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156 lines
5.2 KiB
156 lines
5.2 KiB
STL containers
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##############
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Automatic conversion
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====================
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When including the additional header file :file:`pybind11/stl.h`, conversions
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between ``std::vector<>``/``std::list<>``/``std::array<>``,
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``std::set<>``/``std::unordered_set<>``, and
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``std::map<>``/``std::unordered_map<>`` and the Python ``list``, ``set`` and
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``dict`` data structures are automatically enabled. The types ``std::pair<>``
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and ``std::tuple<>`` are already supported out of the box with just the core
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:file:`pybind11/pybind11.h` header.
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The major downside of these implicit conversions is that containers must be
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converted (i.e. copied) on every Python->C++ and C++->Python transition, which
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can have implications on the program semantics and performance. Please read the
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next sections for more details and alternative approaches that avoid this.
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.. note::
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Arbitrary nesting of any of these types is possible.
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.. seealso::
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The file :file:`tests/test_python_types.cpp` contains a complete
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example that demonstrates how to pass STL data types in more detail.
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.. _opaque:
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Making opaque types
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===================
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pybind11 heavily relies on a template matching mechanism to convert parameters
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and return values that are constructed from STL data types such as vectors,
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linked lists, hash tables, etc. This even works in a recursive manner, for
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instance to deal with lists of hash maps of pairs of elementary and custom
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types, etc.
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However, a fundamental limitation of this approach is that internal conversions
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between Python and C++ types involve a copy operation that prevents
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pass-by-reference semantics. What does this mean?
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Suppose we bind the following function
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.. code-block:: cpp
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void append_1(std::vector<int> &v) {
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v.push_back(1);
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}
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and call it from Python, the following happens:
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.. code-block:: pycon
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>>> v = [5, 6]
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>>> append_1(v)
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>>> print(v)
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[5, 6]
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As you can see, when passing STL data structures by reference, modifications
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are not propagated back the Python side. A similar situation arises when
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exposing STL data structures using the ``def_readwrite`` or ``def_readonly``
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functions:
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.. code-block:: cpp
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/* ... definition ... */
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class MyClass {
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std::vector<int> contents;
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};
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/* ... binding code ... */
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py::class_<MyClass>(m, "MyClass")
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.def(py::init<>())
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.def_readwrite("contents", &MyClass::contents);
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In this case, properties can be read and written in their entirety. However, an
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``append`` operation involving such a list type has no effect:
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.. code-block:: pycon
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>>> m = MyClass()
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>>> m.contents = [5, 6]
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>>> print(m.contents)
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[5, 6]
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>>> m.contents.append(7)
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>>> print(m.contents)
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[5, 6]
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Finally, the involved copy operations can be costly when dealing with very
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large lists. To deal with all of the above situations, pybind11 provides a
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macro named ``PYBIND11_MAKE_OPAQUE(T)`` that disables the template-based
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conversion machinery of types, thus rendering them *opaque*. The contents of
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opaque objects are never inspected or extracted, hence they *can* be passed by
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reference. For instance, to turn ``std::vector<int>`` into an opaque type, add
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the declaration
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.. code-block:: cpp
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PYBIND11_MAKE_OPAQUE(std::vector<int>);
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before any binding code (e.g. invocations to ``class_::def()``, etc.). This
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macro must be specified at the top level (and outside of any namespaces), since
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it instantiates a partial template overload. If your binding code consists of
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multiple compilation units, it must be present in every file preceding any
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usage of ``std::vector<int>``. Opaque types must also have a corresponding
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``class_`` declaration to associate them with a name in Python, and to define a
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set of available operations, e.g.:
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.. code-block:: cpp
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py::class_<std::vector<int>>(m, "IntVector")
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.def(py::init<>())
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.def("clear", &std::vector<int>::clear)
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.def("pop_back", &std::vector<int>::pop_back)
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.def("__len__", [](const std::vector<int> &v) { return v.size(); })
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.def("__iter__", [](std::vector<int> &v) {
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return py::make_iterator(v.begin(), v.end());
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}, py::keep_alive<0, 1>()) /* Keep vector alive while iterator is used */
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// ....
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The ability to expose STL containers as native Python objects is a fairly
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common request, hence pybind11 also provides an optional header file named
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:file:`pybind11/stl_bind.h` that does exactly this. The mapped containers try
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to match the behavior of their native Python counterparts as much as possible.
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The following example showcases usage of :file:`pybind11/stl_bind.h`:
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.. code-block:: cpp
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// Don't forget this
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#include <pybind11/stl_bind.h>
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PYBIND11_MAKE_OPAQUE(std::vector<int>);
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PYBIND11_MAKE_OPAQUE(std::map<std::string, double>);
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// ...
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// later in binding code:
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py::bind_vector<std::vector<int>>(m, "VectorInt");
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py::bind_map<std::map<std::string, double>>(m, "MapStringDouble");
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Please take a look at the :ref:`macro_notes` before using the
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``PYBIND11_MAKE_OPAQUE`` macro.
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.. seealso::
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The file :file:`tests/test_opaque_types.cpp` contains a complete
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example that demonstrates how to create and expose opaque types using
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pybind11 in more detail.
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The file :file:`tests/test_stl_binders.cpp` shows how to use the
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convenience STL container wrappers.
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