2015-10-13 00:57:16 +00:00
|
|
|
About this project
|
|
|
|
|
==================
|
2015-10-18 12:48:24 +00:00
|
|
|
**pybind11** is a lightweight header-only library that exposes C++ types in Python
|
2015-10-13 00:57:16 +00:00
|
|
|
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
|
2015-10-13 21:21:54 +00:00
|
|
|
inferring type information using compile-time introspection.
|
2015-10-13 00:57:16 +00:00
|
|
|
|
|
|
|
|
.. _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
|
2015-10-18 12:48:24 +00:00
|
|
|
everything stripped away that isn't relevant for binding generation. The core
|
|
|
|
|
header files only require ~2K lines of code and depend on Python (2.7 or 3.x)
|
|
|
|
|
and the C++ standard library. This compact implementation was possible thanks
|
|
|
|
|
to some of the new C++11 language features (tuples, lambda functions and
|
2015-10-13 21:21:54 +00:00
|
|
|
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.
|
2015-10-13 00:57:16 +00:00
|
|
|
|
|
|
|
|
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.
|
|
|
|
|
|
2015-10-18 12:48:24 +00:00
|
|
|
- Everything is contained in just a few header files; there no need to link
|
|
|
|
|
against any additional libraries.
|
