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-.. _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;
- }, py::is_operator())
-
-This can be useful for exposing additional operators that don't exist on the
-C++ side, or to perform other types of customization. The ``py::is_operator``
-flag marker is needed to inform pybind11 that this is an operator, which
-returns ``NotImplemented`` when invoked with incompatible arguments rather than
-throwing a type error.
-
-.. 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_virtuals:
-
-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, 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 helper class by
-specifying it as an extra template argument to :class:`class_`. (This can also
-be combined with other template arguments such as a custom holder type; the
-order of template types does not matter). 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.
-
-.. note::
-
- When the overridden type returns a reference or pointer to a type that
- pybind11 converts from Python (for example, numeric values, std::string,
- and other built-in value-converting types), there are some limitations to
- be aware of:
-
- - because in these cases there is no C++ variable to reference (the value
- is stored in the referenced Python variable), pybind11 provides one in
- the PYBIND11_OVERLOAD macros (when needed) with static storage duration.
- Note that this means that invoking the overloaded method on *any*
- instance will change the referenced value stored in *all* instances of
- that type.
-
- - Attempts to modify a non-const reference will not have the desired
- effect: it will change only the static cache variable, but this change
- will not propagate to underlying Python instance, and the change will be
- replaced the next time the overload is invoked.
-
-.. 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, PyAnimal<>> animal(m, "Animal");
- py::class_<Dog, PyDog<>> dog(m, "Dog");
- py::class_<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.
-
-Extended trampoline class functionality
-=======================================
-
-The trampoline classes described in the previous sections are, by default, only
-initialized when needed. More specifically, they are initialized when a python
-class actually inherits from a registered type (instead of merely creating an
-instance of the registered type), or when a registered constructor is only
-valid for the trampoline class but not the registered class. This is primarily
-for performance reasons: when the trampoline class is not needed for anything
-except virtual method dispatching, not initializing the trampoline class
-improves performance by avoiding needing to do a run-time check to see if the
-inheriting python instance has an overloaded method.
-
-Sometimes, however, it is useful to always initialize a trampoline class as an
-intermediate class that does more than just handle virtual method dispatching.
-For example, such a class might perform extra class initialization, extra
-destruction operations, and might define new members and methods to enable a
-more python-like interface to a class.
-
-In order to tell pybind11 that it should *always* initialize the trampoline
-class when creating new instances of a type, the class constructors should be
-declared using ``py::init_alias<Args, ...>()`` instead of the usual
-``py::init<Args, ...>()``. This forces construction via the trampoline class,
-ensuring member initialization and (eventual) destruction.
-
-.. seealso::
-
- See the file :file:`tests/test_alias_initialization.cpp` for complete examples
- showing both normal and forced trampoline instantiation.
-
-.. _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 beginning 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, 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.
-
-The major downside of these implicit conversions is that containers must be
-converted (i.e. copied) on every Python->C++ and C++->Python transition, which
-can have implications on the program semantics and performance. Please read the
-next sections for more details and alternative approaches that avoid this.
-
-.. note::
-
- Arbitrary nesting of any of these types is possible.
-
-.. seealso::
-
- The file :file:`tests/test_python_types.cpp` contains a complete
- example that demonstrates how to pass STL data types in more detail.
-
-.. _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`` operation 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]
-
-Finally, the involved copy operations can be costly when dealing with very
-large lists. To deal with all of the above situations, pybind11 provides a
-macro named ``PYBIND11_MAKE_OPAQUE(T)`` that disables the template-based
-conversion machinery of types, thus rendering them *opaque*. The contents of
-opaque objects are never inspected or extracted, hence they *can* be passed by
-reference. For instance, to turn ``std::vector<int>`` into an opaque type, add
-the declaration
-
-.. 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 (and outside of any namespaces), since
-it 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, e.g.:
-
-.. 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 */
- // ....
-
-The ability to expose STL containers as native Python objects is a fairly
-common request, hence pybind11 also provides an optional header file named
-:file:`pybind11/stl_bind.h` that does exactly this. The mapped containers try
-to match the behavior of their native Python counterparts as much as possible.
-
-The following example showcases usage of :file:`pybind11/stl_bind.h`:
-
-.. code-block:: cpp
-
- // Don't forget this
- #include <pybind11/stl_bind.h>
-
- PYBIND11_MAKE_OPAQUE(std::vector<int>);
- PYBIND11_MAKE_OPAQUE(std::map<std::string, double>);
-
- // ...
-
- // later in binding code:
- py::bind_vector<std::vector<int>>(m, "VectorInt");
- py::bind_map<std::map<std::string, double>>(m, "MapStringDouble");
-
-Please take a look at the :ref:`macro_notes` before using the
-``PYBIND11_MAKE_OPAQUE`` macro.
-
-.. 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.
-
- The file :file:`tests/test_stl_binders.cpp` shows how to use the
- convenience STL container wrappers.
-
-
-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.
-
-C++11 chrono datatypes
-======================
-
-When including the additional header file :file:`pybind11/chrono.h` conversions
-from C++11 chrono datatypes to python datetime objects are automatically enabled.
-This header also enables conversions of python floats (often from sources such
-as `time.monotonic()`, `time.perf_counter()` and `time.process_time()`) into
-durations.
-
-An overview of clocks in C++11
-------------------------------
-
-A point of confusion when using these conversions is the differences between
-clocks provided in C++11. There are three clock types defined by the C++11
-standard and users can define their own if needed. Each of these clocks have
-different properties and when converting to and from python will give different
-results.
-
-The first clock defined by the standard is ``std::chrono::system_clock``. This
-clock measures the current date and time. However, this clock changes with to
-updates to the operating system time. For example, if your time is synchronised
-with a time server this clock will change. This makes this clock a poor choice
-for timing purposes but good for measuring the wall time.
-
-The second clock defined in the standard is ``std::chrono::steady_clock``.
-This clock ticks at a steady rate and is never adjusted. This makes it excellent
-for timing purposes, however the value in this clock does not correspond to the
-current date and time. Often this clock will be the amount of time your system
-has been on, although it does not have to be. This clock will never be the same
-clock as the system clock as the system clock can change but steady clocks
-cannot.
-
-The third clock defined in the standard is ``std::chrono::high_resolution_clock``.
-This clock is the clock that has the highest resolution out of the clocks in the
-system. It is normally a typedef to either the system clock or the steady clock
-but can be its own independent clock. This is important as when using these
-conversions as the types you get in python for this clock might be different
-depending on the system.
-If it is a typedef of the system clock, python will get datetime objects, but if
-it is a different clock they will be timedelta objects.
-
-Conversions Provided
---------------------
-
-C++ to Python
- - ``std::chrono::system_clock::time_point`` → ``datetime.datetime``
- System clock times are converted to python datetime instances. They are
- in the local timezone, but do not have any timezone information attached
- to them (they are naive datetime objects).
-
- - ``std::chrono::duration`` → ``datetime.timedelta``
- Durations are converted to timedeltas, any precision in the duration
- greater than microseconds is lost by rounding towards zero.
-
- - ``std::chrono::[other_clocks]::time_point`` → ``datetime.timedelta``
- Any clock time that is not the system clock is converted to a time delta. This timedelta measures the time from the clocks epoch to now.
-
-Python to C++
- - ``datetime.datetime`` → ``std::chrono::system_clock::time_point``
- Date/time objects are converted into system clock timepoints. Any
- timezone information is ignored and the type is treated as a naive
- object.
-
- - ``datetime.timedelta`` → ``std::chrono::duration``
- Time delta are converted into durations with microsecond precision.
-
- - ``datetime.timedelta`` → ``std::chrono::[other_clocks]::time_point``
- Time deltas that are converted into clock timepoints are treated as
- the amount of time from the start of the clocks epoch.
-
- - ``float`` → ``std::chrono::duration``
- Floats that are passed to C++ as durations be interpreted as a number of
- seconds. These will be converted to the duration using ``duration_cast``
- from the float.
-
- - ``float`` → ``std::chrono::[other_clocks]::time_point``
- Floats that are passed to C++ as time points will be interpreted as the
- number of seconds from the start of the clocks epoch.
-
-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_`, can be passed a template
-type that denotes a special *holder* type that is used to manage references to
-the object. If no such holder type template argument is given, the default for
-a type named ``Type`` 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
-
- PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>);
-
- 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 a holder type 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.
-
-To register a simple exception conversion that translates a C++ exception into
-a new Python exception using the C++ exception's ``what()`` method, a helper
-function is available:
-
-.. code-block:: cpp
-
- py::register_exception<CppExp>(module, "PyExp");
-
-This call creates a Python exception class with the name ``PyExp`` in the given
-module and automatically converts any encountered exceptions of type ``CppExp``
-into Python exceptions of type ``PyExp``.
-
-When more advanced exception translation is needed, the function
-``py::register_exception_translator(translator)`` can be used to register
-functions that can translate arbitrary exception types (and which may include
-additional logic to do so). The function takes a stateless callable (e.g. a
-function pointer or a lambda function without captured variables) with the call
-signature ``void(std::exception_ptr)``.
-
-When a C++ exception is thrown, the registered exception translators are tried
-in reverse order of registration (i.e. the last registered translator gets the
-first shot at handling the exception).
-
-Inside the translator, ``std::rethrow_exception`` should be used within
-a try block to re-throw the exception. One or more catch clauses to catch
-the appropriate exceptions should then be used with each clause using
-``PyErr_SetString`` to set a Python exception or ``ex(string)`` to set
-the python exception to a custom exception type (see below).
-
-To declare a custom Python exception type, declare a ``py::exception`` variable
-and use this in the associated exception translator (note: it is often useful
-to make this a static declaration when using it inside a lambda expression
-without requiring capturing).
-
-
-The following example demonstrates this for a hypothetical exception classes
-``MyCustomException`` and ``OtherException``: the first is translated to a
-custom python exception ``MyCustomError``, while the second is translated to a
-standard python RuntimeError:
-
-.. code-block:: cpp
-
- static py::exception<MyCustomException> exc(m, "MyCustomError");
- py::register_exception_translator([](std::exception_ptr p) {
- try {
- if (p) std::rethrow_exception(p);
- } catch (const MyCustomException &e) {
- exc(e.what());
- } catch (const OtherException &e) {
- PyErr_SetString(PyExc_RuntimeError, e.what());
- }
- });
-
-Multiple exceptions can be handled by a single translator, as shown in the
-example above. 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.
-
-.. seealso::
-
- The file :file:`tests/test_exceptions.cpp` contains examples
- of various custom exception translators and custom exception types.
-
-.. note::
-
- You must call either ``PyErr_SetString`` or a custom exception's call
- operator (``exc(string)``) 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, or
- may be explicity (re-)thrown to delegate it to the other,
- previously-declared existing exception translators.
-
-.. _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 parameter, 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>();
-
-Keyword arguments are also supported. In Python, there is the usual call syntax:
-
-.. code-block:: python
-
- def f(number, say, to):
- ... # function code
-
- f(1234, say="hello", to=some_instance) # keyword call in Python
-
-In C++, the same call can be made using:
-
-.. code-block:: cpp
-
- using pybind11::literals; // to bring in the `_a` literal
- f(1234, "say"_a="hello", "to"_a=some_instance); // keyword call in C++
-
-Unpacking of ``*args`` and ``**kwargs`` is also possible and can be mixed with
-other arguments:
-
-.. code-block:: cpp
-
- // * unpacking
- py::tuple args = py::make_tuple(1234, "hello", some_instance);
- f(*args);
-
- // ** unpacking
- py::dict kwargs = py::dict("number"_a=1234, "say"_a="hello", "to"_a=some_instance);
- f(**kwargs);
-
- // mixed keywords, * and ** unpacking
- py::tuple args = py::make_tuple(1234);
- py::dict kwargs = py::dict("to"_a=some_instance);
- f(*args, "say"_a="hello", **kwargs);
-
-Generalized unpacking according to PEP448_ is also supported:
-
-.. code-block:: cpp
-
- py::dict kwargs1 = py::dict("number"_a=1234);
- py::dict kwargs2 = py::dict("to"_a=some_instance);
- f(**kwargs1, "say"_a="hello", **kwargs2);
-
-.. 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_callbacks.cpp` presents a few examples of calling
- Python functions from C++, including keywords arguments and unpacking.
-
-.. _PEP448: https://www.python.org/dev/peps/pep-0448/
-
-Using Python's print function in C++
-====================================
-
-The usual way to write output in C++ is using ``std::cout`` while in Python one
-would use ``print``. Since these methods use different buffers, mixing them can
-lead to output order issues. To resolve this, pybind11 modules can use the
-:func:`py::print` function which writes to Python's ``sys.stdout`` for consistency.
-
-Python's ``print`` function is replicated in the C++ API including optional
-keyword arguments ``sep``, ``end``, ``file``, ``flush``. Everything works as
-expected in Python:
-
-.. code-block:: cpp
-
- py::print(1, 2.0, "three"); // 1 2.0 three
- py::print(1, 2.0, "three", "sep"_a="-"); // 1-2.0-three
-
- auto args = py::make_tuple("unpacked", true);
- py::print("->", *args, "end"_a="<-"); // -> unpacked True <-
-
-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, you can specify the base class as a template parameter option to
-``class_``, which performs an automated lookup of the corresponding Python
-type. Like the above code, 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, Pet>(m, "Dog")
- .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);
-
-Development of custom type casters
-==================================
-
-In very rare cases, applications may require custom type casters that cannot be
-expressed using the abstractions provided by pybind11, thus requiring raw
-Python C API calls. This is fairly advanced usage and should only be pursued by
-experts who are familiar with the intricacies of Python reference counting.
-
-The following snippets demonstrate how this works for a very simple ``inty``
-type that that should be convertible from Python types that provide a
-``__int__(self)`` method.
-
-.. code-block:: cpp
-
- struct inty { long long_value; };
-
- void print(inty s) {
- std::cout << s.long_value << std::endl;
- }
-
-The following Python snippet demonstrates the intended usage from the Python side:
-
-.. code-block:: python
-
- class A:
- def __int__(self):
- return 123
-
- from example import print
- print(A())
-
-To register the necessary conversion routines, it is necessary to add
-a partial overload to the ``pybind11::detail::type_caster<T>`` template.
-Although this is an implementation detail, adding partial overloads to this
-type is explicitly allowed.
-
-.. code-block:: cpp
-
- namespace pybind11 {
- namespace detail {
- template <> struct type_caster<inty> {
- public:
- /**
- * This macro establishes the name 'inty' in
- * function signatures and declares a local variable
- * 'value' of type inty
- */
- PYBIND11_TYPE_CASTER(inty, _("inty"));
-
- /**
- * Conversion part 1 (Python->C++): convert a PyObject into a inty
- * instance or return false upon failure. The second argument
- * indicates whether implicit conversions should be applied.
- */
- bool load(handle src, bool) {
- /* Extract PyObject from handle */
- PyObject *source = src.ptr();
- /* Try converting into a Python integer value */
- PyObject *tmp = PyNumber_Long(source);
- if (!tmp)
- return false;
- /* Now try to convert into a C++ int */
- value.long_value = PyLong_AsLong(tmp);
- Py_DECREF(tmp);
- /* Ensure return code was OK (to avoid out-of-range errors etc) */
- return !(value.long_value == -1 && !PyErr_Occurred());
- }
-
- /**
- * Conversion part 2 (C++ -> Python): convert an inty instance into
- * a Python object. The second and third arguments are used to
- * indicate the return value policy and parent object (for
- * ``return_value_policy::reference_internal``) and are generally
- * ignored by implicit casters.
- */
- static handle cast(inty src, return_value_policy /* policy */, handle /* parent */) {
- return PyLong_FromLong(src.long_value);
- }
- };
- }
- };
-
-Multiple Inheritance
-====================
-
-pybind11 can create bindings for types that derive from multiple base types
-(aka. *multiple inheritance*). To do so, specify all bases in the template
-arguments of the ``class_`` declaration:
-
-.. code-block:: cpp
-
- py::class_<MyType, BaseType1, BaseType2, BaseType3>(m, "MyType")
- ...
-
-The base types can be specified in arbitrary order, and they can even be
-interspersed with alias types and holder types (discussed earlier in this
-document)---pybind11 will automatically find out which is which. The only
-requirement is that the first template argument is the type to be declared.
-
-There are two caveats regarding the implementation of this feature:
-
-1. When only one base type is specified for a C++ type that actually has
- multiple bases, pybind11 will assume that it does not participate in
- multiple inheritance, which can lead to undefined behavior. In such cases,
- add the tag ``multiple_inheritance``:
-
- .. code-block:: cpp
-
- py::class_<MyType, BaseType2>(m, "MyType", py::multiple_inheritance());
-
- The tag is redundant and does not need to be specified when multiple base
- types are listed.
-
-2. As was previously discussed in the section on :ref:`overriding_virtuals`, it
- is easy to create Python types that derive from C++ classes. It is even
- possible to make use of multiple inheritance to declare a Python class which
- has e.g. a C++ and a Python class as bases. However, any attempt to create a
- type that has *two or more* C++ classes in its hierarchy of base types will
- fail with a fatal error message: ``TypeError: multiple bases have instance
- lay-out conflict``. Core Python types that are implemented in C (e.g.
- ``dict``, ``list``, ``Exception``, etc.) also fall under this combination
- and cannot be combined with C++ types bound using pybind11 via multiple
- inheritance.
diff --git a/docs/advanced/cast/chrono.rst b/docs/advanced/cast/chrono.rst
new file mode 100644
index 0000000..1213e2c
--- /dev/null
+++ b/docs/advanced/cast/chrono.rst
@@ -0,0 +1,81 @@
+Chrono
+======
+
+When including the additional header file :file:`pybind11/chrono.h` conversions
+from C++11 chrono datatypes to python datetime objects are automatically enabled.
+This header also enables conversions of python floats (often from sources such
+as `time.monotonic()`, `time.perf_counter()` and `time.process_time()`) into
+durations.
+
+An overview of clocks in C++11
+------------------------------
+
+A point of confusion when using these conversions is the differences between
+clocks provided in C++11. There are three clock types defined by the C++11
+standard and users can define their own if needed. Each of these clocks have
+different properties and when converting to and from python will give different
+results.
+
+The first clock defined by the standard is ``std::chrono::system_clock``. This
+clock measures the current date and time. However, this clock changes with to
+updates to the operating system time. For example, if your time is synchronised
+with a time server this clock will change. This makes this clock a poor choice
+for timing purposes but good for measuring the wall time.
+
+The second clock defined in the standard is ``std::chrono::steady_clock``.
+This clock ticks at a steady rate and is never adjusted. This makes it excellent
+for timing purposes, however the value in this clock does not correspond to the
+current date and time. Often this clock will be the amount of time your system
+has been on, although it does not have to be. This clock will never be the same
+clock as the system clock as the system clock can change but steady clocks
+cannot.
+
+The third clock defined in the standard is ``std::chrono::high_resolution_clock``.
+This clock is the clock that has the highest resolution out of the clocks in the
+system. It is normally a typedef to either the system clock or the steady clock
+but can be its own independent clock. This is important as when using these
+conversions as the types you get in python for this clock might be different
+depending on the system.
+If it is a typedef of the system clock, python will get datetime objects, but if
+it is a different clock they will be timedelta objects.
+
+Conversions Provided
+--------------------
+
+.. rubric:: C++ to Python
+
+- ``std::chrono::system_clock::time_point`` → ``datetime.datetime``
+ System clock times are converted to python datetime instances. They are
+ in the local timezone, but do not have any timezone information attached
+ to them (they are naive datetime objects).
+
+- ``std::chrono::duration`` → ``datetime.timedelta``
+ Durations are converted to timedeltas, any precision in the duration
+ greater than microseconds is lost by rounding towards zero.
+
+- ``std::chrono::[other_clocks]::time_point`` → ``datetime.timedelta``
+ Any clock time that is not the system clock is converted to a time delta.
+ This timedelta measures the time from the clocks epoch to now.
+
+.. rubric:: Python to C++
+
+- ``datetime.datetime`` → ``std::chrono::system_clock::time_point``
+ Date/time objects are converted into system clock timepoints. Any
+ timezone information is ignored and the type is treated as a naive
+ object.
+
+- ``datetime.timedelta`` → ``std::chrono::duration``
+ Time delta are converted into durations with microsecond precision.
+
+- ``datetime.timedelta`` → ``std::chrono::[other_clocks]::time_point``
+ Time deltas that are converted into clock timepoints are treated as
+ the amount of time from the start of the clocks epoch.
+
+- ``float`` → ``std::chrono::duration``
+ Floats that are passed to C++ as durations be interpreted as a number of
+ seconds. These will be converted to the duration using ``duration_cast``
+ from the float.
+
+- ``float`` → ``std::chrono::[other_clocks]::time_point``
+ Floats that are passed to C++ as time points will be interpreted as the
+ number of seconds from the start of the clocks epoch.
diff --git a/docs/advanced/cast/custom.rst b/docs/advanced/cast/custom.rst
new file mode 100644
index 0000000..50b07db
--- /dev/null
+++ b/docs/advanced/cast/custom.rst
@@ -0,0 +1,79 @@
+Custom type casters
+===================
+
+In very rare cases, applications may require custom type casters that cannot be
+expressed using the abstractions provided by pybind11, thus requiring raw
+Python C API calls. This is fairly advanced usage and should only be pursued by
+experts who are familiar with the intricacies of Python reference counting.
+
+The following snippets demonstrate how this works for a very simple ``inty``
+type that that should be convertible from Python types that provide a
+``__int__(self)`` method.
+
+.. code-block:: cpp
+
+ struct inty { long long_value; };
+
+ void print(inty s) {
+ std::cout << s.long_value << std::endl;
+ }
+
+The following Python snippet demonstrates the intended usage from the Python side:
+
+.. code-block:: python
+
+ class A:
+ def __int__(self):
+ return 123
+
+ from example import print
+ print(A())
+
+To register the necessary conversion routines, it is necessary to add
+a partial overload to the ``pybind11::detail::type_caster<T>`` template.
+Although this is an implementation detail, adding partial overloads to this
+type is explicitly allowed.
+
+.. code-block:: cpp
+
+ namespace pybind11 { namespace detail {
+ template <> struct type_caster<inty> {
+ public:
+ /**
+ * This macro establishes the name 'inty' in
+ * function signatures and declares a local variable
+ * 'value' of type inty
+ */
+ PYBIND11_TYPE_CASTER(inty, _("inty"));
+
+ /**
+ * Conversion part 1 (Python->C++): convert a PyObject into a inty
+ * instance or return false upon failure. The second argument
+ * indicates whether implicit conversions should be applied.
+ */
+ bool load(handle src, bool) {
+ /* Extract PyObject from handle */
+ PyObject *source = src.ptr();
+ /* Try converting into a Python integer value */
+ PyObject *tmp = PyNumber_Long(source);
+ if (!tmp)
+ return false;
+ /* Now try to convert into a C++ int */
+ value.long_value = PyLong_AsLong(tmp);
+ Py_DECREF(tmp);
+ /* Ensure return code was OK (to avoid out-of-range errors etc) */
+ return !(value.long_value == -1 && !PyErr_Occurred());
+ }
+
+ /**
+ * Conversion part 2 (C++ -> Python): convert an inty instance into
+ * a Python object. The second and third arguments are used to
+ * indicate the return value policy and parent object (for
+ * ``return_value_policy::reference_internal``) and are generally
+ * ignored by implicit casters.
+ */
+ static handle cast(inty src, return_value_policy /* policy */, handle /* parent */) {
+ return PyLong_FromLong(src.long_value);
+ }
+ };
+ }} // namespace pybind11::detail
diff --git a/docs/advanced/cast/eigen.rst b/docs/advanced/cast/eigen.rst
new file mode 100644
index 0000000..b83ca9a
--- /dev/null
+++ b/docs/advanced/cast/eigen.rst
@@ -0,0 +1,50 @@
+Eigen
+=====
+
+`Eigen <http://eigen.tuxfamily.org>`_ 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 :ref:`numpy` 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.
+
+.. 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.
diff --git a/docs/advanced/cast/functional.rst b/docs/advanced/cast/functional.rst
new file mode 100644
index 0000000..5d0a01d
--- /dev/null
+++ b/docs/advanced/cast/functional.rst
@@ -0,0 +1,113 @@
+Functional
+##########
+
+The following features must be enabled by including :file:`pybind11/functional.h`.
+
+
+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.
diff --git a/docs/advanced/cast/index.rst b/docs/advanced/cast/index.rst
new file mode 100644
index 0000000..cce3a47
--- /dev/null
+++ b/docs/advanced/cast/index.rst
@@ -0,0 +1,79 @@
+Casting data types
+##################
+
+.. toctree::
+ :maxdepth: 1
+
+ stl
+ functional
+ chrono
+ eigen
+ custom
+
+The following basic data types are supported out of the box (some may require
+an additional extension header to be included). To pass other data structures
+as arguments and return values, refer to the section on binding :ref:`classes`.
+
++---------------------------------+--------------------------+-------------------------------+
+| Data type | Description | Header file |
++=================================+==========================+===============================+
+| ``int8_t``, ``uint8_t`` | 8-bit integers | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``int16_t``, ``uint16_t`` | 16-bit integers | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``int32_t``, ``uint32_t`` | 32-bit integers | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``int64_t``, ``uint64_t`` | 64-bit integers | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``ssize_t``, ``size_t`` | Platform-dependent size | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``float``, ``double`` | Floating point types | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``bool`` | Two-state Boolean type | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``char`` | Character literal | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``wchar_t`` | Wide character literal | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``const char *`` | UTF-8 string literal | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``const wchar_t *`` | Wide string literal | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::string`` | STL dynamic UTF-8 string | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::wstring`` | STL dynamic wide string | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::pair<T1, T2>`` | Pair of two custom types | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::tuple<...>`` | Arbitrary tuple of types | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::reference_wrapper<...>`` | Reference type wrapper | :file:`pybind11/pybind11.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::complex<T>`` | Complex numbers | :file:`pybind11/complex.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::array<T, Size>`` | STL static array | :file:`pybind11/stl.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::vector<T>`` | STL dynamic array | :file:`pybind11/stl.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::list<T>`` | STL linked list | :file:`pybind11/stl.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::map<T1, T2>`` | STL ordered map | :file:`pybind11/stl.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::unordered_map<T1, T2>`` | STL unordered map | :file:`pybind11/stl.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::set<T>`` | STL ordered set | :file:`pybind11/stl.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::unordered_set<T>`` | STL unordered set | :file:`pybind11/stl.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::function<...>`` | STL polymorphic function | :file:`pybind11/functional.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::chrono::duration<...>`` | STL time duration | :file:`pybind11/chrono.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``std::chrono::time_point<...>``| STL date/time | :file:`pybind11/chrono.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``Eigen::Matrix<...>`` | Eigen: dense matrix | :file:`pybind11/eigen.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``Eigen::Map<...>`` | Eigen: mapped memory | :file:`pybind11/eigen.h` |
++---------------------------------+--------------------------+-------------------------------+
+| ``Eigen::SparseMatrix<...>`` | Eigen: sparse matrix | :file:`pybind11/eigen.h` |
++---------------------------------+--------------------------+-------------------------------+
diff --git a/docs/advanced/cast/stl.rst b/docs/advanced/cast/stl.rst
new file mode 100644
index 0000000..bbd2373
--- /dev/null
+++ b/docs/advanced/cast/stl.rst
@@ -0,0 +1,154 @@
+STL containers
+##############
+
+Automatic conversion
+====================
+
+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.
+
+The major downside of these implicit conversions is that containers must be
+converted (i.e. copied) on every Python->C++ and C++->Python transition, which
+can have implications on the program semantics and performance. Please read the
+next sections for more details and alternative approaches that avoid this.
+
+.. note::
+
+ Arbitrary nesting of any of these types is possible.
+
+.. seealso::
+
+ The file :file:`tests/test_python_types.cpp` contains a complete
+ example that demonstrates how to pass STL data types in more detail.
+
+.. _opaque:
+
+Making opaque types
+===================
+
+pybind11 heavily relies on a template matching mechanism to convert parameters
+and return values that are constructed from STL data types such as vectors,
+linked lists, hash tables, etc. This even works in a recursive manner, for
+instance to deal with lists of hash maps of pairs of elementary and custom
+types, etc.
+
+However, a fundamental limitation of this approach is that internal conversions
+between Python and C++ types involve a copy operation that prevents
+pass-by-reference semantics. What does this mean?
+
+Suppose we bind the following function
+
+.. 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`` operation 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]
+
+Finally, the involved copy operations can be costly when dealing with very
+large lists. To deal with all of the above situations, pybind11 provides a
+macro named ``PYBIND11_MAKE_OPAQUE(T)`` that disables the template-based
+conversion machinery of types, thus rendering them *opaque*. The contents of
+opaque objects are never inspected or extracted, hence they *can* be passed by
+reference. For instance, to turn ``std::vector<int>`` into an opaque type, add
+the declaration
+
+.. 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 (and outside of any namespaces), since
+it 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, e.g.:
+
+.. 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 */
+ // ....
+
+The ability to expose STL containers as native Python objects is a fairly
+common request, hence pybind11 also provides an optional header file named
+:file:`pybind11/stl_bind.h` that does exactly this. The mapped containers try
+to match the behavior of their native Python counterparts as much as possible.
+
+The following example showcases usage of :file:`pybind11/stl_bind.h`:
+
+.. code-block:: cpp
+
+ // Don't forget this
+ #include <pybind11/stl_bind.h>
+
+ PYBIND11_MAKE_OPAQUE(std::vector<int>);
+ PYBIND11_MAKE_OPAQUE(std::map<std::string, double>);
+
+ // ...
+
+ // later in binding code:
+ py::bind_vector<std::vector<int>>(m, "VectorInt");
+ py::bind_map<std::map<std::string, double>>(m, "MapStringDouble");
+
+Please take a look at the :ref:`macro_notes` before using the
+``PYBIND11_MAKE_OPAQUE`` macro.
+
+.. 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.
+
+ The file :file:`tests/test_stl_binders.cpp` shows how to use the
+ convenience STL container wrappers.
diff --git a/docs/advanced/classes.rst b/docs/advanced/classes.rst
new file mode 100644
index 0000000..4a423b5
--- /dev/null
+++ b/docs/advanced/classes.rst
@@ -0,0 +1,634 @@
+Classes
+#######
+
+This section presents advanced binding code for classes and it is assumed
+that you are already familiar with the basics from :doc:`/classes`.
+
+.. _overriding_virtuals:
+
+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, 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 helper class by
+specifying it as an extra template argument to :class:`class_`. (This can also
+be combined with other template arguments such as a custom holder type; the
+order of template types does not matter). 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.
+
+.. note::
+
+ When the overridden type returns a reference or pointer to a type that
+ pybind11 converts from Python (for example, numeric values, std::string,
+ and other built-in value-converting types), there are some limitations to
+ be aware of:
+
+ - because in these cases there is no C++ variable to reference (the value
+ is stored in the referenced Python variable), pybind11 provides one in
+ the PYBIND11_OVERLOAD macros (when needed) with static storage duration.
+ Note that this means that invoking the overloaded method on *any*
+ instance will change the referenced value stored in *all* instances of
+ that type.
+
+ - Attempts to modify a non-const reference will not have the desired
+ effect: it will change only the static cache variable, but this change
+ will not propagate to underlying Python instance, and the change will be
+ replaced the next time the overload is invoked.
+
+.. 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, PyAnimal<>> animal(m, "Animal");
+ py::class_<Dog, PyDog<>> dog(m, "Dog");
+ py::class_<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.
+
+Extended trampoline class functionality
+=======================================
+
+The trampoline classes described in the previous sections are, by default, only
+initialized when needed. More specifically, they are initialized when a python
+class actually inherits from a registered type (instead of merely creating an
+instance of the registered type), or when a registered constructor is only
+valid for the trampoline class but not the registered class. This is primarily
+for performance reasons: when the trampoline class is not needed for anything
+except virtual method dispatching, not initializing the trampoline class
+improves performance by avoiding needing to do a run-time check to see if the
+inheriting python instance has an overloaded method.
+
+Sometimes, however, it is useful to always initialize a trampoline class as an
+intermediate class that does more than just handle virtual method dispatching.
+For example, such a class might perform extra class initialization, extra
+destruction operations, and might define new members and methods to enable a
+more python-like interface to a class.
+
+In order to tell pybind11 that it should *always* initialize the trampoline
+class when creating new instances of a type, the class constructors should be
+declared using ``py::init_alias<Args, ...>()`` instead of the usual
+``py::init<Args, ...>()``. This forces construction via the trampoline class,
+ensuring member initialization and (eventual) destruction.
+
+.. seealso::
+
+ See the file :file:`tests/test_alias_initialization.cpp` for complete examples
+ showing both normal and forced trampoline instantiation.
+
+.. _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:
+
+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 a holder type 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<>)
+
+Implicit 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(); });
+
+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;
+ }, py::is_operator())
+
+This can be useful for exposing additional operators that don't exist on the
+C++ side, or to perform other types of customization. The ``py::is_operator``
+flag marker is needed to inform pybind11 that this is an operator, which
+returns ``NotImplemented`` when invoked with incompatible arguments rather than
+throwing a type error.
+
+.. 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.
+
+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
+
+Multiple Inheritance
+====================
+
+pybind11 can create bindings for types that derive from multiple base types
+(aka. *multiple inheritance*). To do so, specify all bases in the template
+arguments of the ``class_`` declaration:
+
+.. code-block:: cpp
+
+ py::class_<MyType, BaseType1, BaseType2, BaseType3>(m, "MyType")
+ ...
+
+The base types can be specified in arbitrary order, and they can even be
+interspersed with alias types and holder types (discussed earlier in this
+document)---pybind11 will automatically find out which is which. The only
+requirement is that the first template argument is the type to be declared.
+
+There are two caveats regarding the implementation of this feature:
+
+1. When only one base type is specified for a C++ type that actually has
+ multiple bases, pybind11 will assume that it does not participate in
+ multiple inheritance, which can lead to undefined behavior. In such cases,
+ add the tag ``multiple_inheritance``:
+
+ .. code-block:: cpp
+
+ py::class_<MyType, BaseType2>(m, "MyType", py::multiple_inheritance());
+
+ The tag is redundant and does not need to be specified when multiple base
+ types are listed.
+
+2. As was previously discussed in the section on :ref:`overriding_virtuals`, it
+ is easy to create Python types that derive from C++ classes. It is even
+ possible to make use of multiple inheritance to declare a Python class which
+ has e.g. a C++ and a Python class as bases. However, any attempt to create a
+ type that has *two or more* C++ classes in its hierarchy of base types will
+ fail with a fatal error message: ``TypeError: multiple bases have instance
+ lay-out conflict``. Core Python types that are implemented in C (e.g.
+ ``dict``, ``list``, ``Exception``, etc.) also fall under this combination
+ and cannot be combined with C++ types bound using pybind11 via multiple
+ inheritance.
diff --git a/docs/advanced/exceptions.rst b/docs/advanced/exceptions.rst
new file mode 100644
index 0000000..3483379
--- /dev/null
+++ b/docs/advanced/exceptions.rst
@@ -0,0 +1,142 @@
+Exceptions
+##########
+
+Built-in exception translation
+==============================
+
+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 translators
+==============================
+
+If the default exception conversion policy described above is insufficient,
+pybind11 also provides support for registering custom exception translators.
+To register a simple exception conversion that translates a C++ exception into
+a new Python exception using the C++ exception's ``what()`` method, a helper
+function is available:
+
+.. code-block:: cpp
+
+ py::register_exception<CppExp>(module, "PyExp");
+
+This call creates a Python exception class with the name ``PyExp`` in the given
+module and automatically converts any encountered exceptions of type ``CppExp``
+into Python exceptions of type ``PyExp``.
+
+When more advanced exception translation is needed, the function
+``py::register_exception_translator(translator)`` can be used to register
+functions that can translate arbitrary exception types (and which may include
+additional logic to do so). The function takes a stateless callable (e.g. a
+function pointer or a lambda function without captured variables) with the call
+signature ``void(std::exception_ptr)``.
+
+When a C++ exception is thrown, the registered exception translators are tried
+in reverse order of registration (i.e. the last registered translator gets the
+first shot at handling the exception).
+
+Inside the translator, ``std::rethrow_exception`` should be used within
+a try block to re-throw the exception. One or more catch clauses to catch
+the appropriate exceptions should then be used with each clause using
+``PyErr_SetString`` to set a Python exception or ``ex(string)`` to set
+the python exception to a custom exception type (see below).
+
+To declare a custom Python exception type, declare a ``py::exception`` variable
+and use this in the associated exception translator (note: it is often useful
+to make this a static declaration when using it inside a lambda expression
+without requiring capturing).
+
+
+The following example demonstrates this for a hypothetical exception classes
+``MyCustomException`` and ``OtherException``: the first is translated to a
+custom python exception ``MyCustomError``, while the second is translated to a
+standard python RuntimeError:
+
+.. code-block:: cpp
+
+ static py::exception<MyCustomException> exc(m, "MyCustomError");
+ py::register_exception_translator([](std::exception_ptr p) {
+ try {
+ if (p) std::rethrow_exception(p);
+ } catch (const MyCustomException &e) {
+ exc(e.what());
+ } catch (const OtherException &e) {
+ PyErr_SetString(PyExc_RuntimeError, e.what());
+ }
+ });
+
+Multiple exceptions can be handled by a single translator, as shown in the
+example above. 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.
+
+.. seealso::
+
+ The file :file:`tests/test_exceptions.cpp` contains examples
+ of various custom exception translators and custom exception types.
+
+.. note::
+
+ You must call either ``PyErr_SetString`` or a custom exception's call
+ operator (``exc(string)``) 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, or
+ may be explicity (re-)thrown to delegate it to the other,
+ previously-declared existing exception translators.
diff --git a/docs/advanced/functions.rst b/docs/advanced/functions.rst
new file mode 100644
index 0000000..5c697b1
--- /dev/null
+++ b/docs/advanced/functions.rst
@@ -0,0 +1,263 @@
+Functions
+#########
+
+Before proceeding with this section, make sure that you are already familiar
+with the basics of binding functions and classes, as explained in :doc:`/basics`
+and :doc:`/classes`. The following guide is applicable to both free and member
+functions, i.e. *methods* in Python.
+
+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.
+
+.. _python_objects_as_args:
+
+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;
+ }
+
+It can be exported:
+
+.. code-block:: cpp
+
+ m.def("print_dict", &print_dict);
+
+And used in Python as usual:
+
+.. code-block:: pycon
+
+ >>> print_dict({'foo': 123, 'bar': 'hello'})
+ key=foo, value=123
+ key=bar, value=hello
+
+For more information on using Python objects in C++, see :doc:`/advanced/pycpp/index`.
+
+Accepting \*args and \*\*kwargs
+===============================
+
+Python provides a useful mechanism to define functions that accept arbitrary
+numbers of arguments and keyword arguments:
+
+.. code-block:: python
+
+ 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);
+
+The class ``py::args`` derives from ``py::tuple`` 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. A demonstration is also available in
+``tests/test_kwargs_and_defaults.cpp``.
+
+.. warning::
+
+ Unlike Python, pybind11 does not allow combining normal parameters with the
+ ``args`` / ``kwargs`` special parameters.
+
+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_v`` notation:
+
+.. code-block:: cpp
+
+ py::class_<MyClass>("MyClass")
+ .def("myFunction", py::arg_v("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);
diff --git a/docs/advanced/misc.rst b/docs/advanced/misc.rst
new file mode 100644
index 0000000..2968f8a
--- /dev/null
+++ b/docs/advanced/misc.rst
@@ -0,0 +1,187 @@
+Miscellaneous
+#############
+
+.. _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 beginning 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 :ref:`overriding_virtuals` 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, 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();
+ }
+
+
+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.
+
+
+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, you can specify the base class as a template parameter option to
+``class_``, which performs an automated lookup of the corresponding Python
+type. Like the above code, 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, Pet>(m, "Dog")
+ .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 {
+ ...
+ };
+
+
+
+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
diff --git a/docs/advanced/pycpp/index.rst b/docs/advanced/pycpp/index.rst
new file mode 100644
index 0000000..6885bdc
--- /dev/null
+++ b/docs/advanced/pycpp/index.rst
@@ -0,0 +1,13 @@
+Python C++ interface
+####################
+
+pybind11 exposes Python types and functions using thin C++ wrappers, which
+makes it possible to conveniently call Python code from C++ without resorting
+to Python's C API.
+
+.. toctree::
+ :maxdepth: 2
+
+ object
+ numpy
+ utilities
diff --git a/docs/advanced/pycpp/numpy.rst b/docs/advanced/pycpp/numpy.rst
new file mode 100644
index 0000000..8b46b7c
--- /dev/null
+++ b/docs/advanced/pycpp/numpy.rst
@@ -0,0 +1,299 @@
+.. _numpy:
+
+NumPy
+#####
+
+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 :doc:`/advanced/cast/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
+
+Arrays
+======
+
+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 parameter, and it ensures that non-conforming arguments are converted
+into an array satisfying the specified requirements instead of trying the next
+function overload.
+
+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.
diff --git a/docs/advanced/pycpp/object.rst b/docs/advanced/pycpp/object.rst
new file mode 100644
index 0000000..8fc165d
--- /dev/null
+++ b/docs/advanced/pycpp/object.rst
@@ -0,0 +1,96 @@
+Python types
+############
+
+Available wrappers
+==================
+
+All major Python types are available as thin C++ wrapper classes. These
+can also be used as function parameters -- see :ref:`python_objects_as_args`.
+
+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`.
+
+Casting back and forth
+======================
+
+In this kind of mixed code, it is often necessary to convert arbitrary C++
+types to Python, which can be done using :func:`py::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`.
+
+Calling Python functions
+========================
+
+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>();
+
+Keyword arguments are also supported. In Python, there is the usual call syntax:
+
+.. code-block:: python
+
+ def f(number, say, to):
+ ... # function code
+
+ f(1234, say="hello", to=some_instance) # keyword call in Python
+
+In C++, the same call can be made using:
+
+.. code-block:: cpp
+
+ using pybind11::literals; // to bring in the `_a` literal
+ f(1234, "say"_a="hello", "to"_a=some_instance); // keyword call in C++
+
+Unpacking of ``*args`` and ``**kwargs`` is also possible and can be mixed with
+other arguments:
+
+.. code-block:: cpp
+
+ // * unpacking
+ py::tuple args = py::make_tuple(1234, "hello", some_instance);
+ f(*args);
+
+ // ** unpacking
+ py::dict kwargs = py::dict("number"_a=1234, "say"_a="hello", "to"_a=some_instance);
+ f(**kwargs);
+
+ // mixed keywords, * and ** unpacking
+ py::tuple args = py::make_tuple(1234);
+ py::dict kwargs = py::dict("to"_a=some_instance);
+ f(*args, "say"_a="hello", **kwargs);
+
+Generalized unpacking according to PEP448_ is also supported:
+
+.. code-block:: cpp
+
+ py::dict kwargs1 = py::dict("number"_a=1234);
+ py::dict kwargs2 = py::dict("to"_a=some_instance);
+ f(**kwargs1, "say"_a="hello", **kwargs2);
+
+.. 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_callbacks.cpp` presents a few examples of calling
+ Python functions from C++, including keywords arguments and unpacking.
+
+.. _PEP448: https://www.python.org/dev/peps/pep-0448/
diff --git a/docs/advanced/pycpp/utilities.rst b/docs/advanced/pycpp/utilities.rst
new file mode 100644
index 0000000..ba0dbef
--- /dev/null
+++ b/docs/advanced/pycpp/utilities.rst
@@ -0,0 +1,57 @@
+Utilities
+#########
+
+Using Python's print function in C++
+====================================
+
+The usual way to write output in C++ is using ``std::cout`` while in Python one
+would use ``print``. Since these methods use different buffers, mixing them can
+lead to output order issues. To resolve this, pybind11 modules can use the
+:func:`py::print` function which writes to Python's ``sys.stdout`` for consistency.
+
+Python's ``print`` function is replicated in the C++ API including optional
+keyword arguments ``sep``, ``end``, ``file``, ``flush``. Everything works as
+expected in Python:
+
+.. code-block:: cpp
+
+ py::print(1, 2.0, "three"); // 1 2.0 three
+ py::print(1, 2.0, "three", "sep"_a="-"); // 1-2.0-three
+
+ auto args = py::make_tuple("unpacked", true);
+ py::print("->", *args, "end"_a="<-"); // -> unpacked True <-
+
+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);
diff --git a/docs/advanced/smart_ptrs.rst b/docs/advanced/smart_ptrs.rst
new file mode 100644
index 0000000..97dc7ab
--- /dev/null
+++ b/docs/advanced/smart_ptrs.rst
@@ -0,0 +1,149 @@
+Smart pointers
+##############
+
+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:
+
+Reference-counting 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_`, can be passed a template
+type that denotes a special *holder* type that is used to manage references to
+the object. If no such holder type template argument is given, the default for
+a type named ``Type`` 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
+
+ PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>);
+
+ 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.
diff --git a/docs/basics.rst b/docs/basics.rst
index 339d559..45272b7 100644
--- a/docs/basics.rst
+++ b/docs/basics.rst
@@ -60,6 +60,19 @@
the tutorial and look at the test cases in the :file:`tests` directory,
which exercise all features of pybind11.
+Header and namespace conventions
+================================
+
+For brevity, all code examples assume that the following two lines are present:
+
+.. code-block:: cpp
+
+ #include <pybind11/pybind11.h>
+
+ namespace py = pybind11;
+
+Some features may require additional headers, but those will be specified as needed.
+
Creating bindings for a simple function
=======================================
@@ -93,6 +106,9 @@
return m.ptr();
}
+.. [#f1] In practice, implementation and binding code will generally be located
+ in separate files.
+
The :func:`PYBIND11_PLUGIN` macro creates a function that will be called when an
``import`` statement is issued from within Python. The next line creates a
module named ``example`` (with the supplied docstring). The method
@@ -235,79 +251,37 @@
// shorthand
m.def("add2", &add, "i"_a=1, "j"_a=2);
+Exporting variables
+===================
+
+To expose a value from C++, use the ``attr`` function to register it in a module
+as shown below. Built-in types and general objects (more on that later) can be
+converted using the function ``py::cast``.
+
+.. code-block:: cpp
+
+ PYBIND11_PLUGIN(example) {
+ py::module m("example", "pybind11 example plugin");
+ m.attr("the_answer") = py::cast(42);
+ m.attr("what") = py::cast("World");
+ return m.ptr();
+ }
+
+These are then accessible from Python:
+
+.. code-block:: pycon
+
+ >>> import example
+ >>> example.the_answer
+ 42
+ >>> example.what
+ 'World'
+
.. _supported_types:
Supported data types
====================
-The following basic data types are supported out of the box (some may require
-an additional extension header to be included). To pass other data structures
-as arguments and return values, refer to the section on binding :ref:`classes`.
-
-+---------------------------------+--------------------------+-------------------------------+
-| Data type | Description | Header file |
-+=================================+==========================+===============================+
-| ``int8_t``, ``uint8_t`` | 8-bit integers | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``int16_t``, ``uint16_t`` | 16-bit integers | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``int32_t``, ``uint32_t`` | 32-bit integers | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``int64_t``, ``uint64_t`` | 64-bit integers | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``ssize_t``, ``size_t`` | Platform-dependent size | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``float``, ``double`` | Floating point types | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``bool`` | Two-state Boolean type | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``char`` | Character literal | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``wchar_t`` | Wide character literal | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``const char *`` | UTF-8 string literal | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``const wchar_t *`` | Wide string literal | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::string`` | STL dynamic UTF-8 string | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::wstring`` | STL dynamic wide string | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::pair<T1, T2>`` | Pair of two custom types | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::tuple<...>`` | Arbitrary tuple of types | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::reference_wrapper<...>`` | Reference type wrapper | :file:`pybind11/pybind11.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::complex<T>`` | Complex numbers | :file:`pybind11/complex.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::array<T, Size>`` | STL static array | :file:`pybind11/stl.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::vector<T>`` | STL dynamic array | :file:`pybind11/stl.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::list<T>`` | STL linked list | :file:`pybind11/stl.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::map<T1, T2>`` | STL ordered map | :file:`pybind11/stl.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::unordered_map<T1, T2>`` | STL unordered map | :file:`pybind11/stl.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::set<T>`` | STL ordered set | :file:`pybind11/stl.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::unordered_set<T>`` | STL unordered set | :file:`pybind11/stl.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::function<...>`` | STL polymorphic function | :file:`pybind11/functional.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::chrono::duration<...>`` | STL time duration | :file:`pybind11/chrono.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``std::chrono::time_point<...>``| STL date/time | :file:`pybind11/chrono.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``Eigen::Matrix<...>`` | Eigen: dense matrix | :file:`pybind11/eigen.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``Eigen::Map<...>`` | Eigen: mapped memory | :file:`pybind11/eigen.h` |
-+---------------------------------+--------------------------+-------------------------------+
-| ``Eigen::SparseMatrix<...>`` | Eigen: sparse matrix | :file:`pybind11/eigen.h` |
-+---------------------------------+--------------------------+-------------------------------+
-
-
-.. [#f1] In practice, implementation and binding code will generally be located
- in separate files.
+A large number of data types are supported out of the box and can be used
+seamlessly as functions arguments, return values or with ``py::cast`` in general.
+For a full overview, see the :doc:`advanced/cast/index` section.
diff --git a/docs/index.rst b/docs/index.rst
index ab9a1fc..cedf652 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -10,15 +10,36 @@
Contents:
.. toctree::
- :maxdepth: 2
+ :maxdepth: 1
intro
+ changelog
+
+.. toctree::
+ :caption: The Basics
+ :maxdepth: 2
+
basics
classes
- advanced
compiling
+
+.. toctree::
+ :caption: Advanced Topics
+ :maxdepth: 2
+
+ advanced/functions
+ advanced/classes
+ advanced/exceptions
+ advanced/smart_ptrs
+ advanced/cast/index
+ advanced/pycpp/index
+ advanced/misc
+
+.. toctree::
+ :caption: Extra Information
+ :maxdepth: 1
+
+ faq
benchmark
limitations
- faq
reference
- changelog
diff --git a/docs/reference.rst b/docs/reference.rst
index 7edc43a..542259e 100644
--- a/docs/reference.rst
+++ b/docs/reference.rst
@@ -215,9 +215,9 @@
.. function:: arg::arg(const char *name)
-.. function:: template <typename T> arg_t<T> arg::operator=(const T &value)
+.. function:: template <typename T> arg_v arg::operator=(T &&value)
-.. class:: template <typename T> arg_t<T> : public arg
+.. class:: arg_v : public arg
Represents a named argument with a default value