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0a0758ce3a
py::class_<T>'s `def_property` and `def_property_static` can now take a `nullptr` as the getter to allow a write-only property to be established (mirroring Python's `property()` built-in when `None` is given for the getter). This also updates properties to use the new nullptr constructor internally.
504 lines
15 KiB
ReStructuredText
504 lines
15 KiB
ReStructuredText
.. _classes:
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Object-oriented code
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####################
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Creating bindings for a custom type
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===================================
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Let's now look at a more complex example where we'll create bindings for a
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custom C++ data structure named ``Pet``. Its definition is given below:
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.. code-block:: cpp
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struct Pet {
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Pet(const std::string &name) : name(name) { }
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void setName(const std::string &name_) { name = name_; }
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const std::string &getName() const { return name; }
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std::string name;
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};
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The binding code for ``Pet`` looks as follows:
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.. code-block:: cpp
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#include <pybind11/pybind11.h>
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namespace py = pybind11;
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PYBIND11_MODULE(example, m) {
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py::class_<Pet>(m, "Pet")
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.def(py::init<const std::string &>())
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.def("setName", &Pet::setName)
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.def("getName", &Pet::getName);
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}
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:class:`class_` creates bindings for a C++ *class* or *struct*-style data
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structure. :func:`init` is a convenience function that takes the types of a
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constructor's parameters as template arguments and wraps the corresponding
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constructor (see the :ref:`custom_constructors` section for details). An
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interactive Python session demonstrating this example is shown below:
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.. code-block:: pycon
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% python
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>>> import example
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>>> p = example.Pet('Molly')
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>>> print(p)
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<example.Pet object at 0x10cd98060>
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>>> p.getName()
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u'Molly'
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>>> p.setName('Charly')
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>>> p.getName()
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u'Charly'
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.. seealso::
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Static member functions can be bound in the same way using
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:func:`class_::def_static`.
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Keyword and default arguments
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=============================
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It is possible to specify keyword and default arguments using the syntax
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discussed in the previous chapter. Refer to the sections :ref:`keyword_args`
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and :ref:`default_args` for details.
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Binding lambda functions
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========================
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Note how ``print(p)`` produced a rather useless summary of our data structure in the example above:
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.. code-block:: pycon
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>>> print(p)
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<example.Pet object at 0x10cd98060>
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To address this, we could bind an utility function that returns a human-readable
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summary to the special method slot named ``__repr__``. Unfortunately, there is no
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suitable functionality in the ``Pet`` data structure, and it would be nice if
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we did not have to change it. This can easily be accomplished by binding a
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Lambda function instead:
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.. code-block:: cpp
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py::class_<Pet>(m, "Pet")
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.def(py::init<const std::string &>())
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.def("setName", &Pet::setName)
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.def("getName", &Pet::getName)
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.def("__repr__",
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[](const Pet &a) {
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return "<example.Pet named '" + a.name + "'>";
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}
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);
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Both stateless [#f1]_ and stateful lambda closures are supported by pybind11.
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With the above change, the same Python code now produces the following output:
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.. code-block:: pycon
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>>> print(p)
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<example.Pet named 'Molly'>
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.. [#f1] Stateless closures are those with an empty pair of brackets ``[]`` as the capture object.
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.. _properties:
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Instance and static fields
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==========================
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We can also directly expose the ``name`` field using the
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:func:`class_::def_readwrite` method. A similar :func:`class_::def_readonly`
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method also exists for ``const`` fields.
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.. code-block:: cpp
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py::class_<Pet>(m, "Pet")
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.def(py::init<const std::string &>())
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.def_readwrite("name", &Pet::name)
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// ... remainder ...
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This makes it possible to write
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.. code-block:: pycon
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>>> p = example.Pet('Molly')
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>>> p.name
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u'Molly'
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>>> p.name = 'Charly'
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>>> p.name
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u'Charly'
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Now suppose that ``Pet::name`` was a private internal variable
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that can only be accessed via setters and getters.
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.. code-block:: cpp
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class Pet {
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public:
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Pet(const std::string &name) : name(name) { }
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void setName(const std::string &name_) { name = name_; }
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const std::string &getName() const { return name; }
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private:
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std::string name;
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};
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In this case, the method :func:`class_::def_property`
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(:func:`class_::def_property_readonly` for read-only data) can be used to
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provide a field-like interface within Python that will transparently call
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the setter and getter functions:
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.. code-block:: cpp
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py::class_<Pet>(m, "Pet")
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.def(py::init<const std::string &>())
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.def_property("name", &Pet::getName, &Pet::setName)
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// ... remainder ...
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Write only properties can be defined by passing ``nullptr`` as the
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input for the read function.
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.. seealso::
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Similar functions :func:`class_::def_readwrite_static`,
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:func:`class_::def_readonly_static` :func:`class_::def_property_static`,
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and :func:`class_::def_property_readonly_static` are provided for binding
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static variables and properties. Please also see the section on
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:ref:`static_properties` in the advanced part of the documentation.
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Dynamic attributes
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==================
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Native Python classes can pick up new attributes dynamically:
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.. code-block:: pycon
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>>> class Pet:
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... name = 'Molly'
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...
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>>> p = Pet()
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>>> p.name = 'Charly' # overwrite existing
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>>> p.age = 2 # dynamically add a new attribute
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By default, classes exported from C++ do not support this and the only writable
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attributes are the ones explicitly defined using :func:`class_::def_readwrite`
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or :func:`class_::def_property`.
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.. code-block:: cpp
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py::class_<Pet>(m, "Pet")
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.def(py::init<>())
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.def_readwrite("name", &Pet::name);
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Trying to set any other attribute results in an error:
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.. code-block:: pycon
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>>> p = example.Pet()
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>>> p.name = 'Charly' # OK, attribute defined in C++
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>>> p.age = 2 # fail
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AttributeError: 'Pet' object has no attribute 'age'
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To enable dynamic attributes for C++ classes, the :class:`py::dynamic_attr` tag
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must be added to the :class:`py::class_` constructor:
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.. code-block:: cpp
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py::class_<Pet>(m, "Pet", py::dynamic_attr())
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.def(py::init<>())
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.def_readwrite("name", &Pet::name);
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Now everything works as expected:
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.. code-block:: pycon
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>>> p = example.Pet()
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>>> p.name = 'Charly' # OK, overwrite value in C++
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>>> p.age = 2 # OK, dynamically add a new attribute
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>>> p.__dict__ # just like a native Python class
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{'age': 2}
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Note that there is a small runtime cost for a class with dynamic attributes.
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Not only because of the addition of a ``__dict__``, but also because of more
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expensive garbage collection tracking which must be activated to resolve
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possible circular references. Native Python classes incur this same cost by
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default, so this is not anything to worry about. By default, pybind11 classes
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are more efficient than native Python classes. Enabling dynamic attributes
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just brings them on par.
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.. _inheritance:
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Inheritance and automatic upcasting
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===================================
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Suppose now that the example consists of two data structures with an
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inheritance relationship:
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.. code-block:: cpp
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struct Pet {
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Pet(const std::string &name) : name(name) { }
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std::string name;
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};
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struct Dog : Pet {
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Dog(const std::string &name) : Pet(name) { }
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std::string bark() const { return "woof!"; }
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};
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There are two different ways of indicating a hierarchical relationship to
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pybind11: the first specifies the C++ base class as an extra template
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parameter of the :class:`class_`:
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.. code-block:: cpp
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py::class_<Pet>(m, "Pet")
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.def(py::init<const std::string &>())
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.def_readwrite("name", &Pet::name);
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// Method 1: template parameter:
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py::class_<Dog, Pet /* <- specify C++ parent type */>(m, "Dog")
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.def(py::init<const std::string &>())
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.def("bark", &Dog::bark);
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Alternatively, we can also assign a name to the previously bound ``Pet``
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:class:`class_` object and reference it when binding the ``Dog`` class:
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.. code-block:: cpp
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py::class_<Pet> pet(m, "Pet");
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pet.def(py::init<const std::string &>())
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.def_readwrite("name", &Pet::name);
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// Method 2: pass parent class_ object:
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py::class_<Dog>(m, "Dog", pet /* <- specify Python parent type */)
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.def(py::init<const std::string &>())
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.def("bark", &Dog::bark);
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Functionality-wise, both approaches are equivalent. Afterwards, instances will
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expose fields and methods of both types:
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.. code-block:: pycon
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>>> p = example.Dog('Molly')
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>>> p.name
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u'Molly'
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>>> p.bark()
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u'woof!'
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The C++ classes defined above are regular non-polymorphic types with an
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inheritance relationship. This is reflected in Python:
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.. code-block:: cpp
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// Return a base pointer to a derived instance
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m.def("pet_store", []() { return std::unique_ptr<Pet>(new Dog("Molly")); });
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.. code-block:: pycon
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>>> p = example.pet_store()
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>>> type(p) # `Dog` instance behind `Pet` pointer
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Pet # no pointer upcasting for regular non-polymorphic types
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>>> p.bark()
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AttributeError: 'Pet' object has no attribute 'bark'
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The function returned a ``Dog`` instance, but because it's a non-polymorphic
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type behind a base pointer, Python only sees a ``Pet``. In C++, a type is only
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considered polymorphic if it has at least one virtual function and pybind11
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will automatically recognize this:
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.. code-block:: cpp
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struct PolymorphicPet {
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virtual ~PolymorphicPet() = default;
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};
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struct PolymorphicDog : PolymorphicPet {
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std::string bark() const { return "woof!"; }
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};
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// Same binding code
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py::class_<PolymorphicPet>(m, "PolymorphicPet");
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py::class_<PolymorphicDog, PolymorphicPet>(m, "PolymorphicDog")
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.def(py::init<>())
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.def("bark", &PolymorphicDog::bark);
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// Again, return a base pointer to a derived instance
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m.def("pet_store2", []() { return std::unique_ptr<PolymorphicPet>(new PolymorphicDog); });
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.. code-block:: pycon
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>>> p = example.pet_store2()
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>>> type(p)
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PolymorphicDog # automatically upcast
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>>> p.bark()
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u'woof!'
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Given a pointer to a polymorphic base, pybind11 performs automatic upcasting
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to the actual derived type. Note that this goes beyond the usual situation in
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C++: we don't just get access to the virtual functions of the base, we get the
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concrete derived type including functions and attributes that the base type may
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not even be aware of.
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.. seealso::
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For more information about polymorphic behavior see :ref:`overriding_virtuals`.
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Overloaded methods
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==================
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Sometimes there are several overloaded C++ methods with the same name taking
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different kinds of input arguments:
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.. code-block:: cpp
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struct Pet {
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Pet(const std::string &name, int age) : name(name), age(age) { }
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void set(int age_) { age = age_; }
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void set(const std::string &name_) { name = name_; }
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std::string name;
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int age;
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};
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Attempting to bind ``Pet::set`` will cause an error since the compiler does not
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know which method the user intended to select. We can disambiguate by casting
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them to function pointers. Binding multiple functions to the same Python name
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automatically creates a chain of function overloads that will be tried in
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sequence.
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.. code-block:: cpp
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py::class_<Pet>(m, "Pet")
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.def(py::init<const std::string &, int>())
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.def("set", (void (Pet::*)(int)) &Pet::set, "Set the pet's age")
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.def("set", (void (Pet::*)(const std::string &)) &Pet::set, "Set the pet's name");
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The overload signatures are also visible in the method's docstring:
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.. code-block:: pycon
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>>> help(example.Pet)
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class Pet(__builtin__.object)
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| Methods defined here:
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| __init__(...)
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| Signature : (Pet, str, int) -> NoneType
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| set(...)
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| 1. Signature : (Pet, int) -> NoneType
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| Set the pet's age
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| 2. Signature : (Pet, str) -> NoneType
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| Set the pet's name
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If you have a C++14 compatible compiler [#cpp14]_, you can use an alternative
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syntax to cast the overloaded function:
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.. code-block:: cpp
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py::class_<Pet>(m, "Pet")
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.def("set", py::overload_cast<int>(&Pet::set), "Set the pet's age")
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.def("set", py::overload_cast<const std::string &>(&Pet::set), "Set the pet's name");
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Here, ``py::overload_cast`` only requires the parameter types to be specified.
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The return type and class are deduced. This avoids the additional noise of
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``void (Pet::*)()`` as seen in the raw cast. If a function is overloaded based
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on constness, the ``py::const_`` tag should be used:
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.. code-block:: cpp
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struct Widget {
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int foo(int x, float y);
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int foo(int x, float y) const;
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};
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py::class_<Widget>(m, "Widget")
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.def("foo_mutable", py::overload_cast<int, float>(&Widget::foo))
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.def("foo_const", py::overload_cast<int, float>(&Widget::foo, py::const_));
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.. [#cpp14] A compiler which supports the ``-std=c++14`` flag
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or Visual Studio 2015 Update 2 and newer.
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.. note::
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To define multiple overloaded constructors, simply declare one after the
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other using the ``.def(py::init<...>())`` syntax. The existing machinery
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for specifying keyword and default arguments also works.
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Enumerations and internal types
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===============================
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Let's now suppose that the example class contains an internal enumeration type,
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e.g.:
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.. code-block:: cpp
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struct Pet {
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enum Kind {
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Dog = 0,
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Cat
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};
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Pet(const std::string &name, Kind type) : name(name), type(type) { }
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std::string name;
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Kind type;
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};
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The binding code for this example looks as follows:
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.. code-block:: cpp
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py::class_<Pet> pet(m, "Pet");
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pet.def(py::init<const std::string &, Pet::Kind>())
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.def_readwrite("name", &Pet::name)
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.def_readwrite("type", &Pet::type);
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py::enum_<Pet::Kind>(pet, "Kind")
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.value("Dog", Pet::Kind::Dog)
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.value("Cat", Pet::Kind::Cat)
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.export_values();
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To ensure that the ``Kind`` type is created within the scope of ``Pet``, the
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``pet`` :class:`class_` instance must be supplied to the :class:`enum_`.
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constructor. The :func:`enum_::export_values` function exports the enum entries
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into the parent scope, which should be skipped for newer C++11-style strongly
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typed enums.
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.. code-block:: pycon
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>>> p = Pet('Lucy', Pet.Cat)
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>>> p.type
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Kind.Cat
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>>> int(p.type)
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1L
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The entries defined by the enumeration type are exposed in the ``__members__`` property:
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.. code-block:: pycon
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>>> Pet.Kind.__members__
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{'Dog': Kind.Dog, 'Cat': Kind.Cat}
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.. note::
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When the special tag ``py::arithmetic()`` is specified to the ``enum_``
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constructor, pybind11 creates an enumeration that also supports rudimentary
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arithmetic and bit-level operations like comparisons, and, or, xor, negation,
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etc.
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.. code-block:: cpp
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py::enum_<Pet::Kind>(pet, "Kind", py::arithmetic())
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...
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By default, these are omitted to conserve space.
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