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92 lines
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ReStructuredText
92 lines
4.0 KiB
ReStructuredText
.. image:: pybind11-logo.png
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About this project
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==================
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**pybind11** is a lightweight header-only library that exposes C++ types in Python
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and vice versa, mainly to create Python bindings of existing C++ code. Its
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goals and syntax are similar to the excellent `Boost.Python`_ library by David
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Abrahams: to minimize boilerplate code in traditional extension modules by
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inferring type information using compile-time introspection.
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.. _Boost.Python: http://www.boost.org/doc/libs/release/libs/python/doc/index.html
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The main issue with Boost.Python—and the reason for creating such a similar
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project—is Boost. Boost is an enormously large and complex suite of utility
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libraries that works with almost every C++ compiler in existence. This
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compatibility has its cost: arcane template tricks and workarounds are
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necessary to support the oldest and buggiest of compiler specimens. Now that
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C++11-compatible compilers are widely available, this heavy machinery has
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become an excessively large and unnecessary dependency.
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Think of this library as a tiny self-contained version of Boost.Python with
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everything stripped away that isn't relevant for binding generation. Without
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comments, the core header files only require ~2.5K lines of code and depend on
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Python (2.7 or 3.x) and the C++ standard library. This compact implementation
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was possible thanks to some of the new C++11 language features (specifically:
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tuples, lambda functions and variadic templates). Since its creation, this
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library has grown beyond Boost.Python in many ways, leading to dramatically
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simpler binding code in many common situations.
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Core features
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*************
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The following core C++ features can be mapped to Python
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- Functions accepting and returning custom data structures per value, reference, or pointer
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- Instance methods and static methods
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- Overloaded functions
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- Instance attributes and static attributes
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- Exceptions
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- Enumerations
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- Iterators and ranges
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- Callbacks
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- Custom operators
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- STL data structures
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- Smart pointers with reference counting like ``std::shared_ptr``
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- Internal references with correct reference counting
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- C++ classes with virtual (and pure virtual) methods can be extended in Python
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Goodies
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*******
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In addition to the core functionality, pybind11 provides some extra goodies:
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- It is possible to bind C++11 lambda functions with captured variables. The
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lambda capture data is stored inside the resulting Python function object.
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- pybind11 uses C++11 move constructors and move assignment operators whenever
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possible to efficiently transfer custom data types.
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- It's easy to expose the internal storage of custom data types through
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Pythons' buffer protocols. This is handy e.g. for fast conversion between
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C++ matrix classes like Eigen and NumPy without expensive copy operations.
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- pybind11 can automatically vectorize functions so that they are transparently
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applied to all entries of one or more NumPy array arguments.
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- Python's slice-based access and assignment operations can be supported with
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just a few lines of code.
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- Everything is contained in just a few header files; there is no need to link
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against any additional libraries.
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- Binaries are generally smaller by a factor of at least 2 compared to
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equivalent bindings generated by Boost.Python. A recent pybind11 conversion
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of `PyRosetta`_, an enourmous Boot.Python binding project, reported a binary
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size reduction of **5.4x** and compile time reduction by **5.8x**.
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- When supported by the compiler, two new C++14 features (relaxed constexpr and
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return value deduction) are used to precompute function signatures at compile
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time, leading to smaller binaries.
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- With little extra effort, C++ types can be pickled and unpickled similar to
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regular Python objects.
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.. _PyRosetta: http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf
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Supported compilers
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*******************
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1. Clang/LLVM (any non-ancient version with C++11 support)
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2. GCC (any non-ancient version with C++11 support)
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3. Microsoft Visual Studio 2015 or newer
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4. Intel C++ compiler v15 or newer
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