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 .. _classes:
 
 Object-oriented code
 ####################
 
 Creating bindings for a custom type
 ===================================
 
 Let's now look at a more complex example where we'll create bindings for a
 custom C++ data structure named ``Pet``. Its definition is given below:
 
 .. code-block:: cpp
 
     struct Pet {
         Pet(const std::string &name) : name(name) { }
         void setName(const std::string &name_) { name = name_; }
         const std::string &getName() const { return name; }
 
         std::string name;
     };
 
 The binding code for ``Pet`` looks as follows:
 
 .. code-block:: cpp
 
     #include <pybind11/pybind11.h>
 
     namespace py = pybind11;
 
     PYBIND11_MODULE(example, m) {
         py::class_<Pet>(m, "Pet")
             .def(py::init<const std::string &>())
             .def("setName", &Pet::setName)
             .def("getName", &Pet::getName);
     }
 
 :class:`class_` creates bindings for a C++ *class* or *struct*-style data
 structure. :func:`init` is a convenience function that takes the types of a
 constructor's parameters as template arguments and wraps the corresponding
 constructor (see the :ref:`custom_constructors` section for details). An
 interactive Python session demonstrating this example is shown below:
 
 .. code-block:: pycon
 
     % python
     >>> import example
     >>> p = example.Pet("Molly")
     >>> print(p)
     <example.Pet object at 0x10cd98060>
     >>> p.getName()
     'Molly'
     >>> p.setName("Charly")
     >>> p.getName()
     'Charly'
 
 .. seealso::
 
     Static member functions can be bound in the same way using
     :func:`class_::def_static`.
 
 Keyword and default arguments
 =============================
 It is possible to specify keyword and default arguments using the syntax
 discussed in the previous chapter. Refer to the sections :ref:`keyword_args`
 and :ref:`default_args` for details.
 
 Binding lambda functions
 ========================
 
 Note how ``print(p)`` produced a rather useless summary of our data structure in the example above:
 
 .. code-block:: pycon
 
     >>> print(p)
     <example.Pet object at 0x10cd98060>
 
 To address this, we could bind a utility function that returns a human-readable
 summary to the special method slot named ``__repr__``. Unfortunately, there is no
 suitable functionality in the ``Pet`` data structure, and it would be nice if
 we did not have to change it. This can easily be accomplished by binding a
 Lambda function instead:
 
 .. code-block:: cpp
 
         py::class_<Pet>(m, "Pet")
             .def(py::init<const std::string &>())
             .def("setName", &Pet::setName)
             .def("getName", &Pet::getName)
             .def("__repr__",
                 [](const Pet &a) {
                     return "<example.Pet named '" + a.name + "'>";
                 }
             );
 
 Both stateless [#f1]_ and stateful lambda closures are supported by pybind11.
 With the above change, the same Python code now produces the following output:
 
 .. code-block:: pycon
 
     >>> print(p)
     <example.Pet named 'Molly'>
 
 .. [#f1] Stateless closures are those with an empty pair of brackets ``[]`` as the capture object.
 
 .. _properties:
 
 Instance and static fields
 ==========================
 
 We can also directly expose the ``name`` field using the
 :func:`class_::def_readwrite` method. A similar :func:`class_::def_readonly`
 method also exists for ``const`` fields.
 
 .. code-block:: cpp
 
         py::class_<Pet>(m, "Pet")
             .def(py::init<const std::string &>())
             .def_readwrite("name", &Pet::name)
             // ... remainder ...
 
 This makes it possible to write
 
 .. code-block:: pycon
 
     >>> p = example.Pet("Molly")
     >>> p.name
     'Molly'
     >>> p.name = "Charly"
     >>> p.name
     'Charly'
 
 Now suppose that ``Pet::name`` was a private internal variable
 that can only be accessed via setters and getters.
 
 .. code-block:: cpp
 
     class Pet {
     public:
         Pet(const std::string &name) : name(name) { }
         void setName(const std::string &name_) { name = name_; }
         const std::string &getName() const { return name; }
     private:
         std::string name;
     };
 
 In this case, the method :func:`class_::def_property`
 (:func:`class_::def_property_readonly` for read-only data) can be used to
 provide a field-like interface within Python that will transparently call
 the setter and getter functions:
 
 .. code-block:: cpp
 
         py::class_<Pet>(m, "Pet")
             .def(py::init<const std::string &>())
             .def_property("name", &Pet::getName, &Pet::setName)
             // ... remainder ...
 
 Write only properties can be defined by passing ``nullptr`` as the
 input for the read function.
 
 .. seealso::
 
     Similar functions :func:`class_::def_readwrite_static`,
     :func:`class_::def_readonly_static` :func:`class_::def_property_static`,
     and :func:`class_::def_property_readonly_static` are provided for binding
     static variables and properties. Please also see the section on
     :ref:`static_properties` in the advanced part of the documentation.
 
 Dynamic attributes
 ==================
 
 Native Python classes can pick up new attributes dynamically:
 
 .. code-block:: pycon
 
     >>> class Pet:
     ...     name = "Molly"
     ...
     >>> p = Pet()
     >>> p.name = "Charly"  # overwrite existing
     >>> p.age = 2  # dynamically add a new attribute
 
 By default, classes exported from C++ do not support this and the only writable
 attributes are the ones explicitly defined using :func:`class_::def_readwrite`
 or :func:`class_::def_property`.
 
 .. code-block:: cpp
 
     py::class_<Pet>(m, "Pet")
         .def(py::init<>())
         .def_readwrite("name", &Pet::name);
 
 Trying to set any other attribute results in an error:
 
 .. code-block:: pycon
 
     >>> p = example.Pet()
     >>> p.name = "Charly"  # OK, attribute defined in C++
     >>> p.age = 2  # fail
     AttributeError: 'Pet' object has no attribute 'age'
 
 To enable dynamic attributes for C++ classes, the :class:`py::dynamic_attr` tag
 must be added to the :class:`py::class_` constructor:
 
 .. code-block:: cpp
 
     py::class_<Pet>(m, "Pet", py::dynamic_attr())
         .def(py::init<>())
         .def_readwrite("name", &Pet::name);
 
 Now everything works as expected:
 
 .. code-block:: pycon
 
     >>> p = example.Pet()
     >>> p.name = "Charly"  # OK, overwrite value in C++
     >>> p.age = 2  # OK, dynamically add a new attribute
     >>> p.__dict__  # just like a native Python class
     {'age': 2}
 
 Note that there is a small runtime cost for a class with dynamic attributes.
 Not only because of the addition of a ``__dict__``, but also because of more
 expensive garbage collection tracking which must be activated to resolve
 possible circular references. Native Python classes incur this same cost by
 default, so this is not anything to worry about. By default, pybind11 classes
 are more efficient than native Python classes. Enabling dynamic attributes
 just brings them on par.
 
 .. _inheritance:
 
 Inheritance and automatic downcasting
 =====================================
 
 Suppose now that the example consists of two data structures with an
 inheritance relationship:
 
 .. code-block:: cpp
 
     struct Pet {
         Pet(const std::string &name) : name(name) { }
         std::string name;
     };
 
     struct Dog : Pet {
         Dog(const std::string &name) : Pet(name) { }
         std::string bark() const { return "woof!"; }
     };
 
 There are two different ways of indicating a hierarchical relationship to
 pybind11: the first specifies the C++ base class as an extra template
 parameter of the :class:`class_`:
 
 .. code-block:: cpp
 
     py::class_<Pet>(m, "Pet")
        .def(py::init<const std::string &>())
        .def_readwrite("name", &Pet::name);
 
     // Method 1: template parameter:
     py::class_<Dog, Pet /* <- specify C++ parent type */>(m, "Dog")
         .def(py::init<const std::string &>())
         .def("bark", &Dog::bark);
 
 Alternatively, we can also assign a name to the previously bound ``Pet``
 :class:`class_` object and reference it when binding the ``Dog`` class:
 
 .. code-block:: cpp
 
     py::class_<Pet> pet(m, "Pet");
     pet.def(py::init<const std::string &>())
        .def_readwrite("name", &Pet::name);
 
     // Method 2: pass parent class_ object:
     py::class_<Dog>(m, "Dog", pet /* <- specify Python parent type */)
         .def(py::init<const std::string &>())
         .def("bark", &Dog::bark);
 
 Functionality-wise, both approaches are equivalent. Afterwards, instances will
 expose fields and methods of both types:
 
 .. code-block:: pycon
 
     >>> p = example.Dog("Molly")
     >>> p.name
     'Molly'
     >>> p.bark()
     'woof!'
 
 The C++ classes defined above are regular non-polymorphic types with an
 inheritance relationship. This is reflected in Python:
 
 .. code-block:: cpp
 
     // Return a base pointer to a derived instance
     m.def("pet_store", []() { return std::unique_ptr<Pet>(new Dog("Molly")); });
 
 .. code-block:: pycon
 
     >>> p = example.pet_store()
     >>> type(p)  # `Dog` instance behind `Pet` pointer
     Pet          # no pointer downcasting for regular non-polymorphic types
     >>> p.bark()
     AttributeError: 'Pet' object has no attribute 'bark'
 
 The function returned a ``Dog`` instance, but because it's a non-polymorphic
 type behind a base pointer, Python only sees a ``Pet``. In C++, a type is only
 considered polymorphic if it has at least one virtual function and pybind11
 will automatically recognize this:
 
 .. code-block:: cpp
 
     struct PolymorphicPet {
         virtual ~PolymorphicPet() = default;
     };
 
     struct PolymorphicDog : PolymorphicPet {
         std::string bark() const { return "woof!"; }
     };
 
     // Same binding code
     py::class_<PolymorphicPet>(m, "PolymorphicPet");
     py::class_<PolymorphicDog, PolymorphicPet>(m, "PolymorphicDog")
         .def(py::init<>())
         .def("bark", &PolymorphicDog::bark);
 
     // Again, return a base pointer to a derived instance
     m.def("pet_store2", []() { return std::unique_ptr<PolymorphicPet>(new PolymorphicDog); });
 
 .. code-block:: pycon
 
     >>> p = example.pet_store2()
     >>> type(p)
     PolymorphicDog  # automatically downcast
     >>> p.bark()
     'woof!'
 
 Given a pointer to a polymorphic base, pybind11 performs automatic downcasting
 to the actual derived type. Note that this goes beyond the usual situation in
 C++: we don't just get access to the virtual functions of the base, we get the
 concrete derived type including functions and attributes that the base type may
 not even be aware of.
 
 .. seealso::
 
     For more information about polymorphic behavior see :ref:`overriding_virtuals`.
 
 
 Overloaded methods
 ==================
 
 Sometimes there are several overloaded C++ methods with the same name taking
 different kinds of input arguments:
 
 .. code-block:: cpp
 
     struct Pet {
         Pet(const std::string &name, int age) : name(name), age(age) { }
 
         void set(int age_) { age = age_; }
         void set(const std::string &name_) { name = name_; }
 
         std::string name;
         int age;
     };
 
 Attempting to bind ``Pet::set`` will cause an error since the compiler does not
 know which method the user intended to select. We can disambiguate by casting
 them to function pointers. Binding multiple functions to the same Python name
 automatically creates a chain of function overloads that will be tried in
 sequence.
 
 .. code-block:: cpp
 
     py::class_<Pet>(m, "Pet")
        .def(py::init<const std::string &, int>())
        .def("set", static_cast<void (Pet::*)(int)>(&Pet::set), "Set the pet's age")
        .def("set", static_cast<void (Pet::*)(const std::string &)>(&Pet::set), "Set the pet's name");
 
 The overload signatures are also visible in the method's docstring:
 
 .. code-block:: pycon
 
     >>> help(example.Pet)
 
     class Pet(__builtin__.object)
      |  Methods defined here:
      |
      |  __init__(...)
      |      Signature : (Pet, str, int) -> NoneType
      |
      |  set(...)
      |      1. Signature : (Pet, int) -> NoneType
      |
      |      Set the pet's age
      |
      |      2. Signature : (Pet, str) -> NoneType
      |
      |      Set the pet's name
 
 If you have a C++14 compatible compiler [#cpp14]_, you can use an alternative
 syntax to cast the overloaded function:
 
 .. code-block:: cpp
 
     py::class_<Pet>(m, "Pet")
         .def("set", py::overload_cast<int>(&Pet::set), "Set the pet's age")
         .def("set", py::overload_cast<const std::string &>(&Pet::set), "Set the pet's name");
 
 Here, ``py::overload_cast`` only requires the parameter types to be specified.
 The return type and class are deduced. This avoids the additional noise of
 ``void (Pet::*)()`` as seen in the raw cast. If a function is overloaded based
 on constness, the ``py::const_`` tag should be used:
 
 .. code-block:: cpp
 
     struct Widget {
         int foo(int x, float y);
         int foo(int x, float y) const;
     };
 
     py::class_<Widget>(m, "Widget")
        .def("foo_mutable", py::overload_cast<int, float>(&Widget::foo))
        .def("foo_const",   py::overload_cast<int, float>(&Widget::foo, py::const_));
 
 If you prefer the ``py::overload_cast`` syntax but have a C++11 compatible compiler only,
 you can use ``py::detail::overload_cast_impl`` with an additional set of parentheses:
 
 .. code-block:: cpp
 
     template <typename... Args>
     using overload_cast_ = pybind11::detail::overload_cast_impl<Args...>;
 
     py::class_<Pet>(m, "Pet")
         .def("set", overload_cast_<int>()(&Pet::set), "Set the pet's age")
         .def("set", overload_cast_<const std::string &>()(&Pet::set), "Set the pet's name");
 
 .. [#cpp14] A compiler which supports the ``-std=c++14`` flag.
 
 .. note::
 
     To define multiple overloaded constructors, simply declare one after the
     other using the ``.def(py::init<...>())`` syntax. The existing machinery
     for specifying keyword and default arguments also works.
 
 Enumerations and internal types
 ===============================
 
 Let's now suppose that the example class contains internal types like enumerations, e.g.:
 
 .. code-block:: cpp
 
     struct Pet {
         enum Kind {
             Dog = 0,
             Cat
         };
 
         struct Attributes {
             float age = 0;
         };
 
         Pet(const std::string &name, Kind type) : name(name), type(type) { }
 
         std::string name;
         Kind type;
         Attributes attr;
     };
 
 The binding code for this example looks as follows:
 
 .. code-block:: cpp
 
     py::class_<Pet> pet(m, "Pet");
 
     pet.def(py::init<const std::string &, Pet::Kind>())
         .def_readwrite("name", &Pet::name)
         .def_readwrite("type", &Pet::type)
         .def_readwrite("attr", &Pet::attr);
 
     py::enum_<Pet::Kind>(pet, "Kind")
         .value("Dog", Pet::Kind::Dog)
         .value("Cat", Pet::Kind::Cat)
         .export_values();
 
     py::class_<Pet::Attributes>(pet, "Attributes")
         .def(py::init<>())
         .def_readwrite("age", &Pet::Attributes::age);
 
 
 To ensure that the nested types ``Kind`` and ``Attributes`` are created within the scope of ``Pet``, the
 ``pet`` :class:`class_` instance must be supplied to the :class:`enum_` and :class:`class_`
 constructor. The :func:`enum_::export_values` function exports the enum entries
 into the parent scope, which should be skipped for newer C++11-style strongly
 typed enums.
 
 .. code-block:: pycon
 
     >>> p = Pet("Lucy", Pet.Cat)
     >>> p.type
     Kind.Cat
     >>> int(p.type)
     1L
 
 The entries defined by the enumeration type are exposed in the ``__members__`` property:
 
 .. code-block:: pycon
 
     >>> Pet.Kind.__members__
     {'Dog': Kind.Dog, 'Cat': Kind.Cat}
 
 The ``name`` property returns the name of the enum value as a unicode string.
 
 .. note::
 
     It is also possible to use ``str(enum)``, however these accomplish different
     goals. The following shows how these two approaches differ.
 
     .. code-block:: pycon
 
         >>> p = Pet("Lucy", Pet.Cat)
         >>> pet_type = p.type
         >>> pet_type
         Pet.Cat
         >>> str(pet_type)
         'Pet.Cat'
         >>> pet_type.name
         'Cat'
 
 .. note::
 
     When the special tag ``py::arithmetic()`` is specified to the ``enum_``
     constructor, pybind11 creates an enumeration that also supports rudimentary
     arithmetic and bit-level operations like comparisons, and, or, xor, negation,
     etc.
 
     .. code-block:: cpp
 
         py::enum_<Pet::Kind>(pet, "Kind", py::arithmetic())
            ...
 
     By default, these are omitted to conserve space.

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