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Copy file name to clipboardExpand all lines: CHANGELOG.md
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@@ -7,8 +7,15 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
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## [Unreleased]
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### Added
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- Add more general visitor `GenericMapPythonVisitor` for map types test `boost::unordered_map<std::string, int>` ([#504](https://github.com/stack-of-tasks/eigenpy/pull/504))
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- Support for non-[default-contructible](https://en.cppreference.com/w/cpp/named_req/DefaultConstructible) types in map types ([#504](https://github.com/stack-of-tasks/eigenpy/pull/504))
Copy file name to clipboardExpand all lines: README.md
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@@ -14,15 +14,16 @@ EigenPy — Versatile and efficient Python bindings between Numpy and Eigen
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**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.
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**EigenPy** provides:
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- full memory sharing between Numpy and Eigen, avoiding memory allocation
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- full support Eigen::Ref avoiding memory allocation
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- full support of the Eigen::Tensor module
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- exposition of the Geometry module of Eigen for easy code prototyping
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- standard matrix decomposion routines of Eigen such as the Cholesky decomposition (SVD and QR decompositions [can be added](#contributing))
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- full support of SWIG objects
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- full support of runtime declaration of Numpy scalar types
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- extended API to expose std::vector types
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- full support of vectorization between C++ and Python (all the hold objects are properly aligned in memory)
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- full memory sharing between Numpy and Eigen, avoiding memory allocation
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- full support Eigen::Ref avoiding memory allocation
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- full support of the Eigen::Tensor module
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- exposition of the Geometry module of Eigen for easy code prototyping
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- standard matrix decomposion routines of Eigen such as the Cholesky decomposition (SVD and QR decompositions [can be added](#contributing))
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- full support of SWIG objects
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- full support of runtime declaration of Numpy scalar types
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- extended API to expose several STL types and some of their Boost equivalents: `optional` types, `std::pair`, maps, variants...
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- full support of vectorization between C++ and Python (all the hold objects are properly aligned in memory)
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