From e1c8edc4b148331083f53c7c3d34766190bb6d99 Mon Sep 17 00:00:00 2001 From: ROUVREAU Vincent Date: Tue, 17 Mar 2020 22:16:23 +0100 Subject: Another proposal to fix #248 --- src/python/doc/bottleneck_distance_sum.inc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'src/python/doc/bottleneck_distance_sum.inc') diff --git a/src/python/doc/bottleneck_distance_sum.inc b/src/python/doc/bottleneck_distance_sum.inc index 6eb0ac19..a01e7f04 100644 --- a/src/python/doc/bottleneck_distance_sum.inc +++ b/src/python/doc/bottleneck_distance_sum.inc @@ -1,5 +1,5 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ | .. figure:: | Bottleneck distance measures the similarity between two persistence | :Author: François Godi | -- cgit v1.2.3 From cf29f4a485d06469d17c6d12d306901fa3c5ab36 Mon Sep 17 00:00:00 2001 From: ROUVREAU Vincent Date: Mon, 23 Mar 2020 18:11:15 +0100 Subject: Shorter headers in sphinx: Introduced in -> Since and Copyright -> License --- src/python/doc/alpha_complex_sum.inc | 4 ++-- src/python/doc/bottleneck_distance_sum.inc | 4 ++-- src/python/doc/cubical_complex_sum.inc | 4 ++-- src/python/doc/cubical_complex_user.rst | 2 +- src/python/doc/nerve_gic_complex_sum.inc | 4 ++-- src/python/doc/persistence_graphical_tools_sum.inc | 4 ++-- src/python/doc/persistent_cohomology_sum.inc | 4 ++-- src/python/doc/persistent_cohomology_user.rst | 2 +- src/python/doc/point_cloud_sum.inc | 4 ++-- src/python/doc/representations_sum.inc | 4 ++-- src/python/doc/rips_complex_sum.inc | 4 ++-- src/python/doc/rips_complex_user.rst | 2 +- src/python/doc/simplex_tree_sum.inc | 4 ++-- src/python/doc/tangential_complex_sum.inc | 4 ++-- src/python/doc/wasserstein_distance_sum.inc | 4 ++-- src/python/doc/witness_complex_sum.inc | 4 ++-- 16 files changed, 29 insertions(+), 29 deletions(-) (limited to 'src/python/doc/bottleneck_distance_sum.inc') diff --git a/src/python/doc/alpha_complex_sum.inc b/src/python/doc/alpha_complex_sum.inc index 00c35155..9e6414d0 100644 --- a/src/python/doc/alpha_complex_sum.inc +++ b/src/python/doc/alpha_complex_sum.inc @@ -4,9 +4,9 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ | .. 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 | + | :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 | :Copyright: MIT (`GPL v3 `_) | + | | the circumradius of the simplex if the circumsphere is empty (the | :License: MIT (`GPL v3 `_) | | | 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 `__ :math:`\geq` 3.1.0 and `CGAL `__ :math:`\geq` 4.11.0 | | | Gabriel otherwise. | | diff --git a/src/python/doc/bottleneck_distance_sum.inc b/src/python/doc/bottleneck_distance_sum.inc index a01e7f04..0de4625c 100644 --- a/src/python/doc/bottleneck_distance_sum.inc +++ b/src/python/doc/bottleneck_distance_sum.inc @@ -4,9 +4,9 @@ +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ | .. 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 | + | :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 | :Copyright: MIT (`GPL v3 `_) | + | Bottleneck distance is the length of | distance at most b, where the distance between points is the sup | :License: MIT (`GPL v3 `_) | | the longest edge | norm in :math:`\mathbb{R}^2`. | | | | | :Requires: `CGAL `__ :math:`\geq` 4.11.0 | +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ diff --git a/src/python/doc/cubical_complex_sum.inc b/src/python/doc/cubical_complex_sum.inc index ab6388e5..28bf8e94 100644 --- a/src/python/doc/cubical_complex_sum.inc +++ b/src/python/doc/cubical_complex_sum.inc @@ -4,9 +4,9 @@ +--------------------------------------------------------------------------+----------------------------------------------------------------------+-----------------------------+ | .. 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..93ca6b24 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 ===================================== ===================================== ===================================== +---------------------------------------------+----------------------------------------------------------------------+ diff --git a/src/python/doc/nerve_gic_complex_sum.inc b/src/python/doc/nerve_gic_complex_sum.inc index d5356eca..7fe55aff 100644 --- a/src/python/doc/nerve_gic_complex_sum.inc +++ b/src/python/doc/nerve_gic_complex_sum.inc @@ -4,9 +4,9 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+------------------------------------------------------------------+ | .. 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 | + | :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. | :Copyright: MIT (`GPL v3 `_) | + | | the image of a scalar-valued function defined on the data. | :License: MIT (`GPL v3 `_) | | | | | | | | :Requires: `CGAL `__ :math:`\geq` 4.11.0 | | | | | diff --git a/src/python/doc/persistence_graphical_tools_sum.inc b/src/python/doc/persistence_graphical_tools_sum.inc index 723c0f78..b68d3d7e 100644 --- a/src/python/doc/persistence_graphical_tools_sum.inc +++ b/src/python/doc/persistence_graphical_tools_sum.inc @@ -4,9 +4,9 @@ +-----------------------------------------------------------------+-----------------------------------------------------------------------+-----------------------------------------------+ | .. 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. | | - | | | :Introduced in: GUDHI 2.0.0 | + | | | :Since: GUDHI 2.0.0 | | | Note that these functions return the matplotlib axis, allowing | | - | | for further modifications (title, aspect, etc.) | :Copyright: MIT | + | | for further modifications (title, aspect, etc.) | :License: MIT | | | | | | | | :Requires: matplotlib, numpy and scipy | +-----------------------------------------------------------------+-----------------------------------------------------------------------+-----------------------------------------------+ diff --git a/src/python/doc/persistent_cohomology_sum.inc b/src/python/doc/persistent_cohomology_sum.inc index 9c29bfaa..0effb50f 100644 --- a/src/python/doc/persistent_cohomology_sum.inc +++ b/src/python/doc/persistent_cohomology_sum.inc @@ -4,9 +4,9 @@ +-----------------------------------------------------------------+-----------------------------------------------------------------------+-----------------------------------------------+ | .. 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. | | | | | | diff --git a/src/python/doc/persistent_cohomology_user.rst b/src/python/doc/persistent_cohomology_user.rst index de83cda1..5f931b3a 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 ===================================== ===================================== ===================================== +-----------------------------------------------------------------+-----------------------------------------------------------------------+ diff --git a/src/python/doc/point_cloud_sum.inc b/src/python/doc/point_cloud_sum.inc index 77245e86..0a159680 100644 --- a/src/python/doc/point_cloud_sum.inc +++ b/src/python/doc/point_cloud_sum.inc @@ -4,9 +4,9 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ | | :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 | + | | | :Since: GUDHI 2.0.0 | | | | | - | | | :Copyright: MIT (`GPL v3 `_) | + | | | :License: MIT (`GPL v3 `_) | | | Parts of this package require CGAL. | | | | | :Requires: `Eigen `__ :math:`\geq` 3.1.0 and `CGAL `__ :math:`\geq` 4.11.0 | | | | | diff --git a/src/python/doc/representations_sum.inc b/src/python/doc/representations_sum.inc index edb8a448..eac89b9d 100644 --- a/src/python/doc/representations_sum.inc +++ b/src/python/doc/representations_sum.inc @@ -4,9 +4,9 @@ +------------------------------------------------------------------+----------------------------------------------------------------+-----------------------------------------------+ | .. 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 | + | | | :Since: GUDHI 3.1.0 | | | | | - | | | :Copyright: MIT | + | | | :License: MIT | | | | | | | | :Requires: scikit-learn | +------------------------------------------------------------------+----------------------------------------------------------------+-----------------------------------------------+ diff --git a/src/python/doc/rips_complex_sum.inc b/src/python/doc/rips_complex_sum.inc index a1f0e469..6feb74cd 100644 --- a/src/python/doc/rips_complex_sum.inc +++ b/src/python/doc/rips_complex_sum.inc @@ -4,9 +4,9 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------+ | .. 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, | | diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst index a27573e8..8efb12e6 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 ==================================================================== ================================ ====================== +-------------------------------------------+----------------------------------------------------------------------+ diff --git a/src/python/doc/simplex_tree_sum.inc b/src/python/doc/simplex_tree_sum.inc index 3c637b8c..a8858f16 100644 --- a/src/python/doc/simplex_tree_sum.inc +++ b/src/python/doc/simplex_tree_sum.inc @@ -4,9 +4,9 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------+ | .. 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 ddc3e609..45ce2a66 100644 --- a/src/python/doc/tangential_complex_sum.inc +++ b/src/python/doc/tangential_complex_sum.inc @@ -4,9 +4,9 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ | .. 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 | + | :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 | :Copyright: MIT (`GPL v3 `_) | + | | extrinsic dimension :math:`d` and exponentially on the intrinsic | :License: MIT (`GPL v3 `_) | | | dimension :math:`k`. | | | | | :Requires: `Eigen `__ :math:`\geq` 3.1.0 and `CGAL `__ :math:`\geq` 4.11.0 | +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/wasserstein_distance_sum.inc b/src/python/doc/wasserstein_distance_sum.inc index 1632befa..0ff22035 100644 --- a/src/python/doc/wasserstein_distance_sum.inc +++ b/src/python/doc/wasserstein_distance_sum.inc @@ -4,9 +4,9 @@ +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ | .. 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 | + | :figclass: align-center | by a perfect matching between the points of the two diagrams (+ all | :Since: 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 | + | 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| :License: 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 | +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ diff --git a/src/python/doc/witness_complex_sum.inc b/src/python/doc/witness_complex_sum.inc index f9c009ab..34d4df4a 100644 --- a/src/python/doc/witness_complex_sum.inc +++ b/src/python/doc/witness_complex_sum.inc @@ -4,9 +4,9 @@ +-------------------------------------------------------------------+----------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+ | .. 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 | + | :alt: Witness complex representation | | :Since: GUDHI 2.0.0 | | :figclass: align-center | The data structure is described in | | - | | :cite:`boissonnatmariasimplextreealgorithmica`. | :Copyright: MIT (`GPL v3 `_ for Euclidean versions only) | + | | :cite:`boissonnatmariasimplextreealgorithmica`. | :License: MIT (`GPL v3 `_ for Euclidean versions only) | | | | | | | | :Requires: `Eigen `__ :math:`\geq` 3.1.0 and `CGAL `__ :math:`\geq` 4.11.0 for Euclidean versions only | +-------------------------------------------------------------------+----------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+ -- cgit v1.2.3 From 7e85b0451c686f043b61cde2e5f78674cf8de248 Mon Sep 17 00:00:00 2001 From: ROUVREAU Vincent Date: Mon, 11 May 2020 09:31:49 +0200 Subject: Double underscore is not the correct syntax --- src/python/doc/alpha_complex_sum.inc | 28 +++++++++++----------- src/python/doc/bottleneck_distance_sum.inc | 22 ++++++++--------- src/python/doc/cubical_complex_user.rst | 4 ++-- src/python/doc/nerve_gic_complex_sum.inc | 26 ++++++++++---------- src/python/doc/nerve_gic_complex_user.rst | 2 +- src/python/doc/persistence_graphical_tools_sum.inc | 22 ++++++++--------- .../doc/persistence_graphical_tools_user.rst | 2 +- src/python/doc/point_cloud.rst | 2 +- src/python/doc/representations_sum.inc | 22 ++++++++--------- src/python/doc/tangential_complex_sum.inc | 22 ++++++++--------- src/python/doc/wasserstein_distance_user.rst | 6 ++--- src/python/doc/witness_complex_sum.inc | 28 +++++++++++----------- src/python/gudhi/persistence_graphical_tools.py | 14 +++++------ src/python/gudhi/point_cloud/knn.py | 4 ++-- src/python/gudhi/representations/metrics.py | 2 +- 15 files changed, 103 insertions(+), 103 deletions(-) (limited to 'src/python/doc/bottleneck_distance_sum.inc') diff --git a/src/python/doc/alpha_complex_sum.inc b/src/python/doc/alpha_complex_sum.inc index 74331333..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 `_) | - | | 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 `__ :math:`\geq` 3.1.0 and `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` | - +----------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + +----------------------------------------------------------------+-------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+ + | .. 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 `_) | + | | 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 `_ :math:`\geq` 3.1.0 and `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/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 `_) | - | the longest edge | norm in :math:`\mathbb{R}^2`. | | - | | | :Requires: `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 `_) | + | the longest edge | norm in :math:`\mathbb{R}^2`. | | + | | | :Requires: `CGAL `_ :math:`\geq` 4.11.0 | + +-----------------------------------------------------------------+----------------------------------------------------------------------+-----------------------------------------------------------------+ + | * :doc:`bottleneck_distance_user` | | + +-----------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst index e6e61d75..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 `Perseus file format `__ section. +The file format is described in details in `Perseus file format `_ 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 `Perseus file format `__) for the specific periodic case: +One can also use Perseus style input files (see `Perseus file format `_) for the specific periodic case: .. testcode:: 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 `_) | - | | | | - | | | :Requires: `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 `_) | + | | | | + | | | :Requires: `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 d5c5438d..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 `_), `geomview `_, `KeplerMapper `_. -Input point clouds are assumed to be OFF files (cf. `OFF file format `__). +Input point clouds are assumed to be OFF files (cf. `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 0f41b420..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 `__ | - +-----------------------------------------------------------------+-----------------------------------------------------------------------+----------------------------------------------------------+ - | * :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 `_ | + +-----------------------------------------------------------------+-----------------------------------------------------------------------+---------------------------------------------------------+ + | * :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 fce628b1..b5a38eb1 100644 --- a/src/python/doc/persistence_graphical_tools_user.rst +++ b/src/python/doc/persistence_graphical_tools_user.rst @@ -67,7 +67,7 @@ of shape (N x 2) encoding a persistence diagram (in a given dimension). Persistence density ------------------- -:Requires: `SciPy `__ +:Requires: `SciPy `_ If you want more information on a specific dimension, for instance: diff --git a/src/python/doc/point_cloud.rst b/src/python/doc/point_cloud.rst index 523a9dfa..ffd8f85b 100644 --- a/src/python/doc/point_cloud.rst +++ b/src/python/doc/point_cloud.rst @@ -16,7 +16,7 @@ File Readers Subsampling ----------- -:Requires: `Eigen `__ :math:`\geq` 3.1.0 and `CGAL `__ :math:`\geq` 4.11.0 +:Requires: `Eigen `_ :math:`\geq` 3.1.0 and `CGAL `_ :math:`\geq` 4.11.0 .. automodule:: gudhi.subsampling :members: diff --git a/src/python/doc/representations_sum.inc b/src/python/doc/representations_sum.inc index cdad4716..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 `_ | + +------------------------------------------------------------------+----------------------------------------------------------------+-------------------------------------------------------------+ + | * :doc:`representations` | + +------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------+ 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 `_) | - | | dimension :math:`k`. | | - | | | :Requires: `Eigen `__ :math:`\geq` 3.1.0 and `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 `_) | + | | dimension :math:`k`. | | + | | | :Requires: `Eigen `_ :math:`\geq` 3.1.0 and `CGAL `_ :math:`\geq` 4.11.0 | + +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+ + | * :doc:`tangential_complex_user` | * :doc:`tangential_complex_ref` | + +----------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst index 2d2e2ae7..96ec7872 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -21,9 +21,9 @@ Distance Functions Optimal Transport ***************** -:Requires: `Python Optimal Transport `__ (POT) :math:`\geq` 0.5.1 +:Requires: `Python Optimal Transport `_ (POT) :math:`\geq` 0.5.1 -This first implementation uses the `Python Optimal Transport `__ +This first implementation uses the `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`. @@ -103,7 +103,7 @@ The output is: Barycenters ----------- -:Requires: `Python Optimal Transport `__ (POT) :math:`\geq` 0.5.1 +:Requires: `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. 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 `_ for Euclidean versions only) | - | | | | - | | | :Requires: `Eigen `__ :math:`\geq` 3.1.0 and `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 `_ for Euclidean versions only) | + | | | | + | | | :Requires: `Eigen `_ :math:`\geq` 3.1.0 and `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/gudhi/persistence_graphical_tools.py b/src/python/gudhi/persistence_graphical_tools.py index e36af304..d59e51a0 100644 --- a/src/python/gudhi/persistence_graphical_tools.py +++ b/src/python/gudhi/persistence_graphical_tools.py @@ -72,11 +72,11 @@ def plot_persistence_barcode( """This function plots the persistence bar code from persistence values list , a np.array of shape (N x 2) (representing a diagram in a single homology dimension), - or from a `persistence diagram `__ file. + or from a `persistence diagram `_ file. :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 `__ file style name + :param persistence_file: A `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 @@ -214,11 +214,11 @@ def plot_persistence_diagram( ): """This function plots the persistence diagram from persistence values list, a np.array of shape (N x 2) representing a diagram in a single - homology dimension, or from a `persistence diagram `__ file`. + homology dimension, or from a `persistence diagram `_ file`. :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 `__ file style name + :param persistence_file: A `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 @@ -369,18 +369,18 @@ def plot_persistence_density( """This function plots the persistence density from persistence values list, np.array of shape (N x 2) representing a diagram in a single homology dimension, - or from a `persistence diagram `__ file. + or from a `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). - :Requires: `SciPy `__ + :Requires: `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 `__ + :param persistence_file: A `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 diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py index 19363097..86008bc3 100644 --- a/src/python/gudhi/point_cloud/knn.py +++ b/src/python/gudhi/point_cloud/knn.py @@ -20,8 +20,8 @@ class KNearestNeighbors: """ Class wrapping several implementations for computing the k nearest neighbors in a point set. - :Requires: `PyKeOps `__, `SciPy `__, - `Scikit-learn `__, and/or `Hnswlib `__ + :Requires: `PyKeOps `_, `SciPy `_, + `Scikit-learn `_, and/or `Hnswlib `_ in function of the selected `implementation`. """ diff --git a/src/python/gudhi/representations/metrics.py b/src/python/gudhi/representations/metrics.py index 0a6dd680..8a32f7e9 100644 --- a/src/python/gudhi/representations/metrics.py +++ b/src/python/gudhi/representations/metrics.py @@ -225,7 +225,7 @@ class BottleneckDistance(BaseEstimator, TransformerMixin): """ This is a class for computing the bottleneck distance matrix from a list of persistence diagrams. - :Requires: `CGAL `__ :math:`\geq` 4.11.0 + :Requires: `CGAL `_ :math:`\geq` 4.11.0 """ def __init__(self, epsilon=None): """ -- cgit v1.2.3