EigenPy — Efficient Python bindings between Numpy/Eigen

EigenPy is an open source framework which allows to bind the famous Eigen C++ library in Python.

EigenPy provides:

• full memory sharing between Numpy and Eigen avoiding memory allocation
• full support Eigen::Ref avoiding memory allocation
• exposition of the Geometry module of Eigen for easy code prototyping
• standard matrix decomposion routines of Eigen such as the Cholesky decomposition, SVD decomposition, QR decomposition, and etc.
• full support of SWIG objects

Setup

The installation of EigenPy on your computer is made easy for Linux/BSD, Mac OS X and Windows environments.

The Conda approach

You simply need this simple line:

conda install eigenpy -c conda-forge

Ubuntu

You can easily install EigenPy from binairies.

Add robotpkg apt repository

1. Add robotpkg as source repository to apt:

sudo sh -c "echo 'deb [arch=amd64] http://robotpkg.openrobots.org/packages/debian/pub $(lsb_release -cs) robotpkg' >> /etc/apt/sources.list.d/robotpkg.list"

2. Register the authentication certificate of robotpkg:

curl http://robotpkg.openrobots.org/packages/debian/robotpkg.key | sudo apt-key add -

3. You need to run at least once apt update to fetch the package descriptions:

sudo apt-get update

Install EigenPy

4. The installation of EigenPy and its dependencies is made through the line:

For Python 2.7

sudo apt install robotpkg-py27-eigenpy

or for Python 3.{5,6,7}

sudo apt install robotpkg-py35-eigenpy

where 35 should be replaced by the python 3 you want to work this (e.g. robotpkg-py36-eigenpy to work with Python 3.6).

Mac OS X

The installation of EigenPy on Mac OS X is made via HomeBrew. You just need to register the tap of the sofware repository.

brew tap gepetto/homebrew-gepetto

and then install EigenPy for Python 3.x with:

brew install eigenpy

Credits

The following people have been involved in the development of EigenPy:

• Justin Carpentier (INRIA): main developer and manager of the project
• Nicolas Mansard (LAAS-CNRS): initial project instructor
• Wolfgang Merkt (University of Edinburgh): ROS integration and support
• Sean Yen (Microsoft): Windows integration
• Loïc Estève (INRIA): Conda integration

If you have taken part to the development of EigenPy, feel free to add your name and contribution here.

Acknowledgments

The development of EigenPy is supported by the Gepetto team @LAAS-CNRS and the Willow team @INRIA.