diff options
Diffstat (limited to 'src/python/doc')
| -rw-r--r-- | src/python/doc/_templates/layout.html | 1 | ||||
| -rw-r--r-- | src/python/doc/alpha_complex_user.rst | 5 | ||||
| -rwxr-xr-x | src/python/doc/conf.py | 5 | ||||
| -rw-r--r-- | src/python/doc/datasets_generators.inc | 14 | ||||
| -rw-r--r-- | src/python/doc/datasets_generators.rst | 105 | ||||
| -rw-r--r-- | src/python/doc/examples.rst | 1 | ||||
| -rw-r--r-- | src/python/doc/img/sphere_3d.png | bin | 0 -> 529148 bytes | |||
| -rw-r--r-- | src/python/doc/index.rst | 5 | ||||
| -rw-r--r-- | src/python/doc/installation.rst | 33 | ||||
| -rw-r--r-- | src/python/doc/wasserstein_distance_user.rst | 29 |
10 files changed, 169 insertions, 29 deletions
diff --git a/src/python/doc/_templates/layout.html b/src/python/doc/_templates/layout.html index cd40a51b..e074b6c7 100644 --- a/src/python/doc/_templates/layout.html +++ b/src/python/doc/_templates/layout.html @@ -194,6 +194,7 @@ <li><a href="/relatedprojects/">Related projects</a></li> <li><a href="/theyaretalkingaboutus/">They are talking about us</a></li> <li><a href="/inaction/">GUDHI in action</a></li> + <li><a href="/etymology/">Etymology</a></li> </ul> </li> <li class="divider"></li> diff --git a/src/python/doc/alpha_complex_user.rst b/src/python/doc/alpha_complex_user.rst index fffcb3db..96e267ef 100644 --- a/src/python/doc/alpha_complex_user.rst +++ b/src/python/doc/alpha_complex_user.rst @@ -163,7 +163,10 @@ As the squared radii computed by CGAL are an approximation, it might happen that :math:`\alpha^2` values do not quite define a proper filtration (i.e. non-decreasing with respect to inclusion). We fix that up by calling :func:`~gudhi.SimplexTree.make_filtration_non_decreasing` (cf. -`C++ version <http://gudhi.gforge.inria.fr/doc/latest/index.html>`_). +`C++ version <https://gudhi.inria.fr/doc/latest/class_gudhi_1_1_simplex__tree.html>`_). + +.. note:: + This is not the case in `exact` version, this is the reason why it is not called in this case. Prune above given filtration value ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/src/python/doc/conf.py b/src/python/doc/conf.py index b06baf9c..e69e2751 100755 --- a/src/python/doc/conf.py +++ b/src/python/doc/conf.py @@ -120,15 +120,12 @@ pygments_style = 'sphinx' # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = 'classic' +html_theme = 'python_docs_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { - "sidebarbgcolor": "#A1ADCD", - "sidebartextcolor": "black", - "sidebarlinkcolor": "#334D5C", "body_max_width": "100%", } diff --git a/src/python/doc/datasets_generators.inc b/src/python/doc/datasets_generators.inc new file mode 100644 index 00000000..8d169275 --- /dev/null +++ b/src/python/doc/datasets_generators.inc @@ -0,0 +1,14 @@ +.. table:: + :widths: 30 40 30 + + +-----------------------------------+--------------------------------------------+--------------------------------------------------------------------------------------+ + | .. figure:: | Datasets generators (points). | :Authors: Hind Montassif | + | img/sphere_3d.png | | | + | | | :Since: GUDHI 3.5.0 | + | | | | + | | | :License: MIT (`LGPL v3 </licensing/>`_) | + | | | | + | | | :Requires: `CGAL <installation.html#cgal>`_ | + +-----------------------------------+--------------------------------------------+--------------------------------------------------------------------------------------+ + | * :doc:`datasets_generators` | + +-----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/datasets_generators.rst b/src/python/doc/datasets_generators.rst new file mode 100644 index 00000000..260c3882 --- /dev/null +++ b/src/python/doc/datasets_generators.rst @@ -0,0 +1,105 @@ + +:orphan: + +.. To get rid of WARNING: document isn't included in any toctree + +=========================== +Datasets generators manual +=========================== + +We provide the generation of different customizable datasets to use as inputs for Gudhi complexes and data structures. + + +Points generators +------------------ + +The module **points** enables the generation of random points on a sphere, random points on a torus and as a grid. + +Points on sphere +^^^^^^^^^^^^^^^^ + +The function **sphere** enables the generation of random i.i.d. points uniformly on a (d-1)-sphere in :math:`R^d`. +The user should provide the number of points to be generated on the sphere :code:`n_samples` and the ambient dimension :code:`ambient_dim`. +The :code:`radius` of sphere is optional and is equal to **1** by default. +Only random points generation is currently available. + +The generated points are given as an array of shape :math:`(n\_samples, ambient\_dim)`. + +Example +""""""" + +.. code-block:: python + + from gudhi.datasets.generators import points + from gudhi import AlphaComplex + + # Generate 50 points on a sphere in R^2 + gen_points = points.sphere(n_samples = 50, ambient_dim = 2, radius = 1, sample = "random") + + # Create an alpha complex from the generated points + alpha_complex = AlphaComplex(points = gen_points) + +.. autofunction:: gudhi.datasets.generators.points.sphere + +Points on a flat torus +^^^^^^^^^^^^^^^^^^^^^^ + +You can also generate points on a torus. + +Two functions are available and give the same output: the first one depends on **CGAL** and the second does not and consists of full python code. + +On another hand, two sample types are provided: you can either generate i.i.d. points on a d-torus in :math:`R^{2d}` *randomly* or on a *grid*. + +First function: **ctorus** +""""""""""""""""""""""""""" + +The user should provide the number of points to be generated on the torus :code:`n_samples`, and the dimension :code:`dim` of the torus on which points would be generated in :math:`R^{2dim}`. +The :code:`sample` argument is optional and is set to **'random'** by default. +In this case, the returned generated points would be an array of shape :math:`(n\_samples, 2*dim)`. +Otherwise, if set to **'grid'**, the points are generated on a grid and would be given as an array of shape: + +.. math:: + + ( ⌊n\_samples^{1 \over {dim}}⌋^{dim}, 2*dim ) + +**Note 1:** The output array first shape is rounded down to the closest perfect :math:`dim^{th}` power. + +**Note 2:** This version is recommended when the user wishes to use **'grid'** as sample type, or **'random'** with a relatively small number of samples (~ less than 150). + +Example +""""""" +.. code-block:: python + + from gudhi.datasets.generators import points + + # Generate 50 points randomly on a torus in R^6 + gen_points = points.ctorus(n_samples = 50, dim = 3) + + # Generate 27 points on a torus as a grid in R^6 + gen_points = points.ctorus(n_samples = 50, dim = 3, sample = 'grid') + +.. autofunction:: gudhi.datasets.generators.points.ctorus + +Second function: **torus** +""""""""""""""""""""""""""" + +The user should provide the number of points to be generated on the torus :code:`n_samples` and the dimension :code:`dim` of the torus on which points would be generated in :math:`R^{2dim}`. +The :code:`sample` argument is optional and is set to **'random'** by default. +The other allowed value of sample type is **'grid'**. + +**Note:** This version is recommended when the user wishes to use **'random'** as sample type with a great number of samples and a low dimension. + +Example +""""""" +.. code-block:: python + + from gudhi.datasets.generators import points + + # Generate 50 points randomly on a torus in R^6 + gen_points = points.torus(n_samples = 50, dim = 3) + + # Generate 27 points on a torus as a grid in R^6 + gen_points = points.torus(n_samples = 50, dim = 3, sample = 'grid') + + +.. autofunction:: gudhi.datasets.generators.points.torus diff --git a/src/python/doc/examples.rst b/src/python/doc/examples.rst index 76e5d4c7..1442f185 100644 --- a/src/python/doc/examples.rst +++ b/src/python/doc/examples.rst @@ -8,6 +8,7 @@ Examples .. only:: builder_html * :download:`alpha_complex_diagram_persistence_from_off_file_example.py <../example/alpha_complex_diagram_persistence_from_off_file_example.py>` + * :download:`alpha_complex_from_generated_points_on_sphere_example.py <../example/alpha_complex_from_generated_points_on_sphere_example.py>` * :download:`alpha_complex_from_points_example.py <../example/alpha_complex_from_points_example.py>` * :download:`alpha_rips_persistence_bottleneck_distance.py <../example/alpha_rips_persistence_bottleneck_distance.py>` * :download:`bottleneck_basic_example.py <../example/bottleneck_basic_example.py>` diff --git a/src/python/doc/img/sphere_3d.png b/src/python/doc/img/sphere_3d.png Binary files differnew file mode 100644 index 00000000..70f3184f --- /dev/null +++ b/src/python/doc/img/sphere_3d.png diff --git a/src/python/doc/index.rst b/src/python/doc/index.rst index 040e57a4..2d7921ae 100644 --- a/src/python/doc/index.rst +++ b/src/python/doc/index.rst @@ -91,3 +91,8 @@ Clustering ********** .. include:: clustering.inc + +Datasets generators +******************* + +.. include:: datasets_generators.inc diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst index 66efe45a..35c344e3 100644 --- a/src/python/doc/installation.rst +++ b/src/python/doc/installation.rst @@ -41,7 +41,7 @@ there. The library uses c++14 and requires `Boost <https://www.boost.org/>`_ :math:`\geq` 1.56.0, `CMake <https://www.cmake.org/>`_ :math:`\geq` 3.5 to generate makefiles, -`NumPy <http://numpy.org>`_, `Cython <https://www.cython.org/>`_ and +`NumPy <http://numpy.org>`_ :math:`\geq` 1.15.0, `Cython <https://www.cython.org/>`_ and `pybind11 <https://github.com/pybind/pybind11>`_ to compile the GUDHI Python module. It is a multi-platform library and compiles on Linux, Mac OSX and Visual @@ -99,20 +99,14 @@ Or install it definitely in your Python packages folder: .. code-block:: bash cd /path-to-gudhi/build/python - # May require sudo or administrator privileges - make install + python setup.py install # add --user to the command if you do not have the permission + # Or 'pip install .' .. note:: - :code:`make install` is only a - `CMake custom targets <https://cmake.org/cmake/help/latest/command/add_custom_target.html>`_ - to shortcut :code:`python setup.py install` command. It does not take into account :code:`CMAKE_INSTALL_PREFIX`. - But one can use :code:`python setup.py install ...` specific options in the python directory: - -.. code-block:: bash - - python setup.py install --prefix /home/gudhi # Install in /home/gudhi directory + But one can use + `alternate location installation <https://docs.python.org/3/install/#alternate-installation>`_. Test suites =========== @@ -200,8 +194,10 @@ A complete configuration would be : Documentation ============= -To build the documentation, `sphinx-doc <http://www.sphinx-doc.org>`_ and -`sphinxcontrib-bibtex <https://sphinxcontrib-bibtex.readthedocs.io>`_ are +To build the documentation, `sphinx-doc <http://www.sphinx-doc.org>`_, +`sphinxcontrib-bibtex <https://sphinxcontrib-bibtex.readthedocs.io>`_, +`sphinxcontrib-paramlinks <https://github.com/sqlalchemyorg/sphinx-paramlinks>`_ and +`python-docs-theme <https://github.com/python/python-docs-theme>`_ are required. As the documentation is auto-tested, `CGAL`_, `Eigen`_, `Matplotlib`_, `NumPy`_, `POT`_, `Scikit-learn`_ and `SciPy`_ are also mandatory to build the documentation. @@ -363,7 +359,7 @@ Python Optimal Transport ------------------------ The :doc:`Wasserstein distance </wasserstein_distance_user>` -module requires `POT <https://pot.readthedocs.io/>`_, a library that provides +module requires `POT <https://pythonot.github.io/>`_, a library that provides several solvers for optimization problems related to Optimal Transport. PyTorch @@ -402,8 +398,9 @@ TensorFlow Bug reports and contributions ***************************** -Please help us improving the quality of the GUDHI library. You may report bugs or suggestions to: - - Contact: gudhi-users@lists.gforge.inria.fr +Please help us improving the quality of the GUDHI library. +You may `report bugs <https://github.com/GUDHI/gudhi-devel/issues>`_ or +`contact us <https://gudhi.inria.fr/contact/>`_ for any suggestions. -GUDHI is open to external contributions. If you want to join our development team, please contact us. +GUDHI is open to external contributions. If you want to join our development team, please take some time to read our +`contributing guide <https://github.com/GUDHI/gudhi-devel/blob/master/.github/CONTRIBUTING.md>`_. diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst index 9ffc2759..76eb1469 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -44,7 +44,7 @@ Basic example ************* This example computes the 1-Wasserstein distance from 2 persistence diagrams with Euclidean ground metric. -Note that persistence diagrams must be submitted as (n x 2) numpy arrays and must not contain inf values. +Note that persistence diagrams must be submitted as (n x 2) numpy arrays. .. testcode:: @@ -67,14 +67,16 @@ We can also have access to the optimal matching by letting `matching=True`. It is encoded as a list of indices (i,j), meaning that the i-th point in X is mapped to the j-th point in Y. An index of -1 represents the diagonal. +It handles essential parts (points with infinite coordinates). However if the cardinalities of the essential parts differ, +any matching has a cost +inf and thus can be considered to be optimal. In such a case, the function returns `(np.inf, None)`. .. testcode:: import gudhi.wasserstein import numpy as np - dgm1 = np.array([[2.7, 3.7],[9.6, 14.],[34.2, 34.974]]) - dgm2 = np.array([[2.8, 4.45], [5, 6], [9.5, 14.1]]) + dgm1 = np.array([[2.7, 3.7],[9.6, 14.],[34.2, 34.974], [3, np.inf]]) + dgm2 = np.array([[2.8, 4.45], [5, 6], [9.5, 14.1], [4, np.inf]]) cost, matchings = gudhi.wasserstein.wasserstein_distance(dgm1, dgm2, matching=True, order=1, internal_p=2) message_cost = "Wasserstein distance value = %.2f" %cost @@ -90,16 +92,31 @@ An index of -1 represents the diagonal. for j in dgm2_to_diagonal: print("point %s in dgm2 is matched to the diagonal" %j) -The output is: + # An example where essential part cardinalities differ + dgm3 = np.array([[1, 2], [0, np.inf]]) + dgm4 = np.array([[1, 2], [0, np.inf], [1, np.inf]]) + cost, matchings = gudhi.wasserstein.wasserstein_distance(dgm3, dgm4, matching=True, order=1, internal_p=2) + print("\nSecond example:") + print("cost:", cost) + print("matchings:", matchings) + + +The output is: .. testoutput:: - Wasserstein distance value = 2.15 + Wasserstein distance value = 3.15 point 0 in dgm1 is matched to point 0 in dgm2 point 1 in dgm1 is matched to point 2 in dgm2 + point 3 in dgm1 is matched to point 3 in dgm2 point 2 in dgm1 is matched to the diagonal point 1 in dgm2 is matched to the diagonal + Second example: + cost: inf + matchings: None + + Barycenters ----------- @@ -181,4 +198,4 @@ Tutorial This `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-Barycenters-of-persistence-diagrams.ipynb>`_ -presents the concept of barycenter, or Fréchet mean, of a family of persistence diagrams.
\ No newline at end of file +presents the concept of barycenter, or Fréchet mean, of a family of persistence diagrams. |
