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804 lines
28 KiB
ReStructuredText
804 lines
28 KiB
ReStructuredText
.. _advanced:
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Advanced topics
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###############
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For brevity, the rest of this chapter assumes that the following two lines are
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present:
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.. code-block:: cpp
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#include <pybind11/pybind11.h>
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namespace py = pybind11;
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Operator overloading
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====================
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Suppose that we're given the following ``Vector2`` class with a vector addition
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and scalar multiplication operation, all implemented using overloaded operators
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in C++.
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.. code-block:: cpp
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class Vector2 {
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public:
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Vector2(float x, float y) : x(x), y(y) { }
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std::string toString() const { return "[" + std::to_string(x) + ", " + std::to_string(y) + "]"; }
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Vector2 operator+(const Vector2 &v) const { return Vector2(x + v.x, y + v.y); }
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Vector2 operator*(float value) const { return Vector2(x * value, y * value); }
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Vector2& operator+=(const Vector2 &v) { x += v.x; y += v.y; return *this; }
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Vector2& operator*=(float v) { x *= v; y *= v; return *this; }
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friend Vector2 operator*(float f, const Vector2 &v) { return Vector2(f * v.x, f * v.y); }
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private:
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float x, y;
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};
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The following snippet shows how the above operators can be conveniently exposed
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to Python.
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.. code-block:: cpp
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#include <pybind11/operators.h>
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PYBIND11_PLUGIN(example) {
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py::module m("example", "pybind11 example plugin");
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py::class_<Vector2>(m, "Vector2")
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.def(py::init<float, float>())
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.def(py::self + py::self)
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.def(py::self += py::self)
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.def(py::self *= float())
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.def(float() * py::self)
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.def("__repr__", &Vector2::toString);
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return m.ptr();
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}
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Note that a line like
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.. code-block:: cpp
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.def(py::self * float())
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is really just short hand notation for
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.. code-block:: cpp
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.def("__mul__", [](const Vector2 &a, float b) {
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return a * b;
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})
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This can be useful for exposing additional operators that don't exist on the
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C++ side, or to perform other types of customization.
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.. note::
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To use the more convenient ``py::self`` notation, the additional
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header file :file:`pybind11/operators.h` must be included.
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.. seealso::
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The file :file:`example/example3.cpp` contains a complete example that
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demonstrates how to work with overloaded operators in more detail.
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Callbacks and passing anonymous functions
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=========================================
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The C++11 standard brought lambda functions and the generic polymorphic
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function wrapper ``std::function<>`` to the C++ programming language, which
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enable powerful new ways of working with functions. Lambda functions come in
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two flavors: stateless lambda function resemble classic function pointers that
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link to an anonymous piece of code, while stateful lambda functions
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additionally depend on captured variables that are stored in an anonymous
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*lambda closure object*.
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Here is a simple example of a C++ function that takes an arbitrary function
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(stateful or stateless) with signature ``int -> int`` as an argument and runs
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it with the value 10.
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.. code-block:: cpp
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int func_arg(const std::function<int(int)> &f) {
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return f(10);
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}
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The example below is more involved: it takes a function of signature ``int -> int``
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and returns another function of the same kind. The return value is a stateful
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lambda function, which stores the value ``f`` in the capture object and adds 1 to
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its return value upon execution.
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.. code-block:: cpp
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std::function<int(int)> func_ret(const std::function<int(int)> &f) {
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return [f](int i) {
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return f(i) + 1;
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};
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}
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After including the extra header file :file:`pybind11/functional.h`, it is almost
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trivial to generate binding code for both of these functions.
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.. code-block:: cpp
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#include <pybind11/functional.h>
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PYBIND11_PLUGIN(example) {
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py::module m("example", "pybind11 example plugin");
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m.def("func_arg", &func_arg);
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m.def("func_ret", &func_ret);
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return m.ptr();
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}
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The following interactive session shows how to call them from Python.
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.. code-block:: python
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$ python
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>>> import example
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>>> def square(i):
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... return i * i
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...
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>>> example.func_arg(square)
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100L
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>>> square_plus_1 = example.func_ret(square)
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>>> square_plus_1(4)
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17L
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>>>
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.. note::
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This functionality is very useful when generating bindings for callbacks in
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C++ libraries (e.g. a graphical user interface library).
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The file :file:`example/example5.cpp` contains a complete example that
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demonstrates how to work with callbacks and anonymous functions in more detail.
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.. warning::
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Keep in mind that passing a function from C++ to Python (or vice versa)
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will instantiate a piece of wrapper code that translates function
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invocations between the two languages. Copying the same function back and
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forth between Python and C++ many times in a row will cause these wrappers
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to accumulate, which can decrease performance.
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Overriding virtual functions in Python
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======================================
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Suppose that a C++ class or interface has a virtual function that we'd like to
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to override from within Python (we'll focus on the class ``Animal``; ``Dog`` is
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given as a specific example of how one would do this with traditional C++
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code).
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.. code-block:: cpp
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class Animal {
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public:
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virtual ~Animal() { }
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virtual std::string go(int n_times) = 0;
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};
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class Dog : public Animal {
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public:
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std::string go(int n_times) {
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std::string result;
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for (int i=0; i<n_times; ++i)
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result += "woof! ";
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return result;
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}
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};
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Let's also suppose that we are given a plain function which calls the
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function ``go()`` on an arbitrary ``Animal`` instance.
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.. code-block:: cpp
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std::string call_go(Animal *animal) {
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return animal->go(3);
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}
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Normally, the binding code for these classes would look as follows:
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.. code-block:: cpp
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PYBIND11_PLUGIN(example) {
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py::module m("example", "pybind11 example plugin");
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py::class_<Animal> animal(m, "Animal");
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animal
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.def("go", &Animal::go);
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py::class_<Dog>(m, "Dog", animal)
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.def(py::init<>());
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m.def("call_go", &call_go);
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return m.ptr();
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}
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However, these bindings are impossible to extend: ``Animal`` is not
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constructible, and we clearly require some kind of "trampoline" that
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redirects virtual calls back to Python.
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Defining a new type of ``Animal`` from within Python is possible but requires a
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helper class that is defined as follows:
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.. code-block:: cpp
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class PyAnimal : public Animal {
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public:
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/* Inherit the constructors */
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using Animal::Animal;
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/* Trampoline (need one for each virtual function) */
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std::string go(int n_times) {
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PYBIND11_OVERLOAD_PURE(
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std::string, /* Return type */
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Animal, /* Parent class */
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go, /* Name of function */
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n_times /* Argument(s) */
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);
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}
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};
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The macro :func:`PYBIND11_OVERLOAD_PURE` should be used for pure virtual
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functions, and :func:`PYBIND11_OVERLOAD` should be used for functions which have
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a default implementation. The binding code also needs a few minor adaptations
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(highlighted):
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.. code-block:: cpp
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:emphasize-lines: 4,6,7
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PYBIND11_PLUGIN(example) {
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py::module m("example", "pybind11 example plugin");
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py::class_<PyAnimal> animal(m, "Animal");
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animal
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.alias<Animal>()
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.def(py::init<>())
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.def("go", &Animal::go);
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py::class_<Dog>(m, "Dog", animal)
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.def(py::init<>());
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m.def("call_go", &call_go);
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return m.ptr();
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}
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Importantly, the trampoline helper class is used as the template argument to
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:class:`class_`, and a call to :func:`class_::alias` informs the binding
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generator that this is merely an alias for the underlying type ``Animal``.
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Following this, we are able to define a constructor as usual.
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The Python session below shows how to override ``Animal::go`` and invoke it via
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a virtual method call.
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.. code-block:: cpp
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>>> from example import *
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>>> d = Dog()
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>>> call_go(d)
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u'woof! woof! woof! '
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>>> class Cat(Animal):
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... def go(self, n_times):
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... return "meow! " * n_times
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...
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>>> c = Cat()
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>>> call_go(c)
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u'meow! meow! meow! '
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.. seealso::
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The file :file:`example/example12.cpp` contains a complete example that
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demonstrates how to override virtual functions using pybind11 in more
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detail.
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Global Interpreter Lock (GIL)
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=============================
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The classes :class:`gil_scoped_release` and :class:`gil_scoped_acquire` can be
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used to acquire and release the global interpreter lock in the body of a C++
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function call. In this way, long-running C++ code can be parallelized using
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multiple Python threads. Taking the previous section as an example, this could
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be realized as follows (important changes highlighted):
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.. code-block:: cpp
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:emphasize-lines: 8,9,33,34
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class PyAnimal : public Animal {
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public:
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/* Inherit the constructors */
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using Animal::Animal;
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/* Trampoline (need one for each virtual function) */
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std::string go(int n_times) {
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/* Acquire GIL before calling Python code */
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gil_scoped_acquire acquire;
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PYBIND11_OVERLOAD_PURE(
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std::string, /* Return type */
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Animal, /* Parent class */
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go, /* Name of function */
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n_times /* Argument(s) */
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);
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}
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};
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PYBIND11_PLUGIN(example) {
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py::module m("example", "pybind11 example plugin");
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py::class_<PyAnimal> animal(m, "Animal");
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animal
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.alias<Animal>()
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.def(py::init<>())
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.def("go", &Animal::go);
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py::class_<Dog>(m, "Dog", animal)
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.def(py::init<>());
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m.def("call_go", [](Animal *animal) -> std::string {
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/* Release GIL before calling into (potentially long-running) C++ code */
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gil_scoped_release release;
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return call_go(animal);
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});
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return m.ptr();
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}
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Passing STL data structures
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===========================
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When including the additional header file :file:`pybind11/stl.h`, conversions
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between ``std::vector<>`` and ``std::map<>`` and the Python ``list`` and
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``dict`` data structures are automatically enabled. The types ``std::pair<>``
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and ``std::tuple<>`` are already supported out of the box with just the core
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:file:`pybind11/pybind11.h` header.
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.. note::
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Arbitrary nesting of any of these types is explicitly permitted.
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.. seealso::
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The file :file:`example/example2.cpp` contains a complete example that
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demonstrates how to pass STL data types in more detail.
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Binding sequence data types, the slicing protocol, etc.
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=======================================================
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Please refer to the supplemental example for details.
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.. seealso::
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The file :file:`example/example6.cpp` contains a complete example that
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shows how to bind a sequence data type, including length queries
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(``__len__``), iterators (``__iter__``), the slicing protocol and other
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kinds of useful operations.
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Return value policies
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=====================
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Python and C++ use wildly different ways of managing the memory and lifetime of
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objects managed by them. This can lead to issues when creating bindings for
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functions that return a non-trivial type. Just by looking at the type
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information, it is not clear whether Python should take charge of the returned
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value and eventually free its resources, or if this is handled on the C++ side.
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For this reason, pybind11 provides a several `return value policy` annotations
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that can be passed to the :func:`module::def` and :func:`class_::def`
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functions. The default policy is :enum:`return_value_policy::automatic``.
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+--------------------------------------------------+---------------------------------------------------------------------------+
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| Return value policy | Description |
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+==================================================+===========================================================================+
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| :enum:`return_value_policy::automatic` | Automatic: copy objects returned as values and take ownership of |
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| | objects returned as pointers |
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+--------------------------------------------------+---------------------------------------------------------------------------+
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| :enum:`return_value_policy::copy` | Create a new copy of the returned object, which will be owned by Python |
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+--------------------------------------------------+---------------------------------------------------------------------------+
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| :enum:`return_value_policy::take_ownership` | Reference the existing object and take ownership. Python will call |
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| | the destructor and delete operator when the reference count reaches zero |
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+--------------------------------------------------+---------------------------------------------------------------------------+
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| :enum:`return_value_policy::reference` | Reference the object, but do not take ownership and defer responsibility |
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| | for deleting it to C++ (dangerous when C++ code at some point decides to |
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| | delete it while Python still has a nonzero reference count) |
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+--------------------------------------------------+---------------------------------------------------------------------------+
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| :enum:`return_value_policy::reference_internal` | Reference the object, but do not take ownership. The object is considered |
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| | be owned by the C++ instance whose method or property returned it. The |
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| | Python object will increase the reference count of this 'parent' by 1 |
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| | to ensure that it won't be deallocated while Python is using the 'child' |
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+--------------------------------------------------+---------------------------------------------------------------------------+
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.. warning::
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Code with invalid call policies might access unitialized memory and free
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data structures multiple times, which can lead to hard-to-debug
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non-determinism and segmentation faults, hence it is worth spending the
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time to understand all the different options above.
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See below for an example that uses the
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:enum:`return_value_policy::reference_internal` policy.
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.. code-block:: cpp
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class Example {
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public:
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Internal &get_internal() { return internal; }
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private:
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Internal internal;
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};
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PYBIND11_PLUGIN(example) {
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py::module m("example", "pybind11 example plugin");
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py::class_<Example>(m, "Example")
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.def(py::init<>())
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.def("get_internal", &Example::get_internal, "Return the internal data", py::return_value_policy::reference_internal)
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return m.ptr();
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}
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Implicit type conversions
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=========================
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Suppose that instances of two types ``A`` and ``B`` are used in a project, and
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that an ``A`` can easily be converted into a an instance of type ``B`` (examples of this
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could be a fixed and an arbitrary precision number type).
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.. code-block:: cpp
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py::class_<A>(m, "A")
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/// ... members ...
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py::class_<B>(m, "B")
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.def(py::init<A>())
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/// ... members ...
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m.def("func",
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[](const B &) { /* .... */ }
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);
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To invoke the function ``func`` using a variable ``a`` containing an ``A``
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instance, we'd have to write ``func(B(a))`` in Python. On the other hand, C++
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will automatically apply an implicit type conversion, which makes it possible
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to directly write ``func(a)``.
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In this situation (i.e. where ``B`` has a constructor that converts from
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``A``), the following statement enables similar implicit conversions on the
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Python side:
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.. code-block:: cpp
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py::implicitly_convertible<A, B>();
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Smart pointers
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==============
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The binding generator for classes (:class:`class_`) takes an optional second
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template type, which denotes a special *holder* type that is used to manage
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references to the object. When wrapping a type named ``Type``, the default
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value of this template parameter is ``std::unique_ptr<Type>``, which means that
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the object is deallocated when Python's reference count goes to zero.
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It is possible to switch to other types of reference counting wrappers or smart
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pointers, which is useful in codebases that rely on them. For instance, the
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following snippet causes ``std::shared_ptr`` to be used instead.
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.. code-block:: cpp
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py::class_<Example, std::shared_ptr<Example>> obj(m, "Example");
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To enable transparent conversions for functions that take shared pointers as an
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argument or that return them, a macro invocation similar to the following must
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be declared at the top level before any binding code:
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.. code-block:: cpp
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PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>);
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.. seealso::
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The file :file:`example/example8.cpp` contains a complete example that
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demonstrates how to work with custom reference-counting holder types in
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more detail.
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.. warning::
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To ensure correct reference counting among Python and C++, the use of
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``std::shared_ptr<T>`` as a holder type requires that ``T`` inherits from
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``std::enable_shared_from_this<T>`` (see cppreference_ for details).
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.. _cppreference: http://en.cppreference.com/w/cpp/memory/enable_shared_from_this
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.. _custom_constructors:
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Custom constructors
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===================
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The syntax for binding constructors was previously introduced, but it only
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works when a constructor with the given parameters actually exists on the C++
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side. To extend this to more general cases, let's take a look at what actually
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happens under the hood: the following statement
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|
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.. code-block:: cpp
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py::class_<Example>(m, "Example")
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.def(py::init<int>());
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|
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is short hand notation for
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|
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.. code-block:: cpp
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py::class_<Example>(m, "Example")
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.def("__init__",
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[](Example &instance, int arg) {
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new (&instance) Example(arg);
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}
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);
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In other words, :func:`init` creates an anonymous function that invokes an
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|
in-place constructor. Memory allocation etc. is already take care of beforehand
|
|
within pybind11.
|
|
|
|
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:
|
|
|
|
+----------------------------+------------------------------+
|
|
| C++ exception type | Python exception type |
|
|
+============================+==============================+
|
|
| :class:`std::exception` | ``Exception`` |
|
|
+----------------------------+------------------------------+
|
|
| :class:`stop_iteration` | ``StopIteration`` (used to |
|
|
| | implement custom iterators) |
|
|
+----------------------------+------------------------------+
|
|
| :class:`index_error` | ``IndexError`` (used to |
|
|
| | indicate out of bounds |
|
|
| | accesses in ``__getitem__``, |
|
|
| | ``__setitem__``, etc.) |
|
|
+----------------------------+------------------------------+
|
|
| :class:`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.
|
|
|
|
Buffer protocol
|
|
===============
|
|
|
|
Python supports an extremely general and convenient approach for exchanging
|
|
data between plugin libraries. Types can expose a buffer view which provides
|
|
fast direct access to the raw internal 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 Matrixes 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>::value(), /* 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
|
|
|
|
py::class_<Eigen::MatrixXd>(m, "MatrixXd")
|
|
.def("__init__", [](Eigen::MatrixXd &m, py::buffer b) {
|
|
/* Request a buffer descriptor from Python */
|
|
py::buffer_info info = b.request();
|
|
|
|
/* Some sanity checks ... */
|
|
if (info.format != py::format_descriptor<double>::value())
|
|
throw std::runtime_error("Incompatible format: expected a double array!");
|
|
|
|
if (info.ndim != 2)
|
|
throw std::runtime_error("Incompatible buffer dimension!");
|
|
|
|
if (info.strides[0] == sizeof(double)) {
|
|
/* Buffer has the right layout -- directly copy. */
|
|
new (&m) Eigen::MatrixXd(info.shape[0], info.shape[1]);
|
|
memcpy(m.data(), info.ptr, sizeof(double) * m.size());
|
|
} else {
|
|
/* Oops -- the buffer is transposed */
|
|
new (&m) Eigen::MatrixXd(info.shape[1], info.shape[0]);
|
|
memcpy(m.data(), info.ptr, sizeof(double) * m.size());
|
|
m.transposeInPlace();
|
|
}
|
|
});
|
|
|
|
.. seealso::
|
|
|
|
The file :file:`example/example7.cpp` contains a complete example that
|
|
demonstrates using the buffer protocol with pybind11 in more detail.
|
|
|
|
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 object 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
|
|
dense array of doubles in C-style ordering.
|
|
|
|
.. code-block:: cpp
|
|
|
|
void f(py::array_t<double> array);
|
|
|
|
When it is invoked with a different type (e.g. an integer), the binding code
|
|
will attempt to cast the input into a NumPy array of the requested type.
|
|
Note that this feature requires the ``pybind11/numpy.h`` header to be included.
|
|
|
|
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 the array elements. The result is returned as a NumPy array of type
|
|
``numpy.dtype.float64``.
|
|
|
|
.. code-block:: python
|
|
|
|
>>> 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 explitly 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);
|
|
}
|
|
);
|
|
|
|
.. seealso::
|
|
|
|
The file :file:`example/example10.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:`tuple`, :class:`list`,
|
|
:class:`dict`, :class:`slice`, :class:`capsule`, :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`.
|
|
|
|
.. seealso::
|
|
|
|
The file :file:`example/example2.cpp` contains a complete example that
|
|
demonstrates passing native Python types in more detail.
|