eigenpy

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  "github_readme_excerpt": "EigenPy \u2014 Versatile and efficient Python bindings between Numpy and Eigen\n======\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://opensource.org/licenses/BSD-2-Clause\"\u003e\u003cimg src=\"https://img.shields.io/badge/License-BSD%202--Clause-green.svg\" alt=\"License\"/\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/stack-of-tasks/eigenpy/workflows/linux.yml\"\u003e\u003cimg alt=\"Build Status\" src=\"https://github.com/stack-of-tasks/eigenpy/actions/workflows/linux.yml/badge.svg?branch=devel\" /\u003e\u003c/a\u003e\n  \u003ca href=\"https://anaconda.org/conda-forge/eigenpy\"\u003e\u003cimg src=\"https://img.shields.io/conda/dn/conda-forge/eigenpy.svg\" alt=\"Conda Downloads\"/\u003e\u003c/a\u003e\n  \u003ca href=\"https://anaconda.org/conda-forge/eigenpy\"\u003e\u003cimg src=\"https://img.shields.io/conda/vn/conda-forge/eigenpy.svg\" alt=\"Conda Version\"/\u003e\u003c/a\u003e\n  \u003ca href=\"https://badge.fury.io/py/eigenpy\"\u003e\u003cimg src=\"https://badge.fury.io/py/eigenpy.svg\" alt=\"PyPI version\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/psf/black\"\u003e\u003cimg src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" alt=\"Code style: black\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/astral-sh/ruff\"\u003e\u003cimg src=\"https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json\" alt=\"Linter: ruff\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n**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.\n\n**EigenPy** provides:\n\n- full memory sharing between Numpy and Eigen, avoiding memory allocation\n- full support Eigen::Ref avoiding memory allocation\n- full support of the Eigen::Tensor module\n- exposition of the Geometry module of Eigen for easy code prototyping\n- standard matrix decomposion routines of Eigen such as the Cholesky, SVD and QR decompositions\n- full support of SWIG objects\n- full support of runtime declaration of Numpy scalar types\n- extended API to expose several STL types and some of their Boost equivalents: `optional` types, `std::pair`, maps, variants...\n- full support of vectorization between C++ and Python (all ",
  "github_repo": "stack-of-tasks/eigenpy",
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