cppyy: Automatic Python-C++ bindings

cppyy is an automatic, run-time, Python-C++ bindings generator, for calling C++ from Python and Python from C++. Run-time generation enables detailed specialization for higher performance, lazy loading for reduced memory use in large scale projects, Python-side cross-inheritance and callbacks for working with C++ frameworks, run-time template instantiation, automatic object downcasting, exception mapping, and interactive exploration of C++ libraries. cppyy delivers this without any language extensions, intermediate languages, or the need for boiler-plate hand-written code. For design and performance, see this PyHPC’16 paper, albeit that the CPython/cppyy performance has been vastly improved since, as well as this CAAS presentation. For a quick teaser, see Jason Turner’s introduction video.

cppyy is based on Cling, the C++ interpreter, to match Python’s dynamism, interactivity, and run-time behavior. Consider this session, showing dynamic, interactive, mixing of C++ and Python features (there are more examples throughout the documentation and in the tutorial):

>>> import cppyy
>>> cppyy.cppdef("""
... class MyClass {
... public:
...     MyClass(int i) : m_data(i) {}
...     virtual ~MyClass() {}
...     virtual int add_int(int i) { return m_data + i; }
...     int m_data;
... };""")
True
>>> from cppyy.gbl import MyClass
>>> m = MyClass(42)
>>> cppyy.cppdef("""
... void say_hello(MyClass* m) {
...     std::cout << "Hello, the number is: " << m->m_data << std::endl;
... }""")
True
>>> MyClass.say_hello = cppyy.gbl.say_hello
>>> m.say_hello()
Hello, the number is: 42
>>> m.m_data = 13
>>> m.say_hello()
Hello, the number is: 13
>>> class PyMyClass(MyClass):
...     def add_int(self, i):  # python side override (CPython only)
...         return self.m_data + 2*i
...
>>> cppyy.cppdef("int callback(MyClass* m, int i) { return m->add_int(i); }")
True
>>> cppyy.gbl.callback(m, 2)             # calls C++ add_int
15
>>> cppyy.gbl.callback(PyMyClass(1), 2)  # calls Python-side override
5
>>>

With a modern C++ compiler having its back, cppyy is future-proof. Consider the following session using boost::any, a capsule-type that allows for heterogeneous containers in C++. The Boost library is well known for its no holds barred use of modern C++ and heavy use of templates:

>>> import cppyy
>>> cppyy.include('boost/any.hpp')       # assumes you have boost installed
>>> from cppyy.gbl import std, boost
>>> val = boost.any()                    # the capsule
>>> val.__assign__(std.vector[int]())    # assign it a std::vector<int>
<cppyy.gbl.boost.any object at 0xf6a8a0>
>>> val.type() == cppyy.typeid(std.vector[int])    # verify type
True
>>> extract = boost.any_cast[int](std.move(val))   # wrong cast
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
cppyy.gbl.boost.bad_any_cast: Could not instantiate any_cast<int>:
  int boost::any_cast(boost::any&& operand) =>
    wrapexcept<boost::bad_any_cast>: boost::bad_any_cast: failed conversion using boost::any_cast
>>> extract = boost.any_cast[std.vector[int]](val) # correct cast
>>> type(extract) is std.vector[int]
True
>>> extract += xrange(100)
>>> len(extract)
100
>>> val.__assign__(std.move(extract))    # move forced
<cppyy.gbl.boost.any object at 0xf6a8a0>
>>> len(extract)                         # now empty (or invalid)
0
>>> extract = boost.any_cast[std.vector[int]](val)
>>> list(extract)
[0, 1, 2, 3, 4, 5, 6, ..., 97, 98, 99]
>>>

Of course, there is no reason to use Boost from Python (in fact, this example calls out for pythonizations), but it shows that cppyy seamlessly supports many advanced C++ features.

cppyy is available for both CPython (v2 and v3) and PyPy, reaching C++-like performance with the latter. It makes judicious use of precompiled headers, dynamic loading, and lazy instantiation, to support C++ programs consisting of millions of lines of code and many thousands of classes. cppyy minimizes dependencies to allow its use in distributed, heterogeneous, development environments.

Background

Bugs and feedback

Please report bugs or requests for improvement on the issue tracker.