slsDetectorPackage/libs/pybind/docs/advanced/cast/stl.rst

STL containers

Automatic conversion

When including the additional header file pybind11/stl.h, conversions between std::vector<>/std::deque<>/std::list<>/std::array<>/std::valarray<>, 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 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.

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

C++17 library containers

The 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:

// `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:

// `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.

Warning

When converting a variant type, pybind11 follows the same rules as when determining which function overload to call (overload_resolution), and so the same caveats hold. In particular, the order in which the variant's alternatives are listed is important, since pybind11 will try conversions in this order. This means that, for example, when converting variant<int, bool>, the bool variant will never be selected, as any Python bool is already an int and is convertible to a C++ int. Changing the order of alternatives (and using variant<bool, int>, in this example) provides a solution.

Note

pybind11 only supports the modern implementation of boost::variant which makes use of variadic templates. This requires Boost 1.56 or newer.

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

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

and call it from Python, the following happens:

>>> 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:

/* ... 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:

>>> 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

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 adds a template instantiation of type_caster. 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.:

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 */
    // ....

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

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 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 pybind11/stl_bind.h:

// 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 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:

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 module_local for more details on module-local bindings.

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