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| author | ROUVREAU Vincent <vincent.rouvreau@inria.fr> | 2020-05-25 08:55:34 +0200 |
|---|---|---|
| committer | ROUVREAU Vincent <vincent.rouvreau@inria.fr> | 2020-05-25 08:55:34 +0200 |
| commit | 8f8f1b6f6197bac633ae059776e7b265e3ef5fb6 (patch) | |
| tree | 39960653868c54b5ab5206ec13caf00fc33a565c /src/python/doc | |
| parent | 0d5556975f7977f6fd41cde4841b1ccd23a66f6b (diff) | |
| parent | 80dc3b26a91280f9da8b9630d983499846d42ea6 (diff) | |
Merge last master version
Diffstat (limited to 'src/python/doc')
31 files changed, 310 insertions, 252 deletions
diff --git a/src/python/doc/alpha_complex_sum.inc b/src/python/doc/alpha_complex_sum.inc index 9e6414d0..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 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 | | :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 ≥ 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 265a82d2..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: docname in docnames - :style: unsrt diff --git a/src/python/doc/bottleneck_distance_sum.inc b/src/python/doc/bottleneck_distance_sum.inc index 0de4625c..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 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 | :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` | | - +-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------+ + +-----------------------------------------------------------------+----------------------------------------------------------------------+-----------------------------------------------------------------+ + | .. 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 206fcb63..89da89d3 100644 --- a/src/python/doc/bottleneck_distance_user.rst +++ b/src/python/doc/bottleneck_distance_user.rst @@ -66,9 +66,3 @@ The output is: Bottleneck distance approximation = 0.81 Bottleneck distance value = 0.75 -Bibliography ------------- - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docname in docnames - :style: unsrt diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst index e8c94bf6..3fd4e27a 100644 --- a/src/python/doc/cubical_complex_user.rst +++ b/src/python/doc/cubical_complex_user.rst @@ -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: docname in 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/index.rst b/src/python/doc/index.rst index c153cdfc..13e51047 100644 --- a/src/python/doc/index.rst +++ b/src/python/doc/index.rst @@ -86,10 +86,3 @@ Point cloud utilities ********************* .. include:: point_cloud_sum.inc - -Bibliography -************ - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docname in docnames - :style: unsrt diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst index 48425d5e..de09c5b3 100644 --- a/src/python/doc/installation.rst +++ b/src/python/doc/installation.rst @@ -12,8 +12,8 @@ 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, +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. @@ -21,7 +21,7 @@ It is a multi-platform library and compiles on Linux, Mac OSX and Visual 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 @@ -30,7 +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 @@ -188,8 +189,14 @@ 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 <https://www.cgal.org/>`_, a C++ library that provides easy @@ -200,7 +207,7 @@ 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 @@ -212,14 +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 @@ -229,8 +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 @@ -251,37 +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_ref.rst b/src/python/doc/nerve_gic_complex_ref.rst index 6a81b7af..abde2e8c 100644 --- a/src/python/doc/nerve_gic_complex_ref.rst +++ b/src/python/doc/nerve_gic_complex_ref.rst @@ -12,10 +12,3 @@ Cover complexes reference manual :show-inheritance: .. automethod:: gudhi.CoverComplex.__init__ - -Bibliography ------------- - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docname in docnames - :style: unsrt diff --git a/src/python/doc/nerve_gic_complex_sum.inc b/src/python/doc/nerve_gic_complex_sum.inc index 7fe55aff..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 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, | :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` | - +----------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------+ + +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------+ + | .. 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 f709ce91..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 ------ @@ -313,10 +313,3 @@ the program outputs again SC.dot which gives the following visualization after u :alt: Visualization with neato Visualization with neato - -Bibliography ------------- - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docname in docnames - :style: unsrt diff --git a/src/python/doc/persistence_graphical_tools_sum.inc b/src/python/doc/persistence_graphical_tools_sum.inc index b68d3d7e..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 40 30 - +-----------------------------------------------------------------+-----------------------------------------------------------------------+-----------------------------------------------+ - | .. 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, 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 91e52703..b5a38eb1 100644 --- a/src/python/doc/persistence_graphical_tools_user.rst +++ b/src/python/doc/persistence_graphical_tools_user.rst @@ -12,9 +12,6 @@ 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:: @@ -36,9 +33,6 @@ This function can display the persistence result as a barcode: 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:: @@ -73,8 +67,7 @@ of shape (N x 2) encoding a persistence diagram (in a given dimension). 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: diff --git a/src/python/doc/persistent_cohomology_sum.inc b/src/python/doc/persistent_cohomology_sum.inc index 0effb50f..a1ff2eee 100644 --- a/src/python/doc/persistent_cohomology_sum.inc +++ b/src/python/doc/persistent_cohomology_sum.inc @@ -12,7 +12,7 @@ | | | | | | 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 506fa3a7..a3f294b2 100644 --- a/src/python/doc/persistent_cohomology_user.rst +++ b/src/python/doc/persistent_cohomology_user.rst @@ -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: docname in docnames - :style: unsrt diff --git a/src/python/doc/point_cloud.rst b/src/python/doc/point_cloud.rst index c0d4b303..ffd8f85b 100644 --- a/src/python/doc/point_cloud.rst +++ b/src/python/doc/point_cloud.rst @@ -16,15 +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: -TimeDelayEmbedding ------------------- +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 0a159680..f955c3ab 100644 --- a/src/python/doc/point_cloud_sum.inc +++ b/src/python/doc/point_cloud_sum.inc @@ -1,15 +1,12 @@ .. table:: :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)` | | | - | | | :Since: GUDHI 2.0.0 | - | | | | - | | | :License: 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, | | + | | estimate a density, 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 eac89b9d..323a0920 100644 --- a/src/python/doc/representations_sum.inc +++ b/src/python/doc/representations_sum.inc @@ -1,14 +1,14 @@ .. table:: :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. | | - | | | :Since: GUDHI 3.1.0 | - | | | | - | | | :License: 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 6feb74cd..f7580714 100644 --- a/src/python/doc/rips_complex_sum.inc +++ b/src/python/doc/rips_complex_sum.inc @@ -11,6 +11,9 @@ | | | | | | 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 c4bbcfb6..819568be 100644 --- a/src/python/doc/rips_complex_user.rst +++ b/src/python/doc/rips_complex_user.rst @@ -348,9 +348,53 @@ until dimension 1 - one skeleton graph in other words), the output is: bottleneck distance and persistence graphical tool will not work properly, this is a known issue. -Bibliography ------------- +Weighted Rips Complex +--------------------- -.. bibliography:: ../../biblio/bibliography.bib - :filter: docname in docnames - :style: unsrt +`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_user.rst b/src/python/doc/simplex_tree_user.rst index 1b272c35..3df7617f 100644 --- a/src/python/doc/simplex_tree_user.rst +++ b/src/python/doc/simplex_tree_user.rst @@ -66,10 +66,3 @@ The output is: ([1, 2], 4.0) ([1], 0.0) ([2], 4.0) - -Bibliography ------------- - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docname in docnames - :style: unsrt diff --git a/src/python/doc/tangential_complex_sum.inc b/src/python/doc/tangential_complex_sum.inc index 45ce2a66..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 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 | :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` | - +----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+ + | .. 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 cf8199cc..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: docname in docnames - :style: unsrt diff --git a/src/python/doc/wasserstein_distance_sum.inc b/src/python/doc/wasserstein_distance_sum.inc index f9308e5e..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 40 30 - +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ - | .. figure:: | The q-Wasserstein distance measures the similarity between two | :Author: Theo Lacombe | - | ../../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 | - | | | | - | | | :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 c24da74d..96ec7872 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -17,12 +17,21 @@ are measured in norm p, for :math:`1 \leq p \leq \infty`. Distance Functions ------------------ -This first implementation uses the Python Optimal Transport library and is based -on ideas from "Large Scale Computation of Means and Cluster for Persistence + +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`. .. 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" @@ -94,6 +103,8 @@ The output is: 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 @@ -164,10 +175,3 @@ The output is: [[0.27916667 0.55416667] [0.7375 0.7625 ] [0.2375 0.2625 ]] - -Bibliography ------------- - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docname in docnames - :style: unsrt diff --git a/src/python/doc/witness_complex_sum.inc b/src/python/doc/witness_complex_sum.inc index 34d4df4a..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 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 | | :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` | - +-------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + +-------------------------------------------------------------------+----------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+ + | .. 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 799f5444..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: docname in 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 + |