STL containers
##############

Automatic conversion
====================

When including the additional header file :file:`pybind11/stl.h`, conversions
between ``std::vector<>``/``std::deque<>``/``std::list<>``/``std::array<>``,
``std::set<>``/``std::unordered_set<>``, and
``std::map<>``/``std::unordered_map<>`` and the Python ``list``, ``set`` and
``dict`` data structures are automatically enabled. The types ``std::pair<>``
and ``std::tuple<>`` are already supported out of the box with just the core
:file:`pybind11/pybind11.h` header.

The major downside of these implicit conversions is that containers must be
converted (i.e. copied) on every Python->C++ and C++->Python transition, which
can have implications on the program semantics and performance. Please read the
next sections for more details and alternative approaches that avoid this.

.. note::

    Arbitrary nesting of any of these types is possible.

.. seealso::

    The file :file:`tests/test_stl.cpp` contains a complete
    example that demonstrates how to pass STL data types in more detail.

.. _cpp17_container_casters:

C++17 library containers
========================

The :file:`pybind11/stl.h` header also includes support for ``std::optional<>``
and ``std::variant<>``. These require a C++17 compiler and standard library.
In C++14 mode, ``std::experimental::optional<>`` is supported if available.

Various versions of these containers also exist for C++11 (e.g. in Boost).
pybind11 provides an easy way to specialize the ``type_caster`` for such
types:

.. code-block:: cpp

    // `boost::optional` as an example -- can be any `std::optional`-like container
    namespace pybind11 { namespace detail {
        template <typename T>
        struct type_caster<boost::optional<T>> : optional_caster<boost::optional<T>> {};
    }}

The above should be placed in a header file and included in all translation units
where automatic conversion is needed. Similarly, a specialization can be provided
for custom variant types:

.. code-block:: cpp

    // `boost::variant` as an example -- can be any `std::variant`-like container
    namespace pybind11 { namespace detail {
        template <typename... Ts>
        struct type_caster<boost::variant<Ts...>> : variant_caster<boost::variant<Ts...>> {};

        // Specifies the function used to visit the variant -- `apply_visitor` instead of `visit`
        template <>
        struct visit_helper<boost::variant> {
            template <typename... Args>
            static auto call(Args &&...args) -> decltype(boost::apply_visitor(args...)) {
                return boost::apply_visitor(args...);
            }
        };
    }} // namespace pybind11::detail

The ``visit_helper`` specialization is not required if your ``name::variant`` provides
a ``name::visit()`` function. For any other function name, the specialization must be
included to tell pybind11 how to visit the variant.

.. note::

    pybind11 only supports the modern implementation of ``boost::variant``
    which makes use of variadic templates. This requires Boost 1.56 or newer.
    Additionally, on Windows, MSVC 2017 is required because ``boost::variant``
    falls back to the old non-variadic implementation on MSVC 2015.

.. _opaque:

Making opaque types
===================

pybind11 heavily relies on a template matching mechanism to convert parameters
and return values that are constructed from STL data types such as vectors,
linked lists, hash tables, etc. This even works in a recursive manner, for
instance to deal with lists of hash maps of pairs of elementary and custom
types, etc.

However, a fundamental limitation of this approach is that internal conversions
between Python and C++ types involve a copy operation that prevents
pass-by-reference semantics. What does this mean?

Suppose we bind the following function

.. code-block:: cpp

    void append_1(std::vector<int> &v) {
       v.push_back(1);
    }

and call it from Python, the following happens:

.. code-block:: pycon

   >>> v = [5, 6]
   >>> append_1(v)
   >>> print(v)
   [5, 6]

As you can see, when passing STL data structures by reference, modifications
are not propagated back the Python side. A similar situation arises when
exposing STL data structures using the ``def_readwrite`` or ``def_readonly``
functions:

.. code-block:: cpp

    /* ... definition ... */

    class MyClass {
        std::vector<int> contents;
    };

    /* ... binding code ... */

    py::class_<MyClass>(m, "MyClass")
        .def(py::init<>())
        .def_readwrite("contents", &MyClass::contents);

In this case, properties can be read and written in their entirety. However, an
``append`` operation involving such a list type has no effect:

.. code-block:: pycon

   >>> m = MyClass()
   >>> m.contents = [5, 6]
   >>> print(m.contents)
   [5, 6]
   >>> m.contents.append(7)
   >>> print(m.contents)
   [5, 6]

Finally, the involved copy operations can be costly when dealing with very
large lists. To deal with all of the above situations, pybind11 provides a
macro named ``PYBIND11_MAKE_OPAQUE(T)`` that disables the template-based
conversion machinery of types, thus rendering them *opaque*. The contents of
opaque objects are never inspected or extracted, hence they *can* be passed by
reference. For instance, to turn ``std::vector<int>`` into an opaque type, add
the declaration

.. code-block:: cpp

    PYBIND11_MAKE_OPAQUE(std::vector<int>);

before any binding code (e.g. invocations to ``class_::def()``, etc.). This
macro must be specified at the top level (and outside of any namespaces), since
it instantiates a partial template overload. If your binding code consists of
multiple compilation units, it must be present in every file (typically via a
common header) preceding any usage of ``std::vector<int>``. Opaque types must
also have a corresponding ``class_`` declaration to associate them with a name
in Python, and to define a set of available operations, e.g.:

.. code-block:: cpp

    py::class_<std::vector<int>>(m, "IntVector")
        .def(py::init<>())
        .def("clear", &std::vector<int>::clear)
        .def("pop_back", &std::vector<int>::pop_back)
        .def("__len__", [](const std::vector<int> &v) { return v.size(); })
        .def("__iter__", [](std::vector<int> &v) {
           return py::make_iterator(v.begin(), v.end());
        }, py::keep_alive<0, 1>()) /* Keep vector alive while iterator is used */
        // ....

.. seealso::

    The file :file:`tests/test_opaque_types.cpp` contains a complete
    example that demonstrates how to create and expose opaque types using
    pybind11 in more detail.

.. _stl_bind:

Binding STL containers
======================

The ability to expose STL containers as native Python objects is a fairly
common request, hence pybind11 also provides an optional header file named
:file:`pybind11/stl_bind.h` that does exactly this. The mapped containers try
to match the behavior of their native Python counterparts as much as possible.

The following example showcases usage of :file:`pybind11/stl_bind.h`:

.. code-block:: cpp

    // Don't forget this
    #include <pybind11/stl_bind.h>

    PYBIND11_MAKE_OPAQUE(std::vector<int>);
    PYBIND11_MAKE_OPAQUE(std::map<std::string, double>);

    // ...

    // later in binding code:
    py::bind_vector<std::vector<int>>(m, "VectorInt");
    py::bind_map<std::map<std::string, double>>(m, "MapStringDouble");

When binding STL containers pybind11 considers the types of the container's
elements to decide whether the container should be confined to the local module
(via the :ref:`module_local` feature).  If the container element types are
anything other than already-bound custom types bound without
``py::module_local()`` the container binding will have ``py::module_local()``
applied.  This includes converting types such as numeric types, strings, Eigen
types; and types that have not yet been bound at the time of the stl container
binding.  This module-local binding is designed to avoid potential conflicts
between module bindings (for example, from two separate modules each attempting
to bind ``std::vector<int>`` as a python type).

It is possible to override this behavior to force a definition to be either
module-local or global.  To do so, you can pass the attributes
``py::module_local()`` (to make the binding module-local) or
``py::module_local(false)`` (to make the binding global) into the
``py::bind_vector`` or ``py::bind_map`` arguments:

.. code-block:: cpp

    py::bind_vector<std::vector<int>>(m, "VectorInt", py::module_local(false));

Note, however, that such a global binding would make it impossible to load this
module at the same time as any other pybind module that also attempts to bind
the same container type (``std::vector<int>`` in the above example).

See :ref:`module_local` for more details on module-local bindings.

.. seealso::

    The file :file:`tests/test_stl_binders.cpp` shows how to use the
    convenience STL container wrappers.