pybind11/docs/intro.rst

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.. image:: pybind11-logo.png
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About this project
==================
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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
goals and syntax are similar to the excellent `Boost.Python`_ library by David
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
The main issue with Boost.Python—and the reason for creating such a similar
project—is Boost. Boost is an enormously large and complex suite of utility
libraries that works with almost every C++ compiler in existence. This
compatibility has its cost: arcane template tricks and workarounds are
necessary to support the oldest and buggiest of compiler specimens. Now that
C++11-compatible compilers are widely available, this heavy machinery has
become an excessively large and unnecessary dependency.
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. The core
header files only require ~2.5K lines of code and depend on Python (2.7 or 3.x)
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and the C++ standard library. This compact implementation was possible thanks
to some of the new C++11 language features (specifically: tuples, lambda
functions and variadic templates). Since its creation, this library has grown
beyond Boost.Python in many ways, leading to dramatically simpler binding code
in many common situations.
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Core features
*************
The following core C++ features can be mapped to Python
- Functions accepting and returning custom data structures per value, reference, or pointer
- Instance methods and static methods
- Overloaded functions
- Instance attributes and static attributes
- Exceptions
- Enumerations
- Callbacks
- Custom operators
- STL data structures
- Smart pointers with reference counting like ``std::shared_ptr``
- Internal references with correct reference counting
- C++ classes with virtual (and pure virtual) methods can be extended in Python
Goodies
*******
In addition to the core functionality, pybind11 provides some extra goodies:
- It is possible to bind C++11 lambda functions with captured variables. The
lambda capture data is stored inside the resulting Python function object.
- pybind11 uses C++11 move constructors and move assignment operators whenever
possible to efficiently transfer custom data types.
- It's easy to expose the internal storage of custom data types through
Pythons' buffer protocols. This is handy e.g. for fast conversion between
C++ matrix classes like Eigen and NumPy without expensive copy operations.
- pybind11 can automatically vectorize functions so that they are transparently
applied to all entries of one or more NumPy array arguments.
- Python's slice-based access and assignment operations can be supported with
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.
- Binaries are generally smaller by a factor of 2 or more compared to
equivalent bindings generated by Boost.Python.
- When supported by the compiler, two new C++14 features (relaxed constexpr and
return value deduction) are used to deduce function signatures at compile
time, leading to smaller binaries.