofaces of simplex(1,7) : " << std::endl; + std::clog << "Third test - 2-dimension Cofaces of simplex(1,7) : " << std::endl; simplex_result = {7, 1, 0}; result.push_back(st.find(simplex_result)); @@ -684,15 +684,15 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(coface_on_simplex_tree, typeST, list_of_tested_var test_cofaces(st, vertex, 1, result); result.clear(); - std::cout << "Cofaces with a codimension too high (codimension + vetices > tree.dimension) :" << std::endl; + std::clog << "Cofaces with a codimension too high (codimension + vetices > tree.dimension) :" << std::endl; test_cofaces(st, vertex, 5, result); - //std::cout << "Cofaces with an empty codimension" << std::endl; + //std::clog << "Cofaces with an empty codimension" << std::endl; //test_cofaces(st, vertex, -1, result); - // std::cout << "Cofaces in an empty simplex tree" << std::endl; + // std::clog << "Cofaces in an empty simplex tree" << std::endl; // typeST empty_tree; // test_cofaces(empty_tree, vertex, 1, result); - //std::cout << "Cofaces of an empty simplex" << std::endl; + //std::clog << "Cofaces of an empty simplex" << std::endl; //vertex.clear(); // test_cofaces(st, vertex, 1, result); @@ -700,8 +700,8 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(coface_on_simplex_tree, typeST, list_of_tested_var BOOST_AUTO_TEST_CASE_TEMPLATE(copy_move_on_simplex_tree, typeST, list_of_tested_variants) { typedef std::vector<typename typeST::Vertex_handle> typeVectorVertex; - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST COPY MOVE CONSTRUCTORS" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST COPY MOVE CONSTRUCTORS" << std::endl; typeST st; typeVectorVertex SimplexVector{2, 1, 0}; @@ -725,11 +725,11 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(copy_move_on_simplex_tree, typeST, list_of_tested_ /* o */ /* 5 */ - std::cout << "Printing st - address = " << &st << std::endl; + std::clog << "Printing st - address = " << &st << std::endl; // Copy constructor typeST st_copy = st; - std::cout << "Printing a copy of st - address = " << &st_copy << std::endl; + std::clog << "Printing a copy of st - address = " << &st_copy << std::endl; // Check the data are the same BOOST_CHECK(st == st_copy); @@ -738,7 +738,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(copy_move_on_simplex_tree, typeST, list_of_tested_ // Move constructor typeST st_move = std::move(st); - std::cout << "Printing a move of st - address = " << &st_move << std::endl; + std::clog << "Printing a move of st - address = " << &st_move << std::endl; // Check the data are the same BOOST_CHECK(st_move == st_copy); @@ -753,7 +753,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(copy_move_on_simplex_tree, typeST, list_of_tested_ BOOST_CHECK(st.num_simplices() == 0); BOOST_CHECK(st.num_vertices() == (size_t)0); - std::cout << "Printing st once again- address = " << &st << std::endl; + std::clog << "Printing st once again- address = " << &st << std::endl; } template<class typeST> @@ -768,22 +768,22 @@ void test_simplex_is_vertex(typeST& st, typename typeST::Simplex_handle sh, type BOOST_AUTO_TEST_CASE(non_contiguous) { typedef Simplex_tree<> typeST; typedef typeST::Simplex_handle Simplex_handle; - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST NON-CONTIGUOUS VERTICES" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST NON-CONTIGUOUS VERTICES" << std::endl; typeST st; typeST::Vertex_handle e[] = {3,-7}; - std::cout << "Insert" << std::endl; + std::clog << "Insert" << std::endl; st.insert_simplex_and_subfaces(e); BOOST_CHECK(st.num_vertices() == 2); BOOST_CHECK(st.num_simplices() == 3); - std::cout << "Find" << std::endl; + std::clog << "Find" << std::endl; Simplex_handle sh = st.find(e); BOOST_CHECK(sh != st.null_simplex()); - std::cout << "Endpoints" << std::endl; + std::clog << "Endpoints" << std::endl; auto p = st.endpoints(sh); test_simplex_is_vertex(st, p.first, 3); test_simplex_is_vertex(st, p.second, -7); - std::cout << "Boundary" << std::endl; + std::clog << "Boundary" << std::endl; auto&& b = st.boundary_simplex_range(sh); auto i = std::begin(b); test_simplex_is_vertex(st, *i, -7); @@ -791,90 +791,6 @@ BOOST_AUTO_TEST_CASE(non_contiguous) { BOOST_CHECK(++i == std::end(b)); } -BOOST_AUTO_TEST_CASE(make_filtration_non_decreasing) { - std::cout << "********************************************************************" << std::endl; - std::cout << "MAKE FILTRATION NON DECREASING" << std::endl; - typedef Simplex_tree<> typeST; - typeST st; - - st.insert_simplex_and_subfaces({2, 1, 0}, 2.0); - st.insert_simplex_and_subfaces({3, 0}, 2.0); - st.insert_simplex_and_subfaces({3, 4, 5}, 2.0); - - /* Inserted simplex: */ - /* 1 */ - /* o */ - /* /X\ */ - /* o---o---o---o */ - /* 2 0 3\X/4 */ - /* o */ - /* 5 */ - - std::cout << "Check default insertion ensures the filtration values are non decreasing" << std::endl; - BOOST_CHECK(!st.make_filtration_non_decreasing()); - - // Because of non decreasing property of simplex tree, { 0 } , { 1 } and { 0, 1 } are going to be set from value 2.0 - // to 1.0 - st.insert_simplex_and_subfaces({0, 1, 6, 7}, 1.0); - - // Inserted simplex: - // 1 6 - // o---o - // /X\7/ - // o---o---o---o - // 2 0 3\X/4 - // o - // 5 - - std::cout << "Check default second insertion ensures the filtration values are non decreasing" << std::endl; - BOOST_CHECK(!st.make_filtration_non_decreasing()); - - // Copy original simplex tree - typeST st_copy = st; - - // Modify specific values for st to become like st_copy thanks to make_filtration_non_decreasing - st.assign_filtration(st.find({0,1,6,7}), 0.8); - st.assign_filtration(st.find({0,1,6}), 0.9); - st.assign_filtration(st.find({0,6}), 0.6); - st.assign_filtration(st.find({3,4,5}), 1.2); - st.assign_filtration(st.find({3,4}), 1.1); - st.assign_filtration(st.find({4,5}), 1.99); - - std::cout << "Check the simplex_tree is rolled back in case of decreasing filtration values" << std::endl; - BOOST_CHECK(st.make_filtration_non_decreasing()); - BOOST_CHECK(st == st_copy); - - // Other simplex tree - typeST st_other; - st_other.insert_simplex_and_subfaces({2, 1, 0}, 3.0); // This one is different from st - st_other.insert_simplex_and_subfaces({3, 0}, 2.0); - st_other.insert_simplex_and_subfaces({3, 4, 5}, 2.0); - st_other.insert_simplex_and_subfaces({0, 1, 6, 7}, 1.0); - - // Modify specific values for st to become like st_other thanks to make_filtration_non_decreasing - st.assign_filtration(st.find({2}), 3.0); - // By modifying just the simplex {2} - // {0,1,2}, {1,2} and {0,2} will be modified - - std::cout << "Check the simplex_tree is repaired in case of decreasing filtration values" << std::endl; - BOOST_CHECK(st.make_filtration_non_decreasing()); - BOOST_CHECK(st == st_other); - - // Modify specific values for st still to be non-decreasing - st.assign_filtration(st.find({0,1,2}), 10.0); - st.assign_filtration(st.find({0,2}), 9.0); - st.assign_filtration(st.find({0,1,6,7}), 50.0); - st.assign_filtration(st.find({0,1,6}), 49.0); - st.assign_filtration(st.find({0,1,7}), 48.0); - // Other copy simplex tree - typeST st_other_copy = st; - - std::cout << "Check the simplex_tree is not modified in case of non-decreasing filtration values" << std::endl; - BOOST_CHECK(!st.make_filtration_non_decreasing()); - BOOST_CHECK(st == st_other_copy); - -} - typedef boost::mpl::list<boost::adjacency_list<boost::setS, boost::vecS, boost::directedS, boost::property<vertex_filtration_t, double>, @@ -896,8 +812,8 @@ typedef boost::mpl::list<boost::adjacency_list<boost::setS, boost::vecS, boost:: boost::property<edge_filtration_t, double>>> list_of_graph_variants; BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insert_graph, Graph, list_of_graph_variants) { - std::cout << "********************************************************************" << std::endl; - std::cout << "INSERT GRAPH" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "INSERT GRAPH" << std::endl; Graph g(3); // filtration value 0 everywhere @@ -924,18 +840,18 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(simplex_tree_insert_graph, Graph, list_of_graph_va st2.insert_graph(g); BOOST_CHECK(st2.num_simplices() == 6); - std::cout << "st1 is" << std::endl; - std::cout << st1 << std::endl; + std::clog << "st1 is" << std::endl; + std::clog << st1 << std::endl; - std::cout << "st2 is" << std::endl; - std::cout << st2 << std::endl; + std::clog << "st2 is" << std::endl; + std::clog << st2 << std::endl; BOOST_CHECK(st1 == st2); } BOOST_AUTO_TEST_CASE_TEMPLATE(insert_duplicated_vertices, typeST, list_of_tested_variants) { - std::cout << "********************************************************************" << std::endl; - std::cout << "TEST INSERT DUPLICATED VERTICES" << std::endl; + std::clog << "********************************************************************" << std::endl; + std::clog << "TEST INSERT DUPLICATED VERTICES" << std::endl; typeST st; typename typeST::Simplex_handle sh; @@ -943,25 +859,25 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(insert_duplicated_vertices, typeST, list_of_tested std::tie(sh, success) = st.insert_simplex_and_subfaces({1}); BOOST_CHECK(success); BOOST_CHECK(sh != st.null_simplex()); - std::cout << "st.dimension(sh)= " << st.dimension(sh) << std::endl; + std::clog << "st.dimension(sh)= " << st.dimension(sh) << std::endl; BOOST_CHECK(st.dimension(sh) == 0); std::tie(sh, success) = st.insert_simplex_and_subfaces({2, 2}); BOOST_CHECK(success); BOOST_CHECK(sh != st.null_simplex()); - std::cout << "st.dimension(sh)= " << st.dimension(sh) << std::endl; + std::clog << "st.dimension(sh)= " << st.dimension(sh) << std::endl; BOOST_CHECK(st.dimension(sh) == 0); std::tie(sh, success) = st.insert_simplex_and_subfaces({3, 3, 3}); BOOST_CHECK(success); BOOST_CHECK(sh != st.null_simplex()); - std::cout << "st.dimension(sh)= " << st.dimension(sh) << std::endl; + std::clog << "st.dimension(sh)= " << st.dimension(sh) << std::endl; BOOST_CHECK(st.dimension(sh) == 0); std::tie(sh, success) = st.insert_simplex_and_subfaces({4, 4, 4, 4}); BOOST_CHECK(success); BOOST_CHECK(sh != st.null_simplex()); - std::cout << "st.dimension(sh)= " << st.dimension(sh) << std::endl; + std::clog << "st.dimension(sh)= " << st.dimension(sh) << std::endl; BOOST_CHECK(st.dimension(sh) == 0); - std::cout << "dimension =" << st.dimension() << " - num_vertices = " << st.num_vertices() + std::clog << "dimension =" << st.dimension() << " - num_vertices = " << st.num_vertices() << " - num_simplices = " << st.num_simplices() << std::endl; BOOST_CHECK(st.dimension() == 0); BOOST_CHECK(st.num_simplices() == st.num_vertices()); @@ -969,10 +885,10 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(insert_duplicated_vertices, typeST, list_of_tested std::tie(sh, success) = st.insert_simplex_and_subfaces({2, 1, 1, 2}); BOOST_CHECK(success); BOOST_CHECK(sh != st.null_simplex()); - std::cout << "st.dimension(sh)= " << st.dimension(sh) << std::endl; + std::clog << "st.dimension(sh)= " << st.dimension(sh) << std::endl; BOOST_CHECK(st.dimension(sh) == 1); - std::cout << "dimension =" << st.dimension() << " - num_vertices = " << st.num_vertices() + std::clog << "dimension =" << st.dimension() << " - num_vertices = " << st.num_vertices() << " - num_simplices = " << st.num_simplices() << std::endl; BOOST_CHECK(st.dimension() == 1); BOOST_CHECK(st.num_simplices() == st.num_vertices() + 1); @@ -982,9 +898,45 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(insert_duplicated_vertices, typeST, list_of_tested BOOST_CHECK(!success); BOOST_CHECK(sh == st.null_simplex()); - std::cout << "dimension =" << st.dimension() << " - num_vertices = " << st.num_vertices() + std::clog << "dimension =" << st.dimension() << " - num_vertices = " << st.num_vertices() << " - num_simplices = " << st.num_simplices() << std::endl; BOOST_CHECK(st.dimension() == 1); BOOST_CHECK(st.num_simplices() == st.num_vertices() + 1); +} +BOOST_AUTO_TEST_CASE_TEMPLATE(generators, typeST, list_of_tested_variants) { + std::cout << "********************************************************************" << std::endl; + std::cout << "TEST FIND GENERATORS" << std::endl; + { + typeST st; + st.insert_simplex_and_subfaces({0,1,2,3,4,5,6},0); + st.assign_filtration(st.find({0,2,4}), 10); + st.assign_filtration(st.find({1,5}), 20); + st.assign_filtration(st.find({1,2,4}), 30); + st.assign_filtration(st.find({3}), 5); + st.make_filtration_non_decreasing(); + BOOST_CHECK(st.filtration(st.find({1,2}))==0); + BOOST_CHECK(st.filtration(st.find({0,1,2,3,4}))==30); + BOOST_CHECK(st.minimal_simplex_with_same_filtration(st.find({0,1,2,3,4,5}))==st.find({1,2,4})); + BOOST_CHECK(st.minimal_simplex_with_same_filtration(st.find({0,2,3}))==st.find({3})); + auto s=st.minimal_simplex_with_same_filtration(st.find({0,2,6})); + BOOST_CHECK(s==st.find({0})||s==st.find({2})||s==st.find({6})); + BOOST_CHECK(st.vertex_with_same_filtration(st.find({2}))==2); + BOOST_CHECK(st.vertex_with_same_filtration(st.find({1,5}))==st.null_vertex()); + BOOST_CHECK(st.vertex_with_same_filtration(st.find({5,6}))>=5); + } + { + typeST st; + st.insert_simplex_and_subfaces({0,1}, 8); + st.insert_simplex_and_subfaces({0,2}, 10); + st.insert_simplex_and_subfaces({3,4}, 6); + st.insert_simplex_and_subfaces({1,2}, 5); + st.insert_simplex_and_subfaces({1,5}, 4); + st.insert_simplex_and_subfaces({0,5}, 3); + st.insert_simplex_and_subfaces({2,5}, 2); + st.insert_simplex_and_subfaces({1,3}, 9); + st.expansion(50); + BOOST_CHECK(st.edge_with_same_filtration(st.find({0,1,2,5}))==st.find({0,2})); + BOOST_CHECK(st.edge_with_same_filtration(st.find({1,5}))==st.find({1,5})); + } } diff --git a/src/Skeleton_blocker/example/Skeleton_blocker_from_simplices.cpp b/src/Skeleton_blocker/example/Skeleton_blocker_from_simplices.cpp index 486827eb..d04ca289 100644 --- a/src/Skeleton_blocker/example/Skeleton_blocker_from_simplices.cpp +++ b/src/Skeleton_blocker/example/Skeleton_blocker_from_simplices.cpp @@ -35,13 +35,13 @@ int main(int argc, char *argv[]) { Complex complex(Gudhi::skeleton_blocker::make_complex_from_top_faces<Complex>(simplices.begin(), simplices.end())); - std::cout << "Simplices:" << std::endl; + std::clog << "Simplices:" << std::endl; for (const Simplex & s : complex.complex_simplex_range()) - std::cout << s << " "; - std::cout << std::endl; + std::clog << s << " "; + std::clog << std::endl; // One blocker as simplex 0123 is not in the complex but all its proper faces are. - std::cout << "Blockers: " << complex.blockers_to_string() << std::endl; + std::clog << "Blockers: " << complex.blockers_to_string() << std::endl; // now build a complex from its full list of simplices simplices.clear(); @@ -53,13 +53,13 @@ int main(int argc, char *argv[]) { simplices.push_back(Simplex(Vertex_handle(2), Vertex_handle(0))); complex = Complex(simplices.begin(), simplices.end()); - std::cout << "Simplices:" << std::endl; + std::clog << "Simplices:" << std::endl; for (const Simplex & s : complex.complex_simplex_range()) - std::cout << s << " "; - std::cout << std::endl; + std::clog << s << " "; + std::clog << std::endl; // One blocker as simplex 012 is not in the complex but all its proper faces are. - std::cout << "Blockers: " << complex.blockers_to_string() << std::endl; + std::clog << "Blockers: " << complex.blockers_to_string() << std::endl; return EXIT_SUCCESS; } diff --git a/src/Skeleton_blocker/example/Skeleton_blocker_iteration.cpp b/src/Skeleton_blocker/example/Skeleton_blocker_iteration.cpp index 7f301047..62084692 100644 --- a/src/Skeleton_blocker/example/Skeleton_blocker_iteration.cpp +++ b/src/Skeleton_blocker/example/Skeleton_blocker_iteration.cpp @@ -45,7 +45,7 @@ int main(int argc, char *argv[]) { // more appropriated! unsigned num_vertices = 0; for (auto v : complex.vertex_range()) { - std::cout << "Vertex " << v << std::endl; + std::clog << "Vertex " << v << std::endl; ++num_vertices; } @@ -65,9 +65,9 @@ int main(int argc, char *argv[]) { else euler -= 1; } - std::cout << "Saw " << num_vertices << " vertices, " << num_edges << " edges and " << num_simplices << " simplices" + std::clog << "Saw " << num_vertices << " vertices, " << num_edges << " edges and " << num_simplices << " simplices" << std::endl; - std::cout << "The Euler Characteristic is " << euler << std::endl; - std::cout << skbl_chrono; + std::clog << "The Euler Characteristic is " << euler << std::endl; + std::clog << skbl_chrono; return EXIT_SUCCESS; } diff --git a/src/Skeleton_blocker/example/Skeleton_blocker_link.cpp b/src/Skeleton_blocker/example/Skeleton_blocker_link.cpp index e634b656..ba7ce43c 100644 --- a/src/Skeleton_blocker/example/Skeleton_blocker_link.cpp +++ b/src/Skeleton_blocker/example/Skeleton_blocker_link.cpp @@ -32,25 +32,25 @@ int main(int argc, char *argv[]) { Simplex tetrahedron(Vertex_handle(0), Vertex_handle(1), Vertex_handle(2), Vertex_handle(3)); complex.add_simplex(tetrahedron); - std::cout << "complex:" << complex.to_string() << std::endl; + std::clog << "complex:" << complex.to_string() << std::endl; // build the link of vertex 1, eg a triangle {0,2,3} auto link = complex.link(Vertex_handle(1)); - std::cout << "link:" << link.to_string() << std::endl; + std::clog << "link:" << link.to_string() << std::endl; // Internally link is a subcomplex of 'complex' and its vertices are stored in a vector. // They can be accessed via Vertex_handle(x) where x is an index of the vector. // In that example, link has three vertices and thus it contains only // Vertex_handle(0),Vertex_handle(1) and Vertex_handle(2) are). for (int i = 0; i < 5; ++i) - std::cout << "link.contains_vertex(Vertex_handle(" << i << ")):" << link.contains_vertex(Vertex_handle(i)) << + std::clog << "link.contains_vertex(Vertex_handle(" << i << ")):" << link.contains_vertex(Vertex_handle(i)) << std::endl; - std::cout << std::endl; + std::clog << std::endl; // To access to the initial vertices eg (0,1,2,3,4), Root_vertex_handle must be used. // For instance, to test if the link contains the vertex that was labeled i: for (int i = 0; i < 5; ++i) - std::cout << "link.contains_vertex(Root_vertex_handle(" << i << ")):" << + std::clog << "link.contains_vertex(Root_vertex_handle(" << i << ")):" << link.contains_vertex(Root_vertex_handle(i)) << std::endl; return EXIT_SUCCESS; diff --git a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker.h b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker.h index bcca851f..653a63fd 100644 --- a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker.h +++ b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker.h @@ -154,8 +154,8 @@ of a simplicial complex. else euler -= 1; } - std::cout << "Saw "<<num_vertices<<" vertices, "<<num_edges<<" edges and "<<num_simplices<<" simplices"<<std::endl; - std::cout << "The Euler Characteristic is "<<euler<<std::endl; + std::clog << "Saw "<<num_vertices<<" vertices, "<<num_edges<<" edges and "<<num_simplices<<" simplices"<<std::endl; + std::clog << "The Euler Characteristic is "<<euler<<std::endl; \endcode @@ -182,13 +182,13 @@ The Euler Characteristic is 1 //get complex from top faces make_complex_from_top_faces(complex,simplices.begin(),simplices.end()); - std::cout << "Simplices:"<<std::endl; + std::clog << "Simplices:"<<std::endl; for(const Simplex & s : complex.star_simplex_range()) - std::cout << s << " "; - std::cout << std::endl; + std::clog << s << " "; + std::clog << std::endl; //One blocker as simplex 0123 is not in the complex but all its proper faces are. - std::cout << "Blockers: "<<complex.blockers_to_string()<<std::endl; + std::clog << "Blockers: "<<complex.blockers_to_string()<<std::endl; //now build a complex from its full list of simplices simplices.clear(); @@ -200,13 +200,13 @@ The Euler Characteristic is 1 simplices.push_back(Simplex(Vertex_handle(2),Vertex_handle(0))); complex = Complex(simplices.begin(),simplices.end()); - std::cout << "Simplices:"<<std::endl; + std::clog << "Simplices:"<<std::endl; for(const Simplex & s : complex.star_simplex_range()) - std::cout << s << " "; - std::cout << std::endl; + std::clog << s << " "; + std::clog << std::endl; //One blocker as simplex 012 is not in the complex but all its proper faces are. - std::cout << "Blockers: "<<complex.blockers_to_string()<<std::endl; + std::clog << "Blockers: "<<complex.blockers_to_string()<<std::endl; \endcode \verbatim ./SkeletonBlockerFromSimplices diff --git a/src/Skeleton_blocker/test/test_skeleton_blocker_complex.cpp b/src/Skeleton_blocker/test/test_skeleton_blocker_complex.cpp index 4336e33b..96438acf 100644 --- a/src/Skeleton_blocker/test/test_skeleton_blocker_complex.cpp +++ b/src/Skeleton_blocker/test/test_skeleton_blocker_complex.cpp @@ -91,10 +91,10 @@ BOOST_AUTO_TEST_CASE(test_skeleton_num_simplices) { BOOST_AUTO_TEST_CASE(test_skeleton_iterator_vertices1) { int n = 10; Complex complex(10); - std::cout << "complex.num_vertices():" << complex.num_vertices() << std::endl; + std::clog << "complex.num_vertices():" << complex.num_vertices() << std::endl; int num_vertex_seen = 0; for (auto vi : complex.vertex_range()) { - std::cout << "vertex:" << vi << std::endl; + std::clog << "vertex:" << vi << std::endl; ++num_vertex_seen; } BOOST_CHECK(num_vertex_seen == n); @@ -104,14 +104,14 @@ BOOST_AUTO_TEST_CASE(test_skeleton_iterator_vertices2) { int n = 10; Complex complex; build_complete(10, complex); - std::cout << "complex.num_vertices():" << complex.num_vertices() << std::endl; - std::cout << "complex.num_edges():" << complex.num_edges() << std::endl; + std::clog << "complex.num_vertices():" << complex.num_vertices() << std::endl; + std::clog << "complex.num_edges():" << complex.num_edges() << std::endl; int num_vertex_seen = 0; for (auto vi : complex.vertex_range(Vertex_handle(2))) { - std::cout << "vertex:" << vi << std::endl; + std::clog << "vertex:" << vi << std::endl; ++num_vertex_seen; } - std::cout << "num_vertex_seen:" << num_vertex_seen << std::endl; + std::clog << "num_vertex_seen:" << num_vertex_seen << std::endl; BOOST_CHECK(num_vertex_seen == (n -1)); } @@ -123,10 +123,10 @@ BOOST_AUTO_TEST_CASE(test_skeleton_iterator_edge) { complex.add_edge_without_blockers(Vertex_handle(i), Vertex_handle(j)); complex.remove_edge(Vertex_handle(2), Vertex_handle(3)); complex.remove_edge(Vertex_handle(3), Vertex_handle(5)); - std::cout << "complex.num_edges():" << complex.num_edges() << std::endl; + std::clog << "complex.num_edges():" << complex.num_edges() << std::endl; int num_edges_seen = 0; for (auto edge : complex.edge_range()) { - std::cout << "edge :" << complex[edge] << std::endl; + std::clog << "edge :" << complex[edge] << std::endl; ++num_edges_seen; } @@ -141,10 +141,10 @@ BOOST_AUTO_TEST_CASE(test_skeleton_iterator_edge2) { complex.add_edge_without_blockers(Vertex_handle(i), Vertex_handle(j)); complex.remove_edge(Vertex_handle(2), Vertex_handle(3)); complex.remove_edge(Vertex_handle(3), Vertex_handle(5)); - std::cout << "complex.num_edges():" << complex.num_edges() << std::endl; + std::clog << "complex.num_edges():" << complex.num_edges() << std::endl; int num_neigbors_seen = 0; for (auto neighbor : complex.vertex_range(Vertex_handle(2))) { - std::cout << "neighbor" << neighbor << std::endl; + std::clog << "neighbor" << neighbor << std::endl; ++num_neigbors_seen; } BOOST_CHECK(num_neigbors_seen == 8); @@ -160,7 +160,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_iterator_triangles) { complex.add_edge_without_blockers(Vertex_handle(i), Vertex_handle(i + 1)); complex.add_edge_without_blockers(Vertex_handle(1), Vertex_handle(6)); - std::cout << complex.to_string() << std::endl; + std::clog << complex.to_string() << std::endl; int num_triangles_seen = 0; //for (auto t : complex.triangle_range(5)){ @@ -214,19 +214,19 @@ BOOST_AUTO_TEST_CASE(test_skeleton_iterator_simplices) { expected_num_simplices[Vertex_handle(5)] = 7; for (auto pair : expected_num_simplices) { - std::cout << "found list: "; + std::clog << "found list: "; unsigned num_simplices_around = 0; for (const auto& simplex : complex.star_simplex_range(pair.first)) { simplex.dimension(); - std::cout << simplex << " - "; + std::clog << simplex << " - "; ++num_simplices_around; } BOOST_CHECK(num_simplices_around == pair.second); - std::cout << std::endl << "current vertex:" << pair.first << " - "; - std::cout << "expected_num_simplices:" << pair.second << " - "; - std::cout << "found:" << num_simplices_around << std::endl; + std::clog << std::endl << "current vertex:" << pair.first << " - "; + std::clog << "expected_num_simplices:" << pair.second << " - "; + std::clog << "found:" << num_simplices_around << std::endl; } } @@ -276,19 +276,19 @@ BOOST_AUTO_TEST_CASE(test_skeleton_iterator_simplices3) { BOOST_AUTO_TEST_CASE(test_skeleton_iterator_simplices4) { Complex empty_complex; for (auto v : empty_complex.vertex_range()) { - std::cout << v; + std::clog << v; BOOST_CHECK(false); } for (auto e : empty_complex.edge_range()) { - std::cout << e; + std::clog << e; BOOST_CHECK(false); } for (auto t : empty_complex.triangle_range()) { - std::cout << t; + std::clog << t; BOOST_CHECK(false); } for (auto s : empty_complex.complex_simplex_range()) { - std::cout << s; + std::clog << s; BOOST_CHECK(false); } } @@ -297,7 +297,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_iterator_coboundary) { Complex c; build_complete(4, c); c.remove_edge(Vertex_handle(1), Vertex_handle(3)); - std::cout << c.to_string(); + std::clog << c.to_string(); Simplex s02(Vertex_handle(0), Vertex_handle(2)); int n = 0; std::set<Simplex> expected; @@ -373,7 +373,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_link0) { auto L2 = complex.link(alpha); BOOST_CHECK(L == L2); - std::cout << L.to_string(); + std::clog << L.to_string(); BOOST_CHECK(L.contains_vertex(*L.get_address(Root_vertex_handle(b)))); BOOST_CHECK(L.contains_vertex(*L.get_address(Root_vertex_handle(d)))); @@ -432,9 +432,9 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_link2) { // Complexes built // Print result - std::cout << "complex complex" << complex.to_string(); - std::cout << std::endl << std::endl; - std::cout << "L= Link_complex(" << alpha << ") : \n" << L.to_string(); + std::clog << "complex complex" << complex.to_string(); + std::clog << std::endl << std::endl; + std::clog << "L= Link_complex(" << alpha << ") : \n" << L.to_string(); auto L2 = complex.link(alpha); BOOST_CHECK(L == L2); @@ -472,9 +472,9 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_link3) { // Complexes built // Print result - std::cout << "complex complex" << complex.to_string(); - std::cout << std::endl << std::endl; - std::cout << "L= Link_complex(" << alpha << ") : \n" << L.to_string(); + std::clog << "complex complex" << complex.to_string(); + std::clog << std::endl << std::endl; + std::clog << "L= Link_complex(" << alpha << ") : \n" << L.to_string(); auto L2 = complex.link(alpha); BOOST_CHECK(L == L2); @@ -529,8 +529,8 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_link5) { // Complexes built // Print result - std::cout << "Complex: " << complex.to_string()<< std::endl << std::endl; - std::cout << "Link: " << L.to_string() << std::endl; + std::clog << "Complex: " << complex.to_string()<< std::endl << std::endl; + std::clog << "Link: " << L.to_string() << std::endl; // verification BOOST_CHECK(L.num_vertices() == 0); @@ -549,8 +549,8 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_link6) { build_link_of_blocker(complex, alpha, link_blocker_alpha); // Print result - std::cout << "Complex: " << complex.to_string()<< std::endl << std::endl; - std::cout << "Link: " << link_blocker_alpha.to_string() << std::endl; + std::clog << "Complex: " << complex.to_string()<< std::endl << std::endl; + std::clog << "Link: " << link_blocker_alpha.to_string() << std::endl; // verification BOOST_CHECK(link_blocker_alpha.num_vertices() == 1); @@ -579,12 +579,12 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_link7) { //the result should be the edge {6,7} plus the blocker {0,1,2} // Print result - std::cout << "Complex: " << complex.to_string()<< std::endl << std::endl; - std::cout << "Link: " << link_blocker_alpha.to_string() << std::endl; + std::clog << "Complex: " << complex.to_string()<< std::endl << std::endl; + std::clog << "Link: " << link_blocker_alpha.to_string() << std::endl; Skeleton_blocker_link_complex link_blocker_alpha_cpy = link_blocker_alpha; - std::cout << "Link copy: " << link_blocker_alpha_cpy.to_string() << std::endl; + std::clog << "Link copy: " << link_blocker_alpha_cpy.to_string() << std::endl; BOOST_CHECK(link_blocker_alpha.num_vertices() == link_blocker_alpha_cpy.num_vertices()); BOOST_CHECK(link_blocker_alpha.num_blockers() == link_blocker_alpha_cpy.num_blockers()); @@ -640,7 +640,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_constructor) { Complex complex(simplices.begin(), simplices.end()); - std::cout << "Constructor 1:\n" << complex.to_string(); + std::clog << "Constructor 1:\n" << complex.to_string(); BOOST_CHECK(complex.num_vertices() == 6); BOOST_CHECK(complex.num_edges() == 10); @@ -677,10 +677,10 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_constructor2) { Complex complex(simplices.begin(), simplices.end()); - std::cout << "Constructor 2:\n" << complex.to_string(); + std::clog << "Constructor 2:\n" << complex.to_string(); for (auto b : complex.const_blocker_range()) { - std::cout << "b:" << b << std::endl; + std::clog << "b:" << b << std::endl; } BOOST_CHECK(complex.num_vertices() == 5); @@ -698,7 +698,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_constructor3) { Complex complex(simplices.begin(), simplices.end()); - std::cout << "Constructor 3:\n" << complex.to_string(); + std::clog << "Constructor 3:\n" << complex.to_string(); BOOST_CHECK(complex.num_blockers() == 1); Sh expected_blocker(Vh(0), Vh(1), Vh(2)); @@ -723,7 +723,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_constructor4) { Complex complex(simplices.begin(), simplices.end()); - std::cout << "Constructor 4:\n" << complex.to_string(); + std::clog << "Constructor 4:\n" << complex.to_string(); BOOST_CHECK(complex.num_blockers() == 1); Sh expected_blocker(Vh(0), Vh(1), Vh(4)); for (auto b : complex.const_blocker_range()) @@ -753,7 +753,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_constructor5) { Complex complex(simplices.begin(), simplices.end()); - std::cout << "Constructor 5:\n" << complex.to_string(); + std::clog << "Constructor 5:\n" << complex.to_string(); BOOST_CHECK(complex.num_vertices() == 6); BOOST_CHECK(complex.num_blockers() == 3); @@ -773,7 +773,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_constructor6) { Complex complex(simplices.begin(), simplices.end()); - std::cout << "Constructor 6:\n" << complex.to_string(); + std::clog << "Constructor 6:\n" << complex.to_string(); BOOST_CHECK(complex.num_vertices() == 4); BOOST_CHECK(complex.num_blockers() == 1); @@ -795,7 +795,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_constructor7) { //get complex from top faces Complex complex(Gudhi::skeleton_blocker::make_complex_from_top_faces<Complex>(simplices.begin(), simplices.end())); - std::cout << "Constructor 7:\n" << complex.to_string(); + std::clog << "Constructor 7:\n" << complex.to_string(); BOOST_CHECK(complex.num_vertices() == 4); BOOST_CHECK(complex.num_blockers() == 1); @@ -818,7 +818,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_complex_constructor8) { //get complex from top faces Complex complex(Gudhi::skeleton_blocker::make_complex_from_top_faces<Complex>(simplices.begin(), simplices.end())); - std::cout << "Constructor 8:\n" << complex.to_string(); + std::clog << "Constructor 8:\n" << complex.to_string(); BOOST_CHECK(complex.num_vertices() == 4); BOOST_CHECK(complex.num_blockers() == 2); diff --git a/src/Skeleton_blocker/test/test_skeleton_blocker_geometric_complex.cpp b/src/Skeleton_blocker/test/test_skeleton_blocker_geometric_complex.cpp index 8cad97a1..9042ddcf 100644 --- a/src/Skeleton_blocker/test/test_skeleton_blocker_geometric_complex.cpp +++ b/src/Skeleton_blocker/test/test_skeleton_blocker_geometric_complex.cpp @@ -36,7 +36,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_off_reader_writer) { Gudhi::skeleton_blocker::Skeleton_blocker_off_reader<Complex> off_reader("test2.off", complex); BOOST_CHECK(off_reader.is_valid()); - std::cout << "complex has " << + std::clog << "complex has " << complex.num_vertices() << " vertices, " << complex.num_blockers() << " blockers, " << complex.num_edges() << " edges and " << @@ -50,8 +50,8 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_off_reader_writer) { Complex same; Gudhi::skeleton_blocker::Skeleton_blocker_off_reader<Complex> off_reader2("tmp.off", same); - std::cout << "\ncomplex:" << complex.to_string() << std::endl; - std::cout << "\nsame:" << same.to_string() << std::endl; + std::clog << "\ncomplex:" << complex.to_string() << std::endl; + std::clog << "\nsame:" << same.to_string() << std::endl; BOOST_CHECK(complex == same); } @@ -61,7 +61,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_abstract_link) { Gudhi::skeleton_blocker::Skeleton_blocker_off_reader<Complex> off_reader("test2.off", complex); BOOST_CHECK(off_reader.is_valid()); - std::cout << "complex has " << + std::clog << "complex has " << complex.num_vertices() << " vertices, " << complex.num_blockers() << " blockers, " << complex.num_edges() << " edges and " << @@ -73,7 +73,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_abstract_link) { auto link_0 = complex.abstract_link(Vertex_handle(0)); - std::cout << "\n link(0):" << link_0.to_string() << std::endl; + std::clog << "\n link(0):" << link_0.to_string() << std::endl; BOOST_CHECK(link_0.num_vertices() == 2); BOOST_CHECK(link_0.num_edges() == 1); @@ -91,13 +91,13 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_abstract_link) { BOOST_CHECK(link_0[*(edge_handle)].second() == Root_vertex_handle(4)); auto link_geometric_0 = complex.link(Vertex_handle(0)); - std::cout << "\n link_geometric(0):" << link_geometric_0.to_string() << std::endl; + std::clog << "\n link_geometric(0):" << link_geometric_0.to_string() << std::endl; BOOST_CHECK(link_0 == link_geometric_0); auto print_point = [&](Vertex_handle v) { - for (auto x : link_geometric_0.point(v)) std::cout << x << " "; - std::cout << std::endl; + for (auto x : link_geometric_0.point(v)) std::clog << x << " "; + std::clog << std::endl; }; std::for_each(link_geometric_0.vertex_range().begin(), link_geometric_0.vertex_range().end(), print_point); diff --git a/src/Skeleton_blocker/test/test_skeleton_blocker_simplifiable.cpp b/src/Skeleton_blocker/test/test_skeleton_blocker_simplifiable.cpp index b714753d..a85d4ff0 100644 --- a/src/Skeleton_blocker/test/test_skeleton_blocker_simplifiable.cpp +++ b/src/Skeleton_blocker/test/test_skeleton_blocker_simplifiable.cpp @@ -49,12 +49,12 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_contraction1) { static_cast<Vertex_handle> (y))); // Print result - std::cout << "complex before complex" << complex.to_string() << std::endl; + std::clog << "complex before complex" << complex.to_string() << std::endl; - std::cout << std::endl << std::endl; + std::clog << std::endl << std::endl; complex.contract_edge(static_cast<Vertex_handle> (a), static_cast<Vertex_handle> (b)); // Print result - std::cout << "ContractEdge(0,1)\n"; + std::clog << "ContractEdge(0,1)\n"; PRINT(complex.to_string()); // verification @@ -89,13 +89,13 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_contraction2) { complex.add_blocker(blocker); // Print result - std::cout << "complex complex" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "complex complex" << complex.to_string(); + std::clog << std::endl << std::endl; complex.contract_edge(static_cast<Vertex_handle> (a), static_cast<Vertex_handle> (b)); - std::cout << "complex.ContractEdge(a,b)" << complex.to_string(); + std::clog << "complex.ContractEdge(a,b)" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << std::endl << std::endl; // there should be one blocker (a,c,d,e) in the complex BOOST_CHECK(complex.contains_blocker(Simplex(static_cast<Vertex_handle> (a), static_cast<Vertex_handle> (x), @@ -110,8 +110,8 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_link_condition1) { complex.add_blocker(Simplex(static_cast<Vertex_handle> (0), static_cast<Vertex_handle> (1), static_cast<Vertex_handle> (2))); // Print result - std::cout << "complex complex" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "complex complex" << complex.to_string(); + std::clog << std::endl << std::endl; BOOST_CHECK(complex.link_condition(Vertex_handle(1), Vertex_handle(2), true)); @@ -125,13 +125,13 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_collapse0) { complex.add_edge_without_blockers(static_cast<Vertex_handle> (2), static_cast<Vertex_handle> (4)); complex.add_edge_without_blockers(static_cast<Vertex_handle> (3), static_cast<Vertex_handle> (4)); // Print result - std::cout << "initial complex :\n" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "initial complex :\n" << complex.to_string(); + std::clog << std::endl << std::endl; Simplex simplex_123(static_cast<Vertex_handle> (1), static_cast<Vertex_handle> (2), static_cast<Vertex_handle> (3)); complex.remove_star(simplex_123); - std::cout << "complex.remove_star(1,2,3):\n" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "complex.remove_star(1,2,3):\n" << complex.to_string(); + std::clog << std::endl << std::endl; // verification BOOST_CHECK(complex.contains_blocker(simplex_123)); @@ -142,13 +142,13 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_collapse1) { build_complete(4, complex); complex.add_blocker(Simplex(Vertex_handle(0), Vertex_handle(1), Vertex_handle(2), Vertex_handle(3))); // Print result - std::cout << "initial complex :\n" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "initial complex :\n" << complex.to_string(); + std::clog << std::endl << std::endl; Simplex simplex_123(Vertex_handle(1), Vertex_handle(2), Vertex_handle(3)); complex.remove_star(simplex_123); - std::cout << "complex.remove_star(1,2,3):\n" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "complex.remove_star(1,2,3):\n" << complex.to_string(); + std::clog << std::endl << std::endl; // verification BOOST_CHECK(complex.contains_blocker(simplex_123)); @@ -164,13 +164,13 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_collapse2) { complex.add_edge_without_blockers(Vertex_handle(3), Vertex_handle(4)); complex.add_blocker(Simplex(Vertex_handle(1), Vertex_handle(2), Vertex_handle(3), Vertex_handle(4))); // Print result - std::cout << "initial complex :\n" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "initial complex :\n" << complex.to_string(); + std::clog << std::endl << std::endl; Simplex sigma(Vertex_handle(1), Vertex_handle(2), Vertex_handle(3)); complex.remove_star(sigma); - std::cout << "complex.remove_star(1,2,3):\n" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "complex.remove_star(1,2,3):\n" << complex.to_string(); + std::clog << std::endl << std::endl; // verification BOOST_CHECK(!complex.contains_blocker(Simplex(Vertex_handle(1), Vertex_handle(2), @@ -187,11 +187,11 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_collapse3) { complex.add_edge_without_blockers(Vertex_handle(3), Vertex_handle(4)); complex.add_blocker(Simplex(Vertex_handle(1), Vertex_handle(2), Vertex_handle(3), Vertex_handle(4))); // Print result - std::cout << "initial complex:\n" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "initial complex:\n" << complex.to_string(); + std::clog << std::endl << std::endl; complex.remove_star(static_cast<Vertex_handle> (2)); - std::cout << "complex after remove star of 2:\n" << complex.to_string(); + std::clog << "complex after remove star of 2:\n" << complex.to_string(); BOOST_CHECK(complex.contains_blocker(Simplex(Vertex_handle(1), Vertex_handle(3), Vertex_handle(4)))); BOOST_CHECK(!complex.contains_blocker(Simplex(Vertex_handle(1), Vertex_handle(2), @@ -202,11 +202,11 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_add_simplex) { Complex complex(4); build_complete(4, complex); complex.add_blocker(Simplex(Vertex_handle(0), Vertex_handle(1), Vertex_handle(3))); - std::cout << "initial complex:\n" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "initial complex:\n" << complex.to_string(); + std::clog << std::endl << std::endl; complex.add_simplex(Simplex(Vertex_handle(0), Vertex_handle(1), Vertex_handle(3))); - std::cout << "complex after add_simplex:\n" << complex.to_string(); + std::clog << "complex after add_simplex:\n" << complex.to_string(); BOOST_CHECK(complex.num_blockers() == 1); BOOST_CHECK(complex.contains_blocker(Simplex(Vertex_handle(0), Vertex_handle(1), Vertex_handle(2), Vertex_handle(3)))); @@ -216,8 +216,8 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_add_simplex2) { Complex complex; build_complete(4, complex); // Print result - std::cout << "initial complex:\n" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "initial complex:\n" << complex.to_string(); + std::clog << std::endl << std::endl; Complex copy(complex.num_vertices()); @@ -232,7 +232,7 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_add_simplex2) { copy.add_simplex(simplex); } - std::cout << "complex after add_simplex:\n" << copy.to_string(); + std::clog << "complex after add_simplex:\n" << copy.to_string(); BOOST_CHECK(complex.num_blockers() == copy.num_blockers()); BOOST_CHECK(complex.num_edges() == copy.num_edges()); @@ -246,11 +246,11 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_add_simplex3) { Simplex sigma(Vertex_handle(0), Vertex_handle(1), Vertex_handle(2)); complex.add_blocker(sigma); // Print result - std::cout << "initial complex:\n" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "initial complex:\n" << complex.to_string(); + std::clog << std::endl << std::endl; complex.add_simplex(sigma); //should create two blockers 0123 and 0124 - std::cout << "complex after adding simplex 012:\n" << complex.to_string(); + std::clog << "complex after adding simplex 012:\n" << complex.to_string(); BOOST_CHECK(complex.num_blockers() == 2); BOOST_CHECK(complex.contains_blocker(Simplex(Vertex_handle(0), Vertex_handle(1), Vertex_handle(2), Vertex_handle(3)))); @@ -292,11 +292,11 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_add_edge) { complex.add_edge(Vertex_handle(i), Vertex_handle((i + 1) % 4)); // Print result - std::cout << "initial complex:\n" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "initial complex:\n" << complex.to_string(); + std::clog << std::endl << std::endl; complex.add_edge(Vertex_handle(1), Vertex_handle(3)); //should create two blockers 013 and 012 - std::cout << "complex after adding edge 13:\n" << complex.to_string(); + std::clog << "complex after adding edge 13:\n" << complex.to_string(); BOOST_CHECK(complex.num_blockers() == 2); BOOST_CHECK(complex.contains_blocker(Simplex(Vertex_handle(0), Vertex_handle(1), Vertex_handle(3)))); BOOST_CHECK(complex.contains_blocker(Simplex(Vertex_handle(1), Vertex_handle(2), Vertex_handle(3)))); @@ -313,12 +313,12 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_remove_popable_blockers) complex.add_blocker(sigma1); complex.add_blocker(sigma2); - std::cout << "complex complex" << complex.to_string(); - std::cout << std::endl << std::endl; - std::cout << "complex.RemovePopableBlockers();" << std::endl; + std::clog << "complex complex" << complex.to_string(); + std::clog << std::endl << std::endl; + std::clog << "complex.RemovePopableBlockers();" << std::endl; complex.remove_popable_blockers(); - std::cout << "complex complex" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << "complex complex" << complex.to_string(); + std::clog << std::endl << std::endl; BOOST_CHECK(complex.num_blockers() == 1); @@ -337,12 +337,12 @@ BOOST_AUTO_TEST_CASE(test_skeleton_blocker_simplifiable_remove_popable_blockers) complex.add_blocker(sigma1); complex.add_blocker(sigma2); - std::cout << "complex complex" << complex.to_string(); - std::cout << std::endl << std::endl; - std::cout << "complex.RemovePopableBlockers();" << std::endl; + std::clog << "complex complex" << complex.to_string(); + std::clog << std::endl << std::endl; + std::clog << "complex.RemovePopableBlockers();" << std::endl; complex.remove_popable_blockers(); - std::cout << "complex complex" << complex.to_string(); + std::clog << "complex complex" << complex.to_string(); - std::cout << std::endl << std::endl; + std::clog << std::endl << std::endl; BOOST_CHECK(complex.num_blockers() == 0); } diff --git a/src/Spatial_searching/example/example_spatial_searching.cpp b/src/Spatial_searching/example/example_spatial_searching.cpp index 034ad24a..8f9151fc 100644 --- a/src/Spatial_searching/example/example_spatial_searching.cpp +++ b/src/Spatial_searching/example/example_spatial_searching.cpp @@ -23,38 +23,38 @@ int main(void) { Points_ds points_ds(points); // 10-nearest neighbor query - std::cout << "10 nearest neighbors from points[20]:\n"; + std::clog << "10 nearest neighbors from points[20]:\n"; auto knn_range = points_ds.k_nearest_neighbors(points[20], 10, true); for (auto const& nghb : knn_range) - std::cout << nghb.first << " (sq. dist. = " << nghb.second << ")\n"; + std::clog << nghb.first << " (sq. dist. = " << nghb.second << ")\n"; // Incremental nearest neighbor query - std::cout << "Incremental nearest neighbors:\n"; + std::clog << "Incremental nearest neighbors:\n"; auto inn_range = points_ds.incremental_nearest_neighbors(points[45]); // Get the neighbors in distance order until we hit the first point for (auto ins_iterator = inn_range.begin(); ins_iterator->first != 0; ++ins_iterator) - std::cout << ins_iterator->first << " (sq. dist. = " << ins_iterator->second << ")\n"; + std::clog << ins_iterator->first << " (sq. dist. = " << ins_iterator->second << ")\n"; // 10-furthest neighbor query - std::cout << "10 furthest neighbors from points[20]:\n"; + std::clog << "10 furthest neighbors from points[20]:\n"; auto kfn_range = points_ds.k_furthest_neighbors(points[20], 10, true); for (auto const& nghb : kfn_range) - std::cout << nghb.first << " (sq. dist. = " << nghb.second << ")\n"; + std::clog << nghb.first << " (sq. dist. = " << nghb.second << ")\n"; // Incremental furthest neighbor query - std::cout << "Incremental furthest neighbors:\n"; + std::clog << "Incremental furthest neighbors:\n"; auto ifn_range = points_ds.incremental_furthest_neighbors(points[45]); // Get the neighbors in distance reverse order until we hit the first point for (auto ifs_iterator = ifn_range.begin(); ifs_iterator->first != 0; ++ifs_iterator) - std::cout << ifs_iterator->first << " (sq. dist. = " << ifs_iterator->second << ")\n"; + std::clog << ifs_iterator->first << " (sq. dist. = " << ifs_iterator->second << ")\n"; // All-near-neighbors search - std::cout << "All-near-neighbors search:\n"; + std::clog << "All-near-neighbors search:\n"; std::vector<std::size_t> rs_result; points_ds.all_near_neighbors(points[45], 0.5, std::back_inserter(rs_result)); K k; for (auto const& p_idx : rs_result) - std::cout << p_idx << " (sq. dist. = " << k.squared_distance_d_object()(points[p_idx], points[45]) << ")\n"; + std::clog << p_idx << " (sq. dist. = " << k.squared_distance_d_object()(points[p_idx], points[45]) << ")\n"; return 0; } diff --git a/src/Subsampling/example/example_choose_n_farthest_points.cpp b/src/Subsampling/example/example_choose_n_farthest_points.cpp index 5cfeb4d8..27cf5d4e 100644 --- a/src/Subsampling/example/example_choose_n_farthest_points.cpp +++ b/src/Subsampling/example/example_choose_n_farthest_points.cpp @@ -23,8 +23,8 @@ int main(void) { Gudhi::subsampling::choose_n_farthest_points(k, points, 100, Gudhi::subsampling::random_starting_point, std::back_inserter(results)); - std::cout << "Before sparsification: " << points.size() << " points.\n"; - std::cout << "After sparsification: " << results.size() << " points.\n"; + std::clog << "Before sparsification: " << points.size() << " points.\n"; + std::clog << "After sparsification: " << results.size() << " points.\n"; return 0; } diff --git a/src/Subsampling/example/example_custom_kernel.cpp b/src/Subsampling/example/example_custom_kernel.cpp index f1eb757b..535bf42a 100644 --- a/src/Subsampling/example/example_custom_kernel.cpp +++ b/src/Subsampling/example/example_custom_kernel.cpp @@ -55,9 +55,9 @@ int main(void) { Gudhi::subsampling::choose_n_farthest_points(k, points, 2, Gudhi::subsampling::random_starting_point, std::back_inserter(results)); - std::cout << "Before sparsification: " << points.size() << " points.\n"; - std::cout << "After sparsification: " << results.size() << " points.\n"; - std::cout << "Result table: {" << results[0] << "," << results[1] << "}\n"; + std::clog << "Before sparsification: " << points.size() << " points.\n"; + std::clog << "After sparsification: " << results.size() << " points.\n"; + std::clog << "Result table: {" << results[0] << "," << results[1] << "}\n"; return 0; } diff --git a/src/Subsampling/example/example_pick_n_random_points.cpp b/src/Subsampling/example/example_pick_n_random_points.cpp index 25266403..316feed1 100644 --- a/src/Subsampling/example/example_pick_n_random_points.cpp +++ b/src/Subsampling/example/example_pick_n_random_points.cpp @@ -21,8 +21,8 @@ int main(void) { K k; std::vector<Point_d> results; Gudhi::subsampling::pick_n_random_points(points, 100, std::back_inserter(results)); - std::cout << "Before sparsification: " << points.size() << " points.\n"; - std::cout << "After sparsification: " << results.size() << " points.\n"; + std::clog << "Before sparsification: " << points.size() << " points.\n"; + std::clog << "After sparsification: " << results.size() << " points.\n"; return 0; } diff --git a/src/Subsampling/example/example_sparsify_point_set.cpp b/src/Subsampling/example/example_sparsify_point_set.cpp index a8caa720..1e2c38c1 100644 --- a/src/Subsampling/example/example_sparsify_point_set.cpp +++ b/src/Subsampling/example/example_sparsify_point_set.cpp @@ -21,8 +21,8 @@ int main(void) { K k; std::vector<Point_d> results; Gudhi::subsampling::sparsify_point_set(k, points, 0.4, std::back_inserter(results)); - std::cout << "Before sparsification: " << points.size() << " points.\n"; - std::cout << "After sparsification: " << results.size() << " points.\n"; + std::clog << "Before sparsification: " << points.size() << " points.\n"; + std::clog << "After sparsification: " << results.size() << " points.\n"; return 0; } diff --git a/src/Subsampling/test/test_pick_n_random_points.cpp b/src/Subsampling/test/test_pick_n_random_points.cpp index 018fb8d2..fafae2af 100644 --- a/src/Subsampling/test/test_pick_n_random_points.cpp +++ b/src/Subsampling/test/test_pick_n_random_points.cpp @@ -49,9 +49,9 @@ BOOST_AUTO_TEST_CASE(test_pick_n_random_points) std::vector<Point_d> results; Gudhi::subsampling::pick_n_random_points(vect, 5, std::back_inserter(results)); - std::cout << "landmark vector contains: "; + std::clog << "landmark vector contains: "; for (auto l: results) - std::cout << l << "\n"; + std::clog << l << "\n"; BOOST_CHECK(results.size() == 5); } diff --git a/src/Subsampling/test/test_sparsify_point_set.cpp b/src/Subsampling/test/test_sparsify_point_set.cpp index 587ab3ad..cdcfbff5 100644 --- a/src/Subsampling/test/test_sparsify_point_set.cpp +++ b/src/Subsampling/test/test_sparsify_point_set.cpp @@ -34,10 +34,10 @@ BOOST_AUTO_TEST_CASE(test_sparsify_point_set) K k; std::vector<Point_d> results; Gudhi::subsampling::sparsify_point_set(k, points, 0.5, std::back_inserter(results)); - std::cout << "Before sparsification: " << points.size() << " points.\n"; - std::cout << "After sparsification: " << results.size() << " points.\n"; + std::clog << "Before sparsification: " << points.size() << " points.\n"; + std::clog << "After sparsification: " << results.size() << " points.\n"; //for (auto p : results) - // std::cout << p << "\n"; + // std::clog << p << "\n"; BOOST_CHECK(points.size() > results.size()); } diff --git a/src/Tangential_complex/test/test_tangential_complex.cpp b/src/Tangential_complex/test/test_tangential_complex.cpp index 46caec54..023c1e1a 100644 --- a/src/Tangential_complex/test/test_tangential_complex.cpp +++ b/src/Tangential_complex/test/test_tangential_complex.cpp @@ -76,14 +76,14 @@ BOOST_AUTO_TEST_CASE(test_mini_tangential) { points.push_back(Point(point.size(), point.begin(), point.end())); point = {1.0, 1.0}; points.push_back(Point(point.size(), point.begin(), point.end())); - std::cout << "points = " << points.size() << std::endl; + std::clog << "points = " << points.size() << std::endl; Kernel k; // Compute the TC TC tc(points, INTRINSIC_DIM, k); tc.compute_tangential_complex(); TC::Num_inconsistencies num_inc = tc.number_of_inconsistent_simplices(); - std::cout << "TC vertices = " << tc.number_of_vertices() << " - simplices = " << num_inc.num_simplices << + std::clog << "TC vertices = " << tc.number_of_vertices() << " - simplices = " << num_inc.num_simplices << " - inc simplices = " << num_inc.num_inconsistent_simplices << " - inc stars = " << num_inc.num_inconsistent_stars << std::endl; @@ -95,7 +95,7 @@ BOOST_AUTO_TEST_CASE(test_mini_tangential) { // Export the TC into a Simplex_tree Gudhi::Simplex_tree<> stree; tc.create_complex(stree); - std::cout << "ST vertices = " << stree.num_vertices() << " - simplices = " << stree.num_simplices() << std::endl; + std::clog << "ST vertices = " << stree.num_vertices() << " - simplices = " << stree.num_simplices() << std::endl; BOOST_CHECK(stree.num_vertices() == 4); BOOST_CHECK(stree.num_simplices() == 6); @@ -109,7 +109,7 @@ BOOST_AUTO_TEST_CASE(test_mini_tangential) { // Export the TC into a Simplex_tree tc.create_complex(stree); - std::cout << "ST vertices = " << stree.num_vertices() << " - simplices = " << stree.num_simplices() << std::endl; + std::clog << "ST vertices = " << stree.num_vertices() << " - simplices = " << stree.num_simplices() << std::endl; BOOST_CHECK(stree.num_vertices() == 4); BOOST_CHECK(stree.num_simplices() == 6); @@ -139,7 +139,7 @@ BOOST_AUTO_TEST_CASE(test_basic_example_throw) { // Compute the TC TC tc(points, INTRINSIC_DIM, k); tc.set_max_squared_edge_length(0.01); - std::cout << "test_basic_example_throw - set_max_squared_edge_length(0.01) to make GUDHI_CHECK fail" << std::endl; + std::clog << "test_basic_example_throw - set_max_squared_edge_length(0.01) to make GUDHI_CHECK fail" << std::endl; BOOST_CHECK_THROW(tc.compute_tangential_complex(), std::invalid_argument); } diff --git a/src/Toplex_map/benchmark/benchmark_tm.cpp b/src/Toplex_map/benchmark/benchmark_tm.cpp index feb5d01c..d078fcf8 100644 --- a/src/Toplex_map/benchmark/benchmark_tm.cpp +++ b/src/Toplex_map/benchmark/benchmark_tm.cpp @@ -25,10 +25,10 @@ typedef std::pair<Simplex_tree<>::Simplex_handle, bool> typePairSimplexBool; class ST_wrapper { public: void insert_simplex(const Simplex& tau) { - /*std::cout << "insert_simplex - " << simplexTree.num_simplices() << " - "; + /*std::clog << "insert_simplex - " << simplexTree.num_simplices() << " - "; for (auto v : tau) - std::cout << v << ", "; - std::cout << std::endl; + std::clog << v << ", "; + std::clog << std::endl; */ simplexTree.insert_simplex_and_subfaces(tau); } @@ -104,22 +104,22 @@ void chrono(int n, int d) { auto c2 = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count(); if (c3 > 0) - std::cout << c1 << "\t \t" << c2 << "\t \t" << c3 << "\t \t" << K.num_maximal_simplices() << std::endl; + std::clog << c1 << "\t \t" << c2 << "\t \t" << c3 << "\t \t" << K.num_maximal_simplices() << std::endl; else - std::cout << c1 << "\t \t" << c2 << "\t \tN/A\t \t" << K.num_maximal_simplices() << std::endl; + std::clog << c1 << "\t \t" << c2 << "\t \tN/A\t \t" << K.num_maximal_simplices() << std::endl; } int main() { for (int d = 5; d <= 40; d += 5) { - std::cout << "d=" << d << " \t Insertions \t Membership \t Contractions \t Size" << std::endl; - std::cout << "T Map \t \t"; + std::clog << "d=" << d << " \t Insertions \t Membership \t Contractions \t Size" << std::endl; + std::clog << "T Map \t \t"; chrono<Toplex_map>(n, d); - std::cout << "Lazy \t \t"; + std::clog << "Lazy \t \t"; chrono<Lazy_toplex_map>(n, d); if (d <= 15) { - std::cout << "ST \t \t"; + std::clog << "ST \t \t"; chrono<ST_wrapper>(n, d); } - std::cout << std::endl; + std::clog << std::endl; } } diff --git a/src/Toplex_map/example/simple_toplex_map.cpp b/src/Toplex_map/example/simple_toplex_map.cpp index 7538c989..c432608e 100644 --- a/src/Toplex_map/example/simple_toplex_map.cpp +++ b/src/Toplex_map/example/simple_toplex_map.cpp @@ -31,72 +31,72 @@ int main(int argc, char* const argv[]) { /* o---o */ /* 1 3 */ - std::cout << "num max simplices = " << tm.num_maximal_simplices() << " - num vertices = " << tm.num_vertices() + std::clog << "num max simplices = " << tm.num_maximal_simplices() << " - num vertices = " << tm.num_vertices() << std::endl; // Browse maximal cofaces Simplex sigma3 = {2, 3}; - std::cout << "Maximal cofaces of {2, 3} are :" << std::endl; + std::clog << "Maximal cofaces of {2, 3} are :" << std::endl; for (auto simplex_ptr : tm.maximal_cofaces(sigma3, 2)) { for (auto v : *simplex_ptr) { - std::cout << v << ", "; + std::clog << v << ", "; } - std::cout << std::endl; + std::clog << std::endl; } // Browse maximal simplices - std::cout << "Maximal simplices are :" << std::endl; + std::clog << "Maximal simplices are :" << std::endl; for (auto simplex_ptr : tm.maximal_simplices()) { for (auto v : *simplex_ptr) { - std::cout << v << ", "; + std::clog << v << ", "; } - std::cout << std::endl; + std::clog << std::endl; } Simplex sigma4 = {1, 3}; assert(tm.membership(sigma4)); Gudhi::Toplex_map::Vertex v = tm.contraction(1, 3); - std::cout << "After contraction(1, 3) - " << v << std::endl; + std::clog << "After contraction(1, 3) - " << v << std::endl; /* Simplex is: */ /* 2 4 */ /* o---o */ /* \5/ */ /* o */ /* 3 */ - std::cout << "num max simplices = " << tm.num_maximal_simplices() << " - num vertices = " << tm.num_vertices() + std::clog << "num max simplices = " << tm.num_maximal_simplices() << " - num vertices = " << tm.num_vertices() << std::endl; // Browse maximal simplices - std::cout << "Maximal simplices are :" << std::endl; + std::clog << "Maximal simplices are :" << std::endl; for (auto simplex_ptr : tm.maximal_simplices()) { for (auto v : *simplex_ptr) { - std::cout << v << ", "; + std::clog << v << ", "; } - std::cout << std::endl; + std::clog << std::endl; } Simplex sigma5 = {3, 4}; assert(tm.membership(sigma5)); v = tm.contraction(3, 4); - std::cout << "After contraction(3, 4) - " << v << std::endl; + std::clog << "After contraction(3, 4) - " << v << std::endl; /* Simplex is: */ /* 2 4 */ /* o---o */ /* \X/ */ /* o */ /* 5 */ - std::cout << "num max simplices = " << tm.num_maximal_simplices() << " - num vertices = " << tm.num_vertices() + std::clog << "num max simplices = " << tm.num_maximal_simplices() << " - num vertices = " << tm.num_vertices() << std::endl; // Browse maximal simplices - std::cout << "Maximal simplices are :" << std::endl; + std::clog << "Maximal simplices are :" << std::endl; for (auto simplex_ptr : tm.maximal_simplices()) { for (auto v : *simplex_ptr) { - std::cout << v << ", "; + std::clog << v << ", "; } - std::cout << std::endl; + std::clog << std::endl; } tm.insert_simplex(sigma1); @@ -109,44 +109,44 @@ int main(int argc, char* const argv[]) { /* 1 3 */ tm.remove_simplex(sigma1); - std::cout << "After remove_simplex(1, 2, 3)" << std::endl; + std::clog << "After remove_simplex(1, 2, 3)" << std::endl; /* Simplex is: */ /* 2 4 */ /* o---o */ /* / \5/ */ /* o---o */ /* 1 3 */ - std::cout << "num max simplices = " << tm.num_maximal_simplices() << " - num vertices = " << tm.num_vertices() + std::clog << "num max simplices = " << tm.num_maximal_simplices() << " - num vertices = " << tm.num_vertices() << std::endl; // Browse maximal simplices - std::cout << "Maximal simplices are :" << std::endl; + std::clog << "Maximal simplices are :" << std::endl; for (auto simplex_ptr : tm.maximal_simplices()) { for (auto v : *simplex_ptr) { - std::cout << v << ", "; + std::clog << v << ", "; } - std::cout << std::endl; + std::clog << std::endl; } tm.remove_vertex(1); - std::cout << "After remove_vertex(1)" << std::endl; + std::clog << "After remove_vertex(1)" << std::endl; /* Simplex is: */ /* 2 4 */ /* o---o */ /* \5/ */ /* o */ /* 3 */ - std::cout << "num max simplices = " << tm.num_maximal_simplices() << " - num vertices = " << tm.num_vertices() + std::clog << "num max simplices = " << tm.num_maximal_simplices() << " - num vertices = " << tm.num_vertices() << std::endl; // Browse maximal simplices - std::cout << "Maximal simplices are :" << std::endl; + std::clog << "Maximal simplices are :" << std::endl; for (auto simplex_ptr : tm.maximal_simplices()) { for (auto v : *simplex_ptr) { - std::cout << v << ", "; + std::clog << v << ", "; } - std::cout << std::endl; + std::clog << std::endl; } return 0; diff --git a/src/Toplex_map/test/lazy_toplex_map_unit_test.cpp b/src/Toplex_map/test/lazy_toplex_map_unit_test.cpp index 639bf35a..994cee8e 100644 --- a/src/Toplex_map/test/lazy_toplex_map_unit_test.cpp +++ b/src/Toplex_map/test/lazy_toplex_map_unit_test.cpp @@ -20,43 +20,43 @@ BOOST_AUTO_TEST_CASE(toplex_map) { using Vertex = Gudhi::Lazy_toplex_map::Vertex; Gudhi::Lazy_toplex_map tm; - std::cout << "insert_simplex {1, 2, 3, 4}" << std::endl; + std::clog << "insert_simplex {1, 2, 3, 4}" << std::endl; std::vector<Vertex> sigma1 = {1, 2, 3, 4}; tm.insert_simplex(sigma1); - std::cout << "insert_simplex {5, 2, 3, 6}" << std::endl; + std::clog << "insert_simplex {5, 2, 3, 6}" << std::endl; std::vector<Vertex> sigma2 = {5, 2, 3, 6}; tm.insert_simplex(sigma2); - std::cout << "insert_simplex {5}" << std::endl; + std::clog << "insert_simplex {5}" << std::endl; std::vector<Vertex> sigma3 = {5}; tm.insert_simplex(sigma3); - std::cout << "insert_simplex {4, 5, 3}" << std::endl; + std::clog << "insert_simplex {4, 5, 3}" << std::endl; std::vector<Vertex> sigma6 = {4, 5, 3}; tm.insert_simplex(sigma6); - std::cout << "insert_simplex {4, 5, 9}" << std::endl; + std::clog << "insert_simplex {4, 5, 9}" << std::endl; std::vector<Vertex> sigma7 = {4, 5, 9}; tm.insert_simplex(sigma7); - std::cout << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 5); std::vector<Vertex> sigma4 = {5, 2, 3}; std::vector<Vertex> sigma5 = {5, 2, 7}; BOOST_CHECK(tm.membership(sigma4)); BOOST_CHECK(!tm.membership(sigma5)); - std::cout << "insert_simplex {5, 2, 7}" << std::endl; + std::clog << "insert_simplex {5, 2, 7}" << std::endl; tm.insert_simplex(sigma5); - std::cout << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 6); BOOST_CHECK(tm.membership(sigma5)); - std::cout << "contraction(4,5)" << std::endl; + std::clog << "contraction(4,5)" << std::endl; auto r = tm.contraction(4, 5); - std::cout << "r=" << r << std::endl; + std::clog << "r=" << r << std::endl; BOOST_CHECK(r == 5); - std::cout << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 6); std::vector<Vertex> sigma8 = {1, 2, 3}; @@ -68,11 +68,11 @@ BOOST_AUTO_TEST_CASE(toplex_map) { BOOST_CHECK(tm.membership(sigma8)); BOOST_CHECK(tm.membership(sigma9)); - std::cout << "remove_simplex({2, 7, r = 5})" << std::endl; + std::clog << "remove_simplex({2, 7, r = 5})" << std::endl; tm.remove_simplex(sigma9); BOOST_CHECK(!tm.membership(sigma9)); - std::cout << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 8); // {2, 7, 5} is removed, but verify its edges are still there @@ -88,71 +88,71 @@ BOOST_AUTO_TEST_CASE(toplex_map_empty_toplex) { using Vertex = Gudhi::Lazy_toplex_map::Vertex; Gudhi::Lazy_toplex_map tm; - std::cout << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 0); - std::cout << "num_vertices = " << tm.num_vertices() << std::endl; + std::clog << "num_vertices = " << tm.num_vertices() << std::endl; BOOST_CHECK(tm.num_vertices() == 0); - std::cout << "Check an empty simplex is a member." << std::endl; + std::clog << "Check an empty simplex is a member." << std::endl; std::vector<Vertex> empty_sigma = {}; BOOST_CHECK(tm.membership(empty_sigma)); - std::cout << "Check the edge 2,7 is not a member." << std::endl; + std::clog << "Check the edge 2,7 is not a member." << std::endl; std::vector<Vertex> edge = {2, 7}; BOOST_CHECK(!tm.membership(edge)); - std::cout << "Insert an empty simplex." << std::endl; + std::clog << "Insert an empty simplex." << std::endl; tm.insert_simplex(empty_sigma); - std::cout << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 0); - std::cout << "num_vertices = " << tm.num_vertices() << std::endl; + std::clog << "num_vertices = " << tm.num_vertices() << std::endl; BOOST_CHECK(tm.num_vertices() == 0); - std::cout << "Check an empty simplex is a member." << std::endl; + std::clog << "Check an empty simplex is a member." << std::endl; BOOST_CHECK(tm.membership(empty_sigma)); - std::cout << "Check the edge 2,7 is not a member." << std::endl; + std::clog << "Check the edge 2,7 is not a member." << std::endl; BOOST_CHECK(!tm.membership(edge)); - std::cout << "Insert edge 2,7." << std::endl; + std::clog << "Insert edge 2,7." << std::endl; tm.insert_simplex(edge); - std::cout << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 1); - std::cout << "num_vertices = " << tm.num_vertices() << std::endl; + std::clog << "num_vertices = " << tm.num_vertices() << std::endl; BOOST_CHECK(tm.num_vertices() == 2); - std::cout << "Check an empty simplex is a member." << std::endl; + std::clog << "Check an empty simplex is a member." << std::endl; BOOST_CHECK(tm.membership(empty_sigma)); - std::cout << "Check the edge 2,7 is a member." << std::endl; + std::clog << "Check the edge 2,7 is a member." << std::endl; BOOST_CHECK(tm.membership(edge)); - std::cout << "contraction(2,7)" << std::endl; + std::clog << "contraction(2,7)" << std::endl; auto r = tm.contraction(2, 7); - std::cout << "r=" << r << std::endl; + std::clog << "r=" << r << std::endl; BOOST_CHECK(r == 7); - std::cout << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 1); - std::cout << "num_vertices = " << tm.num_vertices() << std::endl; + std::clog << "num_vertices = " << tm.num_vertices() << std::endl; BOOST_CHECK(tm.num_vertices() == 1); - std::cout << "Check an empty simplex is a member." << std::endl; + std::clog << "Check an empty simplex is a member." << std::endl; BOOST_CHECK(tm.membership(empty_sigma)); - std::cout << "Check the edge 2,7 is not a member." << std::endl; + std::clog << "Check the edge 2,7 is not a member." << std::endl; BOOST_CHECK(!tm.membership(edge)); - std::cout << "Remove the vertex 7." << std::endl; + std::clog << "Remove the vertex 7." << std::endl; std::vector<Vertex> vertex = {7}; tm.remove_simplex(vertex); - std::cout << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices = " << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 0); - std::cout << "num_vertices = " << tm.num_vertices() << std::endl; + std::clog << "num_vertices = " << tm.num_vertices() << std::endl; BOOST_CHECK(tm.num_vertices() == 0); - std::cout << "Check an empty simplex is a member." << std::endl; + std::clog << "Check an empty simplex is a member." << std::endl; BOOST_CHECK(tm.membership(empty_sigma)); - std::cout << "Check the edge 2,7 is not a member." << std::endl; + std::clog << "Check the edge 2,7 is not a member." << std::endl; BOOST_CHECK(!tm.membership(edge)); } diff --git a/src/Toplex_map/test/toplex_map_unit_test.cpp b/src/Toplex_map/test/toplex_map_unit_test.cpp index 24ec679b..0d0751ff 100644 --- a/src/Toplex_map/test/toplex_map_unit_test.cpp +++ b/src/Toplex_map/test/toplex_map_unit_test.cpp @@ -20,31 +20,31 @@ BOOST_AUTO_TEST_CASE(toplex_map) { using Vertex = Gudhi::Toplex_map::Vertex; Gudhi::Toplex_map tm; - std::cout << "insert_simplex {1, 2, 3, 4}" << std::endl; + std::clog << "insert_simplex {1, 2, 3, 4}" << std::endl; std::vector<Vertex> sigma1 = {1, 2, 3, 4}; tm.insert_simplex(sigma1); - std::cout << "insert_simplex {5, 2, 3, 6}" << std::endl; + std::clog << "insert_simplex {5, 2, 3, 6}" << std::endl; std::vector<Vertex> sigma2 = {5, 2, 3, 6}; tm.insert_simplex(sigma2); - std::cout << "insert_simplex {5}" << std::endl; + std::clog << "insert_simplex {5}" << std::endl; std::vector<Vertex> sigma3 = {5}; tm.insert_simplex(sigma3); - std::cout << "insert_simplex {4, 5, 3}" << std::endl; + std::clog << "insert_simplex {4, 5, 3}" << std::endl; std::vector<Vertex> sigma6 = {4, 5, 3}; tm.insert_simplex(sigma6); - std::cout << "insert_simplex {4, 5, 9}" << std::endl; + std::clog << "insert_simplex {4, 5, 9}" << std::endl; std::vector<Vertex> sigma7 = {4, 5, 9}; tm.insert_simplex(sigma7); - std::cout << "num_maximal_simplices" << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices" << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 4); // Browse maximal simplices - std::cout << "Maximal simplices are :" << std::endl; + std::clog << "Maximal simplices are :" << std::endl; for (auto simplex_ptr : tm.maximal_simplices()) { for (auto v : *simplex_ptr) { - std::cout << v << ", "; + std::clog << v << ", "; } - std::cout << std::endl; + std::clog << std::endl; BOOST_CHECK(tm.maximality(*simplex_ptr)); } @@ -58,37 +58,37 @@ BOOST_AUTO_TEST_CASE(toplex_map) { std::vector<Vertex> sigma5 = {5, 2, 7}; BOOST_CHECK(tm.membership(sigma4)); BOOST_CHECK(!tm.membership(sigma5)); - std::cout << "insert_simplex {5, 2, 7}" << std::endl; + std::clog << "insert_simplex {5, 2, 7}" << std::endl; tm.insert_simplex(sigma5); - std::cout << "num_maximal_simplices" << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices" << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 5); // Browse maximal simplices - std::cout << "Maximal simplices are :" << std::endl; + std::clog << "Maximal simplices are :" << std::endl; for (auto simplex_ptr : tm.maximal_simplices()) { for (auto v : *simplex_ptr) { - std::cout << v << ", "; + std::clog << v << ", "; } - std::cout << std::endl; + std::clog << std::endl; BOOST_CHECK(tm.maximality(*simplex_ptr)); } BOOST_CHECK(tm.membership(sigma5)); - std::cout << "contraction(4,5)" << std::endl; + std::clog << "contraction(4,5)" << std::endl; auto r = tm.contraction(4, 5); - std::cout << "r=" << r << std::endl; + std::clog << "r=" << r << std::endl; BOOST_CHECK(r == 5); - std::cout << "num_maximal_simplices" << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices" << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 4); // Browse maximal simplices - std::cout << "Maximal simplices are :" << std::endl; + std::clog << "Maximal simplices are :" << std::endl; for (auto simplex_ptr : tm.maximal_simplices()) { for (auto v : *simplex_ptr) { - std::cout << v << ", "; + std::clog << v << ", "; } - std::cout << std::endl; + std::clog << std::endl; BOOST_CHECK(tm.maximality(*simplex_ptr)); } @@ -101,19 +101,19 @@ BOOST_AUTO_TEST_CASE(toplex_map) { BOOST_CHECK(tm.membership(sigma8)); BOOST_CHECK(tm.membership(sigma9)); - std::cout << "remove_simplex({2, 7, r = 5})" << std::endl; + std::clog << "remove_simplex({2, 7, r = 5})" << std::endl; tm.remove_simplex(sigma9); BOOST_CHECK(!tm.membership(sigma9)); - std::cout << "num_maximal_simplices" << tm.num_maximal_simplices() << std::endl; + std::clog << "num_maximal_simplices" << tm.num_maximal_simplices() << std::endl; BOOST_CHECK(tm.num_maximal_simplices() == 5); // Browse maximal simplices - std::cout << "Maximal simplices are :" << std::endl; + std::clog << "Maximal simplices are :" << std::endl; for (auto simplex_ptr : tm.maximal_simplices()) { for (auto v : *simplex_ptr) { - std::cout << v << ", "; + std::clog << v << ", "; } - std::cout << std::endl; + std::clog << std::endl; BOOST_CHECK(tm.maximality(*simplex_ptr)); } // {2, 7, 5} is removed, but verify its edges are still there diff --git a/src/Witness_complex/example/example_nearest_landmark_table.cpp b/src/Witness_complex/example/example_nearest_landmark_table.cpp index 441900c1..14101847 100644 --- a/src/Witness_complex/example/example_nearest_landmark_table.cpp +++ b/src/Witness_complex/example/example_nearest_landmark_table.cpp @@ -33,7 +33,7 @@ int main(int argc, char * const argv[]) { Witness_complex witness_complex(nlt); witness_complex.create_complex(simplex_tree, .41); - std::cout << "Number of simplices: " << simplex_tree.num_simplices() << std::endl; + std::clog << "Number of simplices: " << simplex_tree.num_simplices() << std::endl; Persistent_cohomology pcoh(simplex_tree); // initializes the coefficient field for homology diff --git a/src/Witness_complex/example/example_strong_witness_complex_off.cpp b/src/Witness_complex/example/example_strong_witness_complex_off.cpp index 19f73836..583a04ab 100644 --- a/src/Witness_complex/example/example_strong_witness_complex_off.cpp +++ b/src/Witness_complex/example/example_strong_witness_complex_off.cpp @@ -38,8 +38,8 @@ int main(int argc, char* const argv[]) { } point_vector = Point_vector(off_reader.get_point_cloud()); - std::cout << "Successfully read " << point_vector.size() << " points.\n"; - std::cout << "Ambient dimension is " << point_vector[0].dimension() << ".\n"; + std::clog << "Successfully read " << point_vector.size() << " points.\n"; + std::clog << "Ambient dimension is " << point_vector[0].dimension() << ".\n"; // Choose landmarks (decomment one of the following two lines) // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); @@ -52,6 +52,6 @@ int main(int argc, char* const argv[]) { witness_complex.create_complex(simplex_tree, alpha2, lim_dim); end = clock(); - std::cout << "Strong witness complex took " << static_cast<double>(end - start) / CLOCKS_PER_SEC << " s. \n"; - std::cout << "Number of simplices is: " << simplex_tree.num_simplices() << "\n"; + std::clog << "Strong witness complex took " << static_cast<double>(end - start) / CLOCKS_PER_SEC << " s. \n"; + std::clog << "Number of simplices is: " << simplex_tree.num_simplices() << "\n"; } diff --git a/src/Witness_complex/example/example_witness_complex_off.cpp b/src/Witness_complex/example/example_witness_complex_off.cpp index be11c955..3635da78 100644 --- a/src/Witness_complex/example/example_witness_complex_off.cpp +++ b/src/Witness_complex/example/example_witness_complex_off.cpp @@ -42,8 +42,8 @@ int main(int argc, char * const argv[]) { } point_vector = Point_vector(off_reader.get_point_cloud()); - std::cout << "Successfully read " << point_vector.size() << " points.\n"; - std::cout << "Ambient dimension is " << point_vector[0].dimension() << ".\n"; + std::clog << "Successfully read " << point_vector.size() << " points.\n"; + std::clog << "Ambient dimension is " << point_vector[0].dimension() << ".\n"; // Choose landmarks (decomment one of the following two lines) // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); @@ -56,7 +56,7 @@ int main(int argc, char * const argv[]) { witness_complex.create_complex(simplex_tree, alpha2, lim_dim); end = clock(); - std::cout << "Witness complex took " + std::clog << "Witness complex took " << static_cast<double>(end - start) / CLOCKS_PER_SEC << " s. \n"; - std::cout << "Number of simplices is: " << simplex_tree.num_simplices() << "\n"; + std::clog << "Number of simplices is: " << simplex_tree.num_simplices() << "\n"; } diff --git a/src/Witness_complex/example/example_witness_complex_sphere.cpp b/src/Witness_complex/example/example_witness_complex_sphere.cpp index 9e3c972d..78d5db4f 100644 --- a/src/Witness_complex/example/example_witness_complex_sphere.cpp +++ b/src/Witness_complex/example/example_witness_complex_sphere.cpp @@ -47,8 +47,8 @@ int main(int argc, char* const argv[]) { Gudhi::Simplex_tree<> simplex_tree; Point_Vector point_vector, landmarks; generate_points_sphere(point_vector, nbP, 4); - std::cout << "Successfully generated " << point_vector.size() << " points.\n"; - std::cout << "Ambient dimension is " << point_vector[0].size() << ".\n"; + std::clog << "Successfully generated " << point_vector.size() << " points.\n"; + std::clog << "Ambient dimension is " << point_vector[0].size() << ".\n"; // Choose landmarks start = clock(); @@ -62,8 +62,8 @@ int main(int argc, char* const argv[]) { witness_complex.create_complex(simplex_tree, 0); end = clock(); double time = static_cast<double>(end - start) / CLOCKS_PER_SEC; - std::cout << "Witness complex for " << number_of_landmarks << " landmarks took " << time << " s. \n"; - std::cout << "Number of simplices is: " << simplex_tree.num_simplices() << "\n"; + std::clog << "Witness complex for " << number_of_landmarks << " landmarks took " << time << " s. \n"; + std::clog << "Number of simplices is: " << simplex_tree.num_simplices() << "\n"; l_time.push_back(std::make_pair(nbP, time)); } write_data(l_time, "w_time.dat"); diff --git a/src/Witness_complex/test/test_euclidean_simple_witness_complex.cpp b/src/Witness_complex/test/test_euclidean_simple_witness_complex.cpp index 4f718203..9b19f6dc 100644 --- a/src/Witness_complex/test/test_euclidean_simple_witness_complex.cpp +++ b/src/Witness_complex/test/test_euclidean_simple_witness_complex.cpp @@ -82,12 +82,12 @@ BOOST_AUTO_TEST_CASE(simple_witness_complex) { witnesses); eucl_witness_complex.create_complex(complex, 0); - std::cout << "complex.num_simplices() = " << complex.num_simplices() << std::endl; + std::clog << "complex.num_simplices() = " << complex.num_simplices() << std::endl; BOOST_CHECK(complex.num_simplices() == 24); eucl_witness_complex.create_complex(relaxed_complex, 8.01); - std::cout << "relaxed_complex.num_simplices() = " << relaxed_complex.num_simplices() << std::endl; + std::clog << "relaxed_complex.num_simplices() = " << relaxed_complex.num_simplices() << std::endl; BOOST_CHECK(relaxed_complex.num_simplices() == 239); // The corner simplex {0,2,5,7} and its cofaces are missing. @@ -95,12 +95,12 @@ BOOST_AUTO_TEST_CASE(simple_witness_complex) { WitnessComplex witness_complex(nearest_landmark_table); witness_complex.create_complex(complex_ne, 0); - std::cout << "complex.num_simplices() = " << complex_ne.num_simplices() << std::endl; + std::clog << "complex.num_simplices() = " << complex_ne.num_simplices() << std::endl; BOOST_CHECK(complex_ne.num_simplices() == 24); witness_complex.create_complex(relaxed_complex_ne, 8.01); - std::cout << "relaxed_complex.num_simplices() = " << relaxed_complex_ne.num_simplices() << std::endl; + std::clog << "relaxed_complex.num_simplices() = " << relaxed_complex_ne.num_simplices() << std::endl; BOOST_CHECK(relaxed_complex_ne.num_simplices() == 239); @@ -111,10 +111,10 @@ BOOST_AUTO_TEST_CASE(simple_witness_complex) { eucl_strong_witness_complex.create_complex(strong_relaxed_complex, 9.1); eucl_strong_witness_complex.create_complex(strong_relaxed_complex2, 9.1, 2); - std::cout << "strong_relaxed_complex.num_simplices() = " << strong_relaxed_complex.num_simplices() << std::endl; + std::clog << "strong_relaxed_complex.num_simplices() = " << strong_relaxed_complex.num_simplices() << std::endl; BOOST_CHECK(strong_relaxed_complex.num_simplices() == 239); - std::cout << "strong_relaxed_complex2.num_simplices() = " << strong_relaxed_complex2.num_simplices() << std::endl; + std::clog << "strong_relaxed_complex2.num_simplices() = " << strong_relaxed_complex2.num_simplices() << std::endl; BOOST_CHECK(strong_relaxed_complex2.num_simplices() == 92); @@ -124,10 +124,10 @@ BOOST_AUTO_TEST_CASE(simple_witness_complex) { strong_witness_complex.create_complex(strong_relaxed_complex_ne, 9.1); strong_witness_complex.create_complex(strong_relaxed_complex2_ne, 9.1, 2); - std::cout << "strong_relaxed_complex.num_simplices() = " << strong_relaxed_complex_ne.num_simplices() << std::endl; + std::clog << "strong_relaxed_complex.num_simplices() = " << strong_relaxed_complex_ne.num_simplices() << std::endl; BOOST_CHECK(strong_relaxed_complex_ne.num_simplices() == 239); - std::cout << "strong_relaxed_complex2.num_simplices() = " << strong_relaxed_complex2_ne.num_simplices() << std::endl; + std::clog << "strong_relaxed_complex2.num_simplices() = " << strong_relaxed_complex2_ne.num_simplices() << std::endl; BOOST_CHECK(strong_relaxed_complex2_ne.num_simplices() == 92); diff --git a/src/Witness_complex/test/test_simple_witness_complex.cpp b/src/Witness_complex/test/test_simple_witness_complex.cpp index 9e3509d3..7c48cc54 100644 --- a/src/Witness_complex/test/test_simple_witness_complex.cpp +++ b/src/Witness_complex/test/test_simple_witness_complex.cpp @@ -36,7 +36,7 @@ BOOST_AUTO_TEST_CASE(simple_witness_complex) { Witness_complex witness_complex(nlt); BOOST_CHECK(witness_complex.create_complex(stree, 4.1)); - std::cout << "Number of simplices: " << stree.num_simplices() << std::endl; + std::clog << "Number of simplices: " << stree.num_simplices() << std::endl; BOOST_CHECK(stree.num_simplices() == 31); // Check when complex not empty @@ -47,7 +47,7 @@ BOOST_AUTO_TEST_CASE(simple_witness_complex) { BOOST_CHECK(!witness_complex.create_complex(stree2, -0.02)); witness_complex.create_complex(stree2, 4.1, 2); - std::cout << "Number of simplices: " << stree2.num_simplices() << std::endl; + std::clog << "Number of simplices: " << stree2.num_simplices() << std::endl; BOOST_CHECK(stree2.num_simplices() == 25); } diff --git a/src/Witness_complex/utilities/CMakeLists.txt b/src/Witness_complex/utilities/CMakeLists.txt index 3ee0c2f6..d986d2d1 100644 --- a/src/Witness_complex/utilities/CMakeLists.txt +++ b/src/Witness_complex/utilities/CMakeLists.txt @@ -4,10 +4,10 @@ project(Witness_complex_utilities) if(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) add_executable ( Witness_complex_strong_witness_persistence strong_witness_persistence.cpp ) - target_link_libraries(Witness_complex_strong_witness_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) + target_link_libraries(Witness_complex_strong_witness_persistence Boost::program_options) add_executable ( Witness_complex_weak_witness_persistence weak_witness_persistence.cpp ) - target_link_libraries(Witness_complex_weak_witness_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) + target_link_libraries(Witness_complex_weak_witness_persistence Boost::program_options) if (TBB_FOUND) target_link_libraries(Witness_complex_strong_witness_persistence ${TBB_LIBRARIES}) diff --git a/src/Witness_complex/utilities/strong_witness_persistence.cpp b/src/Witness_complex/utilities/strong_witness_persistence.cpp index 75ba1f4b..1f61c77c 100644 --- a/src/Witness_complex/utilities/strong_witness_persistence.cpp +++ b/src/Witness_complex/utilities/strong_witness_persistence.cpp @@ -56,8 +56,8 @@ int main(int argc, char* argv[]) { exit(-1); // ----- >> } witnesses = Point_vector(off_reader.get_point_cloud()); - std::cout << "Successfully read " << witnesses.size() << " points.\n"; - std::cout << "Ambient dimension is " << witnesses[0].dimension() << ".\n"; + std::clog << "Successfully read " << witnesses.size() << " points.\n"; + std::clog << "Ambient dimension is " << witnesses[0].dimension() << ".\n"; // Choose landmarks (decomment one of the following two lines) // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); @@ -69,8 +69,8 @@ int main(int argc, char* argv[]) { strong_witness_complex.create_complex(simplex_tree, max_squared_alpha, lim_d); - std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; - std::cout << " and has dimension " << simplex_tree.dimension() << " \n"; + std::clog << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; + std::clog << " and has dimension " << simplex_tree.dimension() << " \n"; // Sort the simplices in the order of the filtration simplex_tree.initialize_filtration(); @@ -107,7 +107,7 @@ void program_options(int argc, char* argv[], int& nbL, std::string& file_name, s visible.add_options()("help,h", "produce help message")("landmarks,l", po::value<int>(&nbL), "Number of landmarks to choose from the point cloud.")( "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout")( + "Name of file in which the persistence diagram is written. Default print in std::clog")( "max-sq-alpha,a", po::value<Filtration_value>(&max_squared_alpha)->default_value(default_alpha), "Maximal squared relaxation parameter.")( "field-charac,p", po::value<int>(&p)->default_value(11), @@ -128,17 +128,17 @@ void program_options(int argc, char* argv[], int& nbL, std::string& file_name, s po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of a Strong witness complex defined on a set of input points.\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of a Strong witness complex defined on a set of input points.\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Witness_complex/utilities/weak_witness_persistence.cpp b/src/Witness_complex/utilities/weak_witness_persistence.cpp index 0e5b9cc1..93050af5 100644 --- a/src/Witness_complex/utilities/weak_witness_persistence.cpp +++ b/src/Witness_complex/utilities/weak_witness_persistence.cpp @@ -56,8 +56,8 @@ int main(int argc, char* argv[]) { exit(-1); // ----- >> } witnesses = Point_vector(off_reader.get_point_cloud()); - std::cout << "Successfully read " << witnesses.size() << " points.\n"; - std::cout << "Ambient dimension is " << witnesses[0].dimension() << ".\n"; + std::clog << "Successfully read " << witnesses.size() << " points.\n"; + std::clog << "Ambient dimension is " << witnesses[0].dimension() << ".\n"; // Choose landmarks (decomment one of the following two lines) // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); @@ -69,8 +69,8 @@ int main(int argc, char* argv[]) { witness_complex.create_complex(simplex_tree, max_squared_alpha, lim_d); - std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; - std::cout << " and has dimension " << simplex_tree.dimension() << " \n"; + std::clog << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; + std::clog << " and has dimension " << simplex_tree.dimension() << " \n"; // Sort the simplices in the order of the filtration simplex_tree.initialize_filtration(); @@ -107,7 +107,7 @@ void program_options(int argc, char* argv[], int& nbL, std::string& file_name, s visible.add_options()("help,h", "produce help message")("landmarks,l", po::value<int>(&nbL), "Number of landmarks to choose from the point cloud.")( "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout")( + "Name of file in which the persistence diagram is written. Default print in std::clog")( "max-sq-alpha,a", po::value<Filtration_value>(&max_squared_alpha)->default_value(default_alpha), "Maximal squared relaxation parameter.")( "field-charac,p", po::value<int>(&p)->default_value(11), @@ -128,17 +128,17 @@ void program_options(int argc, char* argv[], int& nbL, std::string& file_name, s po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { - std::cout << std::endl; - std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; - std::cout << "of a Weak witness complex defined on a set of input points.\n \n"; - std::cout << "The output diagram contains one bar per line, written with the convention: \n"; - std::cout << " p dim b d \n"; - std::cout << "where dim is the dimension of the homological feature,\n"; - std::cout << "b and d are respectively the birth and death of the feature and \n"; - std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; - - std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; - std::cout << visible << std::endl; + std::clog << std::endl; + std::clog << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::clog << "of a Weak witness complex defined on a set of input points.\n \n"; + std::clog << "The output diagram contains one bar per line, written with the convention: \n"; + std::clog << " p dim b d \n"; + std::clog << "where dim is the dimension of the homological feature,\n"; + std::clog << "b and d are respectively the birth and death of the feature and \n"; + std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::clog << visible << std::endl; exit(-1); } } diff --git a/src/Witness_complex/utilities/witnesscomplex.md b/src/Witness_complex/utilities/witnesscomplex.md index 7ea397b9..3a3a7d83 100644 --- a/src/Witness_complex/utilities/witnesscomplex.md +++ b/src/Witness_complex/utilities/witnesscomplex.md @@ -29,7 +29,7 @@ and `p` is the characteristic of the field *Z/pZ* used for homology coefficients * `-h [ --help ]` Produce help message
* `-l [ --landmarks ]` Number of landmarks to choose from the point cloud.
-* `-o [ --output-file ]` Name of file in which the persistence diagram is written. By default, print in std::cout.
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. By default, print in std::clog.
* `-a [ --max-sq-alpha ]` (default = inf) Maximal squared relaxation parameter.
* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
@@ -60,7 +60,7 @@ and `p` is the characteristic of the field *Z/pZ* used for homology coefficients * `-h [ --help ]` Produce help message
* `-l [ --landmarks ]` Number of landmarks to choose from the point cloud.
-* `-o [ --output-file ]` Name of file in which the persistence diagram is written. By default, print in std::cout.
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. By default, print in std::clog.
* `-a [ --max-sq-alpha ]` (default = inf) Maximal squared relaxation parameter.
* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
diff --git a/src/cmake/modules/GUDHI_boost_test.cmake b/src/cmake/modules/GUDHI_boost_test.cmake index c3b29883..4a13404b 100644 --- a/src/cmake/modules/GUDHI_boost_test.cmake +++ b/src/cmake/modules/GUDHI_boost_test.cmake @@ -9,7 +9,6 @@ if (WITH_GUDHI_BOOST_TEST_COVERAGE) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pg") endif() set(GUDHI_UT_LOG_FORMAT "--log_format=XML") - set(GUDHI_UT_LOG_SINK "--log_sink=${CMAKE_BINARY_DIR}/${unitary_test}_UT.xml") set(GUDHI_UT_LOG_LEVEL "--log_level=test_suite") set(GUDHI_UT_REPORT_LEVEL "--report_level=no") else (WITH_GUDHI_BOOST_TEST_COVERAGE) @@ -19,7 +18,11 @@ else (WITH_GUDHI_BOOST_TEST_COVERAGE) endif(WITH_GUDHI_BOOST_TEST_COVERAGE) function(gudhi_add_boost_test unitary_test) - target_link_libraries(${unitary_test} ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY}) + if (WITH_GUDHI_BOOST_TEST_COVERAGE) + set(GUDHI_UT_LOG_SINK "--log_sink=${CMAKE_BINARY_DIR}/${unitary_test}_UT.xml") + endif(WITH_GUDHI_BOOST_TEST_COVERAGE) + + target_link_libraries(${unitary_test} Boost::unit_test_framework) add_test(NAME ${unitary_test} COMMAND $<TARGET_FILE:${unitary_test}> ${GUDHI_UT_LOG_FORMAT} ${GUDHI_UT_LOG_SINK} ${GUDHI_UT_LOG_LEVEL} ${GUDHI_UT_REPORT_LEVEL}) diff --git a/src/cmake/modules/GUDHI_compilation_flags.cmake b/src/cmake/modules/GUDHI_compilation_flags.cmake index 34c2e065..567fbc40 100644 --- a/src/cmake/modules/GUDHI_compilation_flags.cmake +++ b/src/cmake/modules/GUDHI_compilation_flags.cmake @@ -1,7 +1,6 @@ # This files manage compilation flags required by GUDHI include(TestCXXAcceptsFlag) -include(CheckCXXSourceCompiles) # add a compiler flag only if it is accepted macro(add_cxx_compiler_flag _flag) @@ -12,32 +11,6 @@ macro(add_cxx_compiler_flag _flag) endif() endmacro() -function(can_cgal_use_cxx11_thread_local) - # This is because of https://github.com/CGAL/cgal/blob/master/Installation/include/CGAL/tss.h - # CGAL is using boost thread if thread_local is not ready (requires XCode 8 for Mac). - # The test in https://github.com/CGAL/cgal/blob/master/Installation/include/CGAL/config.h - # #if __has_feature(cxx_thread_local) || \ - # ( (__GNUC__ * 100 + __GNUC_MINOR__) >= 408 && __cplusplus >= 201103L ) || \ - # ( _MSC_VER >= 1900 ) - # #define CGAL_CAN_USE_CXX11_THREAD_LOCAL - # #endif - set(CGAL_CAN_USE_CXX11_THREAD_LOCAL " - int main() { - #ifndef __has_feature - #define __has_feature(x) 0 // Compatibility with non-clang compilers. - #endif - #if __has_feature(cxx_thread_local) || \ - ( (__GNUC__ * 100 + __GNUC_MINOR__) >= 408 && __cplusplus >= 201103L ) || \ - ( _MSC_VER >= 1900 ) - bool has_feature_thread_local = true; - #else - // Explicit error of compilation for CMake test purpose - has_feature_thread_local is not defined - #endif - bool result = has_feature_thread_local; - } ") - check_cxx_source_compiles("${CGAL_CAN_USE_CXX11_THREAD_LOCAL}" CGAL_CAN_USE_CXX11_THREAD_LOCAL_RESULT) -endfunction() - set (CMAKE_CXX_STANDARD 14) enable_testing() @@ -58,16 +31,6 @@ if (DEBUG_TRACES) add_definitions(-DDEBUG_TRACES) endif() -set(GUDHI_CAN_USE_CXX11_THREAD_LOCAL " - int main() { - thread_local int result = 0; - return result; - } ") -check_cxx_source_compiles("${GUDHI_CAN_USE_CXX11_THREAD_LOCAL}" GUDHI_CAN_USE_CXX11_THREAD_LOCAL_RESULT) -if (GUDHI_CAN_USE_CXX11_THREAD_LOCAL_RESULT) - add_definitions(-DGUDHI_CAN_USE_CXX11_THREAD_LOCAL) -endif() - if(CMAKE_BUILD_TYPE MATCHES Debug) message("++ Debug compilation flags are: ${CMAKE_CXX_FLAGS} ${CMAKE_CXX_FLAGS_DEBUG}") else() diff --git a/src/cmake/modules/GUDHI_third_party_libraries.cmake b/src/cmake/modules/GUDHI_third_party_libraries.cmake index 24a34150..0abe66b7 100644 --- a/src/cmake/modules/GUDHI_third_party_libraries.cmake +++ b/src/cmake/modules/GUDHI_third_party_libraries.cmake @@ -6,6 +6,38 @@ if(NOT Boost_FOUND) message(FATAL_ERROR "NOTICE: This program requires Boost and will not be compiled.") endif(NOT Boost_FOUND) +# cf. https://cliutils.gitlab.io/modern-cmake/chapters/packages/Boost.html +# This is needed if your Boost version is newer than your CMake version +# or if you have an old version of CMake (<3.5) +if(NOT TARGET Boost::program_options) + add_library(Boost::program_options IMPORTED INTERFACE) + set_property(TARGET Boost::program_options PROPERTY + INTERFACE_INCLUDE_DIRECTORIES ${Boost_INCLUDE_DIR}) + set_property(TARGET Boost::program_options PROPERTY + INTERFACE_LINK_LIBRARIES ${Boost_LIBRARIES}) +endif() +if(NOT TARGET Boost::filesystem) + add_library(Boost::filesystem IMPORTED INTERFACE) + set_property(TARGET Boost::filesystem PROPERTY + INTERFACE_INCLUDE_DIRECTORIES ${Boost_INCLUDE_DIR}) + set_property(TARGET Boost::filesystem PROPERTY + INTERFACE_LINK_LIBRARIES ${Boost_LIBRARIES}) +endif() +if(NOT TARGET Boost::unit_test_framework) + add_library(Boost::unit_test_framework IMPORTED INTERFACE) + set_property(TARGET Boost::unit_test_framework PROPERTY + INTERFACE_INCLUDE_DIRECTORIES ${Boost_INCLUDE_DIR}) + set_property(TARGET Boost::unit_test_framework PROPERTY + INTERFACE_LINK_LIBRARIES ${Boost_LIBRARIES}) +endif() +if(NOT TARGET Boost::system) + add_library(Boost::system IMPORTED INTERFACE) + set_property(TARGET Boost::system PROPERTY + INTERFACE_INCLUDE_DIRECTORIES ${Boost_INCLUDE_DIR}) + set_property(TARGET Boost::system PROPERTY + INTERFACE_LINK_LIBRARIES ${Boost_LIBRARIES}) +endif() + find_package(GMP) if(GMP_FOUND) INCLUDE_DIRECTORIES(${GMP_INCLUDE_DIR}) @@ -35,6 +67,9 @@ if(CGAL_FOUND) include( ${CGAL_USE_FILE} ) endif() +# For those who dislike bundled dependencies, this indicates where to find a preinstalled Hera. +set(HERA_WASSERSTEIN_INCLUDE_DIR ${CMAKE_SOURCE_DIR}/ext/hera/wasserstein/include CACHE PATH "Directory where one can find Hera's wasserstein.h") + option(WITH_GUDHI_USE_TBB "Build with Intel TBB parallelization" ON) # Find TBB package for parallel sort - not mandatory, just optional. @@ -82,9 +117,6 @@ add_definitions( -DBOOST_ALL_DYN_LINK ) # problem on Mac with boost_system and boost_thread add_definitions( -DBOOST_SYSTEM_NO_DEPRECATED ) -INCLUDE_DIRECTORIES(${Boost_INCLUDE_DIRS}) -LINK_DIRECTORIES(${Boost_LIBRARY_DIRS}) - message(STATUS "boost include dirs:" ${Boost_INCLUDE_DIRS}) message(STATUS "boost library dirs:" ${Boost_LIBRARY_DIRS}) @@ -118,6 +150,25 @@ function( find_python_module PYTHON_MODULE_NAME ) endif() endfunction( find_python_module ) +# For modules that do not define module.__version__ +function( find_python_module_no_version PYTHON_MODULE_NAME ) + string(TOUPPER ${PYTHON_MODULE_NAME} PYTHON_MODULE_NAME_UP) + execute_process( + COMMAND ${PYTHON_EXECUTABLE} -c "import ${PYTHON_MODULE_NAME}" + RESULT_VARIABLE PYTHON_MODULE_RESULT + ERROR_VARIABLE PYTHON_MODULE_ERROR) + if(PYTHON_MODULE_RESULT EQUAL 0) + # Remove carriage return + message ("++ Python module ${PYTHON_MODULE_NAME} found") + set(${PYTHON_MODULE_NAME_UP}_FOUND TRUE PARENT_SCOPE) + else() + message ("PYTHON_MODULE_NAME = ${PYTHON_MODULE_NAME} + - PYTHON_MODULE_RESULT = ${PYTHON_MODULE_RESULT} + - PYTHON_MODULE_ERROR = ${PYTHON_MODULE_ERROR}") + set(${PYTHON_MODULE_NAME_UP}_FOUND FALSE PARENT_SCOPE) + endif() +endfunction( find_python_module_no_version ) + if( PYTHONINTERP_FOUND ) find_python_module("cython") find_python_module("pytest") @@ -127,6 +178,11 @@ if( PYTHONINTERP_FOUND ) find_python_module("sphinx") find_python_module("sklearn") find_python_module("ot") + find_python_module("pybind11") + find_python_module("torch") + find_python_module("pykeops") + find_python_module("eagerpy") + find_python_module_no_version("hnswlib") endif() if(NOT GUDHI_PYTHON_PATH) diff --git a/src/cmake/modules/GUDHI_user_version_target.cmake b/src/cmake/modules/GUDHI_user_version_target.cmake index 0b361a0f..9cf648e3 100644 --- a/src/cmake/modules/GUDHI_user_version_target.cmake +++ b/src/cmake/modules/GUDHI_user_version_target.cmake @@ -26,8 +26,12 @@ add_custom_command(TARGET user_version PRE_BUILD COMMAND ${CMAKE_COMMAND} -E # Generate bib files for Doxygen - cf. root CMakeLists.txt for explanation string(TIMESTAMP GUDHI_VERSION_YEAR "%Y") configure_file(${CMAKE_SOURCE_DIR}/biblio/how_to_cite_gudhi.bib.in "${CMAKE_CURRENT_BINARY_DIR}/biblio/how_to_cite_gudhi.bib" @ONLY) -file(COPY "${CMAKE_SOURCE_DIR}/biblio/how_to_cite_cgal.bib" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/biblio/") file(COPY "${CMAKE_SOURCE_DIR}/biblio/bibliography.bib" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/biblio/") + +# append cgal citation inside bibliography - sphinx cannot deal with more than one bib file +file(READ "${CMAKE_SOURCE_DIR}/biblio/how_to_cite_cgal.bib" CGAL_CITATION_CONTENT) +file(APPEND "${CMAKE_CURRENT_BINARY_DIR}/biblio/bibliography.bib" "${CGAL_CITATION_CONTENT}") + # Copy biblio directory for user version add_custom_command(TARGET user_version PRE_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_BINARY_DIR}/biblio ${GUDHI_USER_VERSION_DIR}/biblio) @@ -54,6 +58,9 @@ add_custom_command(TARGET user_version PRE_BUILD COMMAND ${CMAKE_COMMAND} -E add_custom_command(TARGET user_version PRE_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_SOURCE_DIR}/src/GudhUI ${GUDHI_USER_VERSION_DIR}/GudhUI) +add_custom_command(TARGET user_version PRE_BUILD COMMAND ${CMAKE_COMMAND} -E + copy_directory ${CMAKE_SOURCE_DIR}/ext/hera/wasserstein/include ${GUDHI_USER_VERSION_DIR}/ext/hera/wasserstein/include) + set(GUDHI_DIRECTORIES "doc;example;concept;utilities") set(GUDHI_INCLUDE_DIRECTORIES "include/gudhi") @@ -93,4 +100,4 @@ foreach(GUDHI_MODULE ${GUDHI_MODULES_FULL_LIST}) endforeach() endforeach(GUDHI_INCLUDE_DIRECTORY ${GUDHI_INCLUDE_DIRECTORIES}) -endforeach(GUDHI_MODULE ${GUDHI_MODULES_FULL_LIST})
\ No newline at end of file +endforeach(GUDHI_MODULE ${GUDHI_MODULES_FULL_LIST}) diff --git a/src/common/benchmark/Graph_simplicial_complex_benchmark.cpp b/src/common/benchmark/Graph_simplicial_complex_benchmark.cpp index 0fc145fd..6fe7a887 100644 --- a/src/common/benchmark/Graph_simplicial_complex_benchmark.cpp +++ b/src/common/benchmark/Graph_simplicial_complex_benchmark.cpp @@ -66,7 +66,7 @@ void benchmark_proximity_graph(const std::string& msg, const std::string& off_fi Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file_name); assert(off_reader.is_valid()); - std::cout << "+ " << msg << std::endl; + std::clog << "+ " << msg << std::endl; results_csv << "\"nb_points\";" << "\"nb_simplices\";" @@ -82,7 +82,7 @@ void benchmark_proximity_graph(const std::string& msg, const std::string& off_fi Gudhi::Euclidean_distance()); // benchmark end pg_compute_proximity_graph.end(); - std::cout << pg_compute_proximity_graph; + std::clog << pg_compute_proximity_graph; Gudhi::Simplex_tree<> complex; Gudhi::Clock st_create_clock(" benchmark_proximity_graph - complex creation"); @@ -91,13 +91,13 @@ void benchmark_proximity_graph(const std::string& msg, const std::string& off_fi complex.insert_graph(proximity_graph); // benchmark end st_create_clock.end(); - std::cout << st_create_clock; + std::clog << st_create_clock; results_csv << off_reader.get_point_cloud().size() << ";" << complex.num_simplices() << ";" << pg_compute_proximity_graph.num_seconds() << ";" << st_create_clock.num_seconds() << ";" << std::endl; - std::cout << " benchmark_proximity_graph - nb simplices = " << complex.num_simplices() << std::endl; + std::clog << " benchmark_proximity_graph - nb simplices = " << complex.num_simplices() << std::endl; } int main(int argc, char * const argv[]) { 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 6ea10b88..a33d98cd 100644 --- a/src/common/doc/main_page.md +++ b/src/common/doc/main_page.md @@ -312,7 +312,7 @@ theory is essentially composed of three elements: topological spaces, their homology groups and an evolution scheme. Computation of persistent cohomology using the algorithm of \cite DBLP:journals/dcg/SilvaMV11 and - \cite DBLP:journals/corr/abs-1208-5018 and the Compressed Annotation Matrix implementation of + \cite DBLP:conf/compgeom/DeyFW14 and the Compressed Annotation Matrix implementation of \cite DBLP:conf/esa/BoissonnatDM13 . </td> <td width="15%"> diff --git a/src/common/example/CMakeLists.txt b/src/common/example/CMakeLists.txt index 583a0027..fa8eb98c 100644 --- a/src/common/example/CMakeLists.txt +++ b/src/common/example/CMakeLists.txt @@ -12,6 +12,7 @@ if (DIFF_PATH) add_test(Common_example_vector_double_off_reader_diff_files ${DIFF_PATH} ${CMAKE_CURRENT_BINARY_DIR}/vectordoubleoffreader_result.txt ${CMAKE_CURRENT_BINARY_DIR}/alphacomplexdoc.off.txt) + set_tests_properties(Common_example_vector_double_off_reader_diff_files PROPERTIES DEPENDS Common_example_vector_double_off_reader) endif() if(NOT CGAL_VERSION VERSION_LESS 4.11.0) diff --git a/src/common/example/example_CGAL_3D_points_off_reader.cpp b/src/common/example/example_CGAL_3D_points_off_reader.cpp index 4658d8d5..7f4343f0 100644 --- a/src/common/example/example_CGAL_3D_points_off_reader.cpp +++ b/src/common/example/example_CGAL_3D_points_off_reader.cpp @@ -35,7 +35,7 @@ int main(int argc, char **argv) { int n {}; for (auto point : point_cloud) { ++n; - std::cout << "Point[" << n << "] = (" << point[0] << ", " << point[1] << ", " << point[2] << ")\n"; + std::clog << "Point[" << n << "] = (" << point[0] << ", " << point[1] << ", " << point[2] << ")\n"; } return 0; } diff --git a/src/common/example/example_CGAL_points_off_reader.cpp b/src/common/example/example_CGAL_points_off_reader.cpp index f45683a5..b2bcdbcf 100644 --- a/src/common/example/example_CGAL_points_off_reader.cpp +++ b/src/common/example/example_CGAL_points_off_reader.cpp @@ -36,10 +36,10 @@ int main(int argc, char **argv) { int n {}; for (auto point : point_cloud) { - std::cout << "Point[" << n << "] = "; + std::clog << "Point[" << n << "] = "; for (std::size_t i {0}; i < point.size(); i++) - std::cout << point[i] << " "; - std::cout << "\n"; + std::clog << point[i] << " "; + std::clog << "\n"; ++n; } return 0; diff --git a/src/common/include/gudhi/Clock.h b/src/common/include/gudhi/Clock.h index 00ab2f27..6966aaaa 100644 --- a/src/common/include/gudhi/Clock.h +++ b/src/common/include/gudhi/Clock.h @@ -41,9 +41,9 @@ class Clock { return msg; } - // Print current value to std::cout + // Print current value to std::clog void print() const { - std::cout << *this << std::endl; + std::clog << *this << std::endl; } friend std::ostream& operator<<(std::ostream& stream, const Clock& clock) { diff --git a/src/common/include/gudhi/Debug_utils.h b/src/common/include/gudhi/Debug_utils.h index 38abc06d..f8375b00 100644 --- a/src/common/include/gudhi/Debug_utils.h +++ b/src/common/include/gudhi/Debug_utils.h @@ -27,14 +27,14 @@ #define GUDHI_CHECK_code(CODE) #endif -#define PRINT(a) std::cerr << #a << ": " << (a) << " (DISP)" << std::endl +#define PRINT(a) std::clog << #a << ": " << (a) << " (DISP)" << std::endl // #define DBG_VERBOSE #ifdef DBG_VERBOSE - #define DBG(a) std::cout << "DBG: " << (a) << std::endl - #define DBGMSG(a, b) std::cout << "DBG: " << a << b << std::endl - #define DBGVALUE(a) std::cout << "DBG: " << #a << ": " << a << std::endl - #define DBGCONT(a) std::cout << "DBG: container " << #a << " -> "; for (auto x : a) std::cout << x << ","; std::cout << std::endl + #define DBG(a) std::clog << "DBG: " << (a) << std::endl + #define DBGMSG(a, b) std::clog << "DBG: " << a << b << std::endl + #define DBGVALUE(a) std::clog << "DBG: " << #a << ": " << a << std::endl + #define DBGCONT(a) std::clog << "DBG: container " << #a << " -> "; for (auto x : a) std::clog << x << ","; std::clog << std::endl #else #define DBG(a) (void) 0 #define DBGMSG(a, b) (void) 0 diff --git a/src/common/include/gudhi/Points_3D_off_io.h b/src/common/include/gudhi/Points_3D_off_io.h index 2d110af3..39b79c96 100644 --- a/src/common/include/gudhi/Points_3D_off_io.h +++ b/src/common/include/gudhi/Points_3D_off_io.h @@ -41,7 +41,7 @@ class Points_3D_off_visitor_reader { */ void init(int dim, int num_vertices, int num_faces, int num_edges) { #ifdef DEBUG_TRACES - std::cout << "Points_3D_off_visitor_reader::init - dim=" << dim << " - num_vertices=" << + std::clog << "Points_3D_off_visitor_reader::init - dim=" << dim << " - num_vertices=" << num_vertices << " - num_faces=" << num_faces << " - num_edges=" << num_edges << std::endl; #endif // DEBUG_TRACES if (dim == 3) { @@ -74,11 +74,11 @@ class Points_3D_off_visitor_reader { void point(const std::vector<double>& point) { if (valid_) { #ifdef DEBUG_TRACES - std::cout << "Points_3D_off_visitor_reader::point "; + std::clog << "Points_3D_off_visitor_reader::point "; for (auto coordinate : point) { - std::cout << coordinate << " | "; + std::clog << coordinate << " | "; } - std::cout << std::endl; + std::clog << std::endl; #endif // DEBUG_TRACES // Fill the point cloud point_cloud_.push_back(Point_3(point[0], point[1], point[2])); diff --git a/src/common/include/gudhi/Points_off_io.h b/src/common/include/gudhi/Points_off_io.h index 99371d56..9dc40568 100644 --- a/src/common/include/gudhi/Points_off_io.h +++ b/src/common/include/gudhi/Points_off_io.h @@ -40,7 +40,7 @@ class Points_off_visitor_reader { */ void init(int dim, int num_vertices, int num_faces, int num_edges) { #ifdef DEBUG_TRACES - std::cout << "Points_off_visitor_reader::init - dim=" << dim << " - num_vertices=" << + std::clog << "Points_off_visitor_reader::init - dim=" << dim << " - num_vertices=" << num_vertices << " - num_faces=" << num_faces << " - num_edges=" << num_edges << std::endl; #endif // DEBUG_TRACES if (num_faces > 0) { @@ -66,11 +66,11 @@ class Points_off_visitor_reader { */ void point(const std::vector<double>& point) { #ifdef DEBUG_TRACES - std::cout << "Points_off_visitor_reader::point "; + std::clog << "Points_off_visitor_reader::point "; for (auto coordinate : point) { - std::cout << coordinate << " | "; + std::clog << coordinate << " | "; } - std::cout << std::endl; + std::clog << std::endl; #endif // DEBUG_TRACES // Fill the point cloud point_cloud.push_back(Point_d(point.begin(), point.end())); diff --git a/src/common/include/gudhi/Unitary_tests_utils.h b/src/common/include/gudhi/Unitary_tests_utils.h index 9b86460a..9f995d01 100644 --- a/src/common/include/gudhi/Unitary_tests_utils.h +++ b/src/common/include/gudhi/Unitary_tests_utils.h @@ -20,7 +20,7 @@ template<typename FloatingType > void GUDHI_TEST_FLOAT_EQUALITY_CHECK(FloatingType a, FloatingType b, FloatingType epsilon = std::numeric_limits<FloatingType>::epsilon()) { #ifdef DEBUG_TRACES - std::cout << "GUDHI_TEST_FLOAT_EQUALITY_CHECK - " << a << " versus " << b + std::clog << "GUDHI_TEST_FLOAT_EQUALITY_CHECK - " << a << " versus " << b << " | diff = " << std::fabs(a - b) << " - epsilon = " << epsilon << std::endl; #endif BOOST_CHECK(std::fabs(a - b) <= epsilon); @@ -32,7 +32,7 @@ template<typename FloatingType > FloatingType GUDHI_PROTECT_FLOAT(FloatingType value) { volatile FloatingType protected_value = value; #ifdef DEBUG_TRACES - std::cout << "GUDHI_PROTECT_FLOAT - " << protected_value << std::endl; + std::clog << "GUDHI_PROTECT_FLOAT - " << protected_value << std::endl; #endif return protected_value; } diff --git a/src/common/include/gudhi/distance_functions.h b/src/common/include/gudhi/distance_functions.h index 94cf9ccc..9bbc62b7 100644 --- a/src/common/include/gudhi/distance_functions.h +++ b/src/common/include/gudhi/distance_functions.h @@ -97,7 +97,7 @@ class Minimal_enclosing_ball_radius { Min_sphere ms(boost::size(*point_cloud.begin()), point_cloud.begin(), point_cloud.end()); #ifdef DEBUG_TRACES - std::cout << "Minimal_enclosing_ball_radius = " << std::sqrt(ms.squared_radius()) << " | nb points = " + std::clog << "Minimal_enclosing_ball_radius = " << std::sqrt(ms.squared_radius()) << " | nb points = " << boost::size(point_cloud) << " | dimension = " << boost::size(*point_cloud.begin()) << std::endl; #endif // DEBUG_TRACES diff --git a/src/common/include/gudhi/reader_utils.h b/src/common/include/gudhi/reader_utils.h index db31bf5c..0938f5c1 100644 --- a/src/common/include/gudhi/reader_utils.h +++ b/src/common/include/gudhi/reader_utils.h @@ -220,7 +220,7 @@ template <typename Filtration_value> std::vector<std::vector<Filtration_value>> read_lower_triangular_matrix_from_csv_file(const std::string& filename, const char separator = ';') { #ifdef DEBUG_TRACES - std::cout << "Using procedure read_lower_triangular_matrix_from_csv_file \n"; + std::clog << "Using procedure read_lower_triangular_matrix_from_csv_file \n"; #endif // DEBUG_TRACES std::vector<std::vector<Filtration_value>> result; std::ifstream in; @@ -272,12 +272,12 @@ std::vector<std::vector<Filtration_value>> read_lower_triangular_matrix_from_csv in.close(); #ifdef DEBUG_TRACES - std::cerr << "Here is the matrix we read : \n"; + std::clog << "Here is the matrix we read : \n"; for (size_t i = 0; i != result.size(); ++i) { for (size_t j = 0; j != result[i].size(); ++j) { - std::cerr << result[i][j] << " "; + std::clog << result[i][j] << " "; } - std::cerr << std::endl; + std::clog << std::endl; } #endif // DEBUG_TRACES @@ -294,7 +294,7 @@ Note: the function does not check that birth <= death. template <typename OutputIterator> void read_persistence_intervals_and_dimension(std::string const& filename, OutputIterator out) { #ifdef DEBUG_TRACES - std::cout << "read_persistence_intervals_and_dimension - " << filename << std::endl; + std::clog << "read_persistence_intervals_and_dimension - " << filename << std::endl; #endif // DEBUG_TRACES std::ifstream in(filename); if (!in.is_open()) { @@ -311,11 +311,11 @@ void read_persistence_intervals_and_dimension(std::string const& filename, Outpu double numbers[4]; int n = sscanf(line.c_str(), "%lf %lf %lf %lf", &numbers[0], &numbers[1], &numbers[2], &numbers[3]); #ifdef DEBUG_TRACES - std::cout << "[" << n << "] = "; + std::clog << "[" << n << "] = "; for (int i = 0; i < n; i++) { - std::cout << numbers[i] << ","; + std::clog << numbers[i] << ","; } - std::cout << std::endl; + std::clog << std::endl; #endif // DEBUG_TRACES if (n >= 2) { int dim = (n >= 3 ? static_cast<int>(numbers[n - 3]) : -1); diff --git a/src/common/test/test_distance_matrix_reader.cpp b/src/common/test/test_distance_matrix_reader.cpp index bb619a29..73be8104 100644 --- a/src/common/test/test_distance_matrix_reader.cpp +++ b/src/common/test/test_distance_matrix_reader.cpp @@ -28,15 +28,15 @@ BOOST_AUTO_TEST_CASE( lower_triangular_distance_matrix ) ','); for (auto& i : from_lower_triangular) { for (auto j : i) { - std::cout << j << " "; + std::clog << j << " "; } - std::cout << std::endl; + std::clog << std::endl; } - std::cout << "from_lower_triangular size = " << from_lower_triangular.size() << std::endl; + std::clog << "from_lower_triangular size = " << from_lower_triangular.size() << std::endl; BOOST_CHECK(from_lower_triangular.size() == 5); for (std::size_t i = 0; i < from_lower_triangular.size(); i++) { - std::cout << "from_lower_triangular[" << i << "] size = " << from_lower_triangular[i].size() << std::endl; + std::clog << "from_lower_triangular[" << i << "] size = " << from_lower_triangular[i].size() << std::endl; BOOST_CHECK(from_lower_triangular[i].size() == i); } std::vector<double> expected = {1}; @@ -60,14 +60,14 @@ BOOST_AUTO_TEST_CASE( full_square_distance_matrix ) from_full_square = Gudhi::read_lower_triangular_matrix_from_csv_file<double>("full_square_distance_matrix.csv"); for (auto& i : from_full_square) { for (auto j : i) { - std::cout << j << " "; + std::clog << j << " "; } - std::cout << std::endl; + std::clog << std::endl; } - std::cout << "from_full_square size = " << from_full_square.size() << std::endl; + std::clog << "from_full_square size = " << from_full_square.size() << std::endl; BOOST_CHECK(from_full_square.size() == 7); for (std::size_t i = 0; i < from_full_square.size(); i++) { - std::cout << "from_full_square[" << i << "] size = " << from_full_square[i].size() << std::endl; + std::clog << "from_full_square[" << i << "] size = " << from_full_square[i].size() << std::endl; BOOST_CHECK(from_full_square[i].size() == i); } } diff --git a/src/common/test/test_persistence_intervals_reader.cpp b/src/common/test/test_persistence_intervals_reader.cpp index 8fb4377d..ac8d0981 100644 --- a/src/common/test/test_persistence_intervals_reader.cpp +++ b/src/common/test/test_persistence_intervals_reader.cpp @@ -35,18 +35,18 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_without_dimension ) Persistence_intervals_by_dimension persistence_intervals_by_dimension = Gudhi::read_persistence_intervals_grouped_by_dimension("persistence_intervals_without_dimension.pers"); - std::cout << "\nread_persistence_intervals_grouped_by_dimension - expected\n"; + std::clog << "\nread_persistence_intervals_grouped_by_dimension - expected\n"; for (auto map_iter : expected_intervals_by_dimension) { - std::cout << "key=" << map_iter.first; + std::clog << "key=" << map_iter.first; for (auto vec_iter : map_iter.second) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; } - std::cout << "\nread_persistence_intervals_grouped_by_dimension - read\n"; + std::clog << "\nread_persistence_intervals_grouped_by_dimension - read\n"; for (auto map_iter : persistence_intervals_by_dimension) { - std::cout << "key=" << map_iter.first; + std::clog << "key=" << map_iter.first; for (auto vec_iter : map_iter.second) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; } BOOST_CHECK(persistence_intervals_by_dimension == expected_intervals_by_dimension); @@ -60,13 +60,13 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_without_dimension ) Persistence_intervals persistence_intervals_in_dimension = Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_without_dimension.pers"); - std::cout << "\nread_persistence_intervals_in_dimension - expected\n"; + std::clog << "\nread_persistence_intervals_in_dimension - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; - std::cout << "\nread_persistence_intervals_in_dimension - read\n"; + std::clog << "\nread_persistence_intervals_in_dimension - read\n"; for (auto vec_iter : expected_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); @@ -103,18 +103,18 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_dimension ) Persistence_intervals_by_dimension persistence_intervals_by_dimension = Gudhi::read_persistence_intervals_grouped_by_dimension("persistence_intervals_with_dimension.pers"); - std::cout << "\nread_persistence_intervals_grouped_by_dimension - expected\n"; + std::clog << "\nread_persistence_intervals_grouped_by_dimension - expected\n"; for (auto map_iter : expected_intervals_by_dimension) { - std::cout << "key=" << map_iter.first; + std::clog << "key=" << map_iter.first; for (auto vec_iter : map_iter.second) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; } - std::cout << "\nread_persistence_intervals_grouped_by_dimension - read\n"; + std::clog << "\nread_persistence_intervals_grouped_by_dimension - read\n"; for (auto map_iter : persistence_intervals_by_dimension) { - std::cout << "key=" << map_iter.first; + std::clog << "key=" << map_iter.first; for (auto vec_iter : map_iter.second) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; } BOOST_CHECK(persistence_intervals_by_dimension == expected_intervals_by_dimension); @@ -128,13 +128,13 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_dimension ) Persistence_intervals persistence_intervals_in_dimension = Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers"); - std::cout << "\nread_persistence_intervals_in_dimension - expected\n"; + std::clog << "\nread_persistence_intervals_in_dimension - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; - std::cout << "\nread_persistence_intervals_in_dimension - read\n"; + std::clog << "\nread_persistence_intervals_in_dimension - read\n"; for (auto vec_iter : persistence_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); @@ -143,13 +143,13 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_dimension ) persistence_intervals_in_dimension = Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers", 0); - std::cout << "\nread_persistence_intervals_in_dimension 0 - expected\n"; + std::clog << "\nread_persistence_intervals_in_dimension 0 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; - std::cout << "\nread_persistence_intervals_in_dimension 0 - read\n"; + std::clog << "\nread_persistence_intervals_in_dimension 0 - read\n"; for (auto vec_iter : persistence_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); @@ -159,13 +159,13 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_dimension ) persistence_intervals_in_dimension = Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers", 1); - std::cout << "\nread_persistence_intervals_in_dimension 1 - expected\n"; + std::clog << "\nread_persistence_intervals_in_dimension 1 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; - std::cout << "\nread_persistence_intervals_in_dimension 1 - read\n"; + std::clog << "\nread_persistence_intervals_in_dimension 1 - read\n"; for (auto vec_iter : persistence_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); @@ -173,13 +173,13 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_dimension ) persistence_intervals_in_dimension = Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers", 2); - std::cout << "\nread_persistence_intervals_in_dimension 2 - expected\n"; + std::clog << "\nread_persistence_intervals_in_dimension 2 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; - std::cout << "\nread_persistence_intervals_in_dimension 2 - read\n"; + std::clog << "\nread_persistence_intervals_in_dimension 2 - read\n"; for (auto vec_iter : persistence_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); @@ -188,13 +188,13 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_dimension ) persistence_intervals_in_dimension = Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_dimension.pers", 3); - std::cout << "\nread_persistence_intervals_in_dimension 3 - expected\n"; + std::clog << "\nread_persistence_intervals_in_dimension 3 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; - std::cout << "\nread_persistence_intervals_in_dimension 3 - read\n"; + std::clog << "\nread_persistence_intervals_in_dimension 3 - read\n"; for (auto vec_iter : persistence_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); @@ -212,18 +212,18 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_field ) Persistence_intervals_by_dimension persistence_intervals_by_dimension = Gudhi::read_persistence_intervals_grouped_by_dimension("persistence_intervals_with_field.pers"); - std::cout << "\nread_persistence_intervals_grouped_by_dimension - expected\n"; + std::clog << "\nread_persistence_intervals_grouped_by_dimension - expected\n"; for (auto map_iter : expected_intervals_by_dimension) { - std::cout << "key=" << map_iter.first; + std::clog << "key=" << map_iter.first; for (auto vec_iter : map_iter.second) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; } - std::cout << "\nread_persistence_intervals_grouped_by_dimension - read\n"; + std::clog << "\nread_persistence_intervals_grouped_by_dimension - read\n"; for (auto map_iter : persistence_intervals_by_dimension) { - std::cout << "key=" << map_iter.first; + std::clog << "key=" << map_iter.first; for (auto vec_iter : map_iter.second) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; } BOOST_CHECK(persistence_intervals_by_dimension == expected_intervals_by_dimension); @@ -237,13 +237,13 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_field ) Persistence_intervals persistence_intervals_in_dimension = Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers"); - std::cout << "\nread_persistence_intervals_in_dimension - expected\n"; + std::clog << "\nread_persistence_intervals_in_dimension - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; - std::cout << "\nread_persistence_intervals_in_dimension - read\n"; + std::clog << "\nread_persistence_intervals_in_dimension - read\n"; for (auto vec_iter : persistence_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); @@ -252,13 +252,13 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_field ) persistence_intervals_in_dimension = Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers", 0); - std::cout << "\nread_persistence_intervals_in_dimension 0 - expected\n"; + std::clog << "\nread_persistence_intervals_in_dimension 0 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; - std::cout << "\nread_persistence_intervals_in_dimension 0 - read\n"; + std::clog << "\nread_persistence_intervals_in_dimension 0 - read\n"; for (auto vec_iter : persistence_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); @@ -268,13 +268,13 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_field ) persistence_intervals_in_dimension = Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers", 1); - std::cout << "\nread_persistence_intervals_in_dimension 1 - expected\n"; + std::clog << "\nread_persistence_intervals_in_dimension 1 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; - std::cout << "\nread_persistence_intervals_in_dimension 1 - read\n"; + std::clog << "\nread_persistence_intervals_in_dimension 1 - read\n"; for (auto vec_iter : persistence_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); @@ -282,13 +282,13 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_field ) persistence_intervals_in_dimension = Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers", 2); - std::cout << "\nread_persistence_intervals_in_dimension 2 - expected\n"; + std::clog << "\nread_persistence_intervals_in_dimension 2 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; - std::cout << "\nread_persistence_intervals_in_dimension 2 - read\n"; + std::clog << "\nread_persistence_intervals_in_dimension 2 - read\n"; for (auto vec_iter : persistence_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); @@ -297,13 +297,13 @@ BOOST_AUTO_TEST_CASE( persistence_intervals_with_field ) persistence_intervals_in_dimension = Gudhi::read_persistence_intervals_in_dimension("persistence_intervals_with_field.pers", 3); - std::cout << "\nread_persistence_intervals_in_dimension 3 - expected\n"; + std::clog << "\nread_persistence_intervals_in_dimension 3 - expected\n"; for (auto vec_iter : expected_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; - std::cout << "\nread_persistence_intervals_in_dimension 3 - read\n"; + std::clog << "\nread_persistence_intervals_in_dimension 3 - read\n"; for (auto vec_iter : persistence_intervals_in_dimension) - std::cout << " [" << vec_iter.first << " ," << vec_iter.second << "] "; + std::clog << " [" << vec_iter.first << " ," << vec_iter.second << "] "; BOOST_CHECK(persistence_intervals_in_dimension == expected_intervals_in_dimension); diff --git a/src/python/CMakeLists.txt b/src/python/CMakeLists.txt index b558d4c4..ab08cd6d 100644 --- a/src/python/CMakeLists.txt +++ b/src/python/CMakeLists.txt @@ -32,6 +32,11 @@ function( add_gudhi_debug_info DEBUG_INFO ) endfunction( add_gudhi_debug_info ) if(PYTHONINTERP_FOUND) + if(PYBIND11_FOUND) + add_gudhi_debug_info("Pybind11 version ${PYBIND11_VERSION}") + set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'bottleneck', ") + set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'hera', ") + endif() if(CYTHON_FOUND) set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'off_reader', ") set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'simplex_tree', ") @@ -42,7 +47,6 @@ if(PYTHONINTERP_FOUND) set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'reader_utils', ") set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'witness_complex', ") set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'strong_witness_complex', ") - set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'bottleneck', ") set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'nerve_gic', ") set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'subsampling', ") set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'tangential_complex', ") @@ -52,6 +56,8 @@ if(PYTHONINTERP_FOUND) # Modules that should not be auto-imported in __init__.py set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'representations', ") set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'wasserstein', ") + set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'point_cloud', ") + set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'weighted_rips_complex', ") add_gudhi_debug_info("Python version ${PYTHON_VERSION_STRING}") add_gudhi_debug_info("Cython version ${CYTHON_VERSION}") @@ -73,6 +79,19 @@ if(PYTHONINTERP_FOUND) if(OT_FOUND) add_gudhi_debug_info("POT version ${OT_VERSION}") endif() + if(HNSWLIB_FOUND) + # Does not have a version number... + add_gudhi_debug_info("HNSWlib found") + endif() + if(TORCH_FOUND) + add_gudhi_debug_info("PyTorch version ${TORCH_VERSION}") + endif() + if(PYKEOPS_FOUND) + add_gudhi_debug_info("PyKeOps version ${PYKEOPS_VERSION}") + endif() + if(EAGERPY_FOUND) + add_gudhi_debug_info("EagerPy version ${EAGERPY_VERSION}") + endif() set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-DBOOST_RESULT_OF_USE_DECLTYPE', ") set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-DBOOST_ALL_NO_LIB', ") @@ -86,6 +105,7 @@ if(PYTHONINTERP_FOUND) endif(MSVC) if(CMAKE_COMPILER_IS_GNUCXX) set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-frounding-math', ") + set(GUDHI_PYBIND11_EXTRA_COMPILE_ARGS "${GUDHI_PYBIND11_EXTRA_COMPILE_ARGS}'-fvisibility=hidden', ") endif(CMAKE_COMPILER_IS_GNUCXX) if (CMAKE_CXX_COMPILER_ID MATCHES Intel) set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-fp-model strict', ") @@ -101,37 +121,28 @@ if(PYTHONINTERP_FOUND) set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-DCGAL_EIGEN3_ENABLED', ") endif (EIGEN3_FOUND) - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'off_reader', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'simplex_tree', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'rips_complex', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'cubical_complex', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'periodic_cubical_complex', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'reader_utils', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'witness_complex', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'strong_witness_complex', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'off_reader', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'simplex_tree', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'rips_complex', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'cubical_complex', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'periodic_cubical_complex', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'reader_utils', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'witness_complex', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'strong_witness_complex', ") + set(GUDHI_PYBIND11_MODULES "${GUDHI_PYBIND11_MODULES}'hera', ") if (NOT CGAL_VERSION VERSION_LESS 4.11.0) - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'bottleneck', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'nerve_gic', ") + set(GUDHI_PYBIND11_MODULES "${GUDHI_PYBIND11_MODULES}'bottleneck', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'nerve_gic', ") endif () if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'alpha_complex', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'subsampling', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'tangential_complex', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'euclidean_witness_complex', ") - set(GUDHI_PYTHON_MODULES_TO_COMPILE "${GUDHI_PYTHON_MODULES_TO_COMPILE}'euclidean_strong_witness_complex', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'alpha_complex', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'subsampling', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'tangential_complex', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'euclidean_witness_complex', ") + set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'euclidean_strong_witness_complex', ") endif () if(CGAL_FOUND) - can_cgal_use_cxx11_thread_local() - if (NOT CGAL_CAN_USE_CXX11_THREAD_LOCAL_RESULT) - if(CMAKE_BUILD_TYPE MATCHES Debug) - add_GUDHI_PYTHON_lib("${Boost_THREAD_LIBRARY_DEBUG}") - else() - add_GUDHI_PYTHON_lib("${Boost_THREAD_LIBRARY_RELEASE}") - endif() - message("** Add Boost ${Boost_LIBRARY_DIRS}") - set(GUDHI_PYTHON_LIBRARY_DIRS "${GUDHI_PYTHON_LIBRARY_DIRS}'${Boost_LIBRARY_DIRS}', ") - endif() # Add CGAL compilation args if(CGAL_HEADER_ONLY) add_gudhi_debug_info("CGAL header only version ${CGAL_VERSION}") @@ -176,7 +187,7 @@ if(PYTHONINTERP_FOUND) set(GUDHI_PYTHON_LIBRARY_DIRS "${GUDHI_PYTHON_LIBRARY_DIRS}'${MPFR_LIBRARIES_DIR}', ") message("** Add mpfr ${MPFR_LIBRARIES}") endif(MPFR_FOUND) -endif(CGAL_FOUND) + endif(CGAL_FOUND) # Specific for Mac if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin") @@ -220,7 +231,9 @@ endif(CGAL_FOUND) # Other .py files file(COPY "gudhi/persistence_graphical_tools.py" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi") file(COPY "gudhi/representations" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi/") - file(COPY "gudhi/wasserstein.py" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi") + file(COPY "gudhi/wasserstein" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi") + file(COPY "gudhi/point_cloud" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi") + file(COPY "gudhi/weighted_rips_complex.py" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi") add_custom_command( OUTPUT gudhi.so @@ -232,6 +245,71 @@ endif(CGAL_FOUND) install(CODE "execute_process(COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/setup.py install)") + # Documentation generation is available through sphinx - requires all modules + # Make it first as sphinx test is by far the longest test which is nice when testing in parallel + if(SPHINX_PATH) + if(MATPLOTLIB_FOUND) + if(NUMPY_FOUND) + if(SCIPY_FOUND) + if(SKLEARN_FOUND) + if(OT_FOUND) + if(PYBIND11_FOUND) + if(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) + set (GUDHI_SPHINX_MESSAGE "Generating API documentation with Sphinx in ${CMAKE_CURRENT_BINARY_DIR}/sphinx/") + # User warning - Sphinx is a static pages generator, and configured to work fine with user_version + # Images and biblio warnings because not found on developper version + if (GUDHI_PYTHON_PATH STREQUAL "src/python") + set (GUDHI_SPHINX_MESSAGE "${GUDHI_SPHINX_MESSAGE} \n WARNING : Sphinx is configured for user version, you run it on developper version. Images and biblio will miss") + endif() + # sphinx target requires gudhi.so, because conf.py reads gudhi version from it + add_custom_target(sphinx + WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/doc + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${SPHINX_PATH} -b html ${CMAKE_CURRENT_SOURCE_DIR}/doc ${CMAKE_CURRENT_BINARY_DIR}/sphinx + DEPENDS "${CMAKE_CURRENT_BINARY_DIR}/gudhi.so" + COMMENT "${GUDHI_SPHINX_MESSAGE}" VERBATIM) + + add_test(NAME sphinx_py_test + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" + ${SPHINX_PATH} -b doctest ${CMAKE_CURRENT_SOURCE_DIR}/doc ${CMAKE_CURRENT_BINARY_DIR}/doctest) + + # Set missing or not modules + set(GUDHI_MODULES ${GUDHI_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MODULES") + else(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) + message("++ Python documentation module will not be compiled because it requires a Eigen3 and CGAL version >= 4.11.0") + set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") + endif(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) + else(PYBIND11_FOUND) + message("++ Python documentation module will not be compiled because pybind11 was not found") + set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") + endif(PYBIND11_FOUND) + else(OT_FOUND) + message("++ Python documentation module will not be compiled because POT was not found") + set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") + endif(OT_FOUND) + else(SKLEARN_FOUND) + message("++ Python documentation module will not be compiled because scikit-learn was not found") + set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") + endif(SKLEARN_FOUND) + else(SCIPY_FOUND) + message("++ Python documentation module will not be compiled because scipy was not found") + set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") + endif(SCIPY_FOUND) + else(NUMPY_FOUND) + message("++ Python documentation module will not be compiled because numpy was not found") + set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") + endif(NUMPY_FOUND) + else(MATPLOTLIB_FOUND) + message("++ Python documentation module will not be compiled because matplotlib was not found") + set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") + endif(MATPLOTLIB_FOUND) + else(SPHINX_PATH) + message("++ Python documentation module will not be compiled because sphinx and sphinxcontrib-bibtex were not found") + set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") + endif(SPHINX_PATH) + + # Test examples if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) # Bottleneck and Alpha @@ -377,6 +455,7 @@ endif(CGAL_FOUND) ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/simplex_tree_example.py) add_gudhi_py_test(test_simplex_tree) + add_gudhi_py_test(test_simplex_generators) # Witness add_test(NAME witness_complex_from_nearest_landmark_table_py_test @@ -390,73 +469,31 @@ endif(CGAL_FOUND) add_gudhi_py_test(test_reader_utils) # Wasserstein - if(OT_FOUND) - add_gudhi_py_test(test_wasserstein_distance) - endif(OT_FOUND) + if(OT_FOUND AND PYBIND11_FOUND) + if(TORCH_FOUND AND EAGERPY_FOUND) + add_gudhi_py_test(test_wasserstein_distance) + endif() + add_gudhi_py_test(test_wasserstein_barycenter) + endif() # Representations if(SKLEARN_FOUND AND MATPLOTLIB_FOUND) add_gudhi_py_test(test_representations) endif() - # Documentation generation is available through sphinx - requires all modules - if(SPHINX_PATH) - if(MATPLOTLIB_FOUND) - if(NUMPY_FOUND) - if(SCIPY_FOUND) - if(SKLEARN_FOUND) - if(OT_FOUND) - if(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) - set (GUDHI_SPHINX_MESSAGE "Generating API documentation with Sphinx in ${CMAKE_CURRENT_BINARY_DIR}/sphinx/") - # User warning - Sphinx is a static pages generator, and configured to work fine with user_version - # Images and biblio warnings because not found on developper version - if (GUDHI_PYTHON_PATH STREQUAL "src/python") - set (GUDHI_SPHINX_MESSAGE "${GUDHI_SPHINX_MESSAGE} \n WARNING : Sphinx is configured for user version, you run it on developper version. Images and biblio will miss") - endif() - # sphinx target requires gudhi.so, because conf.py reads gudhi version from it - add_custom_target(sphinx - WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/doc - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${SPHINX_PATH} -b html ${CMAKE_CURRENT_SOURCE_DIR}/doc ${CMAKE_CURRENT_BINARY_DIR}/sphinx - DEPENDS "${CMAKE_CURRENT_BINARY_DIR}/gudhi.so" - COMMENT "${GUDHI_SPHINX_MESSAGE}" VERBATIM) - - add_test(NAME sphinx_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${SPHINX_PATH} -b doctest ${CMAKE_CURRENT_SOURCE_DIR}/doc ${CMAKE_CURRENT_BINARY_DIR}/doctest) - - # Set missing or not modules - set(GUDHI_MODULES ${GUDHI_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MODULES") - else(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) - message("++ Python documentation module will not be compiled because it requires a Eigen3 and CGAL version >= 4.11.0") - set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") - endif(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) - else(OT_FOUND) - message("++ Python documentation module will not be compiled because POT was not found") - set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") - endif(OT_FOUND) - else(SKLEARN_FOUND) - message("++ Python documentation module will not be compiled because scikit-learn was not found") - set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") - endif(SKLEARN_FOUND) - else(SCIPY_FOUND) - message("++ Python documentation module will not be compiled because scipy was not found") - set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") - endif(SCIPY_FOUND) - else(NUMPY_FOUND) - message("++ Python documentation module will not be compiled because numpy was not found") - set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") - endif(NUMPY_FOUND) - else(MATPLOTLIB_FOUND) - message("++ Python documentation module will not be compiled because matplotlib was not found") - set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") - endif(MATPLOTLIB_FOUND) - else(SPHINX_PATH) - message("++ Python documentation module will not be compiled because sphinx and sphinxcontrib-bibtex were not found") - set(GUDHI_MISSING_MODULES ${GUDHI_MISSING_MODULES} "python-documentation" CACHE INTERNAL "GUDHI_MISSING_MODULES") - endif(SPHINX_PATH) + # Time Delay + add_gudhi_py_test(test_time_delay) + + # DTM + if(SCIPY_FOUND AND SKLEARN_FOUND AND TORCH_FOUND AND HNSWLIB_FOUND AND PYKEOPS_FOUND AND EAGERPY_FOUND) + add_gudhi_py_test(test_knn) + add_gudhi_py_test(test_dtm) + endif() + # Weighted Rips + if(SCIPY_FOUND) + add_gudhi_py_test(test_weighted_rips) + endif() # Set missing or not modules set(GUDHI_MODULES ${GUDHI_MODULES} "python" CACHE INTERNAL "GUDHI_MODULES") 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 b5af0d27..3aba0d71 100644 --- a/src/python/doc/alpha_complex_sum.inc +++ b/src/python/doc/alpha_complex_sum.inc @@ -1,17 +1,17 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 - +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ - | .. 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 | :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. | | - | | | | - | | For performances reasons, it is advised to use CGAL ≥ 5.0.0. | | - +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ - | * :doc:`alpha_complex_user` | * :doc:`alpha_complex_ref` | - +----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + +----------------------------------------------------------------+-------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+ + | .. 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 | | :Since: 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 | :License: 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. | | + | | | | + | | For performances reasons, it is advised to use CGAL :math:`\geq` 5.0.0. | | + +----------------------------------------------------------------+-------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+ + | * :doc:`alpha_complex_user` | * :doc:`alpha_complex_ref` | + +----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/alpha_complex_user.rst b/src/python/doc/alpha_complex_user.rst index 60319e84..d49f45b4 100644 --- a/src/python/doc/alpha_complex_user.rst +++ b/src/python/doc/alpha_complex_user.rst @@ -10,9 +10,9 @@ Definition .. include:: alpha_complex_sum.inc `AlphaComplex` is constructing a :doc:`SimplexTree <simplex_tree_ref>` using -`Delaunay Triangulation <http://doc.cgal.org/latest/Triangulation/index.html#Chapter_Triangulations>`_ -:cite:`cgal:hdj-t-19b` from `CGAL <http://www.cgal.org/>`_ (the Computational Geometry Algorithms Library -:cite:`cgal:eb-19b`). +`Delaunay Triangulation <http://doc.cgal.org/latest/Triangulation/index.html#Chapter_Triangulations>`_ +:cite:`cgal:hdj-t-19b` from the `Computational Geometry Algorithms Library <http://www.cgal.org/>`_ +:cite:`cgal:eb-19b`. Remarks ^^^^^^^ @@ -89,25 +89,28 @@ In order to build the alpha complex, first, a Simplex tree is built from the cel Filtration value computation algorithm ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **for** i : dimension :math:`\rightarrow` 0 **do** - **for all** :math:`\sigma` of dimension i - **if** filtration(:math:`\sigma`) is NaN **then** - filtration(:math:`\sigma`) = :math:`\alpha^2(\sigma)` - **end if** +.. code-block:: vim + + for i : dimension → 0 do + for all σ of dimension i + if filtration(σ) is NaN then + filtration(σ) = α²(σ) + end if + for all τ face of σ do // propagate alpha filtration value + if filtration(τ) is not NaN then + filtration(τ) = min( filtration(τ), filtration(σ) ) + else + if τ is not Gabriel for σ then + filtration(τ) = filtration(σ) + end if + end if + end for + end for + end for + + make_filtration_non_decreasing() + prune_above_filtration() - *//propagate alpha filtration value* - - **for all** :math:`\tau` face of :math:`\sigma` - **if** filtration(:math:`\tau`) is not NaN **then** - filtration(:math:`\tau`) = filtration(:math:`\sigma`) - **end if** - **end for** - **end for** - **end for** - - make_filtration_non_decreasing() - - prune_above_filtration() Dimension 2 ^^^^^^^^^^^ @@ -203,9 +206,3 @@ the program output is: [4, 5, 6] -> 22.74 [3, 6] -> 30.25 -CGAL citations -============== - -.. bibliography:: ../../biblio/how_to_cite_cgal.bib - :filter: docnames - :style: unsrt diff --git a/src/python/doc/bottleneck_distance_sum.inc b/src/python/doc/bottleneck_distance_sum.inc index 6eb0ac19..77dc368d 100644 --- a/src/python/doc/bottleneck_distance_sum.inc +++ b/src/python/doc/bottleneck_distance_sum.inc @@ -1,14 +1,14 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 - +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ - | .. figure:: | Bottleneck distance measures the similarity between two persistence | :Author: François Godi | - | ../../doc/Bottleneck_distance/perturb_pd.png | diagrams. It's the shortest distance b for which there exists a | | - | :figclass: align-center | perfect matching between the points of the two diagrams (+ all the | :Introduced in: GUDHI 2.0.0 | - | | diagonal points) such that any couple of matched points are at | | - | Bottleneck distance is the length of | distance at most b, where the distance between points is the sup | :Copyright: MIT (`GPL v3 </licensing/>`_) | - | the longest edge | norm in :math:`\mathbb{R}^2`. | | - | | | :Requires: `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 | - +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ - | * :doc:`bottleneck_distance_user` | | - +-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------+ + +-----------------------------------------------------------------+----------------------------------------------------------------------+-----------------------------------------------------------------+ + | .. figure:: | Bottleneck distance measures the similarity between two persistence | :Author: François Godi | + | ../../doc/Bottleneck_distance/perturb_pd.png | diagrams. It's the shortest distance b for which there exists a | | + | :figclass: align-center | perfect matching between the points of the two diagrams (+ all the | :Since: GUDHI 2.0.0 | + | | diagonal points) such that any couple of matched points are at | | + | Bottleneck distance is the length of | distance at most b, where the distance between points is the sup | :License: MIT (`GPL v3 </licensing/>`_) | + | the longest edge | norm in :math:`\mathbb{R}^2`. | | + | | | :Requires: `CGAL <installation.html#cgal>`_ :math:`\geq` 4.11.0 | + +-----------------------------------------------------------------+----------------------------------------------------------------------+-----------------------------------------------------------------+ + | * :doc:`bottleneck_distance_user` | | + +-----------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/bottleneck_distance_user.rst b/src/python/doc/bottleneck_distance_user.rst index 9435c7f1..89da89d3 100644 --- a/src/python/doc/bottleneck_distance_user.rst +++ b/src/python/doc/bottleneck_distance_user.rst @@ -65,3 +65,4 @@ The output is: Bottleneck distance approximation = 0.81 Bottleneck distance value = 0.75 + diff --git a/src/python/doc/cubical_complex_sum.inc b/src/python/doc/cubical_complex_sum.inc index f200e695..28bf8e94 100644 --- a/src/python/doc/cubical_complex_sum.inc +++ b/src/python/doc/cubical_complex_sum.inc @@ -1,12 +1,12 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 +--------------------------------------------------------------------------+----------------------------------------------------------------------+-----------------------------+ | .. figure:: | The cubical complex is an example of a structured complex useful in | :Author: Pawel Dlotko | | ../../doc/Bitmap_cubical_complex/Cubical_complex_representation.png | computational mathematics (specially rigorous numerics) and image | | - | :alt: Cubical complex representation | analysis. | :Introduced in: GUDHI 2.0.0 | + | :alt: Cubical complex representation | analysis. | :Since: GUDHI 2.0.0 | | :figclass: align-center | | | - | | | :Copyright: MIT | + | | | :License: MIT | | | | | +--------------------------------------------------------------------------+----------------------------------------------------------------------+-----------------------------+ | * :doc:`cubical_complex_user` | * :doc:`cubical_complex_ref` | diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst index 56cf0170..3fd4e27a 100644 --- a/src/python/doc/cubical_complex_user.rst +++ b/src/python/doc/cubical_complex_user.rst @@ -8,7 +8,7 @@ Definition ---------- ===================================== ===================================== ===================================== -:Author: Pawel Dlotko :Introduced in: GUDHI PYTHON 2.0.0 :Copyright: GPL v3 +:Author: Pawel Dlotko :Since: GUDHI PYTHON 2.0.0 :License: GPL v3 ===================================== ===================================== ===================================== +---------------------------------------------+----------------------------------------------------------------------+ @@ -91,7 +91,7 @@ Currently one input from a text file is used. It uses a format inspired from the we allow any filtration values. As a consequence one cannot use ``-1``'s to indicate missing cubes. If you have missing cubes in your complex, please set their filtration to :math:`+\infty` (aka. ``inf`` in the file). -The file format is described in details in :ref:`Perseus file format` file format section. +The file format is described in details in `Perseus file format <fileformats.html#perseus>`_ section. .. testcode:: @@ -120,7 +120,7 @@ conditions are imposed in all directions, then complex :math:`\mathcal{K}` becam various constructors from the file Bitmap_cubical_complex_periodic_boundary_conditions_base.h to construct cubical complex with periodic boundary conditions. -One can also use Perseus style input files (see :doc:`Perseus <fileformats>`) for the specific periodic case: +One can also use Perseus style input files (see `Perseus file format <fileformats.html#perseus>`_) for the specific periodic case: .. testcode:: @@ -158,10 +158,3 @@ Examples. --------- End user programs are available in python/example/ folder. - -Bibliography -============ - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docnames - :style: unsrt diff --git a/src/python/doc/fileformats.rst b/src/python/doc/fileformats.rst index 345dfdba..ae1b00f3 100644 --- a/src/python/doc/fileformats.rst +++ b/src/python/doc/fileformats.rst @@ -80,8 +80,6 @@ Here is a simple sample file in the 3D case:: 1. 1. 1. -.. _Perseus file format: - Perseus ******* diff --git a/src/python/doc/img/barycenter.png b/src/python/doc/img/barycenter.png Binary files differnew file mode 100644 index 00000000..cad6af70 --- /dev/null +++ b/src/python/doc/img/barycenter.png diff --git a/src/python/doc/index.rst b/src/python/doc/index.rst index 3387a64f..13e51047 100644 --- a/src/python/doc/index.rst +++ b/src/python/doc/index.rst @@ -71,6 +71,7 @@ Wasserstein distance .. include:: wasserstein_distance_sum.inc + Persistence representations =========================== @@ -85,10 +86,3 @@ Point cloud utilities ********************* .. include:: point_cloud_sum.inc - -Bibliography -************ - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docnames - :style: unsrt diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst index 40f3f44b..de09c5b3 100644 --- a/src/python/doc/installation.rst +++ b/src/python/doc/installation.rst @@ -12,15 +12,16 @@ The easiest way to install the Python version of GUDHI is using Compiling ********* -The library uses c++14 and requires `Boost <https://www.boost.org/>`_ ≥ 1.56.0, -`CMake <https://www.cmake.org/>`_ ≥ 3.1 to generate makefiles, -`NumPy <http://numpy.org>`_ and `Cython <https://www.cython.org/>`_ to compile +The library uses c++14 and requires `Boost <https://www.boost.org/>`_ :math:`\geq` 1.56.0, +`CMake <https://www.cmake.org/>`_ :math:`\geq` 3.1 to generate makefiles, +`NumPy <http://numpy.org>`_, `Cython <https://www.cython.org/>`_ and +`pybind11 <https://github.com/pybind/pybind11>`_ to compile the GUDHI Python module. It is a multi-platform library and compiles on Linux, Mac OSX and Visual -Studio 2015. +Studio 2017. On `Windows <https://wiki.python.org/moin/WindowsCompilers>`_ , only Python -≥ 3.5 are available because of the required Visual Studio version. +:math:`\geq` 3.5 are available because of the required Visual Studio version. On other systems, if you have several Python/python installed, the version 2.X will be used by default, but you can force it by adding @@ -29,7 +30,8 @@ will be used by default, but you can force it by adding GUDHI Python module compilation =============================== -To build the GUDHI Python module, run the following commands in a terminal: +After making sure that the `Compilation dependencies`_ are properly installed, +one can build the GUDHI Python module, by running the following commands in a terminal: .. code-block:: bash @@ -174,8 +176,8 @@ Documentation To build the documentation, `sphinx-doc <http://www.sphinx-doc.org>`_ and `sphinxcontrib-bibtex <https://sphinxcontrib-bibtex.readthedocs.io>`_ are required. As the documentation is auto-tested, `CGAL`_, `Eigen`_, -`Matplotlib`_, `NumPy`_ and `SciPy`_ are also mandatory to build the -documentation. +`Matplotlib`_, `NumPy`_, `POT`_, `Scikit-learn`_ and `SciPy`_ are +also mandatory to build the documentation. Run the following commands in a terminal: @@ -187,19 +189,25 @@ Run the following commands in a terminal: Optional third-party library **************************** +Compilation dependencies +======================== + +These third party dependencies are detected by `CMake <https://www.cmake.org/>`_. +They have to be installed before performing the `GUDHI Python module compilation`_. + CGAL -==== +---- Some GUDHI modules (cf. :doc:`modules list </index>`), and few examples -require CGAL, a C++ library that provides easy access to efficient and -reliable geometric algorithms. +require `CGAL <https://www.cgal.org/>`_, a C++ library that provides easy +access to efficient and reliable geometric algorithms. The procedure to install this library according to your operating system is detailed `here <http://doc.cgal.org/latest/Manual/installation.html>`_. -The following examples requires CGAL version ≥ 4.11.0: +The following examples require CGAL version :math:`\geq` 4.11.0: .. only:: builder_html @@ -211,14 +219,14 @@ The following examples requires CGAL version ≥ 4.11.0: * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` Eigen -===== +----- Some GUDHI modules (cf. :doc:`modules list </index>`), and few examples require `Eigen <http://eigen.tuxfamily.org/>`_, a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms. -The following examples require `Eigen <http://eigen.tuxfamily.org/>`_ version ≥ 3.1.0: +The following examples require `Eigen <http://eigen.tuxfamily.org/>`_ version :math:`\geq` 3.1.0: .. only:: builder_html @@ -228,8 +236,39 @@ The following examples require `Eigen <http://eigen.tuxfamily.org/>`_ version ≠* :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` +Threading Building Blocks +------------------------- + +`Intel® TBB <https://www.threadingbuildingblocks.org/>`_ lets you easily write +parallel C++ programs that take full advantage of multicore performance, that +are portable and composable, and that have future-proof scalability. + +Having Intel® TBB installed is recommended to parallelize and accelerate some +GUDHI computations. + +Run time dependencies +===================== + +These third party dependencies are detected by Python `import` mechanism at run time. +They can be installed when required. + +EagerPy +------- + +Some Python functions can handle automatic differentiation (possibly only when +a flag `enable_autodiff=True` is used). In order to reduce code duplication, we +use `EagerPy <https://eagerpy.jonasrauber.de/>`_ which wraps arrays from +PyTorch, TensorFlow and JAX in a common interface. + +Hnswlib +------- + +:class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package +`Hnswlib <https://github.com/nmslib/hnswlib>`_ as a backend if explicitly +requested, to speed-up queries. + Matplotlib -========== +---------- The :doc:`persistence graphical tools </persistence_graphical_tools_user>` module requires `Matplotlib <http://matplotlib.org>`_, a Python 2D plotting @@ -250,37 +289,47 @@ The following examples require the `Matplotlib <http://matplotlib.org>`_: * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` +PyKeOps +------- + +:class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package +`PyKeOps <https://www.kernel-operations.io/keops/python/>`_ as a backend if +explicitly requested, to speed-up queries using a GPU. + Python Optimal Transport -======================== +------------------------ The :doc:`Wasserstein distance </wasserstein_distance_user>` module requires `POT <https://pot.readthedocs.io/>`_, a library that provides several solvers for optimization problems related to Optimal Transport. +PyTorch +------- + +`PyTorch <https://pytorch.org/>`_ is currently only used as a dependency of +`PyKeOps`_, and in some tests. + Scikit-learn -============ +------------ The :doc:`persistence representations </representations>` module require `scikit-learn <https://scikit-learn.org/>`_, a Python-based ecosystem of open-source software for machine learning. +:class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package +`scikit-learn <https://scikit-learn.org/>`_ as a backend if explicitly +requested. + SciPy -===== +----- The :doc:`persistence graphical tools </persistence_graphical_tools_user>` and :doc:`Wasserstein distance </wasserstein_distance_user>` modules require `SciPy <http://scipy.org>`_, a Python-based ecosystem of open-source software for mathematics, science, and engineering. -Threading Building Blocks -========================= - -`Intel® TBB <https://www.threadingbuildingblocks.org/>`_ lets you easily write -parallel C++ programs that take full advantage of multicore performance, that -are portable and composable, and that have future-proof scalability. - -Having Intel® TBB installed is recommended to parallelize and accelerate some -GUDHI computations. +:class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package +`SciPy <http://scipy.org>`_ as a backend if explicitly requested. Bug reports and contributions ***************************** diff --git a/src/python/doc/nerve_gic_complex_sum.inc b/src/python/doc/nerve_gic_complex_sum.inc index d633c4ff..7db6c124 100644 --- a/src/python/doc/nerve_gic_complex_sum.inc +++ b/src/python/doc/nerve_gic_complex_sum.inc @@ -1,16 +1,16 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 - +----------------------------------------------------------------+------------------------------------------------------------------------+------------------------------------------------------------------+ - | .. figure:: | Nerves and Graph Induced Complexes are cover complexes, i.e. | :Author: Mathieu Carrière | - | ../../doc/Nerve_GIC/gicvisu.jpg | simplicial complexes that provably contain topological information | | - | :alt: Graph Induced Complex of a point cloud. | about the input data. They can be computed with a cover of the data, | :Introduced in: GUDHI 2.3.0 | - | :figclass: align-center | that comes i.e. from the preimage of a family of intervals covering | | - | | the image of a scalar-valued function defined on the data. | :Copyright: MIT (`GPL v3 </licensing/>`_) | - | | | | - | | | :Requires: `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 | - | | | | - | | | | - +----------------------------------------------------------------+------------------------------------------------------------------------+------------------------------------------------------------------+ - | * :doc:`nerve_gic_complex_user` | * :doc:`nerve_gic_complex_ref` | - +----------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------+ + +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------+ + | .. figure:: | Nerves and Graph Induced Complexes are cover complexes, i.e. | :Author: Mathieu Carrière | + | ../../doc/Nerve_GIC/gicvisu.jpg | simplicial complexes that provably contain topological information | | + | :alt: Graph Induced Complex of a point cloud. | about the input data. They can be computed with a cover of the data, | :Since: GUDHI 2.3.0 | + | :figclass: align-center | that comes i.e. from the preimage of a family of intervals covering | | + | | the image of a scalar-valued function defined on the data. | :License: MIT (`GPL v3 </licensing/>`_) | + | | | | + | | | :Requires: `CGAL <installation.html#cgal>`_ :math:`\geq` 4.11.0 | + | | | | + | | | | + +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------+ + | * :doc:`nerve_gic_complex_user` | * :doc:`nerve_gic_complex_ref` | + +----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/nerve_gic_complex_user.rst b/src/python/doc/nerve_gic_complex_user.rst index 9101f45d..0e67fc78 100644 --- a/src/python/doc/nerve_gic_complex_user.rst +++ b/src/python/doc/nerve_gic_complex_user.rst @@ -13,7 +13,7 @@ Visualizations of the simplicial complexes can be done with either neato (from `graphviz <http://www.graphviz.org/>`_), `geomview <http://www.geomview.org/>`_, `KeplerMapper <https://github.com/MLWave/kepler-mapper>`_. -Input point clouds are assumed to be OFF files (cf. :doc:`fileformats`). +Input point clouds are assumed to be OFF files (cf. `OFF file format <fileformats.html#off-file-format>`_). Covers ------ diff --git a/src/python/doc/persistence_graphical_tools_sum.inc b/src/python/doc/persistence_graphical_tools_sum.inc index 0cdf8072..7ff63ae2 100644 --- a/src/python/doc/persistence_graphical_tools_sum.inc +++ b/src/python/doc/persistence_graphical_tools_sum.inc @@ -1,14 +1,14 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 - +-----------------------------------------------------------------+-----------------------------------------------------------------------+-----------------------------------------------+ - | .. figure:: | These graphical tools comes on top of persistence results and allows | :Author: Vincent Rouvreau | - | img/graphical_tools_representation.png | the user to build easily persistence barcode, diagram or density. | | - | | | :Introduced in: GUDHI 2.0.0 | - | | | | - | | | :Copyright: MIT | - | | | | - | | | :Requires: matplotlib, numpy and scipy | - +-----------------------------------------------------------------+-----------------------------------------------------------------------+-----------------------------------------------+ - | * :doc:`persistence_graphical_tools_user` | * :doc:`persistence_graphical_tools_ref` | - +-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ + +-----------------------------------------------------------------+-----------------------------------------------------------------------+---------------------------------------------------------+ + | .. figure:: | These graphical tools comes on top of persistence results and allows | :Author: Vincent Rouvreau, Theo Lacombe | + | img/graphical_tools_representation.png | the user to display easily persistence barcode, diagram or density. | | + | | | :Since: GUDHI 2.0.0 | + | | Note that these functions return the matplotlib axis, allowing | | + | | for further modifications (title, aspect, etc.) | :License: MIT | + | | | | + | | | :Requires: `Matplotlib <installation.html#matplotlib>`_ | + +-----------------------------------------------------------------+-----------------------------------------------------------------------+---------------------------------------------------------+ + | * :doc:`persistence_graphical_tools_user` | * :doc:`persistence_graphical_tools_ref` | + +-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/persistence_graphical_tools_user.rst b/src/python/doc/persistence_graphical_tools_user.rst index 80002db6..b5a38eb1 100644 --- a/src/python/doc/persistence_graphical_tools_user.rst +++ b/src/python/doc/persistence_graphical_tools_user.rst @@ -12,15 +12,12 @@ Definition Show persistence as a barcode ----------------------------- -.. note:: - this function requires matplotlib and numpy to be available - This function can display the persistence result as a barcode: .. plot:: :include-source: - import matplotlib.pyplot as plot + import matplotlib.pyplot as plt import gudhi off_file = gudhi.__root_source_dir__ + '/data/points/tore3D_300.off' @@ -31,41 +28,53 @@ This function can display the persistence result as a barcode: diag = simplex_tree.persistence(min_persistence=0.4) gudhi.plot_persistence_barcode(diag) - plot.show() + plt.show() Show persistence as a diagram ----------------------------- -.. note:: - this function requires matplotlib and numpy to be available - This function can display the persistence result as a diagram: .. plot:: :include-source: - import matplotlib.pyplot as plot + import matplotlib.pyplot as plt import gudhi # rips_on_tore3D_1307.pers obtained from write_persistence_diagram method persistence_file=gudhi.__root_source_dir__ + \ '/data/persistence_diagram/rips_on_tore3D_1307.pers' - gudhi.plot_persistence_diagram(persistence_file=persistence_file, + ax = gudhi.plot_persistence_diagram(persistence_file=persistence_file, legend=True) - plot.show() + # We can modify the title, aspect, etc. + ax.set_title("Persistence diagram of a torus") + ax.set_aspect("equal") # forces to be square shaped + plt.show() + +Note that (as barcode and density) it can also take a simple `np.array` +of shape (N x 2) encoding a persistence diagram (in a given dimension). + +.. plot:: + :include-source: + + import matplotlib.pyplot as plt + import gudhi + import numpy as np + d = np.array([[0, 1], [1, 2], [1, np.inf]]) + gudhi.plot_persistence_diagram(d) + plt.show() Persistence density ------------------- -.. note:: - this function requires matplotlib, numpy and scipy to be available +:Requires: `SciPy <installation.html#scipy>`_ If you want more information on a specific dimension, for instance: .. plot:: :include-source: - import matplotlib.pyplot as plot + import matplotlib.pyplot as plt import gudhi # rips_on_tore3D_1307.pers obtained from write_persistence_diagram method persistence_file=gudhi.__root_source_dir__ + \ @@ -75,9 +84,9 @@ If you want more information on a specific dimension, for instance: only_this_dim=1) pers_diag = [(1, elt) for elt in birth_death] # Use subplots to display diagram and density side by side - fig, axes = plot.subplots(nrows=1, ncols=2, figsize=(12, 5)) + fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5)) gudhi.plot_persistence_diagram(persistence=pers_diag, axes=axes[0]) gudhi.plot_persistence_density(persistence=pers_diag, dimension=1, legend=True, axes=axes[1]) - plot.show() + plt.show() diff --git a/src/python/doc/persistent_cohomology_sum.inc b/src/python/doc/persistent_cohomology_sum.inc index 4d7b077e..a1ff2eee 100644 --- a/src/python/doc/persistent_cohomology_sum.inc +++ b/src/python/doc/persistent_cohomology_sum.inc @@ -1,18 +1,18 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 +-----------------------------------------------------------------+-----------------------------------------------------------------------+-----------------------------------------------+ | .. figure:: | The theory of homology consists in attaching to a topological space | :Author: Clément Maria | | ../../doc/Persistent_cohomology/3DTorus_poch.png | a sequence of (homology) groups, capturing global topological | | - | :figclass: align-center | features like connected components, holes, cavities, etc. Persistent | :Introduced in: GUDHI 2.0.0 | + | :figclass: align-center | features like connected components, holes, cavities, etc. Persistent | :Since: GUDHI 2.0.0 | | | homology studies the evolution -- birth, life and death -- of these | | - | Rips Persistent Cohomology on a 3D | features when the topological space is changing. Consequently, the | :Copyright: MIT | + | Rips Persistent Cohomology on a 3D | features when the topological space is changing. Consequently, the | :License: MIT | | Torus | theory is essentially composed of three elements: topological spaces, | | | | their homology groups and an evolution scheme. | | | | | | | | Computation of persistent cohomology using the algorithm of | | | | :cite:`DBLP:journals/dcg/SilvaMV11` and | | - | | :cite:`DBLP:journals/corr/abs-1208-5018` and the Compressed | | + | | :cite:`DBLP:conf/compgeom/DeyFW14` and the Compressed | | | | Annotation Matrix implementation of | | | | :cite:`DBLP:conf/esa/BoissonnatDM13`. | | | | | | diff --git a/src/python/doc/persistent_cohomology_user.rst b/src/python/doc/persistent_cohomology_user.rst index de83cda1..a3f294b2 100644 --- a/src/python/doc/persistent_cohomology_user.rst +++ b/src/python/doc/persistent_cohomology_user.rst @@ -7,7 +7,7 @@ Persistent cohomology user manual Definition ---------- ===================================== ===================================== ===================================== -:Author: Clément Maria :Introduced in: GUDHI PYTHON 2.0.0 :Copyright: GPL v3 +:Author: Clément Maria :Since: GUDHI PYTHON 2.0.0 :License: GPL v3 ===================================== ===================================== ===================================== +-----------------------------------------------------------------+-----------------------------------------------------------------------+ @@ -21,7 +21,7 @@ Definition Computation of persistent cohomology using the algorithm of :cite:`DBLP:journals/dcg/SilvaMV11` and -:cite:`DBLP:journals/corr/abs-1208-5018` and the Compressed Annotation Matrix implementation of +:cite:`DBLP:conf/compgeom/DeyFW14` and the Compressed Annotation Matrix implementation of :cite:`DBLP:conf/esa/BoissonnatDM13`. The theory of homology consists in attaching to a topological space a sequence of (homology) groups, capturing global @@ -111,10 +111,3 @@ We provide several example files: run these examples with -h for details on thei * :download:`rips_complex_diagram_persistence_from_distance_matrix_file_example.py <../example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py>` * :download:`random_cubical_complex_persistence_example.py <../example/random_cubical_complex_persistence_example.py>` * :download:`tangential_complex_plain_homology_from_off_file_example.py <../example/tangential_complex_plain_homology_from_off_file_example.py>` - -Bibliography -============ - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docnames - :style: unsrt diff --git a/src/python/doc/point_cloud.rst b/src/python/doc/point_cloud.rst index d668428a..ffd8f85b 100644 --- a/src/python/doc/point_cloud.rst +++ b/src/python/doc/point_cloud.rst @@ -16,7 +16,32 @@ File Readers Subsampling ----------- +:Requires: `Eigen <installation.html#eigen>`_ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`_ :math:`\geq` 4.11.0 + .. automodule:: gudhi.subsampling :members: :special-members: :show-inheritance: + +Time Delay Embedding +-------------------- + +.. autoclass:: gudhi.point_cloud.timedelay.TimeDelayEmbedding + :members: + :special-members: __call__ + +K nearest neighbors +------------------- + +.. automodule:: gudhi.point_cloud.knn + :members: + :undoc-members: + :special-members: __init__ + +Distance to measure +------------------- + +.. automodule:: gudhi.point_cloud.dtm + :members: + :undoc-members: + :special-members: __init__ diff --git a/src/python/doc/point_cloud_sum.inc b/src/python/doc/point_cloud_sum.inc index 85d52de7..4315cea6 100644 --- a/src/python/doc/point_cloud_sum.inc +++ b/src/python/doc/point_cloud_sum.inc @@ -1,15 +1,12 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 - +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ - | | :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` | - +----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + +-----------------------------------+---------------------------------------------------------------+-------------------------------------------------------------------+ + | | :math:`(x_1, x_2, \ldots, x_d)` | Utilities to process point clouds: read from file, subsample, | :Authors: Vincent Rouvreau, Marc Glisse, Masatoshi Takenouchi | + | | :math:`(y_1, y_2, \ldots, y_d)` | find neighbors, embed time series in higher dimension, etc. | | + | | | :Since: GUDHI 2.0.0 | + | | | | + | | | :License: MIT (`GPL v3 </licensing/>`_, BSD-3-Clause, Apache-2.0) | + +-----------------------------------+---------------------------------------------------------------+-------------------------------------------------------------------+ + | * :doc:`point_cloud` | + +-----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/representations.rst b/src/python/doc/representations.rst index 11dcbcf9..041e3247 100644 --- a/src/python/doc/representations.rst +++ b/src/python/doc/representations.rst @@ -10,7 +10,7 @@ Representations manual This module, originally available at https://github.com/MathieuCarriere/sklearn-tda and named sklearn_tda, aims at bridging the gap between persistence diagrams and machine learning, by providing implementations of most of the vector representations for persistence diagrams in the literature, in a scikit-learn format. More specifically, it provides tools, using the scikit-learn standard interface, to compute distances and kernels on persistence diagrams, and to convert these diagrams into vectors in Euclidean space. -A diagram is represented as a numpy array of shape (n,2), as can be obtained from :func:`~gudhi.SimplexTree.persistence_intervals_in_dimension` for instance. Points at infinity are represented as a numpy array of shape (n,1), storing only the birth time. +A diagram is represented as a numpy array of shape (n,2), as can be obtained from :func:`~gudhi.SimplexTree.persistence_intervals_in_dimension` for instance. Points at infinity are represented as a numpy array of shape (n,1), storing only the birth time. The classes in this module can handle several persistence diagrams at once. In that case, the diagrams are provided as a list of numpy arrays. Note that it is not necessary for the diagrams to have the same number of points, i.e., for the corresponding arrays to have the same number of rows: all classes can handle arrays with different shapes. A small example is provided diff --git a/src/python/doc/representations_sum.inc b/src/python/doc/representations_sum.inc index 700828f1..323a0920 100644 --- a/src/python/doc/representations_sum.inc +++ b/src/python/doc/representations_sum.inc @@ -1,14 +1,14 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 - +------------------------------------------------------------------+----------------------------------------------------------------+-----------------------------------------------+ - | .. figure:: | Vectorizations, distances and kernels that work on persistence | :Author: Mathieu Carrière | - | img/sklearn-tda.png | diagrams, compatible with scikit-learn. | | - | | | :Introduced in: GUDHI 3.1.0 | - | | | | - | | | :Copyright: MIT | - | | | | - | | | :Requires: scikit-learn | - +------------------------------------------------------------------+----------------------------------------------------------------+-----------------------------------------------+ - | * :doc:`representations` | - +------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------+ + +------------------------------------------------------------------+----------------------------------------------------------------+-------------------------------------------------------------+ + | .. figure:: | Vectorizations, distances and kernels that work on persistence | :Author: Mathieu Carrière | + | img/sklearn-tda.png | diagrams, compatible with scikit-learn. | | + | | | :Since: GUDHI 3.1.0 | + | | | | + | | | :License: MIT | + | | | | + | | | :Requires: `Scikit-learn <installation.html#scikit-learn>`_ | + +------------------------------------------------------------------+----------------------------------------------------------------+-------------------------------------------------------------+ + | * :doc:`representations` | + +------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/rips_complex_ref.rst b/src/python/doc/rips_complex_ref.rst index 22b5616c..5f3e46c1 100644 --- a/src/python/doc/rips_complex_ref.rst +++ b/src/python/doc/rips_complex_ref.rst @@ -12,3 +12,16 @@ Rips complex reference manual :show-inheritance: .. automethod:: gudhi.RipsComplex.__init__ + +.. _weighted-rips-complex-reference-manual: + +====================================== +Weighted Rips complex reference manual +====================================== + +.. autoclass:: gudhi.weighted_rips_complex.WeightedRipsComplex + :members: + :undoc-members: + :show-inheritance: + + .. automethod:: gudhi.weighted_rips_complex.WeightedRipsComplex.__init__ diff --git a/src/python/doc/rips_complex_sum.inc b/src/python/doc/rips_complex_sum.inc index 857c6893..f7580714 100644 --- a/src/python/doc/rips_complex_sum.inc +++ b/src/python/doc/rips_complex_sum.inc @@ -1,16 +1,19 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------+ | .. figure:: | Rips complex is a simplicial complex constructed from a one skeleton | :Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse | | ../../doc/Rips_complex/rips_complex_representation.png | graph. | | - | :figclass: align-center | | :Introduced in: GUDHI 2.0.0 | + | :figclass: align-center | | :Since: GUDHI 2.0.0 | | | The filtration value of each edge is computed from a user-given | | - | | distance function and is inserted until a user-given threshold | :Copyright: MIT | + | | distance function and is inserted until a user-given threshold | :License: MIT | | | value. | | | | | | | | This complex can be built from a point cloud and a distance function, | | | | or from a distance matrix. | | + | | | | + | | Weighted Rips complex constructs a simplicial complex from a distance | | + | | matrix and weights on vertices. | | +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------+ | * :doc:`rips_complex_user` | * :doc:`rips_complex_ref` | +----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst index a27573e8..819568be 100644 --- a/src/python/doc/rips_complex_user.rst +++ b/src/python/doc/rips_complex_user.rst @@ -8,7 +8,7 @@ Definition ---------- ==================================================================== ================================ ====================== -:Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 +:Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse :Since: GUDHI 2.0.0 :License: GPL v3 ==================================================================== ================================ ====================== +-------------------------------------------+----------------------------------------------------------------------+ @@ -347,3 +347,54 @@ until dimension 1 - one skeleton graph in other words), the output is: points in the persistence diagram will be under the diagonal, and bottleneck distance and persistence graphical tool will not work properly, this is a known issue. + +Weighted Rips Complex +--------------------- + +`WeightedRipsComplex <rips_complex_ref.html#weighted-rips-complex-reference-manual>`_ builds a simplicial complex from a distance matrix and weights on vertices. + + +Example from a distance matrix and weights +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The following example computes the weighted Rips filtration associated with a distance matrix and weights on vertices. + +.. testcode:: + + from gudhi.weighted_rips_complex import WeightedRipsComplex + dist = [[], [1]] + weights = [1, 100] + w_rips = WeightedRipsComplex(distance_matrix=dist, weights=weights) + st = w_rips.create_simplex_tree(max_dimension=2) + print(list(st.get_filtration())) + +The output is: + +.. testoutput:: + + [([0], 2.0), ([1], 200.0), ([0, 1], 200.0)] + +Example from a point cloud combined with DistanceToMeasure +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Combining with DistanceToMeasure, one can compute the DTM-filtration of a point set, as in `this notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-DTM-filtrations.ipynb>`_. + +.. testcode:: + + import numpy as np + from scipy.spatial.distance import cdist + from gudhi.point_cloud.dtm import DistanceToMeasure + from gudhi.weighted_rips_complex import WeightedRipsComplex + pts = np.array([[2.0, 2.0], [0.0, 1.0], [3.0, 4.0]]) + dist = cdist(pts,pts) + dtm = DistanceToMeasure(2, q=2, metric="precomputed") + r = dtm.fit_transform(dist) + w_rips = WeightedRipsComplex(distance_matrix=dist, weights=r) + st = w_rips.create_simplex_tree(max_dimension=2) + print(st.persistence()) + +The output is: + +.. testoutput:: + + [(0, (3.1622776601683795, inf)), (0, (3.1622776601683795, 5.39834563766817)), (0, (3.1622776601683795, 5.39834563766817))] diff --git a/src/python/doc/simplex_tree_ref.rst b/src/python/doc/simplex_tree_ref.rst index 9eb8c199..46b2c1e5 100644 --- a/src/python/doc/simplex_tree_ref.rst +++ b/src/python/doc/simplex_tree_ref.rst @@ -8,7 +8,6 @@ Simplex tree reference manual .. autoclass:: gudhi.SimplexTree :members: - :undoc-members: :show-inheritance: .. automethod:: gudhi.SimplexTree.__init__ diff --git a/src/python/doc/simplex_tree_sum.inc b/src/python/doc/simplex_tree_sum.inc index 5ba58d2b..a8858f16 100644 --- a/src/python/doc/simplex_tree_sum.inc +++ b/src/python/doc/simplex_tree_sum.inc @@ -1,12 +1,12 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------+ | .. figure:: | The simplex tree is an efficient and flexible data structure for | :Author: Clément Maria | | ../../doc/Simplex_tree/Simplex_tree_representation.png | representing general (filtered) simplicial complexes. | | - | :alt: Simplex tree representation | | :Introduced in: GUDHI 2.0.0 | + | :alt: Simplex tree representation | | :Since: GUDHI 2.0.0 | | :figclass: align-center | The data structure is described in | | - | | :cite:`boissonnatmariasimplextreealgorithmica` | :Copyright: MIT | + | | :cite:`boissonnatmariasimplextreealgorithmica` | :License: MIT | | | | | +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------+ | * :doc:`simplex_tree_user` | * :doc:`simplex_tree_ref` | diff --git a/src/python/doc/tangential_complex_sum.inc b/src/python/doc/tangential_complex_sum.inc index d84aa433..22314a2d 100644 --- a/src/python/doc/tangential_complex_sum.inc +++ b/src/python/doc/tangential_complex_sum.inc @@ -1,14 +1,14 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 - +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ - | .. figure:: | A Tangential Delaunay complex is a simplicial complex designed to | :Author: Clément Jamin | - | ../../doc/Tangential_complex/tc_examples.png | reconstruct a :math:`k`-dimensional manifold embedded in :math:`d`- | | - | :figclass: align-center | dimensional Euclidean space. The input is a point sample coming from | :Introduced in: GUDHI 2.0.0 | - | | an unknown manifold. The running time depends only linearly on the | | - | | extrinsic dimension :math:`d` and exponentially on the intrinsic | :Copyright: MIT (`GPL v3 </licensing/>`_) | - | | dimension :math:`k`. | | - | | | :Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 | - +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ - | * :doc:`tangential_complex_user` | * :doc:`tangential_complex_ref` | - +----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+ + | .. figure:: | A Tangential Delaunay complex is a simplicial complex designed to | :Author: Clément Jamin | + | ../../doc/Tangential_complex/tc_examples.png | reconstruct a :math:`k`-dimensional manifold embedded in :math:`d`- | | + | :figclass: align-center | dimensional Euclidean space. The input is a point sample coming from | :Since: GUDHI 2.0.0 | + | | an unknown manifold. The running time depends only linearly on the | | + | | extrinsic dimension :math:`d` and exponentially on the intrinsic | :License: MIT (`GPL v3 </licensing/>`_) | + | | dimension :math:`k`. | | + | | | :Requires: `Eigen <installation.html#eigen>`_ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`_ :math:`\geq` 4.11.0 | + +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+ + | * :doc:`tangential_complex_user` | * :doc:`tangential_complex_ref` | + +----------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/tangential_complex_user.rst b/src/python/doc/tangential_complex_user.rst index 852cf5b6..3d45473b 100644 --- a/src/python/doc/tangential_complex_user.rst +++ b/src/python/doc/tangential_complex_user.rst @@ -194,11 +194,3 @@ The output is: Tangential contains 4 vertices. Inconsistencies has been fixed. - - -Bibliography -============ - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docnames - :style: unsrt diff --git a/src/python/doc/wasserstein_distance_sum.inc b/src/python/doc/wasserstein_distance_sum.inc index a97f428d..c41de017 100644 --- a/src/python/doc/wasserstein_distance_sum.inc +++ b/src/python/doc/wasserstein_distance_sum.inc @@ -1,14 +1,12 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 - +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ - | .. figure:: | The q-Wasserstein distance measures the similarity between two | :Author: Theo Lacombe | - | ../../doc/Bottleneck_distance/perturb_pd.png | persistence diagrams. It's the minimum value c that can be achieved | | - | :figclass: align-center | by a perfect matching between the points of the two diagrams (+ all | :Introduced in: GUDHI 3.1.0 | - | | diagonal points), where the value of a matching is defined as the | | - | Wasserstein distance is the q-th root of the sum of the | q-th root of the sum of all edge lengths to the power q. Edge lengths| :Copyright: MIT | - | edge lengths to the power q. | are measured in norm p, for :math:`1 \leq p \leq \infty`. | | - | | | :Requires: Python Optimal Transport (POT) :math:`\geq` 0.5.1 | - +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ - | * :doc:`wasserstein_distance_user` | | - +-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------+ + +-----------------------------------------------------------------+----------------------------------------------------------------------+-----------------------------------------+ + | .. figure:: | The q-Wasserstein distance measures the similarity between two | :Author: Theo Lacombe, Marc Glisse | + | ../../doc/Bottleneck_distance/perturb_pd.png | persistence diagrams using the sum of all edges lengths (instead of | | + | :figclass: align-center | the maximum). It allows to define sophisticated objects such as | :Since: GUDHI 3.1.0 | + | | barycenters of a family of persistence diagrams. | | + | | | :License: MIT, BSD-3-Clause | + +-----------------------------------------------------------------+----------------------------------------------------------------------+-----------------------------------------+ + | * :doc:`wasserstein_distance_user` | | + +-----------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst index 32999a0c..96ec7872 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -9,17 +9,41 @@ Definition .. include:: wasserstein_distance_sum.inc -This implementation is based on ideas from "Large Scale Computation of Means and Cluster for Persistence Diagrams via Optimal Transport". +The q-Wasserstein distance is defined as the minimal value achieved +by a perfect matching between the points of the two diagrams (+ all +diagonal points), where the value of a matching is defined as the +q-th root of the sum of all edge lengths to the power q. Edge lengths +are measured in norm p, for :math:`1 \leq p \leq \infty`. + +Distance Functions +------------------ + +Optimal Transport +***************** + +:Requires: `Python Optimal Transport <installation.html#python-optimal-transport>`_ (POT) :math:`\geq` 0.5.1 + +This first implementation uses the `Python Optimal Transport <installation.html#python-optimal-transport>`_ +library and is based on ideas from "Large Scale Computation of Means and Cluster for Persistence +Diagrams via Optimal Transport" :cite:`10.5555/3327546.3327645`. -Function --------- .. autofunction:: gudhi.wasserstein.wasserstein_distance +Hera +**** + +This other implementation comes from `Hera +<https://bitbucket.org/grey_narn/hera/src/master/>`_ (BSD-3-Clause) which is +based on "Geometry Helps to Compare Persistence Diagrams" +:cite:`Kerber:2017:GHC:3047249.3064175` by Michael Kerber, Dmitriy +Morozov, and Arnur Nigmetov. + +.. autofunction:: gudhi.hera.wasserstein_distance Basic example -------------- +************* -This example computes the 1-Wasserstein distance from 2 persistence diagrams with euclidean ground metric. +This example computes the 1-Wasserstein distance from 2 persistence diagrams with Euclidean ground metric. Note that persistence diagrams must be submitted as (n x 2) numpy arrays and must not contain inf values. .. testcode:: @@ -27,10 +51,10 @@ Note that persistence diagrams must be submitted as (n x 2) numpy arrays and mus import gudhi.wasserstein import numpy as np - diag1 = np.array([[2.7, 3.7],[9.6, 14.],[34.2, 34.974]]) - diag2 = np.array([[2.8, 4.45],[9.5, 14.1]]) + dgm1 = np.array([[2.7, 3.7],[9.6, 14.],[34.2, 34.974]]) + dgm2 = np.array([[2.8, 4.45],[9.5, 14.1]]) - message = "Wasserstein distance value = " + '%.2f' % gudhi.wasserstein.wasserstein_distance(diag1, diag2, order=1., internal_p=2.) + message = "Wasserstein distance value = " + '%.2f' % gudhi.wasserstein.wasserstein_distance(dgm1, dgm2, order=1., internal_p=2.) print(message) The output is: @@ -38,3 +62,116 @@ The output is: .. testoutput:: Wasserstein distance value = 1.45 + +We can also have access to the optimal matching by letting `matching=True`. +It is encoded as a list of indices (i,j), meaning that the i-th point in X +is mapped to the j-th point in Y. +An index of -1 represents the diagonal. + +.. testcode:: + + import gudhi.wasserstein + import numpy as np + + dgm1 = np.array([[2.7, 3.7],[9.6, 14.],[34.2, 34.974]]) + dgm2 = np.array([[2.8, 4.45], [5, 6], [9.5, 14.1]]) + cost, matchings = gudhi.wasserstein.wasserstein_distance(dgm1, dgm2, matching=True, order=1, internal_p=2) + + message_cost = "Wasserstein distance value = %.2f" %cost + print(message_cost) + dgm1_to_diagonal = matchings[matchings[:,1] == -1, 0] + dgm2_to_diagonal = matchings[matchings[:,0] == -1, 1] + off_diagonal_match = np.delete(matchings, np.where(matchings == -1)[0], axis=0) + + for i,j in off_diagonal_match: + print("point %s in dgm1 is matched to point %s in dgm2" %(i,j)) + for i in dgm1_to_diagonal: + print("point %s in dgm1 is matched to the diagonal" %i) + for j in dgm2_to_diagonal: + print("point %s in dgm2 is matched to the diagonal" %j) + +The output is: + +.. testoutput:: + + Wasserstein distance value = 2.15 + point 0 in dgm1 is matched to point 0 in dgm2 + point 1 in dgm1 is matched to point 2 in dgm2 + point 2 in dgm1 is matched to the diagonal + point 1 in dgm2 is matched to the diagonal + +Barycenters +----------- + +:Requires: `Python Optimal Transport <installation.html#python-optimal-transport>`_ (POT) :math:`\geq` 0.5.1 + +A Frechet mean (or barycenter) is a generalization of the arithmetic +mean in a non linear space such as the one of persistence diagrams. +Given a set of persistence diagrams :math:`\mu_1 \dots \mu_n`, it is +defined as a minimizer of the variance functional, that is of +:math:`\mu \mapsto \sum_{i=1}^n d_2(\mu,\mu_i)^2`. +where :math:`d_2` denotes the Wasserstein-2 distance between +persistence diagrams. +It is known to exist and is generically unique. However, an exact +computation is in general untractable. Current implementation +available is based on (Turner et al., 2014), +:cite:`turner2014frechet` +and uses an EM-scheme to +provide a local minimum of the variance functional (somewhat similar +to the Lloyd algorithm to estimate a solution to the k-means +problem). The local minimum returned depends on the initialization of +the barycenter. +The combinatorial structure of the algorithm limits its +performances on large scale problems (thousands of diagrams and of points +per diagram). + +.. figure:: + ./img/barycenter.png + :figclass: align-center + + Illustration of Frechet mean between persistence + diagrams. + + +.. autofunction:: gudhi.wasserstein.barycenter.lagrangian_barycenter + +Basic example +************* + +This example estimates the Frechet mean (aka Wasserstein barycenter) between +four persistence diagrams. +It is initialized on the 4th diagram. +As the algorithm is not convex, its output depends on the initialization and +is only a local minimum of the objective function. +Initialization can be either given as an integer (in which case the i-th +diagram of the list is used as initial estimate) or as a diagram. +If None, it will randomly select one of the diagrams of the list +as initial estimate. +Note that persistence diagrams must be submitted as +(n x 2) numpy arrays and must not contain inf values. + + +.. testcode:: + + from gudhi.wasserstein.barycenter import lagrangian_barycenter + import numpy as np + + dg1 = np.array([[0.2, 0.5]]) + dg2 = np.array([[0.2, 0.7]]) + dg3 = np.array([[0.3, 0.6], [0.7, 0.8], [0.2, 0.3]]) + dg4 = np.array([]) + pdiagset = [dg1, dg2, dg3, dg4] + bary = lagrangian_barycenter(pdiagset=pdiagset,init=3) + + message = "Wasserstein barycenter estimated:" + print(message) + print(bary) + +The output is: + +.. testoutput:: + + Wasserstein barycenter estimated: + [[0.27916667 0.55416667] + [0.7375 0.7625 ] + [0.2375 0.2625 ]] diff --git a/src/python/doc/witness_complex_sum.inc b/src/python/doc/witness_complex_sum.inc index 71b65a71..4416fec0 100644 --- a/src/python/doc/witness_complex_sum.inc +++ b/src/python/doc/witness_complex_sum.inc @@ -1,18 +1,18 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 - +-------------------------------------------------------------------+----------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+ - | .. figure:: | Witness complex :math:`Wit(W,L)` is a simplicial complex defined on | :Author: Siargey Kachanovich | - | ../../doc/Witness_complex/Witness_complex_representation.png | two sets of points in :math:`\mathbb{R}^D`. | | - | :alt: Witness complex representation | | :Introduced in: GUDHI 2.0.0 | - | :figclass: align-center | The data structure is described in | | - | | :cite:`boissonnatmariasimplextreealgorithmica`. | :Copyright: MIT (`GPL v3 </licensing/>`_ for Euclidean versions only) | - | | | | - | | | :Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 for Euclidean versions only | - +-------------------------------------------------------------------+----------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+ - | * :doc:`witness_complex_user` | * :doc:`witness_complex_ref` | - | | * :doc:`strong_witness_complex_ref` | - | | * :doc:`euclidean_witness_complex_ref` | - | | * :doc:`euclidean_strong_witness_complex_ref` | - +-------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + +-------------------------------------------------------------------+----------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+ + | .. figure:: | Witness complex :math:`Wit(W,L)` is a simplicial complex defined on | :Author: Siargey Kachanovich | + | ../../doc/Witness_complex/Witness_complex_representation.png | two sets of points in :math:`\mathbb{R}^D`. | | + | :alt: Witness complex representation | | :Since: GUDHI 2.0.0 | + | :figclass: align-center | The data structure is described in | | + | | :cite:`boissonnatmariasimplextreealgorithmica`. | :License: MIT (`GPL v3 </licensing/>`_ for Euclidean versions only) | + | | | | + | | | :Requires: `Eigen <installation.html#eigen>`_ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`_ :math:`\geq` 4.11.0 for Euclidean versions only | + +-------------------------------------------------------------------+----------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+ + | * :doc:`witness_complex_user` | * :doc:`witness_complex_ref` | + | | * :doc:`strong_witness_complex_ref` | + | | * :doc:`euclidean_witness_complex_ref` | + | | * :doc:`euclidean_strong_witness_complex_ref` | + +-------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/witness_complex_user.rst b/src/python/doc/witness_complex_user.rst index 7087fa98..08dcd288 100644 --- a/src/python/doc/witness_complex_user.rst +++ b/src/python/doc/witness_complex_user.rst @@ -126,10 +126,3 @@ Example2: Computing persistence using strong relaxed witness complex Here is an example of constructing a strong witness complex filtration and computing persistence on it: * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` - -Bibliography -============ - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docnames - :style: unsrt diff --git a/src/python/doc/zbibliography.rst b/src/python/doc/zbibliography.rst new file mode 100644 index 00000000..e23fcf25 --- /dev/null +++ b/src/python/doc/zbibliography.rst @@ -0,0 +1,10 @@ +:orphan: + +.. To get rid of WARNING: document isn't included in any toctree + +Bibliography +------------ + +.. bibliography:: ../../biblio/bibliography.bib + :style: plain + diff --git a/src/python/example/alpha_complex_diagram_persistence_from_off_file_example.py b/src/python/example/alpha_complex_diagram_persistence_from_off_file_example.py index 4079a469..727af4fa 100755 --- a/src/python/example/alpha_complex_diagram_persistence_from_off_file_example.py +++ b/src/python/example/alpha_complex_diagram_persistence_from_off_file_example.py @@ -1,11 +1,15 @@ #!/usr/bin/env python import argparse +import errno +import os import matplotlib.pyplot as plot import gudhi -""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. - See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - + which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full + license details. Author(s): Vincent Rouvreau Copyright (C) 2016 Inria @@ -41,7 +45,7 @@ args = parser.parse_args() with open(args.file, "r") as f: first_line = f.readline() if (first_line == "OFF\n") or (first_line == "nOFF\n"): - print("#####################################################################") + print("##############################################################") print("AlphaComplex creation from points read in a OFF file") message = "AlphaComplex with max_edge_length=" + repr(args.max_alpha_square) @@ -64,6 +68,7 @@ with open(args.file, "r") as f: gudhi.plot_persistence_diagram(diag, band=args.band) plot.show() else: - print(args.file, "is not a valid OFF file") + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + args.file) f.close() diff --git a/src/python/example/alpha_complex_from_points_example.py b/src/python/example/alpha_complex_from_points_example.py index 844d7a82..465632eb 100755 --- a/src/python/example/alpha_complex_from_points_example.py +++ b/src/python/example/alpha_complex_from_points_example.py @@ -47,7 +47,10 @@ else: print("[4] Not found...") print("dimension=", simplex_tree.dimension()) -print("filtrations=", simplex_tree.get_filtration()) +print("filtrations=") +for simplex_with_filtration in simplex_tree.get_filtration(): + print("(%s, %.2f)" % tuple(simplex_with_filtration)) + print("star([0])=", simplex_tree.get_star([0])) print("coface([0], 1)=", simplex_tree.get_cofaces([0], 1)) diff --git a/src/python/example/alpha_rips_persistence_bottleneck_distance.py b/src/python/example/alpha_rips_persistence_bottleneck_distance.py index d5c33ec8..3e12b0d5 100755 --- a/src/python/example/alpha_rips_persistence_bottleneck_distance.py +++ b/src/python/example/alpha_rips_persistence_bottleneck_distance.py @@ -3,9 +3,14 @@ import gudhi import argparse import math - -""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. - See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +import errno +import os +import numpy as np + +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - + which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full + license details. Author(s): Vincent Rouvreau Copyright (C) 2016 Inria @@ -36,7 +41,7 @@ with open(args.file, "r") as f: first_line = f.readline() if (first_line == "OFF\n") or (first_line == "nOFF\n"): point_cloud = gudhi.read_points_from_off_file(off_file=args.file) - print("#####################################################################") + print("##############################################################") print("RipsComplex creation from points read in a OFF file") message = "RipsComplex with max_edge_length=" + repr(args.threshold) @@ -46,14 +51,15 @@ with open(args.file, "r") as f: points=point_cloud, max_edge_length=args.threshold ) - rips_stree = rips_complex.create_simplex_tree(max_dimension=args.max_dimension) + rips_stree = rips_complex.create_simplex_tree( + max_dimension=args.max_dimension) message = "Number of simplices=" + repr(rips_stree.num_simplices()) print(message) - rips_diag = rips_stree.persistence() + rips_stree.compute_persistence() - print("#####################################################################") + print("##############################################################") print("AlphaComplex creation from points read in a OFF file") message = "AlphaComplex with max_edge_length=" + repr(args.threshold) @@ -67,18 +73,13 @@ with open(args.file, "r") as f: message = "Number of simplices=" + repr(alpha_stree.num_simplices()) print(message) - alpha_diag = alpha_stree.persistence() + alpha_stree.compute_persistence() max_b_distance = 0.0 for dim in range(args.max_dimension): # Alpha persistence values needs to be transform because filtration # values are alpha square values - funcs = [math.sqrt, math.sqrt] - alpha_intervals = [] - for interval in alpha_stree.persistence_intervals_in_dimension(dim): - alpha_intervals.append( - map(lambda func, value: func(value), funcs, interval) - ) + alpha_intervals = np.sqrt(alpha_stree.persistence_intervals_in_dimension(dim)) rips_intervals = rips_stree.persistence_intervals_in_dimension(dim) bottleneck_distance = gudhi.bottleneck_distance( @@ -93,13 +94,13 @@ with open(args.file, "r") as f: print(message) max_b_distance = max(bottleneck_distance, max_b_distance) - print( - "================================================================================" - ) + print("==============================================================") message = "Bottleneck distance is " + repr(max_b_distance) print(message) else: - print(args.file, "is not a valid OFF file") + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + args.file) + f.close() diff --git a/src/python/example/diagram_vectorizations_distances_kernels.py b/src/python/example/diagram_vectorizations_distances_kernels.py index 119072eb..c4a71a7a 100755 --- a/src/python/example/diagram_vectorizations_distances_kernels.py +++ b/src/python/example/diagram_vectorizations_distances_kernels.py @@ -9,26 +9,25 @@ from gudhi.representations import DiagramSelector, Clamping, Landscape, Silhouet TopologicalVector, DiagramScaler, BirthPersistenceTransform,\ PersistenceImage, PersistenceWeightedGaussianKernel, Entropy, \ PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\ - SlicedWassersteinKernel, BottleneckDistance, PersistenceFisherKernel + SlicedWassersteinKernel, BottleneckDistance, PersistenceFisherKernel, WassersteinDistance -D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.], [0., np.inf], [5., np.inf]]) -diags = [D] +D1 = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.], [0., np.inf], [5., np.inf]]) -diags = DiagramSelector(use=True, point_type="finite").fit_transform(diags) -diags = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diags) -diags = DiagramScaler(use=True, scalers=[([1], Clamping(maximum=.9))]).fit_transform(diags) +proc1 = DiagramSelector(use=True, point_type="finite") +proc2 = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]) +proc3 = DiagramScaler(use=True, scalers=[([1], Clamping(maximum=.9))]) +D1 = proc3(proc2(proc1(D1))) -D = diags[0] -plt.scatter(D[:,0],D[:,1]) +plt.scatter(D1[:,0], D1[:,1]) plt.plot([0.,1.],[0.,1.]) plt.title("Test Persistence Diagram for vector methods") plt.show() LS = Landscape(resolution=1000) -L = LS.fit_transform(diags) -plt.plot(L[0][:1000]) -plt.plot(L[0][1000:2000]) -plt.plot(L[0][2000:3000]) +L = LS(D1) +plt.plot(L[:1000]) +plt.plot(L[1000:2000]) +plt.plot(L[2000:3000]) plt.title("Landscape") plt.show() @@ -36,50 +35,39 @@ def pow(n): return lambda x: np.power(x[1]-x[0],n) SH = Silhouette(resolution=1000, weight=pow(2)) -sh = SH.fit_transform(diags) -plt.plot(sh[0]) +plt.plot(SH(D1)) plt.title("Silhouette") plt.show() BC = BettiCurve(resolution=1000) -bc = BC.fit_transform(diags) -plt.plot(bc[0]) +plt.plot(BC(D1)) plt.title("Betti Curve") plt.show() CP = ComplexPolynomial(threshold=-1, polynomial_type="T") -cp = CP.fit_transform(diags) -print("Complex polynomial is " + str(cp[0,:])) +print("Complex polynomial is " + str(CP(D1))) TV = TopologicalVector(threshold=-1) -tv = TV.fit_transform(diags) -print("Topological vector is " + str(tv[0,:])) +print("Topological vector is " + str(TV(D1))) PI = PersistenceImage(bandwidth=.1, weight=lambda x: x[1], im_range=[0,1,0,1], resolution=[100,100]) -pi = PI.fit_transform(diags) -plt.imshow(np.flip(np.reshape(pi[0], [100,100]), 0)) +plt.imshow(np.flip(np.reshape(PI(D1), [100,100]), 0)) plt.title("Persistence Image") plt.show() ET = Entropy(mode="scalar") -et = ET.fit_transform(diags) -print("Entropy statistic is " + str(et[0,:])) +print("Entropy statistic is " + str(ET(D1))) ET = Entropy(mode="vector", normalized=False) -et = ET.fit_transform(diags) -plt.plot(et[0]) +plt.plot(ET(D1)) plt.title("Entropy function") plt.show() -D = np.array([[1.,5.],[3.,6.],[2.,7.]]) -diags2 = [D] +D2 = np.array([[1.,5.],[3.,6.],[2.,7.]]) +D2 = proc3(proc2(proc1(D2))) -diags2 = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diags2) - -D = diags[0] -plt.scatter(D[:,0],D[:,1]) -D = diags2[0] -plt.scatter(D[:,0],D[:,1]) +plt.scatter(D1[:,0], D1[:,1]) +plt.scatter(D2[:,0], D2[:,1]) plt.plot([0.,1.],[0.,1.]) plt.title("Test Persistence Diagrams for kernel methods") plt.show() @@ -88,46 +76,34 @@ def arctan(C,p): return lambda x: C*np.arctan(np.power(x[1], p)) PWG = PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.)) -X = PWG.fit(diags) -Y = PWG.transform(diags2) -print("PWG kernel is " + str(Y[0][0])) +print("PWG kernel is " + str(PWG(D1, D2))) PWG = PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.)) -X = PWG.fit(diags) -Y = PWG.transform(diags2) -print("Approximate PWG kernel is " + str(Y[0][0])) +print("Approximate PWG kernel is " + str(PWG(D1, D2))) PSS = PersistenceScaleSpaceKernel(bandwidth=1.) -X = PSS.fit(diags) -Y = PSS.transform(diags2) -print("PSS kernel is " + str(Y[0][0])) +print("PSS kernel is " + str(PSS(D1, D2))) PSS = PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2]))) -X = PSS.fit(diags) -Y = PSS.transform(diags2) -print("Approximate PSS kernel is " + str(Y[0][0])) +print("Approximate PSS kernel is " + str(PSS(D1, D2))) sW = SlicedWassersteinDistance(num_directions=100) -X = sW.fit(diags) -Y = sW.transform(diags2) -print("SW distance is " + str(Y[0][0])) +print("SW distance is " + str(sW(D1, D2))) SW = SlicedWassersteinKernel(num_directions=100, bandwidth=1.) -X = SW.fit(diags) -Y = SW.transform(diags2) -print("SW kernel is " + str(Y[0][0])) +print("SW kernel is " + str(SW(D1, D2))) + +W = WassersteinDistance(order=2, internal_p=2, mode="pot") +print("Wasserstein distance (POT) is " + str(W(D1, D2))) + +W = WassersteinDistance(order=2, internal_p=2, mode="hera", delta=0.0001) +print("Wasserstein distance (hera) is " + str(W(D1, D2))) W = BottleneckDistance(epsilon=.001) -X = W.fit(diags) -Y = W.transform(diags2) -print("Bottleneck distance is " + str(Y[0][0])) +print("Bottleneck distance is " + str(W(D1, D2))) PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.) -X = PF.fit(diags) -Y = PF.transform(diags2) -print("PF kernel is " + str(Y[0][0])) +print("PF kernel is " + str(PF(D1, D2))) PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1., kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2]))) -X = PF.fit(diags) -Y = PF.transform(diags2) -print("Approximate PF kernel is " + str(Y[0][0])) +print("Approximate PF kernel is " + str(PF(D1, D2))) diff --git a/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py b/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py index 4903667e..e1e572df 100755 --- a/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py +++ b/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py @@ -1,11 +1,15 @@ #!/usr/bin/env python import argparse +import errno +import os import matplotlib.pyplot as plot import gudhi -""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. - See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - + which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full + license details. Author(s): Vincent Rouvreau Copyright (C) 2016 Inria @@ -44,8 +48,9 @@ args = parser.parse_args() with open(args.file, "r") as f: first_line = f.readline() if (first_line == "OFF\n") or (first_line == "nOFF\n"): - print("#####################################################################") - print("EuclideanStrongWitnessComplex creation from points read in a OFF file") + print("##############################################################") + print("EuclideanStrongWitnessComplex creation from points read "\ + "in a OFF file") witnesses = gudhi.read_points_from_off_file(off_file=args.file) landmarks = gudhi.pick_n_random_points( @@ -64,7 +69,8 @@ with open(args.file, "r") as f: witnesses=witnesses, landmarks=landmarks ) simplex_tree = witness_complex.create_simplex_tree( - max_alpha_square=args.max_alpha_square, limit_dimension=args.limit_dimension + max_alpha_square=args.max_alpha_square, + limit_dimension=args.limit_dimension ) message = "Number of simplices=" + repr(simplex_tree.num_simplices()) @@ -79,6 +85,7 @@ with open(args.file, "r") as f: gudhi.plot_persistence_diagram(diag, band=args.band) plot.show() else: - print(args.file, "is not a valid OFF file") + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + args.file) f.close() diff --git a/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py b/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py index 339a8577..58cb2bb5 100755 --- a/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py +++ b/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py @@ -1,11 +1,15 @@ #!/usr/bin/env python import argparse +import errno +import os import matplotlib.pyplot as plot import gudhi -""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. - See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - + which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full + license details. Author(s): Vincent Rouvreau Copyright (C) 2016 Inria @@ -78,6 +82,7 @@ with open(args.file, "r") as f: gudhi.plot_persistence_diagram(diag, band=args.band) plot.show() else: - print(args.file, "is not a valid OFF file") + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + args.file) f.close() diff --git a/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py b/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py index c692e66f..499171df 100755 --- a/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py +++ b/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py @@ -2,10 +2,14 @@ import argparse import matplotlib.pyplot as plot +import errno +import os import gudhi -""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. - See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - + which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full + license details. Author(s): Vincent Rouvreau Copyright (C) 2016 Inria @@ -57,9 +61,10 @@ parser.add_argument( args = parser.parse_args() if is_file_perseus(args.file): - print("#####################################################################") + print("##################################################################") print("PeriodicCubicalComplex creation") - periodic_cubical_complex = gudhi.PeriodicCubicalComplex(perseus_file=args.file) + periodic_cubical_complex = gudhi.PeriodicCubicalComplex( + perseus_file=args.file) print("persistence(homology_coeff_field=3, min_persistence=0)=") diag = periodic_cubical_complex.persistence( @@ -73,4 +78,5 @@ if is_file_perseus(args.file): gudhi.plot_persistence_barcode(diag) plot.show() else: - print(args.file, "is not a valid perseus style file") + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + args.file) diff --git a/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py b/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py index c757aca7..6f992508 100755 --- a/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py +++ b/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py @@ -1,11 +1,15 @@ #!/usr/bin/env python import argparse +import errno +import os import matplotlib.pyplot as plot import gudhi -""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. - See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - + which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full + license details. Author(s): Vincent Rouvreau Copyright (C) 2016 Inria @@ -42,10 +46,11 @@ args = parser.parse_args() with open(args.file, "r") as f: first_line = f.readline() if (first_line == "OFF\n") or (first_line == "nOFF\n"): - print("#####################################################################") + print("##############################################################") print("RipsComplex creation from points read in a OFF file") - message = "RipsComplex with max_edge_length=" + repr(args.max_edge_length) + message = "RipsComplex with max_edge_length=" + \ + repr(args.max_edge_length) print(message) point_cloud = gudhi.read_points_from_off_file(off_file=args.file) @@ -68,6 +73,7 @@ with open(args.file, "r") as f: gudhi.plot_persistence_diagram(diag, band=args.band) plot.show() else: - print(args.file, "is not a valid OFF file") + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + args.file) f.close() diff --git a/src/python/example/rips_complex_from_points_example.py b/src/python/example/rips_complex_from_points_example.py index 59d8a261..c05703c6 100755 --- a/src/python/example/rips_complex_from_points_example.py +++ b/src/python/example/rips_complex_from_points_example.py @@ -22,6 +22,9 @@ rips = gudhi.RipsComplex(points=[[0, 0], [1, 0], [0, 1], [1, 1]], max_edge_lengt simplex_tree = rips.create_simplex_tree(max_dimension=1) -print("filtrations=", simplex_tree.get_filtration()) +print("filtrations=") +for simplex_with_filtration in simplex_tree.get_filtration(): + print("(%s, %.2f)" % tuple(simplex_with_filtration)) + print("star([0])=", simplex_tree.get_star([0])) print("coface([0], 1)=", simplex_tree.get_cofaces([0], 1)) diff --git a/src/python/example/simplex_tree_example.py b/src/python/example/simplex_tree_example.py index 30de00da..c4635dc5 100755 --- a/src/python/example/simplex_tree_example.py +++ b/src/python/example/simplex_tree_example.py @@ -38,8 +38,14 @@ else: print("dimension=", st.dimension()) -st.initialize_filtration() -print("filtration=", st.get_filtration()) +print("simplices=") +for simplex_with_filtration in st.get_simplices(): + print("(%s, %.2f)" % tuple(simplex_with_filtration)) + +print("filtration=") +for simplex_with_filtration in st.get_filtration(): + print("(%s, %.2f)" % tuple(simplex_with_filtration)) + print("filtration[1, 2]=", st.filtration([1, 2])) print("filtration[4, 2]=", st.filtration([4, 2])) diff --git a/src/python/example/tangential_complex_plain_homology_from_off_file_example.py b/src/python/example/tangential_complex_plain_homology_from_off_file_example.py index f0df2189..85bade4a 100755 --- a/src/python/example/tangential_complex_plain_homology_from_off_file_example.py +++ b/src/python/example/tangential_complex_plain_homology_from_off_file_example.py @@ -1,11 +1,15 @@ #!/usr/bin/env python import argparse +import errno +import os import matplotlib.pyplot as plot import gudhi -""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. - See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - + which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full + license details. Author(s): Vincent Rouvreau Copyright (C) 2016 Inria @@ -19,7 +23,7 @@ __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" parser = argparse.ArgumentParser( - description="TangentialComplex creation from " "points read in a OFF file.", + description="TangentialComplex creation from points read in a OFF file.", epilog="Example: " "example/tangential_complex_plain_homology_from_off_file_example.py " "-f ../data/points/tore3D_300.off -i 3" @@ -41,10 +45,11 @@ args = parser.parse_args() with open(args.file, "r") as f: first_line = f.readline() if (first_line == "OFF\n") or (first_line == "nOFF\n"): - print("#####################################################################") + print("##############################################################") print("TangentialComplex creation from points read in a OFF file") - tc = gudhi.TangentialComplex(intrisic_dim=args.intrisic_dim, off_file=args.file) + tc = gudhi.TangentialComplex(intrisic_dim=args.intrisic_dim, + off_file=args.file) tc.compute_tangential_complex() st = tc.create_simplex_tree() @@ -60,6 +65,7 @@ with open(args.file, "r") as f: gudhi.plot_persistence_diagram(diag, band=args.band) plot.show() else: - print(args.file, "is not a valid OFF file") + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + args.file) f.close() diff --git a/src/python/gudhi/alpha_complex.pyx b/src/python/gudhi/alpha_complex.pyx index fff3e920..d75e374a 100644 --- a/src/python/gudhi/alpha_complex.pyx +++ b/src/python/gudhi/alpha_complex.pyx @@ -1,5 +1,7 @@ -# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. -# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - +# which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full +# license details. # Author(s): Vincent Rouvreau # # Copyright (C) 2016 Inria @@ -7,6 +9,7 @@ # Modification(s): # - YYYY/MM Author: Description of the modification +from __future__ import print_function from cython cimport numeric from libcpp.vector cimport vector from libcpp.utility cimport pair @@ -24,11 +27,11 @@ __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) except + + Alpha_complex_interface(vector[vector[double]] points) nogil except + # bool from_file is a workaround for cython to find the correct signature - 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 + + Alpha_complex_interface(string off_file, bool from_file) nogil except + + vector[double] get_point(int vertex) nogil except + + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) nogil except + # AlphaComplex python interface cdef class AlphaComplex: @@ -67,16 +70,20 @@ cdef class AlphaComplex: # The real cython constructor def __cinit__(self, points = None, off_file = ''): + cdef vector[vector[double]] pts if off_file: if os.path.isfile(off_file): - self.thisptr = new Alpha_complex_interface(off_file.encode('utf-8'), True) + self.thisptr = new Alpha_complex_interface( + off_file.encode('utf-8'), True) else: print("file " + off_file + " not found.") else: if points is None: # Empty Alpha construction points=[] - self.thisptr = new Alpha_complex_interface(points) + pts = points + with nogil: + self.thisptr = new Alpha_complex_interface(pts) def __dealloc__(self): @@ -109,6 +116,8 @@ cdef class AlphaComplex: :rtype: SimplexTree """ stree = SimplexTree() + cdef double mas = max_alpha_square cdef intptr_t stree_int_ptr=stree.thisptr - self.thisptr.create_simplex_tree(<Simplex_tree_interface_full_featured*>stree_int_ptr, max_alpha_square) + with nogil: + self.thisptr.create_simplex_tree(<Simplex_tree_interface_full_featured*>stree_int_ptr, mas) return stree diff --git a/src/python/gudhi/bottleneck.cc b/src/python/gudhi/bottleneck.cc new file mode 100644 index 00000000..732cb9a8 --- /dev/null +++ b/src/python/gudhi/bottleneck.cc @@ -0,0 +1,50 @@ +/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + * Author(s): Marc Glisse + * + * Copyright (C) 2020 Inria + * + * Modification(s): + * - YYYY/MM Author: Description of the modification + */ + +#include <gudhi/Bottleneck.h> + +#include <pybind11_diagram_utils.h> + +double bottleneck(Dgm d1, Dgm d2, double epsilon) +{ + // I *think* the call to request() has to be before releasing the GIL. + auto diag1 = numpy_to_range_of_pairs(d1); + auto diag2 = numpy_to_range_of_pairs(d2); + + py::gil_scoped_release release; + + return Gudhi::persistence_diagram::bottleneck_distance(diag1, diag2, epsilon); +} + +PYBIND11_MODULE(bottleneck, m) { + m.attr("__license__") = "GPL v3"; + m.def("bottleneck_distance", &bottleneck, + py::arg("diagram_1"), py::arg("diagram_2"), + py::arg("e") = (std::numeric_limits<double>::min)(), + R"pbdoc( + This function returns the point corresponding to a given vertex. + + :param diagram_1: The first diagram. + :type diagram_1: numpy array of shape (m,2) + :param diagram_2: The second diagram. + :type diagram_2: numpy array of shape (n,2) + :param e: If `e` is 0, this uses an expensive algorithm to compute the + exact distance. + If `e` is not 0, it asks for an additive `e`-approximation, and + currently also allows a small multiplicative error (the last 2 or 3 + bits of the mantissa may be wrong). This version of the algorithm takes + advantage of the limited precision of `double` and is usually a lot + faster to compute, whatever the value of `e`. + Thus, by default, `e` is the smallest positive double. + :type e: float + :rtype: float + :returns: the bottleneck distance. + )pbdoc"); +} diff --git a/src/python/gudhi/bottleneck.pyx b/src/python/gudhi/bottleneck.pyx deleted file mode 100644 index af011e88..00000000 --- a/src/python/gudhi/bottleneck.pyx +++ /dev/null @@ -1,48 +0,0 @@ -# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. -# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. -# Author(s): Vincent Rouvreau -# -# Copyright (C) 2016 Inria -# -# Modification(s): -# - YYYY/MM Author: Description of the modification - -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -import os - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Bottleneck_distance_interface.h" namespace "Gudhi::persistence_diagram": - double bottleneck(vector[pair[double, double]], vector[pair[double, double]], double) - double bottleneck(vector[pair[double, double]], vector[pair[double, double]]) - -def bottleneck_distance(diagram_1, diagram_2, e=None): - """This function returns the point corresponding to a given vertex. - - :param diagram_1: The first diagram. - :type diagram_1: vector[pair[double, double]] - :param diagram_2: The second diagram. - :type diagram_2: vector[pair[double, double]] - :param e: If `e` is 0, this uses an expensive algorithm to compute the - exact distance. - If `e` is not 0, it asks for an additive `e`-approximation, and - currently also allows a small multiplicative error (the last 2 or 3 - bits of the mantissa may be wrong). This version of the algorithm takes - advantage of the limited precision of `double` and is usually a lot - faster to compute, whatever the value of `e`. - - Thus, by default, `e` is the smallest positive double. - :type e: float - :rtype: float - :returns: the bottleneck distance. - """ - if e is None: - # Default value is the smallest double value (not 0, 0 is for exact version) - return bottleneck(diagram_1, diagram_2) - else: - # Can be 0 for exact version - return bottleneck(diagram_1, diagram_2, e) diff --git a/src/python/gudhi/cubical_complex.pyx b/src/python/gudhi/cubical_complex.pyx index cbeda014..ca979eda 100644 --- a/src/python/gudhi/cubical_complex.pyx +++ b/src/python/gudhi/cubical_complex.pyx @@ -1,5 +1,7 @@ -# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. -# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - +# which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full +# license details. # Author(s): Vincent Rouvreau # # Copyright (C) 2016 Inria @@ -7,12 +9,15 @@ # Modification(s): # - YYYY/MM Author: Description of the modification +from __future__ import print_function from cython cimport numeric from libcpp.vector cimport vector from libcpp.utility cimport pair from libcpp.string cimport string from libcpp cimport bool +import errno import os +import sys import numpy as np @@ -30,7 +35,9 @@ cdef extern from "Cubical_complex_interface.h" namespace "Gudhi": cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi": cdef cppclass Cubical_complex_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Cubical_complex::Cubical_complex_interface<>>": Cubical_complex_persistence_interface(Bitmap_cubical_complex_base_interface * st, bool persistence_dim_max) - vector[pair[int, pair[double, double]]] get_persistence(int homology_coeff_field, double min_persistence) + void compute_persistence(int homology_coeff_field, double min_persistence) + vector[pair[int, pair[double, double]]] get_persistence() + vector[vector[int]] cofaces_of_cubical_persistence_pairs() vector[int] betti_numbers() vector[int] persistent_betti_numbers(double from_value, double to_value) vector[pair[double,double]] intervals_in_dimension(int dimension) @@ -87,10 +94,12 @@ cdef class CubicalComplex: if os.path.isfile(perseus_file): self.thisptr = new Bitmap_cubical_complex_base_interface(perseus_file.encode('utf-8')) else: - print("file " + perseus_file + " not found.") + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + perseus_file) else: print("CubicalComplex can be constructed from dimensions and " - "top_dimensional_cells or from a Perseus-style file name.") + "top_dimensional_cells or from a Perseus-style file name.", + file=sys.stderr) def __dealloc__(self): if self.thisptr != NULL: @@ -122,8 +131,31 @@ cdef class CubicalComplex: """ return self.thisptr.dimension() + def compute_persistence(self, homology_coeff_field=11, min_persistence=0): + """This function computes the persistence of the complex, so it can be + accessed through :func:`persistent_betti_numbers`, + :func:`persistence_intervals_in_dimension`, etc. This function is + equivalent to :func:`persistence` when you do not want the list + :func:`persistence` returns. + + :param homology_coeff_field: The homology coefficient field. Must be a + prime number + :type homology_coeff_field: int. + :param min_persistence: The minimum persistence value to take into + account (strictly greater than min_persistence). Default value is + 0.0. + Sets min_persistence to -1.0 to see all values. + :type min_persistence: float. + :returns: Nothing. + """ + if self.pcohptr != NULL: + del self.pcohptr + assert self.__is_defined() + self.pcohptr = new Cubical_complex_persistence_interface(self.thisptr, True) + self.pcohptr.compute_persistence(homology_coeff_field, min_persistence) + def persistence(self, homology_coeff_field=11, min_persistence=0): - """This function returns the persistence of the complex. + """This function computes and returns the persistence of the complex. :param homology_coeff_field: The homology coefficient field. Must be a prime number @@ -136,30 +168,74 @@ cdef class CubicalComplex: :returns: list of pairs(dimension, pair(birth, death)) -- the persistence of the complex. """ - if self.pcohptr != NULL: - del self.pcohptr - if self.thisptr != NULL: - self.pcohptr = new Cubical_complex_persistence_interface(self.thisptr, True) - cdef vector[pair[int, pair[double, double]]] persistence_result - if self.pcohptr != NULL: - persistence_result = self.pcohptr.get_persistence(homology_coeff_field, min_persistence) - return persistence_result + self.compute_persistence(homology_coeff_field, min_persistence) + return self.pcohptr.get_persistence() + + def cofaces_of_persistence_pairs(self): + """A persistence interval is described by a pair of cells, one that creates the + feature and one that kills it. The filtration values of those 2 cells give coordinates + for a point in a persistence diagram, or a bar in a barcode. Structurally, in the + cubical complexes provided here, the filtration value of any cell is the minimum of the + filtration values of the maximal cells that contain it. Connecting persistence diagram + coordinates to the corresponding value in the input (i.e. the filtration values of + the top-dimensional cells) is useful for differentiation purposes. + + This function returns a list of pairs of top-dimensional cells corresponding to + the persistence birth and death cells of the filtration. The cells are represented by + their indices in the input list of top-dimensional cells (and not their indices in the + internal datastructure that includes non-maximal cells). Note that when two adjacent + top-dimensional cells have the same filtration value, we arbitrarily return one of the two + when calling the function on one of their common faces. + + :returns: The top-dimensional cells/cofaces of the positive and negative cells, + together with the corresponding homological dimension, in two lists of numpy arrays of integers. + The first list contains the regular persistence pairs, grouped by dimension. + It contains numpy arrays of shape [number_of_persistence_points, 2]. + The indices of the arrays in the list correspond to the homological dimensions, and the + integers of each row in each array correspond to: (index of positive top-dimensional cell, + index of negative top-dimensional cell). + The second list contains the essential features, grouped by dimension. + It contains numpy arrays of shape [number_of_persistence_points, 1]. + The indices of the arrays in the list correspond to the homological dimensions, and the + integers of each row in each array correspond to: (index of positive top-dimensional cell). + """ + + assert self.pcohptr != NULL, "compute_persistence() must be called before cofaces_of_persistence_pairs()" + + cdef vector[vector[int]] persistence_result + output = [[],[]] + persistence_result = self.pcohptr.cofaces_of_cubical_persistence_pairs() + pr = np.array(persistence_result) + + ess_ind = np.argwhere(pr[:,2] == -1)[:,0] + ess = pr[ess_ind] + max_h = max(ess[:,0])+1 + for h in range(max_h): + hidxs = np.argwhere(ess[:,0] == h)[:,0] + output[1].append(ess[hidxs][:,1]) + + reg_ind = np.setdiff1d(np.array(range(len(pr))), ess_ind) + reg = pr[reg_ind] + max_h = max(reg[:,0])+1 + for h in range(max_h): + hidxs = np.argwhere(reg[:,0] == h)[:,0] + output[0].append(reg[hidxs][:,1:]) + + return output def betti_numbers(self): """This function returns the Betti numbers of the complex. :returns: list of int -- The Betti numbers ([B0, B1, ..., Bn]). - :note: betti_numbers function requires persistence function to be + :note: betti_numbers function requires :func:`compute_persistence` function to be launched first. :note: betti_numbers function always returns [1, 0, 0, ...] as infinity filtration cubes are not removed from the complex. """ - cdef vector[int] bn_result - if self.pcohptr != NULL: - bn_result = self.pcohptr.betti_numbers() - return bn_result + assert self.pcohptr != NULL, "compute_persistence() must be called before betti_numbers()" + return self.pcohptr.betti_numbers() def persistent_betti_numbers(self, from_value, to_value): """This function returns the persistent Betti numbers of the complex. @@ -174,13 +250,11 @@ cdef class CubicalComplex: :returns: list of int -- The persistent Betti numbers ([B0, B1, ..., Bn]). - :note: persistent_betti_numbers function requires persistence + :note: persistent_betti_numbers function requires :func:`compute_persistence` function to be launched first. """ - cdef vector[int] pbn_result - if self.pcohptr != NULL: - pbn_result = self.pcohptr.persistent_betti_numbers(<double>from_value, <double>to_value) - return pbn_result + assert self.pcohptr != NULL, "compute_persistence() must be called before persistent_betti_numbers()" + return self.pcohptr.persistent_betti_numbers(<double>from_value, <double>to_value) def persistence_intervals_in_dimension(self, dimension): """This function returns the persistence intervals of the complex in a @@ -191,13 +265,8 @@ cdef class CubicalComplex: :returns: The persistence intervals. :rtype: numpy array of dimension 2 - :note: intervals_in_dim function requires persistence function to be + :note: intervals_in_dim function requires :func:`compute_persistence` function to be launched first. """ - cdef vector[pair[double,double]] intervals_result - if self.pcohptr != NULL: - intervals_result = self.pcohptr.intervals_in_dimension(dimension) - else: - print("intervals_in_dim function requires persistence function" - " to be launched first.") - return np.array(intervals_result) + assert self.pcohptr != NULL, "compute_persistence() must be called before persistence_intervals_in_dimension()" + return np.array(self.pcohptr.intervals_in_dimension(dimension)) diff --git a/src/python/gudhi/hera.cc b/src/python/gudhi/hera.cc new file mode 100644 index 00000000..ea80a9a8 --- /dev/null +++ b/src/python/gudhi/hera.cc @@ -0,0 +1,56 @@ +/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + * Author(s): Marc Glisse + * + * Copyright (C) 2020 Inria + * + * Modification(s): + * - YYYY/MM Author: Description of the modification + */ + +#include <wasserstein.h> // Hera + +#include <pybind11_diagram_utils.h> + +double wasserstein_distance( + Dgm d1, Dgm d2, + double wasserstein_power, double internal_p, + double delta) +{ + // I *think* the call to request() has to be before releasing the GIL. + auto diag1 = numpy_to_range_of_pairs(d1); + auto diag2 = numpy_to_range_of_pairs(d2); + + py::gil_scoped_release release; + + hera::AuctionParams<double> params; + params.wasserstein_power = wasserstein_power; + // hera encodes infinity as -1... + if(std::isinf(internal_p)) internal_p = hera::get_infinity<double>(); + params.internal_p = internal_p; + params.delta = delta; + // The extra parameters are purposedly not exposed for now. + return hera::wasserstein_dist(diag1, diag2, params); +} + +PYBIND11_MODULE(hera, m) { + m.def("wasserstein_distance", &wasserstein_distance, + py::arg("X"), py::arg("Y"), + py::arg("order") = 1, + py::arg("internal_p") = std::numeric_limits<double>::infinity(), + py::arg("delta") = .01, + R"pbdoc( + Compute the Wasserstein distance between two diagrams. + Points at infinity are supported. + + Parameters: + X (n x 2 numpy array): First diagram + Y (n x 2 numpy array): Second diagram + order (float): Wasserstein exponent W_q + internal_p (float): Internal Minkowski norm L^p in R^2 + delta (float): Relative error 1+delta + + Returns: + float: Approximate Wasserstein distance W_q(X,Y) + )pbdoc"); +} diff --git a/src/python/gudhi/nerve_gic.pyx b/src/python/gudhi/nerve_gic.pyx index 382e71c5..9c89b239 100644 --- a/src/python/gudhi/nerve_gic.pyx +++ b/src/python/gudhi/nerve_gic.pyx @@ -1,5 +1,7 @@ -# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. -# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - +# which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full +# license details. # Author(s): Vincent Rouvreau # # Copyright (C) 2018 Inria @@ -7,11 +9,13 @@ # Modification(s): # - YYYY/MM Author: Description of the modification +from __future__ import print_function from cython cimport numeric from libcpp.vector cimport vector from libcpp.utility cimport pair from libcpp.string cimport string from libcpp cimport bool +import errno import os from libc.stdint cimport intptr_t @@ -96,7 +100,8 @@ cdef class CoverComplex: return self.thisptr != NULL def set_point_cloud_from_range(self, cloud): - """ Reads and stores the input point cloud from a vector stored in memory. + """ Reads and stores the input point cloud from a vector stored in + memory. :param cloud: Input vector containing the point cloud. :type cloud: vector[vector[double]] @@ -104,7 +109,8 @@ cdef class CoverComplex: return self.thisptr.set_point_cloud_from_range(cloud) def set_distances_from_range(self, distance_matrix): - """ Reads and stores the input distance matrix from a vector stored in memory. + """ Reads and stores the input distance matrix from a vector stored in + memory. :param distance_matrix: Input vector containing the distance matrix. :type distance_matrix: vector[vector[double]] @@ -163,7 +169,8 @@ cdef class CoverComplex: """ stree = SimplexTree() cdef intptr_t stree_int_ptr=stree.thisptr - self.thisptr.create_simplex_tree(<Simplex_tree_interface_full_featured*>stree_int_ptr) + self.thisptr.create_simplex_tree( + <Simplex_tree_interface_full_featured*>stree_int_ptr) return stree def find_simplices(self): @@ -182,12 +189,12 @@ cdef class CoverComplex: if os.path.isfile(off_file): return self.thisptr.read_point_cloud(off_file.encode('utf-8')) else: - print("file " + off_file + " not found.") - return False + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + off_file) def set_automatic_resolution(self): """Computes the optimal length of intervals (i.e. the smallest interval - length avoiding discretization artifacts—see :cite:`Carriere17c`) for a + length avoiding discretization artifacts - see :cite:`Carriere17c`) for a functional cover. :rtype: double @@ -214,7 +221,8 @@ cdef class CoverComplex: if os.path.isfile(color_file_name): self.thisptr.set_color_from_file(color_file_name.encode('utf-8')) else: - print("file " + color_file_name + " not found.") + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + color_file_name) def set_color_from_range(self, color): """Computes the function used to color the nodes of the simplicial @@ -235,7 +243,8 @@ cdef class CoverComplex: if os.path.isfile(cover_file_name): self.thisptr.set_cover_from_file(cover_file_name.encode('utf-8')) else: - print("file " + cover_file_name + " not found.") + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + cover_file_name) def set_cover_from_function(self): """Creates a cover C from the preimages of the function f. @@ -268,7 +277,8 @@ cdef class CoverComplex: if os.path.isfile(func_file_name): self.thisptr.set_function_from_file(func_file_name.encode('utf-8')) else: - print("file " + func_file_name + " not found.") + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + func_file_name) def set_function_from_range(self, function): """Creates the function f from a vector stored in memory. @@ -288,7 +298,7 @@ cdef class CoverComplex: def set_graph_from_automatic_rips(self, N=100): """Creates a graph G from a Rips complex whose threshold value is - automatically tuned with subsampling—see. + automatically tuned with subsampling - see :cite:`Carriere17c`. :param N: Number of subsampling iteration (the default reasonable value is 100, but there is no guarantee on how to choose it). @@ -302,14 +312,15 @@ cdef class CoverComplex: """Creates a graph G from a file containing the edges. :param graph_file_name: Name of the input graph file. The graph file - contains one edge per line, each edge being represented by the IDs of - its two nodes. + contains one edge per line, each edge being represented by the IDs + of its two nodes. :type graph_file_name: string """ if os.path.isfile(graph_file_name): self.thisptr.set_graph_from_file(graph_file_name.encode('utf-8')) else: - print("file " + graph_file_name + " not found.") + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + graph_file_name) def set_graph_from_OFF(self): """Creates a graph G from the triangulation given by the input OFF diff --git a/src/python/gudhi/off_reader.pyx b/src/python/gudhi/off_reader.pyx index 7e6d9d80..a3200704 100644 --- a/src/python/gudhi/off_reader.pyx +++ b/src/python/gudhi/off_reader.pyx @@ -1,5 +1,7 @@ -# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. -# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - +# which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full +# license details. # Author(s): Vincent Rouvreau # # Copyright (C) 2016 Inria @@ -7,9 +9,11 @@ # Modification(s): # - YYYY/MM Author: Description of the modification +from __future__ import print_function from cython cimport numeric from libcpp.vector cimport vector from libcpp.string cimport string +import errno import os __author__ = "Vincent Rouvreau" @@ -32,6 +36,6 @@ def read_points_from_off_file(off_file=''): if os.path.isfile(off_file): return read_points_from_OFF_file(off_file.encode('utf-8')) else: - print("file " + off_file + " not found.") - return [] + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + off_file) diff --git a/src/python/gudhi/periodic_cubical_complex.pyx b/src/python/gudhi/periodic_cubical_complex.pyx index 37f76201..06309772 100644 --- a/src/python/gudhi/periodic_cubical_complex.pyx +++ b/src/python/gudhi/periodic_cubical_complex.pyx @@ -7,11 +7,13 @@ # Modification(s): # - YYYY/MM Author: Description of the modification +from __future__ import print_function from cython cimport numeric from libcpp.vector cimport vector from libcpp.utility cimport pair from libcpp.string cimport string from libcpp cimport bool +import sys import os import numpy as np @@ -30,7 +32,9 @@ cdef extern from "Cubical_complex_interface.h" namespace "Gudhi": cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi": cdef cppclass Periodic_cubical_complex_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Cubical_complex::Cubical_complex_interface<Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double>>>": Periodic_cubical_complex_persistence_interface(Periodic_cubical_complex_base_interface * st, bool persistence_dim_max) - vector[pair[int, pair[double, double]]] get_persistence(int homology_coeff_field, double min_persistence) + void compute_persistence(int homology_coeff_field, double min_persistence) + vector[pair[int, pair[double, double]]] get_persistence() + vector[vector[int]] cofaces_of_cubical_persistence_pairs() vector[int] betti_numbers() vector[int] persistent_betti_numbers(double from_value, double to_value) vector[pair[double,double]] intervals_in_dimension(int dimension) @@ -95,12 +99,12 @@ cdef class PeriodicCubicalComplex: if os.path.isfile(perseus_file): self.thisptr = new Periodic_cubical_complex_base_interface(perseus_file.encode('utf-8')) else: - print("file " + perseus_file + " not found.") + print("file " + perseus_file + " not found.", file=sys.stderr) else: print("CubicalComplex can be constructed from dimensions, " "top_dimensional_cells and periodic_dimensions, or from " "top_dimensional_cells and periodic_dimensions or from " - "a Perseus-style file name.") + "a Perseus-style file name.", file=sys.stderr) def __dealloc__(self): if self.thisptr != NULL: @@ -132,8 +136,31 @@ cdef class PeriodicCubicalComplex: """ return self.thisptr.dimension() + def compute_persistence(self, homology_coeff_field=11, min_persistence=0): + """This function computes the persistence of the complex, so it can be + accessed through :func:`persistent_betti_numbers`, + :func:`persistence_intervals_in_dimension`, etc. This function is + equivalent to :func:`persistence` when you do not want the list + :func:`persistence` returns. + + :param homology_coeff_field: The homology coefficient field. Must be a + prime number + :type homology_coeff_field: int. + :param min_persistence: The minimum persistence value to take into + account (strictly greater than min_persistence). Default value is + 0.0. + Sets min_persistence to -1.0 to see all values. + :type min_persistence: float. + :returns: Nothing. + """ + if self.pcohptr != NULL: + del self.pcohptr + assert self.__is_defined() + self.pcohptr = new Periodic_cubical_complex_persistence_interface(self.thisptr, True) + self.pcohptr.compute_persistence(homology_coeff_field, min_persistence) + def persistence(self, homology_coeff_field=11, min_persistence=0): - """This function returns the persistence of the complex. + """This function computes and returns the persistence of the complex. :param homology_coeff_field: The homology coefficient field. Must be a prime number @@ -146,30 +173,74 @@ cdef class PeriodicCubicalComplex: :returns: list of pairs(dimension, pair(birth, death)) -- the persistence of the complex. """ + self.compute_persistence(homology_coeff_field, min_persistence) + return self.pcohptr.get_persistence() + + def cofaces_of_persistence_pairs(self): + """A persistence interval is described by a pair of cells, one that creates the + feature and one that kills it. The filtration values of those 2 cells give coordinates + for a point in a persistence diagram, or a bar in a barcode. Structurally, in the + cubical complexes provided here, the filtration value of any cell is the minimum of the + filtration values of the maximal cells that contain it. Connecting persistence diagram + coordinates to the corresponding value in the input (i.e. the filtration values of + the top-dimensional cells) is useful for differentiation purposes. + + This function returns a list of pairs of top-dimensional cells corresponding to + the persistence birth and death cells of the filtration. The cells are represented by + their indices in the input list of top-dimensional cells (and not their indices in the + internal datastructure that includes non-maximal cells). Note that when two adjacent + top-dimensional cells have the same filtration value, we arbitrarily return one of the two + when calling the function on one of their common faces. + + :returns: The top-dimensional cells/cofaces of the positive and negative cells, + together with the corresponding homological dimension, in two lists of numpy arrays of integers. + The first list contains the regular persistence pairs, grouped by dimension. + It contains numpy arrays of shape [number_of_persistence_points, 2]. + The indices of the arrays in the list correspond to the homological dimensions, and the + integers of each row in each array correspond to: (index of positive top-dimensional cell, + index of negative top-dimensional cell). + The second list contains the essential features, grouped by dimension. + It contains numpy arrays of shape [number_of_persistence_points, 1]. + The indices of the arrays in the list correspond to the homological dimensions, and the + integers of each row in each array correspond to: (index of positive top-dimensional cell). + """ + cdef vector[vector[int]] persistence_result if self.pcohptr != NULL: - del self.pcohptr - if self.thisptr != NULL: - self.pcohptr = new Periodic_cubical_complex_persistence_interface(self.thisptr, True) - cdef vector[pair[int, pair[double, double]]] persistence_result - if self.pcohptr != NULL: - persistence_result = self.pcohptr.get_persistence(homology_coeff_field, min_persistence) - return persistence_result + output = [[],[]] + persistence_result = self.pcohptr.cofaces_of_cubical_persistence_pairs() + pr = np.array(persistence_result) + + ess_ind = np.argwhere(pr[:,2] == -1)[:,0] + ess = pr[ess_ind] + max_h = max(ess[:,0])+1 + for h in range(max_h): + hidxs = np.argwhere(ess[:,0] == h)[:,0] + output[1].append(ess[hidxs][:,1]) + + reg_ind = np.setdiff1d(np.array(range(len(pr))), ess_ind) + reg = pr[reg_ind] + max_h = max(reg[:,0])+1 + for h in range(max_h): + hidxs = np.argwhere(reg[:,0] == h)[:,0] + output[0].append(reg[hidxs][:,1:]) + else: + print("cofaces_of_persistence_pairs function requires persistence function" + " to be launched first.") + return output def betti_numbers(self): """This function returns the Betti numbers of the complex. :returns: list of int -- The Betti numbers ([B0, B1, ..., Bn]). - :note: betti_numbers function requires persistence function to be + :note: betti_numbers function requires :func:`compute_persistence` function to be launched first. - :note: betti_numbers function always returns [1, 0, 0, ...] as infinity + :note: This function always returns the Betti numbers of a torus as infinity filtration cubes are not removed from the complex. """ - cdef vector[int] bn_result - if self.pcohptr != NULL: - bn_result = self.pcohptr.betti_numbers() - return bn_result + assert self.pcohptr != NULL, "compute_persistence() must be called before betti_numbers()" + return self.pcohptr.betti_numbers() def persistent_betti_numbers(self, from_value, to_value): """This function returns the persistent Betti numbers of the complex. @@ -184,13 +255,11 @@ cdef class PeriodicCubicalComplex: :returns: list of int -- The persistent Betti numbers ([B0, B1, ..., Bn]). - :note: persistent_betti_numbers function requires persistence + :note: persistent_betti_numbers function requires :func:`compute_persistence` function to be launched first. """ - cdef vector[int] pbn_result - if self.pcohptr != NULL: - pbn_result = self.pcohptr.persistent_betti_numbers(<double>from_value, <double>to_value) - return pbn_result + assert self.pcohptr != NULL, "compute_persistence() must be called before persistent_betti_numbers()" + return self.pcohptr.persistent_betti_numbers(<double>from_value, <double>to_value) def persistence_intervals_in_dimension(self, dimension): """This function returns the persistence intervals of the complex in a @@ -201,13 +270,8 @@ cdef class PeriodicCubicalComplex: :returns: The persistence intervals. :rtype: numpy array of dimension 2 - :note: intervals_in_dim function requires persistence function to be + :note: intervals_in_dim function requires :func:`compute_persistence` function to be launched first. """ - cdef vector[pair[double,double]] intervals_result - if self.pcohptr != NULL: - intervals_result = self.pcohptr.intervals_in_dimension(dimension) - else: - print("intervals_in_dim function requires persistence function" - " to be launched first.") - return np.array(intervals_result) + assert self.pcohptr != NULL, "compute_persistence() must be called before persistence_intervals_in_dimension()" + return np.array(self.pcohptr.intervals_in_dimension(dimension)) diff --git a/src/python/gudhi/persistence_graphical_tools.py b/src/python/gudhi/persistence_graphical_tools.py index 246280de..6a74a6ca 100644 --- a/src/python/gudhi/persistence_graphical_tools.py +++ b/src/python/gudhi/persistence_graphical_tools.py @@ -5,6 +5,7 @@ # Copyright (C) 2016 Inria # # Modification(s): +# - 2020/02 Theo Lacombe: Added more options for improved rendering and more flexibility. # - YYYY/MM Author: Description of the modification from os import path @@ -14,7 +15,7 @@ import numpy as np from gudhi.reader_utils import read_persistence_intervals_in_dimension from gudhi.reader_utils import read_persistence_intervals_grouped_by_dimension -__author__ = "Vincent Rouvreau, Bertrand Michel" +__author__ = "Vincent Rouvreau, Bertrand Michel, Theo Lacombe" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" @@ -43,6 +44,19 @@ def __min_birth_max_death(persistence, band=0.0): max_death += band return (min_birth, max_death) + +def _array_handler(a): + ''' + :param a: if array, assumes it is a (n x 2) np.array and return a + persistence-compatible list (padding with 0), so that the + plot can be performed seamlessly. + ''' + if isinstance(a[0][1], np.float64) or isinstance(a[0][1], float): + return [[0, x] for x in a] + else: + return a + + def plot_persistence_barcode( persistence=[], persistence_file="", @@ -52,14 +66,17 @@ def plot_persistence_barcode( inf_delta=0.1, legend=False, colormap=None, - axes=None + axes=None, + fontsize=16, ): """This function plots the persistence bar code from persistence values list - or from a :doc:`persistence file <fileformats>`. + , a np.array of shape (N x 2) (representing a diagram + in a single homology dimension), + or from a `persistence diagram <fileformats.html#persistence-diagram>`_ file. - :param persistence: Persistence intervals values list grouped by dimension. - :type persistence: list of tuples(dimension, tuple(birth, death)). - :param persistence_file: A :doc:`persistence file <fileformats>` style name + :param persistence: Persistence intervals values list. Can be grouped by dimension or not. + :type persistence: an array of (dimension, array of (birth, death)) or an array of (birth, death). + :param persistence_file: A `persistence diagram <fileformats.html#persistence-diagram>`_ file style name (reset persistence if both are set). :type persistence_file: string :param alpha: barcode transparency value (0.0 transparent through 1.0 @@ -81,11 +98,16 @@ def plot_persistence_barcode( :param axes: A matplotlib-like subplot axes. If None, the plot is drawn on a new set of axes. :type axes: `matplotlib.axes.Axes` + :param fontsize: Fontsize to use in axis. + :type fontsize: int :returns: (`matplotlib.axes.Axes`): The axes on which the plot was drawn. """ try: import matplotlib.pyplot as plt import matplotlib.patches as mpatches + from matplotlib import rc + plt.rc('text', usetex=True) + plt.rc('font', family='serif') if persistence_file != "": if path.isfile(persistence_file): @@ -101,6 +123,8 @@ def plot_persistence_barcode( print("file " + persistence_file + " not found.") return None + persistence = _array_handler(persistence) + if max_barcodes != 1000: print("Deprecated parameter. It has been replaced by max_intervals") max_intervals = max_barcodes @@ -163,7 +187,7 @@ def plot_persistence_barcode( loc="lower right", ) - axes.set_title("Persistence barcode") + axes.set_title("Persistence barcode", fontsize=fontsize) # Ends plot on infinity value and starts a little bit before min_birth axes.axis([axis_start, infinity, 0, ind]) @@ -183,14 +207,17 @@ def plot_persistence_diagram( inf_delta=0.1, legend=False, colormap=None, - axes=None + axes=None, + fontsize=16, + greyblock=True ): """This function plots the persistence diagram from persistence values - list or from a :doc:`persistence file <fileformats>`. + list, a np.array of shape (N x 2) representing a diagram in a single + homology dimension, or from a `persistence diagram <fileformats.html#persistence-diagram>`_ file`. - :param persistence: Persistence intervals values list grouped by dimension. - :type persistence: list of tuples(dimension, tuple(birth, death)). - :param persistence_file: A :doc:`persistence file <fileformats>` style name + :param persistence: Persistence intervals values list. Can be grouped by dimension or not. + :type persistence: an array of (dimension, array of (birth, death)) or an array of (birth, death). + :param persistence_file: A `persistence diagram <fileformats.html#persistence-diagram>`_ file style name (reset persistence if both are set). :type persistence_file: string :param alpha: plot transparency value (0.0 transparent through 1.0 @@ -214,11 +241,18 @@ def plot_persistence_diagram( :param axes: A matplotlib-like subplot axes. If None, the plot is drawn on a new set of axes. :type axes: `matplotlib.axes.Axes` + :param fontsize: Fontsize to use in axis. + :type fontsize: int + :param greyblock: if we want to plot a grey patch on the lower half plane for nicer rendering. Default True. + :type greyblock: boolean :returns: (`matplotlib.axes.Axes`): The axes on which the plot was drawn. """ try: import matplotlib.pyplot as plt import matplotlib.patches as mpatches + from matplotlib import rc + plt.rc('text', usetex=True) + plt.rc('font', family='serif') if persistence_file != "": if path.isfile(persistence_file): @@ -234,6 +268,8 @@ def plot_persistence_diagram( print("file " + persistence_file + " not found.") return None + persistence = _array_handler(persistence) + if max_plots != 1000: print("Deprecated parameter. It has been replaced by max_intervals") max_intervals = max_plots @@ -256,19 +292,18 @@ def plot_persistence_diagram( # Replace infinity values with max_death + delta for diagram to be more # readable infinity = max_death + delta + axis_end = max_death + delta / 2 axis_start = min_birth - delta - # line display of equation : birth = death - x = np.linspace(axis_start, infinity, 1000) - # infinity line and text - axes.plot(x, x, color="k", linewidth=1.0) - axes.plot(x, [infinity] * len(x), linewidth=1.0, color="k", alpha=alpha) - axes.text(axis_start, infinity, r"$\infty$", color="k", alpha=alpha) # bootstrap band if band > 0.0: + x = np.linspace(axis_start, infinity, 1000) axes.fill_between(x, x, x + band, alpha=alpha, facecolor="red") - + # lower diag patch + if greyblock: + axes.add_patch(mpatches.Polygon([[axis_start, axis_start], [axis_end, axis_start], [axis_end, axis_end]], fill=True, color='lightgrey')) # Draw points in loop + pts_at_infty = False # Records presence of pts at infty for interval in reversed(persistence): if float(interval[1][1]) != float("inf"): # Finite death case @@ -279,10 +314,23 @@ def plot_persistence_diagram( color=colormap[interval[0]], ) else: + pts_at_infty = True # Infinite death case for diagram to be nicer axes.scatter( interval[1][0], infinity, alpha=alpha, color=colormap[interval[0]] ) + if pts_at_infty: + # infinity line and text + axes.plot([axis_start, axis_end], [axis_start, axis_end], linewidth=1.0, color="k") + axes.plot([axis_start, axis_end], [infinity, infinity], linewidth=1.0, color="k", alpha=alpha) + # Infinity label + yt = axes.get_yticks() + yt = yt[np.where(yt < axis_end)] # to avoid ploting ticklabel higher than infinity + yt = np.append(yt, infinity) + ytl = ["%.3f" % e for e in yt] # to avoid float precision error + ytl[-1] = r'$+\infty$' + axes.set_yticks(yt) + axes.set_yticklabels(ytl) if legend: dimensions = list(set(item[0] for item in persistence)) @@ -293,11 +341,11 @@ def plot_persistence_diagram( ] ) - axes.set_xlabel("Birth") - axes.set_ylabel("Death") + axes.set_xlabel("Birth", fontsize=fontsize) + axes.set_ylabel("Death", fontsize=fontsize) + axes.set_title("Persistence diagram", fontsize=fontsize) # Ends plot on infinity value and starts a little bit before min_birth - axes.axis([axis_start, infinity, axis_start, infinity + delta]) - axes.set_title("Persistence diagram") + axes.axis([axis_start, axis_end, axis_start, infinity + delta/2]) return axes except ImportError: @@ -313,18 +361,26 @@ def plot_persistence_density( dimension=None, cmap=None, legend=False, - axes=None + axes=None, + fontsize=16, + greyblock=False ): """This function plots the persistence density from persistence - values list or from a :doc:`persistence file <fileformats>`. Be - aware that this function does not distinguish the dimension, it is + values list, np.array of shape (N x 2) representing a diagram + in a single homology dimension, + or from a `persistence diagram <fileformats.html#persistence-diagram>`_ file. + Be aware that this function does not distinguish the dimension, it is up to you to select the required one. This function also does not handle degenerate data set (scipy correlation matrix inversion can fail). - :param persistence: Persistence intervals values list grouped by dimension. - :type persistence: list of tuples(dimension, tuple(birth, death)). - :param persistence_file: A :doc:`persistence file <fileformats>` - style name (reset persistence if both are set). + :Requires: `SciPy <installation.html#scipy>`_ + + :param persistence: Persistence intervals values list. + Can be grouped by dimension or not. + :type persistence: an array of (dimension, array of (birth, death)) + or an array of (birth, death). + :param persistence_file: A `persistence diagram <fileformats.html#persistence-diagram>`_ + file style name (reset persistence if both are set). :type persistence_file: string :param nbins: Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents (default is 300) @@ -355,11 +411,20 @@ def plot_persistence_density( :param axes: A matplotlib-like subplot axes. If None, the plot is drawn on a new set of axes. :type axes: `matplotlib.axes.Axes` + :param fontsize: Fontsize to use in axis. + :type fontsize: int + :param greyblock: if we want to plot a grey patch on the lower half plane + for nicer rendering. Default False. + :type greyblock: boolean :returns: (`matplotlib.axes.Axes`): The axes on which the plot was drawn. """ try: import matplotlib.pyplot as plt + import matplotlib.patches as mpatches from scipy.stats import kde + from matplotlib import rc + plt.rc('text', usetex=True) + plt.rc('font', family='serif') if persistence_file != "": if dimension is None: @@ -374,6 +439,7 @@ def plot_persistence_density( return None if len(persistence) > 0: + persistence = _array_handler(persistence) persistence_dim = np.array( [ (dim_interval[1][0], dim_interval[1][1]) @@ -418,12 +484,16 @@ def plot_persistence_density( # Make the plot img = axes.pcolormesh(xi, yi, zi.reshape(xi.shape), cmap=cmap) + if greyblock: + axes.add_patch(mpatches.Polygon([[birth.min(), birth.min()], [death.max(), birth.min()], [death.max(), death.max()]], fill=True, color='lightgrey')) + if legend: plt.colorbar(img, ax=axes) - axes.set_xlabel("Birth") - axes.set_ylabel("Death") - axes.set_title("Persistence density") + axes.set_xlabel("Birth", fontsize=fontsize) + axes.set_ylabel("Death", fontsize=fontsize) + axes.set_title("Persistence density", fontsize=fontsize) + return axes except ImportError: diff --git a/src/python/gudhi/point_cloud/__init__.py b/src/python/gudhi/point_cloud/__init__.py new file mode 100644 index 00000000..e69de29b --- /dev/null +++ b/src/python/gudhi/point_cloud/__init__.py diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py new file mode 100644 index 00000000..13e16d24 --- /dev/null +++ b/src/python/gudhi/point_cloud/dtm.py @@ -0,0 +1,70 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Marc Glisse +# +# Copyright (C) 2020 Inria +# +# Modification(s): +# - YYYY/MM Author: Description of the modification + +from .knn import KNearestNeighbors + +__author__ = "Marc Glisse" +__copyright__ = "Copyright (C) 2020 Inria" +__license__ = "MIT" + + +class DistanceToMeasure: + """ + Class to compute the distance to the empirical measure defined by a point set, as introduced in :cite:`dtm`. + """ + + def __init__(self, k, q=2, **kwargs): + """ + Args: + k (int): number of neighbors (possibly including the point itself). + q (float): order used to compute the distance to measure. Defaults to 2. + kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNearestNeighbors`, except that + metric="neighbors" means that :func:`transform` expects an array with the distances + to the k nearest neighbors. + """ + self.k = k + self.q = q + self.params = kwargs + + def fit_transform(self, X, y=None): + return self.fit(X).transform(X) + + def fit(self, X, y=None): + """ + Args: + X (numpy.array): coordinates for mass points. + """ + if self.params.setdefault("metric", "euclidean") != "neighbors": + self.knn = KNearestNeighbors( + self.k, return_index=False, return_distance=True, sort_results=False, **self.params + ) + self.knn.fit(X) + return self + + def transform(self, X): + """ + Args: + X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed", + or distances to the k nearest neighbors if metric is "neighbors" (if the array has more + than k columns, the remaining ones are ignored). + + Returns: + numpy.array: a 1-d array with, for each point of X, its distance to the measure defined + by the argument of :func:`fit`. + """ + if self.params["metric"] == "neighbors": + distances = X[:, : self.k] + else: + distances = self.knn.transform(X) + distances = distances ** self.q + dtm = distances.sum(-1) / self.k + dtm = dtm ** (1.0 / self.q) + # We compute too many powers, 1/p in knn then q in dtm, 1/q in dtm then q or some log in the caller. + # Add option to skip the final root? + return dtm diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py new file mode 100644 index 00000000..86008bc3 --- /dev/null +++ b/src/python/gudhi/point_cloud/knn.py @@ -0,0 +1,328 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Marc Glisse +# +# Copyright (C) 2020 Inria +# +# Modification(s): +# - YYYY/MM Author: Description of the modification + +import numpy + +# TODO: https://github.com/facebookresearch/faiss + +__author__ = "Marc Glisse" +__copyright__ = "Copyright (C) 2020 Inria" +__license__ = "MIT" + + +class KNearestNeighbors: + """ + Class wrapping several implementations for computing the k nearest neighbors in a point set. + + :Requires: `PyKeOps <installation.html#pykeops>`_, `SciPy <installation.html#scipy>`_, + `Scikit-learn <installation.html#scikit-learn>`_, and/or `Hnswlib <installation.html#hnswlib>`_ + in function of the selected `implementation`. + """ + + def __init__(self, k, return_index=True, return_distance=False, metric="euclidean", **kwargs): + """ + Args: + k (int): number of neighbors (possibly including the point itself). + return_index (bool): if True, return the index of each neighbor. + return_distance (bool): if True, return the distance to each neighbor. + implementation (str): choice of the library that does the real work. + + * 'keops' for a brute-force, CUDA implementation through pykeops. Useful when the dimension becomes large (10+) but the number of points remains low (less than a million). Only "minkowski" and its aliases are supported. + * 'ckdtree' for scipy's cKDTree. Only "minkowski" and its aliases are supported. + * 'sklearn' for scikit-learn's NearestNeighbors. Note that this provides in particular an option algorithm="brute". + * 'hnsw' for hnswlib.Index. It can be very fast but does not provide guarantees. Only supports "euclidean" for now. + * None will try to select a sensible one (scipy if possible, scikit-learn otherwise). + metric (str): see `sklearn.neighbors.NearestNeighbors`. + eps (float): relative error when computing nearest neighbors with the cKDTree. + p (float): norm L^p on input points (including numpy.inf) if metric is "minkowski". Defaults to 2. + n_jobs (int): number of jobs to schedule for parallel processing of nearest neighbors on the CPU. + If -1 is given all processors are used. Default: 1. + sort_results (bool): if True, then distances and indices of each point are + sorted on return, so that the first column contains the closest points. + Otherwise, neighbors are returned in an arbitrary order. Defaults to True. + enable_autodiff (bool): if the input is a torch.tensor, jax.numpy.ndarray or tensorflow.Tensor, this + instructs the function to compute distances in a way that works with automatic differentiation. + This is experimental, not supported for all metrics, and requires the package EagerPy. + Defaults to False. + kwargs: additional parameters are forwarded to the backends. + """ + self.k = k + self.return_index = return_index + self.return_distance = return_distance + self.metric = metric + self.params = kwargs + # canonicalize + if metric == "euclidean": + self.params["p"] = 2 + self.metric = "minkowski" + elif metric == "manhattan": + self.params["p"] = 1 + self.metric = "minkowski" + elif metric == "chebyshev": + self.params["p"] = numpy.inf + self.metric = "minkowski" + elif metric == "minkowski": + self.params["p"] = kwargs.get("p", 2) + if self.params.get("implementation") in {"keops", "ckdtree"}: + assert self.metric == "minkowski" + if self.params.get("implementation") == "hnsw": + assert self.metric == "minkowski" and self.params["p"] == 2 + if not self.params.get("implementation"): + if self.metric == "minkowski": + self.params["implementation"] = "ckdtree" + else: + self.params["implementation"] = "sklearn" + if not return_distance: + self.params["enable_autodiff"] = False + + def fit_transform(self, X, y=None): + return self.fit(X).transform(X) + + def fit(self, X, y=None): + """ + Args: + X (numpy.array): coordinates for reference points. + """ + self.ref_points = X + if self.params.get("enable_autodiff", False): + import eagerpy as ep + + X = ep.astensor(X) + if self.params["implementation"] != "keops" or not isinstance(X, ep.PyTorchTensor): + # I don't know a clever way to reuse a GPU tensor from tensorflow in pytorch + # without copying to/from the CPU. + X = X.numpy() + if self.params["implementation"] == "ckdtree": + # sklearn could handle this, but it is much slower + from scipy.spatial import cKDTree + + self.kdtree = cKDTree(X) + + if self.params["implementation"] == "sklearn" and self.metric != "precomputed": + # FIXME: sklearn badly handles "precomputed" + from sklearn.neighbors import NearestNeighbors + + nargs = { + k: v for k, v in self.params.items() if k in {"p", "n_jobs", "metric_params", "algorithm", "leaf_size"} + } + self.nn = NearestNeighbors(self.k, metric=self.metric, **nargs) + self.nn.fit(X) + + if self.params["implementation"] == "hnsw": + import hnswlib + + self.graph = hnswlib.Index("l2", len(X[0])) # Actually returns squared distances + self.graph.init_index( + len(X), **{k: v for k, v in self.params.items() if k in {"ef_construction", "M", "random_seed"}} + ) + n = self.params.get("num_threads") + if n is None: + n = self.params.get("n_jobs", 1) + self.params["num_threads"] = n + self.graph.add_items(X, num_threads=n) + + return self + + def transform(self, X): + """ + Args: + X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed". + + Returns: + numpy.array: if return_index, an array of shape (len(X), k) with the indices (in the argument + of :func:`fit`) of the k nearest neighbors to the points of X. If return_distance, an array of the + same shape with the distances to those neighbors. If both, a tuple with the two arrays, in this order. + """ + if self.params.get("enable_autodiff", False): + # pykeops does not support autodiff for kmin yet, but when it does in the future, + # we may want a special path. + import eagerpy as ep + + save_return_index = self.return_index + self.return_index = True + self.return_distance = False + self.params["enable_autodiff"] = False + try: + newX = ep.astensor(X) + if self.params["implementation"] != "keops" or ( + not isinstance(newX, ep.PyTorchTensor) and not isinstance(newX, ep.NumPyTensor) + ): + newX = newX.numpy() + else: + newX = newX.raw + neighbors = self.transform(newX) + finally: + self.return_index = save_return_index + self.return_distance = True + self.params["enable_autodiff"] = True + # We can implement more later as needed + assert self.metric == "minkowski" + p = self.params["p"] + Y = ep.astensor(self.ref_points) + neighbor_pts = Y[ + neighbors, + ] + diff = neighbor_pts - X[:, None, :] + if isinstance(diff, ep.PyTorchTensor): + # https://github.com/jonasrauber/eagerpy/issues/6 + distances = ep.astensor(diff.raw.norm(p, -1)) + else: + distances = diff.norms.lp(p, -1) + if self.return_index: + return neighbors, distances.raw + else: + return distances.raw + + metric = self.metric + k = self.k + + if metric == "precomputed": + # scikit-learn could handle that, but they insist on calling fit() with an unused square array, which is too unnatural. + if self.return_index: + n_jobs = self.params.get("n_jobs", 1) + # Supposedly numpy can be compiled with OpenMP and handle this, but nobody does that?! + if n_jobs == 1: + neighbors = numpy.argpartition(X, k - 1)[:, 0:k] + if self.params.get("sort_results", True): + X = numpy.take_along_axis(X, neighbors, axis=-1) + ngb_order = numpy.argsort(X, axis=-1) + neighbors = numpy.take_along_axis(neighbors, ngb_order, axis=-1) + else: + ngb_order = neighbors + if self.return_distance: + distances = numpy.take_along_axis(X, ngb_order, axis=-1) + return neighbors, distances + else: + return neighbors + else: + from joblib import Parallel, delayed, effective_n_jobs + from sklearn.utils import gen_even_slices + + slices = gen_even_slices(len(X), effective_n_jobs(n_jobs)) + parallel = Parallel(prefer="threads", n_jobs=n_jobs) + if self.params.get("sort_results", True): + + def func(M): + neighbors = numpy.argpartition(M, k - 1)[:, 0:k] + Y = numpy.take_along_axis(M, neighbors, axis=-1) + ngb_order = numpy.argsort(Y, axis=-1) + return numpy.take_along_axis(neighbors, ngb_order, axis=-1) + + else: + + def func(M): + return numpy.argpartition(M, k - 1)[:, 0:k] + + neighbors = numpy.concatenate(parallel(delayed(func)(X[s]) for s in slices)) + if self.return_distance: + distances = numpy.take_along_axis(X, neighbors, axis=-1) + return neighbors, distances + else: + return neighbors + if self.return_distance: + n_jobs = self.params.get("n_jobs", 1) + if n_jobs == 1: + distances = numpy.partition(X, k - 1)[:, 0:k] + if self.params.get("sort_results"): + # partition is not guaranteed to sort the lower half, although it often does + distances.sort(axis=-1) + else: + from joblib import Parallel, delayed, effective_n_jobs + from sklearn.utils import gen_even_slices + + if self.params.get("sort_results"): + + def func(M): + # Not partitioning in place, because we should not modify the user's array? + r = numpy.partition(M, k - 1)[:, 0:k] + r.sort(axis=-1) + return r + + else: + func = lambda M: numpy.partition(M, k - 1)[:, 0:k] + slices = gen_even_slices(len(X), effective_n_jobs(n_jobs)) + parallel = Parallel(prefer="threads", n_jobs=n_jobs) + distances = numpy.concatenate(parallel(delayed(func)(X[s]) for s in slices)) + return distances + return None + + if self.params["implementation"] == "hnsw": + ef = self.params.get("ef") + if ef is not None: + self.graph.set_ef(ef) + neighbors, distances = self.graph.knn_query(X, k, num_threads=self.params["num_threads"]) + # The k nearest neighbors are always sorted. I couldn't find it in the doc, but the code calls searchKnn, + # which returns a priority_queue, and then fills the return array backwards with top/pop on the queue. + if self.return_index: + if self.return_distance: + return neighbors, numpy.sqrt(distances) + else: + return neighbors + if self.return_distance: + return numpy.sqrt(distances) + return None + + if self.params["implementation"] == "keops": + import torch + from pykeops.torch import LazyTensor + + # 'float64' is slow except on super expensive GPUs. Allow it with some param? + XX = torch.as_tensor(X, dtype=torch.float32) + if X is self.ref_points: + YY = XX + else: + YY = torch.as_tensor(self.ref_points, dtype=torch.float32) + p = self.params["p"] + if p == numpy.inf: + # Requires pykeops 1.4 or later + mat = (LazyTensor(XX[:, None, :]) - LazyTensor(YY[None, :, :])).abs().max(-1) + elif p == 2: # Any even integer? + mat = ((LazyTensor(XX[:, None, :]) - LazyTensor(YY[None, :, :])) ** p).sum(-1) + else: + mat = ((LazyTensor(XX[:, None, :]) - LazyTensor(YY[None, :, :])).abs() ** p).sum(-1) + + if self.return_index: + if self.return_distance: + distances, neighbors = mat.Kmin_argKmin(k, dim=1) + if p != numpy.inf: + distances = distances ** (1.0 / p) + return neighbors, distances + else: + neighbors = mat.argKmin(k, dim=1) + return neighbors + if self.return_distance: + distances = mat.Kmin(k, dim=1) + if p != numpy.inf: + distances = distances ** (1.0 / p) + return distances + return None + + if self.params["implementation"] == "ckdtree": + qargs = {key: val for key, val in self.params.items() if key in {"p", "eps", "n_jobs"}} + distances, neighbors = self.kdtree.query(X, k=self.k, **qargs) + if self.return_index: + if self.return_distance: + return neighbors, distances + else: + return neighbors + if self.return_distance: + return distances + return None + + assert self.params["implementation"] == "sklearn" + if self.return_distance: + distances, neighbors = self.nn.kneighbors(X, return_distance=True) + if self.return_index: + return neighbors, distances + else: + return distances + if self.return_index: + neighbors = self.nn.kneighbors(X, return_distance=False) + return neighbors + return None diff --git a/src/python/gudhi/point_cloud/timedelay.py b/src/python/gudhi/point_cloud/timedelay.py new file mode 100644 index 00000000..5292e752 --- /dev/null +++ b/src/python/gudhi/point_cloud/timedelay.py @@ -0,0 +1,94 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Martin Royer, Yuichi Ike, Masatoshi Takenouchi +# +# Copyright (C) 2020 Inria, Copyright (C) 2020 Fujitsu Laboratories Ltd. +# Modification(s): +# - YYYY/MM Author: Description of the modification + +import numpy as np + + +class TimeDelayEmbedding: + """Point cloud transformation class. Embeds time-series data in the R^d according to + `Takens' Embedding Theorem <https://en.wikipedia.org/wiki/Takens%27s_theorem>`_ and obtains the + coordinates of each point. + + Parameters + ---------- + dim : int, optional (default=3) + `d` of R^d to be embedded. + delay : int, optional (default=1) + Time-Delay embedding. + skip : int, optional (default=1) + How often to skip embedded points. + + Example + ------- + + Given delay=3 and skip=2, a point cloud which is obtained by embedding + a scalar time-series into R^3 is as follows:: + + time-series = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + point cloud = [[1, 4, 7], + [3, 6, 9]] + + Given delay=1 and skip=1, a point cloud which is obtained by embedding + a 2D vector time-series data into R^4 is as follows:: + + time-series = [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]] + point cloud = [[0, 1, 2, 3], + [2, 3, 4, 5], + [4, 5, 6, 7], + [6, 7, 8, 9]] + """ + + def __init__(self, dim=3, delay=1, skip=1): + self._dim = dim + self._delay = delay + self._skip = skip + + def __call__(self, ts): + """Transform method for single time-series data. + + Parameters + ---------- + ts : Iterable[float] or Iterable[Iterable[float]] + A single time-series data, with scalar or vector values. + + Returns + ------- + point cloud : n x dim numpy arrays + Makes point cloud from a single time-series data. + """ + return self._transform(np.array(ts)) + + def fit(self, ts, y=None): + return self + + def _transform(self, ts): + """Guts of transform method.""" + if ts.ndim == 1: + repeat = self._dim + else: + assert self._dim % ts.shape[1] == 0 + repeat = self._dim // ts.shape[1] + end = len(ts) - self._delay * (repeat - 1) + short = np.arange(0, end, self._skip) + vertical = np.arange(0, repeat * self._delay, self._delay) + return ts[np.add.outer(short, vertical)].reshape(len(short), -1) + + def transform(self, ts): + """Transform method for multiple time-series data. + + Parameters + ---------- + ts : Iterable[Iterable[float]] or Iterable[Iterable[Iterable[float]]] + Multiple time-series data, with scalar or vector values. + + Returns + ------- + point clouds : list of n x dim numpy arrays + Makes point cloud from each time-series data. + """ + return [self._transform(np.array(s)) for s in ts] diff --git a/src/python/gudhi/representations/kernel_methods.py b/src/python/gudhi/representations/kernel_methods.py index bfc83aff..596f4f07 100644 --- a/src/python/gudhi/representations/kernel_methods.py +++ b/src/python/gudhi/representations/kernel_methods.py @@ -9,13 +9,83 @@ import numpy as np from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.metrics import pairwise_distances -from .metrics import SlicedWassersteinDistance, PersistenceFisherDistance +from sklearn.metrics import pairwise_distances, pairwise_kernels +from .metrics import SlicedWassersteinDistance, PersistenceFisherDistance, _sklearn_wrapper, pairwise_persistence_diagram_distances, _sliced_wasserstein_distance, _persistence_fisher_distance +from .preprocessing import Padding ############################################# # Kernel methods ############################ ############################################# +def _persistence_weighted_gaussian_kernel(D1, D2, weight=lambda x: 1, kernel_approx=None, bandwidth=1.): + """ + This is a function for computing the persistence weighted Gaussian kernel value from two persistence diagrams. The persistence weighted Gaussian kernel is computed by convolving the persistence diagram points with weighted Gaussian kernels. See http://proceedings.mlr.press/v48/kusano16.html for more details. + + Parameters: + D1: (n x 2) numpy.array encoding the (finite points of the) first diagram. Must not contain essential points (i.e. with infinite coordinate). + D2: (m x 2) numpy.array encoding the second diagram. + bandwidth (double): bandwidth of the Gaussian kernel with which persistence diagrams will be convolved + weight: weight function for the persistence diagram points (default constant function, ie lambda x: 1). This function must be defined on 2D points, ie lists or numpy arrays of the form [p_x,p_y]. + kernel_approx: kernel approximation class used to speed up computation. Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance). + + Returns: + float: the persistence weighted Gaussian kernel value between persistence diagrams. + """ + ws1 = np.array([weight(D1[j,:]) for j in range(len(D1))]) + ws2 = np.array([weight(D2[j,:]) for j in range(len(D2))]) + if kernel_approx is not None: + approx1 = np.sum(np.multiply(ws1[:,np.newaxis], kernel_approx.transform(D1)), axis=0) + approx2 = np.sum(np.multiply(ws2[:,np.newaxis], kernel_approx.transform(D2)), axis=0) + return (1./(np.sqrt(2*np.pi)*bandwidth)) * np.matmul(approx1, approx2.T) + else: + W = np.matmul(ws1[:,np.newaxis], ws2[np.newaxis,:]) + E = (1./(np.sqrt(2*np.pi)*bandwidth)) * np.exp(-np.square(pairwise_distances(D1,D2))/(2*bandwidth*bandwidth)) + return np.sum(np.multiply(W, E)) + +def _persistence_scale_space_kernel(D1, D2, kernel_approx=None, bandwidth=1.): + """ + This is a function for computing the persistence scale space kernel value from two persistence diagrams. The persistence scale space kernel is computed by adding the symmetric to the diagonal of each point in each persistence diagram, with negative weight, and then convolving the points with a Gaussian kernel. See https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Reininghaus_A_Stable_Multi-Scale_2015_CVPR_paper.pdf for more details. + + Parameters: + D1: (n x 2) numpy.array encoding the (finite points of the) first diagram. Must not contain essential points (i.e. with infinite coordinate). + D2: (m x 2) numpy.array encoding the second diagram. + bandwidth (double): bandwidth of the Gaussian kernel with which persistence diagrams will be convolved + kernel_approx: kernel approximation class used to speed up computation. Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance). + + Returns: + float: the persistence scale space kernel value between persistence diagrams. + """ + DD1 = np.concatenate([D1, D1[:,[1,0]]], axis=0) + DD2 = np.concatenate([D2, D2[:,[1,0]]], axis=0) + weight_pss = lambda x: 1 if x[1] >= x[0] else -1 + return 0.5 * _persistence_weighted_gaussian_kernel(DD1, DD2, weight=weight_pss, kernel_approx=kernel_approx, bandwidth=bandwidth) + +def pairwise_persistence_diagram_kernels(X, Y=None, kernel="sliced_wasserstein", **kwargs): + """ + This function computes the kernel matrix between two lists of persistence diagrams given as numpy arrays of shape (nx2). + + Parameters: + X (list of n numpy arrays of shape (numx2)): first list of persistence diagrams. + Y (list of m numpy arrays of shape (numx2)): second list of persistence diagrams (optional). If None, pairwise kernel values are computed from the first list only. + kernel: kernel to use. It can be either a string ("sliced_wasserstein", "persistence_scale_space", "persistence_weighted_gaussian", "persistence_fisher") or a function taking two numpy arrays of shape (nx2) and (mx2) as inputs. If it is a function, make sure that it is symmetric. + **kwargs: optional keyword parameters. Any further parameters are passed directly to the kernel function. See the docs of the various kernel classes in this module. + + Returns: + numpy array of shape (nxm): kernel matrix. + """ + XX = np.reshape(np.arange(len(X)), [-1,1]) + YY = None if Y is None else np.reshape(np.arange(len(Y)), [-1,1]) + if kernel == "sliced_wasserstein": + return np.exp(-pairwise_persistence_diagram_distances(X, Y, metric="sliced_wasserstein", num_directions=kwargs["num_directions"]) / kwargs["bandwidth"]) + elif kernel == "persistence_fisher": + return np.exp(-pairwise_persistence_diagram_distances(X, Y, metric="persistence_fisher", kernel_approx=kwargs["kernel_approx"], bandwidth=kwargs["bandwidth"]) / kwargs["bandwidth_fisher"]) + elif kernel == "persistence_scale_space": + return pairwise_kernels(XX, YY, metric=_sklearn_wrapper(_persistence_scale_space_kernel, X, Y, **kwargs)) + elif kernel == "persistence_weighted_gaussian": + return pairwise_kernels(XX, YY, metric=_sklearn_wrapper(_persistence_weighted_gaussian_kernel, X, Y, **kwargs)) + else: + return pairwise_kernels(XX, YY, metric=_sklearn_wrapper(metric, **kwargs)) + class SlicedWassersteinKernel(BaseEstimator, TransformerMixin): """ This is a class for computing the sliced Wasserstein kernel matrix from a list of persistence diagrams. The sliced Wasserstein kernel is computed by exponentiating the corresponding sliced Wasserstein distance with a Gaussian kernel. See http://proceedings.mlr.press/v70/carriere17a.html for more details. @@ -29,7 +99,7 @@ class SlicedWassersteinKernel(BaseEstimator, TransformerMixin): num_directions (int): number of lines evenly sampled from [-pi/2,pi/2] in order to approximate and speed up the kernel computation (default 10). """ self.bandwidth = bandwidth - self.sw_ = SlicedWassersteinDistance(num_directions=num_directions) + self.num_directions = num_directions def fit(self, X, y=None): """ @@ -39,7 +109,7 @@ class SlicedWassersteinKernel(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. y (n x 1 array): persistence diagram labels (unused). """ - self.sw_.fit(X, y) + self.diagrams_ = X return self def transform(self, X): @@ -52,7 +122,20 @@ class SlicedWassersteinKernel(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise sliced Wasserstein kernel values. """ - return np.exp(-self.sw_.transform(X)/self.bandwidth) + return pairwise_persistence_diagram_kernels(X, self.diagrams_, kernel="sliced_wasserstein", bandwidth=self.bandwidth, num_directions=self.num_directions) + + def __call__(self, diag1, diag2): + """ + Apply SlicedWassersteinKernel on a single pair of persistence diagrams and outputs the result. + + Parameters: + diag1 (n x 2 numpy array): first input persistence diagram. + diag2 (n x 2 numpy array): second input persistence diagram. + + Returns: + float: sliced Wasserstein kernel value. + """ + return np.exp(-_sliced_wasserstein_distance(diag1, diag2, num_directions=self.num_directions)) / self.bandwidth class PersistenceWeightedGaussianKernel(BaseEstimator, TransformerMixin): """ @@ -78,10 +161,7 @@ class PersistenceWeightedGaussianKernel(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. y (n x 1 array): persistence diagram labels (unused). """ - self.diagrams_ = list(X) - self.ws_ = [ np.array([self.weight(self.diagrams_[i][j,:]) for j in range(self.diagrams_[i].shape[0])]) for i in range(len(self.diagrams_)) ] - if self.kernel_approx is not None: - self.approx_ = np.concatenate([np.sum(np.multiply(self.ws_[i][:,np.newaxis], self.kernel_approx.transform(self.diagrams_[i])), axis=0)[np.newaxis,:] for i in range(len(self.diagrams_))]) + self.diagrams_ = X return self def transform(self, X): @@ -94,31 +174,20 @@ class PersistenceWeightedGaussianKernel(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence weighted Gaussian kernel values. """ - Xp = list(X) - Xfit = np.zeros((len(Xp), len(self.diagrams_))) - if len(self.diagrams_) == len(Xp) and np.all([np.array_equal(self.diagrams_[i], Xp[i]) for i in range(len(Xp))]): - if self.kernel_approx is not None: - Xfit = (1./(np.sqrt(2*np.pi)*self.bandwidth)) * np.matmul(self.approx_, self.approx_.T) - else: - for i in range(len(self.diagrams_)): - for j in range(i+1, len(self.diagrams_)): - W = np.matmul(self.ws_[i][:,np.newaxis], self.ws_[j][np.newaxis,:]) - E = (1./(np.sqrt(2*np.pi)*self.bandwidth)) * np.exp(-np.square(pairwise_distances(self.diagrams_[i], self.diagrams_[j]))/(2*np.square(self.bandwidth))) - Xfit[i,j] = np.sum(np.multiply(W, E)) - Xfit[j,i] = Xfit[i,j] - else: - ws = [ np.array([self.weight(Xp[i][j,:]) for j in range(Xp[i].shape[0])]) for i in range(len(Xp)) ] - if self.kernel_approx is not None: - approx = np.concatenate([np.sum(np.multiply(ws[i][:,np.newaxis], self.kernel_approx.transform(Xp[i])), axis=0)[np.newaxis,:] for i in range(len(Xp))]) - Xfit = (1./(np.sqrt(2*np.pi)*self.bandwidth)) * np.matmul(approx, self.approx_.T) - else: - for i in range(len(Xp)): - for j in range(len(self.diagrams_)): - W = np.matmul(ws[i][:,np.newaxis], self.ws_[j][np.newaxis,:]) - E = (1./(np.sqrt(2*np.pi)*self.bandwidth)) * np.exp(-np.square(pairwise_distances(Xp[i], self.diagrams_[j]))/(2*np.square(self.bandwidth))) - Xfit[i,j] = np.sum(np.multiply(W, E)) - - return Xfit + return pairwise_persistence_diagram_kernels(X, self.diagrams_, kernel="persistence_weighted_gaussian", bandwidth=self.bandwidth, weight=self.weight, kernel_approx=self.kernel_approx) + + def __call__(self, diag1, diag2): + """ + Apply PersistenceWeightedGaussianKernel on a single pair of persistence diagrams and outputs the result. + + Parameters: + diag1 (n x 2 numpy array): first input persistence diagram. + diag2 (n x 2 numpy array): second input persistence diagram. + + Returns: + float: persistence weighted Gaussian kernel value. + """ + return _persistence_weighted_gaussian_kernel(diag1, diag2, weight=self.weight, kernel_approx=self.kernel_approx, bandwidth=self.bandwidth) class PersistenceScaleSpaceKernel(BaseEstimator, TransformerMixin): """ @@ -132,7 +201,7 @@ class PersistenceScaleSpaceKernel(BaseEstimator, TransformerMixin): bandwidth (double): bandwidth of the Gaussian kernel with which persistence diagrams will be convolved (default 1.) kernel_approx (class): kernel approximation class used to speed up computation (default None). Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance). """ - self.pwg_ = PersistenceWeightedGaussianKernel(bandwidth=bandwidth, weight=lambda x: 1 if x[1] >= x[0] else -1, kernel_approx=kernel_approx) + self.bandwidth, self.kernel_approx = bandwidth, kernel_approx def fit(self, X, y=None): """ @@ -142,11 +211,7 @@ class PersistenceScaleSpaceKernel(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. y (n x 1 array): persistence diagram labels (unused). """ - self.diagrams_ = list(X) - for i in range(len(self.diagrams_)): - op_D = self.diagrams_[i][:,[1,0]] - self.diagrams_[i] = np.concatenate([self.diagrams_[i], op_D], axis=0) - self.pwg_.fit(X) + self.diagrams_ = X return self def transform(self, X): @@ -159,11 +224,20 @@ class PersistenceScaleSpaceKernel(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence scale space kernel values. """ - Xp = list(X) - for i in range(len(Xp)): - op_X = Xp[i][:,[1,0]] - Xp[i] = np.concatenate([Xp[i], op_X], axis=0) - return self.pwg_.transform(Xp) + return pairwise_persistence_diagram_kernels(X, self.diagrams_, kernel="persistence_scale_space", bandwidth=self.bandwidth, kernel_approx=self.kernel_approx) + + def __call__(self, diag1, diag2): + """ + Apply PersistenceScaleSpaceKernel on a single pair of persistence diagrams and outputs the result. + + Parameters: + diag1 (n x 2 numpy array): first input persistence diagram. + diag2 (n x 2 numpy array): second input persistence diagram. + + Returns: + float: persistence scale space kernel value. + """ + return _persistence_scale_space_kernel(diag1, diag2, bandwidth=self.bandwidth, kernel_approx=self.kernel_approx) class PersistenceFisherKernel(BaseEstimator, TransformerMixin): """ @@ -179,7 +253,7 @@ class PersistenceFisherKernel(BaseEstimator, TransformerMixin): kernel_approx (class): kernel approximation class used to speed up computation (default None). Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance). """ self.bandwidth = bandwidth - self.pf_ = PersistenceFisherDistance(bandwidth=bandwidth_fisher, kernel_approx=kernel_approx) + self.bandwidth_fisher, self.kernel_approx = bandwidth_fisher, kernel_approx def fit(self, X, y=None): """ @@ -189,7 +263,7 @@ class PersistenceFisherKernel(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. y (n x 1 array): persistence diagram labels (unused). """ - self.pf_.fit(X, y) + self.diagrams_ = X return self def transform(self, X): @@ -202,5 +276,18 @@ class PersistenceFisherKernel(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence Fisher kernel values. """ - return np.exp(-self.pf_.transform(X)/self.bandwidth) + return pairwise_persistence_diagram_kernels(X, self.diagrams_, kernel="persistence_fisher", bandwidth=self.bandwidth, bandwidth_fisher=self.bandwidth_fisher, kernel_approx=self.kernel_approx) + + def __call__(self, diag1, diag2): + """ + Apply PersistenceFisherKernel on a single pair of persistence diagrams and outputs the result. + + Parameters: + diag1 (n x 2 numpy array): first input persistence diagram. + diag2 (n x 2 numpy array): second input persistence diagram. + + Returns: + float: persistence Fisher kernel value. + """ + return np.exp(-_persistence_fisher_distance(diag1, diag2, bandwidth=self.bandwidth, kernel_approx=self.kernel_approx)) / self.bandwidth_fisher diff --git a/src/python/gudhi/representations/metrics.py b/src/python/gudhi/representations/metrics.py index 5f9ec6ab..8a32f7e9 100644 --- a/src/python/gudhi/representations/metrics.py +++ b/src/python/gudhi/representations/metrics.py @@ -10,17 +10,168 @@ import numpy as np from sklearn.base import BaseEstimator, TransformerMixin from sklearn.metrics import pairwise_distances -try: - from .. import bottleneck_distance - USE_GUDHI = True -except ImportError: - USE_GUDHI = False - print("Gudhi built without CGAL: BottleneckDistance will return a null matrix") +from gudhi.hera import wasserstein_distance as hera_wasserstein_distance +from .preprocessing import Padding ############################################# # Metrics ################################### ############################################# +def _sliced_wasserstein_distance(D1, D2, num_directions): + """ + This is a function for computing the sliced Wasserstein distance from two persistence diagrams. The Sliced Wasserstein distance is computed by projecting the persistence diagrams onto lines, comparing the projections with the 1-norm, and finally averaging over the lines. See http://proceedings.mlr.press/v70/carriere17a.html for more details. + + Parameters: + D1: (n x 2) numpy.array encoding the (finite points of the) first diagram. Must not contain essential points (i.e. with infinite coordinate). + D2: (m x 2) numpy.array encoding the second diagram. + num_directions (int): number of lines evenly sampled from [-pi/2,pi/2] in order to approximate and speed up the distance computation. + + Returns: + float: the sliced Wasserstein distance between persistence diagrams. + """ + thetas = np.linspace(-np.pi/2, np.pi/2, num=num_directions+1)[np.newaxis,:-1] + lines = np.concatenate([np.cos(thetas), np.sin(thetas)], axis=0) + approx1 = np.matmul(D1, lines) + approx_diag1 = np.matmul(np.broadcast_to(D1.sum(-1,keepdims=True)/2,(len(D1),2)), lines) + approx2 = np.matmul(D2, lines) + approx_diag2 = np.matmul(np.broadcast_to(D2.sum(-1,keepdims=True)/2,(len(D2),2)), lines) + A = np.sort(np.concatenate([approx1, approx_diag2], axis=0), axis=0) + B = np.sort(np.concatenate([approx2, approx_diag1], axis=0), axis=0) + L1 = np.sum(np.abs(A-B), axis=0) + return np.mean(L1) + +def _compute_persistence_diagram_projections(X, num_directions): + """ + This is a function for projecting the points of a list of persistence diagrams (as well as their diagonal projections) onto a fixed number of lines sampled uniformly on [-pi/2, pi/2]. This function can be used as a preprocessing step in order to speed up the running time for computing all pairwise sliced Wasserstein distances / kernel values on a list of persistence diagrams. + + Parameters: + X (list of n numpy arrays of shape (numx2)): list of persistence diagrams. + num_directions (int): number of lines evenly sampled from [-pi/2,pi/2] in order to approximate and speed up the distance computation. + + Returns: + list of n numpy arrays of shape (2*numx2): list of projected persistence diagrams. + """ + thetas = np.linspace(-np.pi/2, np.pi/2, num=num_directions+1)[np.newaxis,:-1] + lines = np.concatenate([np.cos(thetas), np.sin(thetas)], axis=0) + XX = [np.vstack([np.matmul(D, lines), np.matmul(np.matmul(D, .5 * np.ones((2,2))), lines)]) for D in X] + return XX + +def _sliced_wasserstein_distance_on_projections(D1, D2): + """ + This is a function for computing the sliced Wasserstein distance between two persistence diagrams that have already been projected onto some lines. It simply amounts to comparing the sorted projections with the 1-norm, and averaging over the lines. See http://proceedings.mlr.press/v70/carriere17a.html for more details. + + Parameters: + D1: (2n x number_of_lines) numpy.array containing the n projected points of the first diagram, and the n projections of their diagonal projections. + D2: (2m x number_of_lines) numpy.array containing the m projected points of the second diagram, and the m projections of their diagonal projections. + + Returns: + float: the sliced Wasserstein distance between the projected persistence diagrams. + """ + lim1, lim2 = int(len(D1)/2), int(len(D2)/2) + approx1, approx_diag1, approx2, approx_diag2 = D1[:lim1], D1[lim1:], D2[:lim2], D2[lim2:] + A = np.sort(np.concatenate([approx1, approx_diag2], axis=0), axis=0) + B = np.sort(np.concatenate([approx2, approx_diag1], axis=0), axis=0) + L1 = np.sum(np.abs(A-B), axis=0) + return np.mean(L1) + +def _persistence_fisher_distance(D1, D2, kernel_approx=None, bandwidth=1.): + """ + This is a function for computing the persistence Fisher distance from two persistence diagrams. The persistence Fisher distance is obtained by computing the original Fisher distance between the probability distributions associated to the persistence diagrams given by convolving them with a Gaussian kernel. See http://papers.nips.cc/paper/8205-persistence-fisher-kernel-a-riemannian-manifold-kernel-for-persistence-diagrams for more details. + + Parameters: + D1: (n x 2) numpy.array encoding the (finite points of the) first diagram). Must not contain essential points (i.e. with infinite coordinate). + D2: (m x 2) numpy.array encoding the second diagram. + bandwidth (float): bandwidth of the Gaussian kernel used to turn persistence diagrams into probability distributions. + kernel_approx: kernel approximation class used to speed up computation. Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance). + + Returns: + float: the persistence Fisher distance between persistence diagrams. + """ + projection = (1./2) * np.ones((2,2)) + diagonal_projections1 = np.matmul(D1, projection) + diagonal_projections2 = np.matmul(D2, projection) + if kernel_approx is not None: + approx1 = kernel_approx.transform(D1) + approx_diagonal1 = kernel_approx.transform(diagonal_projections1) + approx2 = kernel_approx.transform(D2) + approx_diagonal2 = kernel_approx.transform(diagonal_projections2) + Z = np.concatenate([approx1, approx_diagonal1, approx2, approx_diagonal2], axis=0) + U, V = np.sum(np.concatenate([approx1, approx_diagonal2], axis=0), axis=0), np.sum(np.concatenate([approx2, approx_diagonal1], axis=0), axis=0) + vectori, vectorj = np.abs(np.matmul(Z, U.T)), np.abs(np.matmul(Z, V.T)) + vectori_sum, vectorj_sum = np.sum(vectori), np.sum(vectorj) + if vectori_sum != 0: + vectori = vectori/vectori_sum + if vectorj_sum != 0: + vectorj = vectorj/vectorj_sum + return np.arccos( min(np.dot(np.sqrt(vectori), np.sqrt(vectorj)), 1.) ) + else: + Z = np.concatenate([D1, diagonal_projections1, D2, diagonal_projections2], axis=0) + U, V = np.concatenate([D1, diagonal_projections2], axis=0), np.concatenate([D2, diagonal_projections1], axis=0) + vectori = np.sum(np.exp(-np.square(pairwise_distances(Z,U))/(2 * np.square(bandwidth)))/(bandwidth * np.sqrt(2*np.pi)), axis=1) + vectorj = np.sum(np.exp(-np.square(pairwise_distances(Z,V))/(2 * np.square(bandwidth)))/(bandwidth * np.sqrt(2*np.pi)), axis=1) + vectori_sum, vectorj_sum = np.sum(vectori), np.sum(vectorj) + if vectori_sum != 0: + vectori = vectori/vectori_sum + if vectorj_sum != 0: + vectorj = vectorj/vectorj_sum + return np.arccos( min(np.dot(np.sqrt(vectori), np.sqrt(vectorj)), 1.) ) + +def _sklearn_wrapper(metric, X, Y, **kwargs): + """ + This function is a wrapper for any metric between two persistence diagrams that takes two numpy arrays of shapes (nx2) and (mx2) as arguments. + """ + if Y is None: + def flat_metric(a, b): + return metric(X[int(a[0])], X[int(b[0])], **kwargs) + else: + def flat_metric(a, b): + return metric(X[int(a[0])], Y[int(b[0])], **kwargs) + return flat_metric + +PAIRWISE_DISTANCE_FUNCTIONS = { + "wasserstein": hera_wasserstein_distance, + "hera_wasserstein": hera_wasserstein_distance, + "persistence_fisher": _persistence_fisher_distance, +} + +def pairwise_persistence_diagram_distances(X, Y=None, metric="bottleneck", **kwargs): + """ + This function computes the distance matrix between two lists of persistence diagrams given as numpy arrays of shape (nx2). + + Parameters: + X (list of n numpy arrays of shape (numx2)): first list of persistence diagrams. + Y (list of m numpy arrays of shape (numx2)): second list of persistence diagrams (optional). If None, pairwise distances are computed from the first list only. + metric: distance to use. It can be either a string ("sliced_wasserstein", "wasserstein", "hera_wasserstein" (Wasserstein distance computed with Hera---note that Hera is also used for the default option "wasserstein"), "pot_wasserstein" (Wasserstein distance computed with POT), "bottleneck", "persistence_fisher") or a function taking two numpy arrays of shape (nx2) and (mx2) as inputs. If it is a function, make sure that it is symmetric and that it outputs 0 if called on the same two arrays. + **kwargs: optional keyword parameters. Any further parameters are passed directly to the distance function. See the docs of the various distance classes in this module. + + Returns: + numpy array of shape (nxm): distance matrix + """ + XX = np.reshape(np.arange(len(X)), [-1,1]) + YY = None if Y is None else np.reshape(np.arange(len(Y)), [-1,1]) + if metric == "bottleneck": + try: + from .. import bottleneck_distance + return pairwise_distances(XX, YY, metric=_sklearn_wrapper(bottleneck_distance, X, Y, **kwargs)) + except ImportError: + print("Gudhi built without CGAL") + raise + elif metric == "pot_wasserstein": + try: + from gudhi.wasserstein import wasserstein_distance as pot_wasserstein_distance + return pairwise_distances(XX, YY, metric=_sklearn_wrapper(pot_wasserstein_distance, X, Y, **kwargs)) + except ImportError: + print("POT (Python Optimal Transport) is not installed. Please install POT or use metric='wasserstein' or metric='hera_wasserstein'") + raise + elif metric == "sliced_wasserstein": + Xproj = _compute_persistence_diagram_projections(X, **kwargs) + Yproj = None if Y is None else _compute_persistence_diagram_projections(Y, **kwargs) + return pairwise_distances(XX, YY, metric=_sklearn_wrapper(_sliced_wasserstein_distance_on_projections, Xproj, Yproj)) + elif type(metric) == str: + return pairwise_distances(XX, YY, metric=_sklearn_wrapper(PAIRWISE_DISTANCE_FUNCTIONS[metric], X, Y, **kwargs)) + else: + return pairwise_distances(XX, YY, metric=_sklearn_wrapper(metric, X, Y, **kwargs)) + class SlicedWassersteinDistance(BaseEstimator, TransformerMixin): """ This is a class for computing the sliced Wasserstein distance matrix from a list of persistence diagrams. The Sliced Wasserstein distance is computed by projecting the persistence diagrams onto lines, comparing the projections with the 1-norm, and finally integrating over all possible lines. See http://proceedings.mlr.press/v70/carriere17a.html for more details. @@ -33,8 +184,6 @@ class SlicedWassersteinDistance(BaseEstimator, TransformerMixin): num_directions (int): number of lines evenly sampled from [-pi/2,pi/2] in order to approximate and speed up the distance computation (default 10). """ self.num_directions = num_directions - thetas = np.linspace(-np.pi/2, np.pi/2, num=self.num_directions+1)[np.newaxis,:-1] - self.lines_ = np.concatenate([np.cos(thetas), np.sin(thetas)], axis=0) def fit(self, X, y=None): """ @@ -45,9 +194,6 @@ class SlicedWassersteinDistance(BaseEstimator, TransformerMixin): y (n x 1 array): persistence diagram labels (unused). """ self.diagrams_ = X - self.approx_ = [np.matmul(X[i], self.lines_) for i in range(len(X))] - diag_proj = (1./2) * np.ones((2,2)) - self.approx_diag_ = [np.matmul(np.matmul(X[i], diag_proj), self.lines_) for i in range(len(X))] return self def transform(self, X): @@ -60,31 +206,26 @@ class SlicedWassersteinDistance(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise sliced Wasserstein distances. """ - Xfit = np.zeros((len(X), len(self.approx_))) - if len(self.diagrams_) == len(X) and np.all([np.array_equal(self.diagrams_[i], X[i]) for i in range(len(X))]): - for i in range(len(self.approx_)): - for j in range(i+1, len(self.approx_)): - A = np.sort(np.concatenate([self.approx_[i], self.approx_diag_[j]], axis=0), axis=0) - B = np.sort(np.concatenate([self.approx_[j], self.approx_diag_[i]], axis=0), axis=0) - L1 = np.sum(np.abs(A-B), axis=0) - Xfit[i,j] = np.mean(L1) - Xfit[j,i] = Xfit[i,j] - else: - diag_proj = (1./2) * np.ones((2,2)) - approx = [np.matmul(X[i], self.lines_) for i in range(len(X))] - approx_diag = [np.matmul(np.matmul(X[i], diag_proj), self.lines_) for i in range(len(X))] - for i in range(len(approx)): - for j in range(len(self.approx_)): - A = np.sort(np.concatenate([approx[i], self.approx_diag_[j]], axis=0), axis=0) - B = np.sort(np.concatenate([self.approx_[j], approx_diag[i]], axis=0), axis=0) - L1 = np.sum(np.abs(A-B), axis=0) - Xfit[i,j] = np.mean(L1) + return pairwise_persistence_diagram_distances(X, self.diagrams_, metric="sliced_wasserstein", num_directions=self.num_directions) - return Xfit + def __call__(self, diag1, diag2): + """ + Apply SlicedWassersteinDistance on a single pair of persistence diagrams and outputs the result. + + Parameters: + diag1 (n x 2 numpy array): first input persistence diagram. + diag2 (n x 2 numpy array): second input persistence diagram. + + Returns: + float: sliced Wasserstein distance. + """ + return _sliced_wasserstein_distance(diag1, diag2, num_directions=self.num_directions) class BottleneckDistance(BaseEstimator, TransformerMixin): """ - This is a class for computing the bottleneck distance matrix from a list of persistence diagrams. + This is a class for computing the bottleneck distance matrix from a list of persistence diagrams. + + :Requires: `CGAL <installation.html#cgal>`_ :math:`\geq` 4.11.0 """ def __init__(self, epsilon=None): """ @@ -116,34 +257,26 @@ class BottleneckDistance(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise bottleneck distances. """ - num_diag1 = len(X) - - #if len(self.diagrams_) == len(X) and np.all([np.array_equal(self.diagrams_[i], X[i]) for i in range(len(X))]): - if X is self.diagrams_: - matrix = np.zeros((num_diag1, num_diag1)) - - if USE_GUDHI: - for i in range(num_diag1): - for j in range(i+1, num_diag1): - matrix[i,j] = bottleneck_distance(X[i], X[j], self.epsilon) - matrix[j,i] = matrix[i,j] - else: - print("Gudhi built without CGAL: returning a null matrix") - - else: - num_diag2 = len(self.diagrams_) - matrix = np.zeros((num_diag1, num_diag2)) + Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric="bottleneck", e=self.epsilon) + return Xfit - if USE_GUDHI: - for i in range(num_diag1): - for j in range(num_diag2): - matrix[i,j] = bottleneck_distance(X[i], self.diagrams_[j], self.epsilon) - else: - print("Gudhi built without CGAL: returning a null matrix") + def __call__(self, diag1, diag2): + """ + Apply BottleneckDistance on a single pair of persistence diagrams and outputs the result. - Xfit = matrix + Parameters: + diag1 (n x 2 numpy array): first input persistence diagram. + diag2 (n x 2 numpy array): second input persistence diagram. - return Xfit + Returns: + float: bottleneck distance. + """ + try: + from .. import bottleneck_distance + return bottleneck_distance(diag1, diag2, e=self.epsilon) + except ImportError: + print("Gudhi built without CGAL") + raise class PersistenceFisherDistance(BaseEstimator, TransformerMixin): """ @@ -168,11 +301,6 @@ class PersistenceFisherDistance(BaseEstimator, TransformerMixin): y (n x 1 array): persistence diagram labels (unused). """ self.diagrams_ = X - projection = (1./2) * np.ones((2,2)) - self.diagonal_projections_ = [np.matmul(X[i], projection) for i in range(len(X))] - if self.kernel_approx is not None: - self.approx_ = [self.kernel_approx.transform(X[i]) for i in range(len(X))] - self.approx_diagonal_ = [self.kernel_approx.transform(self.diagonal_projections_[i]) for i in range(len(X))] return self def transform(self, X): @@ -185,60 +313,83 @@ class PersistenceFisherDistance(BaseEstimator, TransformerMixin): Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence Fisher distances. """ - Xfit = np.zeros((len(X), len(self.diagrams_))) - if len(self.diagrams_) == len(X) and np.all([np.array_equal(self.diagrams_[i], X[i]) for i in range(len(X))]): - for i in range(len(self.diagrams_)): - for j in range(i+1, len(self.diagrams_)): - if self.kernel_approx is not None: - Z = np.concatenate([self.approx_[i], self.approx_diagonal_[i], self.approx_[j], self.approx_diagonal_[j]], axis=0) - U, V = np.sum(np.concatenate([self.approx_[i], self.approx_diagonal_[j]], axis=0), axis=0), np.sum(np.concatenate([self.approx_[j], self.approx_diagonal_[i]], axis=0), axis=0) - vectori, vectorj = np.abs(np.matmul(Z, U.T)), np.abs(np.matmul(Z, V.T)) - vectori_sum, vectorj_sum = np.sum(vectori), np.sum(vectorj) - if vectori_sum != 0: - vectori = vectori/vectori_sum - if vectorj_sum != 0: - vectorj = vectorj/vectorj_sum - Xfit[i,j] = np.arccos( min(np.dot(np.sqrt(vectori), np.sqrt(vectorj)), 1.) ) - Xfit[j,i] = Xfit[i,j] - else: - Z = np.concatenate([self.diagrams_[i], self.diagonal_projections_[i], self.diagrams_[j], self.diagonal_projections_[j]], axis=0) - U, V = np.concatenate([self.diagrams_[i], self.diagonal_projections_[j]], axis=0), np.concatenate([self.diagrams_[j], self.diagonal_projections_[i]], axis=0) - vectori = np.sum(np.exp(-np.square(pairwise_distances(Z,U))/(2 * np.square(self.bandwidth)))/(self.bandwidth * np.sqrt(2*np.pi)), axis=1) - vectorj = np.sum(np.exp(-np.square(pairwise_distances(Z,V))/(2 * np.square(self.bandwidth)))/(self.bandwidth * np.sqrt(2*np.pi)), axis=1) - vectori_sum, vectorj_sum = np.sum(vectori), np.sum(vectorj) - if vectori_sum != 0: - vectori = vectori/vectori_sum - if vectorj_sum != 0: - vectorj = vectorj/vectorj_sum - Xfit[i,j] = np.arccos( min(np.dot(np.sqrt(vectori), np.sqrt(vectorj)), 1.) ) - Xfit[j,i] = Xfit[i,j] + return pairwise_persistence_diagram_distances(X, self.diagrams_, metric="persistence_fisher", bandwidth=self.bandwidth, kernel_approx=self.kernel_approx) + + def __call__(self, diag1, diag2): + """ + Apply PersistenceFisherDistance on a single pair of persistence diagrams and outputs the result. + + Parameters: + diag1 (n x 2 numpy array): first input persistence diagram. + diag2 (n x 2 numpy array): second input persistence diagram. + + Returns: + float: persistence Fisher distance. + """ + return _persistence_fisher_distance(diag1, diag2, bandwidth=self.bandwidth, kernel_approx=self.kernel_approx) + +class WassersteinDistance(BaseEstimator, TransformerMixin): + """ + This is a class for computing the Wasserstein distance matrix from a list of persistence diagrams. + """ + def __init__(self, order=2, internal_p=2, mode="pot", delta=0.01): + """ + Constructor for the WassersteinDistance class. + + Parameters: + order (int): exponent for Wasserstein, default value is 2., see :func:`gudhi.wasserstein.wasserstein_distance`. + internal_p (int): ground metric on the (upper-half) plane (i.e. norm l_p in R^2), default value is 2 (euclidean norm), see :func:`gudhi.wasserstein.wasserstein_distance`. + mode (str): method for computing Wasserstein distance. Either "pot" or "hera". + delta (float): relative error 1+delta. Used only if mode == "hera". + """ + self.order, self.internal_p, self.mode = order, internal_p, mode + self.metric = "pot_wasserstein" if mode == "pot" else "hera_wasserstein" + self.delta = delta + + def fit(self, X, y=None): + """ + Fit the WassersteinDistance class on a list of persistence diagrams: persistence diagrams are stored in a numpy array called **diagrams**. + + Parameters: + X (list of n x 2 numpy arrays): input persistence diagrams. + y (n x 1 array): persistence diagram labels (unused). + """ + self.diagrams_ = X + return self + + def transform(self, X): + """ + Compute all Wasserstein distances between the persistence diagrams that were stored after calling the fit() method, and a given list of (possibly different) persistence diagrams. + + Parameters: + X (list of n x 2 numpy arrays): input persistence diagrams. + + Returns: + numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise Wasserstein distances. + """ + if self.metric == "hera_wasserstein": + Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric=self.metric, order=self.order, internal_p=self.internal_p, delta=self.delta) else: - projection = (1./2) * np.ones((2,2)) - diagonal_projections = [np.matmul(X[i], projection) for i in range(len(X))] - if self.kernel_approx is not None: - approx = [self.kernel_approx.transform(X[i]) for i in range(len(X))] - approx_diagonal = [self.kernel_approx.transform(diagonal_projections[i]) for i in range(len(X))] - for i in range(len(X)): - for j in range(len(self.diagrams_)): - if self.kernel_approx is not None: - Z = np.concatenate([approx[i], approx_diagonal[i], self.approx_[j], self.approx_diagonal_[j]], axis=0) - U, V = np.sum(np.concatenate([approx[i], self.approx_diagonal_[j]], axis=0), axis=0), np.sum(np.concatenate([self.approx_[j], approx_diagonal[i]], axis=0), axis=0) - vectori, vectorj = np.abs(np.matmul(Z, U.T)), np.abs(np.matmul(Z, V.T)) - vectori_sum, vectorj_sum = np.sum(vectori), np.sum(vectorj) - if vectori_sum != 0: - vectori = vectori/vectori_sum - if vectorj_sum != 0: - vectorj = vectorj/vectorj_sum - Xfit[i,j] = np.arccos( min(np.dot(np.sqrt(vectori), np.sqrt(vectorj)), 1.) ) - else: - Z = np.concatenate([X[i], diagonal_projections[i], self.diagrams_[j], self.diagonal_projections_[j]], axis=0) - U, V = np.concatenate([X[i], self.diagonal_projections_[j]], axis=0), np.concatenate([self.diagrams_[j], diagonal_projections[i]], axis=0) - vectori = np.sum(np.exp(-np.square(pairwise_distances(Z,U))/(2 * np.square(self.bandwidth)))/(self.bandwidth * np.sqrt(2*np.pi)), axis=1) - vectorj = np.sum(np.exp(-np.square(pairwise_distances(Z,V))/(2 * np.square(self.bandwidth)))/(self.bandwidth * np.sqrt(2*np.pi)), axis=1) - vectori_sum, vectorj_sum = np.sum(vectori), np.sum(vectorj) - if vectori_sum != 0: - vectori = vectori/vectori_sum - if vectorj_sum != 0: - vectorj = vectorj/vectorj_sum - Xfit[i,j] = np.arccos( min(np.dot(np.sqrt(vectori), np.sqrt(vectorj)), 1.) ) + Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric=self.metric, order=self.order, internal_p=self.internal_p, matching=False) return Xfit + + def __call__(self, diag1, diag2): + """ + Apply WassersteinDistance on a single pair of persistence diagrams and outputs the result. + + Parameters: + diag1 (n x 2 numpy array): first input persistence diagram. + diag2 (n x 2 numpy array): second input persistence diagram. + + Returns: + float: Wasserstein distance. + """ + if self.metric == "hera_wasserstein": + return hera_wasserstein_distance(diag1, diag2, order=self.order, internal_p=self.internal_p, delta=self.delta) + else: + try: + from gudhi.wasserstein import wasserstein_distance as pot_wasserstein_distance + return pot_wasserstein_distance(diag1, diag2, order=self.order, internal_p=self.internal_p, matching=False) + except ImportError: + print("POT (Python Optimal Transport) is not installed. Please install POT or use metric='wasserstein' or metric='hera_wasserstein'") + raise diff --git a/src/python/gudhi/representations/preprocessing.py b/src/python/gudhi/representations/preprocessing.py index a39b00e4..a8545349 100644 --- a/src/python/gudhi/representations/preprocessing.py +++ b/src/python/gudhi/representations/preprocessing.py @@ -54,6 +54,18 @@ class BirthPersistenceTransform(BaseEstimator, TransformerMixin): Xfit.append(new_diag) return Xfit + def __call__(self, diag): + """ + Apply BirthPersistenceTransform on a single persistence diagram and outputs the result. + + Parameters: + diag (n x 2 numpy array): input persistence diagram. + + Returns: + n x 2 numpy array: transformed persistence diagram. + """ + return self.fit_transform([diag])[0] + class Clamping(BaseEstimator, TransformerMixin): """ This is a class for clamping values. It can be used as a parameter for the DiagramScaler class, for instance if you want to clamp abscissae or ordinates of persistence diagrams. @@ -142,6 +154,18 @@ class DiagramScaler(BaseEstimator, TransformerMixin): Xfit[i][:,I] = np.squeeze(scaler.transform(np.reshape(Xfit[i][:,I], [-1,1]))) return Xfit + def __call__(self, diag): + """ + Apply DiagramScaler on a single persistence diagram and outputs the result. + + Parameters: + diag (n x 2 numpy array): input persistence diagram. + + Returns: + n x 2 numpy array: transformed persistence diagram. + """ + return self.fit_transform([diag])[0] + class Padding(BaseEstimator, TransformerMixin): """ This is a class for padding a list of persistence diagrams with dummy points, so that all persistence diagrams end up with the same number of points. @@ -186,6 +210,18 @@ class Padding(BaseEstimator, TransformerMixin): Xfit = X return Xfit + def __call__(self, diag): + """ + Apply Padding on a single persistence diagram and outputs the result. + + Parameters: + diag (n x 2 numpy array): input persistence diagram. + + Returns: + n x 2 numpy array: padded persistence diagram. + """ + return self.fit_transform([diag])[0] + class ProminentPoints(BaseEstimator, TransformerMixin): """ This is a class for removing points that are close or far from the diagonal in persistence diagrams. If persistence diagrams are n x 2 numpy arrays (i.e. persistence diagrams with ordinary features), points are ordered and thresholded by distance-to-diagonal. If persistence diagrams are n x 1 numpy arrays (i.e. persistence diagrams with essential features), points are not ordered and thresholded by first coordinate. @@ -259,6 +295,18 @@ class ProminentPoints(BaseEstimator, TransformerMixin): Xfit = X return Xfit + def __call__(self, diag): + """ + Apply ProminentPoints on a single persistence diagram and outputs the result. + + Parameters: + diag (n x 2 numpy array): input persistence diagram. + + Returns: + n x 2 numpy array: thresholded persistence diagram. + """ + return self.fit_transform([diag])[0] + class DiagramSelector(BaseEstimator, TransformerMixin): """ This is a class for extracting finite or essential points in persistence diagrams. @@ -303,3 +351,15 @@ class DiagramSelector(BaseEstimator, TransformerMixin): else: Xfit = X return Xfit + + def __call__(self, diag): + """ + Apply DiagramSelector on a single persistence diagram and outputs the result. + + Parameters: + diag (n x 2 numpy array): input persistence diagram. + + Returns: + n x 2 numpy array: extracted persistence diagram. + """ + return self.fit_transform([diag])[0] diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py index fe26dbe2..46fee086 100644 --- a/src/python/gudhi/representations/vector_methods.py +++ b/src/python/gudhi/representations/vector_methods.py @@ -81,6 +81,18 @@ class PersistenceImage(BaseEstimator, TransformerMixin): return Xfit + def __call__(self, diag): + """ + Apply PersistenceImage on a single persistence diagram and outputs the result. + + Parameters: + diag (n x 2 numpy array): input persistence diagram. + + Returns: + numpy array with shape (number of pixels = **resolution[0]** x **resolution[1]**):: output persistence image. + """ + return self.fit_transform([diag])[0,:] + class Landscape(BaseEstimator, TransformerMixin): """ This is a class for computing persistence landscapes from a list of persistence diagrams. A persistence landscape is a collection of 1D piecewise-linear functions computed from the rank function associated to the persistence diagram. These piecewise-linear functions are then sampled evenly on a given range and the corresponding vectors of samples are concatenated and returned. See http://jmlr.org/papers/v16/bubenik15a.html for more details. @@ -170,6 +182,18 @@ class Landscape(BaseEstimator, TransformerMixin): return Xfit + def __call__(self, diag): + """ + Apply Landscape on a single persistence diagram and outputs the result. + + Parameters: + diag (n x 2 numpy array): input persistence diagram. + + Returns: + numpy array with shape (number of samples = **num_landscapes** x **resolution**): output persistence landscape. + """ + return self.fit_transform([diag])[0,:] + class Silhouette(BaseEstimator, TransformerMixin): """ This is a class for computing persistence silhouettes from a list of persistence diagrams. A persistence silhouette is computed by taking a weighted average of the collection of 1D piecewise-linear functions given by the persistence landscapes, and then by evenly sampling this average on a given range. Finally, the corresponding vector of samples is returned. See https://arxiv.org/abs/1312.0308 for more details. @@ -248,6 +272,18 @@ class Silhouette(BaseEstimator, TransformerMixin): return Xfit + def __call__(self, diag): + """ + Apply Silhouette on a single persistence diagram and outputs the result. + + Parameters: + diag (n x 2 numpy array): input persistence diagram. + + Returns: + numpy array with shape (**resolution**): output persistence silhouette. + """ + return self.fit_transform([diag])[0,:] + class BettiCurve(BaseEstimator, TransformerMixin): """ This is a class for computing Betti curves from a list of persistence diagrams. A Betti curve is a 1D piecewise-constant function obtained from the rank function. It is sampled evenly on a given range and the vector of samples is returned. See https://www.researchgate.net/publication/316604237_Time_Series_Classification_via_Topological_Data_Analysis for more details. @@ -308,6 +344,18 @@ class BettiCurve(BaseEstimator, TransformerMixin): return Xfit + def __call__(self, diag): + """ + Apply BettiCurve on a single persistence diagram and outputs the result. + + Parameters: + diag (n x 2 numpy array): input persistence diagram. + + Returns: + numpy array with shape (**resolution**): output Betti curve. + """ + return self.fit_transform([diag])[0,:] + class Entropy(BaseEstimator, TransformerMixin): """ This is a class for computing persistence entropy. Persistence entropy is a statistic for persistence diagrams inspired from Shannon entropy. This statistic can also be used to compute a feature vector, called the entropy summary function. See https://arxiv.org/pdf/1803.08304.pdf for more details. Note that a previous implementation was contributed by Manuel Soriano-Trigueros. @@ -378,6 +426,18 @@ class Entropy(BaseEstimator, TransformerMixin): return Xfit + def __call__(self, diag): + """ + Apply Entropy on a single persistence diagram and outputs the result. + + Parameters: + diag (n x 2 numpy array): input persistence diagram. + + Returns: + numpy array with shape (1 if **mode** = "scalar" else **resolution**): output entropy. + """ + return self.fit_transform([diag])[0,:] + class TopologicalVector(BaseEstimator, TransformerMixin): """ This is a class for computing topological vectors from a list of persistence diagrams. The topological vector associated to a persistence diagram is the sorted vector of a slight modification of the pairwise distances between the persistence diagram points. See https://diglib.eg.org/handle/10.1111/cgf12692 for more details. @@ -431,6 +491,18 @@ class TopologicalVector(BaseEstimator, TransformerMixin): return Xfit + def __call__(self, diag): + """ + Apply TopologicalVector on a single persistence diagram and outputs the result. + + Parameters: + diag (n x 2 numpy array): input persistence diagram. + + Returns: + numpy array with shape (**threshold**): output topological vector. + """ + return self.fit_transform([diag])[0,:] + class ComplexPolynomial(BaseEstimator, TransformerMixin): """ This is a class for computing complex polynomials from a list of persistence diagrams. The persistence diagram points are seen as the roots of some complex polynomial, whose coefficients are returned in a complex vector. See https://link.springer.com/chapter/10.1007%2F978-3-319-23231-7_27 for more details. @@ -490,3 +562,15 @@ class ComplexPolynomial(BaseEstimator, TransformerMixin): coeff = np.array(coeff[::-1])[1:] Xfit[d, :min(thresh, coeff.shape[0])] = coeff[:min(thresh, coeff.shape[0])] return Xfit + + def __call__(self, diag): + """ + Apply ComplexPolynomial on a single persistence diagram and outputs the result. + + Parameters: + diag (n x 2 numpy array): input persistence diagram. + + Returns: + numpy array with shape (**threshold**): output complex vector of coefficients. + """ + return self.fit_transform([diag])[0,:] diff --git a/src/python/gudhi/rips_complex.pyx b/src/python/gudhi/rips_complex.pyx index deb8057a..72e82c79 100644 --- a/src/python/gudhi/rips_complex.pyx +++ b/src/python/gudhi/rips_complex.pyx @@ -23,12 +23,12 @@ __license__ = "MIT" cdef extern from "Rips_complex_interface.h" namespace "Gudhi": cdef cppclass Rips_complex_interface "Gudhi::rips_complex::Rips_complex_interface": - Rips_complex_interface() - void init_points(vector[vector[double]] values, double threshold) - 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) except + + Rips_complex_interface() nogil + void init_points(vector[vector[double]] values, double threshold) nogil + void init_matrix(vector[vector[double]] values, double threshold) nogil + void init_points_sparse(vector[vector[double]] values, double threshold, double sparse) nogil + void init_matrix_sparse(vector[vector[double]] values, double threshold, double sparse) nogil + void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, int dim_max) nogil except + # RipsComplex python interface cdef class RipsComplex: @@ -97,6 +97,7 @@ cdef class RipsComplex: """ stree = SimplexTree() cdef intptr_t stree_int_ptr=stree.thisptr - self.thisref.create_simplex_tree(<Simplex_tree_interface_full_featured*>stree_int_ptr, - max_dimension) + cdef int maxdim = max_dimension + with nogil: + self.thisref.create_simplex_tree(<Simplex_tree_interface_full_featured*>stree_int_ptr, maxdim) return stree diff --git a/src/python/gudhi/simplex_tree.pxd b/src/python/gudhi/simplex_tree.pxd index 96d14079..e748ac40 100644 --- a/src/python/gudhi/simplex_tree.pxd +++ b/src/python/gudhi/simplex_tree.pxd @@ -21,35 +21,60 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": cdef cppclass Simplex_tree_options_full_featured: pass + cdef cppclass Simplex_tree_simplex_handle "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>::Simplex_handle": + pass + + cdef cppclass Simplex_tree_simplices_iterator "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>::Complex_simplex_iterator": + Simplex_tree_simplices_iterator() nogil + Simplex_tree_simplex_handle& operator*() nogil + Simplex_tree_simplices_iterator operator++() nogil + bint operator!=(Simplex_tree_simplices_iterator) nogil + + cdef cppclass Simplex_tree_skeleton_iterator "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>::Skeleton_simplex_iterator": + Simplex_tree_skeleton_iterator() nogil + Simplex_tree_simplex_handle& operator*() nogil + Simplex_tree_skeleton_iterator operator++() nogil + bint operator!=(Simplex_tree_skeleton_iterator) nogil + + cdef cppclass Simplex_tree_interface_full_featured "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>": - Simplex_tree() - double simplex_filtration(vector[int] simplex) - void assign_simplex_filtration(vector[int] simplex, double filtration) - void initialize_filtration() - int num_vertices() - int num_simplices() - void set_dimension(int dimension) - int dimension() - int upper_bound_dimension() - bool find_simplex(vector[int] simplex) - bool insert_simplex_and_subfaces(vector[int] simplex, - double filtration) - vector[pair[vector[int], double]] get_filtration() - vector[pair[vector[int], double]] get_skeleton(int dimension) - 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) except + - void remove_maximal_simplex(vector[int] simplex) - bool prune_above_filtration(double filtration) - bool make_filtration_non_decreasing() + Simplex_tree() nogil + double simplex_filtration(vector[int] simplex) nogil + void assign_simplex_filtration(vector[int] simplex, double filtration) nogil + void initialize_filtration() nogil + int num_vertices() nogil + int num_simplices() nogil + void set_dimension(int dimension) nogil + int dimension() nogil + int upper_bound_dimension() nogil + bool find_simplex(vector[int] simplex) nogil + bool insert(vector[int] simplex, double filtration) nogil + vector[pair[vector[int], double]] get_star(vector[int] simplex) nogil + vector[pair[vector[int], double]] get_cofaces(vector[int] simplex, int dimension) nogil + void expansion(int max_dim) nogil except + + void remove_maximal_simplex(vector[int] simplex) nogil + bool prune_above_filtration(double filtration) nogil + bool make_filtration_non_decreasing() nogil + void compute_extended_filtration() nogil + vector[vector[pair[int, pair[double, double]]]] compute_extended_persistence_subdiagrams(vector[pair[int, pair[double, double]]] dgm, double min_persistence) nogil + # Iterators over Simplex tree + pair[vector[int], double] get_simplex_and_filtration(Simplex_tree_simplex_handle f_simplex) nogil + Simplex_tree_simplices_iterator get_simplices_iterator_begin() nogil + Simplex_tree_simplices_iterator get_simplices_iterator_end() nogil + vector[Simplex_tree_simplex_handle].const_iterator get_filtration_iterator_begin() nogil + vector[Simplex_tree_simplex_handle].const_iterator get_filtration_iterator_end() nogil + Simplex_tree_skeleton_iterator get_skeleton_iterator_begin(int dimension) nogil + Simplex_tree_skeleton_iterator get_skeleton_iterator_end(int dimension) nogil cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi": cdef cppclass Simplex_tree_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_full_featured>>": - Simplex_tree_persistence_interface(Simplex_tree_interface_full_featured * st, bool persistence_dim_max) - vector[pair[int, pair[double, double]]] get_persistence(int homology_coeff_field, double min_persistence) - vector[int] betti_numbers() - vector[int] persistent_betti_numbers(double from_value, double to_value) - vector[pair[double,double]] intervals_in_dimension(int dimension) - void write_output_diagram(string diagram_file_name) - vector[pair[vector[int], vector[int]]] persistence_pairs() + Simplex_tree_persistence_interface(Simplex_tree_interface_full_featured * st, bool persistence_dim_max) nogil + void compute_persistence(int homology_coeff_field, double min_persistence) nogil + vector[pair[int, pair[double, double]]] get_persistence() nogil + vector[int] betti_numbers() nogil + vector[int] persistent_betti_numbers(double from_value, double to_value) nogil + vector[pair[double,double]] intervals_in_dimension(int dimension) nogil + void write_output_diagram(string diagram_file_name) nogil except + + vector[pair[vector[int], vector[int]]] persistence_pairs() nogil + pair[vector[vector[int]], vector[vector[int]]] lower_star_generators() nogil + pair[vector[vector[int]], vector[vector[int]]] flag_generators() nogil diff --git a/src/python/gudhi/simplex_tree.pyx b/src/python/gudhi/simplex_tree.pyx index b18627c4..9cb24221 100644 --- a/src/python/gudhi/simplex_tree.pyx +++ b/src/python/gudhi/simplex_tree.pyx @@ -7,7 +7,9 @@ # Modification(s): # - YYYY/MM Author: Description of the modification +from cython.operator import dereference, preincrement from libc.stdint cimport intptr_t +import numpy from numpy import array as np_array cimport simplex_tree @@ -31,7 +33,7 @@ cdef class SimplexTree: cdef public intptr_t thisptr # Get the pointer casted as it should be - cdef Simplex_tree_interface_full_featured* get_ptr(self): + cdef Simplex_tree_interface_full_featured* get_ptr(self) nogil: return <Simplex_tree_interface_full_featured*>(self.thisptr) cdef Simplex_tree_persistence_interface * pcohptr @@ -89,7 +91,7 @@ cdef class SimplexTree: (with more :meth:`assign_filtration` or :meth:`make_filtration_non_decreasing` for instance) before calling any function that relies on the filtration property, like - :meth:`initialize_filtration`. + :meth:`persistence`. """ self.get_ptr().assign_simplex_filtration(simplex, filtration) @@ -97,17 +99,10 @@ cdef class SimplexTree: """This function initializes and sorts the simplicial complex filtration vector. - .. note:: - - This function must be launched before - :func:`persistence()<gudhi.SimplexTree.persistence>`, - :func:`betti_numbers()<gudhi.SimplexTree.betti_numbers>`, - :func:`persistent_betti_numbers()<gudhi.SimplexTree.persistent_betti_numbers>`, - or :func:`get_filtration()<gudhi.SimplexTree.get_filtration>` - after :func:`inserting<gudhi.SimplexTree.insert>` or - :func:`removing<gudhi.SimplexTree.remove_maximal_simplex>` - simplices. + .. deprecated:: 3.2.0 """ + import warnings + warnings.warn("Since Gudhi 3.2, calling SimplexTree.initialize_filtration is unnecessary.", DeprecationWarning) self.get_ptr().initialize_filtration() def num_vertices(self): @@ -138,9 +133,9 @@ cdef class SimplexTree: This function is not constant time because it can recompute dimension if required (can be triggered by - :func:`remove_maximal_simplex()<gudhi.SimplexTree.remove_maximal_simplex>` + :func:`remove_maximal_simplex` or - :func:`prune_above_filtration()<gudhi.SimplexTree.prune_above_filtration>` + :func:`prune_above_filtration` methods). """ return self.get_ptr().dimension() @@ -165,9 +160,9 @@ cdef class SimplexTree: This function must be used with caution because it disables dimension recomputation when required (this recomputation can be triggered by - :func:`remove_maximal_simplex()<gudhi.SimplexTree.remove_maximal_simplex>` + :func:`remove_maximal_simplex` or - :func:`prune_above_filtration()<gudhi.SimplexTree.prune_above_filtration>` + :func:`prune_above_filtration` ). """ self.get_ptr().set_dimension(<int>dimension) @@ -181,10 +176,7 @@ cdef class SimplexTree: :returns: true if the simplex was found, false otherwise. :rtype: bool """ - cdef vector[int] csimplex - for i in simplex: - csimplex.push_back(i) - return self.get_ptr().find_simplex(csimplex) + return self.get_ptr().find_simplex(simplex) def insert(self, simplex, filtration=0.0): """This function inserts the given N-simplex and its subfaces with the @@ -201,28 +193,36 @@ cdef class SimplexTree: otherwise (whatever its original filtration value). :rtype: bool """ - cdef vector[int] csimplex - for i in simplex: - csimplex.push_back(i) - return self.get_ptr().insert_simplex_and_subfaces(csimplex, - <double>filtration) + return self.get_ptr().insert(simplex, <double>filtration) - def get_filtration(self): - """This function returns a list of all simplices with their given + def get_simplices(self): + """This function returns a generator with simplices and their given filtration values. + :returns: The simplices. + :rtype: generator with tuples(simplex, filtration) + """ + cdef Simplex_tree_simplices_iterator it = self.get_ptr().get_simplices_iterator_begin() + cdef Simplex_tree_simplices_iterator end = self.get_ptr().get_simplices_iterator_end() + cdef Simplex_tree_simplex_handle sh = dereference(it) + + while it != end: + yield self.get_ptr().get_simplex_and_filtration(dereference(it)) + preincrement(it) + + def get_filtration(self): + """This function returns a generator with simplices and their given + filtration values sorted by increasing filtration values. + :returns: The simplices sorted by increasing filtration values. - :rtype: list of tuples(simplex, filtration) + :rtype: generator with tuples(simplex, filtration) """ - cdef vector[pair[vector[int], double]] filtration \ - = self.get_ptr().get_filtration() - ct = [] - for filtered_complex in filtration: - v = [] - for vertex in filtered_complex.first: - v.append(vertex) - ct.append((v, filtered_complex.second)) - return ct + cdef vector[Simplex_tree_simplex_handle].const_iterator it = self.get_ptr().get_filtration_iterator_begin() + cdef vector[Simplex_tree_simplex_handle].const_iterator end = self.get_ptr().get_filtration_iterator_end() + + while it != end: + yield self.get_ptr().get_simplex_and_filtration(dereference(it)) + preincrement(it) def get_skeleton(self, dimension): """This function returns the (simplices of the) skeleton of a maximum @@ -233,15 +233,12 @@ cdef class SimplexTree: :returns: The (simplices of the) skeleton of a maximum dimension. :rtype: list of tuples(simplex, filtration) """ - cdef vector[pair[vector[int], double]] skeleton \ - = self.get_ptr().get_skeleton(<int>dimension) - ct = [] - for filtered_simplex in skeleton: - v = [] - for vertex in filtered_simplex.first: - v.append(vertex) - ct.append((v, filtered_simplex.second)) - return ct + cdef Simplex_tree_skeleton_iterator it = self.get_ptr().get_skeleton_iterator_begin(dimension) + cdef Simplex_tree_skeleton_iterator end = self.get_ptr().get_skeleton_iterator_end(dimension) + + while it != end: + yield self.get_ptr().get_simplex_and_filtration(dereference(it)) + preincrement(it) def get_star(self, simplex): """This function returns the star of a given N-simplex. @@ -298,17 +295,12 @@ cdef class SimplexTree: .. note:: - Be aware that removing is shifting data in a flat_map - (:func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` to be done). - - .. note:: - The dimension of the simplicial complex may be lower after calling remove_maximal_simplex than it was before. However, - :func:`upper_bound_dimension()<gudhi.SimplexTree.upper_bound_dimension>` + :func:`upper_bound_dimension` method will return the old value, which remains a valid upper bound. If you care, you can call - :func:`dimension()<gudhi.SimplexTree.dimension>` + :func:`dimension` to recompute the exact dimension. """ self.get_ptr().remove_maximal_simplex(simplex) @@ -324,24 +316,14 @@ cdef class SimplexTree: .. note:: - Some simplex tree functions require the filtration to be valid. - prune_above_filtration function is not launching - :func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` - but returns the filtration modification - information. If the complex has changed , please call - :func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` - to recompute it. - - .. note:: - Note that the dimension of the simplicial complex may be lower after calling - :func:`prune_above_filtration()<gudhi.SimplexTree.prune_above_filtration>` + :func:`prune_above_filtration` than it was before. However, - :func:`upper_bound_dimension()<gudhi.SimplexTree.upper_bound_dimension>` + :func:`upper_bound_dimension` will return the old value, which remains a valid upper bound. If you care, you can call - :func:`dimension()<gudhi.SimplexTree.dimension>` + :func:`dimension` method to recompute the exact dimension. """ return self.get_ptr().prune_above_filtration(filtration) @@ -363,7 +345,9 @@ cdef class SimplexTree: :param max_dim: The maximal dimension. :type max_dim: int. """ - self.get_ptr().expansion(max_dim) + cdef int maxdim = max_dim + with nogil: + self.get_ptr().expansion(maxdim) def make_filtration_non_decreasing(self): """This function ensures that each simplex has a higher filtration @@ -372,22 +356,63 @@ cdef class SimplexTree: :returns: True if any filtration value was modified, False if the filtration was already non-decreasing. :rtype: bool + """ + return self.get_ptr().make_filtration_non_decreasing() + def extend_filtration(self): + """ Extend filtration for computing extended persistence. This function only uses the + filtration values at the 0-dimensional simplices, and computes the extended persistence + diagram induced by the lower-star filtration computed with these values. .. note:: - Some simplex tree functions require the filtration to be valid. - make_filtration_non_decreasing function is not launching - :func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` - but returns the filtration modification - information. If the complex has changed , please call - :func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` - to recompute it. + Note that after calling this function, the filtration + values are actually modified within the Simplex_tree. + The function :func:`extended_persistence` + retrieves the original values. + + .. note:: + + Note that this code creates an extra vertex internally, so you should make sure that + the Simplex_tree does not contain a vertex with the largest possible value (i.e., 4294967295). """ - return self.get_ptr().make_filtration_non_decreasing() + self.get_ptr().compute_extended_filtration() + + def extended_persistence(self, homology_coeff_field=11, min_persistence=0): + """This function retrieves good values for extended persistence, and separate the diagrams + into the Ordinary, Relative, Extended+ and Extended- subdiagrams. + + :param homology_coeff_field: The homology coefficient field. Must be a + prime number. Default value is 11. + :type homology_coeff_field: int. + :param min_persistence: The minimum persistence value (i.e., the absolute value of the difference between the persistence diagram point coordinates) to take into + account (strictly greater than min_persistence). Default value is + 0.0. + Sets min_persistence to -1.0 to see all values. + :type min_persistence: float. + :returns: A list of four persistence diagrams in the format described in :func:`persistence`. The first one is Ordinary, the second one is Relative, the third one is Extended+ and the fourth one is Extended-. See https://link.springer.com/article/10.1007/s10208-008-9027-z and/or section 2.2 in https://link.springer.com/article/10.1007/s10208-017-9370-z for a description of these subtypes. + + .. note:: + + This function should be called only if :func:`extend_filtration` has been called first! + + .. note:: + + The coordinates of the persistence diagram points might be a little different than the + original filtration values due to the internal transformation (scaling to [-2,-1]) that is + performed on these values during the computation of extended persistence. + """ + cdef vector[pair[int, pair[double, double]]] persistence_result + if self.pcohptr != NULL: + del self.pcohptr + self.pcohptr = new Simplex_tree_persistence_interface(self.get_ptr(), False) + self.pcohptr.compute_persistence(homology_coeff_field, -1.) + persistence_result = self.pcohptr.get_persistence() + return self.get_ptr().compute_extended_persistence_subdiagrams(persistence_result, min_persistence) + def persistence(self, homology_coeff_field=11, min_persistence=0, persistence_dim_max = False): - """This function returns the persistence of the simplicial complex. + """This function computes and returns the persistence of the simplicial complex. :param homology_coeff_field: The homology coefficient field. Must be a prime number. Default value is 11. @@ -395,7 +420,7 @@ cdef class SimplexTree: :param min_persistence: The minimum persistence value to take into account (strictly greater than min_persistence). Default value is 0.0. - Sets min_persistence to -1.0 to see all values. + Set min_persistence to -1.0 to see all values. :type min_persistence: float. :param persistence_dim_max: If true, the persistent homology for the maximal dimension in the complex is computed. If false, it is @@ -404,13 +429,36 @@ cdef class SimplexTree: :returns: The persistence of the simplicial complex. :rtype: list of pairs(dimension, pair(birth, death)) """ + self.compute_persistence(homology_coeff_field, min_persistence, persistence_dim_max) + return self.pcohptr.get_persistence() + + def compute_persistence(self, homology_coeff_field=11, min_persistence=0, persistence_dim_max = False): + """This function computes the persistence of the simplicial complex, so it can be accessed through + :func:`persistent_betti_numbers`, :func:`persistence_pairs`, etc. This function is equivalent to :func:`persistence` + when you do not want the list :func:`persistence` returns. + + :param homology_coeff_field: The homology coefficient field. Must be a + prime number. Default value is 11. + :type homology_coeff_field: int. + :param min_persistence: The minimum persistence value to take into + account (strictly greater than min_persistence). Default value is + 0.0. + Sets min_persistence to -1.0 to see all values. + :type min_persistence: float. + :param persistence_dim_max: If true, the persistent homology for the + maximal dimension in the complex is computed. If false, it is + ignored. Default is false. + :type persistence_dim_max: bool + :returns: Nothing. + """ if self.pcohptr != NULL: del self.pcohptr - self.pcohptr = new Simplex_tree_persistence_interface(self.get_ptr(), persistence_dim_max) - cdef vector[pair[int, pair[double, double]]] persistence_result - if self.pcohptr != NULL: - persistence_result = self.pcohptr.get_persistence(homology_coeff_field, min_persistence) - return persistence_result + cdef bool pdm = persistence_dim_max + cdef int coef = homology_coeff_field + cdef double minp = min_persistence + with nogil: + self.pcohptr = new Simplex_tree_persistence_interface(self.get_ptr(), pdm) + self.pcohptr.compute_persistence(coef, minp) def betti_numbers(self): """This function returns the Betti numbers of the simplicial complex. @@ -419,16 +467,11 @@ cdef class SimplexTree: :rtype: list of int :note: betti_numbers function requires - :func:`persistence()<gudhi.SimplexTree.persistence>` + :func:`compute_persistence` function to be launched first. """ - cdef vector[int] bn_result - if self.pcohptr != NULL: - bn_result = self.pcohptr.betti_numbers() - else: - print("betti_numbers function requires persistence function" - " to be launched first.") - return bn_result + assert self.pcohptr != NULL, "compute_persistence() must be called before betti_numbers()" + return self.pcohptr.betti_numbers() def persistent_betti_numbers(self, from_value, to_value): """This function returns the persistent Betti numbers of the @@ -445,16 +488,11 @@ cdef class SimplexTree: :rtype: list of int :note: persistent_betti_numbers function requires - :func:`persistence()<gudhi.SimplexTree.persistence>` + :func:`compute_persistence` function to be launched first. """ - cdef vector[int] pbn_result - if self.pcohptr != NULL: - pbn_result = self.pcohptr.persistent_betti_numbers(<double>from_value, <double>to_value) - else: - print("persistent_betti_numbers function requires persistence function" - " to be launched first.") - return pbn_result + assert self.pcohptr != NULL, "compute_persistence() must be called before persistent_betti_numbers()" + return self.pcohptr.persistent_betti_numbers(<double>from_value, <double>to_value) def persistence_intervals_in_dimension(self, dimension): """This function returns the persistence intervals of the simplicial @@ -466,16 +504,11 @@ cdef class SimplexTree: :rtype: numpy array of dimension 2 :note: intervals_in_dim function requires - :func:`persistence()<gudhi.SimplexTree.persistence>` + :func:`compute_persistence` function to be launched first. """ - cdef vector[pair[double,double]] intervals_result - if self.pcohptr != NULL: - intervals_result = self.pcohptr.intervals_in_dimension(dimension) - else: - print("intervals_in_dim function requires persistence function" - " to be launched first.") - return np_array(intervals_result) + assert self.pcohptr != NULL, "compute_persistence() must be called before persistence_intervals_in_dimension()" + return np_array(self.pcohptr.intervals_in_dimension(dimension)) def persistence_pairs(self): """This function returns a list of persistence birth and death simplices pairs. @@ -484,33 +517,68 @@ cdef class SimplexTree: :rtype: list of pair of list of int :note: persistence_pairs function requires - :func:`persistence()<gudhi.SimplexTree.persistence>` + :func:`compute_persistence` function to be launched first. """ - cdef vector[pair[vector[int],vector[int]]] persistence_pairs_result - if self.pcohptr != NULL: - persistence_pairs_result = self.pcohptr.persistence_pairs() - else: - print("persistence_pairs function requires persistence function" - " to be launched first.") - return persistence_pairs_result + assert self.pcohptr != NULL, "compute_persistence() must be called before persistence_pairs()" + return self.pcohptr.persistence_pairs() - def write_persistence_diagram(self, persistence_file=''): + def write_persistence_diagram(self, persistence_file): """This function writes the persistence intervals of the simplicial complex in a user given file name. - :param persistence_file: The specific dimension. + :param persistence_file: Name of the file. :type persistence_file: string. :note: intervals_in_dim function requires - :func:`persistence()<gudhi.SimplexTree.persistence>` + :func:`compute_persistence` function to be launched first. """ - if self.pcohptr != NULL: - if persistence_file != '': - self.pcohptr.write_output_diagram(persistence_file.encode('utf-8')) - else: - print("persistence_file must be specified") + assert self.pcohptr != NULL, "compute_persistence() must be called before write_persistence_diagram()" + self.pcohptr.write_output_diagram(persistence_file.encode('utf-8')) + + def lower_star_persistence_generators(self): + """Assuming this is a lower-star filtration, this function returns the persistence pairs, + where each simplex is replaced with the vertex that gave it its filtration value. + + :returns: First the regular persistence pairs, grouped by dimension, with one vertex per extremity, + and second the essential features, grouped by dimension, with one vertex each + :rtype: Tuple[List[numpy.array[int] of shape (n,2)], List[numpy.array[int] of shape (m,)]] + + :note: lower_star_persistence_generators requires that `persistence()` be called first. + """ + assert self.pcohptr != NULL, "lower_star_persistence_generators() requires that persistence() be called first." + gen = self.pcohptr.lower_star_generators() + normal = [np_array(d).reshape(-1,2) for d in gen.first] + infinite = [np_array(d) for d in gen.second] + return (normal, infinite) + + def flag_persistence_generators(self): + """Assuming this is a flag complex, this function returns the persistence pairs, + where each simplex is replaced with the vertices of the edges that gave it its filtration value. + + :returns: First the regular persistence pairs of dimension 0, with one vertex for birth and two for death; + then the other regular persistence pairs, grouped by dimension, with 2 vertices per extremity; + then the connected components, with one vertex each; + finally the other essential features, grouped by dimension, with 2 vertices for birth. + :rtype: Tuple[numpy.array[int] of shape (n,3), List[numpy.array[int] of shape (m,4)], numpy.array[int] of shape (l,), List[numpy.array[int] of shape (k,2)]] + + :note: flag_persistence_generators requires that `persistence()` be called first. + """ + assert self.pcohptr != NULL, "flag_persistence_generators() requires that persistence() be called first." + gen = self.pcohptr.flag_generators() + if len(gen.first) == 0: + normal0 = numpy.empty((0,3)) + normals = [] + else: + l = iter(gen.first) + normal0 = np_array(next(l)).reshape(-1,3) + normals = [np_array(d).reshape(-1,4) for d in l] + if len(gen.second) == 0: + infinite0 = numpy.empty(0) + infinites = [] else: - print("intervals_in_dim function requires persistence function" - " to be launched first.") + l = iter(gen.second) + infinite0 = np_array(next(l)) + infinites = [np_array(d).reshape(-1,2) for d in l] + return (normal0, normals, infinite0, infinites) diff --git a/src/python/gudhi/wasserstein.py b/src/python/gudhi/wasserstein.py deleted file mode 100644 index db5ddff2..00000000 --- a/src/python/gudhi/wasserstein.py +++ /dev/null @@ -1,97 +0,0 @@ -# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. -# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. -# Author(s): Theo Lacombe -# -# Copyright (C) 2019 Inria -# -# Modification(s): -# - YYYY/MM Author: Description of the modification - -import numpy as np -import scipy.spatial.distance as sc -try: - import ot -except ImportError: - print("POT (Python Optimal Transport) package is not installed. Try to run $ conda install -c conda-forge pot ; or $ pip install POT") - -def _proj_on_diag(X): - ''' - :param X: (n x 2) array encoding the points of a persistent diagram. - :returns: (n x 2) array encoding the (respective orthogonal) projections of the points onto the diagonal - ''' - Z = (X[:,0] + X[:,1]) / 2. - return np.array([Z , Z]).T - - -def _build_dist_matrix(X, Y, order=2., internal_p=2.): - ''' - :param X: (n x 2) numpy.array encoding the (points of the) first diagram. - :param Y: (m x 2) numpy.array encoding the second diagram. - :param internal_p: Ground metric (i.e. norm l_p). - :param order: exponent for the Wasserstein metric. - :returns: (n+1) x (m+1) np.array encoding the cost matrix C. - For 1 <= i <= n, 1 <= j <= m, C[i,j] encodes the distance between X[i] and Y[j], while C[i, m+1] (resp. C[n+1, j]) encodes the distance (to the p) between X[i] (resp Y[j]) and its orthogonal proj onto the diagonal. - note also that C[n+1, m+1] = 0 (it costs nothing to move from the diagonal to the diagonal). - ''' - Xdiag = _proj_on_diag(X) - Ydiag = _proj_on_diag(Y) - if np.isinf(internal_p): - C = sc.cdist(X,Y, metric='chebyshev')**order - Cxd = np.linalg.norm(X - Xdiag, ord=internal_p, axis=1)**order - Cdy = np.linalg.norm(Y - Ydiag, ord=internal_p, axis=1)**order - else: - C = sc.cdist(X,Y, metric='minkowski', p=internal_p)**order - Cxd = np.linalg.norm(X - Xdiag, ord=internal_p, axis=1)**order - Cdy = np.linalg.norm(Y - Ydiag, ord=internal_p, axis=1)**order - Cf = np.hstack((C, Cxd[:,None])) - Cdy = np.append(Cdy, 0) - - Cf = np.vstack((Cf, Cdy[None,:])) - - return Cf - - -def _perstot(X, order, internal_p): - ''' - :param X: (n x 2) numpy.array (points of a given diagram). - :param internal_p: Ground metric on the (upper-half) plane (i.e. norm l_p in R^2); Default value is 2 (Euclidean norm). - :param order: exponent for Wasserstein. Default value is 2. - :returns: float, the total persistence of the diagram (that is, its distance to the empty diagram). - ''' - Xdiag = _proj_on_diag(X) - return (np.sum(np.linalg.norm(X - Xdiag, ord=internal_p, axis=1)**order))**(1./order) - - -def wasserstein_distance(X, Y, order=2., internal_p=2.): - ''' - :param X: (n x 2) numpy.array encoding the (finite points of the) first diagram. Must not contain essential points (i.e. with infinite coordinate). - :param Y: (m x 2) numpy.array encoding the second diagram. - :param internal_p: Ground metric on the (upper-half) plane (i.e. norm l_p in R^2); Default value is 2 (euclidean norm). - :param order: exponent for Wasserstein; Default value is 2. - :returns: the Wasserstein distance of order q (1 <= q < infinity) between persistence diagrams with respect to the internal_p-norm as ground metric. - :rtype: float - ''' - n = len(X) - m = len(Y) - - # handle empty diagrams - if X.size == 0: - if Y.size == 0: - return 0. - else: - return _perstot(Y, order, internal_p) - elif Y.size == 0: - return _perstot(X, order, internal_p) - - M = _build_dist_matrix(X, Y, order=order, internal_p=internal_p) - a = np.full(n+1, 1. / (n + m) ) # weight vector of the input diagram. Uniform here. - a[-1] = a[-1] * m # normalized so that we have a probability measure, required by POT - b = np.full(m+1, 1. / (n + m) ) # weight vector of the input diagram. Uniform here. - b[-1] = b[-1] * n # so that we have a probability measure, required by POT - - # Comptuation of the otcost using the ot.emd2 library. - # Note: it is the Wasserstein distance to the power q. - # The default numItermax=100000 is not sufficient for some examples with 5000 points, what is a good value? - ot_cost = (n+m) * ot.emd2(a, b, M, numItermax=2000000) - - return ot_cost ** (1./order) diff --git a/src/python/gudhi/wasserstein/__init__.py b/src/python/gudhi/wasserstein/__init__.py new file mode 100644 index 00000000..ed225ba4 --- /dev/null +++ b/src/python/gudhi/wasserstein/__init__.py @@ -0,0 +1 @@ +from .wasserstein import wasserstein_distance diff --git a/src/python/gudhi/wasserstein/barycenter.py b/src/python/gudhi/wasserstein/barycenter.py new file mode 100644 index 00000000..d67bcde7 --- /dev/null +++ b/src/python/gudhi/wasserstein/barycenter.py @@ -0,0 +1,146 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Theo Lacombe +# +# Copyright (C) 2019 Inria +# +# Modification(s): +# - YYYY/MM Author: Description of the modification + + +import ot +import numpy as np +import scipy.spatial.distance as sc + +from gudhi.wasserstein import wasserstein_distance + + +def _mean(x, m): + ''' + :param x: a list of 2D-points, off diagonal, x_0... x_{k-1} + :param m: total amount of points taken into account, that is we have (m-k) copies of diagonal + :returns: the weighted mean of x with (m-k) copies of the diagonal + ''' + k = len(x) + if k > 0: + w = np.mean(x, axis=0) + w_delta = (w[0] + w[1]) / 2 * np.ones(2) + return (k * w + (m-k) * w_delta) / m + else: + return np.array([0, 0]) + + +def lagrangian_barycenter(pdiagset, init=None, verbose=False): + ''' + :param pdiagset: a list of ``numpy.array`` of shape `(n x 2)` (`n` can variate), encoding a set of persistence + diagrams with only finite coordinates. + :param init: The initial value for barycenter estimate. + If ``None``, init is made on a random diagram from the dataset. + Otherwise, it can be an ``int`` (then initialization is made on ``pdiagset[init]``) + or a `(n x 2)` ``numpy.array`` enconding a persistence diagram with `n` points. + :type init: ``int``, or (n x 2) ``np.array`` + :param verbose: if ``True``, returns additional information about the barycenter. + :type verbose: boolean + :returns: If not verbose (default), a ``numpy.array`` encoding the barycenter estimate of pdiagset + (local minimum of the energy function). + If ``pdiagset`` is empty, returns ``None``. + If verbose, returns a couple ``(Y, log)`` where ``Y`` is the barycenter estimate, + and ``log`` is a ``dict`` that contains additional informations: + + - `"groupings"`, a list of list of pairs ``(i,j)``. Namely, ``G[k] = [...(i, j)...]``, where ``(i,j)`` indicates that `pdiagset[k][i]`` is matched to ``Y[j]`` if ``i = -1`` or ``j = -1``, it means they represent the diagonal. + + - `"energy"`, ``float`` representing the Frechet energy value obtained. It is the mean of squared distances of observations to the output. + + - `"nb_iter"`, ``int`` number of iterations performed before convergence of the algorithm. + ''' + X = pdiagset # to shorten notations, not a copy + m = len(X) # number of diagrams we are averaging + if m == 0: + print("Warning: computing barycenter of empty diag set. Returns None") + return None + + # store the number of off-diagonal point for each of the X_i + nb_off_diag = np.array([len(X_i) for X_i in X]) + # Initialisation of barycenter + if init is None: + i0 = np.random.randint(m) # Index of first state for the barycenter + Y = X[i0].copy() + else: + if type(init)==int: + Y = X[init].copy() + else: + Y = init.copy() + + nb_iter = 0 + + converged = False # stoping criterion + while not converged: + nb_iter += 1 + K = len(Y) # current nb of points in Y (some might be on diagonal) + G = np.full((K, m), -1, dtype=int) # will store for each j, the (index) + # point matched in each other diagram + #(might be the diagonal). + # that is G[j, i] = k <=> y_j is matched to + # x_k in the diagram i-th diagram X[i] + updated_points = np.zeros((K, 2)) # will store the new positions of + # the points of Y. + # If points disappear, there thrown + # on [0,0] by default. + new_created_points = [] # will store potential new points. + + # Step 1 : compute optimal matching (Y, X_i) for each X_i + # and create new points in Y if needed + for i in range(m): + _, indices = wasserstein_distance(Y, X[i], matching=True, order=2., internal_p=2.) + for y_j, x_i_j in indices: + if y_j >= 0: # we matched an off diagonal point to x_i_j... + if x_i_j >= 0: # ...which is also an off-diagonal point. + G[y_j, i] = x_i_j + else: # ...which is a diagonal point + G[y_j, i] = -1 # -1 stands for the diagonal (mask) + else: # We matched a diagonal point to x_i_j... + if x_i_j >= 0: # which is a off-diag point ! + # need to create new point in Y + new_y = _mean(np.array([X[i][x_i_j]]), m) + # Average this point with (m-1) copies of Delta + new_created_points.append(new_y) + + # Step 2 : Update current point position thanks to groupings computed + to_delete = [] + for j in range(K): + matched_points = [X[i][G[j, i]] for i in range(m) if G[j, i] > -1] + new_y_j = _mean(matched_points, m) + if not np.array_equal(new_y_j, np.array([0,0])): + updated_points[j] = new_y_j + else: # this points is no longer of any use. + to_delete.append(j) + # we remove the point to be deleted now. + updated_points = np.delete(updated_points, to_delete, axis=0) + + # we cannot converge if there have been new created points. + if new_created_points: + Y = np.concatenate((updated_points, new_created_points)) + else: + # Step 3 : we check convergence + if np.array_equal(updated_points, Y): + converged = True + Y = updated_points + + + if verbose: + groupings = [] + energy = 0 + log = {} + n_y = len(Y) + for i in range(m): + cost, edges = wasserstein_distance(Y, X[i], matching=True, order=2., internal_p=2.) + groupings.append(edges) + energy += cost + log["groupings"] = groupings + energy = energy/m + log["energy"] = energy + log["nb_iter"] = nb_iter + + return Y, log + else: + return Y diff --git a/src/python/gudhi/wasserstein/wasserstein.py b/src/python/gudhi/wasserstein/wasserstein.py new file mode 100644 index 00000000..89ecab1c --- /dev/null +++ b/src/python/gudhi/wasserstein/wasserstein.py @@ -0,0 +1,181 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Theo Lacombe +# +# Copyright (C) 2019 Inria +# +# Modification(s): +# - YYYY/MM Author: Description of the modification + +import numpy as np +import scipy.spatial.distance as sc + +try: + import ot +except ImportError: + print("POT (Python Optimal Transport) package is not installed. Try to run $ conda install -c conda-forge pot ; or $ pip install POT") + + +# Currently unused, but Théo says it is likely to be used again. +def _proj_on_diag(X): + ''' + :param X: (n x 2) array encoding the points of a persistent diagram. + :returns: (n x 2) array encoding the (respective orthogonal) projections of the points onto the diagonal + ''' + Z = (X[:,0] + X[:,1]) / 2. + return np.array([Z , Z]).T + + +def _dist_to_diag(X, internal_p): + ''' + :param X: (n x 2) array encoding the points of a persistent diagram. + :param internal_p: Ground metric (i.e. norm L^p). + :returns: (n) array encoding the (respective orthogonal) distances of the points to the diagonal + + .. note:: + Assumes that the points are above the diagonal. + ''' + return (X[:, 1] - X[:, 0]) * 2 ** (1.0 / internal_p - 1) + + +def _build_dist_matrix(X, Y, order, internal_p): + ''' + :param X: (n x 2) numpy.array encoding the (points of the) first diagram. + :param Y: (m x 2) numpy.array encoding the second diagram. + :param order: exponent for the Wasserstein metric. + :param internal_p: Ground metric (i.e. norm L^p). + :returns: (n+1) x (m+1) np.array encoding the cost matrix C. + For 0 <= i < n, 0 <= j < m, C[i,j] encodes the distance between X[i] and Y[j], + while C[i, m] (resp. C[n, j]) encodes the distance (to the p) between X[i] (resp Y[j]) + and its orthogonal projection onto the diagonal. + note also that C[n, m] = 0 (it costs nothing to move from the diagonal to the diagonal). + ''' + Cxd = _dist_to_diag(X, internal_p)**order + Cdy = _dist_to_diag(Y, internal_p)**order + if np.isinf(internal_p): + C = sc.cdist(X,Y, metric='chebyshev')**order + else: + C = sc.cdist(X,Y, metric='minkowski', p=internal_p)**order + Cf = np.hstack((C, Cxd[:,None])) + Cdy = np.append(Cdy, 0) + + Cf = np.vstack((Cf, Cdy[None,:])) + + return Cf + + +def _perstot_autodiff(X, order, internal_p): + ''' + Version of _perstot that works on eagerpy tensors. + ''' + return _dist_to_diag(X, internal_p).norms.lp(order) + +def _perstot(X, order, internal_p, enable_autodiff): + ''' + :param X: (n x 2) numpy.array (points of a given diagram). + :param order: exponent for Wasserstein. Default value is 2. + :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2); Default value is 2 (Euclidean norm). + :param enable_autodiff: If X is torch.tensor, tensorflow.Tensor or jax.numpy.ndarray, make the computation + transparent to automatic differentiation. + :type enable_autodiff: bool + :returns: float, the total persistence of the diagram (that is, its distance to the empty diagram). + ''' + if enable_autodiff: + import eagerpy as ep + + return _perstot_autodiff(ep.astensor(X), order, internal_p).raw + else: + return np.linalg.norm(_dist_to_diag(X, internal_p), ord=order) + + +def wasserstein_distance(X, Y, matching=False, order=2., internal_p=2., enable_autodiff=False): + ''' + :param X: (n x 2) numpy.array encoding the (finite points of the) first diagram. Must not contain essential points + (i.e. with infinite coordinate). + :param Y: (m x 2) numpy.array encoding the second diagram. + :param matching: if True, computes and returns the optimal matching between X and Y, encoded as + a (n x 2) np.array [...[i,j]...], meaning the i-th point in X is matched to + the j-th point in Y, with the convention (-1) represents the diagonal. + :param order: exponent for Wasserstein; Default value is 2. + :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2); + Default value is 2 (Euclidean norm). + :param enable_autodiff: If X and Y are torch.tensor, tensorflow.Tensor or jax.numpy.ndarray, make the computation + transparent to automatic differentiation. This requires the package EagerPy and is currently incompatible + with `matching=True`. + + .. note:: This considers the function defined on the coordinates of the off-diagonal points of X and Y + and lets the various frameworks compute its gradient. It never pulls new points from the diagonal. + :type enable_autodiff: bool + :returns: the Wasserstein distance of order q (1 <= q < infinity) between persistence diagrams with + respect to the internal_p-norm as ground metric. + If matching is set to True, also returns the optimal matching between X and Y. + ''' + n = len(X) + m = len(Y) + + # handle empty diagrams + if n == 0: + if m == 0: + if not matching: + # What if enable_autodiff? + return 0. + else: + return 0., np.array([]) + else: + if not matching: + return _perstot(Y, order, internal_p, enable_autodiff) + else: + return _perstot(Y, order, internal_p, enable_autodiff), np.array([[-1, j] for j in range(m)]) + elif m == 0: + if not matching: + return _perstot(X, order, internal_p, enable_autodiff) + else: + return _perstot(X, order, internal_p, enable_autodiff), np.array([[i, -1] for i in range(n)]) + + if enable_autodiff: + import eagerpy as ep + + X_orig = ep.astensor(X) + Y_orig = ep.astensor(Y) + X = X_orig.numpy() + Y = Y_orig.numpy() + M = _build_dist_matrix(X, Y, order=order, internal_p=internal_p) + a = np.ones(n+1) # weight vector of the input diagram. Uniform here. + a[-1] = m + b = np.ones(m+1) # weight vector of the input diagram. Uniform here. + b[-1] = n + + if matching: + assert not enable_autodiff, "matching and enable_autodiff are currently incompatible" + P = ot.emd(a=a,b=b,M=M, numItermax=2000000) + ot_cost = np.sum(np.multiply(P,M)) + P[-1, -1] = 0 # Remove matching corresponding to the diagonal + match = np.argwhere(P) + # Now we turn to -1 points encoding the diagonal + match[:,0][match[:,0] >= n] = -1 + match[:,1][match[:,1] >= m] = -1 + return ot_cost ** (1./order) , match + + if enable_autodiff: + P = ot.emd(a=a, b=b, M=M, numItermax=2000000) + pairs_X_Y = np.argwhere(P[:-1, :-1]) + pairs_X_diag = np.nonzero(P[:-1, -1]) + pairs_Y_diag = np.nonzero(P[-1, :-1]) + dists = [] + # empty arrays are not handled properly by the helpers, so we avoid calling them + if len(pairs_X_Y): + dists.append((Y_orig[pairs_X_Y[:, 1]] - X_orig[pairs_X_Y[:, 0]]).norms.lp(internal_p, axis=-1).norms.lp(order)) + if len(pairs_X_diag): + dists.append(_perstot_autodiff(X_orig[pairs_X_diag], order, internal_p)) + if len(pairs_Y_diag): + dists.append(_perstot_autodiff(Y_orig[pairs_Y_diag], order, internal_p)) + dists = [dist.reshape(1) for dist in dists] + return ep.concatenate(dists).norms.lp(order).raw + # We can also concatenate the 3 vectors to compute just one norm. + + # Comptuation of the otcost using the ot.emd2 library. + # Note: it is the Wasserstein distance to the power q. + # The default numItermax=100000 is not sufficient for some examples with 5000 points, what is a good value? + ot_cost = ot.emd2(a, b, M, numItermax=2000000) + + return ot_cost ** (1./order) diff --git a/src/python/gudhi/weighted_rips_complex.py b/src/python/gudhi/weighted_rips_complex.py new file mode 100644 index 00000000..0541572b --- /dev/null +++ b/src/python/gudhi/weighted_rips_complex.py @@ -0,0 +1,59 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Raphaël Tinarrage, Yuichi Ike, Masatoshi Takenouchi +# +# Copyright (C) 2020 Inria, Copyright (C) 2020 FUjitsu Laboratories Ltd. +# +# Modification(s): +# - YYYY/MM Author: Description of the modification + +from gudhi import SimplexTree + +class WeightedRipsComplex: + """ + Class to generate a weighted Rips complex from a distance matrix and weights on vertices, + in the way described in :cite:`dtmfiltrations`. + Remark that all the filtration values are doubled compared to the definition in the paper + for the consistency with RipsComplex. + """ + def __init__(self, + distance_matrix, + weights=None, + max_filtration=float('inf')): + """ + Args: + distance_matrix (Sequence[Sequence[float]]): distance matrix (full square or lower triangular). + weights (Sequence[float]): (one half of) weight for each vertex. + max_filtration (float): specifies the maximal filtration value to be considered. + """ + self.distance_matrix = distance_matrix + if weights is not None: + self.weights = weights + else: + self.weights = [0] * len(distance_matrix) + self.max_filtration = max_filtration + + def create_simplex_tree(self, max_dimension): + """ + Args: + max_dimension (int): graph expansion until this given dimension. + """ + dist = self.distance_matrix + F = self.weights + num_pts = len(dist) + + st = SimplexTree() + + for i in range(num_pts): + if 2*F[i] <= self.max_filtration: + st.insert([i], 2*F[i]) + for i in range(num_pts): + for j in range(i): + value = max(2*F[i], 2*F[j], dist[i][j] + F[i] + F[j]) + # max is needed when F is not 1-Lipschitz + if value <= self.max_filtration: + st.insert([i,j], filtration=value) + + st.expansion(max_dimension) + return st + diff --git a/src/python/include/Alpha_complex_interface.h b/src/python/include/Alpha_complex_interface.h index 8614eee3..40de88f3 100644 --- a/src/python/include/Alpha_complex_interface.h +++ b/src/python/include/Alpha_complex_interface.h @@ -58,7 +58,6 @@ class Alpha_complex_interface { void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square) { alpha_complex_->create_complex(*simplex_tree, max_alpha_square); - simplex_tree->initialize_filtration(); } private: diff --git a/src/python/include/Euclidean_strong_witness_complex_interface.h b/src/python/include/Euclidean_strong_witness_complex_interface.h index c1c72737..f94c51ef 100644 --- a/src/python/include/Euclidean_strong_witness_complex_interface.h +++ b/src/python/include/Euclidean_strong_witness_complex_interface.h @@ -50,12 +50,10 @@ class Euclidean_strong_witness_complex_interface { void create_simplex_tree(Gudhi::Simplex_tree<>* simplex_tree, double max_alpha_square, std::size_t limit_dimension) { witness_complex_->create_complex(*simplex_tree, max_alpha_square, limit_dimension); - simplex_tree->initialize_filtration(); } void create_simplex_tree(Gudhi::Simplex_tree<>* simplex_tree, double max_alpha_square) { witness_complex_->create_complex(*simplex_tree, max_alpha_square); - simplex_tree->initialize_filtration(); } std::vector<double> get_point(unsigned vh) { diff --git a/src/python/include/Euclidean_witness_complex_interface.h b/src/python/include/Euclidean_witness_complex_interface.h index 5d7dbdc2..4411ae79 100644 --- a/src/python/include/Euclidean_witness_complex_interface.h +++ b/src/python/include/Euclidean_witness_complex_interface.h @@ -49,12 +49,10 @@ class Euclidean_witness_complex_interface { void create_simplex_tree(Gudhi::Simplex_tree<>* simplex_tree, double max_alpha_square, std::size_t limit_dimension) { witness_complex_->create_complex(*simplex_tree, max_alpha_square, limit_dimension); - simplex_tree->initialize_filtration(); } void create_simplex_tree(Gudhi::Simplex_tree<>* simplex_tree, double max_alpha_square) { witness_complex_->create_complex(*simplex_tree, max_alpha_square); - simplex_tree->initialize_filtration(); } std::vector<double> get_point(unsigned vh) { diff --git a/src/python/include/Nerve_gic_interface.h b/src/python/include/Nerve_gic_interface.h index 5e7f8ae6..ab14c318 100644 --- a/src/python/include/Nerve_gic_interface.h +++ b/src/python/include/Nerve_gic_interface.h @@ -29,7 +29,6 @@ class Nerve_gic_interface : public Cover_complex<std::vector<double>> { public: void create_simplex_tree(Simplex_tree_interface<>* simplex_tree) { create_complex(*simplex_tree); - simplex_tree->initialize_filtration(); } void set_cover_from_Euclidean_Voronoi(int m) { set_cover_from_Voronoi(Gudhi::Euclidean_distance(), m); diff --git a/src/python/include/Persistent_cohomology_interface.h b/src/python/include/Persistent_cohomology_interface.h index 8c79e6f3..e5a3dfba 100644 --- a/src/python/include/Persistent_cohomology_interface.h +++ b/src/python/include/Persistent_cohomology_interface.h @@ -13,9 +13,11 @@ #include <gudhi/Persistent_cohomology.h> +#include <cstdlib> #include <vector> #include <utility> // for std::pair #include <algorithm> // for sort +#include <unordered_map> namespace Gudhi { @@ -23,82 +25,204 @@ template<class FilteredComplex> class Persistent_cohomology_interface : public persistent_cohomology::Persistent_cohomology<FilteredComplex, persistent_cohomology::Field_Zp> { private: + typedef persistent_cohomology::Persistent_cohomology<FilteredComplex, persistent_cohomology::Field_Zp> Base; /* * Compare two intervals by dimension, then by length. */ struct cmp_intervals_by_dim_then_length { - explicit cmp_intervals_by_dim_then_length(FilteredComplex * sc) - : sc_(sc) { } - template<typename Persistent_interval> bool operator()(const Persistent_interval & p1, const Persistent_interval & p2) { - if (sc_->dimension(get < 0 > (p1)) == sc_->dimension(get < 0 > (p2))) - return (sc_->filtration(get < 1 > (p1)) - sc_->filtration(get < 0 > (p1)) - > sc_->filtration(get < 1 > (p2)) - sc_->filtration(get < 0 > (p2))); + if (std::get<0>(p1) == std::get<0>(p2)) { + auto& i1 = std::get<1>(p1); + auto& i2 = std::get<1>(p2); + return std::get<1>(i1) - std::get<0>(i1) > std::get<1>(i2) - std::get<0>(i2); + } else - return (sc_->dimension(get < 0 > (p1)) > sc_->dimension(get < 0 > (p2))); + return (std::get<0>(p1) > std::get<0>(p2)); + // Why does this sort by decreasing dimension? } - FilteredComplex* sc_; }; public: - Persistent_cohomology_interface(FilteredComplex* stptr) - : persistent_cohomology::Persistent_cohomology<FilteredComplex, persistent_cohomology::Field_Zp>(*stptr), - stptr_(stptr) { } - - Persistent_cohomology_interface(FilteredComplex* stptr, bool persistence_dim_max) - : persistent_cohomology::Persistent_cohomology<FilteredComplex, - persistent_cohomology::Field_Zp>(*stptr, persistence_dim_max), + Persistent_cohomology_interface(FilteredComplex* stptr, bool persistence_dim_max=false) + : Base(*stptr, persistence_dim_max), stptr_(stptr) { } - std::vector<std::pair<int, std::pair<double, double>>> get_persistence(int homology_coeff_field, - double min_persistence) { - persistent_cohomology::Persistent_cohomology<FilteredComplex, - persistent_cohomology::Field_Zp>::init_coefficients(homology_coeff_field); - persistent_cohomology::Persistent_cohomology<FilteredComplex, - persistent_cohomology::Field_Zp>::compute_persistent_cohomology(min_persistence); - - // Custom sort and output persistence - cmp_intervals_by_dim_then_length cmp(stptr_); - auto persistent_pairs = persistent_cohomology::Persistent_cohomology<FilteredComplex, - persistent_cohomology::Field_Zp>::get_persistent_pairs(); - std::sort(std::begin(persistent_pairs), std::end(persistent_pairs), cmp); + // TODO: move to the constructors? + void compute_persistence(int homology_coeff_field, double min_persistence) { + Base::init_coefficients(homology_coeff_field); + Base::compute_persistent_cohomology(min_persistence); + } + std::vector<std::pair<int, std::pair<double, double>>> get_persistence() { std::vector<std::pair<int, std::pair<double, double>>> persistence; + auto const& persistent_pairs = Base::get_persistent_pairs(); + persistence.reserve(persistent_pairs.size()); for (auto pair : persistent_pairs) { - persistence.push_back(std::make_pair(stptr_->dimension(get<0>(pair)), - std::make_pair(stptr_->filtration(get<0>(pair)), - stptr_->filtration(get<1>(pair))))); + persistence.emplace_back(stptr_->dimension(get<0>(pair)), + std::make_pair(stptr_->filtration(get<0>(pair)), + stptr_->filtration(get<1>(pair)))); } + // Custom sort and output persistence + cmp_intervals_by_dim_then_length cmp; + std::sort(std::begin(persistence), std::end(persistence), cmp); return persistence; } - std::vector<std::pair<std::vector<int>, std::vector<int>>> persistence_pairs() { - auto pairs = persistent_cohomology::Persistent_cohomology<FilteredComplex, + // This function computes the top-dimensional cofaces associated to the positive and negative + // simplices of a cubical complex. The output format is a vector of vectors of three integers, + // which are [homological dimension, index of top-dimensional coface of positive simplex, + // index of top-dimensional coface of negative simplex]. If the topological feature is essential, + // then the index of top-dimensional coface of negative simplex is arbitrarily set to -1. + std::vector<std::vector<int>> cofaces_of_cubical_persistence_pairs() { + + // Warning: this function is meant to be used with CubicalComplex only!! + + auto&& pairs = persistent_cohomology::Persistent_cohomology<FilteredComplex, persistent_cohomology::Field_Zp>::get_persistent_pairs(); + // Gather all top-dimensional cells and store their simplex handles + std::vector<std::size_t> max_splx; + for (auto splx : stptr_->top_dimensional_cells_range()) + max_splx.push_back(splx); + // Sort these simplex handles and compute the ordering function + // This function allows to go directly from the simplex handle to the position of the corresponding top-dimensional cell in the input data + std::unordered_map<std::size_t, int> order; + //std::sort(max_splx.begin(), max_splx.end()); + for (unsigned int i = 0; i < max_splx.size(); i++) order.emplace(max_splx[i], i); + + std::vector<std::vector<int>> persistence_pairs; + for (auto pair : pairs) { + int h = stptr_->dimension(get<0>(pair)); + // Recursively get the top-dimensional cell / coface associated to the persistence generator + std::size_t face0 = stptr_->get_top_dimensional_coface_of_a_cell(get<0>(pair)); + // Retrieve the index of the corresponding top-dimensional cell in the input data + int splx0 = order[face0]; + + int splx1 = -1; + if (get<1>(pair) != stptr_->null_simplex()){ + // Recursively get the top-dimensional cell / coface associated to the persistence generator + std::size_t face1 = stptr_->get_top_dimensional_coface_of_a_cell(get<1>(pair)); + // Retrieve the index of the corresponding top-dimensional cell in the input data + splx1 = order[face1]; + } + persistence_pairs.push_back({ h, splx0, splx1 }); + } + return persistence_pairs; + } + + std::vector<std::pair<std::vector<int>, std::vector<int>>> persistence_pairs() { std::vector<std::pair<std::vector<int>, std::vector<int>>> persistence_pairs; + auto const& pairs = Base::get_persistent_pairs(); persistence_pairs.reserve(pairs.size()); + std::vector<int> birth; + std::vector<int> death; for (auto pair : pairs) { - std::vector<int> birth; + birth.clear(); if (get<0>(pair) != stptr_->null_simplex()) { for (auto vertex : stptr_->simplex_vertex_range(get<0>(pair))) { birth.push_back(vertex); } } - std::vector<int> death; + death.clear(); if (get<1>(pair) != stptr_->null_simplex()) { + death.reserve(birth.size()+1); for (auto vertex : stptr_->simplex_vertex_range(get<1>(pair))) { death.push_back(vertex); } } - persistence_pairs.push_back(std::make_pair(birth, death)); + persistence_pairs.emplace_back(birth, death); } return persistence_pairs; } + // TODO: (possibly at the python level) + // - an option to return only some of those vectors? + typedef std::pair<std::vector<std::vector<int>>, std::vector<std::vector<int>>> Generators; + + Generators lower_star_generators() { + Generators out; + // diags[i] should be interpreted as vector<array<int,2>> + auto& diags = out.first; + // diagsinf[i] should be interpreted as vector<int> + auto& diagsinf = out.second; + for (auto pair : Base::get_persistent_pairs()) { + auto s = std::get<0>(pair); + auto t = std::get<1>(pair); + int dim = stptr_->dimension(s); + auto v = stptr_->vertex_with_same_filtration(s); + if(t == stptr_->null_simplex()) { + while(diagsinf.size() < dim+1) diagsinf.emplace_back(); + diagsinf[dim].push_back(v); + } else { + while(diags.size() < dim+1) diags.emplace_back(); + auto w = stptr_->vertex_with_same_filtration(t); + auto& d = diags[dim]; + d.insert(d.end(), { v, w }); + } + } + return out; + } + + // An alternative, to avoid those different sizes, would be to "pad" vertex generator v as (v, v) or (v, -1). When using it as index, this corresponds to adding the vertex filtration values either on the diagonal of the distance matrix, or as an extra row or column. + // We could also merge the vectors for different dimensions into a single one, with an extra column for the dimension (converted to type double). + Generators flag_generators() { + Generators out; + // diags[0] should be interpreted as vector<array<int,3>> and other diags[i] as vector<array<int,4>> + auto& diags = out.first; + // diagsinf[0] should be interpreted as vector<int> and other diagsinf[i] as vector<array<int,2>> + auto& diagsinf = out.second; + for (auto pair : Base::get_persistent_pairs()) { + auto s = std::get<0>(pair); + auto t = std::get<1>(pair); + int dim = stptr_->dimension(s); + bool infinite = t == stptr_->null_simplex(); + if(infinite) { + if(dim == 0) { + auto v = *std::begin(stptr_->simplex_vertex_range(s)); + if(diagsinf.size()==0)diagsinf.emplace_back(); + diagsinf[0].push_back(v); + } else { + auto e = stptr_->edge_with_same_filtration(s); + auto&& e_vertices = stptr_->simplex_vertex_range(e); + auto i = std::begin(e_vertices); + auto v1 = *i; + auto v2 = *++i; + GUDHI_CHECK(++i==std::end(e_vertices), "must be an edge"); + while(diagsinf.size() < dim+1) diagsinf.emplace_back(); + auto& d = diagsinf[dim]; + d.insert(d.end(), { v1, v2 }); + } + } else { + auto et = stptr_->edge_with_same_filtration(t); + auto&& et_vertices = stptr_->simplex_vertex_range(et); + auto it = std::begin(et_vertices); + auto w1 = *it; + auto w2 = *++it; + GUDHI_CHECK(++it==std::end(et_vertices), "must be an edge"); + if(dim == 0) { + auto v = *std::begin(stptr_->simplex_vertex_range(s)); + if(diags.size()==0)diags.emplace_back(); + auto& d = diags[0]; + d.insert(d.end(), { v, w1, w2 }); + } else { + auto es = stptr_->edge_with_same_filtration(s); + auto&& es_vertices = stptr_->simplex_vertex_range(es); + auto is = std::begin(es_vertices); + auto v1 = *is; + auto v2 = *++is; + GUDHI_CHECK(++is==std::end(es_vertices), "must be an edge"); + while(diags.size() < dim+1) diags.emplace_back(); + auto& d = diags[dim]; + d.insert(d.end(), { v1, v2, w1, w2 }); + } + } + } + return out; + } + private: // A copy FilteredComplex* stptr_; diff --git a/src/python/include/Rips_complex_interface.h b/src/python/include/Rips_complex_interface.h index a66b0e5b..d98b0226 100644 --- a/src/python/include/Rips_complex_interface.h +++ b/src/python/include/Rips_complex_interface.h @@ -53,7 +53,6 @@ class Rips_complex_interface { rips_complex_->create_complex(*simplex_tree, dim_max); else sparse_rips_complex_->create_complex(*simplex_tree, dim_max); - simplex_tree->initialize_filtration(); } private: diff --git a/src/python/include/Simplex_tree_interface.h b/src/python/include/Simplex_tree_interface.h index 06f31341..56d7c41d 100644 --- a/src/python/include/Simplex_tree_interface.h +++ b/src/python/include/Simplex_tree_interface.h @@ -16,8 +16,6 @@ #include <gudhi/Simplex_tree.h> #include <gudhi/Points_off_io.h> -#include "Persistent_cohomology_interface.h" - #include <iostream> #include <vector> #include <utility> // std::pair @@ -33,19 +31,29 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> { using Simplex_handle = typename Base::Simplex_handle; using Insertion_result = typename std::pair<Simplex_handle, bool>; using Simplex = std::vector<Vertex_handle>; - using Filtered_simplices = std::vector<std::pair<Simplex, Filtration_value>>; + using Simplex_and_filtration = std::pair<Simplex, Filtration_value>; + using Filtered_simplices = std::vector<Simplex_and_filtration>; + using Skeleton_simplex_iterator = typename Base::Skeleton_simplex_iterator; + using Complex_simplex_iterator = typename Base::Complex_simplex_iterator; + using Extended_filtration_data = typename Base::Extended_filtration_data; public: - bool find_simplex(const Simplex& vh) { - return (Base::find(vh) != Base::null_simplex()); + + Extended_filtration_data efd; + + bool find_simplex(const Simplex& simplex) { + return (Base::find(simplex) != Base::null_simplex()); } - void assign_simplex_filtration(const Simplex& vh, Filtration_value filtration) { - Base::assign_filtration(Base::find(vh), filtration); + void assign_simplex_filtration(const Simplex& simplex, Filtration_value filtration) { + Base::assign_filtration(Base::find(simplex), filtration); + Base::clear_filtration(); } bool insert(const Simplex& simplex, Filtration_value filtration = 0) { Insertion_result result = Base::insert_simplex_and_subfaces(simplex, filtration); + if (result.first != Base::null_simplex()) + Base::clear_filtration(); return (result.second); } @@ -79,32 +87,15 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> { void remove_maximal_simplex(const Simplex& simplex) { Base::remove_maximal_simplex(Base::find(simplex)); - Base::initialize_filtration(); + Base::clear_filtration(); } - Filtered_simplices get_filtration() { - Base::initialize_filtration(); - Filtered_simplices filtrations; - for (auto f_simplex : Base::filtration_simplex_range()) { - Simplex simplex; - for (auto vertex : Base::simplex_vertex_range(f_simplex)) { - simplex.insert(simplex.begin(), vertex); - } - filtrations.push_back(std::make_pair(simplex, Base::filtration(f_simplex))); + Simplex_and_filtration get_simplex_and_filtration(Simplex_handle f_simplex) { + Simplex simplex; + for (auto vertex : Base::simplex_vertex_range(f_simplex)) { + simplex.insert(simplex.begin(), vertex); } - return filtrations; - } - - Filtered_simplices get_skeleton(int dimension) { - Filtered_simplices skeletons; - for (auto f_simplex : Base::skeleton_simplex_range(dimension)) { - Simplex simplex; - for (auto vertex : Base::simplex_vertex_range(f_simplex)) { - simplex.insert(simplex.begin(), vertex); - } - skeletons.push_back(std::make_pair(simplex, Base::filtration(f_simplex))); - } - return skeletons; + return std::make_pair(std::move(simplex), Base::filtration(f_simplex)); } Filtered_simplices get_star(const Simplex& simplex) { @@ -131,9 +122,71 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> { return cofaces; } - void create_persistence(Gudhi::Persistent_cohomology_interface<Base>* pcoh) { - Base::initialize_filtration(); - pcoh = new Gudhi::Persistent_cohomology_interface<Base>(*this); + void compute_extended_filtration() { + this->efd = this->extend_filtration(); + return; + } + + std::vector<std::vector<std::pair<int, std::pair<Filtration_value, Filtration_value>>>> compute_extended_persistence_subdiagrams(const std::vector<std::pair<int, std::pair<Filtration_value, Filtration_value>>>& dgm, Filtration_value min_persistence){ + std::vector<std::vector<std::pair<int, std::pair<Filtration_value, Filtration_value>>>> new_dgm(4); + for (unsigned int i = 0; i < dgm.size(); i++){ + std::pair<Filtration_value, Extended_simplex_type> px = this->decode_extended_filtration(dgm[i].second.first, this->efd); + std::pair<Filtration_value, Extended_simplex_type> py = this->decode_extended_filtration(dgm[i].second.second, this->efd); + std::pair<int, std::pair<Filtration_value, Filtration_value>> pd_point = std::make_pair(dgm[i].first, std::make_pair(px.first, py.first)); + if(std::abs(px.first - py.first) > min_persistence){ + //Ordinary + if (px.second == Extended_simplex_type::UP && py.second == Extended_simplex_type::UP){ + new_dgm[0].push_back(pd_point); + } + // Relative + else if (px.second == Extended_simplex_type::DOWN && py.second == Extended_simplex_type::DOWN){ + new_dgm[1].push_back(pd_point); + } + else{ + // Extended+ + if (px.first < py.first){ + new_dgm[2].push_back(pd_point); + } + //Extended- + else{ + new_dgm[3].push_back(pd_point); + } + } + } + } + return new_dgm; + } + + // Iterator over the simplex tree + Complex_simplex_iterator get_simplices_iterator_begin() { + // this specific case works because the range is just a pair of iterators - won't work if range was a vector + return Base::complex_simplex_range().begin(); + } + + Complex_simplex_iterator get_simplices_iterator_end() { + // this specific case works because the range is just a pair of iterators - won't work if range was a vector + return Base::complex_simplex_range().end(); + } + + typename std::vector<Simplex_handle>::const_iterator get_filtration_iterator_begin() { + // Base::initialize_filtration(); already performed in filtration_simplex_range + // this specific case works because the range is just a pair of iterators - won't work if range was a vector + return Base::filtration_simplex_range().begin(); + } + + typename std::vector<Simplex_handle>::const_iterator get_filtration_iterator_end() { + // this specific case works because the range is just a pair of iterators - won't work if range was a vector + return Base::filtration_simplex_range().end(); + } + + Skeleton_simplex_iterator get_skeleton_iterator_begin(int dimension) { + // this specific case works because the range is just a pair of iterators - won't work if range was a vector + return Base::skeleton_simplex_range(dimension).begin(); + } + + Skeleton_simplex_iterator get_skeleton_iterator_end(int dimension) { + // this specific case works because the range is just a pair of iterators - won't work if range was a vector + return Base::skeleton_simplex_range(dimension).end(); } }; diff --git a/src/python/include/Strong_witness_complex_interface.h b/src/python/include/Strong_witness_complex_interface.h index cda5b514..e9ab0c7b 100644 --- a/src/python/include/Strong_witness_complex_interface.h +++ b/src/python/include/Strong_witness_complex_interface.h @@ -41,13 +41,11 @@ class Strong_witness_complex_interface { void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square, std::size_t limit_dimension) { witness_complex_->create_complex(*simplex_tree, max_alpha_square, limit_dimension); - simplex_tree->initialize_filtration(); } void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square) { witness_complex_->create_complex(*simplex_tree, max_alpha_square); - simplex_tree->initialize_filtration(); } private: diff --git a/src/python/include/Tangential_complex_interface.h b/src/python/include/Tangential_complex_interface.h index 698226cc..b1afce94 100644 --- a/src/python/include/Tangential_complex_interface.h +++ b/src/python/include/Tangential_complex_interface.h @@ -90,7 +90,6 @@ class Tangential_complex_interface { void create_simplex_tree(Simplex_tree<>* simplex_tree) { tangential_complex_->create_complex<Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_full_featured>>(*simplex_tree); - simplex_tree->initialize_filtration(); } void set_max_squared_edge_length(double max_squared_edge_length) { diff --git a/src/python/include/Witness_complex_interface.h b/src/python/include/Witness_complex_interface.h index 45e14253..76947e53 100644 --- a/src/python/include/Witness_complex_interface.h +++ b/src/python/include/Witness_complex_interface.h @@ -41,13 +41,11 @@ class Witness_complex_interface { void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square, std::size_t limit_dimension) { witness_complex_->create_complex(*simplex_tree, max_alpha_square, limit_dimension); - simplex_tree->initialize_filtration(); } void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square) { witness_complex_->create_complex(*simplex_tree, max_alpha_square); - simplex_tree->initialize_filtration(); } private: diff --git a/src/python/include/pybind11_diagram_utils.h b/src/python/include/pybind11_diagram_utils.h new file mode 100644 index 00000000..d9627258 --- /dev/null +++ b/src/python/include/pybind11_diagram_utils.h @@ -0,0 +1,39 @@ +/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + * Author(s): Marc Glisse + * + * Copyright (C) 2020 Inria + * + * Modification(s): + * - YYYY/MM Author: Description of the modification + */ + +#include <pybind11/pybind11.h> +#include <pybind11/numpy.h> + +#include <boost/range/counting_range.hpp> +#include <boost/range/adaptor/transformed.hpp> + +namespace py = pybind11; +typedef py::array_t<double> Dgm; + +// Get m[i,0] and m[i,1] as a pair +static auto pairify(void* p, ssize_t h, ssize_t w) { + return [=](ssize_t i){ + char* birth = (char*)p + i * h; + char* death = birth + w; + return std::make_pair(*(double*)birth, *(double*)death); + }; +} + +inline auto numpy_to_range_of_pairs(py::array_t<double> dgm) { + py::buffer_info buf = dgm.request(); + // shape (n,2) or (0) for empty + if((buf.ndim!=2 || buf.shape[1]!=2) && (buf.ndim!=1 || buf.shape[0]!=0)) + throw std::runtime_error("Diagram must be an array of size n x 2"); + // In the case of shape (0), avoid reading non-existing strides[1] even if we won't use it. + ssize_t stride1 = buf.ndim == 2 ? buf.strides[1] : 0; + auto cnt = boost::counting_range<ssize_t>(0, buf.shape[0]); + return boost::adaptors::transform(cnt, pairify(buf.ptr, buf.strides[0], stride1)); + // Be careful that the returned range cannot contain references to dead temporaries. +} diff --git a/src/python/setup.py.in b/src/python/setup.py.in index f993165c..b9f4e3f0 100644 --- a/src/python/setup.py.in +++ b/src/python/setup.py.in @@ -8,16 +8,18 @@ - 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 +import pybind11 __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "MIT" -modules = [@GUDHI_PYTHON_MODULES_TO_COMPILE@] +cython_modules = [@GUDHI_CYTHON_MODULES@] +pybind11_modules = [@GUDHI_PYBIND11_MODULES@] source_dir='@CMAKE_CURRENT_SOURCE_DIR@/gudhi/' extra_compile_args=[@GUDHI_PYTHON_EXTRA_COMPILE_ARGS@] @@ -29,7 +31,7 @@ runtime_library_dirs=[@GUDHI_PYTHON_RUNTIME_LIBRARY_DIRS@] # Create ext_modules list from module list ext_modules = [] -for module in modules: +for module in cython_modules: ext_modules.append(Extension( 'gudhi.' + module, sources = [source_dir + module + '.pyx',], @@ -42,14 +44,32 @@ for module in modules: runtime_library_dirs=runtime_library_dirs, cython_directives = {'language_level': str(sys.version_info[0])},)) +ext_modules = cythonize(ext_modules) + +for module in pybind11_modules: + my_include_dirs = include_dirs + [pybind11.get_include(False), pybind11.get_include(True)] + if module == 'hera': + my_include_dirs = ['@HERA_WASSERSTEIN_INCLUDE_DIR@'] + my_include_dirs + ext_modules.append(Extension( + 'gudhi.' + module, + sources = [source_dir + module + '.cc'], + language = 'c++', + include_dirs = my_include_dirs, + extra_compile_args=extra_compile_args + [@GUDHI_PYBIND11_EXTRA_COMPILE_ARGS@], + extra_link_args=extra_link_args, + libraries=libraries, + library_dirs=library_dirs, + runtime_library_dirs=runtime_library_dirs, + )) + setup( name = 'gudhi', - packages=["gudhi","gudhi.representations"], + packages=find_packages(), # find_namespace_packages(include=["gudhi*"]) author='GUDHI Editorial Board', author_email='gudhi-contact@lists.gforge.inria.fr', version='@GUDHI_VERSION@', url='http://gudhi.gforge.inria.fr/', - ext_modules = cythonize(ext_modules), + ext_modules = ext_modules, install_requires = ['cython','numpy >= 1.9',], - setup_requires = ['numpy >= 1.9',], + setup_requires = ['numpy >= 1.9','pybind11',], ) diff --git a/src/python/test/test_alpha_complex.py b/src/python/test/test_alpha_complex.py index 3761fe16..77121302 100755 --- a/src/python/test/test_alpha_complex.py +++ b/src/python/test/test_alpha_complex.py @@ -40,7 +40,7 @@ def test_infinite_alpha(): assert simplex_tree.num_simplices() == 11 assert simplex_tree.num_vertices() == 4 - assert simplex_tree.get_filtration() == [ + assert list(simplex_tree.get_filtration()) == [ ([0], 0.0), ([1], 0.0), ([2], 0.0), @@ -53,6 +53,7 @@ def test_infinite_alpha(): ([0, 1, 2], 0.5), ([1, 2, 3], 0.5), ] + assert simplex_tree.get_star([0]) == [ ([0], 0.0), ([0, 1], 0.25), @@ -105,7 +106,7 @@ def test_filtered_alpha(): else: assert False - assert simplex_tree.get_filtration() == [ + assert list(simplex_tree.get_filtration()) == [ ([0], 0.0), ([1], 0.0), ([2], 0.0), diff --git a/src/python/test/test_cover_complex.py b/src/python/test/test_cover_complex.py index 32bc5a26..260f6a5c 100755 --- a/src/python/test/test_cover_complex.py +++ b/src/python/test/test_cover_complex.py @@ -9,6 +9,7 @@ """ from gudhi import CoverComplex +import pytest __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2018 Inria" @@ -24,7 +25,8 @@ def test_empty_constructor(): def test_non_existing_file_read(): # Try to open a non existing file cover = CoverComplex() - assert cover.read_point_cloud("pouetpouettralala.toubiloubabdou") == False + with pytest.raises(FileNotFoundError): + cover.read_point_cloud("pouetpouettralala.toubiloubabdou") def test_files_creation(): diff --git a/src/python/test/test_cubical_complex.py b/src/python/test/test_cubical_complex.py index 8c1b2600..5c59db8f 100755 --- a/src/python/test/test_cubical_complex.py +++ b/src/python/test/test_cubical_complex.py @@ -10,6 +10,7 @@ from gudhi import CubicalComplex, PeriodicCubicalComplex import numpy as np +import pytest __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" @@ -25,9 +26,8 @@ def test_empty_constructor(): def test_non_existing_perseus_file_constructor(): # Try to open a non existing file - cub = CubicalComplex(perseus_file="pouetpouettralala.toubiloubabdou") - assert cub.__is_defined() == False - assert cub.__is_persistence_defined() == False + with pytest.raises(FileNotFoundError): + cub = CubicalComplex(perseus_file="pouetpouettralala.toubiloubabdou") def test_dimension_or_perseus_file_constructor(): @@ -147,3 +147,13 @@ def test_connected_sublevel_sets(): periodic_dimensions = periodic_dimensions) assert cub.persistence() == [(0, (2.0, float("inf")))] assert cub.betti_numbers() == [1, 0, 0] + +def test_cubical_generators(): + cub = CubicalComplex(top_dimensional_cells = [[0, 0, 0], [0, 1, 0], [0, 0, 0]]) + cub.persistence() + g = cub.cofaces_of_persistence_pairs() + assert len(g[0]) == 2 + assert len(g[1]) == 1 + assert np.array_equal(g[0][0], np.empty(shape=[0,2])) + assert np.array_equal(g[0][1], np.array([[7, 4]])) + assert np.array_equal(g[1][0], np.array([8])) diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py new file mode 100755 index 00000000..bff4c267 --- /dev/null +++ b/src/python/test/test_dtm.py @@ -0,0 +1,68 @@ +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + Author(s): Marc Glisse + + Copyright (C) 2020 Inria + + Modification(s): + - YYYY/MM Author: Description of the modification +""" + +from gudhi.point_cloud.dtm import DistanceToMeasure +import numpy +import pytest +import torch + + +def test_dtm_compare_euclidean(): + pts = numpy.random.rand(1000, 4) + k = 6 + dtm = DistanceToMeasure(k, implementation="ckdtree") + r0 = dtm.fit_transform(pts) + dtm = DistanceToMeasure(k, implementation="sklearn") + r1 = dtm.fit_transform(pts) + assert r1 == pytest.approx(r0) + dtm = DistanceToMeasure(k, implementation="sklearn", algorithm="brute") + r2 = dtm.fit_transform(pts) + assert r2 == pytest.approx(r0) + dtm = DistanceToMeasure(k, implementation="hnsw") + r3 = dtm.fit_transform(pts) + assert r3 == pytest.approx(r0, rel=0.1) + from scipy.spatial.distance import cdist + + d = cdist(pts, pts) + dtm = DistanceToMeasure(k, metric="precomputed") + r4 = dtm.fit_transform(d) + assert r4 == pytest.approx(r0) + dtm = DistanceToMeasure(k, metric="precomputed", n_jobs=2) + r4b = dtm.fit_transform(d) + assert r4b == pytest.approx(r0) + dtm = DistanceToMeasure(k, implementation="keops") + r5 = dtm.fit_transform(pts) + assert r5 == pytest.approx(r0) + pts2 = torch.tensor(pts, requires_grad=True) + assert pts2.grad is None + dtm = DistanceToMeasure(k, implementation="keops", enable_autodiff=True) + r6 = dtm.fit_transform(pts2) + assert r6.detach().numpy() == pytest.approx(r0) + r6.sum().backward() + assert not torch.isnan(pts2.grad).any() + pts2 = torch.tensor(pts, requires_grad=True) + assert pts2.grad is None + dtm = DistanceToMeasure(k, implementation="ckdtree", enable_autodiff=True) + r7 = dtm.fit_transform(pts2) + assert r7.detach().numpy() == pytest.approx(r0) + r7.sum().backward() + assert not torch.isnan(pts2.grad).any() + + +def test_dtm_precomputed(): + dist = numpy.array([[1.0, 3, 8], [1, 5, 5], [0, 2, 3]]) + dtm = DistanceToMeasure(2, q=1, metric="neighbors") + r = dtm.fit_transform(dist) + assert r == pytest.approx([2.0, 3, 1]) + + dist = numpy.array([[2.0, 2], [0, 1], [3, 4]]) + dtm = DistanceToMeasure(2, q=2, metric="neighbors") + r = dtm.fit_transform(dist) + assert r == pytest.approx([2.0, 0.707, 3.5355], rel=0.01) diff --git a/src/python/test/test_euclidean_witness_complex.py b/src/python/test/test_euclidean_witness_complex.py index c18d2484..f3664d39 100755 --- a/src/python/test/test_euclidean_witness_complex.py +++ b/src/python/test/test_euclidean_witness_complex.py @@ -40,7 +40,7 @@ def test_witness_complex(): assert landmarks[1] == euclidean_witness_complex.get_point(1) assert landmarks[2] == euclidean_witness_complex.get_point(2) - assert simplex_tree.get_filtration() == [ + assert list(simplex_tree.get_filtration()) == [ ([0], 0.0), ([1], 0.0), ([0, 1], 0.0), @@ -78,13 +78,13 @@ def test_strong_witness_complex(): assert landmarks[1] == euclidean_strong_witness_complex.get_point(1) assert landmarks[2] == euclidean_strong_witness_complex.get_point(2) - assert simplex_tree.get_filtration() == [([0], 0.0), ([1], 0.0), ([2], 0.0)] + assert list(simplex_tree.get_filtration()) == [([0], 0.0), ([1], 0.0), ([2], 0.0)] simplex_tree = euclidean_strong_witness_complex.create_simplex_tree( max_alpha_square=100.0 ) - assert simplex_tree.get_filtration() == [ + assert list(simplex_tree.get_filtration()) == [ ([0], 0.0), ([1], 0.0), ([2], 0.0), diff --git a/src/python/test/test_knn.py b/src/python/test/test_knn.py new file mode 100755 index 00000000..a87ec212 --- /dev/null +++ b/src/python/test/test_knn.py @@ -0,0 +1,130 @@ +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + Author(s): Marc Glisse + + Copyright (C) 2020 Inria + + Modification(s): + - YYYY/MM Author: Description of the modification +""" + +from gudhi.point_cloud.knn import KNearestNeighbors +import numpy as np +import pytest + + +def test_knn_explicit(): + base = np.array([[1.0, 1], [1, 2], [4, 2], [4, 3]]) + query = np.array([[1.0, 1], [2, 2], [4, 4]]) + knn = KNearestNeighbors(2, metric="manhattan", return_distance=True, return_index=True) + knn.fit(base) + r = knn.transform(query) + assert r[0] == pytest.approx(np.array([[0, 1], [1, 0], [3, 2]])) + assert r[1] == pytest.approx(np.array([[0.0, 1], [1, 2], [1, 2]])) + + knn = KNearestNeighbors(2, metric="chebyshev", return_distance=True, return_index=False) + knn.fit(base) + r = knn.transform(query) + assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]])) + r = ( + KNearestNeighbors(2, metric="chebyshev", return_distance=True, return_index=False, implementation="keops") + .fit(base) + .transform(query) + ) + assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]])) + r = ( + KNearestNeighbors(2, metric="chebyshev", return_distance=True, return_index=False, implementation="keops", enable_autodiff=True) + .fit(base) + .transform(query) + ) + assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]])) + + knn = KNearestNeighbors(2, metric="minkowski", p=3, return_distance=False, return_index=True) + knn.fit(base) + r = knn.transform(query) + assert np.array_equal(r, [[0, 1], [1, 0], [3, 2]]) + r = ( + KNearestNeighbors(2, metric="minkowski", p=3, return_distance=False, return_index=True, implementation="keops") + .fit(base) + .transform(query) + ) + assert np.array_equal(r, [[0, 1], [1, 0], [3, 2]]) + + dist = np.array([[0.0, 3, 8], [1, 0, 5], [1, 2, 0]]) + knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=False) + r = knn.fit_transform(dist) + assert np.array_equal(r, [[0, 1], [1, 0], [2, 0]]) + knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=True, sort_results=True) + r = knn.fit_transform(dist) + assert np.array_equal(r[0], [[0, 1], [1, 0], [2, 0]]) + assert np.array_equal(r[1], [[0, 3], [0, 1], [0, 1]]) + # Second time in parallel + knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=False, n_jobs=2, sort_results=True) + r = knn.fit_transform(dist) + assert np.array_equal(r, [[0, 1], [1, 0], [2, 0]]) + knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=True, n_jobs=2) + r = knn.fit_transform(dist) + assert np.array_equal(r[0], [[0, 1], [1, 0], [2, 0]]) + assert np.array_equal(r[1], [[0, 3], [0, 1], [0, 1]]) + + +def test_knn_compare(): + base = np.array([[1.0, 1], [1, 2], [4, 2], [4, 3]]) + query = np.array([[1.0, 1], [2, 2], [4, 4]]) + r0 = ( + KNearestNeighbors(2, implementation="ckdtree", return_index=True, return_distance=False) + .fit(base) + .transform(query) + ) + r1 = ( + KNearestNeighbors(2, implementation="sklearn", return_index=True, return_distance=False) + .fit(base) + .transform(query) + ) + r2 = ( + KNearestNeighbors(2, implementation="hnsw", return_index=True, return_distance=False).fit(base).transform(query) + ) + r3 = ( + KNearestNeighbors(2, implementation="keops", return_index=True, return_distance=False) + .fit(base) + .transform(query) + ) + assert np.array_equal(r0, r1) and np.array_equal(r0, r2) and np.array_equal(r0, r3) + + r0 = ( + KNearestNeighbors(2, implementation="ckdtree", return_index=True, return_distance=True) + .fit(base) + .transform(query) + ) + r1 = ( + KNearestNeighbors(2, implementation="sklearn", return_index=True, return_distance=True) + .fit(base) + .transform(query) + ) + r2 = KNearestNeighbors(2, implementation="hnsw", return_index=True, return_distance=True).fit(base).transform(query) + r3 = ( + KNearestNeighbors(2, implementation="keops", return_index=True, return_distance=True).fit(base).transform(query) + ) + assert np.array_equal(r0[0], r1[0]) and np.array_equal(r0[0], r2[0]) and np.array_equal(r0[0], r3[0]) + d0 = pytest.approx(r0[1]) + assert r1[1] == d0 and r2[1] == d0 and r3[1] == d0 + + +def test_knn_nop(): + # This doesn't look super useful... + p = np.array([[0.0]]) + assert None is KNearestNeighbors( + k=1, return_index=False, return_distance=False, implementation="sklearn" + ).fit_transform(p) + assert None is KNearestNeighbors( + k=1, return_index=False, return_distance=False, implementation="ckdtree" + ).fit_transform(p) + assert None is KNearestNeighbors( + k=1, return_index=False, return_distance=False, implementation="hnsw", ef=5 + ).fit_transform(p) + assert None is KNearestNeighbors( + k=1, return_index=False, return_distance=False, implementation="keops" + ).fit_transform(p) + assert None is KNearestNeighbors( + k=1, return_index=False, return_distance=False, metric="precomputed" + ).fit_transform(p) diff --git a/src/python/test/test_rips_complex.py b/src/python/test/test_rips_complex.py index b02a68e1..b86e7498 100755 --- a/src/python/test/test_rips_complex.py +++ b/src/python/test/test_rips_complex.py @@ -32,7 +32,7 @@ def test_rips_from_points(): assert simplex_tree.num_simplices() == 10 assert simplex_tree.num_vertices() == 4 - assert simplex_tree.get_filtration() == [ + assert list(simplex_tree.get_filtration()) == [ ([0], 0.0), ([1], 0.0), ([2], 0.0), @@ -44,6 +44,7 @@ def test_rips_from_points(): ([1, 2], 1.4142135623730951), ([0, 3], 1.4142135623730951), ] + assert simplex_tree.get_star([0]) == [ ([0], 0.0), ([0, 1], 1.0), @@ -95,7 +96,7 @@ def test_rips_from_distance_matrix(): assert simplex_tree.num_simplices() == 10 assert simplex_tree.num_vertices() == 4 - assert simplex_tree.get_filtration() == [ + assert list(simplex_tree.get_filtration()) == [ ([0], 0.0), ([1], 0.0), ([2], 0.0), @@ -107,6 +108,7 @@ def test_rips_from_distance_matrix(): ([1, 2], 1.4142135623730951), ([0, 3], 1.4142135623730951), ] + assert simplex_tree.get_star([0]) == [ ([0], 0.0), ([0, 1], 1.0), diff --git a/src/python/test/test_simplex_generators.py b/src/python/test/test_simplex_generators.py new file mode 100755 index 00000000..8a9b4844 --- /dev/null +++ b/src/python/test/test_simplex_generators.py @@ -0,0 +1,64 @@ +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + Author(s): Marc Glisse + + Copyright (C) 2020 Inria + + Modification(s): + - YYYY/MM Author: Description of the modification +""" + +import gudhi +import numpy as np + + +def test_flag_generators(): + pts = np.array([[0, 0], [0, 1.01], [1, 0], [1.02, 1.03], [100, 0], [100, 3.01], [103, 0], [103.02, 3.03]]) + r = gudhi.RipsComplex(pts, max_edge_length=4) + st = r.create_simplex_tree(max_dimension=50) + st.persistence() + g = st.flag_persistence_generators() + assert np.array_equal(g[0], [[2, 2, 0], [1, 1, 0], [3, 3, 1], [6, 6, 4], [5, 5, 4], [7, 7, 5]]) + assert len(g[1]) == 1 + assert np.array_equal(g[1][0], [[3, 2, 2, 1]]) + assert np.array_equal(g[2], [0, 4]) + assert len(g[3]) == 1 + assert np.array_equal(g[3][0], [[7, 6]]) + # Compare trivial cases (where the simplex is the generator) with persistence_pairs. + # This still makes assumptions on the order of vertices in a simplex and could be more robust. + pairs = st.persistence_pairs() + assert {tuple(i) for i in g[0]} == {(i[0][0],) + tuple(i[1]) for i in pairs if len(i[0]) == 1 and len(i[1]) != 0} + assert {(i[0], i[1]) for i in g[1][0]} == {tuple(i[0]) for i in pairs if len(i[0]) == 2 and len(i[1]) != 0} + assert set(g[2]) == {i[0][0] for i in pairs if len(i[0]) == 1 and len(i[1]) == 0} + assert {(i[0], i[1]) for i in g[3][0]} == {tuple(i[0]) for i in pairs if len(i[0]) == 2 and len(i[1]) == 0} + + +def test_lower_star_generators(): + st = gudhi.SimplexTree() + st.insert([0, 1, 2], -10) + st.insert([0, 3], -10) + st.insert([1, 3], -10) + st.assign_filtration([2], -1) + st.assign_filtration([3], 0) + st.assign_filtration([0], 1) + st.assign_filtration([1], 2) + st.make_filtration_non_decreasing() + st.persistence(min_persistence=-1) + g = st.lower_star_persistence_generators() + assert len(g[0]) == 2 + assert np.array_equal(g[0][0], [[0, 0], [3, 0], [1, 1]]) + assert np.array_equal(g[0][1], [[1, 1]]) + assert len(g[1]) == 2 + assert np.array_equal(g[1][0], [2]) + assert np.array_equal(g[1][1], [1]) + + +def test_empty(): + st = gudhi.SimplexTree() + st.persistence() + assert st.lower_star_persistence_generators() == ([], []) + g = st.flag_persistence_generators() + assert np.array_equal(g[0], np.empty((0, 3))) + assert g[1] == [] + assert np.array_equal(g[2], []) + assert g[3] == [] diff --git a/src/python/test/test_simplex_tree.py b/src/python/test/test_simplex_tree.py index 1822c43b..2137d822 100755 --- a/src/python/test/test_simplex_tree.py +++ b/src/python/test/test_simplex_tree.py @@ -9,6 +9,7 @@ """ from gudhi import SimplexTree +import pytest __author__ = "Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 Inria" @@ -45,7 +46,6 @@ def test_insertion(): assert st.find([2, 3]) == False # filtration test - st.initialize_filtration() assert st.filtration([0, 1, 2]) == 4.0 assert st.filtration([0, 2]) == 4.0 assert st.filtration([1, 2]) == 4.0 @@ -55,7 +55,7 @@ def test_insertion(): assert st.filtration([1]) == 0.0 # skeleton test - assert st.get_skeleton(2) == [ + assert list(st.get_skeleton(2)) == [ ([0, 1, 2], 4.0), ([0, 1], 0.0), ([0, 2], 4.0), @@ -64,7 +64,7 @@ def test_insertion(): ([1], 0.0), ([2], 4.0), ] - assert st.get_skeleton(1) == [ + assert list(st.get_skeleton(1)) == [ ([0, 1], 0.0), ([0, 2], 4.0), ([0], 0.0), @@ -72,12 +72,12 @@ def test_insertion(): ([1], 0.0), ([2], 4.0), ] - assert st.get_skeleton(0) == [([0], 0.0), ([1], 0.0), ([2], 4.0)] + assert list(st.get_skeleton(0)) == [([0], 0.0), ([1], 0.0), ([2], 4.0)] # remove_maximal_simplex test assert st.get_cofaces([0, 1, 2], 1) == [] st.remove_maximal_simplex([0, 1, 2]) - assert st.get_skeleton(2) == [ + assert list(st.get_skeleton(2)) == [ ([0, 1], 0.0), ([0, 2], 4.0), ([0], 0.0), @@ -92,7 +92,6 @@ def test_insertion(): assert st.find([1]) == True assert st.find([2]) == True - st.initialize_filtration() assert st.persistence(persistence_dim_max=True) == [ (1, (4.0, float("inf"))), (0, (0.0, float("inf"))), @@ -126,7 +125,8 @@ def test_expansion(): assert st.num_vertices() == 7 assert st.num_simplices() == 17 - assert st.get_filtration() == [ + + assert list(st.get_filtration()) == [ ([2], 0.1), ([3], 0.1), ([2, 3], 0.1), @@ -149,9 +149,8 @@ def test_expansion(): st.expansion(3) assert st.num_vertices() == 7 assert st.num_simplices() == 22 - st.initialize_filtration() - assert st.get_filtration() == [ + assert list(st.get_filtration()) == [ ([2], 0.1), ([3], 0.1), ([2, 3], 0.1), @@ -248,3 +247,96 @@ def test_make_filtration_non_decreasing(): assert st.filtration([3, 4, 5]) == 2.0 assert st.filtration([3, 4]) == 2.0 assert st.filtration([4, 5]) == 2.0 + +def test_extend_filtration(): + + # Inserted simplex: + # 5 4 + # o o + # / \ / + # o o + # /2\ /3 + # o o + # 1 0 + + st = SimplexTree() + st.insert([0,2]) + st.insert([1,2]) + st.insert([0,3]) + st.insert([2,5]) + st.insert([3,4]) + st.insert([3,5]) + st.assign_filtration([0], 1.) + st.assign_filtration([1], 2.) + st.assign_filtration([2], 3.) + st.assign_filtration([3], 4.) + st.assign_filtration([4], 5.) + st.assign_filtration([5], 6.) + + assert list(st.get_filtration()) == [ + ([0, 2], 0.0), + ([1, 2], 0.0), + ([0, 3], 0.0), + ([3, 4], 0.0), + ([2, 5], 0.0), + ([3, 5], 0.0), + ([0], 1.0), + ([1], 2.0), + ([2], 3.0), + ([3], 4.0), + ([4], 5.0), + ([5], 6.0) + ] + + st.extend_filtration() + + assert list(st.get_filtration()) == [ + ([6], -3.0), + ([0], -2.0), + ([1], -1.8), + ([2], -1.6), + ([0, 2], -1.6), + ([1, 2], -1.6), + ([3], -1.4), + ([0, 3], -1.4), + ([4], -1.2), + ([3, 4], -1.2), + ([5], -1.0), + ([2, 5], -1.0), + ([3, 5], -1.0), + ([5, 6], 1.0), + ([4, 6], 1.2), + ([3, 6], 1.4), + ([3, 4, 6], 1.4), + ([3, 5, 6], 1.4), + ([2, 6], 1.6), + ([2, 5, 6], 1.6), + ([1, 6], 1.8), + ([1, 2, 6], 1.8), + ([0, 6], 2.0), + ([0, 2, 6], 2.0), + ([0, 3, 6], 2.0) + ] + + dgms = st.extended_persistence(min_persistence=-1.) + + assert dgms[0][0][1][0] == pytest.approx(2.) + assert dgms[0][0][1][1] == pytest.approx(3.) + assert dgms[1][0][1][0] == pytest.approx(5.) + assert dgms[1][0][1][1] == pytest.approx(4.) + assert dgms[2][0][1][0] == pytest.approx(1.) + assert dgms[2][0][1][1] == pytest.approx(6.) + assert dgms[3][0][1][0] == pytest.approx(6.) + assert dgms[3][0][1][1] == pytest.approx(1.) + +def test_simplices_iterator(): + st = SimplexTree() + + assert st.insert([0, 1, 2], filtration=4.0) == True + assert st.insert([2, 3, 4], filtration=2.0) == True + + for simplex in st.get_simplices(): + print("simplex is: ", simplex[0]) + assert st.find(simplex[0]) == True + print("filtration is: ", simplex[1]) + assert st.filtration(simplex[0]) == simplex[1] diff --git a/src/python/test/test_subsampling.py b/src/python/test/test_subsampling.py index fe0985fa..31f64e32 100755 --- a/src/python/test/test_subsampling.py +++ b/src/python/test/test_subsampling.py @@ -120,7 +120,6 @@ def test_simple_pick_n_random_points(): # Go furter than point set on purpose for iter in range(1, 10): sub_set = gudhi.pick_n_random_points(points=point_set, nb_points=iter) - print(5) for sub in sub_set: found = False for point in point_set: diff --git a/src/python/test/test_tangential_complex.py b/src/python/test/test_tangential_complex.py index e650e99c..8668a2e0 100755 --- a/src/python/test/test_tangential_complex.py +++ b/src/python/test/test_tangential_complex.py @@ -37,7 +37,7 @@ def test_tangential(): assert st.num_simplices() == 6 assert st.num_vertices() == 4 - assert st.get_filtration() == [ + assert list(st.get_filtration()) == [ ([0], 0.0), ([1], 0.0), ([2], 0.0), @@ -45,6 +45,7 @@ def test_tangential(): ([3], 0.0), ([1, 3], 0.0), ] + assert st.get_cofaces([0], 1) == [([0, 2], 0.0)] assert point_list[0] == tc.get_point(0) diff --git a/src/python/test/test_time_delay.py b/src/python/test/test_time_delay.py new file mode 100755 index 00000000..1ead9bca --- /dev/null +++ b/src/python/test/test_time_delay.py @@ -0,0 +1,43 @@ +from gudhi.point_cloud.timedelay import TimeDelayEmbedding +import numpy as np + + +def test_normal(): + # Sample array + ts = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + # Normal case. + prep = TimeDelayEmbedding() + pointclouds = prep(ts) + assert (pointclouds[0] == np.array([1, 2, 3])).all() + assert (pointclouds[1] == np.array([2, 3, 4])).all() + assert (pointclouds[2] == np.array([3, 4, 5])).all() + assert (pointclouds[3] == np.array([4, 5, 6])).all() + assert (pointclouds[4] == np.array([5, 6, 7])).all() + assert (pointclouds[5] == np.array([6, 7, 8])).all() + assert (pointclouds[6] == np.array([7, 8, 9])).all() + assert (pointclouds[7] == np.array([8, 9, 10])).all() + # Delay = 3 + prep = TimeDelayEmbedding(delay=3) + pointclouds = prep(ts) + assert (pointclouds[0] == np.array([1, 4, 7])).all() + assert (pointclouds[1] == np.array([2, 5, 8])).all() + assert (pointclouds[2] == np.array([3, 6, 9])).all() + assert (pointclouds[3] == np.array([4, 7, 10])).all() + # Skip = 3 + prep = TimeDelayEmbedding(skip=3) + pointclouds = prep(ts) + assert (pointclouds[0] == np.array([1, 2, 3])).all() + assert (pointclouds[1] == np.array([4, 5, 6])).all() + assert (pointclouds[2] == np.array([7, 8, 9])).all() + # Delay = 2 / Skip = 2 + prep = TimeDelayEmbedding(delay=2, skip=2) + pointclouds = prep(ts) + assert (pointclouds[0] == np.array([1, 3, 5])).all() + assert (pointclouds[1] == np.array([3, 5, 7])).all() + assert (pointclouds[2] == np.array([5, 7, 9])).all() + + # Vector series + ts = np.arange(0, 10).reshape(-1, 2) + prep = TimeDelayEmbedding(dim=4) + prep.fit([ts]) + assert (prep.transform([ts])[0] == [[0, 1, 2, 3], [2, 3, 4, 5], [4, 5, 6, 7], [6, 7, 8, 9]]).all() diff --git a/src/python/test/test_wasserstein_barycenter.py b/src/python/test/test_wasserstein_barycenter.py new file mode 100755 index 00000000..f68c748e --- /dev/null +++ b/src/python/test/test_wasserstein_barycenter.py @@ -0,0 +1,46 @@ +from gudhi.wasserstein.barycenter import lagrangian_barycenter +import numpy as np + +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + Author(s): Theo Lacombe + + Copyright (C) 2019 Inria + + Modification(s): + - YYYY/MM Author: Description of the modification +""" + +__author__ = "Theo Lacombe" +__copyright__ = "Copyright (C) 2019 Inria" +__license__ = "MIT" + + +def test_lagrangian_barycenter(): + + dg1 = np.array([[0.2, 0.5]]) + dg2 = np.array([[0.2, 0.7]]) + dg3 = np.array([[0.3, 0.6], [0.7, 0.8], [0.2, 0.3]]) + dg4 = np.array([]) + dg5 = np.array([]) + dg6 = np.array([]) + res = np.array([[0.27916667, 0.55416667], [0.7375, 0.7625], [0.2375, 0.2625]]) + + dg7 = np.array([[0.1, 0.15], [0.1, 0.7], [0.2, 0.22], [0.55, 0.84], [0.11, 0.91], [0.61, 0.75], [0.33, 0.46], [0.12, 0.41], [0.32, 0.48]]) + dg8 = np.array([[0., 4.], [4, 8]]) + + # error crit. + eps = 1e-7 + + + assert np.linalg.norm(lagrangian_barycenter(pdiagset=[dg1, dg2, dg3, dg4],init=3, verbose=False) - res) < eps + assert np.array_equal(lagrangian_barycenter(pdiagset=[dg4, dg5, dg6], verbose=False), np.empty(shape=(0,2))) + assert np.linalg.norm(lagrangian_barycenter(pdiagset=[dg7], verbose=False) - dg7) < eps + Y, log = lagrangian_barycenter(pdiagset=[dg4, dg8], verbose=True) + assert np.linalg.norm(Y - np.array([[1,3], [5, 7]])) < eps + assert np.abs(log["energy"] - 2) < eps + assert np.array_equal(log["groupings"][0] , np.array([[0, -1], [1, -1]])) + assert np.array_equal(log["groupings"][1] , np.array([[0, 0], [1, 1]])) + assert np.linalg.norm(lagrangian_barycenter(pdiagset=[dg8, dg4], init=np.array([[0.2, 0.6], [0.5, 0.7]]), verbose=False) - np.array([[1, 3], [5, 7]])) < eps + assert lagrangian_barycenter(pdiagset = []) is None + diff --git a/src/python/test/test_wasserstein_distance.py b/src/python/test/test_wasserstein_distance.py index 43dda77e..90d26809 100755 --- a/src/python/test/test_wasserstein_distance.py +++ b/src/python/test/test_wasserstein_distance.py @@ -1,6 +1,6 @@ """ This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. - Author(s): Theo Lacombe + Author(s): Theo Lacombe, Marc Glisse Copyright (C) 2019 Inria @@ -8,41 +8,116 @@ - YYYY/MM Author: Description of the modification """ -from gudhi.wasserstein import wasserstein_distance +from gudhi.wasserstein.wasserstein import _proj_on_diag +from gudhi.wasserstein import wasserstein_distance as pot +from gudhi.hera import wasserstein_distance as hera import numpy as np +import pytest __author__ = "Theo Lacombe" __copyright__ = "Copyright (C) 2019 Inria" __license__ = "MIT" +def test_proj_on_diag(): + dgm = np.array([[1., 1.], [1., 2.], [3., 5.]]) + assert np.array_equal(_proj_on_diag(dgm), [[1., 1.], [1.5, 1.5], [4., 4.]]) + empty = np.empty((0, 2)) + assert np.array_equal(_proj_on_diag(empty), empty) -def test_basic_wasserstein(): +def _basic_wasserstein(wasserstein_distance, delta, test_infinity=True, test_matching=True): diag1 = np.array([[2.7, 3.7], [9.6, 14.0], [34.2, 34.974]]) diag2 = np.array([[2.8, 4.45], [9.5, 14.1]]) diag3 = np.array([[0, 2], [4, 6]]) diag4 = np.array([[0, 3], [4, 8]]) - emptydiag = np.array([[]]) + emptydiag = np.array([]) + + # We just need to handle positive numbers here + def approx(x): + return pytest.approx(x, rel=delta) assert wasserstein_distance(emptydiag, emptydiag, internal_p=2., order=1.) == 0. assert wasserstein_distance(emptydiag, emptydiag, internal_p=np.inf, order=1.) == 0. assert wasserstein_distance(emptydiag, emptydiag, internal_p=np.inf, order=2.) == 0. assert wasserstein_distance(emptydiag, emptydiag, internal_p=2., order=2.) == 0. - assert wasserstein_distance(diag3, emptydiag, internal_p=np.inf, order=1.) == 2. - assert wasserstein_distance(diag3, emptydiag, internal_p=1., order=1.) == 4. + assert wasserstein_distance(diag3, emptydiag, internal_p=np.inf, order=1.) == approx(2.) + assert wasserstein_distance(diag3, emptydiag, internal_p=1., order=1.) == approx(4.) + + assert wasserstein_distance(diag4, emptydiag, internal_p=1., order=2.) == approx(5.) # thank you Pythagorician triplets + assert wasserstein_distance(diag4, emptydiag, internal_p=np.inf, order=2.) == approx(2.5) + assert wasserstein_distance(diag4, emptydiag, internal_p=2., order=2.) == approx(3.5355339059327378) + + assert wasserstein_distance(diag1, diag2, internal_p=2., order=1.) == approx(1.4453593023967701) + assert wasserstein_distance(diag1, diag2, internal_p=2.35, order=1.74) == approx(0.9772734057168739) + + assert wasserstein_distance(diag1, emptydiag, internal_p=2.35, order=1.7863) == approx(3.141592214572228) + + assert wasserstein_distance(diag3, diag4, internal_p=1., order=1.) == approx(3.) + assert wasserstein_distance(diag3, diag4, internal_p=np.inf, order=1.) == approx(3.) # no diag matching here + assert wasserstein_distance(diag3, diag4, internal_p=np.inf, order=2.) == approx(np.sqrt(5)) + assert wasserstein_distance(diag3, diag4, internal_p=1., order=2.) == approx(np.sqrt(5)) + assert wasserstein_distance(diag3, diag4, internal_p=4.5, order=2.) == approx(np.sqrt(5)) + + if test_infinity: + diag5 = np.array([[0, 3], [4, np.inf]]) + diag6 = np.array([[7, 8], [4, 6], [3, np.inf]]) + + assert wasserstein_distance(diag4, diag5) == np.inf + assert wasserstein_distance(diag5, diag6, order=1, internal_p=np.inf) == approx(4.) + + + if test_matching: + match = wasserstein_distance(emptydiag, emptydiag, matching=True, internal_p=1., order=2)[1] + assert np.array_equal(match, []) + match = wasserstein_distance(emptydiag, emptydiag, matching=True, internal_p=np.inf, order=2.24)[1] + assert np.array_equal(match, []) + match = wasserstein_distance(emptydiag, diag2, matching=True, internal_p=np.inf, order=2.)[1] + assert np.array_equal(match , [[-1, 0], [-1, 1]]) + match = wasserstein_distance(diag2, emptydiag, matching=True, internal_p=np.inf, order=2.24)[1] + assert np.array_equal(match , [[0, -1], [1, -1]]) + match = wasserstein_distance(diag1, diag2, matching=True, internal_p=2., order=2.)[1] + assert np.array_equal(match, [[0, 0], [1, 1], [2, -1]]) + + + +def hera_wrap(**extra): + def fun(*kargs,**kwargs): + return hera(*kargs,**kwargs,**extra) + return fun - assert wasserstein_distance(diag4, emptydiag, internal_p=1., order=2.) == 5. # thank you Pythagorician triplets - assert wasserstein_distance(diag4, emptydiag, internal_p=np.inf, order=2.) == 2.5 - assert wasserstein_distance(diag4, emptydiag, internal_p=2., order=2.) == 3.5355339059327378 +def pot_wrap(**extra): + def fun(*kargs,**kwargs): + return pot(*kargs,**kwargs,**extra) + return fun - assert wasserstein_distance(diag1, diag2, internal_p=2., order=1.) == 1.4453593023967701 - assert wasserstein_distance(diag1, diag2, internal_p=2.35, order=1.74) == 0.9772734057168739 +def test_wasserstein_distance_pot(): + _basic_wasserstein(pot, 1e-15, test_infinity=False, test_matching=True) + _basic_wasserstein(pot_wrap(enable_autodiff=True), 1e-15, test_infinity=False, test_matching=False) - assert wasserstein_distance(diag1, emptydiag, internal_p=2.35, order=1.7863) == 3.141592214572228 +def test_wasserstein_distance_hera(): + _basic_wasserstein(hera_wrap(delta=1e-12), 1e-12, test_matching=False) + _basic_wasserstein(hera_wrap(delta=.1), .1, test_matching=False) - assert wasserstein_distance(diag3, diag4, internal_p=1., order=1.) == 3. - assert wasserstein_distance(diag3, diag4, internal_p=np.inf, order=1.) == 3. # no diag matching here - assert wasserstein_distance(diag3, diag4, internal_p=np.inf, order=2.) == np.sqrt(5) - assert wasserstein_distance(diag3, diag4, internal_p=1., order=2.) == np.sqrt(5) - assert wasserstein_distance(diag3, diag4, internal_p=4.5, order=2.) == np.sqrt(5) +def test_wasserstein_distance_grad(): + import torch + diag1 = torch.tensor([[2.7, 3.7], [9.6, 14.0], [34.2, 34.974]], requires_grad=True) + diag2 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True) + diag3 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True) + assert diag1.grad is None and diag2.grad is None and diag3.grad is None + dist12 = pot(diag1, diag2, internal_p=2, order=2, enable_autodiff=True) + dist30 = pot(diag3, torch.tensor([]), internal_p=2, order=2, enable_autodiff=True) + dist12.backward() + dist30.backward() + assert not torch.isnan(diag1.grad).any() and not torch.isnan(diag2.grad).any() and not torch.isnan(diag3.grad).any() + diag4 = torch.tensor([[0., 10.]], requires_grad=True) + diag5 = torch.tensor([[1., 11.], [3., 4.]], requires_grad=True) + dist45 = pot(diag4, diag5, internal_p=1, order=1, enable_autodiff=True) + assert dist45 == 3. + dist45.backward() + assert np.array_equal(diag4.grad, [[-1., -1.]]) + assert np.array_equal(diag5.grad, [[1., 1.], [-1., 1.]]) + diag6 = torch.tensor([[5., 10.]], requires_grad=True) + pot(diag6, diag6, internal_p=2, order=2, enable_autodiff=True).backward() + # https://github.com/jonasrauber/eagerpy/issues/6 + # assert np.array_equal(diag6.grad, [[0., 0.]]) diff --git a/src/python/test/test_weighted_rips.py b/src/python/test/test_weighted_rips.py new file mode 100644 index 00000000..7ef48333 --- /dev/null +++ b/src/python/test/test_weighted_rips.py @@ -0,0 +1,63 @@ +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + Author(s): Yuichi Ike and Masatoshi Takenouchi + + Copyright (C) 2020 Inria + + Modification(s): + - YYYY/MM Author: Description of the modification +""" + +from gudhi.weighted_rips_complex import WeightedRipsComplex +from gudhi.point_cloud.dtm import DistanceToMeasure +import numpy as np +from math import sqrt +from scipy.spatial.distance import cdist +import pytest + +def test_non_dtm_rips_complex(): + dist = [[], [1]] + weights = [1, 100] + w_rips = WeightedRipsComplex(distance_matrix=dist, weights=weights) + st = w_rips.create_simplex_tree(max_dimension=2) + assert st.filtration([0,1]) == pytest.approx(200.0) + +def test_compatibility_with_rips(): + distance_matrix = [[0], [1, 0], [1, sqrt(2), 0], [sqrt(2), 1, 1, 0]] + w_rips = WeightedRipsComplex(distance_matrix=distance_matrix,max_filtration=42) + st = w_rips.create_simplex_tree(max_dimension=1) + assert list(st.get_filtration()) == [ + ([0], 0.0), + ([1], 0.0), + ([2], 0.0), + ([3], 0.0), + ([0, 1], 1.0), + ([0, 2], 1.0), + ([1, 3], 1.0), + ([2, 3], 1.0), + ([1, 2], sqrt(2)), + ([0, 3], sqrt(2)), + ] + +def test_compatibility_with_filtered_rips(): + distance_matrix = [[0], [1, 0], [1, sqrt(2), 0], [sqrt(2), 1, 1, 0]] + w_rips = WeightedRipsComplex(distance_matrix=distance_matrix,max_filtration=1.0) + st = w_rips.create_simplex_tree(max_dimension=1) + + assert st.__is_defined() == True + assert st.__is_persistence_defined() == False + + assert st.num_simplices() == 8 + assert st.num_vertices() == 4 + +def test_dtm_rips_complex(): + pts = np.array([[2.0, 2.0], [0.0, 1.0], [3.0, 4.0]]) + dist = cdist(pts,pts) + dtm = DistanceToMeasure(2, q=2, metric="precomputed") + r = dtm.fit_transform(dist) + w_rips = WeightedRipsComplex(distance_matrix=dist, weights=r) + st = w_rips.create_simplex_tree(max_dimension=2) + st.persistence() + persistence_intervals0 = st.persistence_intervals_in_dimension(0) + assert persistence_intervals0 == pytest.approx(np.array([[3.16227766, 5.39834564],[3.16227766, 5.39834564], [3.16227766, float("inf")]])) + |
