Both Python and C++ support object-oriented code through classes and thus it is logical to expose C++ classes as Python ones, including the full inheritance hierarchy.
The C++ code used for the examples below can be found
here, and it is assumed that that code is
loaded at the start of any session.
Download it, save it under the name
features.h, and load it:
>>> import cppyy >>> cppyy.include('features.h') >>>
All bound C++ code starts off from the global C++ namespace, represented in
This namespace, as any other namespace, is treated as a module after it has
Thus, we can import C++ classes that live underneath it:
>>> from cppyy.gbl import Concrete >>> Concrete <class cppyy.gbl.Concrete at 0x2058e30> >>>
Placing classes in the same structure as imposed by C++ guarantees identity,
even if multiple Python modules bind the same class.
There is, however, no necessity to expose that structure to end-users: when
developing a Python package that exposes C++ classes through
cppyy.gbl an “internal” module, and expose the classes in any
structure you see fit.
The C++ names will continue to follow the C++ structure, however, as is needed
for e.g. pickling:
>>> from cppyy.gbl import Namespace >>> Concrete == Namespace.Concrete False >>> n = Namespace.Concrete.NestedClass() >>> type(n) <class cppyy.gbl.Namespace.Concrete.NestedClass at 0x22114c0> >>> type(n).__name__ NestedClass >>> type(n).__module__ cppyy.gbl.Namespace.Concrete >>> type(n).__cpp_name__ Namespace::Concrete::NestedClass >>>
Python and C++ both make a distinction between allocation (
operator new in C++) and initialization (
__init__ in Python,
the constructor call in C++).
When binding, however, there comes a subtle semantic difference: the Python
__new__ allocates memory for the proxy object only, and
initializes the proxy by creating or binding the C++ object.
Thus, no C++ memory is allocated until
The advantages are simple: the proxy can now check whether it is initialized,
because the pointer to C++ memory will be NULL if not; it can be a reference
to another proxy holding the actual C++ memory; and it can now transparently
implement a C++ smart pointer.
__init__ is never called, eg. when a call to the base class
__init__ is missing in a derived class override, then accessing the proxy
will result in a Python
There should not be a reason to call a destructor directly in CPython, but
PyPy uses a garbage collector and that makes it sometimes useful to destruct
a C++ object where you want it destroyed.
Destructors are accessible through the conventional
Accessing an object after it has been destroyed will result in a Python
The output of help shows the inheritance hierarchy, constructors, public
methods, and public data.
Concrete inherits from
Abstract and it has
a constructor that takes an
int argument, with a default value of 42.
>>> from cppyy.gbl import Abstract >>> issubclass(Concrete, Abstract) True >>> a = Abstract() Traceback (most recent call last): File "<console>", line 1, in <module> TypeError: cannot instantiate abstract class 'Abstract' >>> c = Concrete() >>> isinstance(c, Concrete) True >>> isinstance(c, Abstract) True >>> d = Concrete(13) >>>
Just like in C++, interface classes that define pure virtual methods, such
Abstract does, can not be instantiated, but their concrete
As the output of
help showed, the
Concrete constructor takes
an integer argument, that by default is 42.
Python classes that derive from C++ classes can override virtual methods as long as those methods are declared on class instantiation (adding methods to the Python class after the fact will not provide overrides on the C++ side, only on the Python side). Example:
>>> from cppyy.gbl import Abstract, call_abstract_method >>> class PyConcrete(Abstract): ... def abstract_method(self): ... return "Hello, Python World!\n" ... def concrete_method(self): ... pass ... >>> pc = PyConcrete() >>> call_abstract_method(pc) Hello, Python World! >>>
Note that it is not necessary to provide a constructor (
if you do, you must call the base class constructor through the
Python requires that any multiple inheritance (also in pure Python) has an
unambiguous method resolution order (mro), including for classes and thus
also for meta-classes.
In Python2, it was possible to resolve any mro conflicts automatically, but
meta-classes in Python3, although syntactically richer, have functionally
become far more limited.
In particular, the mro is checked in the builtin class builder, instead of
in the meta-class of the meta-class (which in Python3 is the builtin
rather than the meta-class itself as in Python2, another limitation, and
which actually checks the mro a second time for no reason).
The upshot is that a helper is required (
cppyy.multi) to resolve the mro
to support Python3.
The helper is written to also work in Python2.
>>> class PyConcrete(cppyy.multi(cppyy.gbl.Abstract1, cppyy.gbl.Abstract2)): ... def abstract_method1(self): ... return "first message" ... def abstract_method2(self): ... return "second message" ... >>> pc = PyConcrete() >>> cppyy.gbl.call_abstract_method1(pc) first message >>> cppyy.gbl/call_abstract_method2(pc) second message >>>
Contrary to multiple inheritance in Python, in C++ there are no two separate
instances representing the base classes.
Thus, a single
__init__ call needs to construct and initialize all bases,
rather than calling
__init__ on each base independently.
To support this syntax, the arguments to each base class should be grouped
together in a tuple.
If there are no arguments, provide an empty tuple (or omit them altogether,
if these arguments apply to the right-most base(s)).
C++ methods are represented as Python ones: these are first-class objects and can be bound to an instance. If a method is virtual in C++, the proper concrete method is called, whether or not the concrete class is bound. Similarly, if all classes are bound, the normal Python rules apply:
>>> c.abstract_method() called Concrete::abstract_method >>> c.concrete_method() called Concrete::concrete_method >>> m = c.abstract_method >>> m() called Concrete::abstract_method >>>
Data members are implemented as properties, using descriptors.
For example, The
Concrete instances have a public data member
>>> c.m_int, d.m_int (42, 13) >>>
Note however, that the data members are typed: setting them results in a memory write on the C++ side. This is different in Python, where references are replaced, and thus any type will do:
>>> c.m_int = 3.14 # a float does not fit in an int Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: int/long conversion expects an integer object >>> c.m_int = int(3.14) >>> c.m_int, d.m_int (3, 13) >>>
Private and protected data members are not accessible, contrary to Python data members, and C++ const-ness is respected:
>>> c.m_const_int = 71 # declared 'const int' in class definition Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: assignment to const data not allowed >>>
Static C++ data members act like Python class-level data members. They are also represented by property objects and both read and write access behave as expected:
>>> Concrete.s_int # access through class 321 >>> c.s_int = 123 # access through instance >>> Concrete.s_int 123
Many C++ operators can be mapped to their Python equivalent. When the operators are part of the C++ class definition, this is done directly. If they are defined globally, the lookup is done lazily (ie. can resolve after the class definition by loading the global definition or by defining them interactively). Some operators have no Python equivalent and are instead made available by mapping them onto the following conventional functions:
Here is an example of operator usage, using STL iterators directly (note that this is not necessary in practice as STL and STL-like containers work transparently in Python for-loops):
>>> v = cppyy.gbl.std.vector[int](range(3)) >>> i = v.begin() >>> while (i != v.end()): ... print(i.__deref__()) ... _ = i.__preinc__() ... 0 1 2 >>>
operator new and
operator delete, as well as their array
equivalents, are not accessible but will be called as appropriate.
Templated classes are instantiated using square brackets. (For backwards compatibility reasons, parentheses work as well.) The instantiation of a templated class yields a class, which can then be used to create instances.
Templated classes need not pre-exist in the bound code, just their declaration needs to be available. This is true for e.g. all of STL:
>>> cppyy.gbl.std.vector # template metatype <cppyy.Template 'std::vector' object at 0x7fffed2674d0> >>> cppyy.gbl.std.vector(int) # instantiates template -> class <class cppyy.gbl.std.vector<int> at 0x1532190> cppyy.gbl.std.vector[int]() # instantiates class -> object <cppyy.gbl.std.vector<int> object at 0x2341ec0> >>>
The template arguments may be actual types or their names as a string, whichever is more convenient. Thus, the following are equivalent:
>>> from cppyy.gbl.std import vector >>> type1 = vector[Concrete] >>> type2 = vector['Concrete'] >>> type1 == type2 True >>>
Typedefs are simple python references to the actual classes to which they refer.
>>> from cppyy.gbl import Concrete_t >>> Concrete is Concrete_t True >>>