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Wenzel Jakob28f98aa2015-10-13 02:57:16 +02001About this project
2==================
Wenzel Jakob7641c1d2015-10-18 14:48:24 +02003**pybind11** is a lightweight header-only library that exposes C++ types in Python
Wenzel Jakob28f98aa2015-10-13 02:57:16 +02004and vice versa, mainly to create Python bindings of existing C++ code. Its
5goals and syntax are similar to the excellent `Boost.Python`_ library by David
6Abrahams: to minimize boilerplate code in traditional extension modules by
Wenzel Jakob93296692015-10-13 23:21:54 +02007inferring type information using compile-time introspection.
Wenzel Jakob28f98aa2015-10-13 02:57:16 +02008
9.. _Boost.Python: http://www.boost.org/doc/libs/release/libs/python/doc/index.html
10
11The main issue with Boost.Pythonand the reason for creating such a similar
12projectis Boost. Boost is an enormously large and complex suite of utility
13libraries that works with almost every C++ compiler in existence. This
14compatibility has its cost: arcane template tricks and workarounds are
15necessary to support the oldest and buggiest of compiler specimens. Now that
16C++11-compatible compilers are widely available, this heavy machinery has
17become an excessively large and unnecessary dependency.
18
19Think of this library as a tiny self-contained version of Boost.Python with
Wenzel Jakob7641c1d2015-10-18 14:48:24 +020020everything stripped away that isn't relevant for binding generation. The core
21header files only require ~2K lines of code and depend on Python (2.7 or 3.x)
22and the C++ standard library. This compact implementation was possible thanks
23to some of the new C++11 language features (tuples, lambda functions and
Wenzel Jakob93296692015-10-13 23:21:54 +020024variadic templates). Since its creation, this library has grown beyond
25Boost.Python in many ways, leading to dramatically simpler binding code in many
26common situations.
Wenzel Jakob28f98aa2015-10-13 02:57:16 +020027
28Core features
29*************
30The following core C++ features can be mapped to Python
31
32- Functions accepting and returning custom data structures per value, reference, or pointer
33- Instance methods and static methods
34- Overloaded functions
35- Instance attributes and static attributes
36- Exceptions
37- Enumerations
38- Callbacks
39- Custom operators
40- STL data structures
41- Smart pointers with reference counting like ``std::shared_ptr``
42- Internal references with correct reference counting
43- C++ classes with virtual (and pure virtual) methods can be extended in Python
44
45Goodies
46*******
47In addition to the core functionality, pybind11 provides some extra goodies:
48
49- It is possible to bind C++11 lambda functions with captured variables. The
50 lambda capture data is stored inside the resulting Python function object.
51
52- pybind11 uses C++11 move constructors and move assignment operators whenever
53 possible to efficiently transfer custom data types.
54
55- It's easy to expose the internal storage of custom data types through
56 Pythons' buffer protocols. This is handy e.g. for fast conversion between
57 C++ matrix classes like Eigen and NumPy without expensive copy operations.
58
59- pybind11 can automatically vectorize functions so that they are transparently
60 applied to all entries of one or more NumPy array arguments.
61
62- Python's slice-based access and assignment operations can be supported with
63 just a few lines of code.
64
Wenzel Jakob40584ce2015-12-04 23:58:23 +010065- Everything is contained in just a few header files; there is no need to link
Wenzel Jakob7641c1d2015-10-18 14:48:24 +020066 against any additional libraries.