Scikit-Shapes: Shape analysis in Python ======================================= Welcome to the documentation of Scikit-Shapes, an open source library for shape analysis in Python. Our source code is available on `GitHub `_. To get started, check out the installation instructions and have a look at the examples. .. warning:: This library is still in very active development. We expect to release a first usable version in September 2025. Licensing and citations ----------------------- This library is licensed under the permissive `MIT license `_, which is fully compatible with both **academic** and **commercial** applications. Scikit-Shapes provides a user-friendly interface for a collection of research papers, whose authors must be credited. If you use this library in an academic context, **please run** the following piece of code to print a **list of the references that have been used in your experiment**: .. code-block:: python import skshapes as sks # Run your code here ... # At the end of your script, print the relevant references. # This list is updated dynamically by calls to the library functions # and may be displayed in both bibtex and plain text (APA) formats: print(sks.bibliography(style="bibtex")) print(sks.bibliography(style="APA")) Acknowledgements ----------------- Authors: - `Jean Feydy `_ (2023-), project leader. - `Louis Pujol `_ (2023-24), core developer. This library is maintained in the `HeKA team `_, a joint research group between `INRIA `_, `INSERM `_ and the `Université Paris Cité `_. It is funded by `INRIA `_, the `PRAIRIE-PSAI `_ institute, and is distributed under the permissive `MIT license `_. Content ------- .. toctree:: :maxdepth: 1 motivation installation user_guide/index auto_examples/index stubs/skshapes explanation/index contributing