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Bindings between Numpy and Eigen using Boost.Python

Project description

EigenPy — Efficient Python bindings between Numpy/Eigen

License Build Status Conda Downloads Conda Version PyPI version Code style: black

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


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


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] $(lsb_release -cs) robotpkg' >> /etc/apt/sources.list.d/robotpkg.list"
  1. Register the authentication certificate of robotpkg:
curl | sudo apt-key add -
  1. You need to run at least once apt update to fetch the package descriptions:
sudo apt-get update

Install EigenPy

  1. 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


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

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


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

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