docs/advanced.rst

.. _advanced:

Advanced topics
###############

For brevity, the rest of this chapter assumes that the following two lines are
present:

.. code-block:: cpp

    #include <pybind11/pybind11.h>

    namespace py = pybind11;

Exporting constants and mutable objects
=======================================

To expose a C++ constant, use the ``attr`` function to register it in a module
as shown below. The ``int_`` class is one of many small wrapper objects defined
in ``pybind11/pytypes.h``. General objects (including integers) can also be
converted using the function ``cast``.

.. code-block:: cpp

    PYBIND11_PLUGIN(example) {
        py::module m("example", "pybind11 example plugin");
        m.attr("MY_CONSTANT") = py::int_(123);
        m.attr("MY_CONSTANT_2") = py::cast(new MyObject());
    }

Operator overloading
====================

Suppose that we're given the following ``Vector2`` class with a vector addition
and scalar multiplication operation, all implemented using overloaded operators
in C++.

.. code-block:: cpp

    class Vector2 {
    public:
        Vector2(float x, float y) : x(x), y(y) { }

        Vector2 operator+(const Vector2 &v) const { return Vector2(x + v.x, y + v.y); }
        Vector2 operator*(float value) const { return Vector2(x * value, y * value); }
        Vector2& operator+=(const Vector2 &v) { x += v.x; y += v.y; return *this; }
        Vector2& operator*=(float v) { x *= v; y *= v; return *this; }

        friend Vector2 operator*(float f, const Vector2 &v) {
            return Vector2(f * v.x, f * v.y);
        }

        std::string toString() const {
            return "[" + std::to_string(x) + ", " + std::to_string(y) + "]";
        }
    private:
        float x, y;
    };

The following snippet shows how the above operators can be conveniently exposed
to Python.

.. code-block:: cpp

    #include <pybind11/operators.h>

    PYBIND11_PLUGIN(example) {
        py::module m("example", "pybind11 example plugin");

        py::class_<Vector2>(m, "Vector2")
            .def(py::init<float, float>())
            .def(py::self + py::self)
            .def(py::self += py::self)
            .def(py::self *= float())
            .def(float() * py::self)
            .def("__repr__", &Vector2::toString);

        return m.ptr();
    }

Note that a line like

.. code-block:: cpp

            .def(py::self * float())

is really just short hand notation for

.. code-block:: cpp

    .def("__mul__", [](const Vector2 &a, float b) {
        return a * b;
    })

This can be useful for exposing additional operators that don't exist on the
C++ side, or to perform other types of customization.

.. note::

    To use the more convenient ``py::self`` notation, the additional
    header file :file:`pybind11/operators.h` must be included.

.. seealso::

    The file :file:`tests/test_operator_overloading.cpp` contains a
    complete example that demonstrates how to work with overloaded operators in
    more detail.

Callbacks and passing anonymous functions
=========================================

The C++11 standard brought lambda functions and the generic polymorphic
function wrapper ``std::function<>`` to the C++ programming language, which
enable powerful new ways of working with functions. Lambda functions come in
two flavors: stateless lambda function resemble classic function pointers that
link to an anonymous piece of code, while stateful lambda functions
additionally depend on captured variables that are stored in an anonymous
*lambda closure object*.

Here is a simple example of a C++ function that takes an arbitrary function
(stateful or stateless) with signature ``int -> int`` as an argument and runs
it with the value 10.

.. code-block:: cpp

    int func_arg(const std::function<int(int)> &f) {
        return f(10);
    }

The example below is more involved: it takes a function of signature ``int -> int``
and returns another function of the same kind. The return value is a stateful
lambda function, which stores the value ``f`` in the capture object and adds 1 to
its return value upon execution.

.. code-block:: cpp

    std::function<int(int)> func_ret(const std::function<int(int)> &f) {
        return [f](int i) {
            return f(i) + 1;
        };
    }

This example demonstrates using python named parameters in C++ callbacks which
requires using ``py::cpp_function`` as a wrapper. Usage is similar to defining
methods of classes:

.. code-block:: cpp

    py::cpp_function func_cpp() {
        return py::cpp_function([](int i) { return i+1; },
           py::arg("number"));
    }

After including the extra header file :file:`pybind11/functional.h`, it is almost
trivial to generate binding code for all of these functions.

.. code-block:: cpp

    #include <pybind11/functional.h>

    PYBIND11_PLUGIN(example) {
        py::module m("example", "pybind11 example plugin");

        m.def("func_arg", &func_arg);
        m.def("func_ret", &func_ret);
        m.def("func_cpp", &func_cpp);

        return m.ptr();
    }

The following interactive session shows how to call them from Python.

.. code-block:: pycon

    $ python
    >>> import example
    >>> def square(i):
    ...     return i * i
    ...
    >>> example.func_arg(square)
    100L
    >>> square_plus_1 = example.func_ret(square)
    >>> square_plus_1(4)
    17L
    >>> plus_1 = func_cpp()
    >>> plus_1(number=43)
    44L

.. warning::

    Keep in mind that passing a function from C++ to Python (or vice versa)
    will instantiate a piece of wrapper code that translates function
    invocations between the two languages. Naturally, this translation
    increases the computational cost of each function call somewhat. A
    problematic situation can arise when a function is copied back and forth
    between Python and C++ many times in a row, in which case the underlying
    wrappers will accumulate correspondingly. The resulting long sequence of
    C++ -> Python -> C++ -> ... roundtrips can significantly decrease
    performance.

    There is one exception: pybind11 detects case where a stateless function
    (i.e. a function pointer or a lambda function without captured variables)
    is passed as an argument to another C++ function exposed in Python. In this
    case, there is no overhead. Pybind11 will extract the underlying C++
    function pointer from the wrapped function to sidestep a potential C++ ->
    Python -> C++ roundtrip. This is demonstrated in :file:`tests/test_callbacks.cpp`.

.. note::

    This functionality is very useful when generating bindings for callbacks in
    C++ libraries (e.g. GUI libraries, asynchronous networking libraries, etc.).

    The file :file:`tests/test_callbacks.cpp` contains a complete example
    that demonstrates how to work with callbacks and anonymous functions in
    more detail.

Overriding virtual functions in Python
======================================

Suppose that a C++ class or interface has a virtual function that we'd like to
to override from within Python (we'll focus on the class ``Animal``; ``Dog`` is
given as a specific example of how one would do this with traditional C++
code).

.. code-block:: cpp

    class Animal {
    public:
        virtual ~Animal() { }
        virtual std::string go(int n_times) = 0;
    };

    class Dog : public Animal {
    public:
        std::string go(int n_times) override {
            std::string result;
            for (int i=0; i<n_times; ++i)
                result += "woof! ";
            return result;
        }
    };

Let's also suppose that we are given a plain function which calls the
function ``go()`` on an arbitrary ``Animal`` instance.

.. code-block:: cpp

    std::string call_go(Animal *animal) {
        return animal->go(3);
    }

Normally, the binding code for these classes would look as follows:

.. code-block:: cpp

    PYBIND11_PLUGIN(example) {
        py::module m("example", "pybind11 example plugin");

        py::class_<Animal> animal(m, "Animal");
        animal
            .def("go", &Animal::go);

        py::class_<Dog>(m, "Dog", animal)
            .def(py::init<>());

        m.def("call_go", &call_go);

        return m.ptr();
    }

However, these bindings are impossible to extend: ``Animal`` is not
constructible, and we clearly require some kind of "trampoline" that
redirects virtual calls back to Python.

Defining a new type of ``Animal`` from within Python is possible but requires a
helper class that is defined as follows:

.. code-block:: cpp

    class PyAnimal : public Animal {
    public:
        /* Inherit the constructors */
        using Animal::Animal;

        /* Trampoline (need one for each virtual function) */
        std::string go(int n_times) override {
            PYBIND11_OVERLOAD_PURE(
                std::string, /* Return type */
                Animal,      /* Parent class */
                go,          /* Name of function */
                n_times      /* Argument(s) */
            );
        }
    };

The macro :func:`PYBIND11_OVERLOAD_PURE` should be used for pure virtual
functions, and :func:`PYBIND11_OVERLOAD` should be used for functions which have
a default implementation.

There are also two alternate macros :func:`PYBIND11_OVERLOAD_PURE_NAME` and
:func:`PYBIND11_OVERLOAD_NAME` which take a string-valued name argument between
the *Parent class* and *Name of the function* slots. This is useful when the
C++ and Python versions of the function have different names, e.g.
``operator()`` vs ``__call__``.

The binding code also needs a few minor adaptations (highlighted):

.. code-block:: cpp
    :emphasize-lines: 4,6,7

    PYBIND11_PLUGIN(example) {
        py::module m("example", "pybind11 example plugin");

        py::class_<Animal, std::unique_ptr<Animal>, PyAnimal /* <--- trampoline*/> animal(m, "Animal");
        animal
            .def(py::init<>())
            .def("go", &Animal::go);

        py::class_<Dog>(m, "Dog", animal)
            .def(py::init<>());

        m.def("call_go", &call_go);

        return m.ptr();
    }

Importantly, pybind11 is made aware of the trampoline trampoline helper class
by specifying it as the *third* template argument to :class:`class_`. The
second argument with the unique pointer is simply the default holder type used
by pybind11. Following this, we are able to define a constructor as usual.

Note, however, that the above is sufficient for allowing python classes to
extend ``Animal``, but not ``Dog``: see ref:`virtual_and_inheritance` for the
necessary steps required to providing proper overload support for inherited
classes.

The Python session below shows how to override ``Animal::go`` and invoke it via
a virtual method call.

.. code-block:: pycon

    >>> from example import *
    >>> d = Dog()
    >>> call_go(d)
    u'woof! woof! woof! '
    >>> class Cat(Animal):
    ...     def go(self, n_times):
    ...             return "meow! " * n_times
    ...
    >>> c = Cat()
    >>> call_go(c)
    u'meow! meow! meow! '

Please take a look at the :ref:`macro_notes` before using this feature.

.. seealso::

    The file :file:`tests/test_virtual_functions.cpp` contains a complete
    example that demonstrates how to override virtual functions using pybind11
    in more detail.

.. _virtual_and_inheritance:

Combining virtual functions and inheritance
===========================================

When combining virtual methods with inheritance, you need to be sure to provide
an override for each method for which you want to allow overrides from derived
python classes.  For example, suppose we extend the above ``Animal``/``Dog``
example as follows:

.. code-block:: cpp

    class Animal {
    public:
        virtual std::string go(int n_times) = 0;
        virtual std::string name() { return "unknown"; }
    };
    class Dog : public class Animal {
    public:
        std::string go(int n_times) override {
            std::string result;
            for (int i=0; i<n_times; ++i)
                result += bark() + " ";
            return result;
        }
        virtual std::string bark() { return "woof!"; }
    };

then the trampoline class for ``Animal`` must, as described in the previous
section, override ``go()`` and ``name()``, but in order to allow python code to
inherit properly from ``Dog``, we also need a trampoline class for ``Dog`` that
overrides both the added ``bark()`` method *and* the ``go()`` and ``name()``
methods inherited from ``Animal`` (even though ``Dog`` doesn't directly
override the ``name()`` method):

.. code-block:: cpp

    class PyAnimal : public Animal {
    public:
        using Animal::Animal; // Inherit constructors
        std::string go(int n_times) override { PYBIND11_OVERLOAD_PURE(std::string, Animal, go, n_times); }
        std::string name() override { PYBIND11_OVERLOAD(std::string, Animal, name, ); }
    };
    class PyDog : public Dog {
    public:
        using Dog::Dog; // Inherit constructors
        std::string go(int n_times) override { PYBIND11_OVERLOAD_PURE(std::string, Dog, go, n_times); }
        std::string name() override { PYBIND11_OVERLOAD(std::string, Dog, name, ); }
        std::string bark() override { PYBIND11_OVERLOAD(std::string, Dog, bark, ); }
    };

A registered class derived from a pybind11-registered class with virtual
methods requires a similar trampoline class, *even if* it doesn't explicitly
declare or override any virtual methods itself:

.. code-block:: cpp

    class Husky : public Dog {};
    class PyHusky : public Husky {
        using Dog::Dog; // Inherit constructors
        std::string go(int n_times) override { PYBIND11_OVERLOAD_PURE(std::string, Husky, go, n_times); }
        std::string name() override { PYBIND11_OVERLOAD(std::string, Husky, name, ); }
        std::string bark() override { PYBIND11_OVERLOAD(std::string, Husky, bark, ); }
    };

There is, however, a technique that can be used to avoid this duplication
(which can be especially helpful for a base class with several virtual
methods).  The technique involves using template trampoline classes, as
follows:

.. code-block:: cpp

    template <class AnimalBase = Animal> class PyAnimal : public AnimalBase {
        using AnimalBase::AnimalBase; // Inherit constructors
        std::string go(int n_times) override { PYBIND11_OVERLOAD_PURE(std::string, AnimalBase, go, n_times); }
        std::string name() override { PYBIND11_OVERLOAD(std::string, AnimalBase, name, ); }
    };
    template <class DogBase = Dog> class PyDog : public PyAnimal<DogBase> {
        using PyAnimal<DogBase>::PyAnimal; // Inherit constructors
        // Override PyAnimal's pure virtual go() with a non-pure one:
        std::string go(int n_times) override { PYBIND11_OVERLOAD(std::string, DogBase, go, n_times); }
        std::string bark() override { PYBIND11_OVERLOAD(std::string, DogBase, bark, ); }
    };

This technique has the advantage of requiring just one trampoline method to be
declared per virtual method and pure virtual method override.  It does,
however, require the compiler to generate at least as many methods (and
possibly more, if both pure virtual and overridden pure virtual methods are
exposed, as above).

The classes are then registered with pybind11 using:

.. code-block:: cpp

    py::class_<Animal, std::unique_ptr<Animal>, PyAnimal<>> animal(m, "Animal");
    py::class_<Dog, std::unique_ptr<Dog>, PyDog<>> dog(m, "Dog");
    py::class_<Husky, std::unique_ptr<Husky>, PyDog<Husky>> husky(m, "Husky");
    // ... add animal, dog, husky definitions

Note that ``Husky`` did not require a dedicated trampoline template class at
all, since it neither declares any new virtual methods nor provides any pure
virtual method implementations.

With either the repeated-virtuals or templated trampoline methods in place, you
can now create a python class that inherits from ``Dog``:

.. code-block:: python

    class ShihTzu(Dog):
        def bark(self):
            return "yip!"

.. seealso::

    See the file :file:`tests/test_virtual_functions.cpp` for complete examples
    using both the duplication and templated trampoline approaches.

.. _macro_notes:

General notes regarding convenience macros
==========================================

pybind11 provides a few convenience macros such as
:func:`PYBIND11_MAKE_OPAQUE` and :func:`PYBIND11_DECLARE_HOLDER_TYPE`, and
``PYBIND11_OVERLOAD_*``. Since these are "just" macros that are evaluated
in the preprocessor (which has no concept of types), they *will* get confused
by commas in a template argument such as ``PYBIND11_OVERLOAD(MyReturnValue<T1,
T2>, myFunc)``. In this case, the preprocessor assumes that the comma indicates
the beginnning of the next parameter. Use a ``typedef`` to bind the template to
another name and use it in the macro to avoid this problem.


Global Interpreter Lock (GIL)
=============================

The classes :class:`gil_scoped_release` and :class:`gil_scoped_acquire` can be
used to acquire and release the global interpreter lock in the body of a C++
function call. In this way, long-running C++ code can be parallelized using
multiple Python threads. Taking the previous section as an example, this could
be realized as follows (important changes highlighted):

.. code-block:: cpp
    :emphasize-lines: 8,9,33,34

    class PyAnimal : public Animal {
    public:
        /* Inherit the constructors */
        using Animal::Animal;

        /* Trampoline (need one for each virtual function) */
        std::string go(int n_times) {
            /* Acquire GIL before calling Python code */
            py::gil_scoped_acquire acquire;

            PYBIND11_OVERLOAD_PURE(
                std::string, /* Return type */
                Animal,      /* Parent class */
                go,          /* Name of function */
                n_times      /* Argument(s) */
            );
        }
    };

    PYBIND11_PLUGIN(example) {
        py::module m("example", "pybind11 example plugin");

        py::class_<Animal, std::unique_ptr<Animal>, PyAnimal> animal(m, "Animal");
        animal
            .def(py::init<>())
            .def("go", &Animal::go);

        py::class_<Dog>(m, "Dog", animal)
            .def(py::init<>());

        m.def("call_go", [](Animal *animal) -> std::string {
            /* Release GIL before calling into (potentially long-running) C++ code */
            py::gil_scoped_release release;
            return call_go(animal);
        });

        return m.ptr();
    }

Passing STL data structures
===========================

When including the additional header file :file:`pybind11/stl.h`, conversions
between ``std::vector<>``, ``std::list<>``, ``std::set<>``, and ``std::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.

Alternatively it might be desirable to bind STL containers as native C++ classes,
eliminating the need of converting back and forth between C++ representation
and Python one. The downside of this approach in this case users will have to
deal with C++ containers directly instead of using already familiar Python lists
or dicts.

Pybind11 provide set of binder functions to bind various STL containers like vectors,
maps etc. All binder functions are designed to return instances of pybind11::class_
objects so developers can bind extra functions if needed. For complete set of
available functions please see :file:`pybind11/stl_bind.h`. For an example on using
this feature, please see :file:`tests/test_stl_binders.cpp`.

.. note::

    Arbitrary nesting of any of these types is supported.

.. seealso::

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

Binding sequence data types, iterators, the slicing protocol, etc.
==================================================================

Please refer to the supplemental example for details.

.. seealso::

    The file :file:`tests/test_sequences_and_iterators.cpp` contains a
    complete example that shows how to bind a sequence data type, including
    length queries (``__len__``), iterators (``__iter__``), the slicing
    protocol and other kinds of useful operations.

Return value policies
=====================

Python and C++ use wildly different ways of managing the memory and lifetime of
objects managed by them. This can lead to issues when creating bindings for
functions that return a non-trivial type. Just by looking at the type
information, it is not clear whether Python should take charge of the returned
value and eventually free its resources, or if this is handled on the C++ side.
For this reason, pybind11 provides a several `return value policy` annotations
that can be passed to the :func:`module::def` and :func:`class_::def`
functions. The default policy is :enum:`return_value_policy::automatic`.

Return value policies can also be applied to properties, in which case the
arguments must be passed through the :class:`cpp_function` constructor:

.. code-block:: cpp

    class_<MyClass>(m, "MyClass")
        def_property("data"
            py::cpp_function(&MyClass::getData, py::return_value_policy::copy),
            py::cpp_function(&MyClass::setData)
        );

The following table provides an overview of the available return value policies:

.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|

+--------------------------------------------------+----------------------------------------------------------------------------+
| Return value policy                              | Description                                                                |
+==================================================+============================================================================+
| :enum:`return_value_policy::automatic`           | This is the default return value policy, which falls back to the policy    |
|                                                  | :enum:`return_value_policy::take_ownership` when the return value is a     |
|                                                  | pointer. Otherwise, it uses :enum:`return_value::move` or                  |
|                                                  | :enum:`return_value::copy` for rvalue and lvalue references, respectively. |
|                                                  | See below for a description of what all of these different policies do.    |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::automatic_reference` | As above, but use policy :enum:`return_value_policy::reference` when the   |
|                                                  | return value is a pointer. This is the default conversion policy for       |
|                                                  | function arguments when calling Python functions manually from C++ code    |
|                                                  | (i.e. via handle::operator()). You probably won't need to use this.        |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::take_ownership`      | Reference an existing object (i.e. do not create a new copy) and take      |
|                                                  | ownership. Python will call the destructor and delete operator when the    |
|                                                  | object's reference count reaches zero. Undefined behavior ensues when the  |
|                                                  | C++ side does the same.                                                    |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::copy`                | Create a new copy of the returned object, which will be owned by Python.   |
|                                                  | This policy is comparably safe because the lifetimes of the two instances  |
|                                                  | are decoupled.                                                             |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::move`                | Use ``std::move`` to move the return value contents into a new instance    |
|                                                  | that will be owned by Python. This policy is comparably safe because the   |
|                                                  | lifetimes of the two instances (move source and destination) are decoupled.|
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::reference`           | Reference an existing object, but do not take ownership. The C++ side is   |
|                                                  | responsible for managing the object's lifetime and deallocating it when    |
|                                                  | it is no longer used. Warning: undefined behavior will ensue when the C++  |
|                                                  | side deletes an object that is still referenced and used by Python.        |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::reference_internal`  | Indicates that the lifetime of the return value is tied to the lifetime    |
|                                                  | of a parent object, namely the implicit ``this``, or ``self`` argument of  |
|                                                  | the called method or property. Internally, this policy works just like     |
|                                                  | :enum:`return_value_policy::reference` but additionally applies a          |
|                                                  | ``keep_alive<0, 1>`` *call policy* (described in the next section) that    |
|                                                  | prevents the parent object from being garbage collected as long as the     |
|                                                  | return value is referenced by Python. This is the default policy for       |
|                                                  | property getters created via ``def_property``, ``def_readwrite``, etc.)    |
+--------------------------------------------------+----------------------------------------------------------------------------+

.. warning::

    Code with invalid return value policies might access unitialized memory or
    free data structures multiple times, which can lead to hard-to-debug
    non-determinism and segmentation faults, hence it is worth spending the
    time to understand all the different options in the table above.

One important aspect of the above policies is that they only apply to instances
which pybind11 has *not* seen before, in which case the policy clarifies
essential questions about the return value's lifetime and ownership.  When
pybind11 knows the instance already (as identified by its type and address in
memory), it will return the existing Python object wrapper rather than creating
a new copy.

.. note::

    The next section on :ref:`call_policies` discusses *call policies* that can be
    specified *in addition* to a return value policy from the list above. Call
    policies indicate reference relationships that can involve both return values
    and parameters of functions.

.. note::

   As an alternative to elaborate call policies and lifetime management logic,
   consider using smart pointers (see the section on :ref:`smart_pointers` for
   details). Smart pointers can tell whether an object is still referenced from
   C++ or Python, which generally eliminates the kinds of inconsistencies that
   can lead to crashes or undefined behavior. For functions returning smart
   pointers, it is not necessary to specify a return value policy.

.. _call_policies:

Additional call policies
========================

In addition to the above return value policies, further `call policies` can be
specified to indicate dependencies between parameters. There is currently just
one policy named ``keep_alive<Nurse, Patient>``, which indicates that the
argument with index ``Patient`` should be kept alive at least until the
argument with index ``Nurse`` is freed by the garbage collector. Argument
indices start at one, while zero refers to the return value. For methods, index
``1`` refers to the implicit ``this`` pointer, while regular arguments begin at
index ``2``. Arbitrarily many call policies can be specified. When a ``Nurse``
with value ``None`` is detected at runtime, the call policy does nothing.

This feature internally relies on the ability to create a *weak reference* to
the nurse object, which is permitted by all classes exposed via pybind11. When
the nurse object does not support weak references, an exception will be thrown.

Consider the following example: here, the binding code for a list append
operation ties the lifetime of the newly added element to the underlying
container:

.. code-block:: cpp

    py::class_<List>(m, "List")
        .def("append", &List::append, py::keep_alive<1, 2>());

.. note::

    ``keep_alive`` is analogous to the ``with_custodian_and_ward`` (if Nurse,
    Patient != 0) and ``with_custodian_and_ward_postcall`` (if Nurse/Patient ==
    0) policies from Boost.Python.

.. seealso::

    The file :file:`tests/test_keep_alive.cpp` contains a complete example
    that demonstrates using :class:`keep_alive` in more detail.

Implicit type conversions
=========================

Suppose that instances of two types ``A`` and ``B`` are used in a project, and
that an ``A`` can easily be converted into an instance of type ``B`` (examples of this
could be a fixed and an arbitrary precision number type).

.. code-block:: cpp

    py::class_<A>(m, "A")
        /// ... members ...

    py::class_<B>(m, "B")
        .def(py::init<A>())
        /// ... members ...

    m.def("func",
        [](const B &) { /* .... */ }
    );

To invoke the function ``func`` using a variable ``a`` containing an ``A``
instance, we'd have to write ``func(B(a))`` in Python. On the other hand, C++
will automatically apply an implicit type conversion, which makes it possible
to directly write ``func(a)``.

In this situation (i.e. where ``B`` has a constructor that converts from
``A``), the following statement enables similar implicit conversions on the
Python side:

.. code-block:: cpp

    py::implicitly_convertible<A, B>();

.. note::

    Implicit conversions from ``A`` to ``B`` only work when ``B`` is a custom
    data type that is exposed to Python via pybind11.

.. _static_properties:

Static properties
=================

The section on :ref:`properties` discussed the creation of instance properties
that are implemented in terms of C++ getters and setters.

Static properties can also be created in a similar way to expose getters and
setters of static class attributes. It is important to note that the implicit
``self`` argument also exists in this case and is used to pass the Python
``type`` subclass instance. This parameter will often not be needed by the C++
side, and the following example illustrates how to instantiate a lambda getter
function that ignores it:

.. code-block:: cpp

    py::class_<Foo>(m, "Foo")
        .def_property_readonly_static("foo", [](py::object /* self */) { return Foo(); });

Unique pointers
===============

Given a class ``Example`` with Python bindings, it's possible to return
instances wrapped in C++11 unique pointers, like so

.. code-block:: cpp

    std::unique_ptr<Example> create_example() { return std::unique_ptr<Example>(new Example()); }

.. code-block:: cpp

    m.def("create_example", &create_example);

In other words, there is nothing special that needs to be done. While returning
unique pointers in this way is allowed, it is *illegal* to use them as function
arguments. For instance, the following function signature cannot be processed
by pybind11.

.. code-block:: cpp

    void do_something_with_example(std::unique_ptr<Example> ex) { ... }

The above signature would imply that Python needs to give up ownership of an
object that is passed to this function, which is generally not possible (for
instance, the object might be referenced elsewhere).

.. _smart_pointers:

Smart pointers
==============

This section explains how to pass values that are wrapped in "smart" pointer
types with internal reference counting. For the simpler C++11 unique pointers,
refer to the previous section.

The binding generator for classes, :class:`class_`, takes an optional second
template type, which denotes a special *holder* type that is used to manage
references to the object. When wrapping a type named ``Type``, the default
value of this template parameter is ``std::unique_ptr<Type>``, which means that
the object is deallocated when Python's reference count goes to zero.

It is possible to switch to other types of reference counting wrappers or smart
pointers, which is useful in codebases that rely on them. For instance, the
following snippet causes ``std::shared_ptr`` to be used instead.

.. code-block:: cpp

    py::class_<Example, std::shared_ptr<Example> /* <- holder type */> obj(m, "Example");

Note that any particular class can only be associated with a single holder type.

To enable transparent conversions for functions that take shared pointers as an
argument or that return them, a macro invocation similar to the following must
be declared at the top level before any binding code:

.. code-block:: cpp

    PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>);

.. note::

    The first argument of :func:`PYBIND11_DECLARE_HOLDER_TYPE` should be a
    placeholder name that is used as a template parameter of the second
    argument. Thus, feel free to use any identifier, but use it consistently on
    both sides; also, don't use the name of a type that already exists in your
    codebase.

One potential stumbling block when using holder types is that they need to be
applied consistently. Can you guess what's broken about the following binding
code?

.. code-block:: cpp

    class Child { };

    class Parent {
    public:
       Parent() : child(std::make_shared<Child>()) { }
       Child *get_child() { return child.get(); }  /* Hint: ** DON'T DO THIS ** */
    private:
        std::shared_ptr<Child> child;
    };

    PYBIND11_PLUGIN(example) {
        py::module m("example");

        py::class_<Child, std::shared_ptr<Child>>(m, "Child");

        py::class_<Parent, std::shared_ptr<Parent>>(m, "Parent")
           .def(py::init<>())
           .def("get_child", &Parent::get_child);

        return m.ptr();
    }

The following Python code will cause undefined behavior (and likely a
segmentation fault).

.. code-block:: python

   from example import Parent
   print(Parent().get_child())

The problem is that ``Parent::get_child()`` returns a pointer to an instance of
``Child``, but the fact that this instance is already managed by
``std::shared_ptr<...>`` is lost when passing raw pointers. In this case,
pybind11 will create a second independent ``std::shared_ptr<...>`` that also
claims ownership of the pointer. In the end, the object will be freed **twice**
since these shared pointers have no way of knowing about each other.

There are two ways to resolve this issue:

1. For types that are managed by a smart pointer class, never use raw pointers
   in function arguments or return values. In other words: always consistently
   wrap pointers into their designated holder types (such as
   ``std::shared_ptr<...>``). In this case, the signature of ``get_child()``
   should be modified as follows:

.. code-block:: cpp

    std::shared_ptr<Child> get_child() { return child; }

2. Adjust the definition of ``Child`` by specifying
   ``std::enable_shared_from_this<T>`` (see cppreference_ for details) as a
   base class. This adds a small bit of information to ``Child`` that allows
   pybind11 to realize that there is already an existing
   ``std::shared_ptr<...>`` and communicate with it. In this case, the
   declaration of ``Child`` should look as follows:

.. _cppreference: http://en.cppreference.com/w/cpp/memory/enable_shared_from_this

.. code-block:: cpp

    class Child : public std::enable_shared_from_this<Child> { };


Please take a look at the :ref:`macro_notes` before using this feature.

.. seealso::

    The file :file:`tests/test_smart_ptr.cpp` contains a complete example
    that demonstrates how to work with custom reference-counting holder types
    in more detail.

.. _custom_constructors:

Custom constructors
===================

The syntax for binding constructors was previously introduced, but it only
works when a constructor with the given parameters actually exists on the C++
side. To extend this to more general cases, let's take a look at what actually
happens under the hood: the following statement

.. code-block:: cpp

    py::class_<Example>(m, "Example")
        .def(py::init<int>());

is short hand notation for

.. code-block:: cpp

    py::class_<Example>(m, "Example")
        .def("__init__",
            [](Example &instance, int arg) {
                new (&instance) Example(arg);
            }
        );

In other words, :func:`init` creates an anonymous function that invokes an
in-place constructor. Memory allocation etc. is already take care of beforehand
within pybind11.

.. _classes_with_non_public_destructors:

Classes with non-public destructors
===================================

If a class has a private or protected destructor (as might e.g. be the case in
a singleton pattern), a compile error will occur when creating bindings via
pybind11. The underlying issue is that the ``std::unique_ptr`` holder type that
is responsible for managing the lifetime of instances will reference the
destructor even if no deallocations ever take place. In order to expose classes
with private or protected destructors, it is possible to override the holder
type via the second argument to ``class_``. Pybind11 provides a helper class
``py::nodelete`` that disables any destructor invocations. In this case, it is
crucial that instances are deallocated on the C++ side to avoid memory leaks.

.. code-block:: cpp

    /* ... definition ... */

    class MyClass {
    private:
        ~MyClass() { }
    };

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

    py::class_<MyClass, std::unique_ptr<MyClass, py::nodelete>>(m, "MyClass")
        .def(py::init<>)

.. _catching_and_throwing_exceptions:

Catching and throwing exceptions
================================

When C++ code invoked from Python throws an ``std::exception``, it is
automatically converted into a Python ``Exception``. pybind11 defines multiple
special exception classes that will map to different types of Python
exceptions:

.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|

+--------------------------------------+------------------------------+
|  C++ exception type                  |  Python exception type       |
+======================================+==============================+
| :class:`std::exception`              | ``RuntimeError``             |
+--------------------------------------+------------------------------+
| :class:`std::bad_alloc`              | ``MemoryError``              |
+--------------------------------------+------------------------------+
| :class:`std::domain_error`           | ``ValueError``               |
+--------------------------------------+------------------------------+
| :class:`std::invalid_argument`       | ``ValueError``               |
+--------------------------------------+------------------------------+
| :class:`std::length_error`           | ``ValueError``               |
+--------------------------------------+------------------------------+
| :class:`std::out_of_range`           | ``ValueError``               |
+--------------------------------------+------------------------------+
| :class:`std::range_error`            | ``ValueError``               |
+--------------------------------------+------------------------------+
| :class:`pybind11::stop_iteration`    | ``StopIteration`` (used to   |
|                                      | implement custom iterators)  |
+--------------------------------------+------------------------------+
| :class:`pybind11::index_error`       | ``IndexError`` (used to      |
|                                      | indicate out of bounds       |
|                                      | accesses in ``__getitem__``, |
|                                      | ``__setitem__``, etc.)       |
+--------------------------------------+------------------------------+
| :class:`pybind11::value_error`       | ``ValueError`` (used to      |
|                                      | indicate wrong value passed  |
|                                      | in ``container.remove(...)`` |
+--------------------------------------+------------------------------+
| :class:`pybind11::key_error`         | ``KeyError`` (used to        |
|                                      | indicate out of bounds       |
|                                      | accesses in ``__getitem__``, |
|                                      | ``__setitem__`` in dict-like |
|                                      | objects, etc.)               |
+--------------------------------------+------------------------------+
| :class:`pybind11::error_already_set` | Indicates that the Python    |
|                                      | exception flag has already   |
|                                      | been initialized             |
+--------------------------------------+------------------------------+

When a Python function invoked from C++ throws an exception, it is converted
into a C++ exception of type :class:`error_already_set` whose string payload
contains a textual summary.

There is also a special exception :class:`cast_error` that is thrown by
:func:`handle::call` when the input arguments cannot be converted to Python
objects.

Registering custom exception translators
========================================

If the default exception conversion policy described
:ref:`above <catching_and_throwing_exceptions>`
is insufficient, pybind11 also provides support for registering custom
exception translators.

The function ``register_exception_translator(translator)`` takes a stateless
callable (e.g. a function pointer or a lambda function without captured
variables) with the following call signature: ``void(std::exception_ptr)``.

When a C++ exception is thrown, registered exception translators are tried
in reverse order of registration (i.e. the last registered translator gets
a first shot at handling the exception).

Inside the translator, ``std::rethrow_exception`` should be used within
a try block to re-throw the exception. A catch clause can then use
``PyErr_SetString`` to set a Python exception as demonstrated
in :file:`tests/test_exceptions.cpp`.

This example also demonstrates how to create custom exception types
with ``py::exception``.

The following example demonstrates this for a hypothetical exception class
``MyCustomException``:

.. code-block:: cpp

    py::register_exception_translator([](std::exception_ptr p) {
        try {
            if (p) std::rethrow_exception(p);
        } catch (const MyCustomException &e) {
            PyErr_SetString(PyExc_RuntimeError, e.what());
        }
    });

Multiple exceptions can be handled by a single translator. If the exception is
not caught by the current translator, the previously registered one gets a
chance.

If none of the registered exception translators is able to handle the
exception, it is handled by the default converter as described in the previous
section.

.. note::

    You must either call ``PyErr_SetString`` for every exception caught in a
    custom exception translator. Failure to do so will cause Python to crash
    with ``SystemError: error return without exception set``.

    Exceptions that you do not plan to handle should simply not be caught.

    You may also choose to explicity (re-)throw the exception to delegate it to
    the other existing exception translators.

    The ``py::exception`` wrapper for creating custom exceptions cannot (yet)
    be used as a ``py::base``.

.. _opaque:

Treating STL data structures as opaque objects
==============================================

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`` operaton 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]

To deal with both 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, since instantiates a partial template
overload. If your binding code consists of multiple compilation units, it must
be present in every file 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:

.. 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 */
        // ....

Please take a look at the :ref:`macro_notes` before using this feature.

.. 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.

.. _eigen:

Transparent conversion of dense and sparse Eigen data types
===========================================================

Eigen [#f1]_ is C++ header-based library for dense and sparse linear algebra. Due to
its popularity and widespread adoption, pybind11 provides transparent
conversion support between Eigen and Scientific Python linear algebra data types.

Specifically, when including the optional header file :file:`pybind11/eigen.h`,
pybind11 will automatically and transparently convert

1. Static and dynamic Eigen dense vectors and matrices to instances of
   ``numpy.ndarray`` (and vice versa).

2. Returned matrix expressions such as blocks (including columns or rows) and
   diagonals will be converted to ``numpy.ndarray`` of the expression
   values.

3. Returned matrix-like objects such as Eigen::DiagonalMatrix or
   Eigen::SelfAdjointView will be converted to ``numpy.ndarray`` containing the
   expressed value.

4. Eigen sparse vectors and matrices to instances of
   ``scipy.sparse.csr_matrix``/``scipy.sparse.csc_matrix`` (and vice versa).

This makes it possible to bind most kinds of functions that rely on these types.
One major caveat are functions that take Eigen matrices *by reference* and modify
them somehow, in which case the information won't be propagated to the caller.

.. code-block:: cpp

    /* The Python bindings of these functions won't replicate
       the intended effect of modifying the function arguments */
    void scale_by_2(Eigen::Vector3f &v) {
        v *= 2;
    }
    void scale_by_2(Eigen::Ref<Eigen::MatrixXd> &v) {
        v *= 2;
    }

To see why this is, refer to the section on :ref:`opaque` (although that
section specifically covers STL data types, the underlying issue is the same).
The next two sections discuss an efficient alternative for exposing the
underlying native Eigen types as opaque objects in a way that still integrates
with NumPy and SciPy.

.. [#f1] http://eigen.tuxfamily.org

.. seealso::

    The file :file:`tests/test_eigen.cpp` contains a complete example that
    shows how to pass Eigen sparse and dense data types in more detail.

Buffer protocol
===============

Python supports an extremely general and convenient approach for exchanging
data between plugin libraries. Types can expose a buffer view [#f2]_, which
provides fast direct access to the raw internal data representation. Suppose we
want to bind the following simplistic Matrix class:

.. code-block:: cpp

    class Matrix {
    public:
        Matrix(size_t rows, size_t cols) : m_rows(rows), m_cols(cols) {
            m_data = new float[rows*cols];
        }
        float *data() { return m_data; }
        size_t rows() const { return m_rows; }
        size_t cols() const { return m_cols; }
    private:
        size_t m_rows, m_cols;
        float *m_data;
    };

The following binding code exposes the ``Matrix`` contents as a buffer object,
making it possible to cast Matrices into NumPy arrays. It is even possible to
completely avoid copy operations with Python expressions like
``np.array(matrix_instance, copy = False)``.

.. code-block:: cpp

    py::class_<Matrix>(m, "Matrix")
       .def_buffer([](Matrix &m) -> py::buffer_info {
            return py::buffer_info(
                m.data(),                               /* Pointer to buffer */
                sizeof(float),                          /* Size of one scalar */
                py::format_descriptor<float>::format(), /* Python struct-style format descriptor */
                2,                                      /* Number of dimensions */
                { m.rows(), m.cols() },                 /* Buffer dimensions */
                { sizeof(float) * m.rows(),             /* Strides (in bytes) for each index */
                  sizeof(float) }
            );
        });

The snippet above binds a lambda function, which can create ``py::buffer_info``
description records on demand describing a given matrix. The contents of
``py::buffer_info`` mirror the Python buffer protocol specification.

.. code-block:: cpp

    struct buffer_info {
        void *ptr;
        size_t itemsize;
        std::string format;
        int ndim;
        std::vector<size_t> shape;
        std::vector<size_t> strides;
    };

To create a C++ function that can take a Python buffer object as an argument,
simply use the type ``py::buffer`` as one of its arguments. Buffers can exist
in a great variety of configurations, hence some safety checks are usually
necessary in the function body. Below, you can see an basic example on how to
define a custom constructor for the Eigen double precision matrix
(``Eigen::MatrixXd``) type, which supports initialization from compatible
buffer objects (e.g. a NumPy matrix).

.. code-block:: cpp

    /* Bind MatrixXd (or some other Eigen type) to Python */
    typedef Eigen::MatrixXd Matrix;

    typedef Matrix::Scalar Scalar;
    constexpr bool rowMajor = Matrix::Flags & Eigen::RowMajorBit;

    py::class_<Matrix>(m, "Matrix")
        .def("__init__", [](Matrix &m, py::buffer b) {
            typedef Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic> Strides;

            /* Request a buffer descriptor from Python */
            py::buffer_info info = b.request();

            /* Some sanity checks ... */
            if (info.format != py::format_descriptor<Scalar>::format())
                throw std::runtime_error("Incompatible format: expected a double array!");

            if (info.ndim != 2)
                throw std::runtime_error("Incompatible buffer dimension!");

            auto strides = Strides(
                info.strides[rowMajor ? 0 : 1] / sizeof(Scalar),
                info.strides[rowMajor ? 1 : 0] / sizeof(Scalar));

            auto map = Eigen::Map<Matrix, 0, Strides>(
                static_cat<Scalar *>(info.ptr), info.shape[0], info.shape[1], strides);

            new (&m) Matrix(map);
        });

For reference, the ``def_buffer()`` call for this Eigen data type should look
as follows:

.. code-block:: cpp

    .def_buffer([](Matrix &m) -> py::buffer_info {
        return py::buffer_info(
            m.data(),                /* Pointer to buffer */
            sizeof(Scalar),          /* Size of one scalar */
            /* Python struct-style format descriptor */
            py::format_descriptor<Scalar>::format(),
            /* Number of dimensions */
            2,
            /* Buffer dimensions */
            { (size_t) m.rows(),
              (size_t) m.cols() },
            /* Strides (in bytes) for each index */
            { sizeof(Scalar) * (rowMajor ? m.cols() : 1),
              sizeof(Scalar) * (rowMajor ? 1 : m.rows()) }
        );
     })

For a much easier approach of binding Eigen types (although with some
limitations), refer to the section on :ref:`eigen`.

.. seealso::

    The file :file:`tests/test_buffers.cpp` contains a complete example
    that demonstrates using the buffer protocol with pybind11 in more detail.

.. [#f2] http://docs.python.org/3/c-api/buffer.html

NumPy support
=============

By exchanging ``py::buffer`` with ``py::array`` in the above snippet, we can
restrict the function so that it only accepts NumPy arrays (rather than any
type of Python object satisfying the buffer protocol).

In many situations, we want to define a function which only accepts a NumPy
array of a certain data type. This is possible via the ``py::array_t<T>``
template. For instance, the following function requires the argument to be a
NumPy array containing double precision values.

.. code-block:: cpp

    void f(py::array_t<double> array);

When it is invoked with a different type (e.g. an integer or a list of
integers), the binding code will attempt to cast the input into a NumPy array
of the requested type. Note that this feature requires the
:file:``pybind11/numpy.h`` header to be included.

Data in NumPy arrays is not guaranteed to packed in a dense manner;
furthermore, entries can be separated by arbitrary column and row strides.
Sometimes, it can be useful to require a function to only accept dense arrays
using either the C (row-major) or Fortran (column-major) ordering. This can be
accomplished via a second template argument with values ``py::array::c_style``
or ``py::array::f_style``.

.. code-block:: cpp

    void f(py::array_t<double, py::array::c_style | py::array::forcecast> array);

The ``py::array::forcecast`` argument is the default value of the second
template paramenter, and it ensures that non-conforming arguments are converted
into an array satisfying the specified requirements instead of trying the next
function overload.

NumPy structured types
======================

In order for ``py::array_t`` to work with structured (record) types, we first need
to register the memory layout of the type. This can be done via ``PYBIND11_NUMPY_DTYPE``
macro which expects the type followed by field names:

.. code-block:: cpp

    struct A {
        int x;
        double y;
    };

    struct B {
        int z;
        A a;
    };

    PYBIND11_NUMPY_DTYPE(A, x, y);
    PYBIND11_NUMPY_DTYPE(B, z, a);

    /* now both A and B can be used as template arguments to py::array_t */

Vectorizing functions
=====================

Suppose we want to bind a function with the following signature to Python so
that it can process arbitrary NumPy array arguments (vectors, matrices, general
N-D arrays) in addition to its normal arguments:

.. code-block:: cpp

    double my_func(int x, float y, double z);

After including the ``pybind11/numpy.h`` header, this is extremely simple:

.. code-block:: cpp

    m.def("vectorized_func", py::vectorize(my_func));

Invoking the function like below causes 4 calls to be made to ``my_func`` with
each of the array elements. The significant advantage of this compared to
solutions like ``numpy.vectorize()`` is that the loop over the elements runs
entirely on the C++ side and can be crunched down into a tight, optimized loop
by the compiler. The result is returned as a NumPy array of type
``numpy.dtype.float64``.

.. code-block:: pycon

    >>> x = np.array([[1, 3],[5, 7]])
    >>> y = np.array([[2, 4],[6, 8]])
    >>> z = 3
    >>> result = vectorized_func(x, y, z)

The scalar argument ``z`` is transparently replicated 4 times.  The input
arrays ``x`` and ``y`` are automatically converted into the right types (they
are of type  ``numpy.dtype.int64`` but need to be ``numpy.dtype.int32`` and
``numpy.dtype.float32``, respectively)

Sometimes we might want to explicitly exclude an argument from the vectorization
because it makes little sense to wrap it in a NumPy array. For instance,
suppose the function signature was

.. code-block:: cpp

    double my_func(int x, float y, my_custom_type *z);

This can be done with a stateful Lambda closure:

.. code-block:: cpp

    // Vectorize a lambda function with a capture object (e.g. to exclude some arguments from the vectorization)
    m.def("vectorized_func",
        [](py::array_t<int> x, py::array_t<float> y, my_custom_type *z) {
            auto stateful_closure = [z](int x, float y) { return my_func(x, y, z); };
            return py::vectorize(stateful_closure)(x, y);
        }
    );

In cases where the computation is too complicated to be reduced to
``vectorize``, it will be necessary to create and access the buffer contents
manually. The following snippet contains a complete example that shows how this
works (the code is somewhat contrived, since it could have been done more
simply using ``vectorize``).

.. code-block:: cpp

    #include <pybind11/pybind11.h>
    #include <pybind11/numpy.h>

    namespace py = pybind11;

    py::array_t<double> add_arrays(py::array_t<double> input1, py::array_t<double> input2) {
        auto buf1 = input1.request(), buf2 = input2.request();

        if (buf1.ndim != 1 || buf2.ndim != 1)
            throw std::runtime_error("Number of dimensions must be one");

        if (buf1.size != buf2.size)
            throw std::runtime_error("Input shapes must match");

        /* No pointer is passed, so NumPy will allocate the buffer */
        auto result = py::array_t<double>(buf1.size);

        auto buf3 = result.request();

        double *ptr1 = (double *) buf1.ptr,
               *ptr2 = (double *) buf2.ptr,
               *ptr3 = (double *) buf3.ptr;

        for (size_t idx = 0; idx < buf1.shape[0]; idx++)
            ptr3[idx] = ptr1[idx] + ptr2[idx];

        return result;
    }

    PYBIND11_PLUGIN(test) {
        py::module m("test");
        m.def("add_arrays", &add_arrays, "Add two NumPy arrays");
        return m.ptr();
    }

.. seealso::

    The file :file:`tests/test_numpy_vectorize.cpp` contains a complete
    example that demonstrates using :func:`vectorize` in more detail.

Functions taking Python objects as arguments
============================================

pybind11 exposes all major Python types using thin C++ wrapper classes. These
wrapper classes can also be used as parameters of functions in bindings, which
makes it possible to directly work with native Python types on the C++ side.
For instance, the following statement iterates over a Python ``dict``:

.. code-block:: cpp

    void print_dict(py::dict dict) {
        /* Easily interact with Python types */
        for (auto item : dict)
            std::cout << "key=" << item.first << ", "
                      << "value=" << item.second << std::endl;
    }

Available types include :class:`handle`, :class:`object`, :class:`bool_`,
:class:`int_`, :class:`float_`, :class:`str`, :class:`bytes`, :class:`tuple`,
:class:`list`, :class:`dict`, :class:`slice`, :class:`none`, :class:`capsule`,
:class:`iterable`, :class:`iterator`, :class:`function`, :class:`buffer`,
:class:`array`, and :class:`array_t`.

In this kind of mixed code, it is often necessary to convert arbitrary C++
types to Python, which can be done using :func:`cast`:

.. code-block:: cpp

    MyClass *cls = ..;
    py::object obj = py::cast(cls);

The reverse direction uses the following syntax:

.. code-block:: cpp

    py::object obj = ...;
    MyClass *cls = obj.cast<MyClass *>();

When conversion fails, both directions throw the exception :class:`cast_error`.
It is also possible to call python functions via ``operator()``.

.. code-block:: cpp

    py::function f = <...>;
    py::object result_py = f(1234, "hello", some_instance);
    MyClass &result = result_py.cast<MyClass>();

The special ``f(*args)`` and ``f(*args, **kwargs)`` syntax is also supported to
supply arbitrary argument and keyword lists, although these cannot be mixed
with other parameters.

.. code-block:: cpp

    py::function f = <...>;
    py::tuple args = py::make_tuple(1234);
    py::dict kwargs;
    kwargs["y"] = py::cast(5678);
    py::object result = f(*args, **kwargs);

.. seealso::

    The file :file:`tests/test_python_types.cpp` contains a complete
    example that demonstrates passing native Python types in more detail. The
    file :file:`tests/test_kwargs_and_defaults.cpp` discusses usage
    of ``args`` and ``kwargs``.

Default arguments revisited
===========================

The section on :ref:`default_args` previously discussed basic usage of default
arguments using pybind11. One noteworthy aspect of their implementation is that
default arguments are converted to Python objects right at declaration time.
Consider the following example:

.. code-block:: cpp

    py::class_<MyClass>("MyClass")
        .def("myFunction", py::arg("arg") = SomeType(123));

In this case, pybind11 must already be set up to deal with values of the type
``SomeType`` (via a prior instantiation of ``py::class_<SomeType>``), or an
exception will be thrown.

Another aspect worth highlighting is that the "preview" of the default argument
in the function signature is generated using the object's ``__repr__`` method.
If not available, the signature may not be very helpful, e.g.:

.. code-block:: pycon

    FUNCTIONS
    ...
    |  myFunction(...)
    |      Signature : (MyClass, arg : SomeType = <SomeType object at 0x101b7b080>) -> NoneType
    ...

The first way of addressing this is by defining ``SomeType.__repr__``.
Alternatively, it is possible to specify the human-readable preview of the
default argument manually using the ``arg_t`` notation:

.. code-block:: cpp

    py::class_<MyClass>("MyClass")
        .def("myFunction", py::arg_t<SomeType>("arg", SomeType(123), "SomeType(123)"));

Sometimes it may be necessary to pass a null pointer value as a default
argument. In this case, remember to cast it to the underlying type in question,
like so:

.. code-block:: cpp

    py::class_<MyClass>("MyClass")
        .def("myFunction", py::arg("arg") = (SomeType *) nullptr);

Binding functions that accept arbitrary numbers of arguments and keywords arguments
===================================================================================

Python provides a useful mechanism to define functions that accept arbitrary
numbers of arguments and keyword arguments:

.. code-block:: cpp

   def generic(*args, **kwargs):
       # .. do something with args and kwargs

Such functions can also be created using pybind11:

.. code-block:: cpp

   void generic(py::args args, py::kwargs kwargs) {
       /// .. do something with args
       if (kwargs)
           /// .. do something with kwargs
   }

   /// Binding code
   m.def("generic", &generic);

(See ``tests/test_kwargs_and_defaults.cpp``). The class ``py::args``
derives from ``py::list`` and ``py::kwargs`` derives from ``py::dict`` Note
that the ``kwargs`` argument is invalid if no keyword arguments were actually
provided.  Please refer to the other examples for details on how to iterate
over these, and on how to cast their entries into C++ objects.

.. warning::

   Unlike Python, pybind11 does not allow combining normal parameters with the
   ``args`` / ``kwargs`` special parameters.

Partitioning code over multiple extension modules
=================================================

It's straightforward to split binding code over multiple extension modules,
while referencing types that are declared elsewhere. Everything "just" works
without any special precautions. One exception to this rule occurs when
extending a type declared in another extension module. Recall the basic example
from Section :ref:`inheritance`.

.. code-block:: cpp

    py::class_<Pet> pet(m, "Pet");
    pet.def(py::init<const std::string &>())
       .def_readwrite("name", &Pet::name);

    py::class_<Dog>(m, "Dog", pet /* <- specify parent */)
        .def(py::init<const std::string &>())
        .def("bark", &Dog::bark);

Suppose now that ``Pet`` bindings are defined in a module named ``basic``,
whereas the ``Dog`` bindings are defined somewhere else. The challenge is of
course that the variable ``pet`` is not available anymore though it is needed
to indicate the inheritance relationship to the constructor of ``class_<Dog>``.
However, it can be acquired as follows:

.. code-block:: cpp

    py::object pet = (py::object) py::module::import("basic").attr("Pet");

    py::class_<Dog>(m, "Dog", pet)
        .def(py::init<const std::string &>())
        .def("bark", &Dog::bark);

Alternatively, we can rely on the ``base`` tag, which performs an automated
lookup of the corresponding Python type. However, this also requires invoking
the ``import`` function once to ensure that the pybind11 binding code of the
module ``basic`` has been executed.

.. code-block:: cpp

    py::module::import("basic");

    py::class_<Dog>(m, "Dog", py::base<Pet>())
        .def(py::init<const std::string &>())
        .def("bark", &Dog::bark);

Naturally, both methods will fail when there are cyclic dependencies.

Note that compiling code which has its default symbol visibility set to
*hidden* (e.g. via the command line flag ``-fvisibility=hidden`` on GCC/Clang) can interfere with the
ability to access types defined in another extension module. Workarounds
include changing the global symbol visibility (not recommended, because it will
lead unnecessarily large binaries) or manually exporting types that are
accessed by multiple extension modules:

.. code-block:: cpp

    #ifdef _WIN32
    #  define EXPORT_TYPE __declspec(dllexport)
    #else
    #  define EXPORT_TYPE __attribute__ ((visibility("default")))
    #endif

    class EXPORT_TYPE Dog : public Animal {
        ...
    };


Pickling support
================

Python's ``pickle`` module provides a powerful facility to serialize and
de-serialize a Python object graph into a binary data stream. To pickle and
unpickle C++ classes using pybind11, two additional functions must be provided.
Suppose the class in question has the following signature:

.. code-block:: cpp

    class Pickleable {
    public:
        Pickleable(const std::string &value) : m_value(value) { }
        const std::string &value() const { return m_value; }

        void setExtra(int extra) { m_extra = extra; }
        int extra() const { return m_extra; }
    private:
        std::string m_value;
        int m_extra = 0;
    };

The binding code including the requisite ``__setstate__`` and ``__getstate__`` methods [#f3]_
looks as follows:

.. code-block:: cpp

    py::class_<Pickleable>(m, "Pickleable")
        .def(py::init<std::string>())
        .def("value", &Pickleable::value)
        .def("extra", &Pickleable::extra)
        .def("setExtra", &Pickleable::setExtra)
        .def("__getstate__", [](const Pickleable &p) {
            /* Return a tuple that fully encodes the state of the object */
            return py::make_tuple(p.value(), p.extra());
        })
        .def("__setstate__", [](Pickleable &p, py::tuple t) {
            if (t.size() != 2)
                throw std::runtime_error("Invalid state!");

            /* Invoke the in-place constructor. Note that this is needed even
               when the object just has a trivial default constructor */
            new (&p) Pickleable(t[0].cast<std::string>());

            /* Assign any additional state */
            p.setExtra(t[1].cast<int>());
        });

An instance can now be pickled as follows:

.. code-block:: python

    try:
        import cPickle as pickle  # Use cPickle on Python 2.7
    except ImportError:
        import pickle

    p = Pickleable("test_value")
    p.setExtra(15)
    data = pickle.dumps(p, 2)

Note that only the cPickle module is supported on Python 2.7. The second
argument to ``dumps`` is also crucial: it selects the pickle protocol version
2, since the older version 1 is not supported. Newer versions are also fine—for
instance, specify ``-1`` to always use the latest available version. Beware:
failure to follow these instructions will cause important pybind11 memory
allocation routines to be skipped during unpickling, which will likely lead to
memory corruption and/or segmentation faults.

.. seealso::

    The file :file:`tests/test_pickling.cpp` contains a complete example
    that demonstrates how to pickle and unpickle types using pybind11 in more
    detail.

.. [#f3] http://docs.python.org/3/library/pickle.html#pickling-class-instances

Generating documentation using Sphinx
=====================================

Sphinx [#f4]_ has the ability to inspect the signatures and documentation
strings in pybind11-based extension modules to automatically generate beautiful
documentation in a variety formats. The python_example repository [#f5]_ contains a
simple example repository which uses this approach.

There are two potential gotchas when using this approach: first, make sure that
the resulting strings do not contain any :kbd:`TAB` characters, which break the
docstring parsing routines. You may want to use C++11 raw string literals,
which are convenient for multi-line comments. Conveniently, any excess
indentation will be automatically be removed by Sphinx. However, for this to
work, it is important that all lines are indented consistently, i.e.:

.. code-block:: cpp

    // ok
    m.def("foo", &foo, R"mydelimiter(
        The foo function

        Parameters
        ----------
    )mydelimiter");

    // *not ok*
    m.def("foo", &foo, R"mydelimiter(The foo function

        Parameters
        ----------
    )mydelimiter");

.. [#f4] http://www.sphinx-doc.org
.. [#f5] http://github.com/pybind/python_example

Evaluating Python expressions from strings and files
====================================================

pybind11 provides the :func:`eval` and :func:`eval_file` functions to evaluate
Python expressions and statements. The following example illustrates how they
can be used.

Both functions accept a template parameter that describes how the argument
should be interpreted. Possible choices include ``eval_expr`` (isolated
expression), ``eval_single_statement`` (a single statement, return value is
always ``none``), and ``eval_statements`` (sequence of statements, return value
is always ``none``).

.. code-block:: cpp

    // At beginning of file
    #include <pybind11/eval.h>

    ...

    // Evaluate in scope of main module
    py::object scope = py::module::import("__main__").attr("__dict__");

    // Evaluate an isolated expression
    int result = py::eval("my_variable + 10", scope).cast<int>();

    // Evaluate a sequence of statements
    py::eval<py::eval_statements>(
        "print('Hello')\n"
        "print('world!');",
        scope);

    // Evaluate the statements in an separate Python file on disk
    py::eval_file("script.py", scope);