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<h1>About this project<a class="headerlink" href="#about-this-project" title="Permalink to this headline"></a></h1>
<p><strong>pybind11</strong> 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 <a class="reference external" href="http://www.boost.org/doc/libs/release/libs/python/doc/index.html">Boost.Python</a> library by David
Abrahams: to minimize boilerplate code in traditional extension modules by
inferring type information using compile-time introspection.</p>
<p>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&#8217;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.x, or PyPy2.7 &gt;= 5.7) 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.</p>
<div class="section" id="core-features">
<h2>Core features<a class="headerlink" href="#core-features" title="Permalink to this headline"></a></h2>
<p>The following core C++ features can be mapped to Python</p>
<ul class="simple">
<li>Functions accepting and returning custom data structures per value, reference, or pointer</li>
<li>Instance methods and static methods</li>
<li>Overloaded functions</li>
<li>Instance attributes and static attributes</li>
<li>Arbitrary exception types</li>
<li>Enumerations</li>
<li>Callbacks</li>
<li>Iterators and ranges</li>
<li>Custom operators</li>
<li>Single and multiple inheritance</li>
<li>STL data structures</li>
<li>Iterators and ranges</li>
<li>Smart pointers with reference counting like <code class="docutils literal"><span class="pre">std::shared_ptr</span></code></li>
<li>Internal references with correct reference counting</li>
<li>C++ classes with virtual (and pure virtual) methods can be extended in Python</li>
</ul>
</div>
<div class="section" id="goodies">
<h2>Goodies<a class="headerlink" href="#goodies" title="Permalink to this headline"></a></h2>
<p>In addition to the core functionality, pybind11 provides some extra goodies:</p>
<ul class="simple">
<li>Python 2.7, 3.x, and PyPy (PyPy2.7 &gt;= 5.7) are supported with an
implementation-agnostic interface.</li>
<li>It is possible to bind C++11 lambda functions with captured variables. The
lambda capture data is stored inside the resulting Python function object.</li>
<li>pybind11 uses C++11 move constructors and move assignment operators whenever
possible to efficiently transfer custom data types.</li>
<li>It&#8217;s easy to expose the internal storage of custom data types through
Pythons&#8217; buffer protocols. This is handy e.g. for fast conversion between
C++ matrix classes like Eigen and NumPy without expensive copy operations.</li>
<li>pybind11 can automatically vectorize functions so that they are transparently
applied to all entries of one or more NumPy array arguments.</li>
<li>Python&#8217;s slice-based access and assignment operations can be supported with
just a few lines of code.</li>
<li>Everything is contained in just a few header files; there is no need to link
against any additional libraries.</li>
<li>Binaries are generally smaller by a factor of at least 2 compared to
equivalent bindings generated by Boost.Python. A recent pybind11 conversion
of <a class="reference external" href="http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf">PyRosetta</a>, an enormous Boost.Python binding project, reported a binary
size reduction of <strong>5.4x</strong> and compile time reduction by <strong>5.8x</strong>.</li>
<li>When supported by the compiler, two new C++14 features (relaxed constexpr and
return value deduction) are used to precompute function signatures at compile
time, leading to smaller binaries.</li>
<li>With little extra effort, C++ types can be pickled and unpickled similar to
regular Python objects.</li>
</ul>
</div>
<div class="section" id="supported-compilers">
<h2>Supported compilers<a class="headerlink" href="#supported-compilers" title="Permalink to this headline"></a></h2>
<ol class="arabic simple">
<li>Clang/LLVM (any non-ancient version with C++11 support)</li>
<li>GCC 4.8 or newer</li>
<li>Microsoft Visual Studio 2015 or newer</li>
<li>Intel C++ compiler v15 or newer</li>
</ol>
</div>
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