pybind11/README.rst

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**pybind11 — Seamless operability between C++11 and Python**

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`Setuptools example <https://github.com/pybind/python_example>`_
• `Scikit-build example <https://github.com/pybind/scikit_build_example>`_
• `CMake example <https://github.com/pybind/cmake_example>`_

.. start


**pybind11** is a lightweight header-only library that exposes C++ types
in Python and vice versa, mainly to create Python bindings of existing
C++ code. Its goals and syntax are similar to the excellent
`Boost.Python <http://www.boost.org/doc/libs/1_58_0/libs/python/doc/>`_
library by David Abrahams: to minimize boilerplate code in traditional
extension modules by inferring type information using compile-time
introspection.

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 everything stripped away that isn’t relevant for binding
generation. Without comments, the core header files only require ~4K
lines of code and depend on Python (2.7 or 3.5+, or PyPy) 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.

Tutorial and reference documentation is provided at
`pybind11.readthedocs.io <https://pybind11.readthedocs.io/en/latest>`_.
A PDF version of the manual is available
`here <https://pybind11.readthedocs.io/_/downloads/en/latest/pdf/>`_.
And the source code is always available at
`github.com/pybind/pybind11 <https://github.com/pybind/pybind11>`_.


Core features
-------------


pybind11 can map the following core C++ features 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
- Arbitrary exception types
- Enumerations
- Callbacks
- Iterators and ranges
- Custom operators
- Single and multiple inheritance
- 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:

- Python 2.7, 3.5+, and PyPy/PyPy3 7.3 are supported with an
  implementation-agnostic interface.

- 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.

- Everything is contained in just a few header files; there is no need
  to link against any additional libraries.

- Binaries are generally smaller by a factor of at least 2 compared to
  equivalent bindings generated by Boost.Python. A recent pybind11
  conversion of PyRosetta, an enormous Boost.Python binding project,
  `reported <https://graylab.jhu.edu/Sergey/2016.RosettaCon/PyRosetta-4.pdf>`_
  a binary size reduction of **5.4x** and compile time reduction by
  **5.8x**.

- Function signatures are precomputed at compile time (using
  ``constexpr``), leading to smaller binaries.

- With little extra effort, C++ types can be pickled and unpickled
  similar to regular Python objects.

Supported compilers
-------------------

1. Clang/LLVM 3.3 or newer (for Apple Xcode’s clang, this is 5.0.0 or
   newer)
2. GCC 4.8 or newer
3. Microsoft Visual Studio 2015 Update 3 or newer
4. Intel classic C++ compiler 18 or newer (ICC 20.2 tested in CI)
5. Cygwin/GCC (previously tested on 2.5.1)
6. NVCC (CUDA 11.0 tested in CI)
7. NVIDIA PGI (20.9 tested in CI)

About
-----

This project was created by `Wenzel
Jakob <http://rgl.epfl.ch/people/wjakob>`_. Significant features and/or
improvements to the code were contributed by Jonas Adler, Lori A. Burns,
Sylvain Corlay, Eric Cousineau, Aaron Gokaslan, Ralf Grosse-Kunstleve, Trent Houliston, Axel
Huebl, @hulucc, Yannick Jadoul, Sergey Lyskov Johan Mabille, Tomasz Miąsko,
Dean Moldovan, Ben Pritchard, Jason Rhinelander, Boris Schäling, Pim
Schellart, Henry Schreiner, Ivan Smirnov, Boris Staletic, and Patrick Stewart.

We thank Google for a generous financial contribution to the continuous
integration infrastructure used by this project.


Contributing
~~~~~~~~~~~~

See the `contributing
guide <https://github.com/pybind/pybind11/blob/master/.github/CONTRIBUTING.md>`_
for information on building and contributing to pybind11.

License
~~~~~~~

pybind11 is provided under a BSD-style license that can be found in the
`LICENSE <https://github.com/pybind/pybind11/blob/master/LICENSE>`_
file. By using, distributing, or contributing to this project, you agree
to the terms and conditions of this license.

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