diff options
| author | ROUVREAU Vincent <vincent.rouvreau@inria.fr> | 2020-05-11 09:31:49 +0200 |
|---|---|---|
| committer | ROUVREAU Vincent <vincent.rouvreau@inria.fr> | 2020-05-11 09:31:49 +0200 |
| commit | 7e85b0451c686f043b61cde2e5f78674cf8de248 (patch) | |
| tree | 7658315e38e76a2aeb5fd26cf2b5b3c481261ab8 /src/python/doc | |
| parent | 0ed4c3bba47d1375acb49596db2c863c38e9a090 (diff) | |
Double underscore is not the correct syntax
Diffstat (limited to 'src/python/doc')
| -rw-r--r-- | src/python/doc/alpha_complex_sum.inc | 28 | ||||
| -rw-r--r-- | src/python/doc/bottleneck_distance_sum.inc | 22 | ||||
| -rw-r--r-- | src/python/doc/cubical_complex_user.rst | 4 | ||||
| -rw-r--r-- | src/python/doc/nerve_gic_complex_sum.inc | 26 | ||||
| -rw-r--r-- | src/python/doc/nerve_gic_complex_user.rst | 2 | ||||
| -rw-r--r-- | src/python/doc/persistence_graphical_tools_sum.inc | 22 | ||||
| -rw-r--r-- | src/python/doc/persistence_graphical_tools_user.rst | 2 | ||||
| -rw-r--r-- | src/python/doc/point_cloud.rst | 2 | ||||
| -rw-r--r-- | src/python/doc/representations_sum.inc | 22 | ||||
| -rw-r--r-- | src/python/doc/tangential_complex_sum.inc | 22 | ||||
| -rw-r--r-- | src/python/doc/wasserstein_distance_user.rst | 6 | ||||
| -rw-r--r-- | src/python/doc/witness_complex_sum.inc | 28 |
12 files changed, 93 insertions, 93 deletions
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 </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` | - +----------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + +----------------------------------------------------------------+-------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+ + | .. 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/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/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 <fileformats.html#perseus>`__ 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 `Perseus file format <fileformats.html#perseus>`__) 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:: 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 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 <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. `OFF file format <fileformats.html#off-file-format>`__). +Input point clouds are assumed to be OFF files (cf. `OFF file format <fileformats.html#off-file-format>`_). Covers ------ diff --git a/src/python/doc/persistence_graphical_tools_sum.inc b/src/python/doc/persistence_graphical_tools_sum.inc index 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 <installation.html#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 <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 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 <installation.html#scipy>`__ +:Requires: `SciPy <installation.html#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 <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 +:Requires: `Eigen <installation.html#eigen>`_ :math:`\geq` 3.1.0 and `CGAL <installation.html#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 <installation.html#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/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/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 <installation.html#python-optimal-transport>`__ (POT) :math:`\geq` 0.5.1 +: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>`__ +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`. @@ -103,7 +103,7 @@ The output is: Barycenters ----------- -:Requires: `Python Optimal Transport <installation.html#python-optimal-transport>`__ (POT) :math:`\geq` 0.5.1 +: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. 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` | + +-------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ |