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https://github.com/stack-of-tasks/eigenpy
Efficient bindings between Numpy and Eigen using Boost.Python
https://github.com/stack-of-tasks/eigenpy
bindings boost boost-python eigen numpy python python3-library
Last synced: 1 day ago
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Efficient bindings between Numpy and Eigen using Boost.Python
- Host: GitHub
- URL: https://github.com/stack-of-tasks/eigenpy
- Owner: stack-of-tasks
- License: bsd-2-clause
- Created: 2014-07-15T16:55:01.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2024-12-03T08:53:32.000Z (19 days ago)
- Last Synced: 2024-12-13T19:13:35.273Z (9 days ago)
- Topics: bindings, boost, boost-python, eigen, numpy, python, python3-library
- Language: C++
- Homepage:
- Size: 2.26 MB
- Stars: 181
- Watchers: 22
- Forks: 39
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
EigenPy — Versatile and efficient Python bindings between Numpy and Eigen
======
**EigenPy** is an open-source framework that allows the binding of the famous [Eigen](http://eigen.tuxfamily.org) C++ library in Python via Boost.Python.
**EigenPy** provides:
- full memory sharing between Numpy and Eigen, avoiding memory allocation
- full support Eigen::Ref avoiding memory allocation
- full support of the Eigen::Tensor module
- exposition of the Geometry module of Eigen for easy code prototyping
- standard matrix decomposion routines of Eigen such as the Cholesky decomposition (SVD and QR decompositions [can be added](#contributing))
- full support of SWIG objects
- full support of runtime declaration of Numpy scalar types
- extended API to expose several STL types and some of their Boost equivalents: `optional` types, `std::pair`, maps, variants...
- full support of vectorization between C++ and Python (all the hold objects are properly aligned in memory)
## Installation
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:
```bash
conda install eigenpy -c conda-forge
```
### Ubuntu
You can easily install **EigenPy** from binaries.
#### 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 one apt update to fetch the package descriptions:
```bash
sudo apt-get update
```
#### Install EigenPy
4. The installation of **EigenPy** and its dependencies is made through the line:
```bash
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](https://brew.sh/).
You just need to register the tap of the software repository.
```
brew tap gepetto/homebrew-gepetto
```
and then install **EigenPy** for Python 3.x with:
```
brew install eigenpy
```
## Contributing
Standard matrix decomposion routines of Eigen such as the SVD and QR decompositions can be readily added to **EigenPy** following the example of the Cholesky decomposition that is already implemented. Feel free to open a PR if you wrap them for your use case.
## Build/install from source with Pixi
To build **EigenPy** from source the easiest way is to use [Pixi](https://pixi.sh/latest/#installation).
[Pixi](https://pixi.sh/latest/) is a cross-platform package management tool for developers that
will install all required dependencies in `.pixi` directory.
It's used by our CI agent so you have the guarantee to get the right dependencies.
Run the following command to install dependencies, configure, build and test the project:
```bash
pixi run test
```
The project will be built in the `build` directory.
You can run `pixi shell` and build the project with `cmake` and `ninja` manually.
## Credits
The following people have been involved in the development of **EigenPy**:
- [Justin Carpentier](https://jcarpent.github.io) (Inria): main developer and manager of the project
- [Nicolas Mansard](http://projects.laas.fr/gepetto/index.php/Members/NicolasMansard) (LAAS-CNRS): initial project instructor
- [Wolfgang Merkt](http://www.wolfgangmerkt.com/) (University of Edinburgh): ROS integration and support
- [Sean Yen](https://www.linkedin.com/in/seanyentw) (Microsoft): Windows integration
- [Loïc Estève](https://github.com/lesteve) (Inria): Conda integration
- [Wilson Jallet](https://manifoldfr.github.io/) (Inria/LAAS-CNRS): core developer
- [Joris Vaillant](https://github.com/jorisv) (Inria): core developer and manager of the project
If you have taken part in the development of **EigenPy**, feel free to add your name and contribution here.
## Acknowledgments
The development of **EigenPy** is supported by the [Gepetto team](http://projects.laas.fr/gepetto/) [@LAAS-CNRS](http://www.laas.fr) and the [Willow team](https://www.di.ens.fr/willow/) [@INRIA](http://www.inria.fr).