Metadata-Version: 2.1
Name: geomstats
Version: 2.7.0
Summary: Geometric statistics on manifolds
Author-email: Nina Miolane <nmiolane@gmail.com>
License: MIT License
        
        Copyright (c) 2018 Nina Miolane
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: homepage, http://github.com/geomstats/geomstats
Project-URL: documentation, https://geomstats.github.io/
Project-URL: repository, http://github.com/geomstats/geomstats
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
License-File: LICENSE.md
Requires-Dist: joblib >=0.17.0
Requires-Dist: matplotlib >=3.3.4
Requires-Dist: numpy >=1.18.1
Requires-Dist: pandas >=1.1.5
Requires-Dist: scikit-learn >=0.22.1
Requires-Dist: scipy >=1.9
Provides-Extra: all
Requires-Dist: geomstats[backends,dev,opt] ; extra == 'all'
Provides-Extra: autograd
Requires-Dist: autograd >=1.3 ; extra == 'autograd'
Provides-Extra: backends
Requires-Dist: geomstats[autograd,pytorch] ; extra == 'backends'
Provides-Extra: dev
Requires-Dist: geomstats[doc,lint,test] ; extra == 'dev'
Provides-Extra: doc
Requires-Dist: jupyter ; extra == 'doc'
Requires-Dist: nbsphinx ; extra == 'doc'
Requires-Dist: nbsphinx-link ; extra == 'doc'
Requires-Dist: sphinx ; extra == 'doc'
Requires-Dist: sphinx-gallery ; extra == 'doc'
Requires-Dist: pydata-sphinx-theme ; extra == 'doc'
Provides-Extra: graph
Requires-Dist: networkx ; extra == 'graph'
Provides-Extra: lint
Requires-Dist: black ; extra == 'lint'
Requires-Dist: flake8 ; extra == 'lint'
Requires-Dist: flake8-docstrings ; extra == 'lint'
Requires-Dist: Flake8-pyproject ; extra == 'lint'
Requires-Dist: isort ; extra == 'lint'
Requires-Dist: pre-commit ; extra == 'lint'
Provides-Extra: opt
Requires-Dist: geomstats[graph] ; extra == 'opt'
Provides-Extra: pytorch
Requires-Dist: torch >=1.9.1 ; extra == 'pytorch'
Provides-Extra: test
Requires-Dist: pytest ; extra == 'test'
Requires-Dist: pytest-cov ; extra == 'test'
Requires-Dist: coverage ; extra == 'test'
Requires-Dist: jupyter ; extra == 'test'

Geomstats
=========

.. list-table::
   :header-rows: 0

   * - **Code**
     - |PyPI version|\ |Downloads|\  |Zenodo|\
   * - **Continuous Integration**
     - |Build Status|\ |python|\
   * - **Code coverage (np, autograd, torch)**
     - |Coverage Status np|\ |Coverage Status autograd|\ |Coverage Status torch|
   * - **Documentation**
     - |doc|\  |binder|\ |tutorial|\
   * - **Community**
     - |contributions|\  |Slack|\  |Twitter|\

**NEWS**:

- The white papers summarizing the findings from our ICLR 2021 and 2022 challenges of computational differential geometry and topology are available `here (2021) <https://arxiv.org/abs/2108.09810>`__ and `here (2022) <https://arxiv.org/abs/2206.09048>`_.

- Check out our new  `information_geometry <https://github.com/geomstats/geomstats/tree/master/geomstats/information_geometry>`_ module.

Geomstats is an open-source Python package for computations and
statistics on manifolds. The package is organized into two main modules:
``geometry`` and ``learning``.

The module ``geometry`` implements concepts in differential geometry,
and the module ``learning`` implements statistics and learning
algorithms for data on manifolds.

-  To get an overview of ``geomstats``, see our `introductory
   video <https://www.youtube.com/watch?v=Ju-Wsd84uG0&list=PLYx7XA2nY5GejOB1lsvriFeMytD1-VS1B&index=3>`__.
- To understand how ``geomstats`` is built, check out `these slides <https://drive.google.com/file/d/1YxeGeLXrJdVQLBJQ-SYPirQ7V_UisqUw/view?usp=sharing>`__.
-  To get started with ``geomstats``, see the
   `examples <https://github.com/geomstats/geomstats/tree/master/examples>`__
   and
   `notebooks <https://github.com/geomstats/geomstats/tree/master/notebooks>`__
   directories.
-  The documentation of ``geomstats`` can be found on the `documentation
   website <https://geomstats.github.io/>`__.
- Interested in information geometry? Go to our `information_geometry <https://github.com/geomstats/geomstats/tree/master/geomstats/information_geometry>`_ module.
-  To follow the scientific literature on geometric statistics, follow
   our twitter-bot `@geomstats <https://twitter.com/geomstats>`__!

Keep in touch with the community by joining us on our `slack workspace <https://join.slack.com/t/geomstats/shared_invite/zt-15t8k0q7r-Duxt6j6aA3rDOIms2Vd~RQ>`__!


Citing Geomstats
================

If you find ``geomstats`` useful, please kindly cite:

- our research `paper <https://jmlr.org/papers/v21/19-027.html>`__ :

::

    @article{JMLR:v21:19-027,
      author  = {Nina Miolane and Nicolas Guigui and Alice Le Brigant and Johan Mathe and Benjamin Hou and Yann Thanwerdas and Stefan Heyder and Olivier Peltre and Niklas Koep and Hadi Zaatiti and Hatem Hajri and Yann Cabanes and Thomas Gerald and Paul Chauchat and Christian Shewmake and Daniel Brooks and Bernhard Kainz and Claire Donnat and Susan Holmes and Xavier Pennec},
      title   = {Geomstats:  A Python Package for Riemannian Geometry in Machine Learning},
      journal = {Journal of Machine Learning Research},
      year    = {2020},
      volume  = {21},
      number  = {223},
      pages   = {1-9},
      url     = {http://jmlr.org/papers/v21/19-027.html}
    }

- and `Geomstats software version <https://doi.org/10.5281/zenodo.4624475>`__ (citation automatically generated by Zenodo at the bottom right of this link).

We would sincerely appreciate citations to both the original research paper and the software version, to acknowledge authors who started the codebase and made the library possible, together with the crucial work of all contributors who are continuously implementing pivotal new geometries and important learning algorithms, as well as refactoring, testing and documenting the code to democratize geometric statistics and (deep) learning and foster reproducible research in this field.

Install geomstats via pip3
--------------------------

From a terminal (OS X & Linux), you can install geomstats and its
requirements with ``pip3`` as follows:

::

    pip3 install geomstats

This method installs the latest version of geomstats that is uploaded on
PyPi. Note that geomstats is only available with Python3.

Install geomstats via conda
---------------------------

From a terminal (OS X & Linux) or an Anaconda prompt (Windows), you can install geomstats and its
requirements with ``conda`` as follows:

::

    conda install -c conda-forge geomstats

This method installs the latest version of geomstats that is uploaded on
conda-forge. Note that geomstats is only available with Python3.

Install geomstats via Git
-------------------------

From a terminal (OS X & Linux), you can install geomstats and its
requirements via ``git`` as follows:

::

    git clone https://github.com/geomstats/geomstats.git
    cd geomstats
    pip3 install .

This method installs the latest GitHub version of geomstats.


Note that this only installs the minimum requirements. To add the optional,
development, continuous integration and documentation requirements,
refer to the file ``pyproject.toml``.

Install geomstats : Developers
------------------------------

Developers should git clone the master branch of this repository, together with the development requirements
and the optional requirements to enable ``autograd`` and ``pytorch`` backends:

::

    pip3 install geomstats[dev,opt]

Additionally, we recommend installing our pre-commit hook, to ensure that your code
follows our Python style guidelines:

::

    pre-commit install


Choose the backend
------------------

Geomstats can run seamlessly with ``numpy``, ``autograd`` or
``pytorch``. Note that ``autograd`` and ``pytorch`` and requirements are
optional, as geomstats can be used with ``numpy`` only. By default, the
``numpy`` backend is used. The visualizations are only available with
this backend.

To get the ``autograd`` and ``pytorch`` versions compatible with
geomstats, install the optional requirements:

::

    pip3 install geomstats[opt]

To install only the requirements for a given backend do:

::

    pip3 install geomstats[<backend_name>]

You can choose your backend by setting the environment variable
``GEOMSTATS_BACKEND`` to ``numpy``, ``autograd`` or ``pytorch``, and
importing the ``backend`` module. From the command line:

::

    export GEOMSTATS_BACKEND=<backend_name>

and in the Python3 code:

::

    import geomstats.backend as gs

Getting started
---------------

To use ``geomstats`` for learning algorithms on Riemannian manifolds,
you need to follow three steps:

* instantiate the manifold of interest,
* instantiate the learning algorithm of interest,
* run the algorithm.

The data should be represented by a ``gs.array``. This structure
represents numpy arrays, autograd or pytorch tensors, depending on the
choice of backend.

The following code snippet shows the use of tangent Principal Component
Analysis on simulated ``data`` on the space of 3D rotations.

.. code:: python

    from geomstats.geometry.special_orthogonal import SpecialOrthogonal
    from geomstats.learning.pca import TangentPCA

    so3 = SpecialOrthogonal(n=3, point_type="vector")
    metric = so3.bi_invariant_metric

    data = so3.random_uniform(n_samples=10)

    tpca = TangentPCA(metric=metric, n_components=2)
    tpca = tpca.fit(data)
    tangent_projected_data = tpca.transform(data)

All geometric computations are performed behind the scenes. The user
only needs a high-level understanding of Riemannian geometry. Each
algorithm can be used with any of the manifolds and metric implemented
in the package.

To see additional examples, go to the
`examples <https://github.com/geomstats/geomstats/tree/master/examples>`__
or
`notebooks <https://github.com/geomstats/geomstats/tree/master/notebooks>`__
directories.

Contributing
------------

See our
`contributing <https://github.com/geomstats/geomstats/blob/master/docs/contributing/index.rst>`__
guidelines!

Interested? Contact us and join the next hackathons. Previous Geomstats events include:

-  January 2020: hackathon at Inria Sophia-Antipolis, Nice, France
-  April 2020: remote online hackathon
-  March - April 2021: hackathon, hybrid at Inria Sophia-Antipolis / remotely with contributors from around the world
-  July 2021: hackathon at the Geometric Science of Information (GSI) conference, Paris, France
-  August 2021: international Coding Challenge at the International Conference on Learning Representations (ICLR), remotely
-  December 2021: fixit hackathon at the Sorbonne Center for Artificial Intelligence, Paris, France.
-  February 2022: hackathon, hybrid at Inria Sophia-Antipolis / remotely with contributors from around the world
-  April 2022: in-person hackathon at the Villa Cynthia, Saint Raphael, France.
-  April 2022: international Coding Challenge at the International Conference on Learning Representations (ICLR), remotely.
-  June 2022: hakathon at the University of Washington (UW).
-  October 17-21, 2022: hackathon during the trimester `Geometry and Statistics in Data Sciences <https://indico.math.cnrs.fr/category/498/>`__, in Paris.


Acknowledgements
----------------

This work is supported by:

-  the Inria-Stanford associated team `GeomStats <http://www-sop.inria.fr/asclepios/projects/GeomStats/>`__,
-  the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement `G-Statistics <https://team.inria.fr/epione/en/research/erc-g-statistics/>`__ No. 786854),
-  the French society for applied and industrial mathematics (`SMAI <http://smai.emath.fr/>`__),
-  the National Science Foundation (grant NSF DMS RTG 1501767).

.. |Twitter| image:: https://img.shields.io/twitter/follow/geomstats?label=Follow%20%40geomstats%20%20%20%20&style=social
   :target: https://twitter.com/geomstats
.. |PyPI version| image:: https://badge.fury.io/py/geomstats.svg
   :target: https://badge.fury.io/py/geomstats
.. |Build Status| image:: https://github.com/geomstats/geomstats/actions/workflows/test.yml/badge.svg
   :target: https://github.com/geomstats/geomstats/actions/workflows/test.yml
.. |Slack| image:: https://img.shields.io/badge/Slack-Join-yellow
   :target: https://join.slack.com/t/geomstats/shared_invite/zt-15t8k0q7r-Duxt6j6aA3rDOIms2Vd~RQ
.. |Coverage Status np| image:: https://codecov.io/gh/geomstats/geomstats/branch/master/graph/badge.svg?flag=numpy
   :target: https://codecov.io/gh/geomstats/geomstats
.. |Coverage Status autograd| image:: https://codecov.io/gh/geomstats/geomstats/branch/master/graph/badge.svg?flag=autograd
   :target: https://codecov.io/gh/geomstats/geomstats
.. |Coverage Status torch| image:: https://codecov.io/gh/geomstats/geomstats/branch/master/graph/badge.svg?flag=pytorch
   :target: https://codecov.io/gh/geomstats/geomstats
.. |Zenodo| image:: https://zenodo.org/badge/108200238.svg
   :target: https://zenodo.org/badge/latestdoi/108200238
.. |Downloads| image:: https://static.pepy.tech/personalized-badge/geomstats?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=Downloads
   :target: https://pepy.tech/project/geomstats
.. |python| image:: https://img.shields.io/badge/python-3.11+-blue?logo=python
   :target: https://www.python.org/
.. |tutorial| image:: https://img.shields.io/youtube/views/Ju-Wsd84uG0?label=watch&style=social
   :target: https://www.youtube.com/watch?v=Ju-Wsd84uG0
.. |doc| image:: https://img.shields.io/badge/docs-website-brightgreen?style=flat
   :target: https://geomstats.github.io/?badge=latest
.. |binder| image:: https://mybinder.org/badge_logo.svg
   :target: https://mybinder.org/v2/gh/geomstats/geomstats/master?filepath=notebooks
.. |contributions| image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat
   :target: https://geomstats.github.io/contributing.html
